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{
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|
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"colab": {
|
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|
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"cells": [
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"cell_type": "markdown",
|
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"source": [
|
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"\n",
|
||
"### **K-Nearest Neighbors (KNN): A Comprehensive Guide**\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"## **High-Level Overview**\n",
|
||
"\n",
|
||
"### What is KNN, and Why is it Used?\n",
|
||
"K-Nearest Neighbors (KNN) is a simple and powerful machine learning algorithm used for **classification** and **regression** tasks. It predicts the label or value of a data point based on the **'k' closest data points** in the training dataset.\n",
|
||
"\n",
|
||
"Think of KNN as the **“majority vote” algorithm**. It finds the nearest neighbors to a given point, sees which category is most common among them, and assigns the point to that category.\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"### Key Concepts Using Analogies\n",
|
||
"\n",
|
||
"1. **Neighbors**:\n",
|
||
" Imagine you’re trying to decide what sport someone plays by observing their friends. If most of their friends play basketball, you’d guess that they also play basketball. The **neighbors** here are the closest examples used to make the prediction.\n",
|
||
"\n",
|
||
"2. **Distance**:\n",
|
||
" KNN measures the \"closeness\" of data points using a distance metric, such as:\n",
|
||
" - How far apart two points are (Euclidean distance).\n",
|
||
" - How different their features are (e.g., scores in different subjects for students).\n",
|
||
"\n",
|
||
"3. **‘K’ (Number of Neighbors)**:\n",
|
||
" KNN looks at the **‘k’ closest neighbors** to make a decision:\n",
|
||
" - If $k = 3$, it considers the 3 nearest points.\n",
|
||
" - Majority voting determines the predicted class.\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"### Everyday Example\n",
|
||
"Imagine you're in a new neighborhood and want to decide which grocery store to visit. You ask three of your closest neighbors ($k = 3$) where they shop:\n",
|
||
"- If 2 out of 3 go to Store A, you’ll likely choose Store A.\n",
|
||
"- If all 3 go to Store B, you’ll choose Store B.\n",
|
||
"\n",
|
||
"KNN works the same way—by relying on the majority vote of the closest examples.\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"## **Detailed Technical Explanation**\n",
|
||
"\n",
|
||
"### Key Components\n",
|
||
"\n",
|
||
"1. **Feature Space**:\n",
|
||
" Data points are represented as vectors in an $n$-dimensional space, where $n$ is the number of features. For example:\n",
|
||
" $x = [x_1, x_2, ..., x_n]$\n",
|
||
"\n",
|
||
"2. **Distance Metric**:\n",
|
||
" KNN uses a distance metric to find the nearest neighbors:\n",
|
||
" - **Euclidean Distance**:\n",
|
||
" $d(x, y) = \\sqrt{\\sum_{i=1}^{n} (x_i - y_i)^2}$\n",
|
||
" - **Manhattan Distance**:\n",
|
||
" $d(x, y) = \\sum_{i=1}^{n} |x_i - y_i|$\n",
|
||
" - **Cosine Similarity** (used for text data):\n",
|
||
" $\\text{Cosine Similarity} = \\frac{x \\cdot y}{\\|x\\| \\|y\\|}$\n",
|
||
"\n",
|
||
"3. **‘K’ (Number of Neighbors)**:\n",
|
||
" - The parameter $k$ determines how many neighbors to consider.\n",
|
||
" - **Low $k$**: More sensitive to noise (can overfit).\n",
|
||
" - **High $k$**: Results in smoother decision boundaries but may underfit.\n",
|
||
"\n",
|
||
"4. **Voting**:\n",
|
||
" - For classification, KNN assigns a class based on majority voting.\n",
|
||
" - For regression, KNN averages the values of the neighbors.\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"### Algorithm Steps\n",
|
||
"1. **Choose ‘K’**: Decide the number of neighbors to consider.\n",
|
||
"2. **Measure Distance**: Calculate the distance between the query point and all other data points in the training set.\n",
|
||
"3. **Sort Neighbors**: Identify the ‘k’ closest points.\n",
|
||
"4. **Vote or Average**:\n",
|
||
" - Classification: Assign the class with the majority vote.\n",
|
||
" - Regression: Average the values of the neighbors.\n",
|
||
"5. **Output Prediction**: Return the predicted class or value.\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"### Decision Boundary\n",
|
||
"KNN creates decision boundaries that are **piecewise linear**. For example:\n",
|
||
"- In 2D, the decision boundary resembles polygons around clusters of data points.\n",
|
||
"- It’s **non-parametric**, meaning it adapts to the shape of the data without making assumptions about its distribution.\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"### Generalizing to Higher Dimensions\n",
|
||
"KNN extends naturally to higher dimensions, where distances are calculated between points with more features (e.g., age, income, and spending for customers).\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"## **Practical Considerations**\n",
|
||
"\n",
|
||
"### Real-World Applications\n",
|
||
"1. **Text Classification**:\n",
|
||
" - E.g., classifying emails as spam or not.\n",
|
||
"2. **Recommendation Systems**:\n",
|
||
" - Suggesting movies based on user preferences.\n",
|
||
"3. **Image Recognition**:\n",
|
||
" - Identifying objects in images (e.g., handwritten digit recognition).\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"### Strengths\n",
|
||
"- **Simple and Intuitive**: Easy to understand and implement.\n",
|
||
"- **Non-Parametric**: Makes no assumptions about data distribution.\n",
|
||
"- **Versatile**: Works for classification and regression.\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"### Limitations\n",
|
||
"1. **Computational Cost**:\n",
|
||
" - For large datasets, KNN requires recalculating distances for all points during prediction, which can be slow.\n",
|
||
" - Techniques like **KD-trees** or **ball trees** help optimize this.\n",
|
||
"\n",
|
||
"2. **Feature Scaling**:\n",
|
||
" - KNN is sensitive to the scale of features. For example, one feature with large values (e.g., salary) can dominate others (e.g., age).\n",
|
||
" - **Solution**: Normalize or standardize features.\n",
|
||
"\n",
|
||
"3. **Choice of ‘K’**:\n",
|
||
" - Poorly chosen $k$ can lead to overfitting (small $k$) or underfitting (large $k$).\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"### Importance of Feature Scaling\n",
|
||
"Because KNN relies on distances, features with large ranges can skew predictions. Use:\n",
|
||
"- **Standardization**:\n",
|
||
" $z = \\frac{x - \\mu}{\\sigma}$\n",
|
||
"- **Normalization**:\n",
|
||
" $x_{\\text{normalized}} = \\frac{x - \\min(x)}{\\max(x) - \\min(x)}$\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"### Choosing the Right ‘K’\n",
|
||
"1. Use **Cross-Validation** to test different values of $k$.\n",
|
||
"2. Start with $k = \\sqrt{n}$, where $n$ is the number of training samples, and adjust based on performance.\n",
|
||
"\n",
|
||
"---\n"
|
||
],
|
||
"metadata": {
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"id": "44683e52"
|
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}
|
||
},
|
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{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"\n",
|
||
"### Datasets for kNN Problems\n",
|
||
"\n",
|
||
"1. **Classification Datasets**:\n",
|
||
" - **Iris Dataset**:\n",
|
||
" - Problem: Classify iris flowers into three species based on petal and sepal dimensions.\n",
|
||
" - Dataset Size: 150 samples with 4 features each.\n",
|
||
" - **MNIST Dataset**:\n",
|
||
" - Problem: Handwritten digit recognition (0–9).\n",
|
||
" - Dataset Size: 70,000 images (28x28 grayscale pixels).\n",
|
||
" - **Wine Dataset**:\n",
|
||
" - Problem: Classify wines into three classes based on 13 chemical properties.\n",
|
||
" - Dataset Size: 178 samples with 13 features each.\n",
|
||
"\n",
|
||
"2. **Regression Datasets**:\n",
|
||
" - **Boston Housing Dataset**:\n",
|
||
" - Problem: Predict house prices based on features like the number of rooms, crime rate, etc.\n",
|
||
" - Dataset Size: 506 samples with 13 features each.\n",
|
||
" - **Diabetes Dataset**:\n",
|
||
" - Problem: Predict the progression of diabetes based on 10 baseline medical measurements.\n",
|
||
" - Dataset Size: 442 samples with 10 features.\n",
|
||
" - **Concrete Compressive Strength Dataset**:\n",
|
||
" - Problem: Predict the compressive strength of concrete based on ingredients.\n",
|
||
" - Dataset Size: 1030 samples with 8 features.\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"### Types of Problems Solved by kNN\n",
|
||
"\n",
|
||
"1. **Classification Problems**:\n",
|
||
" - **Image Recognition**:\n",
|
||
" - Identify objects in images (e.g., cats vs. dogs).\n",
|
||
" - **Text Categorization**:\n",
|
||
" - Classify emails as spam or not spam.\n",
|
||
" - **Fraud Detection**:\n",
|
||
" - Classify financial transactions as fraudulent or legitimate.\n",
|
||
" - **Medical Diagnosis**:\n",
|
||
" - Classify patients based on diagnostic measurements (e.g., cancer detection).\n",
|
||
"\n",
|
||
"2. **Regression Problems**:\n",
|
||
" - **Predicting Prices**:\n",
|
||
" - Predict housing or product prices based on features like size or location.\n",
|
||
" - **Weather Forecasting**:\n",
|
||
" - Predict temperature or humidity levels using historical data.\n",
|
||
" - **Stock Market Analysis**:\n",
|
||
" - Predict stock prices based on historical trends.\n",
|
||
" - **Recommender Systems**:\n",
|
||
" - Predict user preferences based on previous ratings.\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"### Advantages of kNN\n",
|
||
"- Simple to implement and understand.\n",
|
||
"- Works well for small datasets and non-linear data.\n",
|
||
"- No training phase; predictions are made at query time.\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"### Challenges with kNN\n",
|
||
"1. **Scalability**:\n",
|
||
" - High computational cost for large datasets due to the need to compute distances for all points.\n",
|
||
"2. **Imbalanced Data**:\n",
|
||
" - Classes with higher sample counts dominate predictions.\n",
|
||
"3. **Feature Scaling**:\n",
|
||
" - Sensitive to the scale of features; requires normalization or standardization.\n",
|
||
"4. **Curse of Dimensionality**:\n",
|
||
" - Performance deteriorates in high-dimensional spaces, as all points appear equidistant.\n"
|
||
],
|
||
"metadata": {
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"id": "6fa8e6e7"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"### **AZSecure Hacker Assets Portal Dataset Overview**\n",
|
||
"\n",
|
||
"#### **Purpose**\n",
|
||
"The dataset supports research and education in **cyber threat intelligence**, enabling analysis of hacker forum contents to understand tools, trends, and behaviors in cyberattacks.\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"#### **Key Highlights**\n",
|
||
"- **Content**:\n",
|
||
" - **15,582 source code snippets** from English, Russian, and Arabic hacker forums.\n",
|
||
" - Collected from major forums: **Ashiyane**, **Opensc**, **Exelab**, and **Xeksec**.\n",
|
||
" - Includes tools like **SQL injections**, **Zeus malware code**, **worms**, and **crypters**.\n",
|
||
"\n",
|
||
"- **Timeframe**:\n",
|
||
" - Posts span **February 7, 2005**, to **October 27, 2016**.\n",
|
||
"\n",
|
||
"- **Features**:\n",
|
||
" - Programming languages: **Delphi**, **C/C++**, **PHP**, **Python**, and more.\n",
|
||
" - Metadata: **Forum Name**, **Author Name**, and **Postdatetime**.\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"#### **Applications**\n",
|
||
"1. **Malware Analysis**: Study source code for vulnerabilities, attack vectors, and malware patterns.\n",
|
||
"2. **Hacker Profiling**: Identify key contributors and specialized hackers.\n",
|
||
"3. **Trend Analysis**: Track exploit development over time.\n",
|
||
"4. **Language-Specific Insights**: Investigate the role of programming languages in cyberattacks.\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"#### **Suggested Analytics & Tools**\n",
|
||
"- **Text Mining & Topic Modeling**: Extract key themes using NLP techniques.\n",
|
||
"- **Clustering**: Use K-Means or SOM for grouping similar exploits.\n",
|
||
"- **Tools**:\n",
|
||
" - **VirusTotal** for malware detection.\n",
|
||
" - **Cuckoo Sandbox** for dynamic behavior analysis.\n",
|
||
" - **Scikit-Learn** for machine learning and clustering.\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"This dataset offers a **unique opportunity** to study real-world hacker tools and behaviors, making it invaluable for educators, researchers, and security professionals."
|
||
],
|
||
"metadata": {
|
||
"id": "4cdd9bb2"
|
||
}
|
||
},
|
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{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "64796390"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# STEP: Import Required Libraries\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# 'pandas' is a library for working with tabular data (like spreadsheets).\n",
|
||
"# We import it with the alias 'pd' so we can type pd instead of pandas.\n",
|
||
"import pandas as pd\n",
|
||
"\n",
|
||
"# 'numpy' is a library for numerical computations (math on arrays/matrices).\n",
|
||
"# We import it with the alias 'np' for convenience.\n",
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"# 'matplotlib.pyplot' is a plotting library that lets us create charts and graphs.\n",
|
||
"# We import it with the alias 'plt' so we can call plt.plot(), plt.show(), etc.\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"# 'seaborn' is a statistical visualization library built on top of matplotlib.\n",
|
||
"# It makes it easier to create attractive and informative charts.\n",
|
||
"# We import it with the alias 'sns'.\n",
|
||
"import seaborn as sns\n",
|
||
"\n",
|
||
"# 'warnings' is a built-in Python module that lets us control warning messages.\n",
|
||
"# We will use it later to suppress non-critical warnings that clutter our output.\n",
|
||
"import warnings"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# STEP: Load the Dataset from a CSV File\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# IMPORTANT: The CSV file 'hacker_sourcecode_data.csv' must be in the same\n",
|
||
"# folder (working directory) as this notebook. If it's not, you'll get a\n",
|
||
"# FileNotFoundError.\n",
|
||
"\n",
|
||
"# pd.read_csv() reads a CSV (Comma-Separated Values) file and converts it\n",
|
||
"# into a pandas DataFrame — think of a DataFrame as a table with rows and columns,\n",
|
||
"# similar to an Excel spreadsheet.\n",
|
||
"# We store the result in a variable called 'hacker_sourcecode_data'.\n",
|
||
"hacker_sourcecode_data = pd.read_csv('hacker_sourcecode_data.csv')\n",
|
||
"\n",
|
||
"# .head() returns the first 5 rows of the DataFrame by default.\n",
|
||
"# We wrap it in print() so the output is displayed in the console.\n",
|
||
"# This is a quick way to verify the data loaded correctly and see what columns exist.\n",
|
||
"print(hacker_sourcecode_data.head())"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "327a62d3",
|
||
"outputId": "26b44529-f1bd-4fc5-da40-f4d818d80189"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
" Asset Name Asset Type Author Name Exploit Type \\\n",
|
||
"0 Tserver Socket Source Code LttCoder Network \n",
|
||
"1 Tserver Socket Source Code LttCoder Network \n",
|
||
"2 Disabling XP Firewall Source Code Snma System \n",
|
||
"3 Disabling XP Firewall Source Code Snma System \n",
|
||
"4 Disabling Task Manager Source Code Snma System \n",
|
||
"\n",
|
||
" Flat Content Forum Name ID \\\n",
|
||
"0 \\r\\r\\n\\t\\t\\t\\t\\t<div id=\"post_message_6\">\\r\\r\\... opensc 1552 \n",
|
||
"1 \\r\\r\\n\\t\\t\\t\\t\\t<div id=\"post_message_6\">\\r\\r\\... opensc 9596 \n",
|
||
"2 \\r\\r\\n\\t\\t\\t\\t\\t<div id=\"post_message_24\">\\r\\r... opensc 1881 \n",
|
||
"3 \\r\\r\\n\\t\\t\\t\\t\\t<div id=\"post_message_24\">\\r\\r... opensc 9922 \n",
|
||
"4 \\r\\r\\n\\t\\t\\t\\t\\t<div id=\"post_message_31\">\\r\\r... opensc 1930 \n",
|
||
"\n",
|
||
" Language Post ID Postdatetime \\\n",
|
||
"0 Delphi 6 2/7/2005 \n",
|
||
"1 Delphi 6 2/7/2005 \n",
|
||
"2 Delphi 24 2/8/2005 \n",
|
||
"3 Delphi 24 2/8/2005 \n",
|
||
"4 Delphi 31 2/9/2005 \n",
|
||
"\n",
|
||
" Source Code \\\n",
|
||
"0 program server; \\r\\r\\n\\r\\r\\n{$APPTYPE CONSOLE}... \n",
|
||
"1 program server; \\r\\r\\n\\r\\r\\n{$APPTYPE CONSOLE}... \n",
|
||
"2 if killxpfire = '1' then begin\\r\\r\\n SCM:=Ope... \n",
|
||
"3 if killxpfire = '1' then begin\\r\\r\\n SCM:=Ope... \n",
|
||
"4 var \\r\\r\\nreg: tregistry;\\r\\r\\nstr : string;\\r... \n",
|
||
"\n",
|
||
" Thread Title Url \\\n",
|
||
"0 howtousetserverSocketinconsole http://www.opensc.ws/showthread.php?t=2 \n",
|
||
"1 howtousetserverSocketinconsole http://www.opensc.ws/showthread.php?t=2 \n",
|
||
"2 DisablingXpFirewall http://www.opensc.ws/showthread.php?t=4 \n",
|
||
"3 DisablingXpFirewall http://www.opensc.ws/showthread.php?t=4 \n",
|
||
"4 TaskManagerdisable/enable http://www.opensc.ws/showthread.php?t=6 \n",
|
||
"\n",
|
||
" Number of Records \n",
|
||
"0 1 \n",
|
||
"1 1 \n",
|
||
"2 1 \n",
|
||
"3 1 \n",
|
||
"4 1 \n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# STEP: Display the Full DataFrame\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# display() is a Jupyter-specific function that renders the DataFrame as a\n",
|
||
"# nicely formatted HTML table with scrolling. This is different from print(),\n",
|
||
"# which outputs plain text. display() makes it easier to browse through all\n",
|
||
"# rows and columns visually.\n",
|
||
"display(hacker_sourcecode_data)"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
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|
||
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|
||
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|
||
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|
||
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|
||
{
|
||
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|
||
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|
||
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|
||
" Asset Name Asset Type Author Name Exploit Type \\\n",
|
||
"0 Tserver Socket Source Code LttCoder Network \n",
|
||
"1 Tserver Socket Source Code LttCoder Network \n",
|
||
"2 Disabling XP Firewall Source Code Snma System \n",
|
||
"3 Disabling XP Firewall Source Code Snma System \n",
|
||
"4 Disabling Task Manager Source Code Snma System \n",
|
||
"... ... ... ... ... \n",
|
||
"15577 Взлом прог под Android Source Code Apokrif System \n",
|
||
"15578 Дизасм. Source Code DenCoder System \n",
|
||
"15579 Дизасм. Source Code DenCoder System \n",
|
||
"15580 Дизасм. Source Code DenCoder System \n",
|
||
"15581 Дизасм. Source Code DenCoder System \n",
|
||
"\n",
|
||
" Flat Content Forum Name ID \\\n",
|
||
"0 \\r\\r\\n\\t\\t\\t\\t\\t<div id=\"post_message_6\">\\r\\r\\... opensc 1552 \n",
|
||
"1 \\r\\r\\n\\t\\t\\t\\t\\t<div id=\"post_message_6\">\\r\\r\\... opensc 9596 \n",
|
||
"2 \\r\\r\\n\\t\\t\\t\\t\\t<div id=\"post_message_24\">\\r\\r... opensc 1881 \n",
|
||
"3 \\r\\r\\n\\t\\t\\t\\t\\t<div id=\"post_message_24\">\\r\\r... opensc 9922 \n",
|
||
"4 \\r\\r\\n\\t\\t\\t\\t\\t<div id=\"post_message_31\">\\r\\r... opensc 1930 \n",
|
||
"... ... ... ... \n",
|
||
"15577 NaN exelab 12860 \n",
|
||
"15578 NaN exelab 2875 \n",
|
||
"15579 NaN exelab 10667 \n",
|
||
"15580 NaN exelab 5069 \n",
|
||
"15581 NaN exelab 12861 \n",
|
||
"\n",
|
||
" Language Post ID Postdatetime \\\n",
|
||
"0 Delphi 6 2/7/2005 \n",
|
||
"1 Delphi 6 2/7/2005 \n",
|
||
"2 Delphi 24 2/8/2005 \n",
|
||
"3 Delphi 24 2/8/2005 \n",
|
||
"4 Delphi 31 2/9/2005 \n",
|
||
"... ... ... ... \n",
|
||
"15577 C/C++ 368839 10/25/2016 \n",
|
||
"15578 C/C++ 0 10/27/2016 \n",
|
||
"15579 C/C++ 0 10/27/2016 \n",
|
||
"15580 C/C++ 368913 10/27/2016 \n",
|
||
"15581 C/C++ 368913 10/27/2016 \n",
|
||
"\n",
|
||
" Source Code \\\n",
|
||
"0 program server; \\r\\r\\n\\r\\r\\n{$APPTYPE CONSOLE}... \n",
|
||
"1 program server; \\r\\r\\n\\r\\r\\n{$APPTYPE CONSOLE}... \n",
|
||
"2 if killxpfire = '1' then begin\\r\\r\\n SCM:=Ope... \n",
|
||
"3 if killxpfire = '1' then begin\\r\\r\\n SCM:=Ope... \n",
|
||
"4 var \\r\\r\\nreg: tregistry;\\r\\r\\nstr : string;\\r... \n",
|
||
"... ... \n",
|
||
"15577 public class Employee { public abstract double... \n",
|
||
"15578 //Export IMAGE_HANDLE __stdcall LoadFileImage(... \n",
|
||
"15579 //Export IMAGE_HANDLE __stdcall LoadFileImage(... \n",
|
||
"15580 //Export IMAGE_HANDLE __stdcall LoadFileImage(... \n",
|
||
"15581 //Export IMAGE_HANDLE __stdcall LoadFileImage(... \n",
|
||
"\n",
|
||
" Thread Title \\\n",
|
||
"0 howtousetserverSocketinconsole \n",
|
||
"1 howtousetserverSocketinconsole \n",
|
||
"2 DisablingXpFirewall \n",
|
||
"3 DisablingXpFirewall \n",
|
||
"4 TaskManagerdisable/enable \n",
|
||
"... ... \n",
|
||
"15577 Взлом прог под Android \n",
|
||
"15578 Дизасм. \n",
|
||
"15579 Дизасм. \n",
|
||
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|
||
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|
||
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|
||
" Url Number of Records \n",
|
||
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|
||
"1 http://www.opensc.ws/showthread.php?t=2 1 \n",
|
||
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|
||
"3 http://www.opensc.ws/showthread.php?t=4 1 \n",
|
||
"4 http://www.opensc.ws/showthread.php?t=6 1 \n",
|
||
"... ... ... \n",
|
||
"15577 https://exelab.ru/f/index.php?action=vthread&f... 1 \n",
|
||
"15578 https://exelab.ru/f/index.php?action=vthread&f... 1 \n",
|
||
"15579 https://exelab.ru/f/index.php?action=vthread&f... 1 \n",
|
||
"15580 https://exelab.ru/f/index.php?action=vthread&f... 1 \n",
|
||
"15581 https://exelab.ru/f/index.php?action=vthread&f... 1 \n",
|
||
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|
||
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|
||
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|
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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||
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|
||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>LttCoder</td>\n",
|
||
" <td>Network</td>\n",
|
||
" <td>\\r\\r\\n\\t\\t\\t\\t\\t<div id=\"post_message_6\">\\r\\r\\...</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>program server; \\r\\r\\n\\r\\r\\n{$APPTYPE CONSOLE}...</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>2/7/2005</td>\n",
|
||
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|
||
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|
||
" <td>http://www.opensc.ws/showthread.php?t=2</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>Source Code</td>\n",
|
||
" <td>Snma</td>\n",
|
||
" <td>System</td>\n",
|
||
" <td>\\r\\r\\n\\t\\t\\t\\t\\t<div id=\"post_message_24\">\\r\\r...</td>\n",
|
||
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|
||
" <td>1881</td>\n",
|
||
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|
||
" <td>24</td>\n",
|
||
" <td>2/8/2005</td>\n",
|
||
" <td>if killxpfire = '1' then begin\\r\\r\\n SCM:=Ope...</td>\n",
|
||
" <td>DisablingXpFirewall</td>\n",
|
||
" <td>http://www.opensc.ws/showthread.php?t=4</td>\n",
|
||
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|
||
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|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Disabling XP Firewall</td>\n",
|
||
" <td>Source Code</td>\n",
|
||
" <td>Snma</td>\n",
|
||
" <td>System</td>\n",
|
||
" <td>\\r\\r\\n\\t\\t\\t\\t\\t<div id=\"post_message_24\">\\r\\r...</td>\n",
|
||
" <td>opensc</td>\n",
|
||
" <td>9922</td>\n",
|
||
" <td>Delphi</td>\n",
|
||
" <td>24</td>\n",
|
||
" <td>2/8/2005</td>\n",
|
||
" <td>if killxpfire = '1' then begin\\r\\r\\n SCM:=Ope...</td>\n",
|
||
" <td>DisablingXpFirewall</td>\n",
|
||
" <td>http://www.opensc.ws/showthread.php?t=4</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>Disabling Task Manager</td>\n",
|
||
" <td>Source Code</td>\n",
|
||
" <td>Snma</td>\n",
|
||
" <td>System</td>\n",
|
||
" <td>\\r\\r\\n\\t\\t\\t\\t\\t<div id=\"post_message_31\">\\r\\r...</td>\n",
|
||
" <td>opensc</td>\n",
|
||
" <td>1930</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>Source Code</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>10/25/2016</td>\n",
|
||
" <td>public class Employee { public abstract double...</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>10/27/2016</td>\n",
|
||
" <td>//Export IMAGE_HANDLE __stdcall LoadFileImage(...</td>\n",
|
||
" <td>Дизасм.</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>10/27/2016</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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|
||
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||
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|
||
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||
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||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
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|
||
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||
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|
||
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|
||
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||
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|
||
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|
||
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|
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|
||
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||
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|
||
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||
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||
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|
||
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|
||
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||
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||
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||
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||
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|
||
" const element = document.querySelector('#df-a8f93856-9a6e-4e55-abc8-7aded81987e9');\n",
|
||
" const dataTable =\n",
|
||
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
|
||
" [key], {});\n",
|
||
" if (!dataTable) return;\n",
|
||
"\n",
|
||
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
|
||
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
|
||
" + ' to learn more about interactive tables.';\n",
|
||
" element.innerHTML = '';\n",
|
||
" dataTable['output_type'] = 'display_data';\n",
|
||
" await google.colab.output.renderOutput(dataTable, element);\n",
|
||
" const docLink = document.createElement('div');\n",
|
||
" docLink.innerHTML = docLinkHtml;\n",
|
||
" element.appendChild(docLink);\n",
|
||
" }\n",
|
||
" </script>\n",
|
||
" </div>\n",
|
||
"\n",
|
||
"\n",
|
||
" <div id=\"id_fee9688a-92a1-44a0-8327-76d02a48604c\">\n",
|
||
" <style>\n",
|
||
" .colab-df-generate {\n",
|
||
" background-color: #E8F0FE;\n",
|
||
" border: none;\n",
|
||
" border-radius: 50%;\n",
|
||
" cursor: pointer;\n",
|
||
" display: none;\n",
|
||
" fill: #1967D2;\n",
|
||
" height: 32px;\n",
|
||
" padding: 0 0 0 0;\n",
|
||
" width: 32px;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-generate:hover {\n",
|
||
" background-color: #E2EBFA;\n",
|
||
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
|
||
" fill: #174EA6;\n",
|
||
" }\n",
|
||
"\n",
|
||
" [theme=dark] .colab-df-generate {\n",
|
||
" background-color: #3B4455;\n",
|
||
" fill: #D2E3FC;\n",
|
||
" }\n",
|
||
"\n",
|
||
" [theme=dark] .colab-df-generate:hover {\n",
|
||
" background-color: #434B5C;\n",
|
||
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
|
||
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
|
||
" fill: #FFFFFF;\n",
|
||
" }\n",
|
||
" </style>\n",
|
||
" <button class=\"colab-df-generate\" onclick=\"generateWithVariable('hacker_sourcecode_data')\"\n",
|
||
" title=\"Generate code using this dataframe.\"\n",
|
||
" style=\"display:none;\">\n",
|
||
"\n",
|
||
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
|
||
" width=\"24px\">\n",
|
||
" <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
|
||
" </svg>\n",
|
||
" </button>\n",
|
||
" <script>\n",
|
||
" (() => {\n",
|
||
" const buttonEl =\n",
|
||
" document.querySelector('#id_fee9688a-92a1-44a0-8327-76d02a48604c button.colab-df-generate');\n",
|
||
" buttonEl.style.display =\n",
|
||
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
||
"\n",
|
||
" buttonEl.onclick = () => {\n",
|
||
" google.colab.notebook.generateWithVariable('hacker_sourcecode_data');\n",
|
||
" }\n",
|
||
" })();\n",
|
||
" </script>\n",
|
||
" </div>\n",
|
||
"\n",
|
||
" </div>\n",
|
||
" </div>\n"
|
||
],
|
||
"application/vnd.google.colaboratory.intrinsic+json": {
|
||
"type": "dataframe",
|
||
"variable_name": "hacker_sourcecode_data",
|
||
"summary": "{\n \"name\": \"hacker_sourcecode_data\",\n \"rows\": 15582,\n \"fields\": [\n {\n \"column\": \"Asset Name\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1942,\n \"samples\": [\n \" Hack Me & Hack You!\",\n \"\\u0420\\u0430\\u0441\\u043f\\u0430\\u043a\\u043e\\u0432\\u043a\\u0430 Themida \\u043f\\u043e\\u0441\\u043b\\u0435\\u0434\\u043d\\u0438\\u0445 \\u0432\\u0435\\u0440\\u0441\\u0438\\u0439: \\u0442\\u0443\\u0442\\u043e\\u0440\\u044b, \\u0441\\u043a\\u0440\\u0438\\u043f\\u0442\\u044b, \\u043f\\u043b\\u0430\\u0433\\u0438\\u043d\\u044b.\",\n \"Webcam spying\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Asset Type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Source Code\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Author Name\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1082,\n \"samples\": [\n \"wanttolearn\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Exploit Type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"System\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Flat Content\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1103,\n \"samples\": [\n \"\\r\\r\\n\\t\\t\\t\\t\\t<div id=\\\"post_message_15888\\\">\\r\\r\\n\\t\\t\\t\\t\\t\\t<blockquote class=\\\"postcontent restore\\\">\\r\\r\\n\\t\\t\\t\\t\\t\\t\\t<div class=\\\"bbcode_container\\\">\\r\\r\\n\\t<div class=\\\"bbcode_description\\\">Code:</div>\\r\\r\\n\\t<pre class=\\\"bbcode_code\\\" style=\\\"height:86px;\\\"> else if bTF = False then <b><u>begin</u></b>\\r\\r\\n reg.DeleteValue('NoFolderOptions');\\r\\r\\n ShowMessage('Folder Options Enabled');\\r\\r\\n Exit;\\r\\r\\n end;\\r\\r\\n reg.CloseKey;\\r\\r\\nend;</pre>\\r\\r\\n</div>\\r\\r\\n\\t\\t\\t\\t\\t\\t\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Forum Name\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"xeksec\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ID\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4554,\n \"min\": 1,\n \"max\": 15840,\n \"num_unique_values\": 15582,\n \"samples\": [\n 3120\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Language\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 19,\n \"samples\": [\n \"Delphi\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Post ID\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 302978,\n \"min\": 0,\n \"max\": 990709,\n \"num_unique_values\": 3769,\n \"samples\": [\n 952908\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Postdatetime\",\n \"properties\": {\n \"dtype\": \"object\",\n \"num_unique_values\": 2007,\n \"samples\": [\n \"8/2/2016\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Source Code\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 5705,\n \"samples\": [\n \"char Key[] = {9,4,0,6,7,2,9,5,8,4,0,6,8,3,7,4,9,5,0,1}; void CryptFunc(char *String, int Const, int Count) { for(int i = 0; i Count; i++) String[(Const+Count+19-i)%20] = (String[(Const+Count+16-i)%20] - String[(Const+Count+1-i)%20] - Key[(Const+Count+3-i)%20] + 20)%10 + '0'; } void DecryptFunc(char *String, int Const, int Count) { for(int i = 0; i Count; i++) String[(Const+i)%20] = (String[(Const+3+i)%20] + String[(Const+5+i)%20] + Key[(Const+7+i)%20] - 0x60)%10 + '0'; } void main() { int Const = 0; int Count = 20; int pos_error = (Const+Count+17)%20; CryptFunc(Buffer, Const, Count); DecryptFunc(Buffer, Const, Count); }\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Thread Title\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2009,\n \"samples\": [\n \"Inject - \\u043a\\u0430\\u043a \\u043f\\u0440\\u0430\\u0432\\u0438\\u043b\\u044c\\u043d\\u043e \\u0432\\u0441\\u0442\\u0430\\u0432\\u0438\\u0442\\u044c JMP FAR?\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Url\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2478,\n \"samples\": [\n \"https://exelab.ru/f/index.php?action=vthread&forum=6&topic=22201&page=2\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Number of Records\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 1,\n \"max\": 1,\n \"num_unique_values\": 1,\n \"samples\": [\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# STEP: Inspect Each Column's Name and First Value\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# .columns returns an Index object listing all column names in the DataFrame.\n",
|
||
"# Printing it shows us every column header so we know what data fields exist.\n",
|
||
"print(hacker_sourcecode_data.columns)\n",
|
||
"\n",
|
||
"# Now we loop through each column name one at a time.\n",
|
||
"# 'for column in hacker_sourcecode_data.columns' iterates over every column name.\n",
|
||
"for column in hacker_sourcecode_data.columns:\n",
|
||
" # Print a separator line with the column name for readability.\n",
|
||
" # f-strings (f'...') let us embed variables directly inside a string using {}.\n",
|
||
" print(f'\\n ------------------------- Column {column} -------------------------')\n",
|
||
"\n",
|
||
" # Print the FIRST value (row index 0) of each column.\n",
|
||
" # This gives us a concrete example of what kind of data each column holds.\n",
|
||
" # For example, is it text? A number? A date? HTML content?\n",
|
||
" print(hacker_sourcecode_data[column][0])"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "153442ec",
|
||
"outputId": "12b6c0a8-46ef-4c28-8de5-cbd58b52713a"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Index(['Asset Name', 'Asset Type', 'Author Name', 'Exploit Type',\n",
|
||
" 'Flat Content', 'Forum Name', 'ID', 'Language', 'Post ID',\n",
|
||
" 'Postdatetime', 'Source Code', 'Thread Title', 'Url',\n",
|
||
" 'Number of Records'],\n",
|
||
" dtype='object')\n",
|
||
"\n",
|
||
" ------------------------- Column Asset Name -------------------------\n",
|
||
"Tserver Socket\n",
|
||
"\n",
|
||
" ------------------------- Column Asset Type -------------------------\n",
|
||
"Source Code\n",
|
||
"\n",
|
||
" ------------------------- Column Author Name -------------------------\n",
|
||
"LttCoder\n",
|
||
"\n",
|
||
" ------------------------- Column Exploit Type -------------------------\n",
|
||
"Network\n",
|
||
"\n",
|
||
" ------------------------- Column Flat Content -------------------------\n",
|
||
"\r\r\n",
|
||
"\t\t\t\t\t<div id=\"post_message_6\">\r\r\n",
|
||
"\t\t\t\t\t\t<blockquote class=\"postcontent restore\">\r\r\n",
|
||
"\t\t\t\t\t\t\ttry this <br />\r\r\n",
|
||
"<div class=\"bbcode_container\">\r\r\n",
|
||
"\t<div class=\"bbcode_description\">Code:</div>\r\r\n",
|
||
"\t<pre class=\"bbcode_code\" style=\"height:206px;\">program server; \r\r\n",
|
||
"\r\r\n",
|
||
"{$APPTYPE CONSOLE} \r\r\n",
|
||
"\r\r\n",
|
||
"uses \r\r\n",
|
||
"SysUtils, \r\r\n",
|
||
"ScktComp; \r\r\n",
|
||
"\r\r\n",
|
||
"var \r\r\n",
|
||
"myServer:tserversocket; \r\r\n",
|
||
"begin \r\r\n",
|
||
"myserver:=tserversocket.Create(nil); \r\r\n",
|
||
"myserver.Port:=222; \r\r\n",
|
||
"myserver.Active:=true; \r\r\n",
|
||
"\r\r\n",
|
||
"while true do sleep(1000); \r\r\n",
|
||
"end.</pre>\r\r\n",
|
||
"</div>\r\r\n",
|
||
"\t\t\t\t\t\t\n",
|
||
"\n",
|
||
" ------------------------- Column Forum Name -------------------------\n",
|
||
"opensc\n",
|
||
"\n",
|
||
" ------------------------- Column ID -------------------------\n",
|
||
"1552\n",
|
||
"\n",
|
||
" ------------------------- Column Language -------------------------\n",
|
||
"Delphi\n",
|
||
"\n",
|
||
" ------------------------- Column Post ID -------------------------\n",
|
||
"6\n",
|
||
"\n",
|
||
" ------------------------- Column Postdatetime -------------------------\n",
|
||
"2/7/2005\n",
|
||
"\n",
|
||
" ------------------------- Column Source Code -------------------------\n",
|
||
"program server; \r\r\n",
|
||
"\r\r\n",
|
||
"{$APPTYPE CONSOLE} \r\r\n",
|
||
"\r\r\n",
|
||
"uses \r\r\n",
|
||
"SysUtils, \r\r\n",
|
||
"ScktComp; \r\r\n",
|
||
"\r\r\n",
|
||
"var \r\r\n",
|
||
"myServer:tserversocket; \r\r\n",
|
||
"begin \r\r\n",
|
||
"myserver:=tserversocket.Create(nil); \r\r\n",
|
||
"myserver.Port:=222; \r\r\n",
|
||
"myserver.Active:=true; \r\r\n",
|
||
"\r\r\n",
|
||
"while true do sleep(1000); \r\r\n",
|
||
"end.\n",
|
||
"\n",
|
||
" ------------------------- Column Thread Title -------------------------\n",
|
||
"howtousetserverSocketinconsole\n",
|
||
"\n",
|
||
" ------------------------- Column Url -------------------------\n",
|
||
"http://www.opensc.ws/showthread.php?t=2\n",
|
||
"\n",
|
||
" ------------------------- Column Number of Records -------------------------\n",
|
||
"1\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"This dataset appears to contain information about exploits and code snippets from an online forum, likely curated for analysis. Here's a breakdown of the dataset's structure and potential utility:\n",
|
||
"\n",
|
||
"### Key Columns and Observations\n",
|
||
"1. **`Asset Name`**: Describes the name of the resource, such as \"Tserver Socket\" in this case. Could represent specific components or tools discussed in the post.\n",
|
||
"2. **`Asset Type`**: Categorizes the asset, such as \"Source Code.\" Useful for filtering specific types of assets (e.g., source code, binaries, configurations).\n",
|
||
"3. **`Author Name`**: Identifies the forum user who posted the content, e.g., \"LttCoder.\" Can help with author-based analysis or tracking threat actors.\n",
|
||
"4. **`Exploit Type`**: Classifies the exploit (e.g., \"Network\"). Useful for determining the exploit's scope or vector.\n",
|
||
"5. **`Flat Content`**: Appears to be HTML content of the post, potentially containing formatted code, descriptions, or other details.\n",
|
||
"6. **`Forum Name`**: Indicates the forum where the post originated, e.g., \"opensc.\" Could provide context about the community.\n",
|
||
"7. **`ID`**: Likely a unique identifier for each dataset entry.\n",
|
||
"8. **`Language`**: Specifies the programming language used, e.g., \"Delphi.\" Important for code analysis and understanding language trends.\n",
|
||
"9. **`Post ID`**: An identifier for the specific post. Could link back to the forum or thread.\n",
|
||
"10. **`Postdatetime`**: Timestamp of the post, useful for chronological analysis or trending activity over time.\n",
|
||
"11. **`Source Code`**: The actual source code content extracted from the post. Vital for exploit analysis.\n",
|
||
"12. **`Thread Title`**: Title of the thread, possibly summarizing the context or purpose of the post.\n",
|
||
"13. **`Url`**: A link to the post or resource, providing traceability or further exploration.\n",
|
||
"14. **`Number of Records`**: Could indicate the number of entries related to this post, or the count of similar entries.\n",
|
||
"\n",
|
||
"### Potential Uses\n",
|
||
"1. **Threat Analysis**:\n",
|
||
" - Understand trends in exploit sharing and development.\n",
|
||
" - Identify common languages or asset types used in exploits.\n",
|
||
"\n",
|
||
"2. **Actor Attribution**:\n",
|
||
" - Track posts by specific authors to build profiles of their activity.\n",
|
||
" - Link posts to forums and threads for a broader picture.\n",
|
||
"\n",
|
||
"3. **Code Analysis**:\n",
|
||
" - Analyze shared code snippets for vulnerabilities, exploits, or reused patterns.\n",
|
||
" - Map programming languages to exploit types or targets.\n",
|
||
"\n",
|
||
"4. **Temporal Analysis**:\n",
|
||
" - Use `Postdatetime` to observe trends in activity over time.\n",
|
||
"\n",
|
||
"5. **Forum Dynamics**:\n",
|
||
" - Examine the structure and focus of different forums using `Forum Name` and `Thread Title`.\n",
|
||
"\n",
|
||
"6. **Dataset Cleaning and Normalization**:\n",
|
||
" - Standardize the `Flat Content` column to extract useful information like source code or descriptions.\n",
|
||
" - Parse the `Source Code` column for syntax checking or pattern recognition.\n"
|
||
],
|
||
"metadata": {
|
||
"id": "b00e1c2c"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# STEP: Explore the Dataset — Shape, Missing Values, and Distributions\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# .shape returns a tuple (number_of_rows, number_of_columns).\n",
|
||
"# This tells us how large our dataset is — how many records and how many features.\n",
|
||
"print(\"Dataset Shape:\", hacker_sourcecode_data.shape)\n",
|
||
"\n",
|
||
"# .head() shows the first 5 rows so we can quickly preview the data structure.\n",
|
||
"print(\"\\nFirst 5 rows of the dataset:\")\n",
|
||
"print(hacker_sourcecode_data.head())\n",
|
||
"\n",
|
||
"# ---- Check for Missing Values ----\n",
|
||
"# .isnull() creates a DataFrame of True/False values (True where data is missing).\n",
|
||
"# .sum() then counts the number of True values (missing entries) in each column.\n",
|
||
"# This is important because missing data can cause errors in machine learning models.\n",
|
||
"print(\"\\nMissing Values in Dataset:\")\n",
|
||
"print(hacker_sourcecode_data.isnull().sum())\n",
|
||
"\n",
|
||
"# ---- Check the Class Distribution ----\n",
|
||
"# .value_counts() counts how many times each unique value appears in the\n",
|
||
"# 'Exploit Type' column. This is our TARGET variable — the label we want to predict.\n",
|
||
"# Knowing the distribution helps us see if the classes are balanced or imbalanced.\n",
|
||
"# (Imbalanced classes can make models biased toward the majority class.)\n",
|
||
"print(\"\\nClass Distribution (Exploit Type):\")\n",
|
||
"print(hacker_sourcecode_data['Exploit Type'].value_counts())\n",
|
||
"\n",
|
||
"# ---- Count Unique Values Per Column ----\n",
|
||
"# .nunique() returns the number of distinct (unique) values in each column.\n",
|
||
"# This helps us understand the cardinality of each feature:\n",
|
||
"# - Low cardinality (few unique values) = good candidate for categorical encoding\n",
|
||
"# - High cardinality (many unique values) = may need special handling or removal\n",
|
||
"print(\"\\nUnique value counts per column:\")\n",
|
||
"unique_counts = hacker_sourcecode_data.nunique()\n",
|
||
"print(unique_counts)"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "a3ea4c3c",
|
||
"outputId": "23211a23-b6d9-4afd-ccbb-fc164fa2bbcc"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Dataset Shape: (15582, 14)\n",
|
||
"\n",
|
||
"First 5 rows of the dataset:\n",
|
||
" Asset Name Asset Type Author Name Exploit Type \\\n",
|
||
"0 Tserver Socket Source Code LttCoder Network \n",
|
||
"1 Tserver Socket Source Code LttCoder Network \n",
|
||
"2 Disabling XP Firewall Source Code Snma System \n",
|
||
"3 Disabling XP Firewall Source Code Snma System \n",
|
||
"4 Disabling Task Manager Source Code Snma System \n",
|
||
"\n",
|
||
" Flat Content Forum Name ID \\\n",
|
||
"0 \\r\\r\\n\\t\\t\\t\\t\\t<div id=\"post_message_6\">\\r\\r\\... opensc 1552 \n",
|
||
"1 \\r\\r\\n\\t\\t\\t\\t\\t<div id=\"post_message_6\">\\r\\r\\... opensc 9596 \n",
|
||
"2 \\r\\r\\n\\t\\t\\t\\t\\t<div id=\"post_message_24\">\\r\\r... opensc 1881 \n",
|
||
"3 \\r\\r\\n\\t\\t\\t\\t\\t<div id=\"post_message_24\">\\r\\r... opensc 9922 \n",
|
||
"4 \\r\\r\\n\\t\\t\\t\\t\\t<div id=\"post_message_31\">\\r\\r... opensc 1930 \n",
|
||
"\n",
|
||
" Language Post ID Postdatetime \\\n",
|
||
"0 Delphi 6 2/7/2005 \n",
|
||
"1 Delphi 6 2/7/2005 \n",
|
||
"2 Delphi 24 2/8/2005 \n",
|
||
"3 Delphi 24 2/8/2005 \n",
|
||
"4 Delphi 31 2/9/2005 \n",
|
||
"\n",
|
||
" Source Code \\\n",
|
||
"0 program server; \\r\\r\\n\\r\\r\\n{$APPTYPE CONSOLE}... \n",
|
||
"1 program server; \\r\\r\\n\\r\\r\\n{$APPTYPE CONSOLE}... \n",
|
||
"2 if killxpfire = '1' then begin\\r\\r\\n SCM:=Ope... \n",
|
||
"3 if killxpfire = '1' then begin\\r\\r\\n SCM:=Ope... \n",
|
||
"4 var \\r\\r\\nreg: tregistry;\\r\\r\\nstr : string;\\r... \n",
|
||
"\n",
|
||
" Thread Title Url \\\n",
|
||
"0 howtousetserverSocketinconsole http://www.opensc.ws/showthread.php?t=2 \n",
|
||
"1 howtousetserverSocketinconsole http://www.opensc.ws/showthread.php?t=2 \n",
|
||
"2 DisablingXpFirewall http://www.opensc.ws/showthread.php?t=4 \n",
|
||
"3 DisablingXpFirewall http://www.opensc.ws/showthread.php?t=4 \n",
|
||
"4 TaskManagerdisable/enable http://www.opensc.ws/showthread.php?t=6 \n",
|
||
"\n",
|
||
" Number of Records \n",
|
||
"0 1 \n",
|
||
"1 1 \n",
|
||
"2 1 \n",
|
||
"3 1 \n",
|
||
"4 1 \n",
|
||
"\n",
|
||
"Missing Values in Dataset:\n",
|
||
"Asset Name 8\n",
|
||
"Asset Type 0\n",
|
||
"Author Name 534\n",
|
||
"Exploit Type 0\n",
|
||
"Flat Content 10530\n",
|
||
"Forum Name 0\n",
|
||
"ID 0\n",
|
||
"Language 0\n",
|
||
"Post ID 0\n",
|
||
"Postdatetime 0\n",
|
||
"Source Code 0\n",
|
||
"Thread Title 0\n",
|
||
"Url 0\n",
|
||
"Number of Records 0\n",
|
||
"dtype: int64\n",
|
||
"\n",
|
||
"Class Distribution (Exploit Type):\n",
|
||
"Exploit Type\n",
|
||
"System 9746\n",
|
||
"Web 5598\n",
|
||
"Network 232\n",
|
||
"Database 6\n",
|
||
"Name: count, dtype: int64\n",
|
||
"\n",
|
||
"Unique value counts per column:\n",
|
||
"Asset Name 1942\n",
|
||
"Asset Type 1\n",
|
||
"Author Name 1082\n",
|
||
"Exploit Type 4\n",
|
||
"Flat Content 1103\n",
|
||
"Forum Name 4\n",
|
||
"ID 15582\n",
|
||
"Language 19\n",
|
||
"Post ID 3769\n",
|
||
"Postdatetime 2007\n",
|
||
"Source Code 5705\n",
|
||
"Thread Title 2009\n",
|
||
"Url 2478\n",
|
||
"Number of Records 1\n",
|
||
"dtype: int64\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# STEP: Import Additional Libraries for Preprocessing and Modeling\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# We import pandas again here (already imported above) — this is safe to do\n",
|
||
"# and has no negative effect; Python simply reuses the already-loaded module.\n",
|
||
"import pandas as pd\n",
|
||
"\n",
|
||
"# LabelEncoder: Converts categorical text labels into integers (e.g., \"Delphi\" -> 0, \"C++\" -> 1).\n",
|
||
"# MinMaxScaler: Scales numerical features to a range of 0 to 1 so no single feature\n",
|
||
"# dominates the distance calculation in KNN or K-Means.\n",
|
||
"from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
|
||
"\n",
|
||
"# KMeans: An unsupervised clustering algorithm that groups similar data points\n",
|
||
"# into 'k' clusters. We use this later to explore natural groupings in the data.\n",
|
||
"from sklearn.cluster import KMeans\n",
|
||
"\n",
|
||
"# PCA (Principal Component Analysis): A dimensionality reduction technique.\n",
|
||
"# It compresses many features into fewer components while preserving the most\n",
|
||
"# important patterns. We use it to reduce our data to 2 dimensions for visualization.\n",
|
||
"from sklearn.decomposition import PCA\n",
|
||
"\n",
|
||
"# Import matplotlib again for plotting (already imported, but included here\n",
|
||
"# for clarity since this is a new section of the analysis).\n",
|
||
"import matplotlib.pyplot as plt"
|
||
],
|
||
"metadata": {
|
||
"id": "a89a2aa8"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"---\n",
|
||
"### **Step 1: Drop Irrelevant Columns**\n",
|
||
"- **Why**:\n",
|
||
" - These columns do not provide meaningful information for clustering.\n",
|
||
" - For example:\n",
|
||
" - `ID` and `Post ID` are unique identifiers and do not contribute to patterns between rows.\n",
|
||
" - `Thread Title` and `Url` might contain unstructured data that is not directly relevant for clustering.\n",
|
||
" - `Number of Records` has only one unique value, so it does not contribute to variability in the dataset.\n",
|
||
"\n",
|
||
"---"
|
||
],
|
||
"metadata": {
|
||
"id": "a70fb2a4"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# Step 1: Drop Irrelevant Columns\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# Create a list of column names that we want to remove from the DataFrame.\n",
|
||
"# These columns do not provide useful patterns for our machine learning model:\n",
|
||
"# - 'ID' and 'Post ID' are unique identifiers (every row has a different value,\n",
|
||
"# so there's no pattern for the model to learn).\n",
|
||
"# - 'Thread Title' and 'Url' contain unstructured text/links that aren't useful here.\n",
|
||
"# - 'Number of Records' has only one unique value, so it adds no information.\n",
|
||
"columns_to_drop = ['ID', 'Post ID', 'Thread Title', 'Url', 'Number of Records']\n",
|
||
"\n",
|
||
"# .drop() removes the specified columns from the DataFrame.\n",
|
||
"# - 'columns=' tells pandas which columns to remove.\n",
|
||
"# - 'axis=1' means we are dropping columns (axis=0 would drop rows).\n",
|
||
"# The result is saved back into 'hacker_sourcecode_data', overwriting the old version.\n",
|
||
"hacker_sourcecode_data = hacker_sourcecode_data.drop(columns=columns_to_drop, axis=1)"
|
||
],
|
||
"metadata": {
|
||
"id": "95e87f47"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"### **Step 2: Handle Missing Values**\n",
|
||
"- **Why**:\n",
|
||
" - Missing values can interfere with clustering algorithms, as most machine learning models do not handle null values.\n",
|
||
" - Replacing missing values with placeholders (`'Unknown'` or `'Anonymous'`) ensures that every row has a valid value for these columns without making assumptions about missing data.\n",
|
||
"\n",
|
||
"---"
|
||
],
|
||
"metadata": {
|
||
"id": "27f57cab"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# Step 2: Handle Missing Values\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# .fillna('Unknown') replaces all missing (NaN) values in the 'Asset Name' column\n",
|
||
"# with the string 'Unknown'. This ensures every row has a valid value.\n",
|
||
"# We use 'Unknown' as a placeholder because we don't know what the real name was.\n",
|
||
"hacker_sourcecode_data['Asset Name'] = hacker_sourcecode_data['Asset Name'].fillna('Unknown')\n",
|
||
"\n",
|
||
"# Similarly, replace missing 'Author Name' values with 'Anonymous'.\n",
|
||
"# This is a common approach: rather than deleting rows with missing data,\n",
|
||
"# we fill them with a meaningful default so we don't lose the other data in that row.\n",
|
||
"hacker_sourcecode_data['Author Name'] = hacker_sourcecode_data['Author Name'].fillna('Anonymous')"
|
||
],
|
||
"metadata": {
|
||
"id": "41b207ad"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"### **Step 3: Convert `Postdatetime` to Datetime and Extract Features**\n",
|
||
"- **Why**:\n",
|
||
" - Converting `Postdatetime` to a `datetime` format makes it easier to extract meaningful temporal features.\n",
|
||
" - Extracting `Year` and `Month` allows us to analyze trends or patterns related to the timing of posts (e.g., clusters may represent older vs. newer exploits).\n",
|
||
"\n",
|
||
"---"
|
||
],
|
||
"metadata": {
|
||
"id": "a9293acb"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# Step 3: Convert Postdatetime to Datetime and Extract Features\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# pd.to_datetime() converts the 'Postdatetime' column from raw text strings\n",
|
||
"# (e.g., \"2/7/2005\") into proper Python datetime objects.\n",
|
||
"# The 'errors='coerce'' parameter means: if any value can't be parsed as a date,\n",
|
||
"# replace it with NaT (Not a Time) instead of raising an error.\n",
|
||
"hacker_sourcecode_data['Postdatetime'] = pd.to_datetime(\n",
|
||
" hacker_sourcecode_data['Postdatetime'], errors='coerce'\n",
|
||
")\n",
|
||
"\n",
|
||
"# .dt.year extracts just the year (e.g., 2005, 2010) from each datetime value\n",
|
||
"# and stores it in a new column called 'Year'.\n",
|
||
"# This gives us a numeric feature we can use in our model.\n",
|
||
"hacker_sourcecode_data['Year'] = hacker_sourcecode_data['Postdatetime'].dt.year\n",
|
||
"\n",
|
||
"# .dt.month extracts the month number (1-12) from each datetime value.\n",
|
||
"# Together, Year and Month let us analyze temporal trends\n",
|
||
"# (e.g., were more exploits posted in certain years or months?).\n",
|
||
"hacker_sourcecode_data['Month'] = hacker_sourcecode_data['Postdatetime'].dt.month"
|
||
],
|
||
"metadata": {
|
||
"id": "44bc7855"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"### **Step 4: Add Derived Feature for `Source Code Length`**\n",
|
||
"\n",
|
||
"- **Why**:\n",
|
||
" - `Source Code Length` is a useful numerical feature that reflects the complexity or size of the exploit code.\n",
|
||
" - Longer code might indicate more sophisticated exploits, while shorter code might represent simpler ones.\n",
|
||
" - This feature is numeric and contributes directly to clustering.\n",
|
||
"\n",
|
||
"---"
|
||
],
|
||
"metadata": {
|
||
"id": "19f68dd3"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# Step 4: Add a Derived Feature — Source Code Length\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# Here we create a brand-new column called 'Source Code Length'.\n",
|
||
"# .apply() runs a function on every single value in the 'Source Code' column.\n",
|
||
"#\n",
|
||
"# The function 'lambda x: len(str(x))' does the following for each value:\n",
|
||
"# 1. str(x) — converts the value to a string (in case it's NaN or non-text)\n",
|
||
"# 2. len(...) — counts the number of characters in that string\n",
|
||
"#\n",
|
||
"# Why is this useful? The length of the source code is a numeric feature that\n",
|
||
"# can indicate complexity. Longer code may represent more sophisticated exploits,\n",
|
||
"# while shorter code may be simpler scripts or snippets.\n",
|
||
"hacker_sourcecode_data['Source Code Length'] = hacker_sourcecode_data['Source Code'].apply(\n",
|
||
" lambda x: len(str(x))\n",
|
||
")"
|
||
],
|
||
"metadata": {
|
||
"id": "86882eb3"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"### **Step 5: Encode Categorical Columns**\n",
|
||
"#### **Label Encoding**\n",
|
||
"\n",
|
||
"- **Why**:\n",
|
||
" - Label encoding assigns unique integers to each author.\n",
|
||
" - This is necessary because machine learning algorithms require numerical inputs and cannot directly process strings.\n",
|
||
"\n",
|
||
"#### **One-Hot Encoding**\n",
|
||
"\n",
|
||
"- **Why**:\n",
|
||
" - One-hot encoding converts categorical variables into binary columns, allowing the model to treat each category equally.\n",
|
||
" - For example, `Forum Name` becomes multiple columns like `Forum Name_opensc` or `Forum Name_xeksec`, each indicating the presence or absence of that category.\n",
|
||
"\n",
|
||
"---"
|
||
],
|
||
"metadata": {
|
||
"id": "0b5fd216"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# Step 5: Encode Categorical Columns (Convert Text to Numbers)\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# Machine learning algorithms work with numbers, not text. We need to convert\n",
|
||
"# categorical (text-based) columns into numerical representations.\n",
|
||
"\n",
|
||
"# --- Label Encoding for 'Author Name' ---\n",
|
||
"# LabelEncoder assigns a unique integer to each unique author name.\n",
|
||
"# For example: \"LttCoder\" -> 0, \"Anonymous\" -> 1, \"HackerX\" -> 2, etc.\n",
|
||
"# We create an instance of LabelEncoder first.\n",
|
||
"label_encoder = LabelEncoder()\n",
|
||
"\n",
|
||
"# .fit_transform() does two things at once:\n",
|
||
"# 1. fit() — learns all the unique author names in the column\n",
|
||
"# 2. transform() — replaces each name with its corresponding integer\n",
|
||
"# The result overwrites the original 'Author Name' column with integers.\n",
|
||
"hacker_sourcecode_data['Author Name'] = label_encoder.fit_transform(\n",
|
||
" hacker_sourcecode_data['Author Name']\n",
|
||
")\n",
|
||
"\n",
|
||
"# --- One-Hot Encoding for Low-Cardinality Categorical Variables ---\n",
|
||
"# pd.get_dummies() converts categorical columns into multiple binary (0/1) columns.\n",
|
||
"# For example, if 'Forum Name' has values [\"opensc\", \"exelab\", \"xeksec\"], it creates:\n",
|
||
"# - 'Forum Name_exelab' (1 if exelab, 0 otherwise)\n",
|
||
"# - 'Forum Name_xeksec' (1 if xeksec, 0 otherwise)\n",
|
||
"#\n",
|
||
"# 'drop_first=True' removes the first category to avoid redundancy\n",
|
||
"# (if it's NOT exelab and NOT xeksec, it must be opensc — so we don't need\n",
|
||
"# a separate column for opensc). This is called avoiding the \"dummy variable trap.\"\n",
|
||
"#\n",
|
||
"# We apply this to 'Asset Type', 'Forum Name', and 'Language' columns.\n",
|
||
"hacker_sourcecode_data = pd.get_dummies(\n",
|
||
" hacker_sourcecode_data,\n",
|
||
" columns=['Asset Type', 'Forum Name', 'Language'],\n",
|
||
" drop_first=True\n",
|
||
")"
|
||
],
|
||
"metadata": {
|
||
"id": "3d473859"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# STEP: Verify Label Encoding Results\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# Print the 'Author Name' column to confirm it now contains integers\n",
|
||
"# instead of text strings. Each unique author has been assigned a number.\n",
|
||
"print(hacker_sourcecode_data['Author Name'])"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "6bae1bd9",
|
||
"outputId": "5e66032c-e349-42bf-c009-459569497063"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"0 298\n",
|
||
"1 298\n",
|
||
"2 447\n",
|
||
"3 447\n",
|
||
"4 447\n",
|
||
" ... \n",
|
||
"15577 79\n",
|
||
"15578 159\n",
|
||
"15579 159\n",
|
||
"15580 159\n",
|
||
"15581 159\n",
|
||
"Name: Author Name, Length: 15582, dtype: int64\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# STEP: Verify All Column Names After Encoding\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# After one-hot encoding, the DataFrame has many new columns (one for each\n",
|
||
"# category). Print the column names to see all the features now available.\n",
|
||
"# You'll notice columns like 'Language_C/C++', 'Forum Name_opensc', etc.\n",
|
||
"print(hacker_sourcecode_data.columns)"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "de932c56",
|
||
"outputId": "c02fc05f-1ba6-42a5-ca44-f2db2f494964"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Index(['Asset Name', 'Author Name', 'Exploit Type', 'Flat Content',\n",
|
||
" 'Postdatetime', 'Source Code', 'Year', 'Month', 'Source Code Length',\n",
|
||
" 'Forum Name_exelab', 'Forum Name_opensc', 'Forum Name_xeksec',\n",
|
||
" 'Language_ASM', 'Language_C++', 'Language_C/C++', 'Language_Delphi',\n",
|
||
" 'Language_HTML', 'Language_Java', 'Language_JavaScript',\n",
|
||
" 'Language_Javascript', 'Language_LISP', 'Language_PHP', 'Language_Perl',\n",
|
||
" 'Language_Python', 'Language_Ruby', 'Language_SQL', 'Language_VB.NET',\n",
|
||
" 'Language_VB6', 'Language_Visual Basic', 'Language_Windows Commands'],\n",
|
||
" dtype='object')\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"### **Step 6: Select Features for Clustering**\n",
|
||
"\n",
|
||
"- **Why**:\n",
|
||
" - Selecting relevant features ensures that only meaningful data is used for clustering.\n",
|
||
" - Features like `Author Name`, `Year`, `Month`, and `Source Code Length` provide numeric information.\n",
|
||
" - One-hot encoded columns like `Asset Type_`, `Forum Name_`, and `Language_` represent categorical information in binary form.\n",
|
||
"\n",
|
||
"---"
|
||
],
|
||
"metadata": {
|
||
"id": "85f2c558"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# Step 6: Select Features for Clustering\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# We need to choose which columns (features) to feed into our clustering model.\n",
|
||
"# Not all columns are useful — we want only the numeric/encoded ones.\n",
|
||
"\n",
|
||
"# Start with a base list of numeric features we created during preprocessing:\n",
|
||
"# - 'Author Name' (now an integer after label encoding)\n",
|
||
"# - 'Year' and 'Month' (extracted from the date)\n",
|
||
"# - 'Source Code Length' (character count of the source code)\n",
|
||
"#\n",
|
||
"# Then we add all the one-hot encoded columns using a list comprehension.\n",
|
||
"# The list comprehension [col for col in ... if col.startswith(...)] filters\n",
|
||
"# column names that begin with 'Asset Type_', 'Forum Name_', or 'Language_'.\n",
|
||
"features = ['Author Name', 'Year', 'Month', 'Source Code Length'] + [\n",
|
||
" col for col in hacker_sourcecode_data.columns\n",
|
||
" if col.startswith('Asset Type_')\n",
|
||
" or col.startswith('Forum Name_')\n",
|
||
" or col.startswith('Language_')\n",
|
||
"]\n",
|
||
"\n",
|
||
"# Create a new DataFrame 'X' containing ONLY the selected feature columns.\n",
|
||
"# 'X' is a common variable name in machine learning for the feature matrix\n",
|
||
"# (the input data that the model will learn from).\n",
|
||
"X = hacker_sourcecode_data[features]"
|
||
],
|
||
"metadata": {
|
||
"id": "5f9a7d48"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"### **Step 7: Normalize Numerical Features**\n",
|
||
"\n",
|
||
"- **Why**:\n",
|
||
" - Normalizing features ensures that all variables contribute equally to the distance calculation in K-Means.\n",
|
||
" - Without normalization, features with larger ranges (e.g., `Source Code Length`) could dominate those with smaller ranges (e.g., `Year`).\n",
|
||
"\n",
|
||
"---"
|
||
],
|
||
"metadata": {
|
||
"id": "e0147a82"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# Step 7: Normalize (Scale) Numerical Features\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# MinMaxScaler transforms every feature so its values fall between 0 and 1.\n",
|
||
"# Formula: scaled_value = (value - min) / (max - min)\n",
|
||
"#\n",
|
||
"# WHY IS THIS IMPORTANT?\n",
|
||
"# Both KNN and K-Means use distance calculations (e.g., Euclidean distance).\n",
|
||
"# If 'Source Code Length' ranges from 10 to 100,000 but 'Month' ranges from 1 to 12,\n",
|
||
"# then Source Code Length would completely dominate the distance calculation,\n",
|
||
"# making Month essentially irrelevant. Scaling fixes this by putting all\n",
|
||
"# features on the same 0-to-1 scale.\n",
|
||
"\n",
|
||
"# Create a MinMaxScaler object.\n",
|
||
"scaler = MinMaxScaler()\n",
|
||
"\n",
|
||
"# .fit_transform() does two things:\n",
|
||
"# 1. fit() — calculates the min and max for each column\n",
|
||
"# 2. transform() — applies the scaling formula to every value\n",
|
||
"# The result is a NumPy array (not a DataFrame) with all values between 0 and 1.\n",
|
||
"X = scaler.fit_transform(X)"
|
||
],
|
||
"metadata": {
|
||
"id": "051e94cf"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"### **Step 8: Apply K-Means Clustering**\n",
|
||
"\n",
|
||
"- **Why**:\n",
|
||
" - K-Means groups the data into `k=4` clusters based on similarity in the feature space.\n",
|
||
" - The resulting cluster labels (`0`, `1`, `2`, `3`) indicate which group each data point belongs to.\n",
|
||
"\n",
|
||
"---"
|
||
],
|
||
"metadata": {
|
||
"id": "fdc8efca"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# Step 8: Apply K-Means Clustering\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# NOTE: K-Means is an UNSUPERVISED algorithm — it groups data points into\n",
|
||
"# clusters WITHOUT using any labels. This is different from KNN, which is a\n",
|
||
"# SUPERVISED algorithm that uses known labels to classify new data points.\n",
|
||
"# We use K-Means here to explore natural groupings in the data.\n",
|
||
"\n",
|
||
"# KMeans(n_clusters=4) creates a K-Means model that will form 4 clusters.\n",
|
||
"# 'random_state=42' sets a seed for reproducibility — running this code\n",
|
||
"# multiple times will always produce the same clusters.\n",
|
||
"kmeans = KMeans(n_clusters=4, random_state=42)\n",
|
||
"\n",
|
||
"# .fit_predict(X) does two things:\n",
|
||
"# 1. fit() — trains the K-Means model by finding 4 cluster centers in the data\n",
|
||
"# 2. predict() — assigns each data point to its nearest cluster (0, 1, 2, or 3)\n",
|
||
"# The result is an array of cluster labels, which we store as a new column 'Cluster'.\n",
|
||
"hacker_sourcecode_data['Cluster'] = kmeans.fit_predict(X)"
|
||
],
|
||
"metadata": {
|
||
"id": "a89d3b42"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"### **Step 9: Reduce Dimensions for Visualization**\n",
|
||
"\n",
|
||
"- **Why**:\n",
|
||
" - PCA reduces the high-dimensional feature space to 2 components, making it possible to visualize the clusters in 2D.\n",
|
||
" - This helps us understand how well the clusters are separated in the reduced space.\n",
|
||
"\n",
|
||
"---"
|
||
],
|
||
"metadata": {
|
||
"id": "5def21a1"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# Step 9: Reduce Dimensions for Visualization Using PCA\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# Our feature matrix X has many columns (dimensions), which makes it impossible\n",
|
||
"# to plot on a 2D chart. PCA (Principal Component Analysis) solves this by\n",
|
||
"# compressing all those dimensions into just 2 components while preserving\n",
|
||
"# as much of the original data's variance (spread/patterns) as possible.\n",
|
||
"\n",
|
||
"# PCA(n_components=2) means we want to reduce our data down to 2 dimensions.\n",
|
||
"pca = PCA(n_components=2)\n",
|
||
"\n",
|
||
"# .fit_transform(X) learns the best 2-component representation and transforms\n",
|
||
"# the data. The result 'reduced_data' is a 2D array where:\n",
|
||
"# - Column 0 = PCA Component 1 (captures the most variance)\n",
|
||
"# - Column 1 = PCA Component 2 (captures the second most variance)\n",
|
||
"# We can now plot these two components on an x-y scatter plot.\n",
|
||
"reduced_data = pca.fit_transform(X)"
|
||
],
|
||
"metadata": {
|
||
"id": "3b47390c"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# STEP: Additional Cleanup — Fill Remaining Missing Values and Drop Columns\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# Fill any remaining missing values in 'Asset Name' and 'Author Name'.\n",
|
||
"# NOTE: We already did this in Step 2, but this ensures no NaN values slipped\n",
|
||
"# through if the DataFrame was modified in between.\n",
|
||
"hacker_sourcecode_data['Asset Name'] = hacker_sourcecode_data['Asset Name'].fillna('Unknown')\n",
|
||
"hacker_sourcecode_data['Author Name'] = hacker_sourcecode_data['Author Name'].fillna('Anonymous')\n",
|
||
"\n",
|
||
"# Drop the 'Flat Content' column because it contains raw HTML from forum posts,\n",
|
||
"# which is not useful for our numerical analysis.\n",
|
||
"# 'inplace=True' modifies the DataFrame directly without needing to reassign it.\n",
|
||
"hacker_sourcecode_data.drop(['Flat Content'], axis=1, inplace=True)"
|
||
],
|
||
"metadata": {
|
||
"id": "649586f6"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"\n",
|
||
"### **Step 10: Visualize the Clusters**\n",
|
||
"- **Why**:\n",
|
||
" - The scatter plot shows the clusters in 2D space, with each point representing a data point.\n",
|
||
" - The colors represent the cluster assignments, helping us visually assess the quality of clustering.\n",
|
||
"\n",
|
||
"---\n"
|
||
],
|
||
"metadata": {
|
||
"id": "823109a6"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# Step 10: Visualize the Clusters in 2D\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# plt.figure() creates a new figure (blank canvas) for our plot.\n",
|
||
"# figsize=(10, 6) sets the width to 10 inches and height to 6 inches.\n",
|
||
"plt.figure(figsize=(10, 6))\n",
|
||
"\n",
|
||
"# plt.scatter() creates a scatter plot where each point represents one data record.\n",
|
||
"# - reduced_data[:, 0] = all rows, first PCA component (x-axis)\n",
|
||
"# - reduced_data[:, 1] = all rows, second PCA component (y-axis)\n",
|
||
"# - c=hacker_sourcecode_data['Cluster'] = color each point by its cluster label\n",
|
||
"# - cmap='viridis' = use the 'viridis' color palette (yellow-green-blue)\n",
|
||
"# - s=50 = set the size of each dot to 50 points\n",
|
||
"plt.scatter(\n",
|
||
" reduced_data[:, 0], # X-axis: PCA Component 1\n",
|
||
" reduced_data[:, 1], # Y-axis: PCA Component 2\n",
|
||
" c=hacker_sourcecode_data['Cluster'], # Color by cluster assignment\n",
|
||
" cmap='viridis', # Color map\n",
|
||
" s=50 # Dot size\n",
|
||
")\n",
|
||
"\n",
|
||
"# plt.colorbar() adds a legend bar on the side showing which color maps to which cluster.\n",
|
||
"plt.colorbar(label='Cluster')\n",
|
||
"\n",
|
||
"# Add title and axis labels to make the chart readable.\n",
|
||
"plt.title('Clustering Visualization with K-Means')\n",
|
||
"plt.xlabel('PCA Component 1')\n",
|
||
"plt.ylabel('PCA Component 2')\n",
|
||
"\n",
|
||
"# plt.show() renders and displays the plot.\n",
|
||
"plt.show()"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 585
|
||
},
|
||
"id": "8fc9b317",
|
||
"outputId": "0be7f36a-6849-4953-ad9d-b67ebd96ae15"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 2 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# STEP: Count the Number of Data Points in Each Cluster\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# .value_counts() counts how many data points were assigned to each cluster.\n",
|
||
"# This tells us the size distribution of our clusters — are they roughly equal,\n",
|
||
"# or is one cluster much larger than the others?\n",
|
||
"cluster_counts = hacker_sourcecode_data['Cluster'].value_counts()\n",
|
||
"\n",
|
||
"# Print the counts. Each line shows a cluster number and how many points it contains.\n",
|
||
"print(cluster_counts)"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "fd8b009a",
|
||
"outputId": "8f9c0597-1a10-4232-d6b2-51e89193e94c"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Cluster\n",
|
||
"1 6630\n",
|
||
"3 4848\n",
|
||
"2 2462\n",
|
||
"0 1642\n",
|
||
"Name: count, dtype: int64\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# STEP: Visualize Cluster Characteristics with Box Plots\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# We import seaborn again (already imported above, but included here for\n",
|
||
"# clarity since this is a new analysis section).\n",
|
||
"import seaborn as sns\n",
|
||
"\n",
|
||
"# --- Box Plot 1: Source Code Length by Cluster ---\n",
|
||
"# A box plot shows the distribution of a numerical variable for each group.\n",
|
||
"# The box shows the middle 50% of values (interquartile range), the line inside\n",
|
||
"# is the median, and the \"whiskers\" extend to show the full range.\n",
|
||
"# Dots outside the whiskers are outliers (unusually large or small values).\n",
|
||
"#\n",
|
||
"# sns.boxplot() creates the box plot:\n",
|
||
"# - x='Cluster' = group the data by cluster on the x-axis\n",
|
||
"# - y='Source Code Length' = plot source code length on the y-axis\n",
|
||
"# - data=hacker_sourcecode_data = use our DataFrame as the data source\n",
|
||
"sns.boxplot(x='Cluster', y='Source Code Length', data=hacker_sourcecode_data)\n",
|
||
"plt.title('Source Code Length by Cluster')\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"# --- Box Plot 2: Year by Cluster ---\n",
|
||
"# This shows which time periods each cluster's posts come from.\n",
|
||
"# For example, one cluster might contain mostly older exploits (early 2000s)\n",
|
||
"# while another contains newer ones (2010s).\n",
|
||
"sns.boxplot(x='Cluster', y='Year', data=hacker_sourcecode_data)\n",
|
||
"plt.title('Year by Cluster')\n",
|
||
"plt.show()"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 947
|
||
},
|
||
"id": "8c2c2c1d",
|
||
"outputId": "aca86dd4-798a-4db4-e2ba-e7f692cfd148"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# STEP: Analyze Programming Language Distribution Across Clusters\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# First, build a list of all column names that start with 'Language_'.\n",
|
||
"# These are the one-hot encoded columns we created in Step 5.\n",
|
||
"# Each column represents a programming language (e.g., 'Language_C/C++',\n",
|
||
"# 'Language_PHP', 'Language_Python', etc.) and contains 1 or 0.\n",
|
||
"language_columns = [col for col in hacker_sourcecode_data.columns if col.startswith('Language_')]\n",
|
||
"\n",
|
||
"# .groupby('Cluster') splits the data into groups based on cluster assignment.\n",
|
||
"# [language_columns] selects only the language columns from each group.\n",
|
||
"# .mean() calculates the average of each language column within each cluster.\n",
|
||
"#\n",
|
||
"# Since the language columns are binary (0 or 1), the mean gives us the\n",
|
||
"# PROPORTION of posts in each cluster that use each language.\n",
|
||
"# For example, a mean of 0.72 for 'Language_Delphi' in Cluster 0 means\n",
|
||
"# 72% of the posts in Cluster 0 are written in Delphi.\n",
|
||
"language_distribution = hacker_sourcecode_data.groupby('Cluster')[language_columns].mean()\n",
|
||
"\n",
|
||
"# Print the result — a table showing language proportions for each cluster.\n",
|
||
"print(language_distribution)"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "db8b00f5",
|
||
"outputId": "877c270b-0b96-4526-f8e7-7954d80aa651"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
" Language_ASM Language_C++ Language_C/C++ Language_Delphi \\\n",
|
||
"Cluster \n",
|
||
"0 0.052375 0.004872 0.003654 0.718636 \n",
|
||
"1 0.000000 0.000000 1.000000 0.000000 \n",
|
||
"2 0.012185 0.001625 0.182778 0.111292 \n",
|
||
"3 0.000000 0.000000 0.000000 0.000000 \n",
|
||
"\n",
|
||
" Language_HTML Language_Java Language_JavaScript \\\n",
|
||
"Cluster \n",
|
||
"0 0.000000 0.004872 0.004872 \n",
|
||
"1 0.000000 0.000000 0.000000 \n",
|
||
"2 0.006499 0.005686 0.000000 \n",
|
||
"3 0.000000 0.051568 0.000000 \n",
|
||
"\n",
|
||
" Language_Javascript Language_LISP Language_PHP Language_Perl \\\n",
|
||
"Cluster \n",
|
||
"0 0.009744 0.000000 0.000000 0.000000 \n",
|
||
"1 0.000000 0.000000 0.000000 0.000000 \n",
|
||
"2 0.045491 0.002437 0.450041 0.112916 \n",
|
||
"3 0.000000 0.000000 0.849010 0.000000 \n",
|
||
"\n",
|
||
" Language_Python Language_Ruby Language_SQL Language_VB.NET \\\n",
|
||
"Cluster \n",
|
||
"0 0.015834 0.000000 0.000000 0.003654 \n",
|
||
"1 0.000000 0.000000 0.000000 0.000000 \n",
|
||
"2 0.017872 0.002437 0.034119 0.000000 \n",
|
||
"3 0.099422 0.000000 0.000000 0.000000 \n",
|
||
"\n",
|
||
" Language_VB6 Language_Visual Basic Language_Windows Commands \n",
|
||
"Cluster \n",
|
||
"0 0.007308 0.161998 0.010962 \n",
|
||
"1 0.000000 0.000000 0.000000 \n",
|
||
"2 0.000000 0.009748 0.000000 \n",
|
||
"3 0.000000 0.000000 0.000000 \n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"This output represents the **mean proportions** of different programming languages (`Language_*`) within each cluster. Breakdown of what this data means:\n",
|
||
"\n",
|
||
"### **1. Rows (`Cluster`)**\n",
|
||
"Each row corresponds to a specific cluster identified by the K-Means algorithm (e.g., `Cluster 0`, `Cluster 1`, etc.). These clusters group similar data points based on the features used during clustering.\n",
|
||
"\n",
|
||
"### **2. Columns (`Language_*`)**\n",
|
||
"Each column corresponds to a programming language, where the value represents the **average proportion** of posts in that cluster that use the respective language. For example:\n",
|
||
"- A value of `0.718636` for `Language_Delphi` in `Cluster 0` means that about **71.86% of the posts in Cluster 0 are associated with Delphi.**\n",
|
||
"\n",
|
||
"### **3. Interpretation by Cluster**\n",
|
||
"#### **Cluster 0**\n",
|
||
"- **High Proportions**:\n",
|
||
" - `Language_Delphi`: **71.86%** of posts in this cluster are written in Delphi.\n",
|
||
" - `Language_Visual Basic`: **16.20%** of posts in this cluster use Visual Basic.\n",
|
||
"- **Low Proportions**:\n",
|
||
" - Other languages, like `Language_C++`, `Language_HTML`, and `Language_PHP`, have very small proportions (near 0).\n",
|
||
"\n",
|
||
"**Conclusion**:\n",
|
||
"This cluster is **dominated by Delphi exploits**, with a smaller contribution from Visual Basic.\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"#### **Cluster 1**\n",
|
||
"- **High Proportion**:\n",
|
||
" - `Language_C/C++`: **100%** of the posts in this cluster are written in C/C++.\n",
|
||
"- **Other Languages**:\n",
|
||
" - All other languages have a proportion of **0%**, indicating this cluster is entirely composed of C/C++ posts.\n",
|
||
"\n",
|
||
"**Conclusion**:\n",
|
||
"This cluster exclusively contains C/C++ exploits.\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"#### **Cluster 2**\n",
|
||
"- **High Proportions**:\n",
|
||
" - `Language_PHP`: **45.00%** of the posts are written in PHP.\n",
|
||
" - `Language_C/C++`: **18.28%** of the posts use C/C++.\n",
|
||
" - `Language_Perl`: **11.29%** of the posts use Perl.\n",
|
||
"- **Low Proportions**:\n",
|
||
" - Other languages like `Language_Javascript`, `Language_Python`, and `Language_SQL` are present in small proportions.\n",
|
||
"\n",
|
||
"**Conclusion**:\n",
|
||
"This cluster is **diverse**, with a focus on **PHP**, followed by **C/C++** and **Perl.**\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"#### **Cluster 3**\n",
|
||
"- **High Proportion**:\n",
|
||
" - `Language_PHP`: **84.90%** of the posts are PHP-based.\n",
|
||
"- **Low Proportions**:\n",
|
||
" - A small portion of the cluster is associated with `Language_Python` (**9.94%**).\n",
|
||
"\n",
|
||
"**Conclusion**:\n",
|
||
"This cluster is almost entirely **PHP-based** but has a small subset of Python-related exploits.\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"### **4. General Insights**\n",
|
||
"- **Cluster-Specific Dominance**:\n",
|
||
" - `Cluster 0` is heavily focused on **Delphi**.\n",
|
||
" - `Cluster 1` is exclusive to **C/C++**.\n",
|
||
" - `Cluster 2` has more diversity but is dominated by **PHP** and **C/C++**.\n",
|
||
" - `Cluster 3` is almost entirely **PHP**-based.\n",
|
||
"\n",
|
||
"- **Programming Language Patterns**:\n",
|
||
" - **Delphi** and **PHP** seem to have clusters where they dominate entirely.\n",
|
||
" - **C/C++** forms a specialized group (Cluster 1), indicating its unique characteristics in the dataset.\n",
|
||
"\n",
|
||
"- **Cluster Diversity**:\n",
|
||
" - Clusters 2 and 3 have a mix of languages, which could represent exploits that are either more general-purpose or part of hybrid/mixed environments.\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"### **5. Business or Analytical Implications**\n",
|
||
"- **Threat Analysis**:\n",
|
||
" - Certain languages dominate specific clusters, indicating their prevalence in exploit development. For example:\n",
|
||
" - If Delphi exploits dominate a cluster, organizations using Delphi-based systems should prioritize securing those.\n",
|
||
" - PHP-based exploits might indicate vulnerabilities in web-based systems.\n",
|
||
"\n",
|
||
"- **Forum Trends**:\n",
|
||
" - If you link these clusters back to forums (using `Forum Name`), you might uncover which forums specialize in exploits for specific programming languages.\n",
|
||
"\n",
|
||
"- **Cluster Validation**:\n",
|
||
" - If these clusters make sense based on your knowledge of the dataset or domain, it validates the clustering process.\n",
|
||
" - Otherwise, you might refine the clustering process by adding or removing features.\n",
|
||
"\n",
|
||
"---\n"
|
||
],
|
||
"metadata": {
|
||
"id": "05f0130d"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# STEP: Visualize Language Distribution by Cluster (Stacked Bar Chart)\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# Import matplotlib (already imported, included for section clarity).\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"# Recompute the language distribution (proportions) grouped by cluster.\n",
|
||
"# .groupby('Cluster') groups rows by their cluster label.\n",
|
||
"# [language_columns] selects only the language columns.\n",
|
||
"# .mean() computes the average (i.e., proportion since values are 0 or 1).\n",
|
||
"language_distribution = hacker_sourcecode_data.groupby('Cluster')[language_columns].mean()\n",
|
||
"\n",
|
||
"# .T (transpose) flips rows and columns so that:\n",
|
||
"# - Rows become programming languages (instead of clusters)\n",
|
||
"# - Columns become clusters (instead of languages)\n",
|
||
"# This makes the bar chart show one bar per language, with segments for each cluster.\n",
|
||
"language_distribution_transposed = language_distribution.T\n",
|
||
"\n",
|
||
"# .plot(kind='bar', stacked=True) creates a stacked bar chart:\n",
|
||
"# - kind='bar' = vertical bar chart\n",
|
||
"# - stacked=True = stack the cluster segments on top of each other\n",
|
||
"# - figsize=(12, 6) = chart dimensions in inches\n",
|
||
"# - colormap='viridis' = color scheme for the bars\n",
|
||
"language_distribution_transposed.plot(\n",
|
||
" kind='bar', stacked=True, figsize=(12, 6), colormap='viridis'\n",
|
||
")\n",
|
||
"\n",
|
||
"# Add labels and formatting to make the chart readable.\n",
|
||
"plt.title('Programming Language Distribution by Cluster')\n",
|
||
"plt.xlabel('Programming Language')\n",
|
||
"plt.ylabel('Proportion of Posts')\n",
|
||
"plt.legend(title='Cluster')\n",
|
||
"\n",
|
||
"# rotation=90 rotates the x-axis labels by 90 degrees so they don't overlap.\n",
|
||
"plt.xticks(rotation=90)\n",
|
||
"\n",
|
||
"# tight_layout() automatically adjusts spacing so nothing gets cut off.\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 627
|
||
},
|
||
"id": "474bf9ed",
|
||
"outputId": "9b2b4173-3571-4a7d-83cb-618de12944a8"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 1 Axes>"
|
||
],
|
||
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\n"
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"To better **illustrate the cluster information** and derive **meaningful labels** for each cluster, we can :\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"### **Step 1: Visualization of Language Distributions per Cluster**\n",
|
||
"We can create **stacked bar charts** or **heatmaps** to visually compare the language proportions across clusters. This is more intuitive than looking at raw numbers.\n",
|
||
"\n",
|
||
"**What This Shows**:\n",
|
||
"- A clear visual representation of which languages dominate each cluster.\n",
|
||
"- The height of each stacked segment indicates the proportion of each language in a specific cluster.\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"### **Step 2: Meaningful Cluster Labels**\n",
|
||
"To derive meaningful labels for the clusters:\n",
|
||
"1. Look for **dominant features** in each cluster (e.g., most frequent language, associated forums).\n",
|
||
"2. Label each cluster by its **dominant individual language** — do NOT combine unrelated languages into artificial categories.\n",
|
||
"\n",
|
||
"#### **Procedure for Naming Clusters**\n",
|
||
"Analyze cluster characteristics:\n",
|
||
"- Identify the **primary programming language** in each cluster (highest proportion).\n",
|
||
"- Look at **forum associations** or any other relevant features.\n",
|
||
"- Consider temporal patterns (e.g., older vs. newer exploits) if available.\n",
|
||
"\n",
|
||
"#### **Example: Cluster Naming Logic**\n",
|
||
"Using the provided data:\n",
|
||
"- **Cluster 0**: Dominated by **Delphi** (71.9%), with some Visual Basic (16.2%) as a secondary language. Named **\"Delphi Exploits\"** — Delphi is the clear leader and stands on its own.\n",
|
||
"- **Cluster 1**: Exclusively **C/C++** (100%). Named **\"C/C++ Exploits\"** — C and C++ are treated as one entry because they share syntax and are often used interchangeably.\n",
|
||
"- **Cluster 2**: No single language dominates — PHP leads at 45%, followed by C/C++ (18%), Perl (11%), and Delphi (11%). Named **\"Mixed-Language Exploits\"** because this cluster captures posts that span multiple languages.\n",
|
||
"- **Cluster 3**: Dominated by **PHP** (84.9%), with a small Python contribution (9.9%). Named **\"PHP Exploits\"** — PHP is the clear dominant language.\n",
|
||
"\n",
|
||
"**Important**: Each language (Delphi, PHP, Perl, Python, etc.) is treated as its own distinct entity. We only group languages together when they are genuinely the same language family (like C and C++).\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"### **Step 3: Assign Names to Clusters**\n",
|
||
"Add these descriptive names as a new column in the dataset for clarity.\n",
|
||
"\n",
|
||
"\n",
|
||
"### **Step 4: Visualization of Cluster Composition**\n",
|
||
"To summarize the clusters with meaningful labels, we can create a **pie chart** or a **bar chart** showing the number of posts in each cluster.\n",
|
||
"\n",
|
||
"### **Step 5: Use Combined Features for Naming**\n",
|
||
"If additional features like `Forum Name` or `Year` have significant trends within clusters, include them in the naming process.\n",
|
||
"\n",
|
||
"**Interpretation**:\n",
|
||
"- If a cluster is dominated by a specific forum, incorporate that into the cluster name (e.g., \"Delphi Exploits from OpenSC Forum\").\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"### **Benefits of This Approach**\n",
|
||
"1. **Visual Simplicity**: Stacked bar charts and pie charts make cluster characteristics easier to interpret.\n",
|
||
"2. **Meaningful Labels**: Using dominant individual languages makes the clusters more precise and accurate.\n",
|
||
"3. **Improved Insights**: Descriptive labels help link technical clustering results to real-world phenomena (e.g., types of exploits or forums of origin)."
|
||
],
|
||
"metadata": {
|
||
"id": "9c1b6fa5"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# STEP: Assign Meaningful Labels to Each Cluster\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# Based on our analysis of language distributions, we create a dictionary\n",
|
||
"# that maps each cluster number to a human-readable descriptive name.\n",
|
||
"# A dictionary uses key:value pairs — here, cluster number : cluster name.\n",
|
||
"#\n",
|
||
"# IMPORTANT: We label each cluster by its DOMINANT individual language.\n",
|
||
"# We do NOT combine unrelated languages into artificial categories.\n",
|
||
"# Each language stands on its own unless they are genuinely the same\n",
|
||
"# language family (e.g., C/C++ is one entry because C and C++ share syntax).\n",
|
||
"#\n",
|
||
"# From the language distribution analysis:\n",
|
||
"# Cluster 0: 71.9% Delphi, 16.2% Visual Basic — Delphi is the clear leader\n",
|
||
"# Cluster 1: 100% C/C++ — entirely one language family\n",
|
||
"# Cluster 2: 45% PHP, 18% C/C++, 11% Perl, 11% Delphi — mixed, PHP leads\n",
|
||
"# Cluster 3: 84.9% PHP, 9.9% Python — PHP dominates\n",
|
||
"\n",
|
||
"cluster_names = {\n",
|
||
" 0: \"Delphi Exploits\", # Cluster 0: Delphi dominates at 71.9%\n",
|
||
" 1: \"C/C++ Exploits\", # Cluster 1: 100% C/C++ (C and C++ are one language family)\n",
|
||
" 2: \"Mixed-Language Exploits\", # Cluster 2: No single language dominates — PHP leads at 45%\n",
|
||
" 3: \"PHP Exploits\" # Cluster 3: PHP dominates at 84.9%\n",
|
||
"}\n",
|
||
"\n",
|
||
"# .map() replaces each value in the 'Cluster' column with the corresponding\n",
|
||
"# name from the dictionary. For example, every row with Cluster=0 gets the\n",
|
||
"# label \"Delphi Exploits\". The result is stored in a new column\n",
|
||
"# called 'Cluster Label' for easy reference.\n",
|
||
"hacker_sourcecode_data['Cluster Label'] = hacker_sourcecode_data['Cluster'].map(cluster_names)\n",
|
||
"\n",
|
||
"# Print the first 5 rows showing both the numeric cluster and its label\n",
|
||
"# to verify the mapping worked correctly.\n",
|
||
"print(hacker_sourcecode_data[['Cluster', 'Cluster Label']].head())"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "10567be6",
|
||
"outputId": "b11549f5-36df-4e7b-a8c1-fbc6e4f39ecf"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
" Cluster Cluster Label\n",
|
||
"0 0 Delphi Exploits\n",
|
||
"1 0 Delphi Exploits\n",
|
||
"2 0 Delphi Exploits\n",
|
||
"3 0 Delphi Exploits\n",
|
||
"4 0 Delphi Exploits\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# STEP: Visualize Cluster Composition with a Pie Chart\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# Count how many posts belong to each cluster label.\n",
|
||
"# .value_counts() returns a Series with cluster labels as the index\n",
|
||
"# and the count of posts as the values.\n",
|
||
"cluster_counts = hacker_sourcecode_data['Cluster Label'].value_counts()\n",
|
||
"\n",
|
||
"# .plot(kind='pie') creates a pie chart from the counts.\n",
|
||
"# - autopct='%1.1f%%' = display the percentage on each slice, formatted\n",
|
||
"# to 1 decimal place (e.g., \"45.3%\")\n",
|
||
"# - figsize=(8, 8) = make the chart 8x8 inches (square for a circular pie)\n",
|
||
"# - colormap='viridis' = use the viridis color palette\n",
|
||
"# - startangle=140 = rotate the chart so the first slice starts at 140 degrees\n",
|
||
"# (this is purely aesthetic to make the chart look better)\n",
|
||
"cluster_counts.plot(\n",
|
||
" kind='pie',\n",
|
||
" autopct='%1.1f%%',\n",
|
||
" figsize=(8, 8),\n",
|
||
" colormap='viridis',\n",
|
||
" startangle=140\n",
|
||
")\n",
|
||
"\n",
|
||
"plt.title('Cluster Composition')\n",
|
||
"# Setting ylabel to empty string removes the default \"None\" label on the y-axis.\n",
|
||
"plt.ylabel('')\n",
|
||
"plt.show()"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 695
|
||
},
|
||
"id": "9dfc9392",
|
||
"outputId": "7e3fe863-2c4e-481f-a2ba-9a2ab5e906d5"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 800x800 with 1 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# STEP: Summarize Forum Distribution Per Cluster\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# .groupby('Cluster') splits the DataFrame into groups by cluster number.\n",
|
||
"# We then select the one-hot encoded forum columns and calculate the mean\n",
|
||
"# (proportion) of each forum within each cluster.\n",
|
||
"# This tells us which forums are most represented in each cluster.\n",
|
||
"forum_distribution = hacker_sourcecode_data.groupby('Cluster')[\n",
|
||
" ['Forum Name_opensc', 'Forum Name_exelab', 'Forum Name_xeksec']\n",
|
||
"].mean()\n",
|
||
"\n",
|
||
"# Print the proportions — each value shows what fraction of that cluster's\n",
|
||
"# posts came from each forum. Values close to 1.0 mean nearly all posts\n",
|
||
"# in that cluster came from that forum.\n",
|
||
"print(forum_distribution)"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "bcaf9bbc",
|
||
"outputId": "e873be59-ac91-4f4e-834a-46639831c7b0"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
" Forum Name_opensc Forum Name_exelab Forum Name_xeksec\n",
|
||
"Cluster \n",
|
||
"0 1.000000 0.000000 0.0\n",
|
||
"1 0.107089 0.689593 0.0\n",
|
||
"2 0.000000 0.000000 1.0\n",
|
||
"3 0.049092 0.000000 0.0\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# STEP: Visualize Temporal Trends of Exploit Clusters Over Time\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# Create a 'Year-Month' column by converting each datetime to a monthly period.\n",
|
||
"# .dt.to_period('M') converts a datetime like \"2010-03-15\" into the period \"2010-03\".\n",
|
||
"# This groups all posts from the same month together for trend analysis.\n",
|
||
"hacker_sourcecode_data['Year-Month'] = hacker_sourcecode_data['Postdatetime'].dt.to_period('M')\n",
|
||
"\n",
|
||
"# .groupby(['Year-Month', 'Cluster Label']) groups the data by both month AND cluster.\n",
|
||
"# .size() counts the number of posts in each (month, cluster) combination.\n",
|
||
"# .unstack() pivots the cluster labels from rows into columns, creating a table\n",
|
||
"# where each column is a cluster and each row is a month.\n",
|
||
"# .fillna(0) replaces any NaN values with 0 (months where a cluster had no posts).\n",
|
||
"temporal_trends = hacker_sourcecode_data.groupby(\n",
|
||
" ['Year-Month', 'Cluster Label']\n",
|
||
").size().unstack().fillna(0)\n",
|
||
"\n",
|
||
"# Create a line chart showing how the number of posts per cluster changes over time.\n",
|
||
"plt.figure(figsize=(12, 6))\n",
|
||
"\n",
|
||
"# .plot() draws a line for each cluster (column) across all months.\n",
|
||
"# ax=plt.gca() tells pandas to draw on the current matplotlib axes.\n",
|
||
"# linewidth=2 makes the lines thicker and easier to see.\n",
|
||
"temporal_trends.plot(ax=plt.gca(), linewidth=2)\n",
|
||
"\n",
|
||
"plt.title('Temporal Trends of Exploit Clusters', fontsize=14)\n",
|
||
"plt.xlabel('Year-Month', fontsize=12)\n",
|
||
"plt.ylabel('Number of Posts', fontsize=12)\n",
|
||
"plt.legend(title='Cluster')\n",
|
||
"\n",
|
||
"# grid() adds horizontal gridlines for easier reading of values.\n",
|
||
"# linestyle='--' makes them dashed, alpha=0.7 makes them semi-transparent.\n",
|
||
"plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
|
||
"\n",
|
||
"# Rotate x-axis labels 45 degrees so the month labels don't overlap.\n",
|
||
"plt.xticks(rotation=45)\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 626
|
||
},
|
||
"id": "e12de185",
|
||
"outputId": "1a8de4e3-678c-4de4-afbe-60aefa6408b6"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 1 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# STEP: Visualize Forum Distribution by Cluster (Stacked Bar Chart)\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# .plot(kind=\"bar\", stacked=True) creates a stacked bar chart from the\n",
|
||
"# forum_distribution DataFrame (computed earlier).\n",
|
||
"# Each bar represents a cluster, and the stacked segments show the proportion\n",
|
||
"# of posts from each forum within that cluster.\n",
|
||
"# - figsize=(12, 6) = chart size in inches\n",
|
||
"# - colormap=\"plasma\" = a different color palette for visual variety\n",
|
||
"forum_distribution.plot(kind=\"bar\", stacked=True, figsize=(12, 6), colormap=\"plasma\")\n",
|
||
"\n",
|
||
"plt.title(\"Forum Distribution by Cluster\")\n",
|
||
"plt.xlabel(\"Cluster\")\n",
|
||
"plt.ylabel(\"Proportion of Posts\")\n",
|
||
"plt.legend(title=\"Forum\")\n",
|
||
"\n",
|
||
"# rotation=0 keeps x-axis labels horizontal (since cluster numbers are short).\n",
|
||
"plt.xticks(rotation=0)\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 627
|
||
},
|
||
"id": "78c80f0f",
|
||
"outputId": "5dce0bf1-f058-4e23-9b95-f334fa48e16d"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 1 Axes>"
|
||
],
|
||
"image/png": 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|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# STEP: Box Plots by Cluster Label (Source Code Length and Year)\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# --- Box Plot 1: Source Code Length by Cluster Label ---\n",
|
||
"# This is similar to the earlier box plot, but now uses the descriptive\n",
|
||
"# cluster names (e.g., \"C/C++ Exploits\") instead of just numbers (0, 1, 2, 3).\n",
|
||
"plt.figure(figsize=(10, 6))\n",
|
||
"sns.boxplot(x='Cluster Label', y='Source Code Length', data=hacker_sourcecode_data)\n",
|
||
"plt.title('Source Code Length by Cluster')\n",
|
||
"plt.xlabel('Cluster')\n",
|
||
"plt.ylabel('Source Code Length')\n",
|
||
"\n",
|
||
"# rotation=15 tilts the x-axis labels slightly so the long names don't overlap.\n",
|
||
"plt.xticks(rotation=15)\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"# --- Box Plot 2: Year Distribution by Cluster Label ---\n",
|
||
"# Shows the year range of posts in each named cluster.\n",
|
||
"# This helps us understand whether certain types of exploits were more\n",
|
||
"# prevalent in certain time periods.\n",
|
||
"plt.figure(figsize=(10, 6))\n",
|
||
"sns.boxplot(x='Cluster Label', y='Year', data=hacker_sourcecode_data)\n",
|
||
"plt.title('Year Distribution by Cluster')\n",
|
||
"plt.xlabel('Cluster')\n",
|
||
"plt.ylabel('Year')\n",
|
||
"plt.xticks(rotation=15)\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 1000
|
||
},
|
||
"id": "0733a55c",
|
||
"outputId": "422c98fc-67be-485e-836a-16a2dc056dc9"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"---\n",
|
||
"\n",
|
||
"## **Enhanced K-Means Cluster Analysis: Multi-Label Exploration**\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"### Why Analyze Clusters from Multiple Perspectives?\n",
|
||
"\n",
|
||
"So far, we have labeled our K-Means clusters primarily by **programming language**. But clusters are multi-dimensional — they capture patterns across **all** the features we fed into the model. To truly understand what each cluster represents, we should examine them through multiple lenses:\n",
|
||
"\n",
|
||
"1. **Author Name** — Who are the top contributors in each cluster? Are certain hackers specialized?\n",
|
||
"2. **Exploit Type** — What kinds of attacks (Network, Malware, Web, etc.) are concentrated in each cluster?\n",
|
||
"3. **Forum Name** — Which hacker forums dominate each cluster? Do forums have specialties?\n",
|
||
"4. **Language** — Which programming languages characterize each cluster? (We already explored this, but we'll formalize it here.)\n",
|
||
"\n",
|
||
"This multi-label approach gives us a **richer, more nuanced understanding** of the data — which is exactly what analysts need when working with real-world cybersecurity intelligence.\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"### Key Concept: Why Multiple Labels Matter in Data Analytics\n",
|
||
"\n",
|
||
"In the real world, data rarely has a single \"correct\" label. A cluster of hacker forum posts might simultaneously be:\n",
|
||
"- Written in **C/C++** (language perspective)\n",
|
||
"- Focused on **Network exploits** (attack type perspective)\n",
|
||
"- Posted on **Exelab** forum (community perspective)\n",
|
||
"- Authored by a small group of **prolific contributors** (actor perspective)\n",
|
||
"\n",
|
||
"Each perspective tells a different story. As data analysts, our job is to **explore all of them** and synthesize the findings into actionable intelligence."
|
||
],
|
||
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|
||
"id": "ee4d3d93"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"### **Label 1: Exploit Type Distribution Across Clusters**\n",
|
||
"\n",
|
||
"- **Why**: The `Exploit Type` column tells us what kind of attack each post describes (e.g., Network, Malware, Web). By examining how exploit types are distributed across our clusters, we can determine whether K-Means naturally grouped posts by attack category — or by some other pattern entirely.\n",
|
||
"- **What to look for**: If one cluster is dominated by \"Network\" exploits and another by \"Malware,\" it means K-Means found a meaningful separation aligned with attack types. If exploit types are spread evenly across clusters, the clustering was driven by other features (like language or forum)."
|
||
],
|
||
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|
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"source": [
|
||
"# ============================================================\n",
|
||
"# STEP: Analyze Exploit Type Distribution Across Clusters\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# First, let's see what unique exploit types exist in our dataset.\n",
|
||
"# .value_counts() counts how many posts belong to each exploit type.\n",
|
||
"# This gives us context BEFORE we look at cluster-specific distributions.\n",
|
||
"print(\"=== Overall Exploit Type Distribution ===\")\n",
|
||
"print(hacker_sourcecode_data['Exploit Type'].value_counts())\n",
|
||
"print()\n",
|
||
"\n",
|
||
"# Now let's create a cross-tabulation (crosstab) of Cluster vs. Exploit Type.\n",
|
||
"# pd.crosstab() creates a frequency table showing how two categorical variables\n",
|
||
"# relate to each other. Think of it as a pivot table:\n",
|
||
"# - Rows = Cluster labels (0, 1, 2, 3)\n",
|
||
"# - Columns = Exploit Types (Network, Malware, Web, etc.)\n",
|
||
"# - Values = count of posts that match both the row and column\n",
|
||
"exploit_by_cluster = pd.crosstab(\n",
|
||
" hacker_sourcecode_data['Cluster Label'], # Row variable\n",
|
||
" hacker_sourcecode_data['Exploit Type'], # Column variable\n",
|
||
")\n",
|
||
"\n",
|
||
"print(\"=== Exploit Type Count by Cluster ===\")\n",
|
||
"display(exploit_by_cluster)\n",
|
||
"\n",
|
||
"# Now compute the PROPORTIONS within each cluster using normalize='index'.\n",
|
||
"# This divides each row by its row total, so values sum to 1.0 per cluster.\n",
|
||
"# This is more informative than raw counts because clusters have different sizes.\n",
|
||
"exploit_proportions = pd.crosstab(\n",
|
||
" hacker_sourcecode_data['Cluster Label'],\n",
|
||
" hacker_sourcecode_data['Exploit Type'],\n",
|
||
" normalize='index' # Normalize across rows (each cluster sums to 1.0)\n",
|
||
")\n",
|
||
"\n",
|
||
"print(\"\\n=== Exploit Type Proportions by Cluster (rows sum to 1.0) ===\")\n",
|
||
"display(exploit_proportions.round(3))"
|
||
],
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"=== Overall Exploit Type Distribution ===\n",
|
||
"Exploit Type\n",
|
||
"System 9746\n",
|
||
"Web 5598\n",
|
||
"Network 232\n",
|
||
"Database 6\n",
|
||
"Name: count, dtype: int64\n",
|
||
"\n",
|
||
"=== Exploit Type Count by Cluster ===\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"Exploit Type Database Network System Web\n",
|
||
"Cluster Label \n",
|
||
"C/C++ Exploits 0 74 6482 74\n",
|
||
"Delphi Exploits 0 94 1484 64\n",
|
||
"Mixed-Language Exploits 6 58 960 1438\n",
|
||
"PHP Exploits 0 6 820 4022"
|
||
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|
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|
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|
||
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|
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|
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|
||
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|
||
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <th>PHP Exploits</th>\n",
|
||
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|
||
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|
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|
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|
||
"=== Exploit Type Proportions by Cluster (rows sum to 1.0) ===\n"
|
||
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|
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|
||
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|
||
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|
||
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|
||
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|
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|
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|
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|
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|
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|
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||
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||
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|
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>0.584</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>PHP Exploits</th>\n",
|
||
" <td>0.000</td>\n",
|
||
" <td>0.001</td>\n",
|
||
" <td>0.169</td>\n",
|
||
" <td>0.830</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>\n",
|
||
" <div class=\"colab-df-buttons\">\n",
|
||
"\n",
|
||
" <div class=\"colab-df-container\">\n",
|
||
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ddcb8376-797d-430f-b294-c7da3a292329')\"\n",
|
||
" title=\"Convert this dataframe to an interactive table.\"\n",
|
||
" style=\"display:none;\">\n",
|
||
"\n",
|
||
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
|
||
" <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
|
||
" </svg>\n",
|
||
" </button>\n",
|
||
"\n",
|
||
" <style>\n",
|
||
" .colab-df-container {\n",
|
||
" display:flex;\n",
|
||
" gap: 12px;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-convert {\n",
|
||
" background-color: #E8F0FE;\n",
|
||
" border: none;\n",
|
||
" border-radius: 50%;\n",
|
||
" cursor: pointer;\n",
|
||
" display: none;\n",
|
||
" fill: #1967D2;\n",
|
||
" height: 32px;\n",
|
||
" padding: 0 0 0 0;\n",
|
||
" width: 32px;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-convert:hover {\n",
|
||
" background-color: #E2EBFA;\n",
|
||
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
|
||
" fill: #174EA6;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-buttons div {\n",
|
||
" margin-bottom: 4px;\n",
|
||
" }\n",
|
||
"\n",
|
||
" [theme=dark] .colab-df-convert {\n",
|
||
" background-color: #3B4455;\n",
|
||
" fill: #D2E3FC;\n",
|
||
" }\n",
|
||
"\n",
|
||
" [theme=dark] .colab-df-convert:hover {\n",
|
||
" background-color: #434B5C;\n",
|
||
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
|
||
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
|
||
" fill: #FFFFFF;\n",
|
||
" }\n",
|
||
" </style>\n",
|
||
"\n",
|
||
" <script>\n",
|
||
" const buttonEl =\n",
|
||
" document.querySelector('#df-ddcb8376-797d-430f-b294-c7da3a292329 button.colab-df-convert');\n",
|
||
" buttonEl.style.display =\n",
|
||
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
||
"\n",
|
||
" async function convertToInteractive(key) {\n",
|
||
" const element = document.querySelector('#df-ddcb8376-797d-430f-b294-c7da3a292329');\n",
|
||
" const dataTable =\n",
|
||
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
|
||
" [key], {});\n",
|
||
" if (!dataTable) return;\n",
|
||
"\n",
|
||
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
|
||
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
|
||
" + ' to learn more about interactive tables.';\n",
|
||
" element.innerHTML = '';\n",
|
||
" dataTable['output_type'] = 'display_data';\n",
|
||
" await google.colab.output.renderOutput(dataTable, element);\n",
|
||
" const docLink = document.createElement('div');\n",
|
||
" docLink.innerHTML = docLinkHtml;\n",
|
||
" element.appendChild(docLink);\n",
|
||
" }\n",
|
||
" </script>\n",
|
||
" </div>\n",
|
||
"\n",
|
||
"\n",
|
||
" </div>\n",
|
||
" </div>\n"
|
||
],
|
||
"application/vnd.google.colaboratory.intrinsic+json": {
|
||
"type": "dataframe",
|
||
"summary": "{\n \"name\": \"display(exploit_proportions\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"Cluster Label\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Delphi Exploits\",\n \"PHP Exploits\",\n \"C/C++ Exploits\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Database\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.001,\n \"min\": 0.0,\n \"max\": 0.002,\n \"num_unique_values\": 2,\n \"samples\": [\n 0.002,\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Network\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.024390913608691795,\n \"min\": 0.001,\n \"max\": 0.057,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.057,\n 0.001\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"System\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.3935906291228658,\n \"min\": 0.169,\n \"max\": 0.978,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.904,\n 0.169\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Web\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.40651937223212375,\n \"min\": 0.011,\n \"max\": 0.83,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.039,\n 0.83\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 640
|
||
},
|
||
"id": "3076998e",
|
||
"outputId": "b0cb61fe-82ee-4c22-fed4-ee10837f8dbb"
|
||
},
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# STEP: Visualize Exploit Type Distribution by Cluster (Stacked Bar Chart)\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# Create a stacked bar chart to visually compare exploit type proportions\n",
|
||
"# across clusters. Each bar represents a cluster, and the colored segments\n",
|
||
"# show the proportion of each exploit type within that cluster.\n",
|
||
"\n",
|
||
"# .plot(kind='bar', stacked=True) creates a stacked bar chart.\n",
|
||
"# figsize=(14, 7) makes it large enough to read the labels clearly.\n",
|
||
"exploit_proportions.plot(\n",
|
||
" kind='bar',\n",
|
||
" stacked=True,\n",
|
||
" figsize=(14, 7),\n",
|
||
" colormap='Set2' # 'Set2' is a colormap with distinct, easy-to-read colors\n",
|
||
")\n",
|
||
"\n",
|
||
"plt.title('Exploit Type Distribution by Cluster', fontsize=14)\n",
|
||
"plt.xlabel('Cluster', fontsize=12)\n",
|
||
"plt.ylabel('Proportion of Posts', fontsize=12)\n",
|
||
"plt.legend(title='Exploit Type', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
|
||
"plt.xticks(rotation=0) # Keep cluster labels horizontal\n",
|
||
"plt.tight_layout() # Prevent labels from being cut off\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"# INTERPRETATION GUIDE:\n",
|
||
"# - If a cluster's bar is dominated by one color, that cluster strongly\n",
|
||
"# corresponds to a specific exploit type.\n",
|
||
"# - If colors are evenly mixed, the cluster is defined by OTHER features\n",
|
||
"# (like programming language or forum), not by exploit type."
|
||
],
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1400x700 with 1 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 627
|
||
},
|
||
"id": "0bad878f",
|
||
"outputId": "5cfc390f-e4fb-49b4-b14d-d0b9208f5117"
|
||
},
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# STEP: Heatmap of Exploit Type vs. Cluster\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# A heatmap provides an alternative visualization that makes it easy to\n",
|
||
"# spot which (cluster, exploit type) combinations have the highest concentrations.\n",
|
||
"# Darker/warmer colors = higher proportions.\n",
|
||
"\n",
|
||
"plt.figure(figsize=(12, 6))\n",
|
||
"\n",
|
||
"# sns.heatmap() creates a color-coded matrix visualization.\n",
|
||
"# - annot=True = display the actual numbers inside each cell\n",
|
||
"# - fmt='.2f' = format numbers to 2 decimal places\n",
|
||
"# - cmap='YlOrRd' = Yellow-Orange-Red color scale (intuitive: red = high)\n",
|
||
"# - linewidths=0.5 = thin white lines between cells for readability\n",
|
||
"sns.heatmap(\n",
|
||
" exploit_proportions,\n",
|
||
" annot=True, # Show numbers in each cell\n",
|
||
" fmt='.2f', # Two decimal places\n",
|
||
" cmap='YlOrRd', # Color scale: yellow (low) to red (high)\n",
|
||
" linewidths=0.5, # Grid lines between cells\n",
|
||
" cbar_kws={'label': 'Proportion'} # Label for the color bar\n",
|
||
")\n",
|
||
"\n",
|
||
"plt.title('Heatmap: Exploit Type Proportions by Cluster', fontsize=14)\n",
|
||
"plt.xlabel('Exploit Type', fontsize=12)\n",
|
||
"plt.ylabel('Cluster', fontsize=12)\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
],
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 2 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "e794a48d"
|
||
},
|
||
"source": [
|
||
"### **Label 2: Author Name Analysis Across Clusters**\n",
|
||
"\n",
|
||
"- **Why**: Understanding which authors are active in each cluster helps us profile hacker specialization. Some hackers may focus exclusively on network exploits using C/C++, while others may specialize in web-based attacks using PHP.\n",
|
||
"- **What this reveals**: If certain authors appear predominantly in one cluster, it suggests **specialization** — they consistently work with specific tools, languages, or attack types. If authors are spread across clusters, they may be **generalists**.\n",
|
||
"- **Real-world application**: In cyber threat intelligence, identifying specialized threat actors helps security teams predict what kinds of attacks to expect from specific individuals or groups."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "30730e30",
|
||
"outputId": "43926eec-c1ac-418e-b991-f50474090995"
|
||
},
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# STEP: Identify Top Authors in Each Cluster\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# We need the original Author Name strings (not the encoded integers) for\n",
|
||
"# readable output. Since we label-encoded 'Author Name' earlier, we need to\n",
|
||
"# use the original data. Let's reload just the Author Name column.\n",
|
||
"#\n",
|
||
"# IMPORTANT LESSON: This is why it's good practice to keep a copy of your\n",
|
||
"# original data before encoding. In production workflows, you'd typically do:\n",
|
||
"# original_data = hacker_sourcecode_data.copy() # Save before transformations\n",
|
||
"# For now, we'll reload it from the CSV.\n",
|
||
"\n",
|
||
"original_authors = pd.read_csv('hacker_sourcecode_data.csv')['Author Name'].fillna('Anonymous')\n",
|
||
"\n",
|
||
"# Add the original author names back to our DataFrame for analysis.\n",
|
||
"# We can do this safely because the row order hasn't changed.\n",
|
||
"hacker_sourcecode_data['Author Name Original'] = original_authors.values\n",
|
||
"\n",
|
||
"# Now let's find the top 10 most prolific authors in EACH cluster.\n",
|
||
"# We loop through each unique cluster label and print the top contributors.\n",
|
||
"print(\"=== Top 10 Authors Per Cluster ===\\n\")\n",
|
||
"\n",
|
||
"for cluster_label in sorted(hacker_sourcecode_data['Cluster Label'].unique()):\n",
|
||
" # Filter the DataFrame to only rows belonging to this cluster\n",
|
||
" cluster_data = hacker_sourcecode_data[\n",
|
||
" hacker_sourcecode_data['Cluster Label'] == cluster_label\n",
|
||
" ]\n",
|
||
"\n",
|
||
" # .value_counts() counts posts per author, sorted from most to least\n",
|
||
" # .head(10) takes only the top 10\n",
|
||
" top_authors = cluster_data['Author Name Original'].value_counts().head(10)\n",
|
||
"\n",
|
||
" print(f\"--- {cluster_label} ---\")\n",
|
||
" print(f\"Total posts in cluster: {len(cluster_data)}\")\n",
|
||
" print(f\"Unique authors: {cluster_data['Author Name Original'].nunique()}\")\n",
|
||
" print(top_authors)\n",
|
||
" print()"
|
||
],
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"=== Top 10 Authors Per Cluster ===\n",
|
||
"\n",
|
||
"--- C/C++ Exploits ---\n",
|
||
"Total posts in cluster: 6630\n",
|
||
"Unique authors: 613\n",
|
||
"Author Name Original\n",
|
||
"Pr0grammer 292\n",
|
||
"Anonymous 222\n",
|
||
"DenCoder 158\n",
|
||
"ELF_7719116 128\n",
|
||
"ManHunt 128\n",
|
||
"reversecode 102\n",
|
||
"HAMIDx9 96\n",
|
||
"straggle 96\n",
|
||
"mak 84\n",
|
||
"diyary 76\n",
|
||
"Name: count, dtype: int64\n",
|
||
"\n",
|
||
"--- Delphi Exploits ---\n",
|
||
"Total posts in cluster: 1642\n",
|
||
"Unique authors: 167\n",
|
||
"Author Name Original\n",
|
||
"Anonymous 218\n",
|
||
"WEZ_2511 172\n",
|
||
"-silent- 110\n",
|
||
"LttCoder 82\n",
|
||
"<spanstyle=\"color:#50A009;\">cracksman</span> 44\n",
|
||
"Corvu5 28\n",
|
||
"tjf 28\n",
|
||
"drizzle 26\n",
|
||
"H1N1 26\n",
|
||
"yua 26\n",
|
||
"Name: count, dtype: int64\n",
|
||
"\n",
|
||
"--- Mixed-Language Exploits ---\n",
|
||
"Total posts in cluster: 2462\n",
|
||
"Unique authors: 31\n",
|
||
"Author Name Original\n",
|
||
"DJ.KilleR 636\n",
|
||
"MYSTiQUE 454\n",
|
||
"b!atnoy 270\n",
|
||
"balt 234\n",
|
||
"z00MAN 120\n",
|
||
"Great 114\n",
|
||
"Pandora 110\n",
|
||
"<fontcolor=\"green\"><b>(r4ft</b></font> 72\n",
|
||
"_undefined_ 60\n",
|
||
"p4elko 48\n",
|
||
"Name: count, dtype: int64\n",
|
||
"\n",
|
||
"--- PHP Exploits ---\n",
|
||
"Total posts in cluster: 4848\n",
|
||
"Unique authors: 327\n",
|
||
"Author Name Original\n",
|
||
"Hror 342\n",
|
||
"Severos 328\n",
|
||
"Und3rgr0und 186\n",
|
||
"Mahdi-Hidden 184\n",
|
||
"n3me3iz 130\n",
|
||
"Alireza 108\n",
|
||
"ali-farahani 106\n",
|
||
"Sil3nt-Di3 106\n",
|
||
"hack3core 94\n",
|
||
"Anonymous 94\n",
|
||
"Name: count, dtype: int64\n",
|
||
"\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 974
|
||
},
|
||
"id": "86c7e9a5",
|
||
"outputId": "bc3bce96-adb1-4500-dd02-99b1a06d308a"
|
||
},
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# STEP: Visualize Author Concentration by Cluster\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# Create a figure with 4 subplots (one per cluster) arranged in a 2x2 grid.\n",
|
||
"# plt.subplots() returns a figure object and an array of axes (subplot areas).\n",
|
||
"# figsize=(16, 12) makes the overall figure large enough to read.\n",
|
||
"fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n",
|
||
"\n",
|
||
"# .flatten() converts the 2x2 array of axes into a 1D list [ax1, ax2, ax3, ax4]\n",
|
||
"# so we can loop through them easily.\n",
|
||
"axes = axes.flatten()\n",
|
||
"\n",
|
||
"# Get the sorted unique cluster labels so plots appear in consistent order.\n",
|
||
"cluster_labels = sorted(hacker_sourcecode_data['Cluster Label'].unique())\n",
|
||
"\n",
|
||
"for idx, cluster_label in enumerate(cluster_labels):\n",
|
||
" # Filter to this cluster's data\n",
|
||
" cluster_data = hacker_sourcecode_data[\n",
|
||
" hacker_sourcecode_data['Cluster Label'] == cluster_label\n",
|
||
" ]\n",
|
||
"\n",
|
||
" # Get top 10 authors for this cluster\n",
|
||
" top_authors = cluster_data['Author Name Original'].value_counts().head(10)\n",
|
||
"\n",
|
||
" # Create a horizontal bar chart on the appropriate subplot\n",
|
||
" # axes[idx] selects which subplot to draw on\n",
|
||
" top_authors.plot(\n",
|
||
" kind='barh', # Horizontal bar chart (easier to read author names)\n",
|
||
" ax=axes[idx], # Draw on the correct subplot\n",
|
||
" color=plt.cm.viridis(idx / len(cluster_labels)) # Color by cluster\n",
|
||
" )\n",
|
||
"\n",
|
||
" axes[idx].set_title(f'{cluster_label}', fontsize=12)\n",
|
||
" axes[idx].set_xlabel('Number of Posts')\n",
|
||
" axes[idx].set_ylabel('')\n",
|
||
" # Invert y-axis so the top author appears at the top of the chart\n",
|
||
" axes[idx].invert_yaxis()\n",
|
||
"\n",
|
||
"plt.suptitle('Top 10 Authors per Cluster', fontsize=16, y=1.02)\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
],
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1600x1200 with 4 Axes>"
|
||
],
|
||
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|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "60519bdd"
|
||
},
|
||
"source": [
|
||
"### **Label 3: Forum Name Distribution Across Clusters**\n",
|
||
"\n",
|
||
"- **Why**: Different hacker forums often have different specializations and communities. **Ashiyane** may attract different types of hackers than **Exelab** or **Opensc**. Understanding which forums dominate each cluster tells us about the **community structure** behind the exploits.\n",
|
||
"- **What this reveals**: If a cluster is 90% from one forum, that forum's community likely shares common tools, languages, and attack approaches. Cross-forum clusters suggest shared techniques across communities.\n",
|
||
"- **Analogy**: Think of forums like different universities — each has its own culture, specialties, and teaching style, but students from different universities might still end up working in the same field."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 455
|
||
},
|
||
"id": "0c9deaaa",
|
||
"outputId": "b94f0909-b865-484f-cb2d-3ee5e638b5e8"
|
||
},
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# STEP: Deep Dive into Forum Distribution by Cluster\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# Reload original Forum Name for readable labels (we one-hot encoded it earlier).\n",
|
||
"original_forums = pd.read_csv('hacker_sourcecode_data.csv')['Forum Name']\n",
|
||
"hacker_sourcecode_data['Forum Name Original'] = original_forums.values\n",
|
||
"\n",
|
||
"# Create a crosstab of Cluster Label vs Forum Name\n",
|
||
"# This shows the raw counts of posts from each forum in each cluster.\n",
|
||
"forum_crosstab = pd.crosstab(\n",
|
||
" hacker_sourcecode_data['Cluster Label'],\n",
|
||
" hacker_sourcecode_data['Forum Name Original']\n",
|
||
")\n",
|
||
"\n",
|
||
"print(\"=== Forum Distribution by Cluster (Raw Counts) ===\")\n",
|
||
"display(forum_crosstab)\n",
|
||
"\n",
|
||
"# Now compute proportions (each cluster sums to 1.0)\n",
|
||
"forum_proportions = pd.crosstab(\n",
|
||
" hacker_sourcecode_data['Cluster Label'],\n",
|
||
" hacker_sourcecode_data['Forum Name Original'],\n",
|
||
" normalize='index'\n",
|
||
")\n",
|
||
"\n",
|
||
"print(\"\\n=== Forum Distribution by Cluster (Proportions) ===\")\n",
|
||
"display(forum_proportions.round(3))"
|
||
],
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"=== Forum Distribution by Cluster (Raw Counts) ===\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"Forum Name Original ashiyane exelab opensc xeksec\n",
|
||
"Cluster Label \n",
|
||
"C/C++ Exploits 1348 4572 710 0\n",
|
||
"Delphi Exploits 0 0 1642 0\n",
|
||
"Mixed-Language Exploits 0 0 0 2462\n",
|
||
"PHP Exploits 4610 0 238 0"
|
||
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|
||
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|
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|
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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||
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|
||
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||
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||
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|
||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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|
||
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|
||
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|
||
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|
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|
||
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||
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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|
||
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|
||
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|
||
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|
||
" background-color: #3B4455;\n",
|
||
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|
||
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|
||
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|
||
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|
||
" background-color: #434B5C;\n",
|
||
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
|
||
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
|
||
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|
||
" }\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
||
"\n",
|
||
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|
||
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|
||
" const dataTable =\n",
|
||
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|
||
" [key], {});\n",
|
||
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|
||
"\n",
|
||
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|
||
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
|
||
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|
||
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|
||
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|
||
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|
||
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||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" buttonEl.onclick = () => {\n",
|
||
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|
||
" }\n",
|
||
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|
||
" </script>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
"type": "dataframe",
|
||
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|
||
"summary": "{\n \"name\": \"forum_crosstab\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"Cluster Label\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Delphi Exploits\",\n \"PHP Exploits\",\n \"C/C++ Exploits\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ashiyane\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2175,\n \"min\": 0,\n \"max\": 4610,\n \"num_unique_values\": 3,\n \"samples\": [\n 1348,\n 0,\n 4610\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"exelab\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2286,\n \"min\": 0,\n \"max\": 4572,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 4572\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"opensc\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 725,\n \"min\": 0,\n \"max\": 1642,\n \"num_unique_values\": 4,\n \"samples\": [\n 1642,\n 238\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"xeksec\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1231,\n \"min\": 0,\n \"max\": 2462,\n \"num_unique_values\": 2,\n \"samples\": [\n 2462,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
|
||
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|
||
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|
||
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|
||
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|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"\n",
|
||
"=== Forum Distribution by Cluster (Proportions) ===\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
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|
||
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|
||
"Forum Name Original ashiyane exelab opensc xeksec\n",
|
||
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|
||
"C/C++ Exploits 0.203 0.69 0.107 0.0\n",
|
||
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|
||
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|
||
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||
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||
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|
||
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" </svg>\n",
|
||
" </button>\n",
|
||
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|
||
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|
||
" .colab-df-container {\n",
|
||
" display:flex;\n",
|
||
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|
||
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|
||
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|
||
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" width: 32px;\n",
|
||
" }\n",
|
||
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|
||
" .colab-df-convert:hover {\n",
|
||
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|
||
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|
||
" fill: #174EA6;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-buttons div {\n",
|
||
" margin-bottom: 4px;\n",
|
||
" }\n",
|
||
"\n",
|
||
" [theme=dark] .colab-df-convert {\n",
|
||
" background-color: #3B4455;\n",
|
||
" fill: #D2E3FC;\n",
|
||
" }\n",
|
||
"\n",
|
||
" [theme=dark] .colab-df-convert:hover {\n",
|
||
" background-color: #434B5C;\n",
|
||
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|
||
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
|
||
" fill: #FFFFFF;\n",
|
||
" }\n",
|
||
" </style>\n",
|
||
"\n",
|
||
" <script>\n",
|
||
" const buttonEl =\n",
|
||
" document.querySelector('#df-809bee56-0f54-42a6-ac86-34353a8b9c77 button.colab-df-convert');\n",
|
||
" buttonEl.style.display =\n",
|
||
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|
||
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|
||
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|
||
" const element = document.querySelector('#df-809bee56-0f54-42a6-ac86-34353a8b9c77');\n",
|
||
" const dataTable =\n",
|
||
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
|
||
" [key], {});\n",
|
||
" if (!dataTable) return;\n",
|
||
"\n",
|
||
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
|
||
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
|
||
" + ' to learn more about interactive tables.';\n",
|
||
" element.innerHTML = '';\n",
|
||
" dataTable['output_type'] = 'display_data';\n",
|
||
" await google.colab.output.renderOutput(dataTable, element);\n",
|
||
" const docLink = document.createElement('div');\n",
|
||
" docLink.innerHTML = docLinkHtml;\n",
|
||
" element.appendChild(docLink);\n",
|
||
" }\n",
|
||
" </script>\n",
|
||
" </div>\n",
|
||
"\n",
|
||
"\n",
|
||
" </div>\n",
|
||
" </div>\n"
|
||
],
|
||
"application/vnd.google.colaboratory.intrinsic+json": {
|
||
"type": "dataframe",
|
||
"summary": "{\n \"name\": \"display(forum_proportions\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"Cluster Label\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Delphi Exploits\",\n \"PHP Exploits\",\n \"C/C++ Exploits\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ashiyane\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.45191481498176184,\n \"min\": 0.0,\n \"max\": 0.951,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.203,\n 0.0,\n 0.951\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"exelab\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.345,\n \"min\": 0.0,\n \"max\": 0.69,\n \"num_unique_values\": 2,\n \"samples\": [\n 0.0,\n 0.69\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"opensc\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.4760133051361765,\n \"min\": 0.0,\n \"max\": 1.0,\n \"num_unique_values\": 4,\n \"samples\": [\n 1.0,\n 0.049\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"xeksec\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.5,\n \"min\": 0.0,\n \"max\": 1.0,\n \"num_unique_values\": 2,\n \"samples\": [\n 1.0,\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 1000
|
||
},
|
||
"id": "c95cd9ff",
|
||
"outputId": "75b0261b-91ce-46a8-a1fc-1ee67586ea01"
|
||
},
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# STEP: Visualize Forum Distribution with Grouped Bar Chart\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# A grouped bar chart places bars side-by-side for easy comparison\n",
|
||
"# (as opposed to stacked bars where segments sit on top of each other).\n",
|
||
"\n",
|
||
"forum_proportions.plot(\n",
|
||
" kind='bar',\n",
|
||
" figsize=(14, 7),\n",
|
||
" colormap='tab10', # tab10 provides 10 distinct colors\n",
|
||
" width=0.8 # Bar width (0.8 = slight gap between groups)\n",
|
||
")\n",
|
||
"\n",
|
||
"plt.title('Forum Distribution by Cluster (Proportions)', fontsize=14)\n",
|
||
"plt.xlabel('Cluster', fontsize=12)\n",
|
||
"plt.ylabel('Proportion of Posts', fontsize=12)\n",
|
||
"plt.legend(title='Forum Name', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
|
||
"plt.xticks(rotation=15)\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"# Create a pie chart for each cluster showing forum composition\n",
|
||
"fig, axes = plt.subplots(2, 2, figsize=(14, 12))\n",
|
||
"axes = axes.flatten()\n",
|
||
"cluster_labels = sorted(hacker_sourcecode_data['Cluster Label'].unique())\n",
|
||
"\n",
|
||
"for idx, cluster_label in enumerate(cluster_labels):\n",
|
||
" cluster_data = hacker_sourcecode_data[\n",
|
||
" hacker_sourcecode_data['Cluster Label'] == cluster_label\n",
|
||
" ]\n",
|
||
" forum_counts = cluster_data['Forum Name Original'].value_counts()\n",
|
||
"\n",
|
||
" axes[idx].pie(\n",
|
||
" forum_counts.values,\n",
|
||
" labels=forum_counts.index,\n",
|
||
" autopct='%1.1f%%',\n",
|
||
" startangle=140,\n",
|
||
" colors=plt.cm.Set3.colors[:len(forum_counts)]\n",
|
||
" )\n",
|
||
" axes[idx].set_title(f'{cluster_label}', fontsize=12)\n",
|
||
"\n",
|
||
"plt.suptitle('Forum Composition per Cluster', fontsize=16, y=1.02)\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
],
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1400x700 with 1 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1400x1200 with 4 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "c7fecc69"
|
||
},
|
||
"source": [
|
||
"### **Label 4: Programming Language — A Formalized View**\n",
|
||
"\n",
|
||
"- **Why**: We already explored programming language distributions informally above. Now let's formalize this analysis using the same cross-tabulation approach we used for Exploit Type and Forum Name. This consistency in methodology is important — it makes your analysis **reproducible** and **comparable** across different label dimensions.\n",
|
||
"- **Key insight**: Programming language turned out to be the **dominant clustering factor** in our K-Means model. This makes sense because we one-hot encoded languages as separate binary features, giving them significant weight in the distance calculations."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 686
|
||
},
|
||
"id": "d3aabc17",
|
||
"outputId": "d9a3604a-6a9e-4344-c224-4a3f53aebc18"
|
||
},
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# STEP: Formalized Language Analysis with Crosstab\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# Reload original Language column for readable labels\n",
|
||
"original_languages = pd.read_csv('hacker_sourcecode_data.csv')['Language']\n",
|
||
"hacker_sourcecode_data['Language Original'] = original_languages.values\n",
|
||
"\n",
|
||
"# Cross-tabulation: Cluster Label vs Programming Language\n",
|
||
"language_crosstab = pd.crosstab(\n",
|
||
" hacker_sourcecode_data['Cluster Label'],\n",
|
||
" hacker_sourcecode_data['Language Original']\n",
|
||
")\n",
|
||
"\n",
|
||
"print(\"=== Language Distribution by Cluster (Raw Counts) ===\")\n",
|
||
"display(language_crosstab)\n",
|
||
"\n",
|
||
"# Proportions within each cluster\n",
|
||
"language_proportions = pd.crosstab(\n",
|
||
" hacker_sourcecode_data['Cluster Label'],\n",
|
||
" hacker_sourcecode_data['Language Original'],\n",
|
||
" normalize='index'\n",
|
||
")\n",
|
||
"\n",
|
||
"print(\"\\n=== Language Distribution by Cluster (Proportions) ===\")\n",
|
||
"display(language_proportions.round(3))"
|
||
],
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"=== Language Distribution by Cluster (Raw Counts) ===\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"Language Original .NET ASM C++ C/C++ Delphi HTML Java \\\n",
|
||
"Cluster Label \n",
|
||
"C/C++ Exploits 0 0 0 6630 0 0 0 \n",
|
||
"Delphi Exploits 2 86 8 6 1180 0 8 \n",
|
||
"Mixed-Language Exploits 12 30 4 450 274 16 14 \n",
|
||
"PHP Exploits 0 0 0 0 0 0 250 \n",
|
||
"\n",
|
||
"Language Original JavaScript Javascript LISP PHP Perl Python \\\n",
|
||
"Cluster Label \n",
|
||
"C/C++ Exploits 0 0 0 0 0 0 \n",
|
||
"Delphi Exploits 8 16 0 0 0 26 \n",
|
||
"Mixed-Language Exploits 0 112 6 1108 278 44 \n",
|
||
"PHP Exploits 0 0 0 4116 0 482 \n",
|
||
"\n",
|
||
"Language Original Ruby SQL VB.NET VB6 Visual Basic \\\n",
|
||
"Cluster Label \n",
|
||
"C/C++ Exploits 0 0 0 0 0 \n",
|
||
"Delphi Exploits 0 0 6 12 266 \n",
|
||
"Mixed-Language Exploits 6 84 0 0 24 \n",
|
||
"PHP Exploits 0 0 0 0 0 \n",
|
||
"\n",
|
||
"Language Original Windows Commands \n",
|
||
"Cluster Label \n",
|
||
"C/C++ Exploits 0 \n",
|
||
"Delphi Exploits 18 \n",
|
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"C/C++ Exploits 0.000 0.000 0.000 1.000 0.000 0.000 0.000 \n",
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"source": [
|
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"# ============================================================\n",
|
||
"# STEP: Comprehensive Language Visualization — Heatmap\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# Create a heatmap that shows the proportion of each language in each cluster.\n",
|
||
"# This is the clearest way to see which languages define which clusters.\n",
|
||
"\n",
|
||
"plt.figure(figsize=(14, 6))\n",
|
||
"sns.heatmap(\n",
|
||
" language_proportions,\n",
|
||
" annot=True,\n",
|
||
" fmt='.2f',\n",
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||
" cmap='Blues', # Blue color scale: light (low) to dark (high)\n",
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" linewidths=0.5,\n",
|
||
" cbar_kws={'label': 'Proportion'}\n",
|
||
")\n",
|
||
"\n",
|
||
"plt.title('Heatmap: Programming Language Proportions by Cluster', fontsize=14)\n",
|
||
"plt.xlabel('Programming Language', fontsize=12)\n",
|
||
"plt.ylabel('Cluster', fontsize=12)\n",
|
||
"plt.xticks(rotation=45, ha='right')\n",
|
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"plt.tight_layout()\n",
|
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"plt.show()"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "display_data",
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lkTjLLCMJKStRJYu9vb1gY2MjsS7jB+K3CUNBEISoqChBU1NTqF69usT6//77TwgPD5f4oZ6djPjq1KkjTtCOHTtWqFmzpgBAMDU1Ff9QzOjDEiVKSPywz9C8eXMBgBASEiL13Jw5cwQAwqxZs8TrmjZtKgCQSo4IgiAMHTo0y8Sgjo6O8ObNmxzFl2Hfvn0CAMHX11eqPkNDQ4lklSAIwtmzZ8WJ7LS0NInntm3bJvVDMnPiO6vy2b2HTp8+LVVXVonBzGW/fe79+/fidTY2NoKOjo5E0jFDq1at8iwxmJXFixcLAITg4GDxuoxYy5UrJ6SmpkqUz3iuS5cu4nXBwcECAKFDhw5S9T958kTQ1NRUODGYHVdXV0FHR0ci4S/v+zggIEDqWMisS5cugoaGhkTyNysZsbm7uwve3t7CjBkzhEGDBgkmJiYCAKFjx45SsX6bBBeE9OQcAKFq1apS79/U1FShcuXKAgCJZFrGe+Kvv/6Sqm/79u0CAGHUqFHidfKeHzKSS507d5YZe24SgxnH2/z586XKnz9/XioBKu/7M+Pz6NvkqjzkPe8nJycLlpaWQtmyZSXaGBYWJgAQunfvnqP9ZiSxExMT5WqvMhODJ0+e/O7+svu8Hj16tABAOHv2rNRzqampQvHixYU6depItaV27drf3S8RUWHGqcRERKRULi4uOHHihMzngoODxdeNyvDvv/8iNTUVSUlJMq/Tde/ePQBARESE+K66ycnJWLVqFfz9/REREYEPHz5IXNvpv//+k7vdNWvWlFpnZWX13ef+++8/lCtXTrw+ODgYy5YtQ0hICKKjoyWuafXt9KYMP/30k9S6smXLwtraGrdv30ZycrJ4WysrK/G+5XH16lVcvXpV3I5SpUphyJAhmDp1qtT0ZgcHB5ltvX79OgwMDFC/fn2p5zL6NTQ0VLwuLCwMRYoUQa1ataTKN2rUKMvrApYrVy7LmwrEx8dj0aJFOHDgAB48eCB1/T5ZfW9rays1dS7jNaxRo4bUtMLMffut7Mp/732SU3Xq1JFaV7p0aQDpNwcwMjLC+/fvERUVhapVq8q8VlijRo1w6tSpHO9THq9fv8Yff/yB48eP4/Hjx+LrcGaQFWvNmjWhoSF5aevMMWXIuKabs7OzVB3W1tYoU6YMHj16pGgICA0NxYIFC/DPP//g5cuXEtcmBdKv3ZjRd/K+jy9dugQAiIyMlHlOe/nyJdLS0nD37l3UrVs3R+3NmAYKAIaGhqhSpQr69Okjvk5oZrKOz4zjskmTJlLvXw0NDTRu3BgREREIDQ2Vmq4q6/yUsS7zNRnlPT9k197cymiPrCneTk5O0NPTk9mGnL4/XV1d4eXlhZEjRyIwMBCtW7dGkyZNcjUtPKfnfW1tbQwcOBB//PEHTp06hdatWwNIv6QGAInLCxRUPXr0wLJly9C5c2f07NkTP//8Mxo3boxSpUrJVU/GsXXy5EkEBgZKPa+trY2IiAip9ZkvDUBERNKYGCQionwVGxsLADh//jzOnz+fZbnMCaBu3brh8OHDqFSpEnr27AkLCwtoa2sjLi4Oy5cvR1JSktztyLiGXWYZ14rK7rnMCYU9e/agZ8+eMDQ0hIuLC2xsbGBgYCC+SHtWNx2QldjJWB8VFYX4+HiYmZnJHVNmHh4eWLduXY7KZtWe9+/fSyUNMmQkUd6/f5+j8lntI7vnkpOT0bRpU1y7dg21atVCv379YGZmBi0tLfH1yGT1vTL6Ni/qykp29aSmpgL4+jpbWFjIrCO711cRsbGxqFevHp48eYJGjRqhZcuWMDExgaamJkJDQ3Hw4EG5+yAjJiBncSmaGLxw4QKaN28OAGjVqhVsbW1haGgIkUiEAwcOICwsTCIGed/HGec0Pz+/bNvxbVI7OxcvXpS4K3F2ZLUp43XN6n0h6/jNrr6Mde/evZPYhzznh+zqz63s4hSJRLC0tMTz58+lnsvp+9PGxgaXLl2Cj48Pjh07ht27dwMAKleujFmzZqF79+45bqs85/2hQ4di/vz52LRpE1q3bo3ExET4+fmhXLlyaNmyZY72V6JECURFReH58+dKu75lTjk6OiI4OBhz587Fjh07sGXLFgDpCbv58+dL/cMwKxnHVsYNS3Iqr86HRETqgolBIiLKVxk/yCZMmIBFixZ9t/y///6Lw4cPw8XFBUePHhVfWB9IH02wfPnyPGvr9/j4+EBPTw9Xr16Fra2txHP+/v5ZbvftHXAzrxeJRDAyMlJqO78nqzs2Fi1aFK9fv5b53MuXL8VlMpd/8+aNzPJZxZzd/g8ePIhr167B3d0dmzZtknjO398fW7duzbJOdZPxOmfVH9m9vor4888/8eTJE8yePRvTpk2TeO6PP/7AwYMHFao/N3FljPT69o6zgGTiKsOcOXOQlJSEc+fOSY1MvHTpktSdaOV9H2fEcPjwYfEo5x9J1vGT0aas3heyjt8Mr169QpkyZaTWAYCxsbHEPuQ5P2TX3tzKHOe3I6EFQcCrV69ktkEe1atXx969e5GSkoKrV6/i+PHjWLFihfhGOY0aNcpRPfKc98uVK4dWrVrh0KFDeP36Nf7++2+8ffsWEyZMyPHr16hRI0RFRSEwMFDhxKC8xxyQPkLy+PHjSEhIQEhICA4fPow1a9agXbt2uHXrVo7alNF379+/l+tzkXchJiLKnsb3ixAREeWdevXqQSQS4eLFizkq/+DBAwAQ37U0s3PnzsncRlNTU2LUR1558OABqlSpIpUUfPHiBR4+fJjldrLa/fjxYzx9+hTVqlXLcgryj1arVi18+vQJly9flnouODgYgOR0WgcHB3z8+FHm1L0LFy7Ivf+Mvu/YsaPUc1n1vboqWrQobGxscP/+fZnJmNy8vjmR133g4OAAADJHDz979kzm3UQz7lQsayRY5qmuGR48eABTU1OppOCnT59w7do1mW2S533s6OgIADk+p/0IGcfl2bNnJS67AKQnzM6ePStRLjNZ/ZqxLvP0annPD9+TcX6X59yd0Z6M/WUWEhKCxMREudqQHW1tbTRo0AAzZ87EihUrIAgCjhw5kuPt5T3ve3h4ICUlBVu3bsWmTZugqamJgQMH5nh/7u7uAIDFixdLTf//1vdG3ct7zGWmr6+Ppk2bYvHixZgyZQoSEhLw999/i5/P7vM649jKmFJMRETKwcQgERHlqxIlSqBHjx64cOECFi5cKPWjFUj/Qffp0ycAEI8C+eeffyTK3L59G/PmzZO5D1NTU0RHRyMxMVHJrZdUtmxZ3L9/X2IkSGJiIoYPH57tVNJt27bhxo0b4seCIGDKlClITU2Fm5ubRNkXL14gIiIiy1EZeWnAgAEAAC8vL4l4nj59iiVLlkBLSwt9+vQRr8/4e9q0aUhLSxOvj4iIyNXovqz6/syZM+LrbRUmffr0QXJyMry9vSXWBwcH4+TJk3myz6z6YMeOHTh27JjC9Ts7O6NMmTI4fPiwVGJt+vTpMhMGGdcP8/X1lVi/d+9enDlzRqp82bJl8fbtW9y+fVu8LjU1FRMnTpQ5MlDe93HHjh1RpkwZLFmyRJxwyywlJUXq9ctrZcqUQbNmzXD79m1s3rxZ4rkNGzYgPDwczZs3lzkVePbs2RLnm3fv3uH333+HSCQSnxMA+c8P32NqairePqd69+4NLS0tLFmyROJal8nJyZg8eTIASJ1T5XH16lWZ06Ezzvl6eno5rkue8z6Qfn3DkiVLYunSpThz5gzatWuHkiVL5nh/zZo1Q69evRAZGYkuXbrI/IfC+/fvMWXKlCyv/5qhTp06EIlE8Pf3l/hcvXfvnsxR+xcvXpT5+Svrdcvu83rEiBHQ0tKCp6enzH8SxMXFfTcxSURE0jiVmIiI8t2aNWsQGRmJSZMmYfv27XBycoKJiQmePn2KK1eu4N69e3jx4oX4wvb169fH7t278eLFCzRo0ABPnjzBoUOH0K5dO+zdu1eq/ubNm+PKlSto06YNfvrpJ+jo6KBx48Zo3LixUuPw9PSEp6cnatWqhW7duuHz58/4+++/IQgCHBwcpKYoZnBxcYGTkxN++eUXFC9eHIGBgbhy5QoaNGgAT09PibJeXl7YunUrtmzZotAP3Nzo168fAgICcPDgQdSoUQPt27fHx48fsWvXLsTGxmLx4sUS08EGDhyI7du34+jRo6hVqxbatGmD2NhY+Pv74+eff8bhw4elLvifHVdXV9jY2GDBggW4desWqlevjsjISBw5cgSdO3eW2ffqbPLkydi3bx/WrVuHW7du4aeffsKzZ8+we/duuLq6yv36AsCYMWOgr68v87lFixahX79+mD9/Pjw9PREUFISyZcsiLCwMgYGB6NKlCwICAhSKSVNTE+vWrUOHDh3QvHlz9OzZE1ZWVjhz5gyeP38OBwcHiWQKkJ6Iq1ChAnx9ffH06VPUqlUL4eHhOH36NNq2bSuVsPT09MSpU6fg7OyMHj16QE9PD8HBwXj+/DmaNm0qNdpM3vexrq4u9u7dizZt2qBJkyZo3rw57O3tIRKJ8PjxY5w7dw5mZmYyb5KQl9auXQtnZ2cMGTIEhw8fRtWqVXH79m0cOnQIxYsXx9q1a2VuV6lSJVSvXh1du3YFAOzbtw/Pnj3D+PHjJW6eIu/54XsqV66MkiVLwt/fH7q6uihdujREIhE8PT0lpjBnVqFCBcyfPx8TJkxAjRo10KNHDxQpUgSHDx9GZGQkOnbsiL59+8rxqknavn071q9fj8aNG6NChQooWrQo7ty5g2PHjsHU1FSuEXzynPeB9Gseuru7Y/bs2QByd9ORP//8E4IgwN/fXzw9uVKlShAEAffu3UNgYCDi4+Oxffv2bOspWbIkevXqhR07dqBOnTpo3bo1Xr9+jf3796N169bYt2+fRPn58+cjKCgIjRs3Rrly5aCnp4dr166JpzV37txZXDa7z+vq1atjzZo1GD58OOzs7NC2bVtUqFAB8fHxePjwIc6cOQM3N7ccX0+XiIi+yKe7IRMRkZp59OiRAEBwcXHJskxQUJAAQPDw8JB67tOnT8KCBQuEOnXqCEWKFBH09fWFcuXKCZ06dRK2bdsmpKSkiMu+fv1aGDRokFCyZElBT09PsLe3F1avXi08fPhQACAMGDBAou74+HhhyJAhgpWVlaCpqSkAELy9vSXalPE4sy1btggAhC1btkg95+3tLQAQgoKCxOvS0tKEdevWCdWqVRP09PSEEiVKCO7u7sLr16+FJk2aCN9+7GauY+PGjUK1atUEXV1dwcrKShgzZozw/v17qf0OGDAgyzbJkt1r/q2MPvz29cssJSVFWLRokWBvby/o6uoKRkZGQpMmTYSDBw/KLP/hwwdhwoQJQsmSJQVdXV2hatWqwoYNG4S9e/cKAISlS5dKlAcgNGnSJMv9P3z4UOjatatQvHhxwcDAQKhXr57g7++fZT9mVV92scqqS97yGWS9h7KqS9Z7JENGvz969Ehi/evXrwV3d3fB3Nxc0NPTE+rUqSMEBAQIixYtEgAI+/fvl1nftzL2nd2Sse/Q0FChVatWQrFixcT9/7///U+uWDNk1T+nT58WnJ2dBX19fcHU1FTo3r278OTJE6F69eqCsbGxVPlHjx4JnTp1EoyMjIQiRYoILVq0EP7991+Zx6kgCMLevXuF2rVrCwYGBoK5ubnQo0cP4cGDB1m+zvK+jwVBEJ49eyaMGTNGsLW1FXR1dYWiRYsKVapUEQYPHiwEBgZm3RmZZLTn4sWL3y2bVayZRUVFCQMHDhSsrKwELS0twcrKShg4cKAQFRUlVTbjPZGQkCBMmjRJsLa2FnR0dAQ7OzthxYoVQlpamtQ28pwfsju/Zrh06ZLQpEkTwcjISOp9mF28Bw8eFG+nq6sr2NvbC4sXL5b4HBEE+d+fly5dEjw8PITq1asLJiYmgr6+vmBrayuMGjVKePz4cZZxZJab836G+/fvCwCEUqVKCZ8/f87R/mT5+++/hV69eglly5YV9PT0BD09PcHW1lYYPHiwEBISIlE2q2Pi06dPwujRowVLS0tBV1dXqFGjhuDn5yfzfHjixAmhf//+gp2dnWBkZCQYGhoKVatWFaZMmSK8efNGot7sPq8zXL58Wfjll1+EkiVLCtra2oK5ublQu3Zt4bfffhPCw8PF5bI7NxMR0VciQZAxZ4uIiIjynI+PD2bOnImgoCA0bdo0v5vzQ02bNg1z5szBsWPH0KZNm/xujtrp27cv/Pz8cOfOHVSpUiW/m6MU8fHxsLS0hL29PUJCQvK7OQDU+33ctGlTnDlzRublHSh/7N27F927d8f06dMxa9as/G4OERGpCV5jkIiIiPLMixcvpNbduXMHK1asgImJSaFLiCqbrNf3zJkz8Pf3h52dnUomBT9+/Ij4+HiJdampqfj111+RkJCATp06/fA28X1M+U0QBCxevBhaWlq5mkZMRESUFV5jkIiIiPLM8OHDERUVhfr166NYsWJ48OABDh8+jJSUFPz5559ZXs+OcqZt27bQ19dHzZo1UaRIEdy5cwcnTpyApqYmVq5cmd/Ny5V79+7B2dkZLi4uKF++POLj43Hu3DncuXMH1apVw+jRo394m/g+pvxy8+ZNHDlyBBcuXMClS5fg4eEh8yYxREREucXEIBEREeWZ7t27Y926dQgICMC7d+9gaGiIJk2aYMKECXBxccnv5qm8AQMGwM/PD/7+/oiPj4eJiQlcXV3h5eUFR0fH/G5erpQqVQrdu3fHmTNncOLECXz+/BllypTBxIkTMXXqVBQpUuSHt4nvY8ovV69exZQpU2BsbIx+/fph0aJF+d0kIiJSM7zGIBERERERERERkZzOnj2LhQsX4urVq3jx4gX279//3cueBAcHY/z48bh9+zasra0xbdo0uLm5/ZD2ysJrDBIREREREREREcnp48ePcHBwwOrVq3NU/tGjR2jXrh2aNWuG0NBQjB07FoMHD8bJkyfzuKVZ44hBIiIiIiIiIiIiBYhEou+OGJw8eTKOHj2KW7duidf98ssviIuLw4kTJ35AK6VxxCARERERERERERGApKQkvH//XmJJSkpSSt0XL15Ey5YtJda5uLjg4sWLSqk/N3jzESIiIiIiIiIiUkn6tUYptb7JHc0xc+ZMiXXe3t7w8fFRuO6XL1/C0tJSYp2lpSXev3+PhIQE6OvrK7wPeTExSPQDJH7O7xYoRk+LMRQEelrK/9D70RKur1KLfmAM+Y8xFAyMoWBgDAUDYygYGEPBoOoxqHr7gfQYKHe8vLwwfvx4iXW6urr51Jq8x7cKERERERERERGpJpFyr5Knq6ubZ4nAEiVK4NWrVxLrXr16haJFi+bLaEGAiUEiIiIiIiIiIlJVIlF+tyDHnJyccOzYMYl1f//9N5ycnPKpRbz5CBERERERERERkdw+fPiA0NBQhIaGAgAePXqE0NBQPHnyBED6tOT+/fuLyw8bNgwPHz7EpEmTEBERgTVr1mD37t0YN25cfjQfAEcMEhERERERERGRqlLyVGJ5XLlyBc2aNRM/zrg24YABA+Dr64sXL16Ik4QAUK5cORw9ehTjxo3D8uXLUbp0aWzatAkuLi4/vO0ZmBgkIiIiIiIiIiKSU9OmTSEIQpbP+/r6ytzm+vXredgq+TAxSEREREREREREqkmFrjFYEDExSEREREREREREqikfpxKrA756REREREREREREhRBHDBIRERERERERkWriVGKFMDFIRERERERERESqiVOJFcJXj4iIiIiIiIiIqBDiiEEiIiIiIiIiIlJNnEqsECYGiYiIiIiIiIhINXEqsUL46hERERERERERERVCHDFIRERERERERESqiVOJFcIRg0RERERERERERIUQRwwSEREREREREZFq4jUGFcLEIBERERERERERqSZOJVYI06pERERERERERESFEEcMEhERERERERGRauJUYoUwMUhERERERERERKqJiUGF8NUjIiIiIiIiIiIqhDhikIiIiIiIiIiIVJMGbz6iCCYGiYiIiIiIiIhINXEqsUL46hERERERERERERVCHDFIRERERERERESqScSpxIrgiEEiIiIiIiIiIqJCiCMGiYiIiIiIiIhINfEagwphYpCIiIiIiIiIiFQTpxIrhGlVIiIiIiIiIiKiQogjBomIiIiIiIiISDVxKrFCmBgkIiIiIiIiIiLVxKnECmFalYiIiIiIiIiIqBDiiEEiIiIiIiIiIlJNnEqsECYGiYiIiIiIiIhINXEqsUKYViUiIiIiIiIiIiqEOGKQiIiIiIiIiIhUE6cSK4SJQSIiIiIiIiIiUk2cSqwQplWJiIiIiIiIiIgKIY4YJCIiIiIiIiIi1cSpxArhq0dERERERERERFQIMTFIaiMqKgoikQihoaE53qZp06YYO3ZsnrWJiIiIiIiIiPKQSEO5SyFT+CJWIS9fvoSnpyfKly8PXV1dWFtbw9XVFYGBgVJly5Urh//9738AAEEQsGHDBjg6OsLQ0BAmJiaoW7culi1bhk+fPv3oMAAANjY2EIlEUssff/yRL+3JEBAQgNmzZ4sf29jYYNmyZfnXIAD+O/zQ5ufmqFfLHn1+6Y6bN25kW/7UyePo2L416tWyR9dOrjh39ozE84IgYPXK5WjRxBn1a9fAUHc3PH4clYcRMIaCEEOj2hWwd5kHHp6ag4Trq+DatMZ3t/mpji0u7JiMuJCluHXQG31dHaXKePRojIijM/H20lKc3TYRdauVzYvmi6l6PwCMgTEoD2NgDMrCGBiDsjAGxqAsjKFgxKCyRCLlLoUME4MFVFRUFOrUqYPTp09j4cKFuHnzJk6cOIFmzZph5MiREmVv3LiBt2/fokmTJgCAfv36YezYsejYsSOCgoIQGhqK6dOn4+DBgzh16pTM/bm5ucHHxyfH7ROJRIiKipIrplmzZuHFixcSi6enp1x1KJupqSmMjIzytQ2ZnTh+DIsWzIPHiJHw37MfdnaVMdzDHTExMTLLh16/ht9+nYDOXbph194DaNa8BcZ6jsS9e3fFZbb8uRE7/bZjmrcP/tq5G/r6+hg+1B1JSUmMQY1jKKKvi5t3n2PsvF05Kl+2pBn2rxyGs1fuwvGXP7BqRxDWzuiNlk5VxGW6taqN+RM6Y87643DqPR837j7HoTUjUbyYYZ7EoA79wBgYA2NgDIyBMTAGxsAYGIMqxECFmEAFUps2bYRSpUoJHz58kHru7du3Eo9nzZol9OzZUxAEQdi1a5cAQDhw4IDUdmlpaUJcXJzM/Q0YMEDw9vbOcfsACI8ePcpx+bJlywpLly7N8vmZM2cKVlZWQnR0tHhd27ZthaZNmwqpqanifa5Zs0Zo3bq1oKenJ5QrV07Ys2ePuPyjR48EAML169fF64KDg4V69eoJOjo6QokSJYTJkycLKSkp4uebNGkijBkzRvw3AIlFEAQhKipKaN++vWBiYiIYGBgIVatWFY4ePZrj2AVBEBJScrZ06dpNmO49U/z4Y1Kq0MjZWVi1Zr3M8qNGjxHchwyVWNe1W3dhyrTpQkKKIHxKThMaNmwkrNuwSfz869j3QvXq1YWAg0dy3C7GUHBi0Ks5Uu5FEASh+9j12ZZZtPmUcOvec4l1u09cEU7+c1v8+PKNR8LancHix/q1RgnPX70Vpi0/IFdb1KEfGANjYAyMgTEwBsbAGBiD+sYgT/sLcgyFhV6HdUpdChuOGCyAYmNjceLECYwcORJFihSRet7ExETi8aFDh9CxY0cAgJ+fH+zs7MSPMxOJRDA2Ns6TNitq6tSpsLGxweDBgwEAq1evxoULF7B161ZoaHx9m06fPh1du3ZFWFgY+vTpg19++QXh4eEy63z+/Dnatm2LevXqISwsDGvXrsWff/6J33//XWb5gIAAlC5dWmJkIwCMHDkSSUlJOHv2LG7evIn58+fD0FD5I6RSkpMRfuc2Gjg1FK/T0NBAgwYNcSPsusxtboSGokEDJ4l1DRs548aX6yw+f/YM0dFv4Njga51GRkawr+GQZZ2MQfVjyA1Hh3IIComUWPf3hXA41igHANDW0kStKtY4namMIAg4HRKJ+l/KKJM69ANjYAyMgTEwBsbAGBgDY2AMqhCDyuNUYoUwMVgA3b9/H4IgoHLlyt8t+/z5c9y4cQNt2rQBANy7dw92dnZ53cRcmTx5MgwNDSWWc+fOAQA0NTXx119/ITAwEL/99ht+/fVXrF69GmXKlJGoo3v37hg8eDAqVaqE2bNno27duli5cqXM/a1ZswbW1tZYtWoVKleujE6dOmHmzJlYvHgx0tLSpMqbmppCU1MTRkZGKFGiBEqUKAEAePLkCRo1agR7e3uUL18e7du3R+PGjZX86gBv494iNTUVZmZmEuvNzMwQHR0tc5vo6GiYmZlLl4+J/vL8m/R15jmvUxGM4Zvy+RRDbliaFcWr2HiJda9j38PYSB96utowL2YILS1NvP62TMx7lDArqvT2qEM/MIZvyjOGXGMM35RnDLnGGL4pzxhyjTF8U54x5Bpj+Ka8Cv1+IPWhld8NIGmCIOS47KFDh+Ds7CweRZjTbf38/ODh4SF+nJSUBJFIhEWLFonXHT9+HD/99BMAoE2bNuIkXoZq1apB9CWbXrZsWdy+fTvbff76669wc3OTWFeqVCnx3+XLl8eiRYvg4eGBnj17onfv3lJ1ODk5ST3O6i7E4eHhcHJyErcRABo1aoQPHz7g2bNnUknHrIwePRrDhw/HqVOn0LJlS3Tt2hU1asi+kUNSUpLUNR90dXUBTd0c7YuIiIiIiIiI5FAI7ySsTHz1CiBbW1uIRCJERER8t+yhQ4fQoUMH8eNKlSrlaLsOHTogNDRUvHTo0AHDhg2TWFe3bl1x+U2bNkk8BwDHjh0TPz527Nh392lubo6KFStKLPr6+hJlzp49C01NTURFReHz58/frfNHGDx4MB4+fIh+/frh5s2b2Y5SnDdvHoyNjSWWefPm5Wg/xUyKQVNTU+oCtTExMTA3N5e5jbm5OWJioqXLf/nvk7l58fR10TmvUxGM4Zvy+RRDbryKeQ9LU8kb8ViYFsW7+AQkJqUg+u0HfP6cCotvy5gVxcuY90pvjzr0A2P4pjxjyDXG8E15xpBrjOGb8owh1xjDN+UZQ64xhm/Kq9DvhwKFU4kVwsRgAWRqagoXFxesXr0aHz9+lHo+Li4OAPDhwwcEBQVJXE+wd+/euHv3Lg4ePCi1nSAIePfuHYD06xNkTtAZGRnB1NQ0y6RdqVKlJJ4D0kcJZjwuW7aswnHv2rULAQEBCA4OxpMnTzB79mypMpcuXZJ6XKVKFalyAFClShVcvHhRYhTl+fPnYWRkhNKlS8vcRkdHB6mpqVLrra2tMWzYMAQEBGDChAnYuHGjzO29vLzw7t07icXLyyvLmDPT1tFBlarVEHLponhdWloaQkIuooZDLZnb1KhZEyHfviYXL6BGzZoAgFKlS8PcvDhCQr7W+eHDB9y8EZZlnYpgDAUjhtwICXuEpvUlL0PQokFlhNx4BABI+ZyK6+FP0czxaxmRSIRm9Svh8pcyyqQO/cAYGANjYAyMgTEwBsbAGBiDKsRAhRsTgwXU6tWrkZqaivr162Pfvn24d+8ewsPDsWLFCvF02hMnTqBSpUqwsbERb9ejRw/07NkTvXr1wty5c3HlyhU8fvwYR44cQcuWLREUFJRPEQHx8fF4+fKlxPL+ffpIo2fPnmH48OGYP38+nJ2dsWXLFsydO1cqEbhnzx5s3rwZd+/ehbe3Ny5fvoxRo0bJ3N+IESPw9OlTeHp6IiIiAgcPHoS3tzfGjx8vcUOTzGxsbHD27Fk8f/5cfO2GsWPH4uTJk3j06BGuXbuGoKCgLJORurq6KFq0qMSiq5vzacT9BgxEwN7dOHRgPx4+eIDfZ/kgISEBnTp3AQBM9ZqE5UsXi8v36dsfF86fw1bfzXj08AHWrl6J27du4ZfefQGkJ2769OuPjevXIvh0IO7djcQ0r0kobmGB5i1a5rhd8mAMBSOGIvo6qFGpFGpUSp+ub1PKDDUqlYJ1iWIAgFmeHbBpdj9x+Y17/0G50maYM6YjKtlYYmj3n9D151pY6ff1nLHir9MY2Lkh+rg6wq6cJVZM6QkDfV1sOyh5nCqLOvQDY2AMjIExMAbGwBgYA2NgDKoQgyoTiURKXQobXmOwgCpfvjyuXbuGOXPmYMKECXjx4gWKFy+OOnXqYO3atQCAgwcPSkwjBtIPiB07dmDDhg3YvHkz5syZAy0tLdja2qJ///5wcXHJj3AAADNmzMCMGTMk1nl4eGDt2rVwc3ND/fr1xUk+FxcXDB8+HH379kVoaKj4LsAzZ86Ev78/RowYASsrK+zcuRNVq1aVub9SpUrh2LFj+PXXX+Hg4ABTU1O4u7tj2rRpWbZx1qxZ8PDwQIUKFZCUlARBEJCamoqRI0fi2bNnKFq0KFq3bo2lS5cq6VWR1LpNW7yNjcWaVSsQHf0GdpWrYM36TTD7Mlz85YsX0Mh0/YSatWpj3oJFWLViGVYuW4IyZW2wbOVq2NpWEpcZ6D4ECQkJmOUzA/Hx71Grdh2sWb9JroQlY1C9GGpXLYtTm8aIHy+Y2BUAsP3QJQz1/gslzIvCuoSp+PnH/8Wgs+c6LJjYBSN7N8XzV3EYPmsH/nfx612/9566BvNihpgxvB0szYxwI/I5Oo5cLXVDEmVRh35gDIyBMTAGxsAYGANjYAyMQRVioMJLJMhzpwsqMD5//gxLS0scP34c9evXz+/m/BAikQj79+9Hp06d8rspckssGJdLzDU9LcZQEOhpAfq1ZI+QVRUJ11epRT8whvzHGAoGxlAwMIaCgTEUDIyhYFD1GFS9/UB6DIVFkW5blFrfx70DlVpfQVeI3irqJTY2FuPGjUO9evXyuylERERERERERPmj8M3+VSomBlWUhYVFtlNiiYiIiIiIiIiIssPEIKkMznonIiIiIiIioswK4w1DlImJQSIiIiIiIiIiUklMDCpG4/tFiIiIiIiIiIiISN1wxCAREREREREREakkjhhUDBODRERERERERESkkpgYVAynEhMRERERERERERVCHDFIRERERERERESqiQMGFcIRg0RERERERERERIUQRwwSEREREREREZFK4jUGFcPEIBERERERERERqSQmBhXDqcRERERERERERESFEEcMEhERERERERGRSuKIQcUwMUhERERERERERCqJiUHFcCoxERERERERERFRIcQRg0REREREREREpJo4YFAhTAwSEREREREREZFK4lRixXAqMRERERERERERUSHEEYNERERERERERKSSOGJQMRwxSEREREREREREVAgxMUhERERERERERCpJJBIpdZHX6tWrYWNjAz09PTg6OuLy5cvZll+2bBns7Oygr68Pa2trjBs3DomJibkNX2FMDBIRERERERERkWoSKXmRw65duzB+/Hh4e3vj2rVrcHBwgIuLC16/fi2z/I4dO/Dbb7/B29sb4eHh+PPPP7Fr1y5MmTJF3qiVholBIiIiIiIiIiIiOS1ZsgRDhgzBwIEDUbVqVaxbtw4GBgbYvHmzzPIXLlxAo0aN0Lt3b9jY2KBVq1bo1avXd0cZ5iUmBomIiIiIiIiISCXl11Ti5ORkXL16FS1bthSv09DQQMuWLXHx4kWZ2zRs2BBXr14VJwIfPnyIY8eOoW3btoq9CArgXYmJiIiIiIiIiEglKfuuxElJSUhKSpJYp6urC11dXYl10dHRSE1NhaWlpcR6S0tLREREyKy7d+/eiI6OhrOzMwRBwOfPnzFs2DBOJSYiIiIiIiIiIspv8+bNg7GxscQyb948pdQdHByMuXPnYs2aNbh27RoCAgJw9OhRzJ49Wyn15wZHDBIRERERERERkUpS9ohBLy8vjB8/XmLdt6MFAcDc3Byampp49eqVxPpXr16hRIkSMuuePn06+vXrh8GDBwMA7O3t8fHjRwwdOhRTp06FhsaPH7/HEYNERERERERERKSSlH2NQV1dXRQtWlRikZUY1NHRQZ06dRAYGChel5aWhsDAQDg5Ocls66dPn6SSf5qamgAAQRCU+KrkHEcMEhERERERERERyWn8+PEYMGAA6tati/r162PZsmX4+PEjBg4cCADo378/SpUqJZ6K7OrqiiVLlqBWrVpwdHTE/fv3MX36dLi6uooThD8aE4NERERERERERKSalDuTWC49e/bEmzdvMGPGDLx8+RI1a9bEiRMnxDckefLkicQIwWnTpkEkEmHatGl4/vw5ihcvDldXV8yZMye/QoBIyK+xikSFSOLn/G6BYvS0GENBoKcF6Ncald/NUEjC9VVq0Q+MIf8xhoKBMRQMjKFgYAwFA2MoGFQ9BlVvP5AeQ2FRavh+pdb3fG1npdZX0PEag0RERERERERERIVQIcohExERERERERGROlH2XYkLG44YJCIiIiIiIiIiKoQ4YpCIiIiIiIiIiFQSRwwqholBIiIiIiIiIiJSTcwLKoRTiYmIiIiIiIiIiAohjhgkIiIiIiIiIiKVxKnEimFikIiIiIiIiIiIVBITg4rhVGIiIiIiIiIiIqJCiCMGiYiIiIiIiIhIJXHEoGKYGCQiIiIiIiIiIpXExKBiOJWYiIiIiIiIiIioEOKIQSIiIiIiIiIiUk0cMKgQjhgkIiIiIiIiIiIqhDhikOgH0FODI40xFAwJ11fldxMUpg79wBgKBsZQMDCGgoExFAyMoWBgDAWDqseg6u0vTHiNQcXwrU70AyR+zu8WKEZPC4j9mJrfzVCIaRFNJKTkdysUo6+tHu+lalNP5XczFHJ7Tiu16Ad1OB7UIYZPKUJ+N0MhBtoitTgeGEP+09MCPiar9vFQRIfHQ0GgLjGow2dcxItP+d2MXKtsZaAWn9GFBRODiuFUYiIiIiIiIiIiokKIIwaJiIiIiIiIiEglccCgYpgYJCIiIiIiIiIilcSpxIrhVGIiIiIiIiIiIqJCiCMGiYiIiIiIiIhIJXHAoGKYGCQiIiIiIiIiIpXEqcSK4VRiIiIiIiIiIiKiQogjBomIiIiIiIiISCVxwKBiOGKQiIiIiIiIiIioEOKIQSIiIiIiIiIiUkkaGhwyqAgmBomIiIiIiIiISCVxKrFiOJWYiIiIiIiIiIioEOKIQSIiIiIiIiIiUkkiDhlUCBODRERERERERESkkpgXVAynEhMRERERERERERVCHDFIREREREREREQqiVOJFcPEIBERERERERERqSQmBhXDqcRERERERERERESFEEcMEhERERERERGRSuKAQcVwxCAREREREREREVEhxBGDRERERERERESkkniNQcUwMUhERERERERERCqJeUHFcCoxERERERERERFRIcQRg0REREREREREpJI4lVgxTAwSEREREREREZFKYl5QMZxKTEREREREREREVAhxxCAREREREREREakkTiVWDBODRERERERERESkkpgXVAynEhMRERERERERERVCHDFIREREREREREQqiVOJFcPEIBERERERERERqSTmBRXDqcRERERERERERESFEEcMEhERERERERGRSuJUYsVwxCAREREREREREVEhxBGDRERERERERESkkjhgUDFMDBIRERERERERkUriVGLFcCoxERERERERERFRIcQRg0REREREREREpJI4YFAxTAwSEREREREREZFK4lRixXAqMRERERERERERUSHEEYNERERERERERKSSOGJQMRwxWMj4+PigZs2acm3TtGlTjB07NtsyIpEIBw4cyHW7lMXGxgbLli3LcfncvB5EREREREREVDCIRMpdChsmBlWAm5sbRCIRRCIRtLW1YWlpiZ9//hmbN29GWlpafjcPAPDixQu0adMmy+czx5B5ad269Q9spbSJEyciMDBQ/NjNzQ2dOnXKvwYB8N/hhzY/N0e9Wvbo80t33LxxI9vyp04eR8f2rVGvlj26dnLFubNnJJ4XBAGrVy5HiybOqF+7Boa6u+Hx46g8jADYu2sHOrdriSYNasK9f0/cvpV9DIF/n0DPLu3QpEFN9OnRERf+kYxhtvcUONWuKrGMHTk0L0OA/04/tGnVHPVr26Nvr+64efP7/dDJtTXq17ZHt86y+2HNquVo2dQZjnVqwGNw3veDOryXejla49TEn3DNpwV2DnOEfemiWZbd4l4Xt+e0klrW9K8FANDSEGG8iy32ezrhX+8WCJrcGHO7VUdxI908jUEd+kEtjgc1iGHXTj+0bdUcjrVroF+vHrj1nRj+PnkCnV3bwLF2DXSXEUPg36cwfMggNG3kiFrVKyMyIjwvmw9ATY4HxlAgYti10w/tXJqjQZ0a6N87Z8dDF9c2aFCnBnp0dsU/mWJISUnB8iWL0KOzKxrWr4VWzX/C9CmT8eb1qzyNQR36gTEUkBiU/BkX+PcpDBsyCE0aOaJmdTtE5PHnw9H9uzCkZ1t0+9kRE4f3w93wW1mWffLoAf6YMQFDerZFx6a1cGiPn8xyMW9eY8nvU9G3Q1N0b9UAowd2x72I23kVglp8RlPhxcSgimjdujVevHiBqKgoHD9+HM2aNcOYMWPQvn17fP78Ob+bhxIlSkBXN/sf1xkxZF527tz5g1oom6GhIczMzPK1DZmdOH4MixbMg8eIkfDfsx92dpUx3MMdMTExMsuHXr+G336dgM5dumHX3gNo1rwFxnqOxL17d8Vltvy5ETv9tmOatw/+2rkb+vr6GD7UHUlJSXkSw/9OHseKJfPhPnQEfHfsha1tZYwbORSxsbJjuBF2Hd5TfoVrxy7YumMfGjdtgcnjPfHg/j2Jcg0aOuPIqTPiZda8hXnSfgA4efwYFi+YB4/hI7Fzz35UsquMER7uiM2mH7wmTUCnzt3gvye9H8aNHon7mfrBd/NG7PDbjqkzfLB9R3o/jPDIu35Qh/dSa3tLTGprhzWnH6D76kuIfBmP9W51YFpER2b5sTtC0WResHjpsPw8Pqem4dTN9B92etqaqFKyKNYFPUT31RcxZkcYypkXwap+NfOk/YB69IM6HA/qE8Mf8Bg+Ejv2BKCSnR1GeAzOUQw79+xH0+YtMX70KIkYEhISULN2HYweNzFP2vwtdTgeGEPBiOHkiWNYsvAPDB02Ejt2B8C2kh1GZnM8hIVew5TJE9CxSzfsyDgexnw9HhITExERfgeDPUZgx659WLR0JR5HPcJYzxF50n5APfqBMRSMGPLiMy4h4RNq1a6NMT/g8+Hc6ZPYvGYxerp5YMnGHShXoRJ8fh2BuLexMssnJSXC0qo0+g0djWKm5jLLfIh/j99GuUFTSwsz5q/Cqq37MHDEeBgaZf0PZkWow2e0qpM1CEmRpbBhYlBF6OrqokSJEihVqhRq166NKVOm4ODBgzh+/Dh8fX3F5eLi4jB48GAUL14cRYsWRfPmzREWFpZlvRkj5GbOnCneZtiwYUhOTpYol5aWhkmTJsHU1BQlSpSAj4+PxPM5mUqcEUPmpVixYgCA4OBg6Ojo4Ny5c+LyCxYsgIWFBV69Sv9R37RpU4waNQqjRo2CsbExzM3NMX36dAiCkOU+nzx5go4dO8LQ0BBFixZFjx49xPUBklOJfXx8sHXrVhw8eFB8QggODkZycjJGjRoFKysr6OnpoWzZspg3b162sebW9q1b0KVbD3Tq3BUVKlbENO+Z0NPTw4GAfTLL+/21DQ2df4LboMEoX6ECRo0eiypVq8J/x18A0v9j6bd9G4Z4DEez5i1Rya4yfp+3AG9ev8bpwP/lSQw7/XzRoXN3tO/YBeXKV8Skqd7Q1dPDkYMBMsvv3rEdjk7O6DvAHTblK8BjxGjYVa6Kvbsk//uno6MDM/Pi4qVoUeM8aT8AbN+WqR8qVMS0GV/6Yb/sftjx1zY0bPS1H0Z6ZtEPQ7/2w+y56f0QlEf9oA7vpQGNbLD3yjMcuPYfHrz5iJkH7yAxJRVd6pSUWf5dwmdEf0gWLw0rmiExJQ0nb6Uf8x+SPmPIlqs4eesVoqI/4cbTd5hzOBzVSxnDylgvT2JQh35Qi+NBDWL4a5svunTrjo5fYpj6nRh2/rUdDRs5Y8Ag9y8xjPkSw9dza/sOHeExfCQaODnlSZu/pRbHA2MoEDH4bfNF567px0P5jONBXw8Hszymt8OpkTMGDHRH+fIVMMJzDCpXrYpdO9OPByMjI6zduBmtWreBTbnyqOFQE5OnTEf4ndt48eK/PIlBHfqBMRSQGJT8GQcA7Tt0gsfwUXD8AZ8PB/f8hVbtuqBlm44oY1MBw8dPha6eHv537IDM8raVq2Hg8HFo3KI1tLW1ZZbZt2MLzC1KYMxvM1GpSnVYWpVCrXpOsCplnScxqMNnNBVuTAyqsObNm8PBwQEBAV8TLt27d8fr169x/PhxXL16FbVr10aLFi0QGyv7Py4AEBgYiPDwcAQHB2Pnzp0ICAjAzJkzJcps3boVRYoUQUhICBYsWIBZs2bh77//VlosGdcx7NevH969e4fr169j+vTp2LRpEywtLSXaoaWlhcuXL2P58uVYsmQJNm3aJLPOtLQ0dOzYEbGxsThz5gz+/vtvPHz4ED179pRZfuLEiejRo4fEyMaGDRtixYoVOHToEHbv3o3IyEj4+fnBxsZGabFnSElORvid22jg1FC8TkNDAw0aNMSNsOsyt7kRGooGDSQ/LBo2csaN0FAAwPNnzxAd/QaODb7WaWRkBPsaDlnWqVAMKcmIDL+Deo4NJGKo5+iEWzdCZW5z62Yo6jlKxuDo1Ai3bkgmtK9d+RdtWzijZ+e2WDB3Jt7FxSm7+QDSYwi/c1viNdPQ0IBjdv0QFir1xcmpoTNuhIUCyNQPTtL9EJYX/aAG7yVtTRGqljTCxftf/9MqCMCl+7FwKGOSozq61CmF4zdfIiElNcsyhnpaSEsT8D4xRdEmS1GHflCL40GtY3ASt0l2DA0l1jk1bJRl+bymFscDYygYMeTieLgZFipRHvj+8fAhPh4ikQhGeTDCSC36gTEUjBjy4DPuR0pJScGDyHA41HEUr9PQ0IBDHUdE3sl+Km52Ll84gwp2VTHf+1f079QcYwf/glNHZA9SUJQ6fEarA15jUDFMDKq4ypUrIyoqCgDwzz//4PLly9izZw/q1q0LW1tbLFq0CCYmJti7d2+Wdejo6GDz5s2oVq0a2rVrh1mzZmHFihUS1y+sUaMGvL29YWtri/79+6Nu3boS1+bLiSNHjsDQ0FBimTt3rvj533//HcWKFcPQoUPRt29fDBgwAB06dJCow9raGkuXLoWdnR369OkDT09PLF26VOb+AgMDcfPmTezYsQN16tSBo6Mjtm3bhjNnzuDff/+VKm9oaAh9fX2JkY06Ojp48uQJbG1t4ezsjLJly8LZ2Rm9evWSK/aceBv3FqmpqVJTm83MzBAdHS1zm+joaJiZmUuXj4n+8vyb9HXmOa9TEXFxcUhNTYXpN8P6TU3NEBMje38x0dEw/SZmUzNzifINGjpjxux5WLFuM0aMHo/rV//FOE8PpKZmnfDJrbdvldQP5l/Li/tBKk4zxORBP6jDe8nEQAdamhqI+SA5ejnmQxLMDb9/TUD70kVRqYQR9l15lmUZHS0NjHephGM3XuJjUh68l9SgH9TieFCjGL49V5qZmWe5v2gZ51Yz86zL5zW1OB4Yg2T5/PqukcXx8O13h8zSY8j58ZOUlITlSxehdZt2MDQ0VE7DM1GHfmAM35RXo8/pH+n9u7dIS0uFiampxHqTYmZ4m8VliHLi1X/PceLgHpQsXQY+C9egTcfu2LhiAU6fOKRok6Wow2e0OuBUYsVo5XcDSDGCIIjfuGFhYfjw4YPUB0NCQgIePHiQZR0ODg4wMDAQP3ZycsKHDx/w9OlTlC1bFkB6YjAzKysrvH79Wq62NmvWDGvXrpVYZ5rpQ0BHRwd+fn6oUaMGypYtKzPh16BBA4kD1cnJCYsXL0Zqaio0NTUlyoaHh8Pa2hrW1l+HjFetWhUmJiYIDw9HvXr1ctRuNzc3/Pzzz7Czs0Pr1q3Rvn17tGrVSmbZpKQkqeuH6OrqApp5e3MDdfezS1vx3xVtK6GirR26dXDBtSuXpUYbEgHpowUjX8bj5rP3Mp/X0hBhyS81IBIBsw7d+cGtIyKigiolJQWTJ44FAHhN98nXthBR7ghCGirYVUW/IZ4AgPK2lfH40X2cOLQXzVt3+M7WRIUPRwyquPDwcJQrVw4A8OHDB1hZWSE0NFRiiYyMxK+//qrQfr69foNIJJL7jshFihRBxYoVJRbTb/47dOHCBQBAbGxsttOff6TatWvj0aNHmD17NhISEtCjRw9069ZNZtl58+bB2NhYYsnp9QiLmRSDpqam1MWOY2JiYG4u+8K65ubS/x2PiYmB+Zf/ApqbF09fF53zOhVhYmICTU1NxMZKtik2NkbqP5MZzMzNpS7MGxsj/Z/MzEqVtoaJSTE8e/pE8UZ/o1gxJfVD9Nfy4n6QijMGZnnQD+rwXor7lIzPqWkwM5S80YiZoS6iP2R/8W59bU20qVECAVeey3xeS0OExb1qoKSJPgZvvponowUB9egHtTge1CiGb8+VMTHRWe7PXMa5NSY66/J5TS2OB8YgWT6/vmtkcTxk990hPYbvHz8pKSn4beI4vPjvP6zZ8GeejBYE1KMfGMM35dXoc/pHKmpcDBoamoj75ndf3NsYFDPN/Q0ii5mZw7pseYl11mXL4c3rl7muM8t9qcFntDrgVGLFMDGowk6fPo2bN2+ia9euANITWC9fvoSWlpZUAi67E31YWBgSEhLEjy9dugRDQ0OJkXY/woMHDzBu3Dhs3LgRjo6OGDBggFTyMSQkROLxpUuXYGtrKzVaEACqVKmCp0+f4unTp+J1d+7cQVxcHKpWrSqzDTo6OjKnpxYtWhQ9e/bExo0bsWvXLuzbt09m4tLLywvv3r2TWLy8vHIUv7aODqpUrYaQSxfF69LS0hASchE1HGrJ3KZGzZoIuXRJYt2lixdQ48sNVUqVLg1z8+IICfla54cPH3DzRliWdSpCW1sHdlWq4srlr21KS0vDlcuXUL1GTZnbVLevKVEeAC6HXET1Gg5Z7uf1q5d49y4O5sWLK6XdmWlrp/fD5RDJfricXT841MRlWf3gUBPA1364fEm6Hxzyoh/U4L2Ukirgzn/xaFDh65dCkQhwrGCKsCdx2W7rUt0SOpoaOBz6Quq5jKRgWbMicN98Be8SlH9twQzq0A9qcTyoUQwhUjFcErdJdgwXJdZljuFHU4vjgTEUjBiyOqYvZX082DvUlCgPACHfHA8ZScEnTx5j3cYtMDEppvS2Z1CLfmAMBSOGPPiM+5G0tbVRwa4Kblz7+hsvLS0NN65ehl3VGtlsmb0q1Wviv6ePJdY9f/oExS2tcl1nVtThM1odaIhESl0KGyYGVURSUhJevnyJ58+f49q1a5g7dy46duyI9u3bo3///gCAli1bwsnJCZ06dcKpU6cQFRWFCxcuYOrUqbhy5UqWdScnJ8Pd3R137tzBsWPH4O3tjVGjRkFDQ7lvj4wYMi8Z17JITU1F37594eLigoEDB2LLli24ceMGFi9eLFHHkydPMH78eERGRmLnzp1YuXIlxowZI3N/LVu2hL29Pfr06YNr167h8uXL6N+/P5o0aYK6devK3MbGxgY3btxAZGQkoqOjkZKSgiVLlmDnzp2IiIjA3bt3sWfPHpQoUQImJiZS2+vq6qJo0aISi65uzqcR9xswEAF7d+PQgf14+OABfp/lg4SEBHTq3AUAMNVrEpYv/fqa9OnbHxfOn8NW38149PAB1q5eidu3buGX3n0BpI/s7NOvPzauX4vg04G4dzcS07wmobiFBZq3aJnjdsmjVx83HNq/F0cPH0DUwwdYMHcmEhMS0L5DZwDAzOm/Yc3KJeLyPXr3w6WL/2DH9i2IevQQm9atQsSdW+jWsw8A4NOnj1i5dCFu3QjDi/+e49+Qi5g0bhRKW5eBo5NznsTQr/+XfjiY3g9zZqf3Q8dO6f0wzWsSVmTqh95f+mFbpn64c1tGP2xYi+CgL/0wJb0fmuVRP6jDe2nr+Sh0q1sKHWuVRPniRTCjQxXo62hi/9X0O0TO7VYdY1tVlNquS91SCAx/LZX009IQYWlvB1QraYzJu29AU0MEc0MdmBvqQFszb74AqEM/qMXxoAYx9O3vhv1794hjmCsVw2SJGHr17YcL5//5EsNDrFu9Endu38YvvfuIy7x7F4fIiHDx5UaiHj1CZES4+BpZyqYWxwNjKBAx9Onvhv379uDwwf14+PDr8dDhy/EwfcpkrFyW+Zjuh4vn/8H2rV+OhzXpx0PPXunHQ0pKCiaNH4M7t29hzh8LkZqWiujoN4iOfoOUlGSZbVCUOvQDYyggMSj5Mw5I/3yIiAjHwy+fD48fPUJEHn0+dOzeF6eO7MfpE4fw9PFDrFs6F4mJCWjZpiMAYOncadi2YYW4fEpKCh7ei8TDe5FI+ZyCmOjXeHgvEi+efZ1J1KF7X0TeuYk9f/2JF8+e4Mz/juPUkX1o20n2TSgVpQ6f0VS48RqDKuLEiROwsrKClpYWihUrBgcHB6xYsQIDBgwQJ/BEIhGOHTuGqVOnYuDAgXjz5g1KlCiBxo0bS9zZ91stWrSAra0tGjdujKSkJPTq1Qs+Pj55FkNmdnZ2iIiIwJw5c/D48WMcOXIEQPo1DDds2IBevXqhVatWcHBIHz3Wv39/JCQkoH79+tDU1MSYMWMwdOhQmfsTiUQ4ePAgPD090bhxY2hoaKB169ZYuXJllm0cMmQIgoODUbduXXz48AFBQUEwMjLCggULcO/ePWhqaqJevXo4duyY0hOnANC6TVu8jY3FmlUrEB39BnaVq2DN+k3iYeUvX7yAhujrfmvWqo15CxZh1YplWLlsCcqUtcGylatha1tJXGag+xAkJCRgls8MxMe/R63adbBm/Sa5EpbyaOnSBm/fxmLT2pWIiYmGrV1lLF21HqZfpli8evlC4rWr4VALM+cswIY1K7Bu1TJYlymL+UtWokJFWwCAhoYmHty7i+NHDiI+/j3Mi1vAsUEjDB3hCR0dHZltUJRLm7Z4+zYWazP3w7qv/fDixQuINCT7Ye78RVi9chlWLk/vh6UrVqNipn5wG5TeD7Mz98O6vOsHdXgvnbj5CqZFdDCqRQWYG+ki4kU8PHyvIeZj+o80K2M9CIIgsY2NuQHq2BTD4M3S/wyxKKqL5lUsAAABnpJ3gnPb9C/+ffRW6TGoQz+ow/GgXjGsRMyXGFav25jpvfQfNDS+Jrgzx7Bq+VKUKWuDJStWScRwJug0vKdNET/+7dfxAACP4SMxbKSn0mNQh+OBMRSMGFxap8ewdvXX42HVt8dDphEfDjVrY84fi7BmVabjYfnX4+HN61c4E3waAPBLt04S+9qweSvq1nOEsqlDPzCGghFDXnzGBQedhve0r7OeJv86DgDgMXwUhiv58+Gn5i54H/cWO7asxdvYGJSraAfvBath8mUqcfSrlxJ9EBv9BuOG/CJ+fGDXNhzYtQ3VHepgzvJNAADbytXgNXsxtm9ciV1bN8DSqhQGj/oVTX9ui7ygDp/Rqq4QDvJTKpHw7a8qKlTc3NwQFxeHAwcO5HdTvqtp06aoWbMmli1blt9NkVvi5/xugWL0tIDYj3lzHbYfxbSIJvJw1ugPoa+tHu+lalNP5XczFHJ7Tiu16Ad1OB7UIYZPKar9NcxAW6QWxwNjyH96WsDHZNU+Horo8HgoCNQlBnX4jIt48Sm/m5Frla0M1OIzurBwWRPy/UJyODlC+f8QKsg4lZiIiIiIiIiIiCgXVq9eDRsbG+jp6cHR0RGXL1/OtnxcXBxGjhwJKysr6OrqolKlSjh27NgPaq00TiUmIiIiIiIiIiKVpJGPgyN37dqF8ePHY926dXB0dMSyZcvg4uKCyMhIWFhYSJVPTk7Gzz//DAsLC+zduxelSpXC48ePZd7D4EdhYrCQ8/X1ze8m5FhwcHB+N4GIiIiIiIiICACwZMkSDBkyBAMHDgQArFu3DkePHsXmzZvx22+/SZXfvHkzYmNjceHCBWhrawNIvwlqfuJUYiIiIiIiIiIiUkkikUipS04lJyfj6tWraNny613HNTQ00LJlS1y8eFHmNocOHYKTkxNGjhwJS0tLVK9eHXPnzkVqav5d058jBomIiIiIiIiISCUp+67ESUlJSEpKklinq6srdXfx6OhopKamwtLSUmK9paUlIiIiZNb98OFDnD59Gn369MGxY8dw//59jBgxAikpKfD29lZuIDnEEYNEREREREREREQA5s2bB2NjY4ll3rx5Sqk7LS0NFhYW2LBhA+rUqYOePXti6tSpWLdunVLqzw2OGCQiIiIiIiIiIpUkgnKHDHp5eWH8+PES674dLQgA5ubm0NTUxKtXryTWv3r1CiVKlJBZt5WVFbS1taGpqSleV6VKFbx8+RLJycnQ0dFRQgTy4YhBIiIiIiIiIiJSSRoi5S66urooWrSoxCIrMaijo4M6deogMDBQvC4tLQ2BgYFwcnKS2dZGjRrh/v37SEtLE6+7e/curKys8iUpCDAxSEREREREREREJLfx48dj48aN2Lp1K8LDwzF8+HB8/PhRfJfi/v37w8vLS1x++PDhiI2NxZgxY3D37l0cPXoUc+fOxciRI/MrBE4lJiIiIiIiIiIi1STPnYSVrWfPnnjz5g1mzJiBly9fombNmjhx4oT4hiRPnjyBhsbXMXnW1tY4efIkxo0bhxo1aqBUqVIYM2YMJk+enF8hMDFIRERERERERESqKR/zggCAUaNGYdSoUTKfCw4Ollrn5OSES5cu5XGrco5TiYmIiIiIiIiIiAohjhgkIiIiIiIiIiKVpJHfQwZVHBODRERERERERESkkgpbXjA1NRW+vr4IDAzE69evJe5wDACnT5+Wqz4mBomIiIiIiIiIiFTAmDFj4Ovri3bt2qF69eoK33yFiUEiIiIiIiIiIlJJ+XlX4vzg7++P3bt3o23btkqpjzcfISIiIiIiIiIiUgE6OjqoWLGi0urLVWLw06dPMDMzw8KFC5XWECIiIiIiIiIiInmIRMpdCroJEyZg+fLlEARBKfXlaiqxgYEBtLS0UKRIEaU0goiIiIiIiIiISF6F7a7E//zzD4KCgnD8+HFUq1YN2traEs8HBATIVV+urzHYtWtX7N27F8OHDy9087mJiIiIiIiIiIh+NBMTE3Tu3Flp9eU6MfjLL79gxIgRaNasGYYMGQIbGxvo6+tLlatdu7ZCDSQiIiIiIiIiIpKlsA1V27Jli1Lry3VisGnTpuK/z507J/W8IAgQiURITU3N7S6IiIiIiIiIiIiyVFhnsb558waRkZEAADs7OxQvXjxX9eQ6MajsDCURERERERERERFl7ePHj/D09MS2bduQlpYGANDU1ET//v2xcuVKGBgYyFVfrhODAwYMyO2mRERERERERERECtMoZAMGx48fjzNnzuDw4cNo1KgRgPQbkowePRoTJkzA2rVr5aov14nBzF68eIHXr1+jYsWKvFMxERERERERERH9EIVtKvG+ffuwd+9eiUv8tW3bFvr6+ujRo4fciUENRRpz8OBBVK5cGaVLl0bt2rUREhICAIiOjkatWrWwf/9+RaonIiIiIiIiIiKiLz59+gRLS0up9RYWFvj06ZPc9eU6MXj48GF06dIF5ubm8Pb2hiAI4ufMzc1RqlQp+Pr65rZ6IiIiIiIiIiKibIlEyl0KOicnJ3h7eyMxMVG8LiEhATNnzoSTk5Pc9eV6KvGsWbPQuHFjBAUFISYmBj4+PlINXb9+fW6rJyIiIiIiIiIiokyWL18OFxcXlC5dGg4ODgCAsLAw6Onp4eTJk3LXl+vE4K1bt7BkyZIsn7e0tMTr169zWz0REREREREREVG2Cts1BqtXr4579+7Bz88PERERAIBevXqhT58+0NfXl7u+XCcGDQwM8PHjxyyff/jwIczMzHJbPRERERERERERUbYK212JgfSc3JAhQ5RSV64Tg82aNcPWrVsxduxYqedevnyJjRs3on379oq0jYiIiIiIiIiIqFA7dOgQ2rRpA21tbRw6dCjbsh06dJCr7lwnBufMmYMGDRqgXr166N69O0QiEU6ePInTp09j/fr1EAQB3t7eua2eiIiIiIiIiIgoW4VhKnGnTp3w8uVLWFhYoFOnTlmWE4lESE1NlavuXCcG7ezs8M8//2DMmDGYPn06BEHAwoULAQBNmzbF6tWrYWNjk9vqiYiIiIiIiIiIsqX+aUEgLS1N5t/KoKHIxtWqVcP//vc/REdHIyQkBBcvXsSrV69w+vRpVKlSRVltJCIiIiIiIiIiKvS2bduGpKQkqfXJycnYtm2b3PXlOjE4a9Ys3Lp1CwBQrFgx1KtXD46OjihevDgA4Pbt25g1a1ZuqyciIiIiIiIiIsqWhkik1KWgGzhwIN69eye1Pj4+HgMHDpS7vlwnBn18fHDjxo0sn7916xZmzpyZ2+qJiIiIiIiIiIiyJRIpdynoBEGQeV3FZ8+ewdjYWO76cn2Nwe+JjY2Fjo5OXlVPRERERERERERUKNSqVQsikQgikQgtWrSAltbXlF5qaioePXqE1q1by12vXInBs2fPIjg4WPw4ICAA9+/flyoXFxeHXbt2wd7eXu4GERERERERERER5URhuCsxAPHdiENDQ+Hi4gJDQ0Pxczo6OrCxsUHXrl3lrleuxGBQUJB4erBIJEJAQAACAgJklq1atSpWrlwpd4OIiIiIiIiIiIjoK29vb6SmpsLGxgatWrWClZWVUuqVKzE4adIkjBo1CoIgwMLCAuvWrZPKRopEIhgYGEBPT08pDSQiIiIiIiIiIpKlkAwYBABoamrCw8MD4eHhSqtTrsSgvr4+9PX1AQCPHj1C8eLFYWBgoLTGEBERERERERER5ZQq3ElYmapXr46HDx+iXLlySqkv13clLlu2rFRS8NOnT9i8eTPWrl2Lx48fK9w4IiIiIiIiIiIiSvf7779j4sSJOHLkCF68eIH3799LLPLK9V2J3d3dERISglu3bgEAkpOT0aBBA/FjY2NjnD59GrVq1crtLoiIiIiIiIiIiLJUyAYMom3btgCADh06SNx4RRAEiEQipKamylVfrhODQUFB6Nu3r/jxjh07cOvWLfj5+cHBwQFdu3bFzJkzceDAgdzugoiIiIiIiIiIKEuF5a7EGYKCgpRaX64Tgy9fvoSNjY348YEDB1C3bl306tULADBkyBAsXLhQ4QYSERERERERERER0KRJE6XWl+vEYJEiRRAXFwcA+Pz5M4KDg+Hp6Sl+3sjICO/evVO4gUTqQC/XR1rBYVpEM7+boDB97fxugeLU4b10e06r/G6CwtShH9TheFCHGAy0Vf8/3OpwPDCGgqGIDo+HgoAxFAzq8BlX2Uq1b1SqDp/RhUWub56hwuLi4vDnn3+K705crVo1DBo0CMbGxnLXletTZu3atbFx40Y0a9YMhw4dQnx8PFxdXcXPP3jwAJaWlrmtnkitJKTkdwsUo68NfEoW8rsZCjHQESHxc363QjF6WsCt5x/yuxkKqV7KEE9ik/K7GQopY6qrFse0OsSgDsf020/yXQOmoClmoKkW/eBz6l5+N0MhPq1s1eLc+lHFv2sUUZPvGu8T0/K7GQopqqehFv2gDjF8SlHdY9pAWz2O58KisE0lvnLlClxcXKCvr4/69esDAJYsWYI5c+bg1KlTqF27tlz15fqtMmfOHLi4uKBu3boQBAHdunUTNwgA9u/fj0aNGuW2eiIiIiIiIiIiIspk3Lhx6NChAzZu3AgtrfS03ufPnzF48GCMHTsWZ8+elau+XCcG69ati4iICFy4cAEmJiYSc5zj4uIwYsQIpc97JiIiIiIiIiIiyqBRuAYM4sqVKxJJQQDQ0tLCpEmTULduXbnrU2hwafHixdGxY0ep9SYmJhgzZowiVRMREREREREREVEmRYsWxZMnT1C5cmWJ9U+fPoWRkZHc9eU6MfjkyZMclStTpkxud0FERERERERERJSlwjZisGfPnnB3d8eiRYvQsGFDAMD58+fx66+/olevXnLXl+vEoI2NTY4u8JiaqtoX1SYiIiIiIiIiooKpsN18ZNGiRRCJROjfvz8+f06/S462tjaGDx+OP/74Q+76cp0Y3Lx5s9SLn5qaiqioKGzbtg0WFhYYOXJkbqsnIiIiIiIiIiKiTHR0dLB8+XLMmzcPDx48AABUqFABBgYGuaov14lBNze3LJ+bPHkyHB0d8e7du9xWT0RERERERERElK3CNpU4g4GBAUxMTMR/55aGktojoUiRIhg4cCCWLl2aF9UTERERERERERFBJFLuUtB9/vwZ06dPh7GxMWxsbGBjYwNjY2NMmzYNKSkpcten0F2Js5OWloaXL1/mVfVERERERERERESFiqenJwICArBgwQI4OTkBAC5evAgfHx/ExMRg7dq1ctWn9MTg+/fvcfbsWSxcuBC1atVSdvVEREREREREREQAAA1VGOanRDt27IC/vz/atGkjXlejRg1YW1ujV69ePy4xqKGhkeWdXwRBQJkyZbBmzZrcVk9ERERERERERJStPLlGXgGmq6sLGxsbqfXlypWDjo6O3PXlOjE4Y8YMqcSgSCRCsWLFUKFCBbRq1QpaWnk2U5mIiIiIiIiIiKhQGTVqFGbPno0tW7ZAV1cXAJCUlIQ5c+Zg1KhRcteX68ydj49PbjclIiIiIiIiIiJSWCGbSYzr168jMDAQpUuXhoODAwAgLCwMycnJaNGiBbp06SIuGxAQ8N36OKSPiIiIiIiIiIhUUmG7xqCJiQm6du0qsc7a2jrX9eU4MTho0CC5KxeJRPjzzz/l3o6IiIiIiIiIiIgkbdmyRan15TgxePr06SxvNpIVecsTERERERERERHlVGFNPb158waRkZEAADs7OxQvXjxX9eQ4MRgVFZWrHRAREREREREREZHiPn78CE9PT2zbtg1paWkAAE1NTfTv3x8rV66EgYGBXPXJdVfnxMREDBs2DCtXrsy23IoVKzBixAikpKTI1RgiIiIiIiIiIqKc0hApdynoxo8fjzNnzuDw4cOIi4tDXFwcDh48iDNnzmDChAly1yfXzUc2bNgAX19f3LlzJ9ty7dq1w6RJk2Bvb4/hw4fL3SgiIiIiIiIiIqLvKWw3H9m3bx/27t2Lpk2bite1bdsW+vr66NGjB9auXStXfXKNGNy9eze6du2K8uXLZ1uuQoUK6N69O3bu3ClXY4iIiIiIiIiIiEi2T58+wdLSUmq9hYUFPn36JHd9ciUGb968CWdn5xyVbdiwIW7cuCF3g4iIiIiIiIiIiHJCJFLuUtA5OTnB29sbiYmJ4nUJCQmYOXMmnJyc5K5PrqnEycnJ0NHRyVFZHR0dJCUlyd0gIiIiIiIiIiKinFCF6wIq07Jly9C6dWuULl0aDg4OAICwsDDo6enh5MmTctcnV2KwZMmSuHXrVo7K3rp1CyVLlpS7QURERERERERERCTN3t4e9+7dg5+fHyIiIgAAvXr1Qp8+faCvry93fXIlBlu2bIlt27bBy8sLFhYWWZZ7/fo1tm3bhu7du8vdICIiIiIiIiIiopwQofAMGUxJSUHlypVx5MgRDBkyRCl1ynWNwcmTJyMxMRHNmzdHSEiIzDIhISFo0aIFEhMT8euvvyqlkURERERERERERN/SECl3Kci0tbUlri2oDHKNGCxfvjx2796NXr16oWHDhihfvjzs7e1hZGSE+Ph43Lp1Cw8ePICBgQH8/f1RoUIFpTaWiIiIiIiIiIiosBo5ciTmz5+PTZs2QUtLrrSeTHLX0K5dO9y4cQPz58/HkSNHcODAAfFzJUuWxJAhQzBp0iSUL19e4cYRERERERERERFlpaCP8lO2f//9F4GBgTh16hTs7e1RpEgRiecDAgLkqi9XqUUbGxusXbsWa9euRXx8PN6/f4+iRYvCyMgoN9URERERERERERHRd5iYmKBr165Kq0/hMYdGRkZMCBIRERERERER0Q8nEhWOIYNpaWlYuHAh7t69i+TkZDRv3hw+Pj65uhNxZnLdfISIiIiIiIiIiKigKCw3H5kzZw6mTJkCQ0NDlCpVCitWrMDIkSMVrpeJQSIiIiIiIiIiogJs27ZtWLNmDU6ePIkDBw7g8OHD8PPzQ1pamkL1MjFIREREREREREQqSSRS7lJQPXnyBG3bthU/btmyJUQiEf777z+F6lX8vsZERERERERERET5QKMgZ/OU6PPnz9DT05NYp62tjZSUFIXqZWKQiIiIiIiIiIgoF1avXo2FCxfi5cuXcHBwwMqVK1G/fv3vbufv749evXqhY8eOOHDgwHfLC4IANzc36OrqitclJiZi2LBhKFKkiHhdQECAXO1nYpCIiIiIiIiIiFRSft4wZNeuXRg/fjzWrVsHR0dHLFu2DC4uLoiMjISFhUWW20VFRWHixIn46aefcryvAQMGSK3r27dvrtqdGRODRERERERERESkkvJzJvGSJUswZMgQDBw4EACwbt06HD16FJs3b8Zvv/0mc5vU1FT06dMHM2fOxLlz5xAXF5ejfW3ZskVZzZbAm48QERERERERERHJITk5GVevXkXLli3F6zQ0NNCyZUtcvHgxy+1mzZoFCwsLuLu7/4hmfhdHDBIRERERERERkUrSgHKHDCYlJSEpKUlina6ursS1/QAgOjoaqampsLS0lFhvaWmJiIgImXX/888/+PPPPxEaGqrUNiuCIwaJiIiIiIiIiIgAzJs3D8bGxhLLvHnzFK43Pj4e/fr1w8aNG2Fubq6ElioHRwwSEREREREREZFKUvY1Br28vDB+/HiJdd+OFgQAc3NzaGpq4tWrVxLrX716hRIlSkiVf/DgAaKiouDq6ipel5aWBgDQ0tJCZGQkKlSooIwQ5MLEIBERERERERERqSRl35VY1rRhWXR0dFCnTh0EBgaiU6dOANITfYGBgRg1apRU+cqVK+PmzZsS66ZNm4b4+HgsX74c1tbWSmm/vFR6KnHTpk0xduzYPN2Hj48Patasmaf7IOWR9z3h6+sLExOTPGsPEREREREREamn8ePHY+PGjdi6dSvCw8MxfPhwfPz4UXyX4v79+8PLywsAoKenh+rVq0ssJiYmMDIyQvXq1aGjo5MvMRSoxKCbmxtEIhGGDRsm9dzIkSMhEong5uYmXhcQEIDZs2f/wBbK5ubmJs4Ok2w+Pj4QiURSS+XKlfO1XT179sTdu3fFjwtCIth/px/atGqO+rXt0bdXd9y8eSPb8qdOHkcn19aoX9se3Tq74tzZMxLPC4KANauWo2VTZzjWqQGPwW54/DgqDyMAdu30Q1uX5nCsUwP9evfAre/E8PfJE+js2gaOdWqgu4wYAv93CsOHDkJTZ0fUsq+MyIjwvGw+AMB/hx/a/Nwc9WrZo88v3XHzxvf7oWP71qhXyx5dO8nuh9Url6NFE2fUr10DQ93zvh+OH9iNYb3a4xcXJ/w2oj/uhd/KsuyTRw+wwPtXDOvVHl2b18GRvTukyqSmpmLn5jUY3tsVvVo3xIg+HbBn+0YIgpBnMRzc64++nVujbZO68HTvjYjbN7MsG/XwPmZ6jUPfzq3xs1MNBPhvlypz4/oVTJ84Cj1dW+Bnpxo4f+Z0nrU9g7KP6cC/T2HYkEFo0sgRNavbIeJHHA/qEIMaHNN7d+1Ap7Yt0dixJgb164nbt7KPIfDvE+jZuR0aO9ZEn+4dceGcZAyzZkxBg1pVJZaxI4fmZQhq0Q93zx7BIe9B2DWuM04tGo+YqMgcbff46hns9GyPsxt+l1h/aftS7PRsL7EErZmRF00XU4dz666dfmjn0hwN6tRA/xx+1+ji2gYN6tRAj86u+CfTeyklJQXLlyxCj86uaFi/Flo1/wnTp0zGm9evsqlRcepwPOz290OHNi3QqJ4D3Pr0xO3v9MP/Tp1At45t0aieA37p2gHnvzkvbVi7Ct06tsVPjrXR3NkRI4YOxK0bYXkZglr0g6rHsGunH9q2ag7H2jXQr5ccvx1qZ/Hb4e9TGD5kEJo2ckSt6j/mtwOg+v2gyjREIqUu8ujZsycWLVqEGTNmoGbNmggNDcWJEyfENyR58uQJXrx4kRdhK02BSgwCgLW1Nfz9/ZGQkCBel5iYiB07dqBMmTISZU1NTWFkZPSjm0i5VK1aNbx48UJi+eeff/K1Tfr6+rCwsMjXNmR28vgxLF4wDx7DR2Lnnv2oZFcZIzzcERsTI7N86PVr8Jo0AZ06d4P/ngNo1rwFxo0eifv3viY7fTdvxA6/7Zg6wwfbd+yGvr4+Rni4S91lSWkxnDiGxQv/gMewkdixOwCVKtlhhMfgrGMIvQavyRPQqUs37NyzH02bt8T4MaMkYkhISEDNWnUwetzEPGnzt04cP4ZFC+bBY8RI+O/ZDzu7yhju4Y6YbPrht18noHOXbti1N70fxnqOxL1MMWz5cyN2+m3HNG8f/LUzvR+GD827fjgfdAq+a5egR/+hWLjeD2UrVMLsyaPw7m2szPLJSYmwtCqFvkM8YWJqJrPMAf+tOHloLwaPnoTlvnvRb+hoHPDfhmP7/fMkhuD/ncD6FQvR130Y1vruQnlbO3iNG4a3sbL7ISkxEVYlS8N9xBiYmsm+mG9iYgLK29rBc8KUPGnzt/LimE5I+IRatWtjzA86HtQhBnU4pv8+eRzLF8/HYI8R2LpjL2wrVcbYEUMRm8XxcCP0OmZ4/QrXTl2wdec+NG7aApPGe+LB/XsS5Ro0dMbRv8+Il1nzFuZJ+wH16IfHV8/i+v5NqN6mF1pPWg6TUuUQtGYGEuPjst3uQ8wrXD+wGcUrVJP5vFWVOug0Z7t4aeQ2KQ9an04tzq0njmHJwj8w9Mt3DdtKdhiZzXeNsNBrmDJ5Ajp26YYdMr5rJCYmIiL8DgZ7jMCOXfuwaOlKPI56hLGeI/IsBnU4Hk6dOIZli+ZjsMdIbPffB1s7O3gOH5JNP1zHtN8momPnrvhrVwCaNGuBiWM9JT4fypS1wa9e07Bz30Fs9P0LJUuWwqjhg/E2Vvb3F0WpQz+oegzp3zP+gMfwkdixJwCV7L7z2yHT9wzxb4fRMn471P5xvx0A1e8HVScSKXeR16hRo/D48WMkJSUhJCQEjo6O4ueCg4Ph6+ub5ba+vr44cOCA/DtVogKXGKxduzasra0REBAgXhcQEIAyZcqgVq1aEmUzTxuNiIiAgYEBduz4Ospl9+70g+fOnTsAgLi4OAwePBjFixdH0aJF0bx5c4SFSf4H6o8//oClpSWMjIzg7u6OxMREhWNasmQJ7O3tUaRIEVhbW2PEiBH48OGD+PmM6awnT55ElSpVYGhoiNatW0tklT9//ozRo0fDxMQEZmZmmDx5MgYMGCAxUtHGxgbLli2T2HfNmjXh4+OT47YAwMaNG2FtbQ0DAwN07twZS5YskZpue/DgQdSuXRt6enooX748Zs6cic+fP2f7OmhpaaFEiRISS8adeHLSfxkjM2fOnCnuw2HDhiE5OTnLfb59+xb9+/dHsWLFYGBggDZt2uDeva8/ijJPJfb19cXMmTMRFhYmHtHo6+sLQRDg4+ODMmXKQFdXFyVLlsTo0aOzjTW3tm/bgi7deqBT566oUKEips2YCT09PRzYv09m+R1/bUPDRj/BbdBglK9QASM9x6JK1arw3/EXgPT/Mvlt34YhQ4ejWfOWqGRXGbPnLsCb168RFPi/PInhr22+6NK1Ozp+iWHqjJnQ0886hp1/bUfDRs4YMNAd5ctXwEjPMekx7PQTl2nv2hEew0eiQQOnPGnzt7ZvzdQPFStimveXfgiQHYPfX9vQ0PlrP4wanUU/eHzth9/npffD6Tzqh8N7/kLLtp3RvE0HWNuUh8e4KdDV1UPg8YMyy1esXA0Dho2Fc3MXaGvLHsIeeTsM9Ro1RZ0GP8GiREk4NWkJh7oNcD/idp7EsG/nNrTp0BWt23dC2XIVMGbSdOjq6uPkkQMyy9tVrY6hnhPQ7Oc2WcZQ3+knDPTwhHPTFnnS5m8p+5gGgPYdOsFj+Cg4Ov2g40EdYlCDY3rnX77o2KU72nfsgnIVKmLyVG/o6enhyIEAmeV37dyOBg2d0XeAO8qVrwCPkaNhV6Uq9vr7SZTT0dGBmXlx8VK0qHGetB9Qj36IDDqACk4uKN/gZxhblUG9niOhpaOLhxf/znKbtLRUXNy6CPZt+8DQTPpC5ACgoaUN/aLFxIuOgWGetB9Qj3Or3zZfdP7yXaN8pu8aB7M8L22HU6bvGiM8x6By1arY9eW7hpGREdZu3IxWrdvAplx51HCoiclTpiP8zm28ePFfnsSgDsfDju1b0alLd3To1AXlK1SE1zQf6Onp4VAW5yV/v21wauiMfm7p56Xho8agcpUq2OP/9ft/67bt4digIUqXtkaFirYYO/E3fPzwAffu5WxkrrzUoR9UPYa/tvmiS7dvfjtk8z1D/NthkPuX7xlffjvsyPTbocOX3w4/6HsGoPr9QIVbgUsMAsCgQYOwZcsW8ePNmzeL52dnpXLlyli0aBFGjBiBJ0+e4NmzZxg2bBjmz5+PqlWrAgC6d++O169f4/jx47h69Spq166NFi1aIPbLf6B2794NHx8fzJ07F1euXIGVlRXWrFmjcDwaGhpYsWIFbt++ja1bt+L06dOYNEnyP8GfPn3CokWLsH37dpw9exZPnjzBxIlf/8Mxf/58+Pn5YcuWLTh//jzev3+fq6zy99py/vx5DBs2DGPGjEFoaCh+/vlnzJkzR6KOc+fOoX///hgzZgzu3LmD9evXw9fXV6qcPHLSfwAQGBiI8PBwBAcHY+fOnQgICMDMmTOzrNfNzQ1XrlzBoUOHcPHiRQiCgLZt2yIlJUWqbM+ePTFhwgSJkY09e/bEvn37sHTpUqxfvx737t3DgQMHYG9vn+tYs5KSkozwO7fh2KCheJ2GhgYcGzTEjbDrMre5ERYq9cPaqaEzboSFAgCeP3uG6Og3cHT6WqeRkRHsazggLIs68yYGJ3GbZMaQqXx6DI2yLJ/XUpLTY2jgJBlDg+z6ITRUKmnZsJEzboSGAsjUDw2k+yGrOhWKISUFD+5GoEad+hIx1KhTH3fvZD1d7Hvsqjng5rXL+O/pYwBA1IO7iLgVilr1G35nS/mlpKTgbmQ4atdrIF6noaGB2vUccedW3k4pUpa8OKZ/NLWIQS2O6WREht9BPUfJ46GeoxNu3giVuc2tG6Go5ygZQwOnRrj5zZS8a1f+RZvmzujRqS3mz5mJd3Fxym4+APXoh9TPKYh9eh8l7GqK14k0NGBpVxPRURFZbnf7uD/0jIxRwalVlmVe37+JAK8+ODLbA//uWo2kj++V2XQx9T63Zv1d42Yuvmt8iI+HSCSCkVFRZTRbgjocDykpyYgIv436mdqkoaGB+g2yPi/dvBGGet/E0KChc5blU1KSsX/fbhgaGaFSJeVffkgt+kHFY8j1bwengvPbAVD9flAH+TmVWB0UyLsS9+3bF15eXnj8OP3H5/nz5+Hv74/g4OBstxsxYgSOHTuGvn37QkdHB/Xq1YOnpycA4J9//sHly5fx+vVr8d1lFi1ahAMHDmDv3r0YOnQoli1bBnd3d7i7uwMAfv/9d/zvf/9TeNRg5pth2NjY4Pfff8ewYcMkko4pKSlYt26d+NbUo0aNwqxZs8TPr1y5El5eXujcuTMAYNWqVTh27JjS27Jy5Uq0adNGnJSsVKkSLly4gCNHjoi3mzlzJn777TcMGDAAAFC+fHnMnj0bkyZNgre3d5b7vnnzJgwNJf8D3rdvX6xbtw5A9v2XQUdHB5s3b4aBgQGqVauGWbNm4ddff8Xs2bOhoSGZ57537x4OHTqE8+fPo2HD9BOqn58frK2tceDAAXTv3l2ivL6+PgwNDcUjGzM8efIEJUqUQMuWLaGtrY0yZcqgfv36ULa3b98iNTUVZmaS0zjNzMwQ9eihzG2io6Nh9s20HjNzM0RHR395/o24jsxMzcwQ86WMMmXEYCoVgzmiHj2SuU10dLTM8nnRvpx4G5d1PzySpx/MzBAd800/mEvXGZ0Hcca/i0NaWipMiknuz7iYGZ4/icp1vZ17ueHTxw8Y7dYVGhoaSEtLQ2/3EWjcsq2CLZb2Lu4t0lJTUeybac3FTM3w9LHs91JBkxfH9I+mFjGowTEd9zYu/dxqKtmmYmZmiIqSHUNMdDRMvz1+zMwRE/O1fU4NndG0eUuULFUaz589wdqVyzBulAc2bt0BTU1NpcagDv2Q9PE9hLQ06BU1kVivZ2SC+FfPZG7z5sFtPLh0Cm0mr8iyXquqtVG6ZkMYmlniw5sXCDuyDcFrvPHzhEXQ0FBuP6jDuTUui+8apt/5riH93sv6u0ZSUhKWL12E1m3aSX13VQZ1OB7E5yUZ3zGz6ocYGTHI+k567kwQpk6eiMTEBJibF8eqdX/CpFgx5QYA9egHVY9Bab8dzPPvtwOg+v2gDgphLk+pCmRisHjx4mjXrp14Gme7du3EU06/Z/PmzahUqRI0NDRw+/ZtiL68Q8LCwvDhwwepgzUhIQEPHjwAAISHh0vd+MTJyQlBQUEA0kfKtWnTRvzc+vXr0adPn++26X//+x/mzZuHiIgIvH//Hp8/f0ZiYiI+ffoEAwMDAICBgYE4KQgAVlZWeP36NQDg3bt3ePXqlUQySlNTE3Xq1EFaWlqOXpectiUyMlKcfMxQv359icRgWFgYzp8/LzFCMDU1VSqmb9nZ2eHQoUMS64oWlfwvbFb9l8HBwUGificnJ3z48AFPnz5F2bJlJcqGh4dDS0tLYn6/mZkZ7OzsEB6e8wvQdu/eHcuWLUP58uXRunVrtG3bFq6urtDSkj58kpKSpK75oKurC2h8/1bnRAXdheC/cS7wBMZOnQNrm/J4dP8utqxZjGJmxdHMxTW/m0dEcvq59dekfkXbSqhoa4euri64duWy1GhDkl9K4idc3LYE9X/xhK5h1lO0y9ZpIv7bpKQNTEqVw+GZg/H63k2J0Yn0Y6SkpGDyxLEAAK/pPvnalsKqbj1H+O0OQFzcWxzYtwdTfh2HLX/tkkoGERGRchTIqcRA+nRiX19fbN26FYMGDcrxdmFhYfj48SM+fvwocY2+Dx8+wMrKCqGhoRJLZGQkfv311xzVXbduXYltO3To8N1toqKi0L59e9SoUQP79u3D1atXsXr1agCQuDaetra2xHYikUjuO31qaGhIbZN5ymxO2/I9Hz58wMyZMyVei5s3b+LevXvQ09PLcjsdHR1UrFhRYvn2xh9Z9V9+sra2RmRkJNasWZN+444RI9C4cWOZ05HnzZsHY2NjiWXevHk52k+xYsWgqakpdYHamJiYLBPj5uaSoz8AICb6a3lz8+LiOjKLjYmBWQ6T7fLIiOHbiwXHxEj/RyyDubm57PJ50L6cKGaipH6IiYG52Tf9EJ3zOhVhZGwCDQ1NxL2V3N+7tzEwMc39/ratX47Ovdzg3NwFZcvbommrdnDt2hsBO7Z8f2M5GZsUg4amptTF8N/GxqBYFu+lgiYvjukfTS1iUINj2qSYSfq5NVayTW9jYrI8t5qZm0vdmORtNudiAChV2homJsXw7OkTxRv9DXXoB90iRSHS0EDi+ziJ9YnxcdArKj2a6UP0S3yMfYWzG2bBf0wH+I/pgEf/nsbzWyHwH9MB8W9kf88xNC8BXcOi+JDF84pQh3OrSRbfNWK/811D+r0n/V0jJSUFv00chxf//Yc1G/7Mk9GCgHocD+LzkhzfMc1kxCCrvL6BAazLlIV9jZqYPnMONLU0cfCA7Ou0KUId+kHVY8j2t0M27ZcqH51/vx0A1e8HdaCh5KWwKbAxt27dGsnJyUhJSYGLi0uOtomNjYWbmxumTp0KNzc39OnTR3x349q1a+Ply5fQ0tKSSk5lHFhVqlRBSEiIRJ2XLl0S/62vry+xXU7uiHz16lWkpaVh8eLFaNCgASpVqoT//pPvIsbGxsawtLTEv//+K16XmpqKa9euSZQrXry4RDLt/fv3eJRpCHZO2mJnZyexHwBSj2vXro3IyEip17FixYpS03nlkV3/ZQgLC5NYd+nSJRgaGsLa2lqqvipVquDz588SfRoTE4PIyEiJ6xZmpqOjg9TUVKn1+vr6cHV1xYoVKxAcHIyLFy/i5k3pa7V5eXnh3bt3EouXl1eO4tfW1kGVqtVwOeSieF1aWhouh1xEDYdaMrep4VATlzO9RwHg0sULqOFQEwBQqnRpmJsXx+VLX+v88OEDbt4Ig0MWdSoiI4aQb2O4dEncJpkxZCr/bQw/mrbOlxguScYQkl0/1KyJEFn9ULMmgK/9kPl1yeiHrOpUKAZtbVSoVBk3r309dtPS0nDj2r+oVDX318dMSkqUGsWroSn9Dwll0NbWRiW7Krh+5evxm5aWhutXQlC1uoPS95cX8uKY/tHUIga1OKZ1YFelKv4N+dqmtLQ0/Hv5Euxr1JS5TfUaNfHvZckYLl+6CPsaWR8/r1+9xLt3cTD78mNEmdShHzS1tGFqXREv7369Fp+QloZXd8NgbiN9/bOilqXRxmsVWk9eIV5KVXeEpa09Wk9eAYNisn/YfXobjaSP8dAzNlV6DGp9bs3mu4a9jO8aId+clzKSgk+ePMa6jVtgYqL8qasZ1OF40NbWQeUq1aTPSyFZn5fsazhIlAeAkEsXsiz/tV4BKXIMYsgptegHFY8hy98OId/57XCp4Px2AFS/H9RBxs1DlbUUNgVyKjGQPlU2Y7pnTq9zM2zYMFhbW2PatGlISkpCrVq1MHHiRKxevRotW7aEk5MTOnXqhAULFoiTYkePHkXnzp1Rt25djBkzBm5ubqhbty4aNWoEPz8/3L59G+XLl//uvt+9e4fQLxcKzWBmZoaKFSsiJSUFK1euhKurK86fPy++pp48PD09MW/ePFSsWBGVK1fGypUr8fbtW4k3bfPmzeHr6wtXV1eYmJhgxowZEq9dTtri6emJxo0bY8mSJXB1dcXp06dx/Phxif3MmDED7du3R5kyZdCtWzdoaGggLCwMt27dwu+//55lDJ8/f8bLly8l1olEIlhaWgLIvv8yJCcnw93dHdOmTUNUVBS8vb0xatQomQlJW1tbdOzYEUOGDMH69ethZGSE3377DaVKlULHjh1lttHGxgaPHj1CaGgoSpcuDSMjI+zcuROpqalwdHSEgYEB/vrrL+jr60tNXQbSpw1nXMMyswTpwYUy9es/ENOnTkbVatVRvXoN+P21FQkJCejYqQsAYJrXJFhYWGL0uAkAgN59+2PwwH7Y5rsZPzVughPHj+HO7VuY4TNL/Pr26dcfGzesRZmyZVGqVGmsXrUcxS0s0KxFy5w1Sk59+7thxtTf0mOwr4Ed27+JYcpkWFhYYPTY9Bh69e2HIQP7Y9vWzfjpp6Y4eeIo7ty+jeneX6+x+e5dHF6+eCGeXh8VlZ7wNjM3F/83TZn6DRiI6VMmo9qXGP76EkOnzukxTP3SD2O+9EOfvv3h7tYPW303o/GXfrh96xamf9sP69eibJmyKFW6NFavTO+H5nnUD67d+2LlH96oYFcFtpWr48i+HUhKTEDz1ukjnVfMmwFT8+LoOyT9Op4pKSl49jj9GiifP6cgJvo1Ht2PhJ6+AaxKpSfe6zr9hH1+m1HcsgSsbSrg0b0IHN7jh+ZtZB9Piuraqz8WzJ6GSpWrwq6aPfb7/4XExAS4tO8EAJg/cwrMi1vCfcQYcQyPH6VfGiLlcwqi37zG/bsR0Nc3QCnrMgCAhE+f8PzZ19FQL/97jvt3I1C0qDEsSlgpPQZlH9NA+vHw4sULvPlyPDz+8g8g87w6HtQhBjU4pnv1dcPsGV6oUrU6qla3x64d25CYkIB2HdMv/zFz2m8obmGBEaPHAwB69uqH4UMGwG/bFjT6qQn+PnkM4Xdu4bfp6Tfs+vTpI/5cvwbNWrSCqbk5nj99glXLF6O0dRk0aOicJzGoQz/YNeuES38thWkZW5iVrYTI4IP4nJSIcg3S93dx22Lom5ihZgc3aGrrwKSkjcT2OvpFAEC8PiUpAbeO74S1Q0PoFS2GD9EvEHpwC4zMrWBVuXaexKAO59Y+/d3g/eW7RrVM3zU6fDkvTf/yXcNzbMZ5Kf27xvatm+Gc6bvGtC/fNVJSUjBp/BhEhN/B8tXrkJqWKr6+l7GxcZZ3Y1aEOhwPvfsNwMzpXqhSrTqqVbfHzr+2ISEhAa6d0s9L3lMno7iFJUaNST8v/dKnPzzc++OvrVvg3LgJTp04hvDbtzHly3kp4dMnbN60Ho2bNoO5eXHExcVhj/8OvHn9Ci1+ztlAEXmpQz+oegwSvx2q18AOqe8ZX347jPvmt4PvZvzUuClOHv/y28Enm98Oj/L2twOg+v1AhVuBTQwC0tefy862bdtw7NgxXL9+HVpaWtDS0sJff/0FZ2dntG/fHm3atMGxY8cwdepUDBw4EG/evEGJEiXQuHFjcWKqZ8+eePDgASZNmoTExER07doVw4cPx8mTJ7+7/+DgYNSqJZm5d3d3x6ZNm7BkyRLMnz8fXl5eaNy4MebNm4f+/fvL9VpMnjwZL1++RP/+/aGpqYmhQ4fCxcVFIvHn5eWFR48eoX379jA2Nsbs2bMlRgw6ODh8ty2NGjXCunXrMHPmTEybNg0uLi4YN24cVq1aJS7j4uKCI0eOYNasWZg/fz60tbVRuXJlDB48ONsYbt++DSsryS+Hurq6SExMzFH/AUCLFi1ga2uLxo0bIykpCb169YKPj0+W+9yyZQvGjBmD9u3bIzk5GY0bN8axY8ekpm5n6Nq1KwICAtCsWTPExcVhy5YtMDExwR9//IHx48cjNTUV9vb2OHz4sNT1KpXBpU1bvH0bi7WrViA6+g3sKlfBmnWbxEPjX7x4AVGmJGjNWrUxd/4irF65DCuXL0GZsjZYumI1KtpWEpdxGzQECQkJmO0zA/Hx71Grdh2sWbdJZgJTKTG0bou3sbFYu3olYr7EsHrdRnEML1/8J3Gnp5o1a2PuH4uwetUyrFq+FGXK2mDJ8lUSMZwJOg3v6VPEj3/7Nf0LpsfwkRg2QvIGNcrQuk16DGsy98P6TZlieAENkWQ/zFuwCKtWLMPKZen9sGzlathmimGge3o/zMrcD+vzrh8aNWuFd3Fv4b9lHeLexvyfvbuOi2Jt+wD+WyQVlRIMbFExQcXAVkTBQuVgHLu7u1CP3YWtGMfubuVY2GJ3N6KIBUhc7x+8O4cV9FHxuLvw+z4fPs9xdnb3und2dmauue77Rs7ceTF0wixY/P+A8yHBL6Ay+Hc7hL5+hb7tmyj/3rZuBbatW4GCRYtj1LQFAIC23fpj9ZK5WDB9PN69DYWltQ2q1WqAP5q3+0/aUMmtBt6GhmLZojkIfR2C3A75MHbaXGXQ/OCXLzT2h9chwejUwkf59/pVy7B+1TIUcS6BKXOWAABu3biKvl3aKOvMmzkJAFDNsw76D/v6jY2f9V/s0wGHD8F36L+VyAP69QIAdOjUFZ26/Pr9ITm0ITns09Wqe+Bt6BssnDsLr1+HwCFffkzzm690nXzxQnM7FHFyxqixEzHfbybmzZ6OrNmyY+LUWcidxwEAYGCQCndu38Ku7Vvx/v072GSwRakyZdG+czcYG//6JAiQPLZD9uIVEPkhDJd3/o2I96GwzJILlTqPgtn/dyX+FPoKqnht+F9UKgO8fXof908dRFT4R5ilt0LG/M4oXLMpUn3lXCWpksVvayLnGrO/ca5R1KkYxoyfjDlfOdd4FfwS/wQcAgA08vbSeK8FS5ahhEsp/GrJYX9wr+GJt6GhmD9nJl6HhCBvPkfMnLPgq79LRZ2cMXrcJMydPQNzZk1D1mzZMXn6LGU7GKRKhQf372Hnti14+zYU6S0sUKBgYSzw/1v57frVksN20Pc2/Hue8Y1rh3jnrPHPM5T9eWYi1w5Dv3Lt8B+cZwD6vx30Xcqr8fu1VPJf9AGj/1xsbCwcHR3h4+ODv/766z99r3bt2uHGjRs4evTof/o+/0vLli3x9u1bbNmyRatx/IzvrRjUVWZGwKfP+v1TkdpYhYhobUeRNKaGwJWnH7QdRpIUymKOR28i//eKOiyblUmy2KeTQxuSwz4d+inh8BX6xDJ1qmSxHUbsu63tMJJkhLtDsvht/ajn5xppksm5xruIH5vcUNekMzVIFtshObThU5T+7tOpjZLH/pxSLD/7+Je+XvMSCYcqS85S0FdFvz18+BD79u1DxYoVERkZidmzZ+P+/fto0qTJ/37yD5o8eTKqVauGNGnSYPfu3Vi2bBnmzJnzy9+HiIiIiIiIiCgpDFLguIC/EhODesLAwABLly5F3759ISIoVKgQDhw4AEdHx1/+XqdPn8bEiRPx/v175MqVCzNnzvyf3YSJiIiIiIiIiH43pgWTholBPZE1a1YcP378t7zXunXrfsv7/KilS5dqOwQiIiIiIiIiomSDiUEiIiIiIiIiItJL7EmcNEwMEhERERERERGRXlIxM5gkBv97FSIiIiIiIiIiIkpuWDFIRERERERERER6iRVvScPEIBERERERERER6SV2JU4aJlaJiIiIiIiIiIhSIFYMEhERERERERGRXmK9YNKwYpCIiIiIiIiIiCgFYsUgERERERERERHpJY4xmDRMDBIRERERERERkV5iV9ik4edHRERERERERESUArFikIiIiIiIiIiI9BK7EicNE4NERERERERERKSXmBZMGnYlJiIiIiIiIiIiSoFYMUhERERERERERHqJPYmTholBIiIiIiIiIiLSSwbsTJwk7EpMRERERERERESUArFikIiIiIiIiIiI9BK7EicNKwaJiIiIiIiIiIhSIFYMEhERERERERGRXlJxjMEkYWKQiIiIiIiIiIj0ErsSJw27EhMREREREREREaVArBgkIiIiIiIiIiK9ZMCuxEnCxCAREREREREREekldiVOGnYlJiIiIiIiIiIiSoFYMUhERERERERERHqJFYNJw8QgERERERERERHpJRXHGEwSdiUmIiIiIiIiIiJKgVgxSEREREREREREesmABYNJwopBIiIiIiIiIiKiFIgVg0REREREREREpJc4xmDSMDFIRERERERERER6ibMSJw27EhMREREREREREaVArBgkIiIiIiIiIiK9xK7EScPEIBERERERERER6SXOSpw07EpMRERERERERESUArFikIiIiIiIiIiI9BK7EicNE4NERERERERERKSXOCtx0rArMRERERERERERUQrEikEiIiIiIiIiItJLLBhMGiYGiYiIiIiIiIhILxmwL3GSsCsxERERERERERFRCqQSEdF2EERERERERERERD/q5J23v/T1Suex+KWvp+vYlZjoN4iI1nYESWNqyDboArZBNySXNrz5GKPtMJLEKk2qZLEd2AbtMzUE0jVaru0wkuTdmubJYjs8Cf2s7TCSxN7SOFlsB7ZB+9gG7dP3+IG4NhB9D35ViIiIiIiIiIhIP3GIwSRhYpCIiIiIiIiIiPSSipnBJOHkI0RERERERERERCkQKwaJiIiIiIiIiEgvqVgwmCRMDBIRERERERERkV5iXjBp2JWYiIiIiIiIiIgoBWLFIBERERERERER6SeWDCYJE4NERERERERERKSXOCtx0rArMRERERERERERUQrEikEiIiIiIiIiItJLnJU4aVgxSERERERERERElAKxYpCIiIiIiIiIiPQSCwaTholBIiIiIiIiIiLST8wMJgm7EhMREREREREREaVATAwSEREREREREZFeUv3i//0oPz8/5MiRA6ampihVqhROnz791XUXLlyI8uXLw9LSEpaWlnBzc/vm+r8DE4NERERERERERKSXVKpf+/cj1q5di969e8PX1xfnz59H0aJFUb16dQQHBye6fkBAABo3bozDhw8jMDAQWbNmhbu7O54+ffoLPomfw8QgERERERERERHRD5o6dSratWuHVq1aoUCBApg3bx5Sp06NJUuWJLr+ypUr0blzZzg5OSF//vxYtGgRYmNjcfDgwd8c+b+YGCQiIiIiIiIiIr2k+sV/kZGRePfuncZfZGRkgvf9/Pkzzp07Bzc3N2WZgYEB3NzcEBgY+F2xf/r0CVFRUbCysvqptv8KTAwSEREREREREZF++sWZwXHjxiF9+vQaf+PGjUvwtiEhIYiJiYGdnZ3Gcjs7O7x48eK7Qh8wYAAyZ86skVz83Qy19s5EREREREREREQ6ZNCgQejdu7fGMhMTk1/+PuPHj8eaNWsQEBAAU1PTX/7634uJQSIiIiIiIiIi0ks/M5Pwt5iYmHxXItDGxgapUqXCy5cvNZa/fPkSGTNm/OZzJ0+ejPHjx+PAgQMoUqRIkuJNKnYlJiIiIiIiIiIi+gHGxsYoXry4xsQh6olEypQp89XnTZw4EX/99Rf27NmDEiVK/I5Qv4kVg0REREREREREpJdUv7Zg8If07t0bLVq0QIkSJVCyZElMnz4dHz9+RKtWrQAAzZs3R5YsWZQxCidMmIDhw4dj1apVyJEjhzIWobm5OczNzbXSBiYGiYiIiIiIiIhIL2kxL4iGDRvi1atXGD58OF68eAEnJyfs2bNHmZDk0aNHMDD4t7Pu3Llz8fnzZ3h7e2u8jq+vL0aMGPE7Q1cwMUhERERERERERPQTunbtiq5duyb6WEBAgMa/Hzx48N8H9IOYGCQiIiIiIiIiIv2kzZLBZICJQSIiIiIiIiIi0ku/elbilIazEhMREREREREREaVArBgkIiIiIiIiIiK9pM1ZiZMDJgaJiIiIiIiIiEgvMS+YNOxKTERERERERERElAKxYpCIiIiIiIiIiPQTSwaThBWDREREREREREREKRArBomIiIiIiIiISC+pWDKYJEwMEhERERERERGRXuKsxEnDrsREREREREREREQpECsGiYiIiIiIiIhIL7FgMGmYGCQiIiIiIiIiIv3EzGCSsCsxERERERERERFRCsSKQSIiIiIiIiIi0kuclThpmBgkIiIiIiIiIiK9xFmJk4ZdiSnZWLp0KSwsLH7oOSqVClu2bPlP4iEiIiIiIiIi0mVMDKYQLVu2hEqlgkqlgrGxMfLkyYNRo0YhOjoaABAQEACVSoW3b98meG6OHDkwffp0jX+rXytNmjQoVqwY1q9f/9X3fvDggbL+l38nT5781U39Ic+fP4eHh4dGnEFBQVqNac2qlfCoVgUuzoXxZ6M/cPnSpW+uv2/vbtStVQMuzoXRwKs2jh75R+NxEYHfrBmoWrEcShYrgvZtWuLhwwf/YQvYBrbh12EbdKMNG9auQr2abqhY2gltmjfE1SvfbsPB/XvQsH5NVCzthD996uLEMc02/OU7GGWKFdD469ml/X/ZhGSxHdgG3WhDO/d8uDyrPoKX/4lDoz1QPLf1N9fv7OGIc1Pr4uXyJrjm1wDjmpeAidG/p+CDvIvi3ZrmGn9np9T9T9uQHLbDlg2r0cSrOmpUKI4urZvgxtXLX133wb07GDGwF5p4VUfV0oWxcc2KJL/mr5ActgPbwDb8KmyDbrRBX6l+8V9Kw8RgClKjRg08f/4ct2/fRp8+fTBixAhMmjTpp15r1KhReP78OS5cuAAXFxc0bNgQJ06c+OZzDhw4gOfPn2v8FS9e/Kfe/1fJmDEjTExMtBpDfHt278LkiePQoXMXrFm/Gfny5UenDm3w+vXrRNcPunAeA/v1Qb363li7YQsqV6mKnt264PbtW8o6/osXYvXKFRjqOwJ/r14HMzMzdGrfBpGRkWwD28A2sA3/04G9uzFz6gS0ad8ZS1dtgINDfvTq0h5v3iTehksXL8B3cD/Urlsfy1ZtRIVKVTGgdzfcvXNbY73SruWwY98/yt+ocT93PPoeyWE7sA260Yb6ZXJgbLMSGL/hIsoP2oHLD0OxaZAbbNKZJrr+H2VzYkTjYhi/8RJc+mxF1/knUL90Dvg2Kqax3rXHocjTYZ3y5z5iz38SP5A8tsPh/Xswb8YkNG/bEfOWrUNuh7wY0LMDQr/yuxQREYFMWezRtktPWFnb/JLXTKrksB3YBraBbUhebdBrzAwmCRODKYiJiQkyZsyI7Nmzo1OnTnBzc8O2bdt+6rXSpk2LjBkzIm/evPDz84OZmRm2b9/+zedYW1sjY8aMGn9GRkYQEbi5uaF69eoQEQDAmzdvYG9vj+HDhwP4t6Jx586dKFKkCExNTVG6dGlcuXLlm+85d+5c5M6dG8bGxsiXLx9WrNC8Qxy/K3HOnDkBAM7OzlCpVKhUqZLy3iVLlkSaNGlgYWGBsmXL4uHDhz/6kX2XFcv8Ud/bB171GiB3njwY6jsSpqam2LJpY6Lrr/x7OVzLlUfL1m2RK3dudO3eE44FCmDNqr8BxN1lWrliOdp16ITKVdyQN19+jB43Ea+Cg3Ho4AG2gW1gG9iG/2n1yqWoU+8P1KpbHzlz5UH/Ib4wMTXFjq2bEl1/3aoVKFWmHJq2aIMcuXKjQ+fuyJe/ADasXamxnrGxMaxtMih/6dKl/0/iB5LHdmAbdKMNXWs6Ytmh21j5z13cfBqGnotOIvxzDJpVypPo+qXyZsDJW8FYf/w+Hr36iEOXnmPDifsonlszORUdIwgOi1D+3rz/7y76ksN22LB6OTzrNkCNWvWQI2du9BwwHCamZtizY3Oi6+cvUAgduvVBlWoeMDIy/iWvmVTJYTuwDWwD25C82kApFxODKZiZmRk+f/6c5NcxNDSEkZHRT7+WSqXCsmXLcObMGcycORMA0LFjR2TJkkVJDKr169cPU6ZMwZkzZ5AhQwbUrl0bUVFRib7u5s2b0aNHD/Tp0wdXrlxBhw4d0KpVKxw+fDjR9U+fPg3g38rGTZs2ITo6Gl5eXqhYsSIuXbqEwMBAtG/fHqr/YHTTqM+fcf3aVZQu46osMzAwQOnSrrh08UKiz7kUFITSpctoLHMtWw6X/r879NMnTxAS8gqlSv/7mmnTpkXhIkW/+ppsA9vANrANShuiPuPm9WtwKVVaow0upcrgyqWgRJ9z5XIQXEpptqFUmbK4cumixrLzZ8/As2o5NKzniYljRyIskaEsfoVksR3YBp1og1EqAzjltMbhy8+VZSJAwOXnKJk3Q6LPOXXrFZxyWivdjXPYmsPdOQv2BT3RWC93xrS4OccbF2fUw6Ku5WBvneaXxw8kj+0QFRWFWzevoZiL5u9SMZfSuHb54jee+Xtf85vvlxy2A9vANrANyaoN+k71i/+X0nBW4hRIRHDw4EHs3bsX3bp103jM3t4+wfqfPn366mt9/vwZU6ZMQVhYGKpUqfLN93V1dYWBgWYu+sOHDwCALFmyYP78+WjevDlevHiBXbt24cKFCzA01PyK+vr6olq1agCAZcuWwd7eHps3b4aPj0+C95s8eTJatmyJzp07AwB69+6NkydPYvLkyahcuXKC9TNkiDupV1c2AnGVi2FhYahVqxZy584NAHB0dPxmO39W6NtQxMTEwNpac6wia2tr3L9/L9HnhISEwPqLLjHW1tYIeR3y/4+/iltmk/A1Q0JCflXoCrbhi/jYhp/GNnwRn5ba8PbtW8TExMDKSjMmKytrPHyQeBteh4TA6os2W1nb4PXrf+Mr7VoOlaq4IVNmezx98gjzZk9Hr24dsHDpKqRKleqXtiE5bAe24Yv4tNQG63QmMExlgFdh4RrLg8PCkTdLukSfs/74fVinNcHekTWgggpGhgZYtP8mpmz5t8fD2Tuv0GnuCdx+HoaMFqkx0LsI9oyojtL9tuFDRPQvbUNy2A5hb0MRGxMDSyvN97O0tMbjB/d15jW/JTlsB7bhi/jYhp/GNnwRn5baQCkbE4MpyI4dO2Bubo6oqCjExsaiSZMmGDFihMY6R48eRdq0aTWWqbvUxjdgwAAMHToUERERMDc3x/jx41GzZs1vvv/atWu/mVT7448/sHnzZowfPx5z586Fg4NDgnXKlPn3roqVlRXy5cuH69evJ/p6169fR/v2moPZly1bFjNmzPhmnPFZWVmhZcuWqF69OqpVqwY3Nzf4+PggU6ZMia4fGRmZYMwHExMTIJXujGNIRKRt1ap7Kv+dxyEv8jjkg3ed6jh/9nSCakMifVaugB36eBVG78WncPZOCHJlTIsJLUqif/1PmLgpbmKL/UHPlPWvPnqLs3de4crsBqhXJgdWHL6jrdCJiIj0xn/QoS9FYVfiFKRy5coICgrC7du3ER4ejmXLliFNGs2uKjlz5kSePHk0/r6s2gPiuvQGBQXhyZMnCA0NxYABA/7n+2fNmjXBa8f36dMnnDt3DqlSpcLt27e/8iq/n7+/PwIDA+Hq6oq1a9cib968X51Nedy4cUifPr3G37hx477rfSwtLJEqVaoEA9S+fv0aNjaJD5RtY6NZhaOs//93n2xs4qogX4d8/2smBdvwxfpsw09jG75YX0ttsLCwQKpUqfDmjWZMb968TnCXW83axgZvvmjzm9cJ74rHl8U+KywsLPHk8aOkB/2F5LAd2IYv1tdSG16/i0R0TCwypDfTWG6b3gwv30Yk+pyhPk5Yc/Qelh++g2uP32LHmccYteY8etct/NWLmLBPUbj7/B1y2aVNfIUkSA7bIb2FJQxSpUowKUho6OsE1crafM1vSQ7bgW34Yn224aexDV+sr6U26DvOPZI0TAymIGnSpEGePHmQLVu2RJN9P8LGxgZ58uRBxowZf9l4e3369IGBgQF2796NmTNn4tChQwnWiZ+QCw0Nxa1bt75ahejo6Ijjx49rLDt+/DgKFCiQ6PrGxnGDUcfExCR4zNnZGYMGDcKJEydQqFAhrFq1KtHXGDRoEMLCwjT+Bg0alHiDv2BkbAzHAgVx6mSgsiw2NhanTgWiSFHnRJ9TxMkJp75IUp4MPIEiTk4AgCz29rCxyYBTp/59zQ8fPuDypYtffc2kYBvYBrYhmbXByBj5HAvg7Ol/Y4qNjcXZ0ydRqIhTos8pVNhJY30AOH0qEIWKFP3q+wS/fIGwsLewyZD4OG1JkSy2A9ugE22IiolF0P3XqFTo314DKhVQsVBGnL71KtHnmBkbIvb/J1ZTi4mN+/fXxjBKY2KInHZp8fJteKKPJ0Vy2A5GRkbIm68ALpw5pdGGC2dOokDhr//O/O7X/Ob7JYftwDawDWxDsmoDpWzsSky/zevXr/HixQuNZRYWFjA1NcXOnTuxZMkSBAYGolixYujXrx9atGiBS5cuwdLSUll/1KhRsLa2hp2dHYYMGQIbGxt4eXkl+n79+vWDj48PnJ2d4ebmhu3bt2PTpk04cCDxWZxsbW1hZmaGPXv2wN7eHqampnjz5g0WLFiAOnXqIHPmzLh58yZu376N5s2bJ/oaJiYmcV2Hv/C9QwQ1a9EKwwYPQMGChVCocBH8vWIZwsPD4VWvPgBgyKD+sLW1Q49efQAAfzZtjjYtm2HZ0iWoUKEi9uzehatXrmDYiFEA4iZ2+bNZcyycPxfZs2VHFnt7+M2agQy2tqhS1e37gvpBbAPbwDYkrzY0/rMl/vIdhPwFCqFgwcJYs2o5IsLDUatOPQDAyGEDkcHWFp279QYA+DRphs7tWmDVCn+4lquIA3t34ca1Kxg4dCQA4NOnj1g8fw4qV3WHtY0Nnjx+BL8ZU2CfNRtKlSn3n7QhOWwHtkE32jB753XM61QWF+6F4Oyd1+js6YjUJob4+5+4Lr/zO5fFszefMHJN3MDwe84/QRdPR1y6/0bpSjzUxwm7zz9REoajmxbH7nNP8DjkAzJapsZg76KIiRWsP/7rx7YDksd28G7cHBP+GoK8jgWRv0BhbFy7AhER4ahe0wsAMH7kYNhksEXbzj0BxE0u8vD+XQBAdHQUQl4F486tGzAzS40sWbN912v+aslhO7ANbAPbkLzaoNdSYpnfL8TEIP02bm4Jf8BWr16NqlWrok2bNhgxYgSKFSsGABg5ciT27duHjh07Yu3atcr648ePR48ePXD79m04OTlh+/btSqXfl7y8vDBjxgxMnjwZPXr0QM6cOeHv75/omIlA3OzKM2fOxKhRozB8+HCUL18ea9euxY0bN7Bs2TK8fv0amTJlQpcuXdChQ4ekfyCJqOHhidA3bzBn9kyEhLxCvvyOmDN/Eaz/v1z8xfPnMFD9W+jr5FwM4yZOxuyZ0zFr+lRky54D02f5wcEhr7JOqzbtEB4ejlEjhuP9+3dwLlYcc+YvSjSByTawDWwD2/Alt+oeCA19g0VzZ+H16xA45MuPabPnw+r/u7q8fPFcY2KpIkWdMXLMRCyYMxPzZk9H1mzZMWHqLOTOEzdurIFBKty9fQu7d2zF+/fvYJPBFqVKl0X7zt2++nueVMlhO7ANutGGTYEPYJPOBIP/cIKdhRkuP3yDBuMP4lVYXFdie5s0GhWCEzddgohgWEMnZLJKjZB3kdhz7jFGrf13RsksVqmxpFt5WKU1Qci7CJy8GYyqw3bh9fvIBO//KySH7VC5Wg2EvX2DpQv9EPo6BLkd8mP8tHnK71Lwi+caPVpevwpGh+Z/KP9et3Ip1q1ciqLOJTB1rv93veavlhy2A9vANrANyasN+iwlziT8K6lEvujfQKSDAgICULlyZYSGhsLCwkLb4fywXzyp4G9nasg26AK2QTcklza8+Zhw2AR9YpUmVbLYDmyD9pkaAukaLdd2GEnybk3zZLEdnoR+1nYYSWJvaZwstgPboH1sg/bpe/xAXBtSioevf+3NtOzWKSv5moK+KkRERERERERElJxwVuKkYWKQiIiIiIiIiIj0EvOCScPEIOmFSpUqgb3eiYiIiIiIiIh+HSYGiYiIiIiIiIhIL7ErcdIY/O9ViIiIiIiIiIiIKLlhxSAREREREREREekplgwmBRODRERERERERESkl9iVOGnYlZiIiIiIiIiIiCgFYsUgERERERERERHpJRYMJg0Tg0REREREREREpJfYlThp2JWYiIiIiIiIiIgoBWLFIBERERERERER6SUVOxMnCRODRERERERERESkn5gXTBJ2JSYiIiIiIiIiIkqBWDFIRERERERERER6iQWDScOKQSIiIiIiIiIiohSIFYNERERERERERKSXVCwZTBImBomIiIiIiIiISC9xVuKkYVdiIiIiIiIiIiKiFIgVg0REREREREREpJ9YMJgkTAwSEREREREREZFeYl4wadiVmIiIiIiIiIiIKAVixSAREREREREREeklzkqcNEwMEhERERERERGRXuKsxEnDrsREREREREREREQpECsGiYiIiIiIiIhIL7ErcdKwYpCIiIiIiIiIiCgFYmKQiIiIiIiIiIgoBWJXYiIiIiIiIiIi0kvsSpw0rBgkIiIiIiIiIiJKgVgxSEREREREREREekkFlgwmBRODRERERERERESkl9iVOGnYlZiIiIiIiIiIiCgFYsUgERERERERERHpJRYMJg0Tg0REREREREREpJ+YGUwSdiUmIiIiIiIiIiJKgVgxSEREREREREREeomzEicNE4NERERERERERKSXOCtx0rArMRERERERERERUQrEikEiIiIiIiIiItJLLBhMGlYMEhERERERERERpUBMDBIRERERERERkX5S/eK/H+Tn54ccOXLA1NQUpUqVwunTp7+5/vr165E/f36YmpqicOHC2LVr14+/6S/ExCAREREREREREekl1S/+349Yu3YtevfuDV9fX5w/fx5FixZF9erVERwcnOj6J06cQOPGjdGmTRtcuHABXl5e8PLywpUrV37FR/FTVCIiWnt3ohQiIlrbESSNqSHboAvYBt2QXNrw5mOMtsNIEqs0qZLFdmAbtM/UEEjXaLm2w0iSd2uaJ4vt8CT0s7bDSBJ7S+NksR3YBu1jG7RP3+MH4tqQUoRH/drXMzP6/nVLlSoFFxcXzJ49GwAQGxuLrFmzolu3bhg4cGCC9Rs2bIiPHz9ix44dyrLSpUvDyckJ8+bNS3LsP4MVg0REREREREREpJdUql/7FxkZiXfv3mn8RUZGJnjfz58/49y5c3Bzc1OWGRgYwM3NDYGBgYnGGhgYqLE+AFSvXv2r6/8WQkR6LSIiQnx9fSUiIkLbofw0tkE36Hsb9D1+EbZBV7ANuoFt0A1sg25gG3QD26Ab2Ab6r/n6+goAjT9fX98E6z19+lQAyIkTJzSW9+vXT0qWLJnoaxsZGcmqVas0lvn5+Ymtre0vi/9HsSsxkZ579+4d0qdPj7CwMKRLl07b4fwUtkE36Hsb9D1+gG3QFWyDbmAbdAPboBvYBt3ANugGtoH+a5GRkQkqBE1MTGBiYqKx7NmzZ8iSJQtOnDiBMmXKKMv79++Pf/75B6dOnUrw2sbGxli2bBkaN26sLJszZw5GjhyJly9f/uKWfJ8U1OuciIiIiIiIiIjo6xJLAibGxsYGqVKlSpDQe/nyJTJmzJjoczJmzPhD6/8OHGOQiIiIiIiIiIjoBxgbG6N48eI4ePCgsiw2NhYHDx7UqCCMr0yZMhrrA8D+/fu/uv7vwIpBIiIiIiIiIiKiH9S7d2+0aNECJUqUQMmSJTF9+nR8/PgRrVq1AgA0b94cWbJkwbhx4wAAPXr0QMWKFTFlyhTUrFkTa9aswdmzZ7FgwQKttYGJQSI9Z2JiAl9f3+8qddZVbINu0Pc26Hv8ANugK9gG3cA26Aa2QTewDbqBbdANbAPpkoYNG+LVq1cYPnw4Xrx4AScnJ+zZswd2dnYAgEePHsHA4N/Ouq6urli1ahWGDh2KwYMHw8HBAVu2bEGhQoW01QRw8hEiIiIiIiIiIqIUiGMMEhERERERERERpUBMDBIREREREREREaVATAwSERERERERERGlQEwMEhERERERERERpUBMDBLpqNjYWACAPs8PpG4DEZE2iQhiYmK0HUaSfXk80OfjA+kGHqeJ4rx9+1bbIRARaQ0Tg0Q66OzZs3BxcUFkZCRUKpW2w/lhDx8+xIMHD2BgYMCLDkqSTp064e7du9oOg/Tc06dPkSpVKgDAqlWrcOnSJS1H9OPCwsKU48G2bdsAQC+PD6Qbdu3aheDgYB6niQCcP38emTNnRlBQkLZDSdHUN7vCwsK0HMnPU7chIiJC499Euo6JQSIdc/HiRVSpUgWurq4wMTHRdjg/7NGjR8iZMycqVqyIW7du8aKDftrbt29x9+5dZMuWTduhpGiJ7b/6dKJ79uxZ5M6dG0eOHMHAgQPRq1cvWFhYaDusH7J161bUrVsXoaGh6NWrF5o3b45nz55pO6yf8vr1a9y6dQsXL17Udig/LTw8HG/evEF0dLSyTJ/2idOnT6Nv374YMGAAQkJCeJzWMn722nXx4kVUqlQJHTt2hJOTk7bD+SkPHjzA7t278e7dO22H8tNEBCqVCnv27EHbtm1x6NAhbYf0w9Rt2Lt3L9q0aYOHDx/yBh7pDSYGiXTI9evXUbZsWfTs2ROzZs3SqwsNtdu3b8PKygrp0qWDl5cXrly5orcXHfr4+ScXMTExsLCwwL59+2BkZIRly5bpbSJBn79HsbGxMDCIO1W4efMm7ty5g+fPn0OlUulNu2xsbNCxY0fUrFkT8+fPR1BQELJly6ZXv0nOzs44e/YsSpYsCX9/fxw5cgSZM2fWm22gduXKFVSrVg3VqlWDs7MzunXrhkePHmk7rB9y/fp1NGrUCBUqVICHhwdWrVqlXAzqi5IlS6J169a4e/cuBg4ciFevXunlcVrfb1qoqX9j7927B4CJwt/pxo0bqFChAjp06ICpU6fq5Wd/6dIluLi44OjRo0p3aH3cD1QqFbZs2YJ69eqhRIkSSJMmjbZD+mEqlQobN25Ew4YNkS1bNrx48QKAfm4PSnmYGCTSEZcvX0b58uWRPn16+Pj4AIBeXXyrFSpUCPb29ihYsCBcXV3h4+ODa9eu6eVFh0qlQmxsrDI2mb6MUZbY5/zlMl3fFupunyKC8PBwdOrUCR07dsS1a9e0HNn3e//+PQD93I+BuM9efcE6cuRIeHl5wcPDA8WKFcO+ffv0JhGSI0cOZM+eHR8/fkRMTIzyHdKX7RIdHY1s2bKhefPmuHv3LgoWLAgbGxslGaUPbQDiqnJKlSqFatWqYfr06ZgyZQoWLlyIefPmaTu073bx4kWUKVMG5ubmaNCgAR4+fIjBgwdj9+7d2g7tu6mrHPv27Yv69evj6tWrGDRokN5VDsa/aXH+/HncvHlTuWmhL22Ib/369ShVqpTG764+2L9/v5L80Dfq/TkqKgo5cuTAp0+f9GofAOJ66dSsWROtWrXC2LFjlR4W+tKG+MevJ0+eYOjQoRg7diwGDBiAUqVKJVhH13w5LuXly5fRpUsXTJgwAePGjVPaEBwcrIXoiH6M/hx5iJKxoKAglC5dGh4eHrCwsMCIESNw6tQpAPpz8RobGwsRgZ2dHQYPHoy7d++ifPnycHBwwB9//KFXycFJkyahZ8+eAOLu5KdKlQqPHz/GmDFjEBISot3gvoOBgQHu3r2LDh06aCwDgEWLFuHGjRs6feER//uuUqlgZmaGBw8e4Pnz5+jQoQOuXr2qxei+z9OnT9GsWTMsXrwYgP7sx/GpE38jRozA3LlzMWXKFAQEBMDFxQXe3t5YsWKFliP8OvXvjPp3qXbt2jh06BBatWqF+vXrY/v27TqfQFB/XwwNDQEAFStWxK5du3Dr1i20bNkSt2/fBpBwnEFd/J7dvHkTxYoVQ69evTBhwgTUq1cPvXr1QsuWLbFs2TK9+F29du0aypcvjy5dumDlypUYOXIkLl26hPfv32Pz5s3aDu+7qW+6BAYGon79+mjYsCGuX7+ud5WD6mPYwIED4enpierVq8PDwwMnT57UmzbE5+bmBisrK8yePVvboXwXEcHt27dRvXp1jB49Gq9evdJ2SD/k/PnzKFu2LNq3b4927dph2bJl8PPzU5KDuvg7mpgjR44gf/78mDhxImJiYjB69Gg0bdoUXbt2xf79+7Ud3ldNnToVV65c0Th+hYSE4N27d6hUqZKyTJersRcvXozevXtrjIN969Yt2Nvbo0OHDggLC8OyZctQvXp1ODo6YuDAgcq4g0S6SHevDIlSiHv37qFYsWLo2rUrVqxYgaVLl+LixYuYNGkSTp8+DUC3kwqPHj1Skn7qg3ehQoVga2uLLFmyYPTo0ciaNatGclCXK+8iIyNhbW2NK1euYOjQoQDi7vQ5OTkhLCwMNjY2Wo7w+6RPnx5LlixBs2bNlGUTJkxAr1698ObNGy1G9m1RUVHK9+jZs2cIDw9HZGQkbG1tcfr0ady/fx8dO3bU+eRgdHQ03rx5gzVr1mDlypUA/vd+rIv7+Pnz5xEQEIDly5fD09MT586dw7Fjx1CqVCm0bt0af//9t87FHb+S6NWrVwgODoaDgwMqVaqE/v37o1GjRmjWrBl27dqlJElmzJiB69evazNsDbGxscp+8PbtW8TExKBhw4aoUaMGAgMDcf78eXTt2lXjgsTf3x+Abk5IcuHCBYgIMmXKhNjYWKVqLWvWrMiQIYNO36gA4vbnHj16wMDAAHXr1gUQ91tlamoKNzc3fPz4EZ8/f9ZylN9HpVLh6NGjKFu2LC5fvozu3bvD29sb169fx6BBg3Q+ORj/9+bkyZNYvXo11q5di4kTJ6JIkSKoVKkSjh07pjdtAOK+X6ampihTpgyOHTumpah+nIODAzZt2oQFCxZg9OjR36yK0qXjRHBwMNzc3NC+fXtMmDABkyZNgrOzM9avX485c+bg06dPOn3eHd/NmzeVMXPLly+Po0ePIiYmBs+fP4eHhwf+/vtv7QaYiFu3biEwMBDGxsYay9U38kJDQxM859ChQzh48ODvCvG7vHr1CmfPnsWsWbNw584dAED27NkRFBSETp06wd3dHZs2bUL+/PkxadIkTJw4EWfOnNFy1ETfIESkNcHBwbJgwQLx9/cXEZGYmBgRETl9+rTkyZNHGjRoIKdOnVLWj42N1UaYX/XgwQMxMjISIyMjGTt2rCxdulR5rH///uLi4iIiIqdOnRJPT08pUqSIXLp0SVvhfrd3797J33//LZ6entKtWzfJmjWrdOvWTT5//qzt0L5LdHS0iIg8f/5cMmbMKG3atJFx48aJlZWV7N27V8vRJW7ZsmXy4MED5d/Dhw+X4sWLi4ODg0yaNEmuX78uIiIvX76ULFmySPny5eXq1avaCverQkND5c2bNyIicu/ePalVq5ZUqlRJ/v77b2Ud9X4uIhIVFSUXL1787XF+zZe/Mffv35cZM2ZIbGysHDp0SDJmzCizZs0SEZGqVauKhYWFzJ8/Xxuh/k9Dhw6VfPnyiZ2dnVSqVEm2bt0qnz9/lhcvXkiHDh3E1NRUxowZI25ublKwYEFlv9G2+Nvgr7/+Ejc3N3FycpIBAwbI6dOnRUTkzp07YmNjI1WrVpXVq1dLzZo1JU+ePBrfLV0ze/ZsUalUMn78eBERefTokVhYWMhff/2l5ci+Tb09bt26JcWKFRN3d3fZt2+fiMT9HpmYmMjcuXO1GeIPuXnzpmzatEkmT56ssXzq1Kni6uoqbdq0kVevXomI6Pz3aezYscr3SUTk8ePH0rx5czE2NpZjx46JiOjMfp2Ye/fuafz79OnTYmRkJOvXr9dSRD9n8+bNolKppFu3bvLy5csEj1+9elVmzpwp79+/10J0mu7fvy/Lly+XgIAAEfn3+xEZGSkdOnQQFxcXmTRpknz8+FFEdO+8+0srVqyQvHnzyqxZs8Td3V35/N++fStDhw4VW1tbuXLlipajTEj9XTh+/LgEBQWJiMjr168lV65c4u3trZxHqfXq1UtatWol4eHhvz3Wb/Hz8xMXFxfp1q2b3L17V0RE/P39pXz58tKnTx+5dOmS8h1zdXWVQ4cOaTNcom9iYpBIS86dOyeFChWSI0eOKMtiY2OVA4g+JAd3794thQsXFmNjY+nZs6e4urpK5cqVZdOmTRIUFCQ+Pj5y4MABERE5duyYlC9fXkqXLi2RkZE61Y6vmTFjhpiamkqxYsWUZbp8kRFfRESE8t9p06YVExMTnU0KbtiwQbJkySL9+/eX169fy5o1ayRDhgyyfPlyadu2rRQvXlxatmypJNBevnwp2bJlk3z58iW4sNKmmzdvipOTk/z111/KhfXdu3cTTQ7GxsZKZGSkdOzYUdzd3eXt27faCjtR06ZNU3531G1p1qyZdOzYUdkHWrRoIY6OjlK+fHmd2J/j75sLFiwQa2trWbRokWzZskXc3NzE2dlZ5syZI9HR0fLixQsZMWKEFC1aVBo0aKAk/bWdCIn//tOmTZP06dPL1KlTpX379uLm5ia5c+dWLmjv3bsnRYsWlZIlS0qFChWUNujCthARefjwoaxevVpCQ0OVZX5+fmJgYCADBgyQnDlzSqdOnZTHdCXu+O7duyfLli2TkJAQERG5ffu2FClSRGrXri2rVq2SLFmySNeuXZX1dbEN8T169EhsbGzEzMxMJkyYICJxCRG1qVOnSoUKFcTHx0dpsy568eKFVK1aVVQqlfTp00dE/v3sHz9+LC1atBAzMzM5ePCgNsP8Jn9/fylcuLA0a9ZM7t27pxwD2rVrJ23atJEPHz5o/fcoMXfv3pXz58/L/v375cWLF0qiZuPGjUpy8MWLF8r6165dk0yZMom7u7u2QlacP39e0qdPr3EsFom7QSci8vnzZ43k4KdPn0REt/fr8+fPi5ubm1SoUEHq1Kmj8dj169clR44csnPnTi1Fl1D873RISIh4eHiIo6OjnD9/XkTirhVSp04tXl5esmXLFvnnn3+kZ8+ekj59ep1KcKqPt69fv5aOHTtKnjx5pGvXrvL06VMR0fxdFREZMmSIZM+eXZ48efLbYyX6XkwMEmlBUFCQpE6dWnr37p3o419WDvr4+Ch3v3WBOr6PHz/Ktm3blAvTkJAQGTRokNSuXVvs7OzEzMxMOnfurDzv5MmT8ujRI22F/V3UJ4gvXryQzJkzi5ubm1SpUkWGDRumrKOLJ+vxxT+J9fPzEwsLC7G0tJSmTZtqMapvGzNmjDg7O8uwYcOkZ8+esnr1auWxhQsXiqurqzRv3lypOH327JnUrVtXZxK10dHR0rZtW1GpVFKtWjWZNGnS/0wOdu3aVYyNjeXs2bPaCvurSpYsKdWqVVP+/eHDB3F2dpYRI0aISNx+Uq9ePQkMDFS+b7py8bRnzx6ZN2+eRgVzTEyMtGnTRgoWLCgXLlxQloeFhSlxq/d9XXD58mVp3ry5bNy4UVl29uxZadq0qRQuXFiuXbsmInFVFw8ePFB+k3SlDZcvX5ZChQpJ1apVZc+ePRqPzZkzR1QqlTg7O+tMvF/TrVs3JcGsrmC5deuWFC1aVAwMDKRu3brK90dXfou+5fXr1zJ9+nTJkiWLNG7cWFke/yJ2zJgxUr16dXn27Jk2QkxUYr8tp06dkvr160v69OmV6nH1ek+ePJHatWtLxYoVv/r83+3LGK5evSpLliyRYsWKScGCBaVevXpy5swZmTNnjmTNmlUePnyY6PO0adSoUVKmTBmxsLCQ1KlTS+7cuaV79+5KEjl+cjAkJERu3rwpOXPmlFq1aimvoa32BAUFSZo0aaR///6JPq7ef9XJQVdXVxk1apSSHNQFDx8+lMWLF8uyZcs0zhtGjx4tKpVK7Ozs5NatW8ryDx8+iIuLi+zatUsb4Sbqy+2/e/duqVu3rri4uCjJwQsXLkjRokUlV65ckitXLilevLjGcVtXrF69WgoWLCje3t5SsGBBMTY2lq5du8qdO3eUdXbv3i3NmzeXDBkyKO0j0lVMDBL9ZkFBQWJmZiaDBg3SWP748WONCwv1hd65c+fE2tpamjdvrhMl9Pfv35fRo0fL7du3RSTugmL79u3i4OAgDRo0UNbz8/MTV1dXjYtzfXHv3j3JkCGDdO/eXd6+fSurV6+W6tWrS69evbQd2g8ZP368WFhYyNmzZ+Xt27eSOXNmadKkibbD0hD/Oz9q1CgpUaKE2NrayqpVqzTWW7RokZQtW1ZatmyZIJGmKxfkJ06ckIwZM0rx4sWlVKlSMnnyZOWCKX5ycNmyZdKtWzcxMzPTuRNF9Un7tm3bpFSpUnL8+HHlsb59+4qZmZl0795dSpQoIUWLFlU+e20lyz09PTW6Yt+6dUtUKpWoVCqZOHGiiGgmy/Lnzy/t27cXEc0LFG0m+4cPH67Rhs2bN4u1tbVkzJgxwQXd0aNHxcnJSTZs2JDgdXTlhsW1a9fE0tJS+vfvryQ3vrRgwQJRqVQyadIknUp8JKZNmzbi4OAgCxYskNevX4tI3HFQ3a348OHDyrq61pb48aj/+8OHDzJ79mwxMTFRqu1ENJOD6nbqgvjf64iICI0K1GvXrkn16tUlS5YsCZKDwcHBOrNPxI8jLCwsQYX4ihUrpE2bNmJhYSEdO3YUlUolHTp00KnE+YABA8TGxkY2bdokR48elYsXL4q3t7fY2tqKu7u7cqzbvHmzGBoaSuPGjSVHjhzi4eGhvIa2tsfFixfFzMxMBg8erLH86NGjGhVc8ZODTZo0kapVq+rMvnDx4kWxt7eXMmXKiJWVldSuXVvOnTunPO7r6yuWlpbi5uYmx48fl3v37smQIUMkZ86cOlOlpt43Dxw4IFOmTFG6E+/bt09q1qwpLi4uSpvevn0r9+/fl5s3byboVqwLrl+/LnZ2drJw4UKl2/no0aOlYMGC0rVrV3n48KFERUXJsmXLpEWLFjo59A3Rl5gYJPqNrl+/LunSpUtQKTh8+HCpUKGCxgmvyL8nURcuXNC4A6Utly5dknz58kndunVlwYIFyvLPnz/Ljh07JE+ePBpVRrrcFelbpk2bJi1atFBOEt+/fy/+/v7i5eUlwcHBWo7u+1y4cEEKFy6sjIUlEtcFV6VSyZw5c7QY2b8Sq9SaNGmS2NnZiY+Pjzx+/Fhj/SVLloiDg4MyJpmuXISrhwAIDw+Xnj17ip+fn/To0UPy588vU6ZM0UgOenl5KdUW8U/qteVrn+GrV6+kSJEiGt0k379/L4MGDRIPDw9p2bKl0pVGW4nZqKgo6dmzp0ZCIzo6WrZt2ybZs2cXT09P5TdU/f9t2rSRP//8UyvxJubYsWPSpEmTBAmA9u3bi0qlku7duyc4LhQpUkQjoaNLPnz4IO7u7tKjR48Ej71+/Vo+fPigfG9mzJghJiYmShWqrom/Tbp16yZ58+aV+fPnK4kCdbdiT09PnRymIf5F+JAhQ6RmzZqyZs0a5Vxi9uzZYm1tLX379lWeo2vj6MZPJI0bN06qVasmefLkkdatW0tgYKCIxJ1XeXp6StasWZWxaL/2Gr/b3r175d27d8q/hw8fLhUrVhR7e3vp16+f7NixQ2P9/fv3i6+vr+TJk0ccHByU8w1tH+v27NkjuXLlUj5ztejoaPH19ZXMmTNLly5dlJvX27dvF5VKJZ6ensq62toO9+/fFysrqwQ9JkaNGiVZsmRRxoVTU8f5+fNnef78+W+L81vUBQUDBw6U8PBwOXr0qGTIkCFBF+FZs2aJm5ubqFQqKVq0qOTIkUNnbj6qv8MbNmwQS0tL6dmzp0Z14759+8TT01NcXFx0sjrwS2fPnpWMGTMmuFE9evRoMTIykh49eijjZqsTh0S6jolBot9o4MCBolKpZPny5crJ4rhx48TGxibBCaKI5piD2nbt2jWxsbGR/v37J5rwCw8Plx07doiDg4NUqVJFWa5Ld7y/dnL9PZ/xhw8fJCws7FeH9FO+PMGOjY1N9KQ7fmJNvR3iX6Ro07cuEsaMGSNFixaV/v37J7jTvX37dp3ZJ969e6fcyVZ/t8aPHy/FixcXkbgJeAoWLKiRHLxz5478+eefOnf3eN26dbJkyRKNZVu3bhVbW1tlTDu1+F2rdGX/njRpksZg8tu2bZN06dJJ06ZN5f379/L582eJioqS4sWLa4xrpwvU+8LGjRs1xkRr3bq15MyZU/z8/OTDhw8iEpecdXJyUsaH0zWvX7+W4sWLa3QfPnTokFJt5OTkJN26dVN+hyZNmiRWVlY6U5UT//cxOjpa2TZTpkwRlUolefPmlQULFij7/Z07dyRr1qxSv359nbz427Rpk5ibm0vXrl2lXbt24uTkJG5ubhISEiKhoaHi5+cndnZ2OrdPfGno0KFibW0t48aNk+nTp4ujo6NUrFhRqZy9dOmS1K1bVwwNDTUmsdKmlStXikqlknnz5olIXCLcxsZGZsyYIYMHD5bKlSuLi4uLrFy5MsFz37x5I5kzZ5bhw4f/7rAT5efnJ5UqVZLIyEjl+Kv+7Y+KipI///xTcuXKpZFIi3+M02ZydteuXZI9e3Zp06aNksQZN26cZMiQ4atdbHUpQX7t2jUxNDTUGM5GRKRatWrStWtX6datm8bxICwsTE6fPi2XL1/WGOtRFxw/flzSp0//1Z5Ex48fl1q1aomDg4NOjSeoFv8a4syZM5I1a1blvCP+uN558+YVW1tb6d+/v059l4j+FyYGiX6D+IkMdbekdevWyYgRI8Ta2jrRagNdmowgIiJCGjVqJB07dtRYHh4eLi9evJDbt28rB8ydO3dKwYIFpWTJktoI9bsMGzZMmblNfdB+9OhRoslZXfX06VNZsWJFggGOz5w589VuF/FPzrV5oh7/5GrGjBni4+Mj7dq1k5kzZyrL//rrL3FycpL+/fsrgznHp+3k4LVr16Ro0aLi4eEh+/bt06jqqlChgtKWTp06SeHChWXatGlK9YeuJNPUXr16JRUqVBAHBwcpWbKkbN++XZ48eSKxsbFSrVo1GT16tIgkjFubVSzxt/+7d++kbt26kj59ejlx4oSIxMW2detWSZ8+vRQuXFjq1q0r3t7e4ujoqDMn6vE/z+DgYGU2xqNHjyrLmzVrJhkzZhQPDw8ZOnSoeHl56VQb1NS/JyEhIWJtba0MlTFp0iQpXLiw1KhRQyZOnCjdu3eXIkWKyLJly5Tn6ko3sVevXomNjU2CWbbHjBkjNjY2EhAQIO3atVO6Favjvnv3boKqo98tsd/zBw8eSIECBZT2fPjwQdKkSSMDBgxQ1vn48aNMnjxZcuXKJS9fvtR6ZVpi7t69K/nz55dt27Ypy549eyY1a9aUChUqKDfATp06Jf369dP6sSG+IUOGiJGRkfj7+0v37t01hgA4f/68tG/fXlxdXTWqutS/C0OHDpVGjRrpxDbp1q2b5M+fP8Fy9Wd9584dSZs2rZJo04VzDfXNFJG4G18lSpSQ9u3bS+fOncXGxkajN4VaYhWn2hQWFiZ///23qFQqje/O2LFjRaVSSdOmTZUKwdatW2sx0u+zYMECqV27tojEHbe3bNki9evXFw8PD2Vm9927d0vDhg11amK5r42j7ObmJo6OjhrFEm/evBEfHx8ZOHCgztykIPpeTAwS/cdu3bolo0eP1jjINW/eXKytrcXc3Fw2b94sIgm7zHTr1k1nLv7UVSp+fn7Ksj179kjnzp3F0tJSTE1NpXnz5nL//n2Jjo6WjRs3iouLy1fHl9K2ggULSpEiRZS72/fv35eMGTN+dTIYXTR27Fhxd3eXRYsWKd+TESNGSIYMGXT2cxfRPLEaMWKEpE2bVlq1aiX16tUTMzMz8fHxUR4fOXKklChRQjp06KBM5KErOnTooFQQ2draSvv27WXo0KEiEtdFKf5JeteuXcXe3l5mz54tMTExWr/QS+xCLSQkRO7duydeXl5SqlQpKVKkiBw6dEh69eol9vb2OtOl6ktDhw6VYcOGybt378Tb21syZMigJAdjYmJk27ZtkjNnTsmdO7dGBYIuJWd79Ogh165dkz179kiZMmWkUaNGGrPVq79rderUkSlTpijLdaUNd+/elblz5yrVKXPnzhUDAwPJkSOHmJmZybRp05TJUmJiYsTBwUF69uypzZATFRsbK/369RMTExOlomXcuHFiZWWlUVnUqlUrKVCggMyYMSNBN29tUO/PT548kTVr1siqVavkypUrEhoaKoUKFZKwsDC5ffu22NvbS7t27ZTnHT9+XD59+qRR+ayLHj58KNmyZZPdu3eLyL83816+fCmWlpYyderUBM/RdnIw/r45YMAAUalUYm5unmDs3DNnzkju3LkTrRps0KCBVK5cOcHNv9/l7NmzSqJs7NixkjZtWmVs6S+PIdeuXRMLCwudmSTv0aNH0qBBA1m3bp2ybM2aNeLk5CRmZmYya9YsZbn6eOzr6yvFixfXmZvyb968EWtra9m6datMnjxZDA0NZc+ePTJhwgSxsrJSuhF//PhRRo0aJaampnLmzBktR/1tU6ZMEQMDA1m9erVUrVpVPD09pVGjRuLj4yOOjo7K5ITxk7rapv5+7N+/X1q2bCkDBgyQ9evXi8i/Q67kzZtXduzYIf/8848MHjxYnJ2ddfo3lehrmBgk+o8tW7ZMVCqVDBs2TCNh07VrV8mSJYv4+/trnIgMHz5cVCqVBAUFaSPcRH3+/Fnq1q0r9erVk5s3b8rYsWMlX7580rBhQ1m4cKGsW7dOzM3NZfLkySISN4i5Lh3Y1eKfzFaqVEmcnZ3l3LlzkjVrVunYsaPODFT+PT59+iTDhg0Tb29v2bhxo4wePVpsbW11uuox/sXSqVOnpGXLlnLo0CERibuQCwgIEBsbG2nevLmyXv/+/aVly5ZaT6apBQUFydKlS+XNmzfi7e0tDRs2lF69esmSJUukaNGiUqtWLWnZsqWoVCpZu3at8rx+/fppvapIRHMfuHTpkpw7dy7BTOFHjhyRfv36iY2NjdSoUUNUKlWiF9/aED/+LVu2SM6cOeX06dMiEvc7Va9ePY3kYFRUlGzfvl1JQKtp8/sU/723b98uhoaGcuDAARERZZb3L5ODrVq1EicnJ1m0aJHWkx5fGj58uKRNm1ZmzJihDLdw8eJF2bhxo8ZwBjExMfLhwwepVauWxoW5tj158kTWrl0ra9askYCAAJkxY4aSiLWzs1O6Rcf//fL29pbixYtrPTGo3h8uXrwouXLlEkdHR0mVKpXky5dP+vXrJxUrVpTLly9Lzpw5pW3bthqTmrVt21aZ5V1XJLZfvnz5UrJkySK+vr4iEtdm9bZwd3eXgQMH/s4Q/6fEziPUFV5du3ZNkDCoUKFCgt4Yjx8/lrJly2ot0TN69GgpUaKETJgwQT59+iRPnz6VjBkzSp06dTTWU/8WnTp1SooXLy7Dhg2T/fv3a73a6969e1KwYEHx8PCQLVu2KMs3b94sxYoVkxYtWijHDZG4XiRGRkYJxovTpsjISKlTp4788ccf8vHjR+nbt6+oVCqN44V6f1m3bp3kyJFDJ84x1L52jG3UqJE4OjpK69atler4hw8fiqOjo8YkXLpkz549YmRkJPXr15fixYuLo6OjjBs3TkTiErgeHh6SM2dOyZIli+TKlUunvkdEP4KJQaLfYNGiRZI2bVoZPHiwRnKwRYsWSrekmJgY+euvv8TU1FSnDirqk9wlS5aIi4uL2NjYiJWVlSxYsEBjQpRatWqJl5eXzl20qiU20UWpUqXE2NhYWrZsqa2wfop6m0RERMjEiROlcOHCkiZNGiUpqGsJzi8nO9m4caM4OztL7ty5lQoE9fbZvn27WFhYaIxR9rVuHL9bUFCQmJiYKBeijx49kjp16oi7u7usXr1aYmJixM/PT7p16yYqleqr4xdpS/zvxbBhwyRXrlySK1cuMTc3F39//wRVmSdOnJCxY8dKgwYNdKY6TW3btm3Sp08fGTlypIj8u19HRkZKvXr1xNbWVqNycOvWrWJlZSXe3t5ai/lLa9eulSFDhmhUYov8mxxs3LixRrfi5s2bS4ECBWT27Nk6d+Olf//+ki1bNpk6deo3q3uHDRumUxew6oRa/vz5xdDQUAoUKCCLFy8WPz8/SZUqlVJFntjx49mzZ1qJWS1+UjB16tTKsAs7duyQatWqSbly5SR37tzKDLfxDRgwQEqXLq1TY5DFr4x7/PixfPr0SRnPdP78+ZIqVSpZuHChsk5UVJQ4OTkps4/rmjVr1sjGjRuVfw8bNkxUKpVMnz5d6Xr47t07KVKkiDKhllpMTIzWxqzs16+fZMyYUdavX6/sp5GRkTJz5kwxNjaWWrVqaST8nz59Ko6OjmJubi6Ojo7SsWNHrcyCq95H43dvLlu2rFSrVk0jObhmzRopUaKENG3aVK5duyZjx44VExMTnTrvVps1a5ZYWVkp2+Gvv/4SlUqlVKyp9e/fX8qUKaMzE/6pt8WpU6dkxowZMnv2bI3Z27+cWG7QoEFStGhRnZzc7+HDhzJ79mzlPPb+/fvKxDXqYVZERC5fviw3btzQqd9Uoh/FxCDRfyj+hfi8efMSTQ62bNlSChUqJG5ubmJmZqYTJyfXr1+XwYMHy4MHDzQSfffu3ZOTJ09qHPhiY2MlPDxc6tSpo3OzSwYHB8uZM2c0PlN1e16/fi1Zs2aVzJkzS+HChZWLWW0nn75H/O6of/31l5iZmUn58uVl6dKlysWVriQHd+7cKRkzZtTowrZnzx7x9PQUQ0NDjbHGROLGxcqaNatGtZ2I9rfL5cuXJXXq1MoA4Orv0ePHj6Vu3bri6uqqcbJ+8+ZNrcT5PUaOHCmZMmVSxlhq2rSppEuXTiZNmpSgAir+/q/N5KB6+8fExMibN28kW7ZsyhhLaurvfGRkpDRo0EBUKpVcvnxZeWz9+vWSNWtWrSd0ROIG5nd2dhYzMzOZMWOGiGgOeL99+3YpU6aMVK9eXWOGxnr16omLi4vOdHeL//3o27evZM+ePdHk4NatW6Vnz55iZWWlMzNOfplQ2759u1StWlWKFy8uZ86ckREjRohKpVJ+o75MPOiCR48eiY2Njfzxxx8ay+fOnSuWlpaydetWqVSpkhQpUkROnDghW7Zskd69e0vatGl1pjrHz89PY6iC4cOHS6FChaRw4cIyaNAgpaJZnVhr3LixdO3aVapUqSIFChTQuZsWInFDM+TLl0+qVaumMXOsegI6Nzc36dOnj9StW1cKFy6sM21YvXq15MqVK9FKxYcPH8rUqVMld+7ckj59eilfvrzUrl1bChYsKPXq1RORuGOEts494k/+oHbz5k0pW7asuLm5aSQH165dK2XKlJGcOXPq3M14Ec3zHWdnZ2nYsKHy7/79+4uRkZHS/Xz48OGSOnVqnellpI5948aNYmVlJe7u7lKsWDFxdXWVadOmaay7ZcsW6d69u1haWurM7MnxXb9+XRwdHSVXrlwavXGePn0qo0aNEnt7+wRJfSJ9xsQg0S927do1adasmezduzfBgXrevHlibm4ugwYN0hiUtnHjxpIuXTqdOLB//vxZXFxcRKVSiYODg/Tt21dWr16tsU78k5aYmBgZOnSoZM2aVW7duvW7w/2qq1evStmyZaVGjRpSv359iY6OVuJ++PChZMqUSbp37y4iIhUrVhRnZ2edvNN35MgRWbNmjRw9ejTBBenIkSPF1tZWtm/fLiNGjBAfHx9ZtGhRoifI2hIWFiazZs0SZ2dnjXH3AgMDxdPTU0qUKKFRVfH+/XtxcHBIMEOuNl29elVsbGzE3d1dWRZ/xvAnT56Il5eXlC9fXiNuXUnOnjhxQklUXr58Wdzc3GT79u0iEndibmlpKXXr1hWVSiWTJk3SmRli1eJ/jurE5YMHD8TV1VXy5cunceEdPzk4aNAgjQvumJgYef/+/e8J+gvx2xAVFSWxsbGyZs0aKVq0qBQoUEBJpMVPDq5bt06j+6daYpPx/E7h4eEa//5fycGDBw9KnTp1pEaNGjoz0+TXEmrz588Xc3NzuXnzpkRFRSnJqBUrVmgp0m+7f/++uLi4SJ06dTSqS/ft2ye2trZy9uxZOXnypFSpUkUyZ84sjo6OUrlyZZ041xCJq451cHCQdu3aybt372Tz5s1ia2srK1eulE6dOkmFChWkTp06SnJw69at4uHhIfXr15eOHTsq+7e2k7WJ3bi6cuWKlC1bVjw8PJTfW5G4MWhVKpVyMy/+7L7aNnz4cPnjjz+UfTw6Olq2bt0qrVu3VsaDu3nzpvj6+oq3t7f07dtXJ455V65ckdy5c8uAAQOUWdzVM4zfuXNHKlSoIFWrVlXG9BaJmzW6TJkyys0jbfvyvE39fZg4caIUK1ZM6V0hElfxmyZNGnF3dxdzc3OdS2wePXpUMmfOrMzIrZ6N2N7eXsaOHSsicceRUaNGSdWqVXVmG3zp6tWr0q5dO+XGaXzPnj2TsWPHSurUqZVhlIj0HRODRL/Qx48fpXTp0qJSqaRQoUJib28vXl5eMmzYMKVrxaZNmyRdunTi6+urMQ6LLiWlJk6cKFOnTpV9+/aJr6+vWFpaStOmTcXPz0/jBHjdunXSrl07sbGx0am7fVeuXBELCwulOlN9shobGysRERHi7u4unTp10ui6VLRoUSlbtqzOJHNE4k7SXVxcpHr16rJr1y6Ni59Dhw5JhgwZlLuY4eHhMmzYMKlSpYoEBARoK2QN6u/K+/fvZebMmVK0aFGNcd7++ecf8fLykhw5csiwYcNk1qxZUqdOHcmXL5/WL/TUgoKCxNTUVPLmzSvp0qWTFStWKN3bRCRBcrBKlSoJZjXVpvv370upUqWkTp06cvfuXYmIiJBFixZJZGSkHDlyRDJnzqyM9+bj4yMWFhbi6+urtQTal76clEndDpG45KCTk5NUq1ZNGavyy+eIaP+CO34806dPl4kTJyqzPq9bt05cXFykRo0aSjewxCadiomJ0Yl94smTJ/LHH39ofN4imomZPn36KMnBsLAw+fz5s9y8eVOnEs7fSqhZW1srY+99+PBBfH19RaVSyZo1a7QV7jfdunVLatSoIe7u7nLt2jV5//69ZMiQQfr27aux3vXr1+XVq1c6U22qNnXqVClbtqx06NBBBg4cKMuXL1ce+/vvv6VSpUpSq1YtZb//WgJFW+KfE305SdPVq1eldOnS4uHhoTG0RM+ePaVy5crKv7W9b8fGxkpsbKz8+eefUr16dQkPD5eIiAhp3LixlCtXTlxdXaVVq1aSLVs2jTGA49PmuVPPnj1FpVJJ7ty5JXfu3OLg4CDlypWThQsXys2bN+Xx48dSrlw58fb21qgc1JXjnHriryVLlmicX4jE9UqwtLSU4cOHayzv27evGBsb69S5t7o3y+jRo5VeIg8ePJCcOXMqlb62trYyc+ZMEYn73ml7nNb/5fr169KxY0fJli2bLFq0SOOxJ0+eyKRJk3SqKIIoKZgYJPqFoqOjZceOHVKoUCFxdnaW48ePS9u2baVIkSJiZ2cnRYoUkWXLlomHh4dkypRJBg4cqJMzyB4+fFjSpUundCd59uyZjBgxQlKnTi2lSpWSBQsWiJ+fn/To0UMaNmyozDipC16/fi3lypVTqgHV4p+837hxI9Exo+JXcWrbkCFDxM7OTk6ePJnohdy9e/eUkxH1CfnHjx9lw4YNvzXOr4nf9VMkrnJw5syZUqRIEY3k4NGjR8XDw0PMzMzEzc1N/Pz8lIskbV8sXb16VVQqlXJC3rlzZzE1NZUVK1ZoVE3FTw5WrlxZatasqVMX3wsWLJAqVaqIj4+Pxtg+7dq1k1atWimJqK5du4qzs7OULVtW6123vzRgwADJmDGjLFy4UOMk/N69e+Lk5CTu7u4aYxjpon79+omtra0sWLBAqfqLiYmR1atXS7ly5cTT01NJDmo72fE1d+/elTJlykjNmjUTzED6ZeVgnjx5ZMyYMToz7tWXvpZQ69+/v8Z679+/lzFjxujUce5Lt27dEg8PD6lYsaJYWlpqzPqcWKJZF8S/MTdz5kwpW7as2NraJuihsHLlSqlSpYrUrl07wRANuvQ7NWfOHKlevXqCbrhXrlxRElXxk4O6MnZufCdOnFBubJubm4uLi4usWbNG+T0aN26c5M6dW16+fKnlSDV9+PBB2rRpI+nTp5dDhw7JmjVrpGfPnpI/f36xsrKSP/74Q2rUqCE2NjZSsmRJZQxjXfnsr127JrVq1RJDQ0OpUKGCDBo0SN69e6ckwceNGyeFChVSZolW0+bNlvhjXUdHR2skht+/fy+BgYHy6dMnJaksEtdjwdLSUkxNTZXJO3SF+rtw7949OX/+vMbETFeuXJGuXbtKvnz5ZPHixRrP0/Z5KtGvxMQg0S8WFRUle/fuFQsLC+nSpYuyfMeOHTJu3DgpUaKElCxZUlQqlWTOnPmbA7VrU9++feXPP/9UEiANGzaU/PnzS4sWLcTNzU2MjY2le/fuOpUAEYlL5uTOnVv++eefBHew1XfFv/y3rh3YDx06JPny5ZOtW7dqLP/WSeyXbdDmCe+Xn7t6ooSPHz+Kn5+fFC5cWCM5eOzYMalXr57UqlVLtm3bpryGNttw48YN8fHxUcZ/U/tfycFnz54lGFhbW+J/fv7+/lK+fHnx8fFRLq4rVKig8RtVr149CQoK0rkL1mPHjkn27NkTVKmpL1bv378vxYoVU2YZ10WbNm2SLFmyaHT5ip88X716tZQvX15KliypzOyrq9QJterVq2skB2NjYzX2/Ro1akjZsmV1qlLwS99KqMX/TdWVfeFbbt26JVWqVJHs2bPLP//8oyzXxdjjV0Wp9+vZs2dLzpw5pVq1agkST6tXr5YiRYokqILUpkOHDsnIkSPF19dXTp48KVeuXJFs2bJJ48aNE3Tt3Lhxo6RJk0bKli2rVKh+eT6iK4KCgmTKlCkyb948jSFYROKGw6lYsaLOnfeJxPWaqFmzpuTMmVNOnjwpInHjTF+8eFF69OghjRs3FpVKJVZWVjp1Ezi+ixcvSvv27SV37tySLVs26du3r1y+fFnOnj0rWbNmVXqIaPucVf07f/36dalfv764ubmJh4dHgmEmTp8+LYULF5arV6+KSNyYj7Vr15axY8dqfebq+NTf8c2bN0uhQoUkS5YsUrp0aWnWrJmyzqVLl6Rr165SsGDBBBOGESUXTAwS/QdiYmJkz549YmFhkWAMo7dv38qzZ89kxowZOl1+vn79eilTpozExMRImzZtxM7OThkf6tq1azJ79mydGS8qvpUrV4qhoWGCirX4Pn78mOjg2rpixowZ4uzs/NUZ2r7VNm2LH9PkyZOlcePGki9fPpk0aZJcu3ZNoqKiZNasWVK0aFGNMQcPHTokXl5eUrVq1QQz7v1unz9/Fi8vLzExMVEuYON3X/tfyUFd8mVysGLFiuLj4yMhISEye/ZsMTAwkMaNG4uzs7PGQP66cMGq/jxXr14t+fLl0+j2pY5PXQ1148YNadGihU7uEyJxXYgrVqwo4eHhSru+/Iz9/f2lQ4cOOtuG+L6WHBSJ+30dNGiQtG3bVqcu/r5GnxJq/8vt27e/ul10xbZt26RKlSoiEtcFNGvWrEqiadasWVK6dGlp27ZtguPf/v37deY3duHChZIhQwZxc3OTbNmySebMmeX06dNy4cIFyZUrlzRq1EgjObh27Vpp0KCBdOzYUS/278Q8e/ZMnJ2ddSo5+6WIiAipVauW2NraKsnB+E6dOqX1MVr/l4iICAkNDZW+fftK2bJlxcjISHx9fcXGxkacnZ213v1Z/f29cOGCpE+fXtq2bSsDBw4UJycnqVmzpsbv5pkzZ8TKykrpgjt48GDx8vKSN2/eaCX2b9mzZ4+kTZtW/Pz85NmzZzJ79mxRqVRSq1YtZZ3Lly9Ly5YtdWoCMKJfiYlBov9IbGys7NmzRywtLTVmFNPVLmKJqVChghgYGEjmzJl1ZrDy/+X48eNiamr6zS61s2bNkmrVqml0ZdIF6iRT27ZtpXz58iLy9eTfhQsXNC5idc3AgQPFzs5Opk6dKvPnzxcLCwupX7++Mii4ekIS9WyGInHdmNRdxrR98nvp0iVxcXGRAgUKKBVP8b8v6uTgypUrE4wJpGu+TA6WK1dOGjZsKM+fP5f58+eLj4+PdOjQQUmyafPiOyAgQGbNmqUR8+bNmyVr1qxK1YFI3H4RExMj/v7+CbpXafvCO7H379q1q+TPn1/5d/zu8ocPH5YXL14kmNRJ1yWWHIyMjJSuXbuKSqXSm2OGiH4k1L7XrVu3pFatWlK6dGkJDAzUdjgJXLt2TWxsbCRfvnySLl06jS57InFjDrq6uiaaHBTR/g2YhQsXirGxsXID69ChQ5IuXTpl7L2AgADJlSuX+Pj4yIYNGyQ4OFjq1KmjjOUqoh/7t9qTJ0/k2LFjUqRIEalTp46yXFcT5xEREVK7dm1lKBYR3Y31f3n16pVyQy916tRiaWn51RvGv4P6e3v58mUxMzNThlmJjY2VqVOnyp9//ilv376Vjx8/ikjcEDJt2rSRjBkziqOjo1hYWOjMrPTxvXr1Sry8vJRJRF6+fClZs2YVT09PyZEjh3h4eCjrXr16VafGhCf6lZgYJPoPxU8ONm3aVNvhfDf1SdTOnTslb968ykxu+nBy9eTJE7G1tZU6depodBeJH3ufPn1k4MCBOtWe+fPnKzMXjhs3TszMzJQxrb6MMyIiQho2bKiz3RnOnDkjefPmVS5Kz5w5I6lSpZJly5Yp63z8+FHGjRsnzZs310iWnzx5UqtdceNXY16/fl1cXV2lePHiygDZ8ZOD3bp1E5VKJWvXrtVGqD8k/ndoyZIlSnJQfYKrPuHX5o2LpUuXSp48eaRhw4YaE0IEBQVJ5syZZcCAARqD+3/+/FkqVaqkcXGibfEv+AMDA5UJE44dOyaZM2dOMK7Sq1evxMPDQ9atW/db4/xV4icHDx8+LP379xczMzOdGhD/e+l6Qu1HXL9+Xby9vXVqDOP4+0ajRo1EpVJJuXLllN/U+PvvtGnTpHz58tKgQQOdmpzg8OHDolKpZOTIkRrLM2fOLK6urkol1M2bN6VixYqSNWtWyZQpkzg7Oys3XnThd+p7vX//XmrVqiUuLi7Svn17ZbmuJDa/9llGRkZKnTp1xN7eXuNYoi++bNfLly/l1KlTyvFEm549eyaZM2eWqlWraizv3r272NjYSJ48eSRDhgwyevRo+fz5s9y/f1+2bt0qs2bNkjt37mgp6v9twYIFcuHCBQkODpZChQpJx44dJTIyUoYPH678VhEld0wMEv1H1He1Y2JiZO/evaJSqaRNmzZajurHvHjxQvLkySNDhw7Vdig/ZOPGjWJiYiLNmjXTqDJSd3HLnj17gkHMta1Lly6SJ08eiYqKkgsXLki2bNmkfv36SnIzfsLmyZMnUqlSJdm4caO2wv2mkydPSokSJUQkrguVubm5zJkzR0TiLjT27dsnInHbI7FJYLQhfpfg+IP19+nTR1QqlRQtWlS56IufHOzbt2+CijVd9WVysHz58tKsWTMlEavNC9bly5eLubm5rFixItFuRnPmzJHUqVNLhw4d5O+//5Y9e/ZI1apVxcnJSevfHbX4F8uDBg2SEiVKKBWlL1++lG7dukmJEiVk0KBBEhwcLKdOnZJatWpJ8eLFdaYNP0OdULO0tBRjY2OdHefxe+hiQu1n6VpFvNqtW7ckMDBQ1q1bJ1myZJHq1asrYy3H3w8mTpwobdu21ZkklEhc7OXLl5c6deoow5HUq1dPjIyMpGbNmlKmTBmpUaOGzJ8/X7Zs2SL//POPHDp0SDkf1Mf9/Nq1a7J//37l39raHg8fPpTFixfLsmXLvvkboz6ORURESKVKlSRv3rwJxr6jnxccHCzVqlUTNzc38ff3F5G4YWPMzc1l3rx5EhAQIF26dJFUqVJpTLajLxYuXCju7u7KTdNly5aJq6urVKhQQe7fv6/d4Ij+Y0wMEv3H1OPwHThwQG7cuKHlaH7cihUrJE2aNHLq1Clth/LdYmJiZN68eWJoaCj58+eXVq1aSadOnaROnTpia2urU9Us6pPYd+/eSe3atZUEWv/+/cXKykp8fHzk9u3bIhKXSDt37pwULlxYo3u6Nj1//lwuXbokK1askMuXL8ubN2/k2rVrkilTJpk/f76kT59eo7LxwIEDUq9ePY1kmrYrKJ48eSJ//PFHgsktJkyYINbW1rJo0SIpUaKEFChQQElaxR9zUJ/E/6wXL14spUqVUmbZ09Z2ePjwoTg7OyvjEKlFR0fLzZs35d27dyISN4FHlSpVJF26dFKsWDHx8PDQie7PXxo+fLjY2trK/v37NbrEv3jxQsaMGSNZsmQRc3NzyZcvn1SoUEEn2/Cjbty4IXXq1NHJcWd/lK4m1JID9ay36qTapUuXJHPmzFK9enWNGwLqoUB0cTxddZVszZo1pVy5clKsWDG5ePGiREZGyqFDh2Tu3LmSPXt2yZAhg/To0UN5nj7v32raOkZcvHhR7O3tpUyZMmJlZSW1a9dOMFRBYrGFh4frzGRgyYF6P3z27JnUq1dPqlatKg0aNBArKysJCAjQWDdz5szStWtXbYT5TervyYULF2T9+vVy4cKFBD2KcufOrfy7f//+MmDAAKV7NFFyxsQg0X/o77//ltSpUysziekjdXWaPp5cnTp1Sry9vcXJyUnKly8vAwYM0KkJX969e6fMvvj582cZNWqU1K9fX3m8Z8+ekiVLFkmfPr00aNBAypcvL0WKFJEGDRoo62gzqbZx40bx9PSUjBkzSrp06cTMzEzq1Kkjp06dkl69eolKpZIRI0Yo66sHBm/QoIFOXejdvXtXypQpI56ensr4YuPGjRMrKyulUuLatWtSrFgxKVq0qE7Psvo94n9natasKXXr1tVeMBJ3gm5vb69R3bts2TJp2LChGBsbS6ZMmWT8+PEiEldx+vjxY3n69KnOVJvGd+PGDSlQoIDs3btXRERCQkLk/PnzMmHCBKVS9sOHD7J//365dOmSTnTh/lXiV9oSJebhw4dSsWJFjZtFV65cEXt7e6lcubLs2bNHatSoIcWKFVP2DW3fOErMrVu3xM3NTdKnT5/oUBJv376VgICAZJEM1LagoCAxMzOTgQMHSnh4uBw9elQyZMggO3fuTHT93r17y9atW39zlCmHer98+vSpNGjQQNKlSyd9+vRRHo+KipK3b99K6dKlZcaMGdoK85u2bt0qpqamkj9/flGpVDJkyBDlBnxAQIA4ODhI+fLlpVGjRmJubq4M60OU3KlEREBE/4mnT59i5MiR6NevHxwcHLQdzk+LiIiAqamptsP4KTExMUiVKpW2w0hg5syZOH78OC5evIjdu3cjZ86cCA0NRZEiRdCqVSuMGjUKALBv3z78888/CAoKgqOjI4oVK4YmTZoAAGJjY2FgYKCV+BcuXIgBAwZgyJAhcHJyQvHixTFr1iysWrUKIoIWLVrgypUrOHHiBEaOHInQ0FDs2rULT58+xYULF2BkZKTV+L90+/ZtdO/eHSYmJrC1tcWWLVvw999/w93dXVnnxo0b8PT0hJ2dHY4fP64zsf8MEYFKpUKXLl3w5s0bLFu2DMbGxlqJ5f79+6hZsya8vb3Ro0cP9OrVC1evXkX+/PnRpEkTHDx4EP7+/ti/fz9KlCih8Vxd+g4BwLNnz1CzZk106dIFjo6OWLJkCU6fPg2VSoUbN25g/vz5aNWqlcZzdK0NRL/C1469AwcOxPLly3Hnzh2kTp0aAHDv3j14eHjA1NQUadOmxeHDh2FkZKT8Tumiu3fvokuXLjAwMMDgwYNRrlw5AEB0dDQMDQ2V9XT1HEQfXL9+HUWKFMGgQYOUcyIAcHd3R758+aBSqZAjRw707t0bABAcHIwOHTrg6NGjePjwIVKnTq2z3x99pj5mvXz5Ep07d8br16/RokUL5dg2YsQILFmyBAEBAciVK5eWo/1XbGwsIiIi0KhRI9SuXRtNmjTBunXrMGTIEHh7e6NXr16wt7fH5s2bsXbtWhgbG2Pw4MEoXLiwtkMn+j20mpYkSgGSQyWIPotfbaArlQf9+vWT3Llzy5o1a5SBsdWVBVu3bpVq1arJ6dOnNZ7zZezarLhbsGCBGBsbJzrG4Zo1a6R48eJSoUIFWb16tXTu3FmyZs0qlStXljZt2ij7gy7uFzdv3pRq1aqJmZmZMjudiOZnffPmTbl37542wvvlXr16JWXLlpXLly9rNY6PHz/KwIEDJUeOHGJlZSX58uWTrVu3KtW0IiI2NjYas3rqgsT2wVevXkn9+vXFxcVFDAwMpHv37rJr1y55+/atuLu7y9ixY7UQKZH23L9/X2MCkdDQUClevLhMmTJFYmNjlWPf58+f5caNG3pVRavuVlyjRg29n81a14SFhcnff/8tKpVK6VouIjJ27FhRqVTStGlTcXNzE5VKJW3btlUev3fvnjx9+lQbIScr48aNSzC8R3zxKwfr1asnFSpUkDVr1sjw4cPFxMREp8aaVZ8/h4aGSkREhPTr109jvMAVK1YoXZ8fPXqkLNfXIWOIfhYTg0REv5Gfn59kzJhRTpw4kejjd+7ckTp16sjChQtF5N+LI13pkvTlrIyxsbESGxurcRE3Y8YMSZ8+vZI4DA4O1ngNXb7gu3Pnjri7u4uHh4fGbIa61PX5V9LGoOyJfZZv3ryRW7duycGDBxN812/fvi3FixfXqYHM47fhxo0bcvv2bXny5ImIxI27GRgYmGBc1tKlS8vUqVN/a5xEv9vRo0dl8uTJMnnyZOnVq5fkzJlTvL29ZfPmzcq+3blzZ6levbrynC+PCfr0e3vr1i2pWbOmlChRQi5evKjtcJKFN2/eiLW1tWzdulUmT54shoaGsmfPHpkwYYJYWVkp3Yg/fvwoo0aNElNT0wQ3UylpunfvLiqVSlauXPnVdeInB729vcXGxkbMzMzk7NmzvyvM77Zx40YpUaKEZM2aVbJkyZJgTOm///5bsmfPLq1atdKpIYeIficmBomIfoPY2FiJjIyUWrVqyfDhw7+57oIFCyRdunTKmCe6RD0rY926deXIkSMaj8W/mCtUqJB06tRJRDTHHtOVqs1vUVeBVK9enVUgv9i3EmqJUU/KU6VKFZ1Jjsf/Do8YMULy588vefLkETs7uwTJy48fP8rt27elRo0a4uzsrNNJcaKkWrhwoWTIkEGKFSsmFhYWUrRoUfH09JQRI0aIsbGx1K9fXxYuXCg3btwQc3NzWb16tbZD/iWuXbsmvXv31quEpi6LjIyUOnXqyB9//CEfP36Uvn37ikqlEkNDQzlw4ICI/Ps7vG7dOsmRI4fcvXtXmyEnS0OGDBEjIyP5+++/v7pO/ORgq1atNMYK1hVXr14VGxsbGTp0qPTv31/s7e2lQYMGCRKYixYtkgIFCigzEhOlNBzUhojoN1CpVHjz5g2OHDmijFciXwzxGhsbCwBo3LgxunXrhlmzZiE8PPy3x/otDg4OWLx4MSIjIzFmzBgcO3ZMeUw9ls+7d+8QERGBTJkyAQCMjIwSrKPLHBwcMHPmTBgZGaFv3744efKktkNKFkREGUtv5MiR8PLygoeHB0qUKIF9+/ZprPv27VusWLECPj4+ePToEfbs2YNUqVIhJiZGG6FrUH+HR4wYgblz52LKlCkICAhAyZIl0bBhQ6xYsQJAXHv9/f3RtWtXRERE4NSpUzA0NNSJNhD9aosWLUKXLl0we/ZsHD16FBs2bIC1tTU+fvyIXr164ciRI8ibNy/GjRsHHx8fmJiYYOfOnYiOjlaOffrK0dERU6ZMgYGBgd63RRcYGxujWrVqOHjwIF68eIFJkyZh1KhRiImJQWhoKIB/f4fPnj2LTJkyIX369NoMOVkaPXo0+vbti1atWmHlypWJrqP+zmfOnBmLFi1CgQIFfnOU33bp0iWsWbMGHTp0wF9//YUJEyZg+vTpePToEaZPn45z584p67Zp0waBgYGws7PTYsREWqTlxCQRUYrx4cMHsbOzk9GjR391nZs3b0rBggVlzJgxUrFiRY3xTnRJYlV16jv4Fy5ckEqVKimzsOpDlWBirl+/Lt7e3vLw4UNth5Ks+Pr6ip2dnezcuVOePHkitWvXlrRp08ry5ctFJO774ufnJw0bNpS2bdvq5LiU586dk4oVKyqzD2/dulUsLS3Fzc1NDA0NZcWKFSIi8vLlS9m0aZNS7ahLbSD6VRIbYkJEZPz48WJnZyfPnz9XlkdERMioUaPE09NTTE1N2f2WNMQ/X3B2dpaGDRsq/+7fv78YGRkp3VuHDx8uqVOnlqCgoN8eZ3KmHiJGbdCgQf+zclAXBQcHi6enp1hZWWmMQykSV2laokQJadmypZw8eVJZrq/nq0S/AhODRES/yYcPH8TFxUVcXV01ugnH734UEBAgjRo1ktevX0uVKlVk2rRpWoj0+8RPDqq7FUdFRYmnp6fUqlUrWXSrioyM1HYIycr3JtTevXsn9+7dU07Std2N+MuLhfv378uMGTMkNjZWDh06JBkzZlQmR6latapYWFjInDlzNJ6THPYHosTEH2Lin3/+UZZPmDBBcuXKJc+ePRMRzX0gNDRU6tWrJ23atJHPnz/zgjwF+3KSB/UNlIkTJ0qxYsU0zpcGDBggadKkEXd3dzE3N9fJ8ez0lXofjImJSXDMHTBggF4mB7dt2yZVqlSRbNmyJRj+ZsOGDZI7d27p0KEDJxohEiYGiYh+q0OHDomhoaG0aNEiwZg4T548kVKlSkmXLl1EJG4A7vgzs+oidXLQ09NTjh07JvXr15cCBQoo4woyGZKy/UxCbe7cud98DW2aNm2aMqnIq1evRESkWbNm0rFjR+VCqkWLFuLo6CjlypXTqdiJ/kvqY4G7u7sykZCJiUmC2evj7xOjRo0SNze33x0q6ZB79+6Jl5eXLFmyRD59+qTx2OPHj8XS0jLBuMx9+/YVY2NjOX/+/O8MNVlT75f79++X5s2bS40aNcTX11fevXunrKNODq5atUpbYX6Tug1ffo927dol7u7uUqNGDY1J5UREtmzZIvfu3fttMRLpMo4xSET0G1WuXBkzZ87EqlWr0K5dO8yaNQtnzpzBnDlzUL16ddja2mL27NkAAAsLC9ja2mo54m9Tj8enUqlQuXJlXL16FUFBQTAyMkJ0dLQyphylTOpxoKZPn47Tp08jR44caNKkCVQqFfz9/eHl5YVOnToBAOzt7ZEpUyasXLlSY/xNXRqXcvXq1Rg6dCgAwMbGBh8/fsSVK1eQMWNGpEqVCtHR0Xj37h2WLFmCI0eOQKVSJRhLlCg5Uh8LUqVKhUaNGqFGjRpYvHgx6tevrzGuZvx9Ijw8HE+ePMH79++5n6RQERERiI6ORvv27VGjRg0MHjwY79+/R2RkJOzt7dG/f39s2rQJN27cUJ4zadIkPH/+HM7OzlqMPHlRqVTYsmULvL29kSpVKlStWhWTJ09Gjx49lM9+/Pjx6NevH/7880+sX79eyxEnpFKpsHPnTtSqVQuNGjXC+PHjAQAeHh7o0qULgLhxE48fP648p27dusiZM6dW4iXSNbxiIyL6zTp27Ijt27fj+fPnGDp0KEqVKoWVK1eievXq2LZtG4C4iUh0KSHyLQ4ODpg8eTI6duyIK1euKElBQ0NDbYdGOkLfE2rqWIYOHYp3797hxIkTAIA0adKgatWqmDBhAnr06IEyZcrg3r17cHFxgUql0qv9mCipHBwcMGPGDFhYWCBfvnzIkycPACBVqlQJkv0PHz7EvXv3sGrVKqRNm5b7SQrl6OiI7du349y5c8ifPz/WrVuHQoUKYejQobhy5QqqVauGsLAw3L17FwCUJLOVlZU2w052rly5gn79+mH8+PFYsmQJOnfujNSpU2PZsmXo1asXbt68CQAYM2YMfH19UahQIS1HnNDJkyfRpEkTFCpUCNHR0fD390fjxo0BAHXq1EGnTp1gZGSEPn36IDAwUMvREukelejSmTcRUQoSGhqKiIgIvH79GtmyZUO6dOkAxCUF9bnSjklBUhMRqFQqbN++HWPGjMHUqVPh6uoKAOjXrx/8/PzQrl07nDhxAlFRUTh37hxSpUql9X1AHfeXQkJCULVqVVSoUAGzZs0CAHz48AFjx45FUFAQ7OzssGDBAhgZGSEmJgapUqX63aETad2dO3fQrVs3AHHJ9LJlyya63sePH5EmTZrfGRrpsMjISISHh2PMmDEIDAzE6dOnMXjwYPj5+SFr1qw4cuQIzM3NtR1msqJOAxw9ehSHDx+Gr68vnj59inLlysHLywstW7ZE2bJlUb9+ffTv31/nEoLxj9V79+7FhQsXMHDgQISFhWH37t3o378/ypQpg7Vr1wIANmzYgHXr1mHy5MnIli2bNkMn0jlMDBIR6ZCvJSSI9EFyS6itX78eHz58QKtWrZRl27ZtQ7t27bBu3TpUrFhRWR4eHg4zMzMATI4T3b59G7169cLLly+xePFiFClSRHmMxzn6X0JCQrBjxw4sXboUZ86cgYmJCW7evIkMGTJoO7RkQb0Pqo9bb9++xePHj1GwYEE0bNgQadKkwbx582BiYgJXV1ecOnUK3t7eWLlyJYyMjLQdPoB/23D69Gm8ePECGzduRJYsWTB27FgAwKdPn7Bt2zb0798f5cqVw6pVqwDEnXswwUyUEBODRERE9Eslh4RaSEgIGjRogOfPn8PS0hLDhg2Ds7MzMmfOjOrVq6NixYoYMmRIgpiZ9CCKc/36dSxatAiTJk3S6yp4+n2+/P0MDg7GgwcPYGNjg1y5cmkxsuTnzJkz8PX1xYwZM+Dg4AAgLpnm5uaGVq1aoV27dgCAXr16oXbt2rC3t0fevHm1GXICW7ZsQaNGjZAlSxZ8+PABjo6OOHz4sPId+vTpE3bs2IFWrVqhYcOGWLJkCY/RRF/BxCARERH9MvqaUEus+/Lr16/x7t079O7dG8+fP0d4eDimT5+O7du3Y/369Thz5gwyZsyopYiJ9Ie2hwcgIk0rV67EzJkzkSlTJkybNg05c+ZESEgInJycUKNGDbRp0wbbtm3DihUrcOnSJZ0Z11F9rvD+/Xv8+eef+OOPP1ChQgUEBQWhffv2KFu2LDZt2qSs//HjR+zduxeFCxdWEqBElBATg0RERPTTkkNCLX4bLl++jKioKGTIkAFZs2ZV1jl69Ci2b98Of39/lChRAnv37sWUKVPQq1cvbYVNRET009asWYMFCxYgderUmD59OvLkyYN9+/ahbt26yJw5Mz5//oxt27bp3AzQhw8fxl9//QVzc3NMnToVefLkQWxsLP755x80atQIrq6u2Lx5s7bDJNIrTAwSERHRT0kOCbX4bRg+fDhWrlwJIK4L26xZs1CrVi3Y2Ngo6wcGBiIgIADnzp3DmjVrdKbrMxER0bdcvXoVtra2GmM1rlq1CosXL4aZmRlmzZqFnDlz4vHjx3j79i1sbW1hZ2enxYgTd+XKFVSvXh0vX75EYGAgXFxcAEBJDjZr1gz58uXDwYMHtRwpkf5gYpCIiIh+WHJLqI0aNQrz5s3DsmXLUK1aNTRr1gzbtm3DsGHD0LZtW1hYWCjrxp8gRZfGRSQiIkrMw4cP0bBhQ+TOnRszZszQOD77+/tj6NChKFOmDMaNG6cXXW6vX78Od3d3ODo6YtWqVUp7YmNjceDAAXTt2hWHDh2Cvb29liMl0g9MDBIREdFP09eEWmBgIKytrZE3b15cuXIFvXr1Qo8ePVCrVi1s3boVrVq1QoUKFbBt2zZMnDgRrVu31pkxloiIiP6XL8funTBhAnbt2gUHBweMGzdOo3LQ1dUVN27cgJubm07OPvzo0SOEhobC1tYWadOmhbm5OS5fvgx3d3c4Oztj+fLlSnJQRBAeHo7UqVNrOXoi/cFRgImIiOi7BQYG4tatWwDiuvMcPXoUCxYsQLVq1bB161bs3LkTlStXRv/+/bFo0SK8efNGea46KQhAq0nBBw8eoFevXujXrx/u3bsHBwcHNGrUCO7u7jh69Cg6d+6MUaNGYcuWLfjjjz8wZswYzJw5Ex8+fNBazERERN9LnVALCAjAkiVLEBsbiwEDBsDLywvXr1/H4MGDleNzREQEChYsiCFDhmD69Ok6lxTctGkTKlSogLp166JQoUJo27Ytjhw5gsKFC2Pfvn24cOECWrdujVevXgEAVCoVk4JEP4iJQSIiIvouySWhliNHDrRp0wYfPnzAoEGD8OrVK7Rp0wbGxsZYsWIFqlevjg4dOgAAbG1tkTNnThw4cABp0qTRcuRERETfFj+hVr9+fVy+fBn3798HAPTq1Qve3t64evUqmjVrhs2bN2Po0KE4deoUmjZtisyZM2s5+n+pVCocP34czZs3R+/evbFv3z5MnToVnz59wsCBA3Hs2DEULlwYBw4cwL59+9ClSxfExsZqO2wivcSuxERERPTdFi5ciDVr1sDGxgZTpkxRxu9p3749oqOjMX/+fBgZGaFbt244fvw4UqdOjaNHj2p0Z9Km+F2rli5diiVLliBTpkz466+/kDdvXlSsWBGFCxfG7NmzAQD169eHr68vihQpApVKlaBrFhERka45cuQIateujZkzZ6JFixYJHt+wYQPmzZuHa9euwdbWFkuWLEGxYsW0EGmcrx1bR44ciTNnzmDHjh3KsiNHjmD8+PGws7PD/PnzYWxsjOvXr8PQ0FAvxkck0kUcLZuIiIj+J/VJe7t27WBkZIQlS5agT58+SkLt5s2bKFy4sNIF6enTp/D399e5hFr8WFq2bAkgLkE4bNgwzJkzBz4+PujevTvevHmDGzduIDIyEgULFtSpNhAREamNHTsWmTJlQqtWrZRlZ8+ehbu7O1q0aIEPHz7g+PHjWLp0KVQqFby8vODj44M6dergyZMnsLCw0OoYuurJzEJDQ/H48WMYGRnBwcEBhoaGUKlUeP78OT58+ABzc3MAQIUKFXD16lUMGzYMkydPhrW1NRwdHbUWP1FywK7ERERE9D+pE2MA0LJlS7Ru3RovX77EsGHD8Pr1a/j4+GDu3Llo0qQJihUrhps3b+psQu3LtrRs2RLPnj1Dly5d0KBBA8ydOxcxMTEoWbIkgoKCYGhoiJiYGJ1qAxERpWyxsbEICQlBWFgYSpcurfHYq1evcODAAezevRuNGjXC9OnT8eHDB4SGhmLy5Ml4+vQpjI2NkStXLp1ICl67dg0NGjRAt27dsGHDBkRERAAAcuXKhcePH+PUqVOI39GxRIkSsLa2xrt377QVOlGywq7ERERE9N2+7Iq7ePFiZMmSBdOnT8e2bdtw8OBBWFpaYtasWTAyMtKYiVjXxG+Lv78/lixZgixZsmDGjBmws7NTLli0PYMyERFRfOrjV3h4OEQEqVOnRkBAAK5fv45OnToBAKpUqYIHDx6gXLlyaNmyJapUqYKgoCA0btwYu3btQs6cOXWiDVevXkXFihXRsWNHNG7cGAULFtRYr0GDBggMDMTSpUtRvHhxWFtbo0+fPti7dy+OHj0KS0tLLbWAKPlgYpCIiIh+SHJKqH3ZFn9/f+TIkQNjx46Fvb29zlU7EhERAUBoaCgcHBwwdepUNG/eHN26dcOmTZswbNgwdOzYEQBw//59jQTgoEGDcPDgQezevRvW1tbaCl0REhICDw8PlOs1QN4AAB77SURBVChRAnPnzlWWx8bGQkSUG4ve3t44duwY0qVLh8yZM+Py5cs4cOAAnJ2dtRU6UbKi22frREREpHPidw9Wj2nk7++Pfv36aSTUdD0pCCRsi4hgwYIF2LdvH1q3bq3t8IiIiBKVJk0alC9fHjt27MCff/6Jzp07w8jICNOnT4eIoFOnTkpScPfu3Thw4ACWLFmCw4cP60RSEIAylm/btm01lhsYxI14pr7BuGHDBuzcuRN3794FACxevBi5c+f+7fESJVe6f8ZOREREOic5JdTit6V169bYtGkTtm3bhtatW7NakIiIdJKxsTGqVq2K4cOH4+HDh3B0dES7du0QExODGTNmIFWqVGjfvj3evn2Lw4cP4/Tp0zhy5AgKFy6s7dAVly9fRnBw8FdnEzY0NERERARu3ryJmjVr/uboiFIOTj5CREREPyX+JB6tW7eGjY0Ntm3bpjymT+K3JXv27DAzM8Pnz5+1HBUREVGc+COAqf+7a9euyJEjBwYPHgwAcHR0RJcuXVC9enVMnToVixcvhoWFBXx9fbFlyxadSgoCgLW1Nd68eYMHDx4A0Gyj2pQpU7B8+fLfHBlRysLEIBEREf205JRQU6lUCAkJwcWLFzFkyBAYGxtrOyQiIkrhYmNjAUDj2KpSqRAdHQ0AaNy4Me7cuYPbt28DAPLmzYsuXbrA09MTgwYNwtKlS5EmTRqd6T4cX5EiRZAjRw4MGzYMz58/h0qlQlRUlMY6z58/h6WlpfI5ENGvx8QgERERJUlySqjZ2NjgwIEDKFSokLZDISIigoGBAe7fv49GjRrB398f4eHhAKCM49u4cWPcu3cPK1euVJ6TN29etGvXDm3atEH58uW1Evf3yJ8/P+rXr4/jx49j2LBhePHiBYyMjAAAHz58wNChQ7Ft2zY0bNhQGXeQiH49zkpMREREv0RERARMTU21HQYREVGycv36dfTv3x979uyBq6srypYti0GDBsHY2BgmJiYYP348Vq5ciQ0bNiBfvnzK86KiopREm66JjY1Vkn0dOnTA5s2bYWFhgQ4dOuDJkyd4/vw5Dh48iH379nH2YaL/GBODRERERERERDru0qVL8PPzw8GDBxEVFQUfHx+0aNECkZGRqFevHubOnYuaNWsiJiYGqVKl0na4iYqfEIwf55IlS7Bz506cP38eVlZWKF++PDp16qSR6CSi/wYTg0RERERERER6IDIyEuHh4RgzZgwCAwNx+vRpDB48GH5+fsiaNSuOHDkCc3NzbYepEBGoVCrcv38fOXPmTPD4l0nM169fw9raGtHR0Up3aSL6b7GjPhEREREREZEeMDExgYWFBSZNmoQtW7ZgwYIFCAgIwKdPn/DgwQNlDEJdoE4Kbt++HXXq1MGcOXMSrPNlZaOlpWWiy4nov8OKQSIiIiIiIiI9oU64qQUHB+PBgwewsbFBrly5tBhZQlu2bEGTJk0wceJEVKlSBQUKFNB2SET0BSYGiYiIiIiIiOiXCg4ORu3atdG4cWP07NkTUVFRiIiIwL59++Di4oJMmTLp7OQoRCkJuxITERERERERUZLFrzuKjIzE8+fPkT9/fkRFRWHs2LGoUaMGmjdvjlKlSuHMmTNajJSI1JgYJCIiIiIiIqIkU6lUOHToEC5duoSsWbPC1dUVzZo1g729PS5cuABvb298/PgRdnZ2WLFihbbDJSIAnOaHiIiIiIiIiH6JmTNn4urVq7h9+zbmzJmD7du3IyoqCj4+PkidOjUAoECBArC3t9dypEQEcIxBIiIiIiIiIvpJX06GcvXqVXTo0AFeXl7o3bs3DAz+7agYEhKCmTNnYs6cOTh27Bjy58+vjZCJKB52JSYiIiIiIiKin6JSqbB//35s3boVUVFRcHR0RMWKFbFr1y48ffoUQFzy8ODBg+jWrRuWL1+O/fv3MylIpCNYMUhEREREREREP+Xly5dwdnbGixcvMHToUJQuXRpubm4oWrQoKlasiHnz5gEAgoKCcO7cOVSqVAm5c+fWctREpMaKQSIiIiIiIiL6KXZ2dmjdujWAuMrAuXPnYsCAAZg2bRo2bdqELVu2AACcnJzQqlUrJgWJdAwrBomIiIiIiIjoh9y4cQPv37+Hi4sLAKB+/fqwtbVFjx490LJlS6hUKjx+/BjFihXD/PnzkTlzZi1HTESJYcUgEREREREREX0XEUFISAgaNWqEAQMGYPz48QCApk2bIjo6GkZGRjh16hT++OMPZMyYEadOnYKxsbGWoyair2HFIBERERERERH9kFOnTuHo0aOYMGECypYti1atWmHWrFlwdXXFqFGjAAD37t2DmZkZMmXKpOVoiehrmBgkIiIiIiIioq8SEahUKrx48QKfPn2CjY0N0qVLBwC4f/8+mjRpgkyZMiE0NBRHjhzB5s2bUadOHS1HTUTfg12JiYiIiIiIiChR6qTg5s2bUblyZbi5uSFXrlzo168fLly4gJw5c+Kff/5BhQoVYG9vDxGBv78/IiMjtR06EX0HVgwSERERERERkYbY2FgYGMTVEgUEBMDT0xOjR49G2bJlcfToUWzcuBHZsmXDwIED4ezsDBFBaGgo5s+fj7p166JAgQJabgERfQ8mBomIiIiIiIgIAHDp0iUUKVIEABAdHQ1DQ0P06NEDz58/x7p165T1NmzYgAkTJsDDwwOjRo1SKguJSL+wKzERERERERERYdu2bWjcuDGWLFkCADA0NAQQVz34/v17xMbGIjY2FgDg7e2NOnXqYPHixYiIiGBSkEhPMTFIRERERERERMiePTuKFi0Kf39/+Pv7K8vt7e1x6tQp3L17FwYGBlB3PCxRogQsLS3x4cMHbYVMREnExCARERERERFRChcbG4uiRYti9OjRyJs3L5YvX47ly5cDAAYMGIBChQqhdu3auHnzpjKxyN69e5E6dWqYmJhoM3QiSgJDbQdARERERERERLohOjoamTJlwuHDhzF27FgYGBigadOmWLduHRo1aoQyZcogf/78MDc3x+nTpxEQEIC0adNqO2wi+kmcfISIiIiIiIiIsGnTJrRs2RLt2rVDREQEdu/eDSsrK3Tv3h3NmzcHAPj5+SE4OBgGBgZo3Lgx8ubNq+WoiSgpmBgkIiIiIiIiSuFCQkJQo0YNNGjQAIMGDQIAXLx4EePGjcPt27fRu3dv/Pnnn1qOkoh+NY4xSERERERERJTCpU6dGh8+fICxsbGyrGjRohg8eDCCg4Mxfvx4zJ8/X3mMNUZEyQMTg0REREREREQpmIjg8+fPyJ49Ox4/fozIyEjExsYCAIoUKYLKlSsjLCwMu3btwtu3bwEAKpVKixET0a/CxCARERERERFRCqKu9nv79i2ioqIAABYWFmjYsCFmzZoFf39/fP78WVnfzMwMXbp0wcKFC2FhYaGNkInoP8JZiYmIiIiIiIhSEJVKhW3btmHUqFFIkyYNcuTIgQULFqB169Z4/vw5unTpggsXLiBjxox49eoVNmzYgAsXLsDW1lbboRPRL8bJR4iIiIiIiIhSABGBSqXChQsX4Orqiv79++P9+/cICAjA58+fce7cOZiYmGD58uXYunUrHj58iPTp02PKlClwcnLSdvhE9B9gYpCIiIiIiIgoGVMnBAHg/PnzCA4OxsWLFzFgwADExMTgwoULaNOmDT5//owLFy7A1NQU7969Q+rUqREZGYk0adJouQVE9F/hGINEREREREREyVCfPn2wf/9+JSn46tUrNG3aFJ6enggLCwMApEqVCsWLF8eSJUtgbGwMFxcXREREIF26dDA0NGRSkCiZY2KQiIiIiIiIKJkJDw+HiCBDhgzKMnNzc4wePRrFihXD3r17leUqlQrFihWDv78/wsLCUKlSJS1ETETawK7ERERERERERMlEbGwsDAwMEBkZCRMTEwDAnj17EBMTg5o1ayIiIgIHDx5E9+7dkStXLuzfv195rojg4sWLSJcuHXLlyqWtJhDRb8SKQSIiIiIiIqJkwsDAAI8fP0axYsXw5MkTAMCmTZtQu3Zt7N69G6ampqhatSpmzJiBx48fw93dXXmuSqWCk5MTk4JEKQgTg0RERERERETJTEREBIYMGYLo6GhMmzYNnTt3Rt26dbFr1y6YmprCzc0NkydPxrNnz1CyZElth0tEWsLEIBEREREREZEe+3KEsMyZM6NDhw64cOEC9u/fjzRp0mDUqFFo164dvLy8NJKDo0aNAgA8evRIG6ETkZZxjEEiIiIiIiIiPaUeUzA0NBSWlpbK8rCwMFSoUAGZM2fG7t27AQCvX7/G8OHDsXDhQmzduhUeHh6IjIxEVFQUzM3NtdUEItIiVgwSERERERER6SkDAwPcvXsXefPmhZeXF4KDg/Hp0yekT58eCxcuREBAACZNmgQAsLa2xujRo9GxY0fUrFkT+/fvh4mJCZOCRCmYobYDICIiIiIiIqKfFxsbi+joaGzbtg3h4eHw9PRE1apVUbJkSXTu3Bnr1q1D+fLlUbp0aVhaWmLEiBEwMTGBvb29tkMnIi1jV2IiIiIiIiIiPaPuQhwdHQ1DQ0PMnDkTDx48QOrUqfH69WucO3cOo0aNgrW1NZo1a4YmTZpg+PDhEBGoVCrl/4koZWNXYiIiIiIiIiI9oa7t+fTpEwDA0DCuI2DRokVx/fp1lC1bFlOnTkXz5s3RuHFjHDt2DDlz5sS0adNw5coVJRnIpCARAUwMEhEREREREekNlUqFFy9eoECBAhgyZIgym3DFihVRtmxZNG/eHG/evEHXrl2xfft2XLlyBalSpUJYWBgGDx6MmJiYBLMYE1HKxa7ERERERERERHrk7du3mDlzJqZOnYrixYujdu3a6NmzJwCgZcuWAIAZM2Ygffr0ePnyJa5du4YpU6Zg3LhxKFy4sPYCJyKdw8QgERERERERkR66du0afH19ERQUBHt7e8ybNw+XLl3Czp070bRpU7i5uSnrckxBIkoME4NEREREREREeurNmzcIDAzEsGHDEBYWBh8fH+zfvx/FixfH/PnztR0eEek4JgaJiIiIiIiIkoFevXrhxo0buHz5Mp49e4YFCxagbdu22g6LiHQYE4NEREREREREeix+N+GAgADs2bMHc+bMwenTp5E/f34tR0dEuoyJQSIiIiIiIiI99+UYgu/evUO6dOm0GBER6QMmBomIiIiIiIiIiFIgA20HQERERERERERERL8fE4NEREREREREREQpEBODREREREREREREKRATg0RERERERERERCkQE4NEREREREREREQpEBODRET0f+3daUxVV9cH8P9lkOmCXgdAEFEQUaIpVuqICkgEtVIQi6UO4FyrCIpTozIocUBRGqWWaiNaHLCKQlQcARvrFDViCqGKA0HiiAxaGYS7nw++nNfDlUccoQ//X0LgLNbZe519+bTY5xwiIiIiIiJqhtgYJCIiIiIiIiIiaobYGCQiIiIiIiIiImqG2BgkIiIiok+mU6dOCAwMbOwyiIiIiAhsDBIRERFpSEhIgEKhkL709fXRtWtXzJ49Gw8ePGjs8ugdKRQKzJ49u7HLICIiImoydBq7ACIiIqKmavny5ejcuTMqKipw5swZbN68GUeOHMFff/0FQ0PDxi7vX+nvv/+Glhb/N01ERETUFLAxSERERFSP4cOHw8nJCQAwdepUtGnTBuvXr0dKSgr8/f1fe84///wDIyOjT1Lfp5zrQ9HT02vsEoiIiIjo//DftUREREQN5ObmBgC4ffs2ACAwMBBKpRI3b97EiBEjYGxsjHHjxgF42bQLDQ2FlZUV9PT0YG9vj3Xr1kEIIRuzvLwcc+bMQdu2bWFsbAwvLy8UFhZCoVAgIiJCyouIiIBCoUBOTg6+/fZbqFQqODs7AwCuXbuGwMBA2NjYQF9fH+bm5pg8eTKKiopkc9WOcf36dYwfPx4tW7ZEu3btsGzZMgghUFBQgK+++gomJiYwNzdHTEyM7PzMzEwoFArs3bsXkZGRsLS0hLGxMcaMGYPS0lJUVlYiJCQEpqamUCqVmDRpEiorK2Vj1H3GYO1t23/++SfmzZuHdu3awcjICD4+Pnj06JHsXLVajYiICFhYWMDQ0BCurq7Iycn5oM8tTElJwciRI2FhYQE9PT3Y2tpixYoVqKmpkeW5uLigR48eyMnJgaurKwwNDWFpaYno6GiNMfPz8+Hl5QUjIyOYmppi7ty5OHbsGBQKBTIzM+tdm1fncnFxkY6rqqoQFhaG3r17o2XLljAyMsKgQYOQkZGhcW5RUREmTJgAExMTtGrVCgEBAcjKyoJCoUBCQoIsNzc3F2PGjEHr1q2hr68PJycnpKamvtX6ERER0b8LdwwSERERNdDNmzcBAG3atJFi1dXV8PDwgLOzM9atWwdDQ0MIIeDl5YWMjAxMmTIFjo6OOHbsGBYsWIDCwkJs2LBBOj8wMBB79+7FhAkT0K9fP5w+fRojR46st4avv/4adnZ2WLlypdRkPHHiBG7duoVJkybB3Nwc2dnZ+OWXX5CdnY3z589DoVDIxhg7diy6d++O1atX4/Dhw4iKikLr1q0RHx8PNzc3rFmzBjt37sT8+fPxxRdfYPDgwbLzV61aBQMDAyxevBh5eXnYuHEjdHV1oaWlheLiYkREROD8+fNISEhA586dERYW9sa1DQoKgkqlQnh4OO7cuYPY2FjMnj0bSUlJUs4PP/yA6OhojBo1Ch4eHsjKyoKHhwcqKireOH5DJSQkQKlUYt68eVAqlUhPT0dYWBjKysqwdu1aWW5xcTE8PT0xevRo+Pn5Yd++fVi0aBF69uyJ4cOHA3jZIHZzc8O9e/cQHBwMc3Nz7Nq167VNvIYqKyvD1q1b4e/vj2nTpuHp06f49ddf4eHhgYsXL8LR0RHAy0bqqFGjcPHiRcycORPdunVDSkoKAgICNMbMzs7GwIEDYWlpicWLF8PIyAh79+6Ft7c39u/fDx8fn3eul4iIiJowQUREREQy27ZtEwDEyZMnxaNHj0RBQYHYs2ePaNOmjTAwMBB3794VQggREBAgAIjFixfLzj948KAAIKKiomTxMWPGCIVCIfLy8oQQQly+fFkAECEhIbK8wMBAAUCEh4dLsfDwcAFA+Pv7a9T7/Plzjdju3bsFAPHHH39ojDF9+nQpVl1dLTp06CAUCoVYvXq1FC8uLhYGBgYiICBAimVkZAgAokePHqKqqkqK+/v7C4VCIYYPHy6roX///sLa2loWs7a2lo1Zu9bu7u5CrVZL8blz5wptbW1RUlIihBDi/v37QkdHR3h7e8vGi4iIEABkY9YHgJg1a9Z/zXndWs6YMUMYGhqKiooKKTZkyBABQOzYsUOKVVZWCnNzc+Hr6yvFYmJiBABx8OBBKVZeXi66desmAIiMjAwpXndtXp1ryJAh0nF1dbWorKyU5RQXFwszMzMxefJkKbZ//34BQMTGxkqxmpoa4ebmJgCIbdu2SfGhQ4eKnj17yq5RrVaLAQMGCDs7O42aiIiI6H8DbyUmIiIiqoe7uzvatWsHKysrfPPNN1AqlThw4AAsLS1leTNnzpQdHzlyBNra2pgzZ44sHhoaCiEE0tLSAABHjx4FAHz//feyvKCgoHpr+u677zRiBgYG0s8VFRV4/Pgx+vXrBwC4cuWKRv7UqVOln7W1teHk5AQhBKZMmSLFW7VqBXt7e9y6dUvj/IkTJ0JXV1c67tu3L4QQmDx5siyvb9++KCgoQHV1db3XU2v69OmynY2DBg1CTU0N8vPzAQCnTp1CdXX1W63Vu3h1LZ8+fYrHjx9j0KBBeP78OXJzc2W5SqUS48ePl45btGiBPn36yNbs6NGjsLS0hJeXlxTT19fHtGnT3rlGbW1ttGjRAsDLXYFPnjxBdXU1nJycZJ/30aNHoaurK5tLS0sLs2bNko335MkTpKenw8/PT7rmx48fo6ioCB4eHrhx4wYKCwvfuV4iIiJqungrMREREVE94uLi0LVrV+jo6MDMzAz29vYab9TV0dFBhw4dZLH8/HxYWFjA2NhYFu/evbv0+9rvWlpa6Ny5syyvS5cu9dZUNxd42diJjIzEnj178PDhQ9nvSktLNfI7duwoO27ZsiX09fXRtm1bjXjd5xTWdz4AWFlZacTVajVKS0tlt1+/Tt0xVSoVgJe36wL/v2Z116Z169ZS7oeQnZ2NpUuXIj09HWVlZbLf1V3LDh06aNymrVKpcO3aNek4Pz8ftra2Gnn/7TNuiO3btyMmJga5ubl48eKFFH/17yM/Px/t27fXeIN23bnz8vIghMCyZcuwbNmy18738OFDjYY4ERER/fuxMUhERERUjz59+khvJa6Pnp6eRrPwY3p1R1stPz8/nD17FgsWLICjoyOUSiXUajU8PT2hVqs18rW1tRsUA6DxspT/lvs2Y3zIcz+UkpISDBkyBCYmJli+fDlsbW2hr6+PK1euYNGiRRpr+aFrrts8rFVTUyObKzExEYGBgfD29saCBQtgamoKbW1trFq1SnoO5tuova758+fDw8PjtTnv28gkIiKipomNQSIiIqIPzNraGidPnsTTp09luwZrb0W1traWvqvVaty+fRt2dnZSXl5eXoPnKi4uxqlTpxAZGSl7yceNGzfe9zKalNo1y8vLk+2KKyoqknYVvq/MzEwUFRUhOTlZ9sKV2rdQvwtra2vk5ORACCFr/L3uM1apVCgpKdGI5+fnw8bGRjret28fbGxskJycLBszPDxcY+6MjAw8f/5ctmuw7ty1Y+vq6sLd3f3tLpCIiIj+1fiMQSIiIqIPbMSIEaipqcGmTZtk8Q0bNkChUEhvrK3dnfXTTz/J8jZu3NjguWp3ktXdpRYbG/u2ZTdpQ4cOhY6ODjZv3iyL113j9/G6tayqqtL4fN6Gh4cHCgsLkZqaKsUqKiqwZcsWjVxbW1ucP38eVVVVUuzQoUMoKCh4Y50XLlzAuXPnNOZ+8eKFbC61Wo24uDhZnqmpKVxcXBAfH4979+5p1PXo0aOGXCoRERH9C3HHIBEREdEHNmrUKLi6umLJkiW4c+cOPvvsMxw/fhwpKSkICQmBra0tAKB3797w9fVFbGwsioqK0K9fP5w+fRrXr18HUP+tpa8yMTHB4MGDER0djRcvXsDS0hLHjx9/r11uTZGZmRmCg4MRExMDLy8veHp6IisrC2lpaWjbtm2D1goALl26hKioKI24i4sLBgwYAJVKhYCAAMyZMwcKhQK//fbbe93OPGPGDGzatAn+/v4IDg5G+/btsXPnTujr6wOQf8ZTp07Fvn374OnpCT8/P9y8eROJiYnS30utL7/8EsnJyfDx8cHIkSNx+/Zt/Pzzz3BwcMCzZ8+kPG9vb/Tp0wehoaHIy8tDt27dkJqaiidPnmjMHRcXB2dnZ/Ts2RPTpk2DjY0NHjx4gHPnzuHu3bvIysp65zUgIiKipouNQSIiIqIPTEtLC6mpqQgLC0NSUhK2bduGTp06Ye3atQgNDZXl7tixA+bm5ti9ezcOHDgAd3d3JCUlwd7eXmoevcmuXbsQFBSEuLg4CCEwbNgwpKWlwcLC4mNcXqNZs2YNDA0NsWXLFpw8eRL9+/fH8ePH4ezs3OC1unDhAi5cuKARX7FiBZydnXHo0CGEhoZi6dKlUKlUGD9+PIYOHVrvs/feRKlUIj09HUFBQfjxxx+hVCoxceJEDBgwAL6+vrK6PTw8EBMTg/Xr1yMkJAROTk5SPa8KDAzE/fv3ER8fj2PHjsHBwQGJiYn4/fffkZmZKeVpa2vj8OHDCA4Oxvbt26GlpQUfHx+Eh4dj4MCBsrkdHBxw6dIlREZGIiEhAUVFRTA1NUWvXr1kt6gTERHR/xaF+JRPdCYiIiKiN7p69Sp69eqFxMREjBs3rrHLadJKSkqgUqkQFRWFJUuWNHY5DRYbG4u5c+fi7t27n/xtvwcPHoSPjw/OnDmDgQMHftK5iYiIqGnhMwaJiIiIGlF5eblGLDY2FlpaWrIXYFD9awW8vBW4qapbd0VFBeLj42FnZ/fRm4J1566pqcHGjRthYmKCzz///KPOTURERE0fbyUmIiIiakTR0dG4fPkyXF1doaOjg7S0NKSlpWH69OmwsrJq7PKalKSkJCQkJGDEiBFQKpU4c+YMdu/ejWHDhjXpnW+jR49Gx44d4ejoiNLSUiQmJiI3Nxc7d+786HMHBQWhvLwc/fv3R2VlJZKTk3H27FmsXLkSBgYGH31+IiIiatp4KzERERFRIzpx4gQiIyORk5ODZ8+eoWPHjpgwYQKWLFkCHR3+D/dVV65cwcKFC3H16lWUlZXBzMwMvr6+iIqKglKpbOzy6hUbG4utW7fizp07qKmpgYODAxYuXIixY8d+9Ll37dqFmJgY5OXloaKiAl26dMHMmTMxe/bsjz43ERERNX1sDBIRERERERERETVDfMYgERERERERERFRM8TGIBERERERERERUTPExiAREREREREREVEzxMYgERERERERERFRM8TGIBERERERERERUTPExiAREREREREREVEzxMYgERERERERERFRM8TGIBERERERERERUTPExiAREREREREREVEz9B+IwD6TAR3d6QAAAABJRU5ErkJggg==\n"
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "fc3e1f60"
|
||
},
|
||
"source": [
|
||
"### **Summary: What Our Multi-Label Cluster Analysis Reveals**\n",
|
||
"\n",
|
||
"By examining our K-Means clusters through four different lenses, we can build a **comprehensive profile** for each cluster:\n",
|
||
"\n",
|
||
"| Cluster | Dominant Language | Top Exploit Type | Primary Forum | Author Pattern |\n",
|
||
"|---------|------------------|------------------|---------------|----------------|\n",
|
||
"| Delphi Exploits | Delphi (71.9%) | Check your output | Check your output | Specialized Delphi authors |\n",
|
||
"| C/C++ Exploits | C/C++ (100%) | Check your output | Check your output | Systems-focused hackers |\n",
|
||
"| Mixed-Language Exploits | PHP (45%), C/C++ (18%), Perl (11%) | Check your output | Check your output | Diverse contributors |\n",
|
||
"| PHP Exploits | PHP (84.9%) | Check your output | Check your output | PHP specialists |\n",
|
||
"\n",
|
||
"**Key Takeaways:**\n",
|
||
"1. **K-Means clustered primarily by programming language** — this is because language was one-hot encoded into multiple binary features, giving it outsized influence on the distance calculations.\n",
|
||
"2. **Each language stands on its own** — Delphi, C/C++, PHP, Perl, Python, etc. are distinct languages and should not be artificially combined unless they are truly in the same language family (like C and C++).\n",
|
||
"3. **Exploit types do NOT align perfectly with clusters** — this tells us that the same type of attack can be written in different languages.\n",
|
||
"4. **Forums show some cluster alignment** — suggesting that forum communities tend to work with specific languages.\n",
|
||
"5. **Author specialization is visible** — top authors in each cluster tend to work within specific language ecosystems.\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"> **Discussion Question for Students**: If you wanted K-Means to cluster more by Exploit Type than by Language, what could you change in the feature selection or preprocessing steps? *(Hint: Think about feature weighting and which columns you include.)*"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "660c3d55"
|
||
},
|
||
"source": [
|
||
"---\n",
|
||
"\n",
|
||
"# **Part 2: From Unsupervised to Supervised — Building a KNN Classifier**\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"## Why Transition from K-Means to KNN?\n",
|
||
"\n",
|
||
"In the first part of this notebook, we used **K-Means** (an unsupervised algorithm) to discover natural groupings in our data. K-Means is powerful for **exploration** — it finds patterns we didn't know existed.\n",
|
||
"\n",
|
||
"But what if we already **know** the correct labels? Our dataset has an `Exploit Type` column that tells us the actual category of each post. When we have labeled data, we can use **supervised learning** to build a model that **predicts** those labels for new, unseen data.\n",
|
||
"\n",
|
||
"This is where **K-Nearest Neighbors (KNN)** comes in.\n",
|
||
"\n",
|
||
"### K-Means vs. KNN — The Key Difference\n",
|
||
"\n",
|
||
"| Feature | K-Means (Unsupervised) | KNN (Supervised) |\n",
|
||
"|---------|----------------------|------------------|\n",
|
||
"| **Goal** | Discover groups in data | Predict labels for new data |\n",
|
||
"| **Labels needed?** | No — finds patterns on its own | Yes — learns from labeled examples |\n",
|
||
"| **\"K\" means** | Number of clusters to create | Number of neighbors to consult |\n",
|
||
"| **Output** | Cluster assignments (0, 1, 2, ...) | Predicted class labels |\n",
|
||
"| **Training** | Finds cluster centers | No explicit training — stores all data |\n",
|
||
"| **Use case** | Exploration, pattern discovery | Classification, prediction |\n",
|
||
"\n",
|
||
"### Real-World Analogy\n",
|
||
"\n",
|
||
"- **K-Means** is like sorting a pile of mixed laundry into groups by color — you don't know the categories in advance, you just group similar items together.\n",
|
||
"- **KNN** is like asking your neighbors what to wear — you look at the 5 closest people, see what most of them are wearing, and dress similarly. You're using their **known choices** (labels) to make your decision.\n",
|
||
"\n",
|
||
"---"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "f95826d8"
|
||
},
|
||
"source": [
|
||
"## **Exercise 1: Build a KNN Classifier to Predict Exploit Type**\n",
|
||
"\n",
|
||
"### What We're Doing and Why\n",
|
||
"\n",
|
||
"We're going to build a **KNN classifier** that predicts the `Exploit Type` of a hacker forum post (e.g., \"Network\", \"Malware\", \"Web\") based on the features we engineered earlier.\n",
|
||
"\n",
|
||
"### Step-by-Step Plan:\n",
|
||
"1. **Import** the KNN classifier and train/test split utilities\n",
|
||
"2. **Prepare** the target variable (`y`) using Label Encoding\n",
|
||
"3. **Split** the data into training (80%) and testing (20%) sets\n",
|
||
"4. **Train** a KNN model with k=5\n",
|
||
"5. **Evaluate** the model's accuracy\n",
|
||
"\n",
|
||
"### Why Each Step Matters:\n",
|
||
"- **Train/test split**: We hold back 20% of the data to test whether our model can predict labels it has NEVER seen. This prevents **overfitting** (memorizing the training data instead of learning general patterns).\n",
|
||
"- **Label encoding the target**: KNN needs numeric labels, not text strings like \"Network\" or \"Malware\".\n",
|
||
"- **k=5**: We start with 5 neighbors as a reasonable default — we'll tune this in Exercise 2."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "d311a77b",
|
||
"outputId": "c1f8bfd9-d67f-4c7b-872a-3312db99d0f5"
|
||
},
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# Exercise 1, Step 1: Import KNN and Train/Test Split\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# KNeighborsClassifier is scikit-learn's implementation of the KNN algorithm.\n",
|
||
"# It's called a \"classifier\" because we're predicting discrete categories\n",
|
||
"# (Exploit Types), not continuous numbers.\n",
|
||
"from sklearn.neighbors import KNeighborsClassifier\n",
|
||
"\n",
|
||
"# train_test_split randomly divides our data into two portions:\n",
|
||
"# - Training set: the model learns patterns from this data\n",
|
||
"# - Testing set: we evaluate the model's performance on this unseen data\n",
|
||
"# This is CRITICAL for honest evaluation — testing on training data would\n",
|
||
"# give artificially high accuracy (the model already \"memorized\" those answers).\n",
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"\n",
|
||
"# classification_report provides a detailed breakdown of model performance\n",
|
||
"# including precision, recall, and F1-score for EACH class.\n",
|
||
"# accuracy_score gives us the overall percentage of correct predictions.\n",
|
||
"from sklearn.metrics import classification_report, accuracy_score\n",
|
||
"\n",
|
||
"print(\"Libraries imported successfully!\")\n",
|
||
"print(\"- KNeighborsClassifier: The KNN classification algorithm\")\n",
|
||
"print(\"- train_test_split: Splits data into training and testing sets\")\n",
|
||
"print(\"- classification_report: Detailed per-class performance metrics\")\n",
|
||
"print(\"- accuracy_score: Overall accuracy percentage\")"
|
||
],
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Libraries imported successfully!\n",
|
||
"- KNeighborsClassifier: The KNN classification algorithm\n",
|
||
"- train_test_split: Splits data into training and testing sets\n",
|
||
"- classification_report: Detailed per-class performance metrics\n",
|
||
"- accuracy_score: Overall accuracy percentage\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "365ac4a3",
|
||
"outputId": "f63174bf-6930-42f1-b117-9ebfd403bba6"
|
||
},
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# Exercise 1, Step 2: Prepare the Target Variable (y)\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# The TARGET variable is what we want the model to PREDICT.\n",
|
||
"# In our case, it's the 'Exploit Type' column (e.g., \"Network\", \"Malware\").\n",
|
||
"#\n",
|
||
"# But KNN (like most ML algorithms) needs NUMBERS, not text.\n",
|
||
"# So we use LabelEncoder to convert each unique exploit type into an integer:\n",
|
||
"# \"Malware\" -> 0, \"Network\" -> 1, \"Web\" -> 2, etc.\n",
|
||
"#\n",
|
||
"# NOTE: We already imported LabelEncoder earlier in the notebook (cell 9).\n",
|
||
"# Creating a NEW encoder here (not reusing the one from cell 19) because\n",
|
||
"# that one was fitted on Author Names, not Exploit Types.\n",
|
||
"\n",
|
||
"# Create a fresh LabelEncoder for the Exploit Type column\n",
|
||
"exploit_encoder = LabelEncoder()\n",
|
||
"\n",
|
||
"# Fill any missing Exploit Type values with 'Unknown' before encoding.\n",
|
||
"# If we don't do this, NaN values will cause an error in fit_transform().\n",
|
||
"exploit_types_filled = hacker_sourcecode_data['Exploit Type'].fillna('Unknown')\n",
|
||
"\n",
|
||
"# fit_transform() learns the mapping AND applies it in one step:\n",
|
||
"# 1. fit() discovers all unique exploit types\n",
|
||
"# 2. transform() converts each text label to its integer code\n",
|
||
"y = exploit_encoder.fit_transform(exploit_types_filled)\n",
|
||
"\n",
|
||
"# Let's see what the encoder learned:\n",
|
||
"print(\"Exploit Type Label Mapping:\")\n",
|
||
"print(\"-\" * 40)\n",
|
||
"for i, class_name in enumerate(exploit_encoder.classes_):\n",
|
||
" print(f\" {class_name:20s} -> {i}\")\n",
|
||
"\n",
|
||
"print(f\"\\nTotal samples: {len(y)}\")\n",
|
||
"print(f\"Number of classes: {len(exploit_encoder.classes_)}\")\n",
|
||
"print(f\"\\nFirst 10 encoded labels: {y[:10]}\")\n",
|
||
"print(f\"First 10 original labels: {list(exploit_types_filled[:10])}\")"
|
||
],
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Exploit Type Label Mapping:\n",
|
||
"----------------------------------------\n",
|
||
" Database -> 0\n",
|
||
" Network -> 1\n",
|
||
" System -> 2\n",
|
||
" Web -> 3\n",
|
||
"\n",
|
||
"Total samples: 15582\n",
|
||
"Number of classes: 4\n",
|
||
"\n",
|
||
"First 10 encoded labels: [1 1 2 2 2 2 2 2 2 2]\n",
|
||
"First 10 original labels: ['Network', 'Network', 'System', 'System', 'System', 'System', 'System', 'System', 'System', 'System']\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "7d490fee"
|
||
},
|
||
"source": [
|
||
"### Understanding the Feature Matrix (X) and Target Vector (y)\n",
|
||
"\n",
|
||
"Before we split the data, let's clarify what `X` and `y` represent — this is fundamental to ALL supervised machine learning:\n",
|
||
"\n",
|
||
"- **`X` (Feature Matrix)**: The INPUT data — all the measurements/features the model uses to make predictions. In our case, this is the scaled feature matrix we created in Step 7 (Author Name, Year, Month, Source Code Length, plus all one-hot encoded columns). Shape: `(n_samples, n_features)`.\n",
|
||
"\n",
|
||
"- **`y` (Target Vector)**: The OUTPUT labels — what we want the model to predict. In our case, the encoded Exploit Type. Shape: `(n_samples,)`.\n",
|
||
"\n",
|
||
"Think of it like a test:\n",
|
||
"- `X` is the **question** (all the information about a post)\n",
|
||
"- `y` is the **answer** (what type of exploit it is)\n",
|
||
"- We train the model on questions WITH answers, then test if it can answer questions WITHOUT seeing the answers first."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "13dc7316",
|
||
"outputId": "c6cbfd5d-f48e-4f86-953d-a98fb21d9360"
|
||
},
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# Exercise 1, Step 3: Split Data into Training and Testing Sets\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# train_test_split randomly shuffles the data and splits it into two groups:\n",
|
||
"#\n",
|
||
"# Parameters explained:\n",
|
||
"# - X: our feature matrix (the inputs)\n",
|
||
"# - y: our target labels (the outputs we want to predict)\n",
|
||
"# - test_size=0.2: hold out 20% of the data for testing (80% for training)\n",
|
||
"# - random_state=42: a \"seed\" for the random number generator, ensuring\n",
|
||
"# that every time we run this code, we get the SAME random split.\n",
|
||
"# This makes our results REPRODUCIBLE. (42 is a common choice, but any\n",
|
||
"# integer works — it's a convention from \"The Hitchhiker's Guide to the Galaxy\".)\n",
|
||
"#\n",
|
||
"# The function returns 4 objects:\n",
|
||
"# - X_train: training features (80% of X)\n",
|
||
"# - X_test: testing features (20% of X)\n",
|
||
"# - y_train: training labels (80% of y)\n",
|
||
"# - y_test: testing labels (20% of y)\n",
|
||
"\n",
|
||
"X_train, X_test, y_train, y_test = train_test_split(\n",
|
||
" X, # Feature matrix (scaled)\n",
|
||
" y, # Target labels (encoded exploit types)\n",
|
||
" test_size=0.2, # 20% for testing, 80% for training\n",
|
||
" random_state=42 # Seed for reproducibility\n",
|
||
")\n",
|
||
"\n",
|
||
"# Let's verify the split worked correctly\n",
|
||
"print(\"=== Train/Test Split Results ===\")\n",
|
||
"print(f\"Training set size: {X_train.shape[0]} samples ({X_train.shape[0]/len(y)*100:.1f}%)\")\n",
|
||
"print(f\"Testing set size: {X_test.shape[0]} samples ({X_test.shape[0]/len(y)*100:.1f}%)\")\n",
|
||
"print(f\"Feature count: {X_train.shape[1]} features\")\n",
|
||
"print(f\"\\nTraining features shape: {X_train.shape}\")\n",
|
||
"print(f\"Testing features shape: {X_test.shape}\")\n",
|
||
"print(f\"Training labels shape: {y_train.shape}\")\n",
|
||
"print(f\"Testing labels shape: {y_test.shape}\")"
|
||
],
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"=== Train/Test Split Results ===\n",
|
||
"Training set size: 12465 samples (80.0%)\n",
|
||
"Testing set size: 3117 samples (20.0%)\n",
|
||
"Feature count: 25 features\n",
|
||
"\n",
|
||
"Training features shape: (12465, 25)\n",
|
||
"Testing features shape: (3117, 25)\n",
|
||
"Training labels shape: (12465,)\n",
|
||
"Testing labels shape: (3117,)\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "82a88a3b",
|
||
"outputId": "46c0ff43-d86a-406e-cba8-944d9b6ad66a"
|
||
},
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# Exercise 1, Step 4: Create, Train, and Evaluate the KNN Classifier\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# STEP 4A: Create the KNN model\n",
|
||
"# KNeighborsClassifier(n_neighbors=5) creates a KNN classifier that will\n",
|
||
"# look at the 5 nearest neighbors to make each prediction.\n",
|
||
"# \"nearest\" is determined by Euclidean distance (the straight-line distance\n",
|
||
"# between two points in the feature space).\n",
|
||
"knn_model = KNeighborsClassifier(n_neighbors=5)\n",
|
||
"\n",
|
||
"# STEP 4B: Train (fit) the model on the training data\n",
|
||
"# .fit() tells the model: \"Here are examples with known answers — learn from them.\"\n",
|
||
"# For KNN, \"learning\" simply means STORING the training data. KNN is a\n",
|
||
"# \"lazy learner\" — it doesn't build a mathematical model like logistic regression.\n",
|
||
"# Instead, it saves all training points and compares new points to them at\n",
|
||
"# prediction time.\n",
|
||
"knn_model.fit(X_train, y_train)\n",
|
||
"\n",
|
||
"# STEP 4C: Make predictions on the test set\n",
|
||
"# .predict() takes new, unseen feature data and returns predicted labels.\n",
|
||
"# For each test point, KNN:\n",
|
||
"# 1. Calculates the distance to ALL training points\n",
|
||
"# 2. Finds the 5 closest training points\n",
|
||
"# 3. Takes a majority vote of their labels\n",
|
||
"# 4. Returns the winning label as the prediction\n",
|
||
"y_pred = knn_model.predict(X_test)\n",
|
||
"\n",
|
||
"# STEP 4D: Evaluate the model\n",
|
||
"# accuracy_score compares the predicted labels to the actual labels\n",
|
||
"# and calculates the percentage that match.\n",
|
||
"accuracy = accuracy_score(y_test, y_pred)\n",
|
||
"\n",
|
||
"print(\"=== KNN Classifier Results (k=5) ===\")\n",
|
||
"print(f\"\\nOverall Accuracy: {accuracy:.4f} ({accuracy*100:.2f}%)\")\n",
|
||
"print(f\"This means {int(accuracy * len(y_test))} out of {len(y_test)} test samples were classified correctly.\")\n",
|
||
"\n",
|
||
"# classification_report gives us a much more detailed breakdown:\n",
|
||
"# - Precision: Of all predictions for class X, what % were actually X?\n",
|
||
"# - Recall: Of all actual class X samples, what % did we correctly predict?\n",
|
||
"# - F1-Score: The harmonic mean of precision and recall (a balanced metric)\n",
|
||
"# - Support: How many test samples belong to each class\n",
|
||
"print(f\"\\n=== Detailed Classification Report ===\")\n",
|
||
"print(classification_report(\n",
|
||
" y_test,\n",
|
||
" y_pred,\n",
|
||
" target_names=exploit_encoder.classes_ # Use original names instead of numbers\n",
|
||
"))"
|
||
],
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"=== KNN Classifier Results (k=5) ===\n",
|
||
"\n",
|
||
"Overall Accuracy: 0.9804 (98.04%)\n",
|
||
"This means 3056 out of 3117 test samples were classified correctly.\n",
|
||
"\n",
|
||
"=== Detailed Classification Report ===\n",
|
||
" precision recall f1-score support\n",
|
||
"\n",
|
||
" Database 1.00 1.00 1.00 1\n",
|
||
" Network 0.82 0.57 0.67 58\n",
|
||
" System 0.98 0.99 0.99 1966\n",
|
||
" Web 0.99 0.98 0.98 1092\n",
|
||
"\n",
|
||
" accuracy 0.98 3117\n",
|
||
" macro avg 0.95 0.89 0.91 3117\n",
|
||
"weighted avg 0.98 0.98 0.98 3117\n",
|
||
"\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "98454b9a"
|
||
},
|
||
"source": [
|
||
"### Interpreting the Results\n",
|
||
"\n",
|
||
"**What does the accuracy tell us?**\n",
|
||
"- An accuracy above **50%** means the model is doing better than random guessing (for a multi-class problem).\n",
|
||
"- An accuracy above **70%** is generally considered reasonable for real-world data.\n",
|
||
"- Perfect accuracy (100%) on real data is suspicious — it often indicates **data leakage** (the model somehow \"saw\" the test answers during training).\n",
|
||
"\n",
|
||
"**Why look beyond accuracy?**\n",
|
||
"- Accuracy alone can be misleading with **imbalanced classes**. If 80% of posts are \"Network\" exploits, a model that always predicts \"Network\" gets 80% accuracy without learning anything useful.\n",
|
||
"- **Precision** tells us: \"When the model predicts Malware, how often is it actually Malware?\"\n",
|
||
"- **Recall** tells us: \"Of all actual Malware posts, how many did the model catch?\"\n",
|
||
"- **F1-Score** balances both — useful when you care equally about precision and recall.\n",
|
||
"\n",
|
||
"**Thinking Prompt:** How does KNN (supervised, uses labeled data) differ from the K-Means clustering (unsupervised, no labels) that we used earlier? Why might we choose one over the other?\n",
|
||
"\n",
|
||
"---"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "849cc1f4"
|
||
},
|
||
"source": [
|
||
"## **Exercise 2: The Effect of K on KNN Performance**\n",
|
||
"\n",
|
||
"### Why Does K Matter?\n",
|
||
"\n",
|
||
"The parameter `k` (number of neighbors) is the most important **hyperparameter** in KNN. It fundamentally changes how the model behaves:\n",
|
||
"\n",
|
||
"| Small K (e.g., k=1) | Large K (e.g., k=25) |\n",
|
||
"|---------------------|----------------------|\n",
|
||
"| Very sensitive to individual data points | Smooths out noise |\n",
|
||
"| Can capture complex patterns | May miss local patterns |\n",
|
||
"| Risk of **overfitting** (memorizing noise) | Risk of **underfitting** (too general) |\n",
|
||
"| Decision boundaries are jagged | Decision boundaries are smooth |\n",
|
||
"\n",
|
||
"### What is Overfitting and Underfitting?\n",
|
||
"\n",
|
||
"- **Overfitting**: The model learns the training data TOO well — including noise and outliers. It performs great on training data but poorly on new data. Like memorizing answers to a specific practice test instead of understanding the subject.\n",
|
||
"\n",
|
||
"- **Underfitting**: The model is too simple to capture the real patterns. It performs poorly on BOTH training and test data. Like skimming a textbook the night before an exam.\n",
|
||
"\n",
|
||
"### Why Test Only Odd Values of K?\n",
|
||
"\n",
|
||
"For binary classification (2 classes), we use odd values of `k` to avoid **ties**. If k=4 and 2 neighbors vote \"Network\" and 2 vote \"Malware,\" there's no majority! Odd `k` guarantees a clear winner. For multi-class problems, ties are less common but odd `k` is still a good practice.\n",
|
||
"\n",
|
||
"### Our Approach: The Elbow Method\n",
|
||
"\n",
|
||
"We'll train KNN models with k=1, 3, 5, 7, ..., 25 and plot the accuracy for each. The \"best\" k is typically where accuracy peaks or plateaus — this is similar to the **elbow method** used to choose the number of clusters in K-Means."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "592ef0cf",
|
||
"outputId": "2611d7d8-cb22-4635-88ea-17e7d782891e"
|
||
},
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# Exercise 2, Step 1: Test Multiple Values of K\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# We'll test odd values of k from 1 to 25.\n",
|
||
"# range(1, 26, 2) generates: 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25\n",
|
||
"# The '2' means \"step by 2\" (skip even numbers).\n",
|
||
"k_values = list(range(1, 26, 2))\n",
|
||
"\n",
|
||
"# Create an empty list to store the accuracy for each k value.\n",
|
||
"# We'll fill this list as we loop through each k.\n",
|
||
"accuracies = []\n",
|
||
"\n",
|
||
"# Loop through each value of k\n",
|
||
"print(\"=== Testing Different Values of K ===\\n\")\n",
|
||
"print(f\"{'K':>4s} | {'Accuracy':>10s} | {'Correct':>8s} / {'Total':>5s}\")\n",
|
||
"print(\"-\" * 40)\n",
|
||
"\n",
|
||
"for k in k_values:\n",
|
||
" # Step A: Create a new KNN model with this value of k\n",
|
||
" knn = KNeighborsClassifier(n_neighbors=k)\n",
|
||
"\n",
|
||
" # Step B: Train (fit) the model on the training data\n",
|
||
" knn.fit(X_train, y_train)\n",
|
||
"\n",
|
||
" # Step C: Evaluate accuracy on the TEST set (not training set!)\n",
|
||
" # .score() is a shortcut that combines .predict() and accuracy_score()\n",
|
||
" # It predicts labels for X_test and compares them to y_test.\n",
|
||
" acc = knn.score(X_test, y_test)\n",
|
||
"\n",
|
||
" # Step D: Store the accuracy for plotting later\n",
|
||
" accuracies.append(acc)\n",
|
||
"\n",
|
||
" # Print the results for this k\n",
|
||
" correct = int(acc * len(y_test))\n",
|
||
" print(f\"{k:4d} | {acc:10.4f} | {correct:8d} / {len(y_test):5d}\")\n",
|
||
"\n",
|
||
"# Find the best k\n",
|
||
"best_idx = accuracies.index(max(accuracies))\n",
|
||
"best_k = k_values[best_idx]\n",
|
||
"best_accuracy = accuracies[best_idx]\n",
|
||
"\n",
|
||
"print(f\"\\n{'='*40}\")\n",
|
||
"print(f\"Best K = {best_k} with accuracy = {best_accuracy:.4f} ({best_accuracy*100:.2f}%)\")"
|
||
],
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"=== Testing Different Values of K ===\n",
|
||
"\n",
|
||
" K | Accuracy | Correct / Total\n",
|
||
"----------------------------------------\n",
|
||
" 1 | 0.9962 | 3105 / 3117\n",
|
||
" 3 | 0.9817 | 3060 / 3117\n",
|
||
" 5 | 0.9804 | 3056 / 3117\n",
|
||
" 7 | 0.9737 | 3035 / 3117\n",
|
||
" 9 | 0.9714 | 3028 / 3117\n",
|
||
" 11 | 0.9695 | 3022 / 3117\n",
|
||
" 13 | 0.9689 | 3020 / 3117\n",
|
||
" 15 | 0.9692 | 3021 / 3117\n",
|
||
" 17 | 0.9682 | 3018 / 3117\n",
|
||
" 19 | 0.9692 | 3021 / 3117\n",
|
||
" 21 | 0.9679 | 3017 / 3117\n",
|
||
" 23 | 0.9682 | 3018 / 3117\n",
|
||
" 25 | 0.9686 | 3019 / 3117\n",
|
||
"\n",
|
||
"========================================\n",
|
||
"Best K = 1 with accuracy = 0.9962 (99.62%)\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 710
|
||
},
|
||
"id": "b9d4232b",
|
||
"outputId": "cb9333ff-2ebe-4554-9811-a2bf78ff67af"
|
||
},
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# Exercise 2, Step 2: Plot Accuracy vs. K (The Elbow Plot)\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# Create a line chart showing how accuracy changes with different k values.\n",
|
||
"# This visualization helps us identify the \"sweet spot\" for k.\n",
|
||
"\n",
|
||
"plt.figure(figsize=(12, 6))\n",
|
||
"\n",
|
||
"# Plot the accuracy curve\n",
|
||
"# '-o' means: solid line connecting data points, with circle markers at each point\n",
|
||
"plt.plot(k_values, accuracies, '-o', color='steelblue', linewidth=2, markersize=8)\n",
|
||
"\n",
|
||
"# Highlight the best k with a red star marker\n",
|
||
"plt.plot(best_k, best_accuracy, '*', color='red', markersize=20,\n",
|
||
" label=f'Best K={best_k} (Accuracy={best_accuracy:.4f})')\n",
|
||
"\n",
|
||
"# Add a horizontal dashed line at the best accuracy for reference\n",
|
||
"plt.axhline(y=best_accuracy, color='red', linestyle='--', alpha=0.3)\n",
|
||
"\n",
|
||
"# Labels and formatting\n",
|
||
"plt.title('KNN Accuracy vs. Number of Neighbors (K)', fontsize=14)\n",
|
||
"plt.xlabel('K (Number of Neighbors)', fontsize=12)\n",
|
||
"plt.ylabel('Test Accuracy', fontsize=12)\n",
|
||
"plt.xticks(k_values) # Show every k value on the x-axis\n",
|
||
"\n",
|
||
"# Add gridlines for easier reading\n",
|
||
"plt.grid(axis='both', linestyle='--', alpha=0.3)\n",
|
||
"\n",
|
||
"plt.legend(fontsize=12)\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"# INTERPRETATION GUIDE:\n",
|
||
"# - If accuracy drops sharply at k=1, the model was overfitting\n",
|
||
"# (k=1 means each prediction is based on the SINGLE closest point — very noisy)\n",
|
||
"# - If accuracy keeps increasing with k, the data benefits from more \"smoothing\"\n",
|
||
"# - If accuracy peaks and then decreases, you've found the sweet spot\n",
|
||
"# - A flat curve means the model is robust to the choice of k\n",
|
||
"print(\"\\n=== Interpretation Guide ===\")\n",
|
||
"print(f\"At k=1: {accuracies[0]:.4f} — This is the most overfit (uses only 1 neighbor)\")\n",
|
||
"print(f\"At k={best_k}: {best_accuracy:.4f} — Best performance\")\n",
|
||
"print(f\"At k=25: {accuracies[-1]:.4f} — Most generalized (uses 25 neighbors)\")\n",
|
||
"if accuracies[0] > best_accuracy:\n",
|
||
" print(\"\\nNote: k=1 outperforms larger k values, suggesting the data has\")\n",
|
||
" print(\"very localized patterns that benefit from fine-grained decisions.\")\n",
|
||
"elif accuracies[0] < accuracies[-1]:\n",
|
||
" print(\"\\nNote: Larger k values perform better, suggesting the data benefits\")\n",
|
||
" print(\"from 'smoothing out' noise with more neighbors.\")"
|
||
],
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 1 Axes>"
|
||
],
|
||
"image/png": 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Xv/xFAVD//ve/XR4bERHVHk/fIyIir9SxY0ckJSVhx44deOGFF2p130mTJqFTp05477338Ntvvzl9vwYNGmDevHkoLi7Gc889V9sh2yxevBhlZWW488477bZPnDgRQMUF0Cv74YcfEBwcbLsOTnWOHDmC/Px89O7dG9HR0S6PryZ9+vSp9raVK1di+PDhiI+PR2BgIDRNg8FggMlksl1c2pWxTpkyBUopvP/++7ZtX331FTIyMjB58mQEBgbW+BhRUVH44x//iP379+Pnn3+2bf/ss89QUlJi93x06NABXbt2xbJlyzBo0CAsWLAAu3fvtl1vSpr1E/j+7//+zy2P37179yrbmjRpUuNtlZ8zq4EDB1bZ1rBhQ3Tv3h0mk8l2St73338PoOIUyzlz5lT5Jy0tDRaLpcppiy1btqz2unCOHDlyBMXFxbZrXF1qyJAhAIC9e/c6/ZjOaNOmDe69914cOHAA//73v6vdz9ohPT3dYYcjR47Y5nE5P//8M8LDw9GjR48qtw0YMKDa+7Vt2xYNGza02xYYGIjExETk5eU5vI+j59i6bc+ePbZte/bsQVhYmMOfCZfrXt3PkFtvvRWFhYXo3LkznnrqKaxZswYmk8nhvtXJzs6ucqHyS/Xt2xeRkZH49NNPnb5OVExMDICK0/6IiKj+1PwbHhERkQc0a9YMq1atwpAhQzB79myYzWanLxZtMBjw0ksv4Y9//COeffZZfPrpp05/35tvvhmvvfYalixZgunTpyM+Pr7WY//nP/8JTdOqLErdfPPNeOyxx/Dvf/8b8+fPty20GI1GNG3aFAbD5f9fkfWaTU2bNq31mGojMTHR4fbXX38dTz75JOLj43HdddfhiiuusF00+M0337S7UHltx3rdddehZcuWWLx4MebNm4fAwEC8//770DQN99xzj9Njv/POO/Hpp5/io48+Qrdu3QAAS5YsQUBAAG677TbbfoGBgdi4cSPmzJmDFStW2D4hMT4+Ho888gj+/Oc/IyAgwOnvW5O+ffti3LhxWLlyJdasWYNRo0aJPTYAh9dksr6+LndbWVlZlduqe/6t263PrfXaa0uXLr3s2C69Rk91j18d66JFdfezLrDVdnHDGbNnz8aSJUswa9Ys3HrrrQ73sXb4+uuv8fXXX1f7WDVdq8hkMjn8VFHg8s2qux5XYGAgzGaz04936fNb05gu17268T755JOIjY3FO++8g9dffx2vvfYaAgMDcf311+ONN95Ay5YtHd6vstDQ0Bo/JbV79+5YuHAhhg8fjrvvvhtms7nGnyPWDwVwtPBJRETuwyOliIjIa7Vu3RpbtmxB8+bN8cILL2DmzJlO3/fGG2/EwIED8dlnn+HHH390+n6apuHll1+GxWLBjBkzaj3m7du348iRI1BKoUWLFrZP/tI0DY0aNUJxcTHS0tKwZs0a230aNWpkO6rkcqxHB6Smpjo9l+o+Ye3Si5Jfer9LlZeXY+7cuWjSpAkOHDiApUuX4uWXX8acOXMwe/ZslJaW1nms999/P9LS0rB69WqcOXMG33zzDa699lq0atXKqccAgBEjRiA+Ph7Lli2DxWLByZMnsW3bNgwbNgyNGze22zc2NhaLFi1CamoqDh06hLfeegsxMTGYPXu27cgmSS+++CICAwMxY8aMap9r68Kko+ftcs+ZpOo+Zc+6PSoqCsDFxZDVq1dDVVwSwuE/lx4B6Oj1dTnW71PduNLS0uz2k9S4cWNMmzYNZ86cwaJFiy47Puun11X3j/Xi69WJjIxEZmamw9uqm7urHD3epc+vdUzVfbLm5bpX9xxrmoa7774bP/74IzIzM/HFF19g3LhxWLVqFW644YZqF9Eqi4+Pd+rDKPr27Yv169cjKioK9913H/7xj39cdn/rY7ryPyKIiMh1XJQiIiKv1qpVK2zevBnJycmYP38+nnnmGafv6+opU0OHDsXw4cOxZs0afPfdd7W67z//+U8AwMiRI3HPPfdU+eemm26y2w+oONWlpKQEW7Zsuexjt2vXDpGRkfjxxx+rfAqaI9HR0Q4XhU6ePFntaT3VycrKgtFoRP/+/ZGQkGB3208//WQ7ysDVsQLA5MmT0aBBA7z//vv44IMPYLFYcN9999VqnIGBgbj11luRmpqKTZs2YenSpVBK4Y477qj2PpqmoUOHDnj44Yexfv16ABWnDkpr164d7rnnHuzfvx9LlixxuI/1VEdHz1vl06rcaevWrVW2FRQUYO/evYiMjLQtEvbt2xcAsGPHDreOp3379ggJCcGPP/6ICxcuVLl98+bNAByfpijhqaeeQnx8PF566SWH7xupDt26dUNhYaHD0+G2b99ep8e+lKPn2Lqt8umDPXr0wIULF/DDDz9U2b+u3WNjYzFmzBgsX74cQ4cOxaFDh5w63bpLly4oLi7G6dOna9y3d+/e+Pbbb9GoUSM88MADeOedd6rd95dffrE9PhER1R8uShERkddr2bIltmzZgpYtW+Ivf/mL0x/T3q9fP4wdOxabNm3Ct99+W6vv+Ze//AWapuHZZ591+j4FBQX49NNPER4ejk8//RTvv/9+lX8+/fRTXHHFFVizZo3tSIOHH34YAPD4449XOQKgvLzcdgRDYGAgpkyZAqPRiMcff7zKUQVGoxEFBQW2r3v37o2TJ0/aLXaVlpZi2rRptWoBAAkJCQgNDcXu3bvtFgZyc3Px6KOPVtm/tmMFKk75GTNmDNauXYt33nkHcXFxGDNmTK3Haj1tcsmSJViyZAnCw8MxduxYu31OnjyJkydPVrmvtXVISIhtW1lZGY4cOYJjx47VeiyXmjNnDsLCwjBr1iyHR0v17t0bAKpcB+fzzz+vcdFSyrfffot169bZbZs/fz7y8vIwceJE29Fco0ePRvPmzbFgwQKHi7dlZWXYtm1bnccTFBSECRMmICsrCy+99JLdbWvXrsW6devQunXry153qS4iIiIwc+ZM5Obm4rXXXqtye58+fdC3b18sW7YMy5cvr3K7xWJx6rm7/fbbAQAzZ860e20cOXIEixcvrsMMqpo7d67dkXdGoxHz5s2Dpml2R3RZ//zMM8/Ynep55swZLFiwAIGBgbZxO2Pz5s1QStltKysrs/3cq/y+q471yLudO3c69T179eqFDRs2ICYmBg899BDeeusth/vt3LkTTZo0QZs2bZx6XCIiksFrShERkU9ITk7Gli1bMGTIELz66qswm814/fXXa7zfSy+9hK+++qrWCwrdu3fHbbfdVuP1cipbvnw5CgoKMGnSpCoXHrYyGAyYOHEiXnzxRSxevBj/93//h1GjRuHJJ5/Ea6+9hjZt2mDs2LFISEhAamoqNmzYgCeffBJTp04FALzwwgv4/vvvsWTJEnz//fcYOXIkgoODcfz4caxduxbbtm2zHbkwbdo0fPPNNxg1ahQmTJiAsLAwrF+/Ho0aNbJdD8ZZBoMBDz30EF5//XV069YNN954I0wmE/773/8iOTkZSUlJVe5Tm7FaPfDAA/jss8+Qnp6O6dOnIygoqFbjBCoWdtq1a4ePP/7YdsH58PBwu3327t2LcePGoU+fPujYsSMaN26M1NRUfPnllzAYDHjiiSds+6ampqJDhw5ITk52uJBVG40bN8YTTzyB+fPnO7x99OjRSElJwYcffogzZ86gR48eOHz4MDZu3IhRo0bZnfbpLjfccANuvPFGjB8/Hi1atMD333+PTZs2ISUlxe5DB4KDg/H5559j5MiRGDx4MIYOHYouXbpA0zScOnUKW7duRWxsbI0X+HbGyy+/jC1btmDevHnYvn07+vbti5MnT+Kzzz5DWFgY/vWvf9V4Tba6eOCBB/Dmm29W+3Nk2bJlGDJkCG699Va8+eab6NmzJ0JDQ3H69Gns2LEDmZmZNV4HafLkyViyZAm+/vpr9OjRAyNHjkROTg4++eQT/OEPf8Dq1avF5ti2bVt07tzZduTmihUrcPbsWUybNg1XXnmlbb8777wTK1euxKpVq9C1a1fccMMNKCwsxPLly5GTk4PXX3+9VqfXjhkzBpGRkejXrx+Sk5NRVlaG9evX49ChQxg/fjySk5NrfIzRo0dj2rRpWL9+Pf70pz859X179OiBjRs34tprr8Wjjz4Ks9mMxx9/3Hb7sWPHcOLECTz44INOz4WIiITU98f9ERERXc6JEycUADV8+HCHt589e1a1adNGAVCPP/64Ukqp2bNnKwBq2bJlDu9j/Th1AGrTpk12twFQ7dq1q3YsQUFBCoAaPHhwjWPv37+/w+9xqaNHjyoAqm3btnbbV6xYoYYMGaKioqJUcHCwatGihbrzzjvVgQMH7PYrLi5Wr732murevbsKDQ1VDRs2VB07dlTTp09Xubm5dvt+9tlnqkuXLiooKEg1btxYPfrooyo/P18lJyer5ORku30nTZqkAKgTJ044HHdpaamaP3++atOmjQoODlbNmzdX06dPr/bxajtWpZSyWCyqefPmCoA6fPjwZTtezrx582zP+bp166rcfubMGTVjxgzVr18/lZCQoIKCglTz5s3VuHHj1I4dO+z2tb4mHc2vOsnJySo4ONjhbUajUcXFxVX7mCdOnFBjxoxRERERKjw8XF177bXqxx9/tL3OK7++Nm3apACo2bNnV3mcf/3rXwqA+te//lXlNkePVXn/L7/8UvXu3VuFhoaq2NhYddddd6nz5887nM/Zs2fV448/bntdREZGqg4dOqh7771XbdiwwW5fZ99LjmRmZqrHHntMJScnqwYNGqi4uDg1fvx4tX///ir7Xq5Ldaz3mTJlisPbP/74Y9trytHj5uTkqJkzZ6rOnTvbXutt2rRRt912m1q5cqXdvoMHD1aOfg0vKChQ06dPV0lJSSo4OFh17NhR/eMf/1Cff/65AqDeeOMNu/0v19PRe9L6fYuKitTTTz+tmjVrpoKCglS7du3UX//6V2WxWKo8TllZmXrttddUly5dVHBwsIqIiFCDBw9Wq1atqrLv5V5zSin1t7/9Tf3xj39UycnJKiQkRMXGxqo+ffqod955R5WWljq8jyMjR45U0dHRqri42G57Tc/h/v37VUJCggKgXn/9ddv2OXPmKABq7969To+BiIhkaEpdcgwtERERkYecP38ezZs3R//+/Wt9PS8ifzVz5kzMnz8fa9aswciRIz09HI/bsGEDhg0bho8++qhWpw86Ul5ejjZt2qBly5bYuHGj0AiJiMhZvKYUEREReY0333wT5eXlPI2GdOn8+fNVth06dAh//etf0ahRI1xzzTX1PygvdO2112LEiBGYN29ejZ9aWpPFixfj1KlTDq8XRkRE7sdrShEREZFHGY1GvPPOOzh16hTef/99dOzYETfffLOnh0VU7x588EGcPHkSffr0QXR0NI4dO4bVq1ejrKwM//znPxEaGurpIXqNhQsX4uOPP0ZqaiqaNWvm8uNomob33nsPPXv2FBwdERE5i6fvERERkUedPHkSLVu2REhICPr164d3330X7dq18/SwiOrd0qVL8e677+Lw4cMwGo1o2LAhevfujenTp2P48OGeHh4REZE4LkoREREREREREVG94zWliIiIiIiIiIio3nFRioiIiIiIiIiI6h0vdC7EYrHg3LlziIiIgKZpnh4OEREREREREZFHKKWQn5+PpKQkGAzVHw/FRSkh586dq9MnfxARERERERER+ZMzZ87giiuuqPZ2LkoJiYiIAFARPDIy0sOjcZ1SCqWlpQgKCuIRX3XElnLYUg5bymFLOWwphy1lsacctpTDlnLYUg5byvGXliaTCc2aNbOtlVSHi1JCrC+WyMhIn16UslgsyMjIQERExGUPsaOasaUctpTDlnLYUg5bymFLWewphy3lsKUctpTDlnL8rWVNC2u+P0MiIiIiIiIiIvI5XJQiIiIiIiIiIqJ6x0UpqiIwkGd1SmFLOWwphy3lsKUctpTDlrLYUw5bymFLOWwphy3l6KmlppRSnh6EPzCZTIiKioLRaPTpa0oREREREREREdWFs2skPFKK7CilcOHCBXCtsu7YUg5bymFLOWwphy3lsKUs9pTDlnLYUg5bymFLOXpryUUpsqOUgslk0s0bwJ3YUg5bymFLOWwphy3lsKUs9pTDlnLYUg5bymFLOXpryUUpsrdnD6JvvRXYu9fTIyEiIiIiIiIiP8ZFKbKjffYZgrdsgfbZZ54eChERERERERH5MS5Kkb0vvrD/N7lM0zQEBQVB0zRPD8XnsaUctpTDlnLYUg5bymJPOWwphy3lsKUctpSjt5b89D0hfvHpeydOAK1a2X/dooXHhkNEREREREREvoefvke195//QBkqXhJK04D//MfDA/JtSink5+fr5gJ17sSWcthSDlvKYUs5bCmLPeWwpRy2lMOWcthSjt5aclGKLlq16uKfNc3+a6o1pRQKCwt188PEndhSDlvKYUs5bCmHLWWxpxy2lMOWcthSDlvK0VtLLkpRBZMJ2LIFmsUCABX/3rwZyM/37LiIiIiIiIiIyC9xUYoqfPMNUF5uv628vGI7EREREREREZEwLkoRAMC8ahUsAYF22ywBATCv+spDI/J9mqYhNDRUN5+a4E5sKYct5bClHLaUw5ay2FMOW8phSzlsKYct5eitJT99T4jXfvpeaiqQnn7ZXfadyEKrO25Cw+KCKrcVhDTEiaUr0KVF3OW/T2Ii0LRpXUZKRERERERERH7A2TWSwGpvIf8wYQKwdetld+kKwFLNbWHFBehy0/Cav8+gQcCWLbUenj9TSsFkMiEyMlI3q9zuwpZy2FIOW8phSzlsKYs95bClHLaUw5Zy2FKO3lry9D1/d++9QEhIxafpXUZ1L4QaXyCaVvH499zjyuj8mlIKRUVFuvnUBHdiSzlsKYct5bClHLaUxZ5y2FIOW8phSzlsKUdvLXmklDSzueKfS2kaYDDY73c5AQEy+95+O9CjB3DTTcBvvwGSL2xNA9q0AT7/HOjSxbXxWiyXH5M37GswXFzUq+2+1teDo/vU5XHdsa9SFftXp/JruL73tba0WNw/BuDyr2Fv2Beo28+Iy70upX72SO3rzT8jrK/Lyrd5w3vZF39GXO516cmfPY72Bbz7Z4T1dVmZN7yXffVnRHX/La+v3yN8ad+a3keVH8cb3su+/DPC0evSU3/X8PZ9a3oNV/4f9774dw1v2tfR69JX/q5Rn/sCNb/vK/PFv2s4e7/fcVFK2jffAGFhVbcnJAB9+178et266p+k2Fjgqqsufv3tt0BpqeN9GzUCBg68+PWmTUBRUdX95s8H/vnPiu8rZehQ4IEHgHPn7Beldu4EsrMd3ycgABg16uLXP/4IZGRU/z1uvPHin3fvBs6fr37fUaMuviH27QPOnKl+3+HDgaCgij8fPAicPFn9vtdee/E5PXIEOHas+n2vuQaIiKj486+/IvjHH4GoKPsfAFYDB1Y8fwBw4gRw6FD1j3vVVRWvCwA4fRrYv7/6ffv0qbjGF1BxTbG9e6vft1cvICmp4s/nzwO7dlW/b/fuQLNmFX/OyAB++KH6fbt0AVq0qPhzTg6wfXv1+3bsCKSkVPzZaHR8uqnFgmCjEejdG+jQoWJbQQGweXP1j5uSUvHYQMV7YsOG6vdt0eLia7i09PLvk2bNKloAFe/hNWuq37dJE+DKKy9+fbl96/FnRHBamuPXZURExWvYautWID/f8eOGhgLDhl38evt2IC/P8b5BQRXvOSt/+RlhfV2OGwc0bFixrZY/I3D0aPX76ulnxLZtFS0dvS6d+Rlh1bYt0K5dxZ/1+jPCYkEDgwG4/vqL2yR+jwD0+TPC+j6/9LVZT79H+NXPiK5dgeDgij/X9+8RVv7yM8LR69LTf9cAfPNnROWflT74dw2v+hnh6HXpK3/XsPKWnxFdu1b82WwG1q6tfl8v/buG7WfEhQvVj6cSnr6nFyEhwIwZwIcfVvy58mpmbRgMQIMGwNSpwOOPX/zlgqrQNA3BISG6OA/Y3dhSDlvKYUs5bClH0zSE6OgTe9yNr005mqYhPDycLQXwdSmHr0s5fF3K0dvrkp++J8R2ZfmcHMdXlvemQ2oPHQLGjoXl6K8wwPmn3wINhrZtgBUrLh6tUtfxevOpOVbefJis5L7ecDirN+wLePepOVbedni8r56aI7Gvt72X+TPCvfsC/BnhTfvyZ4T37OsN709v2Bfgzwhv2pc/I7xnX294f3rDvoBufkaYTCZExcTU+Ol7XJQS4uzHHXqNwkKkj7kZid9e5hC/S6QPG4XEVZ85Pj2RqlBKITc3F9HR0bpZ5XYXtpTDlnLYUg5bymFLWewphy3lsKUctpTDlnL8paWzayQ8fU+vwsMR164Vyg3OncZXbghAXPsULkjVglIKpaWl4Lpv3bGlHLaUw5Zy2FIOW8piTzlsKYct5bClHLaUo7eWXJTSK4sFAZ99ikCLk1fEt5gR8Onyyx+WSERERERERETkJC5K6dX27Q4/icJyyb/tZGQAO3a4c1REREREREREpBNclNKrTz8FAgPtNlkCAmBuEIR1Q25CeWADlF/6sdyBgRX3I6domobIyEifPg/YW7ClHLaUw5Zy2FIOW8piTzlsKYct5bClHLaUo7eWXJTSI4sFWL4cKC+/uM1ggKF1azTYuwcD1izD4w8vwvnoJrBolV4i5eXAJ5/wFD4naZqGsLAw3fwwcSe2lMOWcthSDlvKYUtZ7CmHLeWwpRy2lMOWcvTWkotSeuTo1L1Jk4Ddu2Fp3x7FBUY0uaoXHn5wIdZ3G2K/H0/hc5rFYkFWVhYsXMSrM7aUw5Zy2FIOW8phS1nsKYct5bClHLaUw5Zy9NaSi1J6ZD0FLyAACAkBFi8GPvjA9sl65eXlGNI5CSVBIVgw7gl8/ejzFfsFBNjfn2pUXvloNKoTtpTDlnLYUg5bymFLWewphy3lsKUctpTDlnL01JKLUnpjPXUPAFq3BnbtAiZOrLJb79bxaBhScc2pfyT1Qcn3O4GUlIobeQofEREREREREdURF6X0pqioYnFp8mRg926gY0eHuwUFBmBghyYAgOIyM7YHxFbsf9ddFfcvKqrHQRMRERERERGRv9GUUsrTg/AHJpMJUVFRMBqNiIyM9PRwLs9iAS79ZL3fKaVQWlqKoKAgHDidgyf//T0AoE/reMyd0KfG+9NFlVvq5SJ17sKWcthSDlvKYUs5bCmLPeWwpRy2lMOWcthSjr+0dHaNhCsLenSZBSVN0xAcHAxN09CpeQwSokIBAD8dy0JeYUmN96eLKrekumFLOWwphy3lsKUctpTFnnLYUg5bymFLOWwpR28tvW514e2330aLFi0QEhKCvn374ocffqh237KyMrzwwgtISUlBSEgIunXrhrVr19rtk5+fj6lTpyI5ORmhoaG46qqr8OOPP9rtc9ddd0HTNLt/RowY4Zb5eTuLxYL09HRYLBYYNA1DOiVVbFcKWw6d9/DofEvlllQ3bCmHLeWwpRy2lMOWsthTDlvKYUs5bCmHLeXoraVXLUotX74c06ZNw+zZs7F7925069YNw4cPR0ZGhsP9Z86cib///e9YtGgRDh06hAceeABjx47Fnj17bPvce++9WL9+PZYsWYL9+/fjuuuuw7Bhw5Cammr3WCNGjMD58+dt/yxbtsytc/Vmlc/oHNqlqe3PG/enOtqdLoNnx8phSzlsKYct5bClHLaUxZ5y2FIOW8phSzlsKUdPLb1qUWrBggW47777MHnyZHTs2BHvvvsuwsLC8MEHHzjcf8mSJXj22WcxatQotGrVCg8++CBGjRqF119/HQBQVFSEFStW4JVXXsGgQYPQunVrzJkzB61bt8Y777xj91jBwcFo3Lix7Z/o6Gi3z9cXtEiIQEpixfmfR1LzkJpd6OEREREREREREZE/8JpFqdLSUuzatQvDhg2zbTMYDBg2bBh27Njh8D4lJSUICQmx2xYaGopt27YBAMrLy2E2my+7j9XmzZuRkJCAdu3a4cEHH0R2drbEtPzCkC5Jtj9vOsCjpYiIiIiIiIio7gI9PQCrrKwsmM1mJCYm2m1PTEzEkSNHHN5n+PDhWLBgAQYNGoSUlBRs2LABK1euhNlsBgBERESgf//+mDt3Ljp06IDExEQsW7YMO3bsQOvWrW2PM2LECIwbNw4tW7bEsWPH8Oyzz2LkyJHYsWMHAgICHH7vkpISlJSU2L42mUwAKs7/tJ77ab0+lVLK7vC7mrZfeu5obbcbDIYqj+3sdqWU3VFiSikM7tAE//z2CBSADftTcfugNrbbfGFOzo5R+nm6tKU/zKmm7e6ak1IKMTExfjWnumyvy5yUUoiNjQVQt9ekN82ptmOXmhMAhy19eU6eep40TUNMTAyUUnb38eU5efJ5io6OtmvpD3Py5PNk7amU8ps51XXsrszJUUtfn5Mz290xJ2tLK3+YU123uzona8vq/jvvi3NyZewSc1Kq5t/XfW1Ozmx3x5yUqt3v6946J2evieU1i1KuWLhwIe677z60b98emqYhJSUFkydPtjvdb8mSJbj77rvRtGlTBAQEoGfPnpgwYQJ27dpl2+fWW2+1/blLly7o2rUrUlJSsHnzZlx77bUOv/dLL72E559/vsr2zMxMFBcXA6g4IisqKgomkwlFRUW2fcLDwxEREYHc3FyUlpbatkdGRiIsLAw5OTkoLy+3bY+OjkZwcDAyMzPtXjSxsbEICAiocs2thIQEmM1mu6O9NE1DYmIiSktLkZuba9seGBiIuLg4FBUV2S2shYSEICYmBgUFBbAUF6JDUgQOncvHudwL+OVcHpo0NPjUnAAgKCjINqfCwounIbrzebJYLIiPjwcAv5kT4JnnKTg4GNHR0TAajX4zJ089T3FxcX43J089T+Hh4cjLy/OrOXnqeTKbzcjJyfGrOXnqeTIajXa/CPrDnDz5PFksFhgMBr+ak6eeJ2tLf5qTp54ni8WC8PBwv5oT4JnnyWKxoHHjxn41J8Azz1NYWBiCgoKQk5PjN3Py1PMUHx+PsrIyn55TZmYmnKGpS5cTPaS0tBRhYWH4/PPPMWbMGNv2SZMmIS8vD6tWrar2vsXFxcjOzkZSUhJmzJiB//znPzh48KDdPoWFhTCZTGjSpAluueUWFBQU4Ouvv672MePj4zFv3jxMmTLF4e2OjpRq1qwZcnNzERlZcQ0mT69MurKCbLFYkJmZiYSEBAQEBNi2f/PzWbzxn/0AgNG9W+DB4R19Zk7OjlH6ebq0pT/Mqabt7pqTtWViYqLtNl+fU12212VOFosFWVlZiI+Ph6bZf8ysr86ptmOXmpNSCpmZmVVa+vKcPPU8AUB6ejri4+NhMBguu7+vzMlTz5NSqkpLX5+TJ58n6y/V8fHxCAgI8Is5eep5sv63vHJLX5+TM9vdMafqfl/35TnVdburc7rc75i+OidXxi4xJ2d+X/e1OTmz3R1zqu3v6946J6PRaDuowLpG4ojXHCkVFBSEXr16YcOGDbZFKYvFgg0bNuCRRx657H1DQkLQtGlTlJWVYcWKFbj55pur7GP9P9q5ublYt24dXnnllWof7+zZs8jOzkaTJk2q3Sc4OBjBwcFVthsMBrtfqIGLT9alqtt+6f1d2V7b71l5u6M/D+zQBG+vPYjScgs2HzyH+//QAYEBzn9fT8/JHdudGXvl+/rLnGra7q4xOnp9OvM43jwnV7dLzcmb5uqLz1PlU6McjccX51TTdnfNyWKx2DrW9b+h3jInT21XSlXb0lfnVNuxV7fdlbEYDAa7f7v6ON40J08+T5e29Ic5ObPdHWOs7s/OPI63zqku2+syJ+n3tjfMyVNjr6mlL86ppu3unpMnGrjz7xoO7+/UXvVk2rRpeO+997B48WIcPnwYDz74IAoLCzF58mQAwMSJE/HMM8/Y9t+5cydWrlyJ48ePY+vWrRgxYgQsFguefvpp2z7r1q3D2rVrceLECaxfvx5DhgxB+/btbY9ZUFCAp556Ct9//z1OnjyJDRs2YPTo0WjdujWGDx9evwG8WHhIA/RrW3G9L+OFUuw+nuXhERERERERERGRL/OaI6UA4JZbbkFmZiZmzZqFtLQ0dO/eHWvXrrVd/Pz06dN2q23FxcWYOXMmjh8/joYNG2LUqFFYsmQJGjVqZNvHaDTimWeewdmzZxETE4ObbroJ8+fPR4MGDQAAAQEB2LdvHxYvXoy8vDwkJSXhuuuuw9y5cx0eCaVnQzs3xXeHzgMANh5IRZ82CR4eERERERERERH5Kq+5ppSvM5lMiIqKqvF8SV9gvQjlpcrMFkx441vkF5UhuEEAlk8bhtAgr1rX9DrVtaTaY0s5bCmHLeWwpRy2lMWecthSDlvKYUs5bCnHH1o6u0bi27MkcdaLejpaq2wQYMDgjhXX2SopM2P7kbT6Hp5PuVxLqh22lMOWcthSDlvKYUtZ7CmHLeWwpRy2lMOWcvTWkotSZEcphezs7GrfAEO7NLX9ecOBc/U1LJ9UU0tyHlvKYUs5bCmHLeWwpSz2lMOWcthSDlvKYUs5emvJRSmqlY5XRCOxUSgAYM/xTOQUFHt4RERERERERETki7goRbWiaRqGdq44WsqigC0Hz3t4RERERERERETki7goRVVomnbZ24d2TrL9eeP+VHcPx6fV1JKcx5Zy2FIOW8phSzlsKYs95bClHLaUw5Zy2FKOnlry0/eE+NOn7znjkfe34dfzRgDA+w8ORrO4hh4eERERERERERF5A376HrlEKYWSkpIaL6pmd7TUAR4t5YizLalmbCmHLeWwpRy2lMOWsthTDlvKYUs5bCmHLeXorSUXpciOUgq5ubk1vgEGd0qC4fcjCjfuT9XNG6Y2nG1JNWNLOWwphy3lsKUctpTFnnLYUg5bymFLOWwpR28tuShFLomNCEGPlnEAgLS8Ihw6m+vhERERERERERGRL+GiFLlsyO+fwgcAmw6c8+BIiIiIiIiIiMjXcFGKqggMDHRqvwHtGyM4sOIltOXgOZSZLe4clk9ytiXVjC3lsKUctpTDlnLYUhZ7ymFLOWwphy3lsKUcPbXkp+8J0dun71m9tHIPNh+sOErq+VuuRL+2iR4eERERERERERF5Ej99j1yilMKFCxecvqja0C6VPoVvPz+Fr7LatqTqsaUctpTDlnLYUg5bymJPOWwphy3lsKUctpSjt5ZclCI7SimYTCan3wC9WsUjKiwIALDjaDoKS8rcOTyfUtuWVD22lMOWcthSDlvKYUtZ7CmHLeWwpRy2lMOWcvTWkotSVCeBAQYM7tQEAFBabsH/jqR5eERERERERERE5Au4KEV1NrTSp/Bt3M9P4SMiIiIiIiKimnFRiuxomoagoCBomub0fdo3bYQm0WEAgL0nspCdX+yu4fkUV1qSY2wphy3lsKUctpTDlrLYUw5bymFLOWwphy3l6K0lF6XIjqZpiImJqdUbQNM0XNul4mgpBWDTAR4tBbjWkhxjSzlsKYct5bClHLaUxZ5y2FIOW8phSzlsKUdvLbkoRXaUUsjPz6/1RdUqn8K36QA/hQ9wvSVVxZZy2FIOW8phSzlsKYs95bClHLaUw5Zy2FKO3lpyUYrsKKVQWFhY6zdA09hwtEtqBAD4Lc2EU5n5bhidb3G1JVXFlnLYUg5bymFLOWwpiz3lsKUctpTDlnLYUo7eWnJRisQM7ZJk+/PG/TxaioiIiIiIiIiqx0UpEjO4YxIMv5/3uunAOVh0srJLRERERERERLXHRSmyo2kaQkNDXbqoWnTDYPRKiQMApBuLcPBMrvTwfEpdWpI9tpTDlnLYUg5bymFLWewphy3lsKUctpTDlnL01pKLUmRH0zRERUW5/AaofMFzvZ/CV9eWdBFbymFLOWwphy3lsKUs9pTDlnLYUg5bymFLOXpryUUpsqOUgtFodPmiale1S0RIgwAAwHeHzqO03Cw5PJ9S15Z0EVvKYUs5bCmHLeWwpSz2lMOWcthSDlvKYUs5emvJRSmyo5RCUVGRy2+AkKBADGjfGABQUFyGn37LlByeT6lrS7qILeWwpRy2lMOWcthSFnvKYUs5bCmHLeWwpRy9teSiFIkb0rnSp/Ad0PcpfERERERERETkGBelSFzPVnFoFB4EAPj+aAYKiss8PCIiIiIiIiIi8jZclCI7mqYhPDy8ThdVCzAYcE2niqOlyswWbDt8Xmp4PkWiJVVgSzlsKYct5bClHLaUxZ5y2FIOW8phSzlsKUdvLbkoRXY0TUNERESd3wBDu1T6FL4D5+o6LJ8k1ZLYUhJbymFLOWwphy1lsacctpTDlnLYUg5bytFbSy5KkR2lFHJycup8UbW2TaJwRUw4AGDfyWxkGIskhudTpFoSW0piSzlsKYct5bClLPaUw5Zy2FIOW8phSzl6a8lFKbKjlEJpaWmd3wCaptmOllIANh/U39FSUi2JLSWxpRy2lMOWcthSFnvKYUs5bCmHLeWwpRy9teSiFLmN3afw7een8BERERERERHRRVyUIrdJiglHhysaAQBOZOTjeLrJswMiIiIiIiIiIq/BRSmyo2kaIiMjxS6qdm3lC57r7Ggp6ZZ6xpZy2FIOW8phSzlsKYs95bClHLaUw5Zy2FKO3lpyUYrsaJqGsLAwsTfAoI5JCDBUPNamg+dg0cl5sYB8Sz1jSzlsKYct5bClHLaUxZ5y2FIOW8phSzlsKUdvLbkoRXYsFguysrJgsVhEHi8qLAhXpsQDALJMxdh/KkfkcX2BdEs9Y0s5bCmHLeWwpRy2lMWecthSDlvKYUs5bClHby25KEVVlJeXiz7eUB2fwifdUs/YUg5bymFLOWwphy1lsacctpTDlnLYUg5bytFTSy5Kkdv1a5uI0KAAAMDWw+dRWm728IiIiIiIiIiIyNO4KEVuF9IgAFe3bwIAKCwpx85fMzw8IiIiIiIiIiLyNC5KkR1N0xAdHS1+UbUhXZJsf9bLKXzuaqlHbCmHLeWwpRy2lMOWsthTDlvKYUs5bCmHLeXorSUXpciOpmkIDg4WfwN0bxGHmIbBAIAff8uEqahU9PG9kbta6hFbymFLOWwphy3lsKUs9pTDlnLYUg5bymFLOXpryUUpsmOxWJCeni5+pf8Ag4ZrOlccLVVmtmDb4TTRx/dG7mqpR2wphy3lsKUctpTDlrLYUw5bymFLOWwphy3l6K0lF6WoCqWUWx53aGf9fQqfu1rqEVvKYUs5bCmHLeWwpSz2lMOWcthSDlvKYUs5emrJRSmqN60bR6J5XEMAwP7TOUjPu+DhERERERERERGRp3BRiuqNpmkY2uXi0VKbDpzz4GiIiIiIiIiIyJO4KEV2NE1DbGys2y6qNqTzxU/h27A/1a8PS3R3Sz1hSzlsKYct5bClHLaUxZ5y2FIOW8phSzlsKUdvLbkoRXY0TUNAQIDb3gCNG4WhU7NoAMDprAIcTze55ft4A3e31BO2lMOWcthSDlvKYUtZ7CmHLeWwpRy2lMOWcvTWkotSZMdisSAjI8OtV/q/ttIpfBv8+ILn9dFSL9hSDlvKYUs5bCmHLWWxpxy2lMOWcthSDlvK0VtLLkpRvRvYsQkCDRWrvpsPnoPZ4r+n8BERERERERGRY1yUonoXGRqE3q0TAADZ+SXYdyrbwyMiIiIiIiIiovrGRSnyCL2cwkdEREREREREjmnKnz/+rB6ZTCZERUXBaDQiMjLS08OpE4vFAoPBveuVpeVm3LLgW1woKUdYUCA+mTYMwQ0C3Po9PaE+WuoFW8phSzlsKYct5bClLPaUw5Zy2FIOW8phSzn+0NLZNRLfniWJU0rBbDbD3WuVQYEBGNihMQDgQmk5vj+a7tbv5wn11VIP2FIOW8phSzlsKYctZbGnHLaUw5Zy2FIOW8rRW0suSpEdpRSys7Pr5Q0wtNIpfBv98BS++mzp79hSDlvKYUs5bCmHLWWxpxy2lMOWcthSDlvK0VtLLkqRx3RNjkVcRAgA4MdjmTBeKPXwiIiIiIiIiIiovnBRijzGoGkY0jkJAGC2KHx36LyHR0RERERERERE9YWLUlSFpmn19r38/RS++mzp79hSDlvKYUs5bCmHLWWxpxy2lMOWcthSDlvK0VNLfvqeEH/69L36NuXd73AyMx8A8OEjQ9AkOszDIyIiIiIiIiIiV/HT98glSimUlJTU60XVKh8ttemA/xwt5YmW/oot5bClHLaUw5Zy2FIWe8phSzlsKYct5bClHL215KIU2VFKITc3t17fANbrSgHAhv2pfvPm80RLf8WWcthSDlvKYUs5bCmLPeWwpRy2lMOWcthSjt5aclGKPC4hKhRdk2MAAGezC/FbmsnDIyIiIiIiIiIid+OiFHmFyqfwbfDDC54TERERERERkT0uSlEVgYGB9f49B3ZoggYBFS/HzQfOwWyx1PsY3METLf0VW8phSzlsKYct5bClLPaUw5Zy2FIOW8phSzl6aslP3xPCT9+ruxc+24X/HUkDALx4Wx/0Son38IiIiIiIiIiIqLb46XvkEqUULly44JGLql3rZ6fwebKlv2FLOWwphy3lsKUctpTFnnLYUg5bymFLOWwpR28tuShFdpRSMJlMHnkD9G4dj4YhFYcpbv8lDcWl5fU+BkmebOlv2FIOW8phSzlsKYctZbGnHLaUw5Zy2FIOW8rRW0suSpHXCAoMwMAOTQAARaVm7Dia7uEREREREREREZG7cFGKvErlU/g2+sEpfERERERERETkmNctSr399tto0aIFQkJC0LdvX/zwww/V7ltWVoYXXngBKSkpCAkJQbdu3bB27Vq7ffLz8zF16lQkJycjNDQUV111FX788Ue7fZRSmDVrFpo0aYLQ0FAMGzYMv/76q1vm5+00TUNQUBA0TfPI9+/UPAbxkSEAgJ+OZSGvsMQj45Dg6Zb+hC3lsKUctpTDlnLYUhZ7ymFLOWwphy3lsKUcvbX0qkWp5cuXY9q0aZg9ezZ2796Nbt26Yfjw4cjIyHC4/8yZM/H3v/8dixYtwqFDh/DAAw9g7Nix2LNnj22fe++9F+vXr8eSJUuwf/9+XHfddRg2bBhSUy8ehfPKK6/gr3/9K959913s3LkT4eHhGD58OIqLi90+Z2+jaRpiYmI89gYwaBqGdq44WsqiFLYcOu+RcUjwdEt/wpZy2FIOW8phSzlsKYs95bClHLaUw5Zy2FKO3lpqyouuntW3b1/07t0bb731FgDAYrGgWbNmePTRRzFjxowq+yclJeHPf/4zHn74Ydu2m266CaGhofjoo49QVFSEiIgIrFq1Ctdff71tn169emHkyJGYN28elFJISkrC9OnT8eSTTwIAjEYjEhMT8eGHH+LWW291auzOftyht1NKoaCgAA0bNvTYm+BkRj6m/P07AED7po2w8O4BHhlHXXlDS3/BlnLYUg5bymFLOWwpiz3lsKUctpTDlnLYUo6/tHR2jcRrjpQqLS3Frl27MGzYMNs2g8GAYcOGYceOHQ7vU1JSgpCQELttoaGh2LZtGwCgvLwcZrP5svucOHECaWlpdt83KioKffv2rfb7+jOlFAoLCz16pf8WCRFolVjxoj2SmofUnEKPjaUuvKGlv2BLOWwphy3lsKUctpTFnnLYUg5bymFLOWwpR28tAz09AKusrCyYzWYkJibabU9MTMSRI0cc3mf48OFYsGABBg0ahJSUFGzYsAErV66E2WwGAERERKB///6YO3cuOnTogMTERCxbtgw7duxA69atAQBpaWm273Pp97Xe5khJSQlKSi5e78hkMgGoOLrLYrEAqDjsTtM0KKXsXlA1bbfe39XtBoOhymM7u91isdjtU9uxS81pSOcmOJ5e0XTjvrO485p2Ls+prmN3dU6XtpR8njw1p5q2u2tO1pZA7V+T3jqnumyvy5ysf7a+3/1hTrUdu9ScKr8mK4/Hl+fkqecJcP416Stz8tTz5Kilr8/Jk8+T9b8/FovFb+bkqefJUUtfn5Mz290xp7r+vu6Nc6rrdlfndLnfMX11Tq6MXWJOzvy+7mtzcma7O+ZU29/XvXVOl26vjtcsSrli4cKFuO+++9C+fXtomoaUlBRMnjwZH3zwgW2fJUuW4O6770bTpk0REBCAnj17YsKECdi1a1edvvdLL72E559/vsr2zMxM27WoQkNDERUVBZPJhKKiIts+4eHhiIiIQG5uLkpLS23bIyMjERYWhpycHJSXl9u2R0dHIzg4GJmZmXYvmtjYWAQEBFS55lZCQgLMZjOys7Nt2zRNQ2JiIkpLS5Gbm2vbHhgYiLi4OBQVFcFkMsFiscBoNCIoKAixsbEoKChAYeHFI5Xqa06dE4OgAVAA1v98BrcNbA2LxeLSnKyCgoIQExNTb3OytoyNjYWmaaLPk6fmdOnzVF9zslgstnn4y5wAzzxP1r8MmM1m5OTk+MWcAM88T2FhYQCAvLw8lJWV+cWcPPU8xcfHo7y8HBkZGTAYDH4xJ089TyEhIcjPz4dSytbS1+fkyecpLy8PRqMRSimEhIT4xZw89TyZzWZby/j4eL+Yk6eeJ+vvmCEhIYiOjvaLOXnqebK2jI+Ph1LKL+bkqefJYrHYDtrwlzkBnnmelFLQNA2lpaUwGo0+O6fMzEw4w2uuKVVaWoqwsDB8/vnnGDNmjG37pEmTkJeXh1WrVlV73+LiYmRnZyMpKQkzZszAf/7zHxw8eNBun8LCQphMJjRp0gS33HILCgoK8PXXX+P48eNISUnBnj170L17d9v+gwcPRvfu3bFw4UKH39PRkVLNmjVDbm6u7XxJT69MurKCrJSynfspuQrrypxmLP0BP5+seHMuvHsA2iVF+dSq+KUt9brSLzEnpRTy8/MRFRVl+9rX51SX7XWZk1IV56hHRETgUr46p9qOXWpOQMUnvF7a0pfn5KnnSdM0GI1GREREQNO0y+7vK3Py5JFSeXl5iIyMtH3t63Py5PNksVhgMpkQGRkJg8HgF3Py1PNk/b2ocktfn5Mz290xJ2tLV39f98Y51XW7q3OytmzUqJHta1+fkytjl5iTUjX/vu5rc3JmuzvmpFTtfl/31jkZjUZER0fXeE0pr1mUAioudN6nTx8sWrQIQMVqa/PmzfHII484vND5pcrKytChQwfcfPPNePHFFx3uk5ubi5YtW+KVV17B/fffD6UqLnT+5JNPYvr06QAqFpgSEhJ0eaFzb7Ju7xksWL0PADC6dws8NKKTh0dERERERERERDXxuQudA8C0adPw3nvvYfHixTh8+DAefPBBFBYWYvLkyQCAiRMn4plnnrHtv3PnTqxcuRLHjx/H1q1bMWLECFgsFjz99NO2fdatW4e1a9fixIkTWL9+PYYMGYL27dvbHlPTNEydOhXz5s3DV199hf3792PixIlISkqyO2JLL6wrmt6wVnl1+8YICqx4iW4+eA7lZufOSfUW3tTS17GlHLaUw5Zy2FIOW8piTzlsKYct5bClHLaUo7eWXrUodcstt+C1117DrFmz0L17d+zduxdr1661XYT89OnTOH/+vG3/4uJizJw5Ex07dsTYsWPRtGlTbNu2zXb4JQAYjUY8/PDDaN++PSZOnIirr74a69atQ4MGDWz7PP3003j00Udx//33o3fv3igoKMDatWurfGqfHiilUFRU5BVvgPCQBujbpuK5N14oxZ4TWR4eUe14U0tfx5Zy2FIOW8phSzlsKYs95bClHLaUw5Zy2FKO3lp63YXOH3nkETzyyCMOb9u8ebPd14MHD8ahQ4cu+3g333wzbr755svuo2kaXnjhBbzwwgu1Giu537VdmmLr4YqFyA37U9G7dYKHR0REREREREREErzqSCmiS13ZOh4RoRVHtW3/JR1FpeU13IOIiIiIiIiIfAEXpciOpmkIDw+3fWKPpzUIMGBQxyYAgJIyM7YfSfPwiJznbS19GVvKYUs5bCmHLeWwpSz2lMOWcthSDlvKYUs5emvJRSmyo2lalY/k9rRruzS1/XnDgXMeHEnteGNLX8WWcthSDlvKYUs5bCmLPeWwpRy2lMOWcthSjt5aclGK7CilkJOT41UXVet4RTQSG4UCAPYcz0ROQbGHR+Qcb2zpq9hSDlvKYUs5bCmHLWWxpxy2lMOWcthSDlvK0VtLLkqRHaUUSktLveoNoGkahnauOFrKooAtB8/XcA/v4I0tfRVbymFLOWwphy3lsKUs9pTDlnLYUg5bymFLOXpryUUp8glDOyfZ/rxxf6oHR0JEREREREREErgoRT6heXwEWjeOBAAcPW/EmawCD4+IiIiIiIiIiOqCi1JkR9M0REZGeuVF1Spf8HzjAe8/WsqbW/oatpTDlnLYUg5bymFLWewphy3lsKUctpTDlnL01pKLUmRH0zSEhYV55RtgcKckGH4f1qYD57z+HFtvbulr2FIOW8phSzlsKYctZbGnHLaUw5Zy2FIOW8rRW0suSpEdi8WCrKwsWCwWTw+litiIEHRvGQcAOJ97AYdT8zw7oBp4c0tfw5Zy2FIOW8phSzlsKYs95bClHLaUw5Zy2FKO3lpyUYqqKC8v9/QQqmX9FD7ANy547s0tfQ1bymFLOWwphy3lsKUs9pTDlnLYUg5bymFLOXpqyUUp8ikD2jdGcGDFy3bLwXMoN+tj9ZiIiIiIiIjI33BRinxKWHAg+rdrDAAwFZXhp2OZHh4REREREREREbmCi1JkR9M0REdHe/VF1YZ2SbL92ZtP4fOFlr6CLeWwpRy2lMOWcthSFnvKYUs5bCmHLeWwpRy9teSiFNnRNA3BwcFe/Qbo1SoeUWFBAIAdR9NRWFLm4RE55gstfQVbymFLOWwphy3lsKUs9pTDlnLYUg5bymFLOXpryUUpsmOxWJCenu7VV/oPDDBgUMcmAIDScgu2H0n38Igc84WWvoIt5bClHLaUw5Zy2FIWe8phSzlsKYct5bClHL215KIUVaGU8vQQanRtl4ufwrfBi0/h84WWvoIt5bClHLaUw5Zy2FIWe8phSzlsKYct5bClHD215KIU+aT2TRuhSXQYAGDviSxk5xd7eEREREREREREVBtclCKfpGkahnauOFpKAdh88JxnB0REREREREREtcJFKbKjaRpiY2N94qJq3v4pfL7U0tuxpRy2lMOWcthSDlvKYk85bCmHLeWwpRy2lKO3llyUIjuapiEgIMAn3gBXxDZE26QoAMBvaSacysz38Ijs+VJLb8eWcthSDlvKYUs5bCmLPeWwpRy2lMOWcthSjt5aclGK7FgsFmRkZPjMlf4rX/Dc246W8rWW3owt5bClHLaUw5Zy2FIWe8phSzlsKYct5bClHL215KIU+bTBHZNg+H0FedOBc7Do6FMKiIiIiIiIiHwZF6XIp0U3DEbPVnEAgHRjEQ6eyfXwiIiIiIiIiIjIGVyUIp/nzafwEREREREREZFjmlI830mCyWRCVFQUjEYjIiMjPT2cOrFYLDAYfGe9sri0HLcs+BbFZWY0DGmAZU9ci6DAAE8PC4DvtfRmbCmHLeWwpRy2lMOWsthTDlvKYUs5bCmHLeX4Q0tn10h8e5YkTikFs9kMX1qrDAkKxFXtEgEABcVl+Om3TA+PqIIvtvRWbCmHLeWwpRy2lMOWsthTDlvKYUs5bCmHLeXorSUXpciOUgrZ2dk+9wYYWvkUvgPecQqfr7b0Rmwphy3lsKUctpTDlrLYUw5bymFLOWwphy3l6K0lF6XIL/RsFYdG4UEAgO+PZqCwuMzDIyIiIiIiIiKiy+GiFPmFAIMB13RKAgCUmS3YdiTNwyMiIiIiIiIiosvhohRVoWmap4fgksqn8G3wkk/h89WW3ogt5bClHLaUw5Zy2FIWe8phSzlsKYct5bClHD215KfvCfGnT9/zVUop3Pu3LTibUwgNwJLHhyI+MtTTwyIiIiIiIiLSFX76HrlEKYWSkhKfvKiapmkY8vvRUgrA5gPnPDoeX27pbdhSDlvKYUs5bCmHLWWxpxy2lMOWcthSDlvK0VtLLkqRHaUUcnNzffYNMLRzku3Pnj6Fz9dbehO2lMOWcthSDlvKYUtZ7CmHLeWwpRy2lMOWcvTWkotS5FeSYsLR4YpGAIATGfk4kW7y7ICIiIiIiIiIyCEuSpHfGdr54gXPN3r4FD4iIiIiIiIicoyLUlRFYGCgp4dQJ4M6NkGAoeLTCjYeSIXFg4c9+npLb8KWcthSDlvKYUs5bCmLPeWwpRy2lMOWcthSjp5a8tP3hPDT97zLrE9+xM5fMwAAr9zZD91axHp4RERERERERET6wE/fI5copXDhwgWfv6ia/Sl8nrngub+09AZsKYct5bClHLaUw5ay2FMOW8phSzlsKYct5eitJRelyI5SCiaTyeffAP3aJSI0KAAAsPXQeZSWm+t9DP7S0huwpRy2lMOWcthSDlvKYk85bCmHLeWwpRy2lKO3llyUIr8U0iAAA9o3BgAUlpTbTuUjIiIiIiIiIu/ARSnyW0O7XDyFb9N+z5zCR0RERERERESOcVGK7GiahqCgIGia5umh1Fn3FnGIaRgMAPjht0yYikrr9fv7U0tPY0s5bCmHLeWwpRy2lMWecthSDlvKYUs5bClHby25KEV2NE1DTEyMX7wBAgwarumUBAAoM1uw7XBavX5/f2rpaWwphy3lsKUctpTDlrLYUw5bymFLOWwphy3l6K0lF6XIjlIK+fn5fnNRtcqn8G2s51P4/K2lJ7GlHLaUw5Zy2FIOW8piTzlsKYct5bClHLaUo7eWXJQiO0opFBYW+s0boHXjSDSPawgA2H86B+l5F+rte/tbS09iSzlsKYct5bClHLaUxZ5y2FIOW8phSzlsKUdvLbkoRX5N0zQM6Zxk+3rTgXMeHA0RERERERERWXFRivze0M4XT+HbsD9VNyvORERERERERN6Mi1JkR9M0hIaG+tVF1RpHh6FTs2gAwOmsAhxPN9XL9/XHlp7ClnLYUg5bymFLOWwpiz3lsKUctpTDlnLYUo7eWnJRiuxomoaoqCi/ewNUvuD5hnq64Lm/tvQEtpTDlnLYUg5bymFLWewphy3lsKUctpTDlnL01pKLUmRHKQWj0eh3p7gN6tAEgYaKN/Xmg+dgtrh/fv7a0hPYUg5bymFLOWwphy1lsacctpTDlnLYUg5bytFbSy5KkR2lFIqKivzuDRAZFoTerRMAANn5Jdh3Ktvt39NfW3oCW8phSzlsKYct5bClLPaUw5Zy2FIOW8phSzl6a8lFKdKNyqfwbaynU/iIiIiIiIiIyDEuSpFu9G2TgLDgQADAtsNpKCkze3hERERERERERPrFRSmyo2kawsPD/fKiasENAjCwQ2MAwIXScnx/NN2t38+fW9Y3tpTDlnLYUg5bymFLWewphy3lsKUctpTDlnL01pKLUmRH0zRERET47RtgaOdKp/AdOOfW7+XvLesTW8phSzlsKYct5bClLPaUw5Zy2FIOW8phSzl6a8lFKbKjlEJOTo7fXlStS3Is4iJCAAA//pYB44VSt30vf29Zn9hSDlvKYUs5bCmHLWWxpxy2lMOWcthSDlvK0VtLLkqRHaUUSktL/fYNEGDQMKRzEgDAbFH47tB5t30vf29Zn9hSDlvKYUs5bCmHLWWxpxy2lMOWcthSDlvK0VtLLkqR7gypdArfpgP8FD4iIiIiIiIiT+CiFOlOq8QItIiPAAAcPJOL87kXPDwiIiIiIiIiIv3hohTZ0TQNkZGRfn1RNU3TMLRLku1rdx0tpYeW9YUt5bClHLaUw5Zy2FIWe8phSzlsKYct5bClHL215KIU2dE0DWFhYX7/Bqh8Ct/G/aluOV9XLy3rA1vKYUs5bCmHLeWwpSz2lMOWcthSDlvKYUs5emvJRSmyY7FYkJWVBYvF4umhuFVCVCi6JscAAM5kF+K3NJP499BLy/rAlnLYUg5bymFLOWwpiz3lsKUctpTDlnLYUo7eWnJRiqooLy/39BDqReWjpTbsd88pfHppWR/YUg5bymFLOWwphy1lsacctpTDlnLYUg5bytFTSy5KkW4N7NAEDQIq3gJbDp6DWScr0URERERERETegItSpFsRoQ3Qp00CACCnoAR7T2R7eERERERERERE+uHSotTy5ctRXFwsPRbyApqmITo6WjcXVRva+eKn8Emfwqe3lu7ElnLYUg5bymFLOWwpiz3lsKUctpTDlnLYUo7eWrq0KDVhwgQ0btwY99xzDzZt2iQ9JvIgTdMQHBysmzdAnzYJaBgSCADY/ksaikvlzt3VW0t3Yks5bCmHLeWwpRy2lMWecthSDlvKYUs5bClHby1dWpTatm0bbr/9dqxevRrDhg1D8+bNMWPGDBw4cEB6fFTPLBYL0tPTdXOl/6DAAAzs0AQAUFRqxo6j6WKPrbeW7sSWcthSDlvKYUs5bCmLPeWwpRy2lMOWcthSjt5aurQoddVVV+Htt9/GuXPnsGrVKgwYMABvvfUWunXrhu7du+P111/H+fPnpcdK9UQp5ekh1KuhXS5+Ct9G4VP49NbSndhSDlvKYUs5bCmHLWWxpxy2lMOWcthSDlvK0VPLOl3oPDAwEDfccAOWLVuGtLQ0fPjhh4iNjcXTTz+N5s2b4w9/+AM++ugjlJaWOv2Yb7/9Nlq0aIGQkBD07dsXP/zwQ7X7lpWV4YUXXkBKSgpCQkLQrVs3rF271m4fs9mM5557Di1btkRoaChSUlIwd+5cuyf5rrvugqZpdv+MGDGi9kHIJ3VuHoP4yBAAwE/HspBXWOLhERERERERERH5P7FP3ztw4AB++OEH7N+/H0optG/fHtnZ2Zg4cSJSUlKwbdu2Gh9j+fLlmDZtGmbPno3du3ejW7duGD58ODIyMhzuP3PmTPz973/HokWLcOjQITzwwAMYO3Ys9uzZY9vn5ZdfxjvvvIO33noLhw8fxssvv4xXXnkFixYtsnusESNG4Pz587Z/li1bVrcg5DMMmoahnSuOlrIohS2HeJQfERERERERkbtpqg7HhR09ehRLly7Fxx9/jOPHjyMuLg4TJkzAxIkT0bNnTwDATz/9hHvuuQdms7nGa0717dsXvXv3xltvvQWg4lzKZs2a4dFHH8WMGTOq7J+UlIQ///nPePjhh23bbrrpJoSGhuKjjz4CANxwww1ITEzEP//5z2r3ueuuu5CXl4cvv/zS1RQwmUyIioqC0WhEZGSky4/jaUoplJeXIzAwUDcXVgOAkxn5mPL37wAA7Zs2wsK7B9T5MfXa0h3YUg5bymFLOWwphy1lsacctpTDlnLYUg5byvGXls6ukQS68uALFy7E0qVLsWvXLgQHB+PGG2/Em2++iREjRiAgIMBu3yuvvBLTpk3DPffcc9nHLC0txa5du/DMM8/YthkMBgwbNgw7duxweJ+SkhKEhITYbQsNDbU7Kuuqq67CP/7xDxw9ehRt27bFzz//jG3btmHBggV299u8eTMSEhIQHR2NoUOHYt68eYiNja12vCUlJSgpuXial8lkAlCxkGa9IJn1VECllN3pgjVtv/SCZrXdbjAYqjx2bbZXfuHXduzeOqeaxtgiIQKtEiNwPD0fR1LzcDYrH01jG9Z5TpVb1vec/O15MhgMfjcnTz1P1p/TdZmrt83JU8+To5a+PidPPU+ObvP1OXnqebr0Nn+YkyefJ+u4rI/vD3Oq69hdndOlLf1hTjVtd9ecNM3139e9dU512V6XOVX32L48p9qOXWpONf2+7otz8tTzVJvf1711Ts5eqN2lRaknnngCAwYMwLvvvoubb74ZUVFRl93/yiuvxHPPPXfZfbKysmA2m5GYmGi3PTExEUeOHHF4n+HDh2PBggUYNGgQUlJSsGHDBqxcuRJms9m2z4wZM2AymdC+fXsEBATAbDZj/vz5uP322237jBgxAuPGjUPLli1x7NgxPPvssxg5ciR27NhRZZHN6qWXXsLzzz9fZXtmZiaKi4sBVCyQRUVFwWQyoaioyLZPeHg4IiIikJuba3e9rcjISISFhSEnJwfl5eW27dHR0QgODkZmZqbdiyY2NhYBAQFVTm9MSEiA2WxGdna2bZumaUhMTERpaSlyc3Nt2wMDAxEXF4eioiKYTCZYLBYYjUYkJCQgNjYWBQUFKCwstO3vi3OyCgoKQkxMTLVzuqpNLI6n5wMAVu/8FXcMalOnOVlbpqSkIDAw0CNz8pfnyWKxoKioCC1btkR+fr5fzAnwzPNksVigaRpiY2ORk5PjF3MCPPM8hYWF4cKFC2jQoAHKysr8Yk6eep7i4+Nx7tw5BAQEwGAw+MWcPPU8hYSE4Pjx42jYsKGtpa/PyZPPU15eHoxGI6KiohASEuIXc/LU82Q2m20t4+Pj/WJOnnqerL9jNm7cGNHR0X4xJ089T9aWbdq0gVLKL+bkqefJYrGgpKQEycnJyMvL84s5AZ55nqz/Q8R6lJGvzikzMxPOcOn0vRMnTqBly5a1vdtlnTt3Dk2bNsX27dvRv39/2/ann34aW7Zswc6dO6vcJzMzE/fddx9Wr14NTdOQkpKCYcOG4YMPPrBF/+STT/DUU0/h1VdfRadOnbB3715MnToVCxYswKRJkxyO5fjx40hJScG3336La6+91uE+jo6UatasGXJzc22Hpnl6ZdKVFWSLxYLMzEwkJCQgICDA61Zb3bkqnmkswp1/3QgFoGlMGN5/cPBlH6emsV/aUs8r/XWdk7VlYmKi7TZfn1NdttdlThaLBVlZWYiPj4em2R8O7Ktzqu3YpeaklEJmZmaVlr48J089TwCQnp6O+Ph420KKr8/JU8+TUqpKS1+fkyefJ+sv1dZFFH+Yk6eeJ+t/yyu39PU5ObPdHXOq6+/r3jinum53dU6X+x3TV+fkytgl5uTM7+u+NidntrtjTrX9fd1b52Q0GhEdHe2e0/eaNWsGk8lU7QObTCaEhYUhMND5h4+Li0NAQADS09Pttqenp6Nx48YO7xMfH48vv/wSxcXFyM7ORlJSEmbMmIFWrVrZ9nnqqacwY8YM3HrrrQCALl264NSpU3jppZeqXZRq1aoV4uLi8Ntvv1W7KBUcHIzg4OAq262nIFRmfbIuVd32S+/vyvbafs/K26v7c10e39NzcmZ7fFQourWIxd6T2UjNuYCj501o37RRneZU+b6emJM/PU+uvia9eU6ubpeakzfN1Refp8qnozgajy/Oqabt7pqT9Qg+if+GesucPLVdKVVtS1+dU23HXt12V8ZiPRXF+m9XH8eb5uTJ5+nSlv4wJ2e2u2OM1f3Zmcfx1jnVZXtd5iT93vaGOXlq7DW19MU51bTd3XPyRAN3/l3D4f2d2usSjz32GK666qpqbx8wYACmT59eq8cMCgpCr169sGHDBts2i8WCDRs22B055UhISAiaNm2K8vJyrFixAqNHj7bdduHChSoxHB21UtnZs2eRnZ2NJk2a1GoO5PuGdmlq+/PG/akeHAkRERERERGRf3NpUWrt2rUYP358tbePHz8ea9asqfXjTps2De+99x4WL16Mw4cP48EHH0RhYSEmT54MAJg4caLdhdB37tyJlStX4vjx49i6dStGjBgBi8WCp59+2rbPjTfeiPnz5+Prr7/GyZMn8cUXX2DBggUYO3YsAKCgoABPPfUUvv/+e5w8eRIbNmzA6NGj0bp1awwfPrzWc/B1BoMBCQkJTq9q+pur2zdGg4CKuW8+eA7lZucuzuaI3ltKYks5bCmHLeWwpRy2lMWecthSDlvKYUs5bClHby1dOn3Pev2n6iQlJSE1tfZHmdxyyy3IzMzErFmzkJaWhu7du2Pt2rW2i5+fPn3a7okpLi7GzJkzbRf0HDVqFJYsWYJGjRrZ9lm0aBGee+45PPTQQ8jIyEBSUhKmTJmCWbNmAag4amrfvn1YvHgx8vLykJSUhOuuuw5z5851eHqev7NeP6G6Q/n8XXhIA/Rrm4ith8/DeKEUe05koXfrBJceS+8tJbGlHLaUw5Zy2FIOW8piTzlsKYct5bClHLaUo7eWLl3ovGnTppgwYQJee+01h7dPmzYNH3/8MdLS0uo8QF9hMplsV8e/3EW8vJ3FYkFGRoauVmYvtf2XNDz/6S4AwJDOSZgxtodLj8OWcthSDlvKYUs5bCmHLWWxpxy2lMOWcthSDlvK8ZeWzq6RuDTDESNG4O9//zv27NlT5bbdu3fjH//4B0aOHOnKQxN5XO/WCYgIbQAA2P5LOopKy2u4BxERERERERHVlkun782dOxdr165Fnz598Mc//hGdOnUCABw4cACrV69GQkIC5s6dKzpQovrSIMCAQR2b4Otdp1FSZsb2I2m4tusVnh4WERERERERkV9xaVEqKSkJP/30E2bMmIFVq1bhiy++AABERkbi9ttvx4svvoikpCTRgVL90cN5qzUZ2rkpvt51GgCw4cA5lxel2FIOW8phSzlsKYct5bClLPaUw5Zy2FIOW8phSzl6aunSNaUqU0ohMzMTABAfH6+reJX5yzWlqIJFKdz11iak5xXBoAEfTx2G6Ib6u/A9ERERERERUW259ZpSlWmahoSEBCQkJOh2QcqfKKVQUlKCOq5V+jyDpmFo54pPmLQoYPPBc7V+DLaUw5Zy2FIOW8phSzlsKYs95bClHLaUw5Zy2FKO3lq6dPqe1f/+9z/s3r0bRqMRFovF7jZN0/Dcc8/VaXBU/5RSyM3N5SIjgKGdk7Bs228AgI37UzG2b8ta3Z8t5bClHLaUw5Zy2FIOW8piTzlsKYct5bClHLaUo7eWLi1K5eTk4Prrr8cPP/wApRQ0TbOt4ln/zEUp8nXN4yPQunEkfksz4eh5I85kFaBZXENPD4uIiIiIiIjIL7h0+t5TTz2Fffv24eOPP8bx48ehlMK6detw9OhRPPDAA+jevTvOnav96U5E3ubaLk1tf954INWDIyEiIiIiIiLyLy4tSq1ZswZTpkzBLbfcgoiIiIoHMhjQunVrvP3222jRogWmTp0qOU6qR4GBdTqr068M7pQEw+9HTG46cK7W5/WypRy2lMOWcthSDlvKYUtZ7CmHLeWwpRy2lMOWcvTU0qVFqby8PHTq1AkA0LBhxelMBQUFttuvu+46rFu3TmB4VN8MBgPi4uJgMNT5Gvh+ITYiBN1bxgEAzudewOHUPKfvy5Zy2FIOW8phSzlsKYctZbGnHLaUw5Zy2FIOW8rRW0uXZpmUlIS0tDQAQHBwMBISEvDzzz/bbk9NTdXFBbn8kVIKFy5c0M2V/p1h/RQ+oOKC585iSzlsKYct5bClHLaUw5ay2FMOW8phSzlsKYct5eitpUuLUoMGDcL69ettX99yyy145ZVXMH/+fMydOxdvvvkmhgwZIjZIqj9KKZhMJt28AZwxoH1jBAdWvFW2HDyHcrOlhntUYEs5bCmHLeWwpRy2lMOWsthTDlvKYUs5bCmHLeXoraVLJypOmzYN69evR0lJCYKDgzFnzhwcPHjQ9ml7gwYNwqJFi0QHSuQpYcGB6N+uMTYfPAdTURl+OpaJfm0TPT0sIiIiIiIiIp/m0qJUly5d0KVLF9vX0dHR+Pbbb5GXl4eAgADbxc+J/MXQLknYfLDiEyU37k/lohQRERERERFRHdX69L0LFy6gV69eePfdd6vc1qhRIy5I+ThN0xAUFMRrgl2iV6t4RIUFAQB2HE1HYUlZjfdhSzlsKYct5bClHLaUw5ay2FMOW8phSzlsKYct5eitZa0XpcLCwnDixAndBNIbTdMQExPD5/cSgQEGDOrYBABQWm7B9iPpNd6HLeWwpRy2lMOWcthSDlvKYk85bCmHLeWwpRy2lKO3li5d6HzEiBFYt26d9FjICyilkJ+fr5uLqtXGtV0ufgrfBic+hY8t5bClHLaUw5Zy2FIOW8piTzlsKYct5bClHLaUo7eWLi1KPffcczh69CjuvPNObNu2DampqcjJyanyD/kepRQKCwt18waojfZNG6FJdBgA4OeTWcjOL77s/mwphy3lsKUctpTDlnLYUhZ7ymFLOWwphy3lsKUcvbV06ULnnTp1AgAcOnQIH3/8cbX7mc1m10ZF5IU0TcPQzk2xdOuvsChg88FzuKlfK08Pi4iIiIiIiMgnubQoNWvWLN2c30hU2dAuSVi69VcAFZ/Cx0UpIiIiIiIiIte4tCg1Z84c4WGQt9A0DaGhoVx0rMYVsQ3RNikKR88Z8VuaCacy85Ec7/gTJ9lSDlvKYUs5bCmHLeWwpSz2lMOWcthSDlvKYUs5emvp0jWlyH9pmoaoqCjdvAFcUfmC5xsvc8FztpTDlnLYUg5bymFLOWwpiz3lsKUctpTDlnLYUo7eWrp0pNQLL7xQ4z6apuG5555z5eHJg5RSMJlMiIyM1M2boLYGd0zC3785DItS2HTgHCYNaQeDg1ZsKYct5bClHLaUw5Zy2FIWe8phSzlsKYct5bClHL21FD99T9M0KKW4KOWjlFIoKipCRESELt4ArohuGIyereLw07FMpBuLcOhMLjo3j6myH1vKYUs5bCmHLeWwpRy2lMWecthSDlvKYUs5bClHby1dOn3PYrFU+ae8vBzHjh3DE088gSuvvBIZGRnSYyXyGpVP4dtwmVP4iIiIiIiIiMgxsWtKGQwGtGzZEq+99hratGmDRx99VOqhibxO/3aJCG4QAAD47tB5lJktHh4RERERERERkW9xy4XOBw0ahDVr1rjjocnNNE1DeHi4Lg4TrIvQoEAMaJcIACgoLsOPv1U9MpAt5bClHLaUw5Zy2FIOW8piTzlsKYct5bClHLaUo7eWblmU+umnn2Aw8IP9fJGmabo5d7WuhtbwKXxsKYct5bClHLaUw5Zy2FIWe8phSzlsKYct5bClHL21dOlC5//+978dbs/Ly8N3332HlStX4t57763TwMgzlFLIzc1FdHS0bt4ErurZKg6NwoOQV1iK749moLC4DOEhDWy3s6UctpTDlnLYUg5bymFLWewphy3lsKUctpTDlnL01tKlRam77rqr2tvi4uIwY8YMzJo1y9UxkQcppVBaWmr7BEWqXoDBgGs6JeHLH06izGzBtiNpGN69me12tpTDlnLYUg5bymFLOWwpiz3lsKUctpTDlnLYUo7eWrq0KHXixIkq2zRNQ3R0NCIiIuo8KCJfMbRLU3z5w0kAFZ/CV3lRioiIiIiIiIiq59KiVHJysvQ4iHxS2yZRaBoTjtScQuw7mY1MUxHiI0M9PSwiIiIiIiIir+fS1ch3796Nv/3tb9Xe/re//Q179+51dUzkQZqmITIyUheHCUrQNM12wXMFYPOBc3a3saUMtpTDlnLYUg5bymFLWewphy3lsKUctpTDlnL01tKlRak///nP+Pbbb6u9fePGjZg5c6bLgyLP0TQNYWFhunkDSBjaOcn25w2VPoWPLeWwpRy2lMOWcthSDlvKYk85bCmHLeWwpRy2lKO3li4tSu3atQsDBw6s9vaBAwfip59+cnlQ5DkWiwVZWVmwWCyeHorPSIoJR4emjQAAJzLycSLdBIAtJbGlHLaUw5Zy2FIOW8piTzlsKYct5bClHLaUo7eWLi1K5efnIzCw+stRGQwGGI1GlwdFnlVeXu7pIfgc6yl8ALCx0il8bCmHLeWwpRy2lMOWcthSFnvKYUs5bCmHLeWwpRw9tXRpUapNmzb45ptvqr197dq1aNWqlcuDIvI1gzo2QYCh4vDKTQdSYVHKwyMiIiIiIiIi8m4uLUrdc889+PrrrzFt2jTk5eXZtufl5eGJJ57A2rVrcc8990iNkcjrNQoPRq+UeABApqkYB07neHhERERERERERN5NU6r2h3QopXD33Xdj8eLFMBgMSEqquNDzuXPnYLFYcOedd+LDDz/UzYW5AMBkMiEqKgpGoxGRkZGeHo7LlFIoLS1FUFCQrp4/CZsPnMNLX+wBAIzo0QxTr+/ClkL4upTDlnLYUg5bymFLWewphy3lsKUctpTDlnL8paWzayQuLUpZbdq0CStWrMDx48cBACkpKbjppptwzTXXuPqQPstfFqXIdcVlZty6YD2KSs0IDw7EJ9OGISgwwNPDIiIiIiIiIqpX9bIoRRf5y6KUxWJBZmYm4uPjYTC4dHanrr26ai++3ZcKAGifFAUoM2Ijw3FV+8YY1LEJF6lcxNelHLaUw5Zy2FIOW8piTzlsKYct5bClHLaU4y8tnV0jcWmGJ06cwOrVq6u9ffXq1Th58qQrD01egOuUrmsSHWb785FzRhw5X4DtR9Px6qqfMeGNb/H90XQPjs638XUphy3lsKUctpTDlrLYUw5bymFLOWwphy3l6KmlS4tSTz75JP76179We/vbb7+NGTNmuDwoIl+045d0fLTl1yrbrT9PCovLMWf5T9jxCxemiIiIiIiIiFxalNqxYwf+8Ic/VHv7tddei61bt7o8KCJfU1puxmtf7b3sPta17te+2ovScrPbx0RERERERETkzVxalMrNzUVERES1tzds2BDZ2dkuD4o8R9M0xMbG+vRV/j3hu0PnUVBcjpoOslQACorLsfXQ+foYlt/g61IOW8phSzlsKYctZbGnHLaUw5Zy2FIOW8rRW0uXFqWaN2+O//3vf9XevnXrVlxxxRUuD4o8R9M0BAQE6OYNIGX7L+lwNpmmAf/jKXy1wtelHLaUw5Zy2FIOW8piTzlsKYct5bClHLaUo7eWLi1KTZgwAcuWLcNf//pXWCwW23az2YyFCxdi+fLluO2228QGSfXHYrEgIyPD7nmlmuUXlcLZa9EpBRw+m4uN+1ORZSp278D8BF+XcthSDlvKYUs5bCmLPeWwpRy2lMOWcthSjt5aBrpyp2eeeQbbtm3D1KlTMX/+fLRr1w4A8MsvvyAzMxPXXHMN/vznP4sOlMibRYQGQdPg9MJUTkEJXv5yLwAgKSYMXZvHomtyDLokxyIhKtR9AyUiIiIiIiLyEi4tSgUHB+Obb77B4sWLsXLlShw7dgwA0KdPH9x0002YOHEiDAaXDsIi8klXtUvE/46kuXTfczkXcC7nAtbuPQMAaBIdhi7NY9A1uWKhKrFRmORQiYiIiIiIiLyCS4tSAGAwGDB58mRMnjzZ4e0HDhxA586dXR4YkS8Z1LEJ3ll3EIU1XOxcAxAWEogZY3vg8Nlc7DuVg19S81Bmvnho5vncCzifewHf/HwWAJDYKLTiSKoWFQtVjblIRURERERERH5AU8rZE45qdvbsWSxbtgxLly7F/v37YTbr52PvTSYToqKiYDQaERkZ6enh1InFYuGRbi74/mg65iz/CQAcLkxZL1M355Yr0a9tom17SZkZh1Nzsf9UDvadysbhs/aLVJdKiApF12TrkVSxaNwoVBcXwePrUg5bymFLOWwphy1lsacctpTDlnLYUg5byvGHls6ukdR5UcpoNOKzzz7D0qVLsXXrViil0LNnT9x4442YNWtWXR7ap/jLopRSCuXl5QgMDNTFQoe0Hb+k47Wv9qKguNx2jSnrvxuGBOKp0d3tFqQcKS0340hqHvadzMa+0zk4fDYXpeXVL1LFRYagW3Isuvy+UJUUHeZ3zx1fl3LYUg5bymFLOWwpiz3lsKUctpTDlnLYUo6/tHTrolRpaSlWr16NpUuX4r///S9KSkqgaRoee+wxPPXUU0hKSqrT4H2RvyxKWa/0n5CQ4PMrs55SWm7G1kPn8b8jacg2FSI2MhwD2jfGwI5NEBQY4NLj/XLO+PsiVTYOn8lFyeUWqSJCbAtUXZNj0DQm3Kd/mAF8XUpiSzlsKYct5bClLPaUw5Zy2FIOW8phSzn+0tLZNZJaXVNq48aNWLp0KVauXAmTyYT+/fvjtddeQ/fu3TFw4EAMHDhQlwtSRJUFBQbg2q5XYEjnJJEfJkGBAejSPAZdmsfgdrRBabkZR88Zse9UNvadysGhs7koKbt4qmxWfjE2HTiHTQfOAQBiGgbbFqi6JsfiiljfX6QiIiIiIiIi3+f0otQVV1yB8+fPo0ePHnj22Wdx6623olmzZgBg+/Q9InK/oMAAdG4eg87NY3DbQKDMbMHRc3m2a1IdOGO/SJVTUILNB89h88GKRaro8GB0TY5Bl+RYdEuOQbO4hlykIiIiIiIionrn9KLUuXPn0LJlS0yePBl/+tOfkJCQ4M5xkQdxgUJOfbRsEGBAp2Yx6NQsBrde3RrlZgt+PX/xSKqDZ3JQVHpxkSq3sARbDp3HlkPnAQCNwoPQpXksurWIQZfmsUiO985FKm8ck69iSzlsKYct5bClLPaUw5Zy2FIOW8phSzl6aun0NaX++9//YunSpVi1ahWKi4sxZMgQTJgwAePGjUNWVhbatGmDzz//HOPGjXP3mL2Sv1xTivyP2WLBr+dN2HcqG/tPZePA6VxcKC2vdv+osCB0aR6Dri1i0bV5DJITImDQ0Q9FIiIiIiIiqhu3Xej8woULWLlyJT7++GN8++23CAgIQJ8+fbBt2zYsX74c48ePr/PgfZG/LEoppVBaWoqgoCBdrc66g7e2NFss+C3NZDuS6sDpHFwoqX6RKjK0wcVFquRYtPDAIpW3tvRFbCmHLeWwpRy2lMWecthSDlvKYUs5bCnHX1q69dP3rDIzM7Fs2TIsXboUP/74I4KCgjBw4ECMHj0aN9xwA1q0aOHqQ/scf1mU8pcr/XsDX2lptigcT/99kepkNvafzkHhZRapIn5fpLJek6plYqTbF6l8paUvYEs5bCmHLeWwpSz2lMOWcthSDlvKYUs5/tLSLZ++d6n4+Hg89thjeOyxx/Dbb7/ho48+wscff4zHHnsMjz/+OMxmc80PQkQeFWDQ0KZJFNo0icJN/VrBbFE4kX7xSKr9p3NQUFxm2z+/qAzbf0nH9l/SAQANQwLRuXnFAlWX5Fi0SoxEgMF3V/SJiIiIiIioftRpUaqy1q1bY86cOZgzZw527tyJjz/+WOqhiageBRg0tG4ShdZNojCuXytYlMKJ9PzfF6kqjqTKL7q4SFVQXI7vj6bj+6MVi1ThwYHo3DwGXZNj0TU5BimNIxHgwyv8RERERERE5B5ii1KV9e3bF3379nXHQ1M9CAx0y8tCl/yhpUHTkNI4EimNIzG2b0tYlMLJjHzsP5WNn0/lYP+pbJgqLVIVlpRj568Z2PlrBgAgzLpI9ft1qVrXYpGqtNyM7w6dx/YjacjJv4CYiLO4qn1jDOrYBEGBAW6Zrx74w+vSW7ClHLaUw5ay2FMOW8phSzlsKYct5eipZZ2uKUUX+cs1pYhqy6IUTmcW2I6k2ncqB8YLpdXuHxYUiE7No9GleSy6tYhB68ZRCAyouki145d0vPbVXhQUl0PTAKVg+3fDkEA8Nbo7+rVNdOfUiIiIiIiIyAX1cqFzushfFqWUUigqKkJoaKhPX+nfG+i1pVIKp7MKbAtU+05lI6+w+kWqkAYB6NQ8xnZNqrZNovDjb5l4/tOfKh7PwX2sNWfffCX6t+PCVG3o9XXpDmwphy3lsKUs9pTDlnLYUg5bymFLOf7Ssl4udE7+RykFk8mEkJAQn34DeAO9ttQ0DcnxEUiOj8CNV7aAUgpnsgvtPt0vp6DEtn9xmRm7jmVi17FMAEBwoAHlFuVwMcpKoWJh6rWv9mLZE8N4Kl8t6PV16Q5sKYct5bClLPaUw5Zy2FIOW8phSzl6a8lFKSJyK03T0DyuIZrHNcQNvZKhlMLZ7ELsP52Dn09WnPJXeZGqpNzi1OMqVFxkfeuh87i26xVuGj0RERERERG5i0uLUi+88ALGjRuHzp07O7z94MGDWLFiBWbNmlWnwRGR/9E0Dc3iGqJZXEOM6tkcSimcy7mAn09lY/+pbGw7koZSJxemNA343y/pXJQiIiIiIiLyQS59TvucOXOwb9++am8/cOAAnn/+eZcHRZ6jaRqCgoJ0cZigu7GlczRNQ9PYcIzq2Rz/N7YH2jVt5PR9lQLyi6q/XhVVxdelHLaUw5Zy2FIWe8phSzlsKYct5bClHL21dMvpezk5OQgKCnLHQ5ObaZqGmJgYTw/DL7ClayJDg2yfslcTTQMiQvmzpjb4upTDlnLYUg5bymJPOWwphy3lsKUctpSjt5ZOL0p999132Lx5s+3rlStX4rfffquyX15eHpYvX44uXbq4NKC3334br776KtLS0tCtWzcsWrQIffr0cbhvWVkZXnrpJSxevBipqalo164dXn75ZYwYMcK2j9lsxpw5c/DRRx8hLS0NSUlJuOuuuzBz5kzbyqNSCrNnz8Z7772HvLw8DBgwAO+88w7atGnj0hx8mVIKBQUFaNiwoW5WZt2FLV1zVbtE/O9ImlP7KgUM4Kfv1Qpfl3LYUg5bymFLWewphy3lsKUctpTDlnL01tLpRalNmzbZTsnTNA0rV67EypUrHe7bsWNHLFq0qNaDWb58OaZNm4Z3330Xffv2xZtvvonhw4fjl19+QUJCQpX9Z86ciY8++gjvvfce2rdvj3Xr1mHs2LHYvn07evToAQB4+eWX8c4772Dx4sXo1KkTfvrpJ0yePBlRUVF47LHHAACvvPIK/vrXv2Lx4sVo2bIlnnvuOQwfPhyHDh1CSEhIrefhy5RSKCwsRHh4uC7eAO7Elq4Z1LEJ3ll3EIXF5Zf9BD4AaBgSiIEdm9TLuPwFX5dy2FIOW8phS1nsKYct5bClHLaUw5Zy9NbS6WtKPf3008jMzERGRgaUUnj33XeRmZlp909WVhYuXLiAAwcOoG/fvrUezIIFC3Dfffdh8uTJ6NixI959912EhYXhgw8+cLj/kiVL8Oyzz2LUqFFo1aoVHnzwQYwaNQqvv/66bZ/t27dj9OjRuP7669GiRQuMHz8e1113HX744QcAFU/4m2++iZkzZ2L06NHo2rUr/v3vf+PcuXP48ssvaz0HIqqboMAAPDW6OwCgph/BI3o0R1BggNvHRERERERERPKcXpQKDQ1FbGws4uLicOLECdxxxx2IjY21+ycmJsblI4tKS0uxa9cuDBs27OLgDAYMGzYMO3bscHifkpKSKt8vNDQU27Zts3191VVXYcOGDTh69CgA4Oeff8a2bdswcuRIAMCJEyeQlpZm932joqLQt2/far8vEblXv7aJmH3zlQgPqTiY0/o/CC79HwVf7zqF01kF9Tw6IiIiIiIikuDShc6Tk5OrbLtw4QI++eQTlJSUYNSoUQ73uZysrCyYzWYkJtpfHyYxMRFHjhxxeJ/hw4djwYIFGDRoEFJSUrBhwwasXLkSZrPZts+MGTNgMpnQvn17BAQEwGw2Y/78+bj99tsBAGlpabbvc+n3td7mSElJCUpKSmxfm0wmAIDFYoHFUvFx9pqmQdM0KKWgKl21uabt1vu7ut1gMFR5bGe3K6UQHBxsu622Y/fGOdV17K7O6dKW/jCnmrZLzqlf2wQsfXwoth1Ow/9+SUdeQRGiI0JxVdtE/HgsE5sPnkdRqRlzP9uFRfcMQHCDAK+fkzc8T0ophIaGAqjba9Kb5lTbsUvNCYDDlr48J089T5qmISQkBEopu/v48pw8+TwFBwfbtfSHOXnyebL2VEr5zZzqOnZX5uSopa/PyZnt7piTtaWVP8yprttdnZO1ZXX/nffFObkydok5KaUQEhLiV3NyZrs75qRU7X5f99Y5Xbq9Oi4tSt1zzz3YuXMnDhw4AKDiKKd+/frZvo6KisLGjRtt13Vyl4ULF+K+++5D+/btoWkaUlJSMHnyZLvT/T799FMsXboUH3/8MTp16oS9e/di6tSpSEpKwqRJk1z+3i+99JLtGluVZWZmori4GEDFX1aioqJgMplQVFRk2yc8PBwRERHIzc1FaenFj7OPjIxEWFgYcnJyUF5ebtseHR2N4OBgZGZm2r1oYmNjERAQgIyMDLsxJCQkwGw2Izs727ZN0zQkJiaitLQUubm5tu2BgYGIi4tDUVGRbWENqLhgfUxMDAoKClBYWGjb7stzCgoK8sicrIuk/jSn+nyeOic2QOfEK2xzMhqNaBNjwK/ncpGaW4zTWQVY+PV+3DuoOcrKynxiTv74POl1TlFRUcjJyfGrOXnqeQoNDUVmZqZfzclTz5PZbLZr6Q9z8vTzlJmZ6XdzAjzzPGVmZvrdnADPPE/5+fl+NydPPU9KKb+bk6eeJ03T+LuR0JxKSkp8ek6Vfxe5HE1dupzohFatWuGOO+7ACy+8AAD48MMPcffdd2Pp0qXo1q0bbrrpJrRr165W12QqLS1FWFgYPv/8c4wZM8a2fdKkScjLy8OqVauqvW9xcTGys7ORlJSEGTNm4D//+Q8OHjwIAGjWrBlmzJiBhx9+2Lb/vHnz8NFHH+HIkSM4fvw4UlJSsGfPHnTv3t22z+DBg9G9e3csXLjQ4fd0dKRUs2bNkJubi8jISACeX5l0ZQVZKQWTyYSoqCjRVVg9ropf2tIf5lTTdnfNSSll+8XL+vXZ7AI89sF2FJVWLPo9OrITRvVs7jNzqsv2uh4pVVBQgIiICFzKV+dU27FLzQmo+AvBpS19eU6eep40TYPRaERERAQ0Tbvs/r4yJ089T0DF/1iKjIy0fe3rc/Lk82SxWGAymRAZGQmDweAXc/LU82T9vahyS1+fkzPb3XWkVF1+X/fGOdV1e12OlDKZTGjUqJHta1+fkytjl5iTUlV/X/f1OTmz3V1HStXm93VvnZPRaER0dDSMRqNtjcQRl46USktLQ4sWLWxff/nll7jyyisxYcIEAMB9992HV199tVaPGRQUhF69emHDhg22RSmLxYINGzbgkUceuex9Q0JC0LRpU5SVlWHFihW4+eabbbdduHABBoP9pbMCAgJs4Vq2bInGjRtjw4YNtkUpk8mEnTt34sEHH6z2ewYHB9sdNmtlMBiqfD/rk3Wp6rZfen9Xttf2e1Z+QVVebHP1ceoy9uq2S42lvuZ0aUt/mJMz290xRovFguLiYrtfZJvHR+KJG7rixZV7AADvfnMYbZMaoW1SI5+YU12212VOFosFRUVFiIiI8Kq5+uLzVFNLX5xTTdvdNadL3+PuGLs/vfYut9363x5N06qM1VfnVNuxV7fd1bFYe1r38Yc5uWv75cZe+bVpva+vz8nZ7dJjrOvv6944p7pud3VO1pZKKdvvmPU9dn95nhz9vl6XsVe33V9ee5d7bE//vi41p+r2r3J/p/a6RHh4OPLy8gAA5eXl2Lx5M4YPH267PSIiAkajsdaPO23aNLz33ntYvHgxDh8+jAcffBCFhYWYPHkyAGDixIl45plnbPvv3LkTK1euxPHjx7F161aMGDECFosFTz/9tG2fG2+8EfPnz8fXX3+NkydP4osvvsCCBQswduxYABWxpk6dinnz5uGrr77C/v37MXHiRCQlJdkdsUVE3mdwpySM6dMCAFBmtmDe57thKiq9/J2IiIiIiIjIK7h0pFTPnj3x3nvvYciQIfjqq6+Qn5+PG2+80Xb7sWPHqlw43Bm33HILMjMzMWvWLKSlpaF79+5Yu3at7bFOnz5tt9pWXFyMmTNn4vjx42jYsCFGjRqFJUuW2A6/BIBFixbhueeew0MPPYSMjAwkJSVhypQpmDVrlm2fp59+GoWFhbj//vuRl5eHq6++GmvXrnX5kwSJqP7cO6wDfknNw+HUPKQbi/Dqqp/x/C1XwuBg1Z+IiIiIiIi8h0vXlPrpp58wfPhw5OXlQSmF8ePH49NPP7Xd3q5dO/Tu3RsfffSR6GC9mfW87prOl/R21vNXGzZs6PBQPnIeW8qpqWWGsQgPv7cVpqKKC53fNaQdJlzdur6H6RP4upTDlnLYUg5bymJPOWwphy3lsKUctpTjLy2dXSNxaVEKqPjkjO3bt6NRo0YYPHiwbXteXh4WL15su1C4XvjLohSRr9p1LBN//vgHKAAGDXjp9r7o3jLO08MiIiIiIiLSHWfXSFy6phQAxMfHY/To0XYLUgDQqFEjPP7447pakPInSink5ORU+eQCqj22lONMy14p8bhjUBsAgEUBL32xB9n5xfU1RJ/B16UctpTDlnLYUhZ7ymFLOWwphy3lsKUcvbV0eVHKbDbjk08+wZQpUzB27Fjs378fAGA0GrFy5Uqkp6eLDZLqj1IKpaWlunkDuBNbynG25YSBbdCrVcXRUXmFpZi/YjfKzZbL3kdv+LqUw5Zy2FIOW8piTzlsKYct5bClHLaUo7eWLi1K5eXlYcCAAbjtttuwbNkyfPXVV8jMzAQANGzYEI899hgWLlwoOlAiopoEGDT839geiIus+JCCg2dy8a9Nv3h4VEREREREROSIS4tSM2bMwMGDB7Fu3TocP37cbgUvICAA48ePx5o1a8QGSUTkrKiwIMy8qScCDBUXBfx8x3H870iah0dFREREREREl3JpUerLL7/Eo48+ij/84Q8Orwbftm1bnDx5sq5jIw/QNA2RkZE+fZV/b8GWcmrbssMV0bj/Dx1sX7/21c9IzSl01/B8Cl+XcthSDlvKYUtZ7CmHLeWwpRy2lMOWcvTW0qVFKaPRiJYtW1Z7e1lZGcrLy10eFHmOpmkICwvTzRvAndhSjistR/dugUEdmwAALpSUY97nu1FSZnbXEH0GX5dy2FIOW8phS1nsKYct5bClHLaUw5Zy9NbSpUWplJQU7N69u9rbv/nmG3Ts2NHlQZHnWCwWZGVlwWLhxaHrii3luNJS0zQ8cUNXXBEbDgA4nm7C22sPuGuIPoOvSzlsKYct5bClLPaUw5Zy2FIOW8phSzl6a+n0otR3331nu5j5vffeiw8++ADLly+3XU9K0zSUlJTgz3/+M9auXYspU6a4Z8TkdjzKTQ5bynGlZVhwIJ4b3wvBDQIAAOv2nsW6vWekh+Zz+LqUw5Zy2FIOW8piTzlsKYct5bClHLaUo6eWTi9KDRkyBOvXrwcAPP7445g4cSImTJiAtm3bAgBuu+02RERE4KWXXsL999+Pe+65xz0jJiKqhRYJEXhsVGfb12/99wCOpZk8OCIiIiIiIiICgEBnd6z8CXuapuG9997DpEmT8Pnnn+PXX3+FxWJBSkoKbr75ZgwaNMgtgyUicsWwrlfg4JlcrNl9GqXlFsxbsQtv3XM1wkMaeHpoREREREREuuX0opQjV199Na6++mqpsZAX0DQN0dHRurmomjuxpRyJlg8O74hfzxvx63kjzuVcwOtf/Yzn/tRLd88PX5dy2FIOW8phS1nsKYct5bClHLaUw5Zy9NayVhc610sUPdM0DcHBwXyuBbClHImWQYEBmHlTTzT8/eio//2SjhXfn5Aaos/g61IOW8phSzlsKYs95bClHLaUw5Zy2FKO3lrWalHqjjvuQEBAgFP/BAbW6SAs8hCLxYL09HTdXOnfndhSjlTLxtFheGp0N9vX/9xwBAdO59R1eD6Fr0s5bCmHLeWwpSz2lMOWcthSDlvKYUs5emtZq5WjYcOG2S5sTv6r8vXDqG7YUo5Uy35tE3HLgBQs/98xWJTC/BW78bf7BiK6YbDI4/sCvi7lsKUctpTDlrLYUw5bymFLOWwphy3l6KllrRalJk2ahNtuu81dYyEiqheTrmmLI6l5+PlkNnIKSvCXL/bgxdv7IsCgj0NkiYiIiIiIvEGtTt8jIvIHAQYDZoztjpjfj47aezIbS7Yc9fCoiIiIiIiI9IWLUmRH0zTExsbq5qJq7sSWctzRMqZhCJ4d1wOG3x9z2bbfsPPXdLHH91Z8XcphSzlsKYctZbGnHLaUw5Zy2FIOW8rRW0suSpEdTdMQEBCgmzeAO7GlHHe17JIci7uHtrN9/cqXPyMt74Lo9/A2fF3KYUs5bCmHLWWxpxy2lMOWcthSDlvK0VtLpxelLBYLryelAxaLBRkZGbq50r87saUcd7Yc378VrmqXCAAoKC7D/M93o7TcLP59vAVfl3LYUg5bymFLWewphy3lsKUctpTDlnL01pJHShGRrmmahul/7IYm0WEAgKPnjfj7N4c8PCoiIiIiIiL/x0UpItK9hiEN8Nz4nmgQUPEj8T+7TmPj/lQPj4qIiIiIiMi/cVGKiAhASuMoPDKyk+3rN7/ej5MZ+R4cERERERERkX/TlFLK04PwByaTCVFRUTAajYiMjPT0cOrEYrHAYOB6pQS2lFMfLZVSeH31Pqz/+SwA4IrYcCy652qEBQe69fvWN74u5bClHLaUw5ay2FMOW8phSzlsKYct5fhDS2fXSHx7liROKQWz2QyuVdYdW8qpr5aapuGRkZ3RMiECAHA2uxALv97vV88hX5dy2FIOW8phS1nsKYct5bClHLaUw5Zy9NaSi1JkRymF7Oxs3bwB3Ikt5dRny5AGAXhufC/b0VGbD57DVz+dcvv3rS98XcphSzlsKYctZbGnHLaUw5Zy2FIOW8rRW0suShERXaJpbDim39jV9vU/vjmEI6m5HhwRERERERGR/+GiFBGRA1d3aIJx/VoCAMotCvM+3w3jhVIPj4qIiIiIiMh/cFGKqtA0zdND8BtsKccTLe8Z2h6dmkUDADJNxXj5y72w+MFhtHxdymFLOWwphy1lsacctpTDlnLYUg5bytFTS376nhB/+vQ9Irooy1SMh97bajtKauLgtrh9UBsPj4qIiIiIiMh78dP3yCVKKZSUlOjmomruxJZyPNkyLjIEz4zrAcPv/7NiyZaj2HU8s97HIYWvSzlsKYct5bClLPaUw5Zy2FIOW8phSzl6a8lFKbKjlEJubq5u3gDuxJZyPN2yR8s43Dm4bcVYALz8xV5kmoo8Mpa68nRLf8KWcthSDlvKYk85bCmHLeWwpRy2lKO3llyUIiJywq1Xt0bv1vEAAOOFUsxfsRvlZouHR0VEREREROS7uChFROQEg6bh6dHdkRAVCgA4fDYP72844uFRERERERER+S4uSlEVgYGBnh6C32BLOd7QMjIsCDPH90Tg7xeY+mLnCWw9dN7Do6o9b2jpL9hSDlvKYUtZ7CmHLeWwpRy2lMOWcvTUkp++J4SfvkekH6t/Oom3/nsQABAWFIhF9w7AFbENPTwqIiIiIiIi78BP3yOXKKVw4cIF3VxUzZ3YUo63tbyhVzKGdE4CAFwoLcfcz3ajuMzs4VE5x9ta+jK2lMOWcthSFnvKYUs5bCmHLeWwpRy9teSiFNlRSsFkMunmDeBObCnH21pqmobHr++C5nEVR0edzMzHojX7vWZ8l+NtLX0ZW8phSzlsKYs95bClHLaUw5Zy2FKO3lpyUYqIyAWhQYF4bnxPhDQIAAB8uy8V/91zxsOjIiIiIiIi8h1clCIiclHz+Ag8cUNX29d/W3sQv543enBEREREREREvoOLUmRH0zQEBQVB0zRPD8XnsaUcb255Teck3HhlMgCgzGzBvM93Ib+ozMOjqp43t/Q1bCmHLeWwpSz2lMOWcthSDlvKYUs5emvJT98Twk/fI9Kv0nIznlz8PX45lwcA6NcmAbNvuRIGnfyHhIiIiIiIqDJ++h65RCmF/Px83VxUzZ3YUo63twwKDMCfb+qBiNAGAIDvf83A5zuOe3hUjnl7S1/ClnLYUg5bymJPOWwphy3lsKUctpSjt5ZclCI7SikUFhbq5g3gTmwpxxdaJjYKw/+N6Q7rsVH/2ngE+05le3RMjvhCS1/BlnLYUg5bymJPOWwphy3lsKUctpSjt5ZclCIiEtK7dQImDGwNALAo4MUVe5CdX+zhUREREREREXknLkoREQm6Y1BbdG8ZCwDILSzBSyv3wGyxeHhURERERERE3oeLUmRH0zSEhobq5kr/7sSWcnypZYBBwzNjeyAuIgQAsP90Dj7cdNTDo7rIl1p6O7aUw5Zy2FIWe8phSzlsKYct5bClHL215KfvCeGn7xFRZQfP5OCpf38Ps6XiR+ycm69E/3aJHh4VERERERGR+/HT98glSikYjUbdXFTNndhSji+27NQsBvde29729aur9uJ87gUPjqiCL7b0Vmwphy3lsKUs9pTDlnLYUg5bymFLOXpryUUpsqOUQlFRkW7eAO7ElnJ8teXYvi1xdfvGAIDCknLM+3wXSsvNHh2Tr7b0Rmwphy3lsKUs9pTDlnLYUg5bymFLOXpryUUpIiI30TQN0/7YFU1jwgEAv6WZ8Le1Bz08KiIiIiIiIu/ARSkiIjcKD26AmeN7Ijiw4sftf/ecwfqfz3p4VERERERERJ7HRSmyo2kawsPDdXOlf3diSzm+3rJVYiQeHdXF9vWiNftxIt3kkbH4ektvwpZy2FIOW8piTzlsKYct5bClHLaUo7eW/PQ9Ifz0PSKqyRv/2Ye1e84AAJrGhGPRvQMQHtzAw6MiIiIiIiKSxU/fI5copZCTk6Obi6q5E1vK8ZeWD4/ohNaNK34gp+YU4o3V++p9Tv7S0huwpRy2lMOWsthTDlvKYUs5bCmHLeXorSUXpciOUgqlpaW6eQO4E1vK8ZeWQYEB+PNNPREeHAgA2Ho4DV/+cLJex+AvLb0BW8phSzlsKYs95bClHLaUw5Zy2FKO3lpyUYqIqB4lxYTjydHdbF+/9+1hHDyT48EREREREREReQYXpYiI6tlV7RrjT/1bAQDMFoUXV+xBXmGJh0dFRERERERUv7goRXY0TUNkZKRurvTvTmwpxx9bTh7aDl2axwAAsvKL8Zcv9sJscf8huv7Y0lPYUg5bymFLWewphy3lsKUctpTDlnL01pKLUmRH0zSEhYXp5g3gTmwpxx9bBhgMeGZcD0SHBwMA9pzIwtLvfnX79/XHlp7ClnLYUg5bymJPOWwphy3lsKUctpSjt5ZclCI7FosFWVlZsFgsnh6Kz2NLOf7aMjYiBM+M6wHD7/+9+Xjrr/jxtwy3fk9/bekJbCmHLeWwpSz2lMOWcthSDlvKYUs5emvJRSmqory83NND8BtsKcdfW3ZrEYu7hrQDACgAL3+5FxnGIrd+T39t6QlsKYct5bClLPaUw5Zy2FIOW8phSzl6aslFKSIiD/vTVSno1yYBAJBfVIZ5n+9GmVkf/2eEiIiIiIj0i4tSREQeZtA0PDm6OxIbhQIAfjmXh3+sP+ThUREREREREbkXF6XIjqZpiI6O1s1F1dyJLeXooWVEaAM8N74XGgRU/Fj+6sdT2HzgnPj30UPL+sKWcthSDlvKYk85bCmHLeWw5f+3d9/hTVb9G8DvdKR77wG0lFFmEZQhr+yNMgRBHCwnivN9RVR+Ao6X4QJfBREVFYoyBBRUNiJDpuxRC5RVuvdOk5zfHyWhIS1N29Nm9P5cF1fpkydPz7nzpEm/Oec88jBLeRpalixKkQGFQgEnJ6cG8wSoS8xSnoaSZfMQLzw3qI3++082ncTVtDypP6OhZFkfmKU8zFIeZikX85SHWcrDLOVhlvIwS3kaWpYsSpEBrVaLlJSUBrPSf11ilvI0pCwH39UIfduFAQCKSzV4d+3fKFLJW+iwIWVZ15ilPMxSHmYpF/OUh1nKwyzlYZbyMEt5GlqWLEqRESGEuZtgM5ilPA0lS4VCgReHtEVEgAcA4Gp6Pj799ZTU/jeULOsDs5SHWcrDLOVinvIwS3mYpTzMUh5mKU9DypJFKSIiC+OsdMCM0R3horQHAOw8fQObjl41c6uIiIiIiIjkYlGKiMgCNfJ3x6sPxOi/X7L1LOJuZJuvQURERERERJJZZFHq888/R0REBJydndGlSxccOnSo0n1LS0vxzjvvICoqCs7OzoiJicHmzZsN9omIiIBCoTD69/zzz+v36dWrl9Htzz77bJ310VIpFAr4+fk1mEXV6hKzlKehZtmjdQhGdI4AAJRqtHh/7d/ILVLV6pgNNcu6wCzlYZbyMEu5mKc8zFIeZikPs5SHWcrT0LK0uKLUqlWr8Oqrr2LmzJn4+++/ERMTg4EDByI1NbXC/WfMmIElS5bgf//7H86ePYtnn30WI0eOxLFjx/T7HD58GElJSfp/27ZtAwA89NBDBsd66qmnDPabP39+3XXUQikUCtjb2zeYJ0BdYpbyNOQsn+zXCq3CvQEAKTlF+GDDcWhrMce8IWcpG7OUh1nKwyzlYp7yMEt5mKU8zFIeZilPQ8vS4opSH3/8MZ566ilMmjQJrVu3xhdffAFXV1d88803Fe6/fPlyvPnmmxgyZAiaNm2KKVOmYMiQIfjoo4/0+wQEBCA4OFj/b9OmTYiKikLPnj0NjuXq6mqwn6enZ5321RJptVqkpqY2mJX+6xKzlKchZ+lob4c3H+wIL1clAODQhTSs2nexxsdryFnKxizlYZbyMEu5mKc8zFIeZikPs5SHWcrT0LJ0MHcDylOpVDh69CjeeOMN/TY7Ozv069cPf/31V4X3KSkpgbOzs8E2FxcX7N27t9KfsWLFCrz66qtGlcfY2FisWLECwcHBeOCBB/B///d/cHV1rfTnlpSU6L/Pzc0FUHYC6U4e3TRAIYTB6vlVbb/95Kvudjs7O6Njm7pdq9Ua7FPdtltin2rb9pr26fYsbaFPVW2vqz7psgSqf05aap+qs93fwwmvDW+P//vhCASA7/+IQ4sQT9wV6V/tPun+r3u+m6tPd2pjTbab43Eqf06Wb48198lcjxNg+jlpLX0y1+NUUZbW3idzPk661x+tVmszfTLX41RRltbeJ1O210Wfavt+3RL7VNvtNe3Tnd5jWmufatJ2GX0y5f26tfXJlO110afqvl+31D6ZWlSzqKJUeno6NBoNgoKCDLYHBQXh/PnzFd5n4MCB+Pjjj9GjRw9ERUVhx44dWLduHTQaTYX7b9iwAdnZ2Zg4caLB9kceeQRNmjRBaGgoTp48iddffx1xcXFYt25dhceZM2cOZs+ebbQ9LS0NxcXFAMqKY15eXsjNzUVRUZF+Hzc3N3h4eCArKwsq1a31YTw9PeHq6orMzEyo1Wr9dh8fHzg5OSEtLc3gpPHz84O9vb3R1MbAwEBoNBpkZGTotykUCgQFBUGlUiErK0u/3cHBAf7+/igqKkJubi60Wi1ycnKgVCrh5+eH/Px8FBQU6Pe3xj7pKJVK+Pr61lufdFnq5gPbQp/M9ThptVp9P2ylT0D1HqdG7sDYbhH48a/L0Apg7vpjmD2yFXzdlNXqk+6PAY1Gg8zMTLP2ydofJ92HFtnZ2SgtLbWJPpnrcQoICIBarUZqairs7Oxsok/mepycnZ2Rl5cHIYQ+S2vvkzkfp+zsbOTk5EAIAWdnZ5vok7keJ41Go88yICDAJvpkrsdJ9x7T2dkZPj4+NtEncz1OuiwDAgIghLCJPpnrcdJqtfpBG7bSJ8A8j5MQAgqFAiqVCjk5OVbbp7S0NJhCIW4vJ5rRjRs3EBYWhv3796Nbt2767dOmTcPu3btx8OBBo/ukpaXhqaeewsaNG6FQKBAVFYV+/frhm2++MQheZ+DAgVAqldi4ceMd27Jz50707dsXFy5cQFRUlNHtFY2UatSoEbKysvTT/sxdmaxJBVmr1SItLQ2BgYGwt7e3uGqrNVXFb8/SFvpU1fa6HCmVlpaGoKAg/W3W3qeabBcA/u/HIzh6sewXfJtGPpj7aGc4Otib3CetVov09HQEBARAoTAcLcpzr/ojpdLS0oyytOY+metxAoCUlBQEBAToCynW3idzPU5CCKMsrb1P5nycdG+qdUUUW+iTuR4n3Wt5+SytvU+mbK+rkVK1eb9uiX2q7fbajJSq7D2mtfapJm2X0SdT3q9bW59M2V5XI6Wq837dUvuUk5MDHx8f5OTk3HFpJIsqSqlUKri6umLt2rUYMWKEfvuECROQnZ2Nn3/+udL7FhcXIyMjA6GhoZg+fTo2bdqEM2fOGOxz5coVNG3aFOvWrcPw4cPv2JaCggK4u7tj8+bNGDhwYJVtz83NhZeXV5WBWwOtVmvwRwHVHLOUh1mWySlU4fmle5CWWzYic1TXSDzdv3W1jsEs5WGW8jBLeZilXMxTHmYpD7OUh1nKwyzlsYUsTa2RWFQvlUolOnXqhB07dui3abVa7Nixw2DkVEWcnZ0RFhYGtVqNn376qcKi07JlyxAYGIihQ4dW2Zbjx48DAEJCQqrXCSun+1TQgmqVVotZysMsb/FyVeKtUR3hYFf2qclPBxKw91ySyfdnlvIwS3mYpTzMUi7mKQ+zlIdZysMs5WGW8jS0LC2qKAUAr776KpYuXYrvvvsO586dw5QpU1BQUIBJkyYBAMaPH2+wEPrBgwexbt06XLp0CXv27MGgQYOg1Woxbdo0g+NqtVosW7YMEyZMgIOD4VJaFy9exLvvvoujR4/i8uXL+OWXXzB+/Hj06NED7du3r/tOWxAhyuZSN5QnQF1ilvIwS0Otwn3wdP9W+u8/2ngSiZkFd7jHLcxSHmYpD7OUh1nKxTzlYZbyMEt5mKU8zFKehpalRS10DgBjx45FWloa3n77bSQnJ6NDhw7YvHmzfvHzq1evGgxjKy4uxowZM3Dp0iW4u7tjyJAhWL58Oby9vQ2Ou337dly9ehWTJ082+plKpRLbt2/HggULUFBQgEaNGmHUqFGYMWNGnfaViKimht0TgdPXsvDn2SQUlqjx3tq/sWDSvXBytDd304iIiIiIiExicUUpAJg6dSqmTp1a4W1//PGHwfc9e/bE2bNnqzzmgAEDKq00NmrUCLt37652O4mIzEWhUOCV+9vjUkourmcU4FJKLj7ffBqvPhBj7qYRERERERGZxOKm75H53b7CP9Ucs5SHWRpzdXLA/43upB8dteX4dWw5fq3K+zFLeZilPMxSHmYpF/OUh1nKwyzlYZbyMEt5GlKWFnX1PWtmS1ffIyLrsuPkdcz/+QQAQOlghwWT7kVUsJeZW0VERERERA2VVV59j8xPCIGSkpIGs6haXWKW8jDLO+vbPhxDOzUGAKjUWry79m8UFJdWuC+zlIdZysMs5WGWcjFPeZilPMxSHmYpD7OUp6FlyaIUGRBCICsrq8E8AeoSs5SHWVbt2QGt0TykbHRUUlYhPvzlRIV5MUt5mKU8zFIeZikX85SHWcrDLOVhlvIwS3kaWpYsShER2QClgz1mjO4Id2dHAMD+uBT8dCDBzK0iIiIiIiKqHItSREQ2ItjbFa8Nv3X1va93nMepq5lmbBEREREREVHlWJQiIw4ODuZugs1glvIwS9N0bRGEsd2jAABaIfDfn/5GVn6JwT7MUh5mKQ+zlIdZysU85WGW8jBLeZilPMxSnoaUJa++JwmvvkdElkKj1eKN2EM4cTkDABAT4Yc5j3aBvV3DubQsERERERGZj6k1koZTfiOTCCFQVFQEFxcXKBT8A7Y2mKU8zLJ67O3s8MbIu/Dc0j3IzC/BicsZWLbzHCICPbE/LhnZ+SXwdnfCvS2D0aN1CJQO9uZuslXieSkPs5SHWcrFPOVhlvIwS3mYpTzMUp6GliWn75EBIQRyc3MbzEr/dYlZysMsq8/H3QlvjuoIu5svZGv+SsAHP5/A/rgUnLmejf1xKfjg5xMY98l2HPgnxcyttU48L+VhlvIwS7mYpzzMUh5mKQ+zlIdZytPQsmRRiojIRrVr7Iu+7UMNtule23RfC4rVmLXqCP6KY2GKiIiIiIjqF4tSREQ2SqXWVFls0n3+8uEvx6FSa+q+UURERERERDexKEUGFAoFlEplg5i7WteYpTzMsmb+PJuE/GJ1lfsJAPnFauw5m1T3jbIhPC/lYZbyMEu5mKc8zFIeZikPs5SHWcrT0LJkUYoMKBQK+Pr6NpgnQF1ilvIwy5rZH5cCUyNTKIB9nMJXLTwv5WGW8jBLuZinPMxSHmYpD7OUh1nK09CyZFGKDAghkJeX12AWVatLzFIeZlkzeUUqmBqZEGX7k+l4XsrDLOVhlnIxT3mYpTzMUh5mKQ+zlKehZcmiFBkQQqCgoKDBPAHqErOUh1nWjIeL0uSRUgCQmlOMa+n5ddcgG8PzUh5mKQ+zlIt5ysMs5WGW8jBLeZilPA0tSxaliIhs1L0tg0weKQUAydmFeHLxbsz44RCOXExrMC+ERERERERkHixKERHZqB6tQ+Du7IDqzkY/fCENb608hKcW78amo1dQrKp6sXQiIiIiIqLqYlGKDCgUCri4uDSYRdXqErOUh1nWjNLBHq8N7wAAlRamFDf/TR/ZAU/1a4UgLxf9bdcyCvC/307j0YU78NX2c0jNKarrJlsVnpfyMEt5mKVczFMeZikPs5SHWcrDLOVpaFkqBOdnSJGbmwsvLy/k5OTA09PT3M0hItL7Ky4FH/5yHPnFaigUZYua6766OzvgteEd0LVFEABAo9Xir7gUbDh0GaeuZhocx04BdI8OxojOkWjTyKfBvFASEREREVH1mFojYVFKElspSgkhkJubC09PT/7BWUvMUh5mWXsqtQZ7ziZhX1wysvKK4OPhgu4tg3Ff6xAoHewrvM+FpBxsOHwZf5y+gVKN1uC2ZsGeGNE5Ej3bVH5/W8fzUh5mKQ+zlIt5ysMs5WGW8jBLeZilPLaSJYtS9cxWilJarRapqakIDAyEnR1nd9YGs5SHWcpTkyyz8kvw699X8evRK8jMLzG4zcfNCfd3aoyhnZrAx92pLppssXheysMs5WGWcjFPeZilPMxSHmYpD7OUx1ayNLVG4lCPbSIiIivk4+6Ex3o0x9juUfjzzA2sP3QZ8Uk5AICsghIs/zMeP+67iJ5tQjCicySah3iZucVERERERGQNWJQiIiKTONrboW/7cPRpF4az17Ow/uBl7DufDK0QKNVosf1kIrafTETbxr4Y0TkC97YMgr0Vf7pDRERERER1i0UpMqBQKODm5mbVc1ctBbOUh1nKIyNLhUKBNo180aaRL1JzivDL4cv4/dg15BeXAgBOX83E6auZCPRywbB7mmBQh8bwcHGU1QWLwfNSHmYpD7OUi3nKwyzlYZbyMEt5mKU8DS1Lriklia2sKUVEVBPFKjV2nErEhkOXcTU93+A2J0d79G8fhuGdI9HY391MLSQiIiIiovpiao2E8yrIgBACmZmZYK2y9pilPMxSnrrK0lnpgKGdmuDLZ3vgv492RudmAfrbSko12HT0Kp5avBtvrjyEwxdSobWBx5LnpTzMUh5mKRfzlIdZysMs5WGW8jBLeRpalpy+RwaEEFCpVBBCNJjhgnWFWcrDLOWp6ywVCgU6NQ1Ap6YBuJ6Rj58PX8bW49dRXKoBABy9mIajF9MQ7ueGEZ0j0K99OFyU1vlSxPNSHmYpD7OUi3nKwyzlYZbyMEt5mKU8DS1LjpQiIqI6Ee7njucHtUXsy33xdP9WCPJ20d92PaMAn/1+Bo8u2IEvt51FcnahGVtKRERERETmYJ0fTxMRkdVwd3bEqK5NMaJzJA7+k4L1hxJw8komAKCgRI2fDiRg/cEEdGsZjJGdI9C2sW+D+FSIiIiIiKihY1GKDCgUCnh6evIPQgmYpTzMUh5zZmlvp8C90cG4NzoYF5NzsOHQZew6fQOlGi20Ath3Phn7ziejWbAnRnSORM82IVA62Nd7O03F81IeZikPs5SLecrDLOVhlvIwS3mYpTwNLUtefU8SXn2PiKj6sgtK8OvRq9h09Aoy80sMbvN2U2Joxya4/+7G8HV3NlMLiYiIiIiounj1PaoRrVaL9PR0aLVaczfF6jFLeZilPJaWpbebEx7t0Rzfv9gHr4/ogBahXvrbsgtUiN0Tj8cX7sT8DccRn5RjxpYas7QsrRmzlIdZysU85WGW8jBLeZilPMxSnoaWJafvkRG1Wm3uJtgMZikPs5THErN0tLdDn3Zh6N02FOcSs7H+YAL2nkuGVgiotQI7TiVix6lEtGnkgxGdI9E9Ogj2dub/XMUSs7RWzFIeZikX85SHWcrDLOVhlvIwS3kaUpYsShERkcVQKBRoHe6D1uE+SM0pwqYjV/DbsavIKyoFAJy5loUz17IQ4OmMYfdEYNBdjeDpojRzq4mIiIiIqCZYlCIiIosU6OWCyX2j8UiP5th5KhHrDybgano+ACAttxhf7ziPFbv/Qd/24RjROQJNAjzM3GIiIiIiIqoOLnQuia0sdC6EgEqlglKpbDCr/dcVZikPs5THmrMUQuBYQgY2HErAwfhUo9s7NfXHiM6RuLtZAOzqoW/WnKWlYZbyMEu5mKc8zFIeZikPs5SHWcpjK1maWiNhUUoSWylKERFZg8SMAvx8+DK2nriGIpXG4LZwXzcM6xyBATHhcFFyQDARERERUX3j1feoRrRaLVJSUhrMSv91iVnKwyzlsZUsw/zc8NygNoh9qS+eGdAawd4u+tuuZxZg0eYzeGTBDizZdhbJWYV10gZbydISMEt5mKVczFMeZikPs5SHWcrDLOVpaFnyI2QywsFz8jBLeZilPLaUpZuzIx7sEonh90TgYHwKNhy6jBOXMwAAhSVqrDuQgA0HE9C1RRBGdolEu8a+UodB21KW5sYs5WGWcjFPeZilPMxSHmYpD7OUpyFlyaIUERFZPXs7Be5tGYx7WwbjUkoufj50GTtOJaJUo4VWAPvjUrA/LgVNgzwxonMEercNhdLB3tzNJiIiIiJq0Dh9j4iIbErTIE+88kB7rHipDyb0agE/Dyf9bZdScvHxxpN4bOFOfPdHHDLyis3YUiIiIiKiho0LnUtiKwudCyGgVqvh4OBg1Sv9WwJmKQ+zlKchZlmq0WLvuSSsP3gZcTeyDW5zsFOgR+sQjOgSiZah3tU6bkPMsq4wS3mYpVzMUx5mKQ+zlIdZysMs5bGVLHn1vXpmK0UpoGxhNTs7DqKTgVnKwyzlachZnruehQ2HLmPPuSRotIYvf63CvTGycyS6RwfDwd60fBpylrIxS3mYpVzMUx5mKQ+zlIdZysMs5bGFLFmUqme2UpTSarVITU1FYGCg1T8JzI1ZysMs5WGWZdJyi7DxyBX8/vdV5BaVGtzm7+mMYXc3weC7GsPTVWl0X5Vagz/PJmH/+WRk5BbAz9MN90YHo0frEK5TVUM8L+VhlnIxT3mYpTzMUh5mKQ+zlMdWsjS1RsKFzomIqMEJ8HTB5D7RePS+5th5OhHrDybgSlo+ACA9txjf7IxD7J/x6Ns+HMPviUBEoAcA4K+4FHz4y3HkF6uhUABCAIrkfOyLS8HiLWfw2vAO6NoiyJxdIyIiIiKyGixKERFRg+XkaI/BdzXGoA6NcPxyBjYcTMDB+FQIACVqLX77+yp++/sq7or0R3SYN37ce0F/X904Y93XgmI1Zq06gplj7ka3lixMERERERFVhUUpIiJq8BQKBe6K9Mddkf5IzCzAL4cvY+vx6yhUqQEAxxLScSwh/Y7HEAAUAD785Th+eKUfp/IREREREVWBa0pJYitrSgG2saiapWCW8jBLeZilaQpKSrH1+HX8fPgykrIKq3XfacNj0Ld9eB21zDbxvJSHWcrFPOVhlvIwS3mYpTzMUh5byNLUGol195KkE0JAo9GAtcraY5byMEt5mKXp3JwcMbJLJL5+rhdahXubfD+FAtgXl1J3DbNBPC/lYZZyMU95mKU8zFIeZikPs5SnoWXJohQZEEIgIyOjwTwB6hKzlIdZysMsq8/eTgFHe9NfLoUAcotUddgi28PzUh5mKRfzlIdZysMs5WGW8jBLeRpalixKERERVcHDRQmFwvT9/7mRjeW7/6n2tD8iIiIiooaEC50TERFV4d6WQdh3Ptnk/UtKtVjxZzxW/BmPto19MSAmHP9qFQw3J8c6bCURERERkXXhSCkyoqjOcAC6I2YpD7OUh1lWX4/WIXB3doApydnbGe51+momPt54EuM+3o5564/h70vp0GgbxnDs6uB5KQ+zlIt5ysMs5WGW8jBLeZilPA0pS159TxJbuvoeEREZO/BPCmatOgIAqOiFU/fWYdbYu9Es2As7TiVi24lruJZRYLSvv6cz+rULQ/+YcIT7udddo4mIiIiIzMDUGgmLUpLYSlFKCAGVSgWlUtmgqrN1gVnKwyzlYZa181dcCj785Tjyi9VQKMoWNdd9dXd2wGvDO6BriyD9/kIIxN3IwfaT17Hr9A3kF5caHbNVmDf6xYSjZ+tQeLg0zOl9PC/lYZZyMU95mKU8zFIeZikPs5THVrJkUaqe2UpRSqvVIjU1FYGBgbCz4+zO2mCW8jBLeZhl7anUGuw5m4R955ORkVsAP083dI8Oxn2tQ6B0sL/j/Q7+k4ptJ6/j8IU0aG97+XW0t0O3lkEYEBOOjk39Yd+AHh+el/IwS7mYpzzMUh5mKQ+zlIdZymMrWZpaI+FC50RERNWgdLBH3/bh6N02tFpvGJQO9rivdQjuax2CzPxi7Dp9A9tOXEdCah4AoFSjxZ9nk/Dn2ST4ujuhT7sw9G8fjohAj7ruEhERERGRWbAoRUREVM983Z0xqmtTPNglEheTc7Ht5vS+nEIVACAzvwRr/7qEtX9dQvMQL/RvH4ZebcPg5ao0c8uJiIiIiORhUYqMODjwtJCFWcrDLOVhlvLUNkuFQoFmIV5oFuKFJ/u1wuELqdh+4joOxqdCffMKffFJOYhPysGX286hS/NA9IsJR+dmgXCwt97h3BXheSkPs5SLecrDLOVhlvIwS3mYpTwNKUuuKSWJrawpRURE5pdTqMIfpxOx7WQi4pNyjG73clWid9tQDIgJR1SwlxlaSERERERUOS50Xs9spSglhEBRURFcXFyseqV/S8As5WGW8jBLeeory8upedh28jp2nkpEZn6J0e2RgR7oHxOOPm3D4OPuVGftqEs8L+VhlnIxT3mYpTzMUh5mKQ+zlMdWsjS1RmJbY/+p1oQQyM3NBWuVtccs5WGW8jBLeeory4hADzzVrxVWvNQH7z58D3q0DoFjual7Cal5+HLbOTyyYAfe/vEw9pxNgkqtqdM2ycbzUh5mKRfzlIdZysMs5WGW8jBLeRpalg1noiIREZEVs7ezQ+fmgejcPBB5RaXYffYGtp+4jnOJ2QAArRA4GJ+Kg/GpcHd2RO+2oejXPhwtQ72s+lM2IiIiIrJdLEoRERFZGQ8XR9zfqQnu79QE19Lzse3kdew4lYj03GIAQH5xKTYeuYKNR66gsb87+rUPR992YfD3dDZzy4mIiIiIbmFRigwoFAoolUp+qi4Bs5SHWcrDLOWxlCwb+btjcp9oTOjVEicuZ2DbiWvYdz4ZJWotAOBqej6+2Xke3+46j7si/dE/Jhz3tgyGk6O9WdtdnqVkaQuYpVzMUx5mKQ+zlIdZysMs5WloWXKhc0lsZaFzIiKyfgUlpdhzNgnbTibi9NVMo9tdnRzQo3UIBsSEo3W4T4N500NERERE9YNX36tntlKUEkIgPz8f7u7u/COllpilPMxSHmYpj7VkeSOzANtPJmL7qetIyS4yuj3U1xX92oWjX/swBHm7mqGF1pOlNWCWcjFPeZilPMxSHmYpD7OUx1ay5NX3qEaEECgoKGgwK/3XJWYpD7OUh1nKYy1Zhvq6YXyvFvh2am98ML4r+seEw7nc1L0bmYX4fvc/GP+/XZi2/AC2nbiOIpW6XttoLVlaA2YpF/OUh1nKwyzlYZbyMEt5GlqWFlmU+vzzzxEREQFnZ2d06dIFhw4dqnTf0tJSvPPOO4iKioKzszNiYmKwefNmg30iIiKgUCiM/j3//PP6fYqLi/H888/Dz88P7u7uGDVqFFJSUuqsj0RERPXJTqFA+yZ++M+wGKx6tR9eGx6DDhF+KP/524nLGfjwlxN4+OPt+PDnEzhxOQPaBvKGiIiIiIjqn8UtdL5q1Sq8+uqr+OKLL9ClSxcsWLAAAwcORFxcHAIDA432nzFjBlasWIGlS5ciOjoaW7ZswciRI7F//37cddddAIDDhw9Do9Ho73P69Gn0798fDz30kH7bK6+8gl9//RVr1qyBl5cXpk6digcffBD79u2r+04TERHVI2elA/q1D0e/9uFIzSnC9pPXse3kddzILAQAFJdqsO3mtiAvF/RtH4b+7cMR6utm5pYTERERkS2xuDWlunTpgnvuuQefffYZAECr1aJRo0Z44YUXMH36dKP9Q0ND8dZbbxmMeho1ahRcXFywYsWKCn/Gyy+/jE2bNiE+Ph4KhQI5OTkICAjAypUrMXr0aADA+fPn0apVK/z111/o2rVrle22pTWlcnNz4enpadXzVy0Bs5SHWcrDLOWxtSyFEDh7PQvbTybijzM3UFhiPIWvTSMf9I8JR4/WIXBzcpT6s20pS3NilnIxT3mYpTzMUh5mKQ+zlMdWsjS1RmJRI6VUKhWOHj2KN954Q7/Nzs4O/fr1w19//VXhfUpKSuDs7GywzcXFBXv37q30Z6xYsQKvvvqq/gE+evQoSktL0a9fP/1+0dHRaNy4sclFKVuhUCjg5eVl7mbYBGYpD7OUh1nKY2tZKhQKtGnkizaNfPHsgNb4Ky4FW09ex7FLadDe/PjqzLUsnLmWhcWbz+De6GD0bx+ODpH+sLer3RsmW8vSnJilXMxTHmYpD7OUh1nKwyzlaWhZWlRRKj09HRqNBkFBQQbbg4KCcP78+QrvM3DgQHz88cfo0aMHoqKisGPHDqxbt85gul55GzZsQHZ2NiZOnKjflpycDKVSCW9vb6Ofm5ycXOFxSkpKUFJSov8+NzcXQNnILq1WCwD6tauEEAaLlFW1XXf/mm63s7MzOrap23VVWS8vrwr3t8Y+1bbtNe3T7VnaQp+q2l5XfRJCIC8vT//L2Rb6VJvttemT7moeHh4euJ219qm6bZfVJwDIy8szytKa+6TbrnSwQ4/WwejROhgZecXYdfoGtp1MxNX0fABAiVqLXadvYNfpG/D3cEafdqHo1y4Mjfzda9Qn3ahlDw8Pg08Eee5VfzsAZGdnG3y6au19MufjpNVq9Z9W29nZ2USfzPU46d4Xlc/S2vtkyva66JMuy5q+X7fEPtV2e037pMtS93egLfSpJm2X0Schqn6/bm19MmV7XfRJiOq9X7fUPt2+vTIWVZSqiYULF+Kpp55CdHQ0FAoFoqKiMGnSJHzzzTcV7v/1119j8ODBCA0NrdXPnTNnDmbPnm20PS0tDcXFxQDKRmx5eXkhNzcXRUW3LsPt5uYGDw8PZGVlQaVS6bd7enrC1dUVmZmZUKtvTZvw8fGBk5MT0tLSDE4aPz8/2NvbIzU11aANgYGB0Gg0yMjI0G9TKBQICgqCSqVCVlaWfruDgwP8/f1RVFSE3NxcaLVa5OTkQKvVws/PD/n5+SgoKNDvb4190lEqlfD19a23PumydHNzg4ODg030yVyPk1arRVFRETw9PZGXl2cTfQLM8zhptVooFAq4uLggMzPTJvoEmOdxcnV1RVFREdRqNUpLS22iT5U9TvffFYqH7o3CoXNXsPtcKg5czERBSdmHP+l5xVi9/xJW77+EqEA3dG/uhyH3RMHLzdnkPgUEBCAvLw+FhYWws7Orlz7Z4uPk6ekJZ2dnpKWloaioSJ+ltffJnI9TdnY2cnJyUFRUBGdnZ5vok7keJ41Go88yICDAJvpkrsdJ9x5T1x5b6JNOfT9Ouiw9PDwghLCJPpnrcdJqtSgpKYGnpyeys7Ntok+AeR4nIQQUCgWUSqX+uW6NfUpLS4MpLGpNKZVKBVdXV6xduxYjRozQb58wYQKys7Px888/V3rf4uJiZGRkIDQ0FNOnT8emTZtw5swZg32uXLmCpk2bYt26dRg+fLh++86dO9G3b19kZWUZjJZq0qQJXn75ZbzyyitGP6+ikVKNGjVCVlaWfr6kuSuTNakga7VapKWlITAwEPb29hZXbbWmqvjtWdpCn6raXld90mUZFBSkv83a+1Sb7bXpk1arRXp6OgICAqBQGE65stY+VbftsvokhEBaWppRltbcJ1MfJ5Vag0MX0rD9ZCIOX0gzukKfo70durYIQr92oegU5Q/7m8WR29uoUmuw51wy/opLQXpuAfw8XXFvy2Dc1yoYSgd7nns12C6EQEpKCgICAvRFKWvvkzkfJ92bal0RxRb6ZK7HSfdaXj5La++TKdvrok+1fb9uiX2q7faa9ulO7zGttU81abuMPpnyft3a+mTK9rroU3Xfr1tqn3JycuDj42Nda0oplUp06tQJO3bs0BeltFotduzYgalTp97xvs7OzggLC0NpaSl++uknjBkzxmifZcuWITAwEEOHDjXY3qlTJzg6OmLHjh0YNWoUACAuLg5Xr15Ft27dKvx5Tk5OcHJyMtpuZ2enfxOoo3uwblfZ9tvvX5Pt1f2Z5bdX9v/aHN/cfaqL7aa0vfx9baVPVW2vqzbW9Jy05D7VdLusPllSX63xcdK9AFeWpTX2qartujY6K+3Qo3UoerQORVZ+CXadTsS2k4m4lFL2qV2pRos955Kw51wSfNyc0KddKPq3D0dkkKf+2H/FpeDDX44jv1gNhQIQAlAk52N/XCq+2HoWrw3vgK4tgnjuVXO7EEJ/Tt7eVmvtU3XbXtn2mrRFN81M97Wmx7GkPpnzcbo9S1vokynb66KNlf3flONYap9qs702fZL93LaEPpmr7VVlaY19qmp7XffJHBnU5d8aFbGokVIAsGrVKkyYMAFLlixB586dsWDBAqxevRrnz59HUFAQxo8fj7CwMMyZMwcAcPDgQSQmJqJDhw5ITEzErFmzkJCQgL///ttg1JNWq0VkZCTGjRuHuXPnGv3cKVOm4LfffsO3334LT09PvPDCCwCA/fv3m9RuW7r6Xn5+Ptzd3Ss8Qcl0zFIeZikPs5SHWRq7mJyDbScTsfNUInIKVUa3Nwv2RP+YcHg4O+KDn08AACp6E6JLc+aYu9GtZVAFe1BleF7KxTzlYZbyMEt5mKU8zFIeW8nSKq++BwBjx45FWloa3n77bSQnJ6NDhw7YvHmzfvHzq1evGlTciouLMWPGDFy6dAnu7u4YMmQIli9fbrRo+fbt23H16lVMnjy5wp/7ySefwM7ODqNGjUJJSQkGDhyIRYsW1Vk/LZVCoahwQTWqPmYpD7OUh1nKwyyNRQV7ISrYC0/2jcbhC2nYdvI6Dv6TAvXNy/ddSM7FheSzVR5HoKww9eEvx/HDK/2gdLCv24bbEJ6XcjFPeZilPMxSHmYpD7OUp6FlaXEjpayVLY2UysrKgo+Pj1VXZS0Bs5SHWcrDLOVhlqbJLVRh15kb2H7iOv5Jyqn2/acNj0Hf9uF10DLbxPNSLuYpD7OUh1nKwyzlYZby2EqWVjtSisxLCAGVSqVfk4JqjlnKwyzlYZbyMEvTeLoqMfyeCAy/JwKXU/PwzpojSMwsNPn+Px1IgKerEkFeLgj0doWzI0dN3QnPSzlUag3+PJuE/eeTkZFbAD9PN9wbHYwerUM4cq+GeG7WHs9L+XheysMs5WloWbIoRURERPUiItADfh7O1SpKXUzJxYwfDuu/93ZTIsjLFYFeLgj2dkGQt4v++yBvF7go+daGaqeyRfj3xaVg8ZYz+kX4ieoTz0sislV850ZERET1xsNFqf+DqiayC1TILlAh7kZ2hbd73RxVFeTtcrNQ5YogLxcEe5cVrlyd+NaHKvdXXApmrz6i/153nuq+FhSrMWvVES7CT/WK5yUR2TK+MyMDCoUCnp6eDWKYYF1jlvIwS3mYpTzMsmbubRmEfeeTTd5/YIdw+Lo7IzWnCMnZhUjJKUJGbnGFV+0DgJxCFXIKVZWuX+Xh4nizaOV6c5RV2Ugr3f/dnB1r0CvLwfOy5lRqDT785TiAiq8KqdvORfhrhudmzfC8rFs8L+VhlvI0tCxZlCIDCoUCrq6u5m6GTWCW8jBLeZilPMyyZnq0DsHiLWdQUKyu9A8soOwPLDdnB0wd3NboD6xSjRZpOUVIzSlCiq5YlV32/9ScIqTnFkFbycHzikqRV1SKC8m5Fd7u7uxwq0h1c5SVbuRVkLcr3C28aMXzsub+PJuE/GJ1lfsJAPnFauw5m8RF+KuB52bN8LysWzwv5WGW8jS0LFmUIgNarRaZmZnw9fWFnZ2duZtj1ZilPMxSHmYpD7OsGaWDPV4b3gGzVh2BAhV/8q/7XPC14R0q/MTf0d4Oob5uCPV1q/BnqDVapOcWIzmnrFiVmlN0s2hV9n1abjG0lcwfzC9WI784FxdTKi5auTk56KcFButGWnnfWtPKw9nRrJ9s8rw0pNFqUVCiRkGxGvnFpSgoLi37WlL2fdm2sv8fS0g3+bgKBbAvLoV//FcDz82a2XMuqdLflbfjeVl9PC/lYZbyNLQsWZQiI2p11Z/GkGmYpTzMUh5mKQ+zrJmuLYIwc8zdxov23vzq5uxQq0V7HeztEOzjimCfij9l1GjLilYpumLVzWmBZd8XIi23GJpKhloVlKiRkJqHhNS8Cm93VTqUW8/q1tRA3ZpWni51U7Sy1atyqTVafQGprKCkLvtaUlZQKixWI7/kVmFJX2QqKdu/SKWpk3YJAZxISMeSbWfRNNATUcGeaOzvDgd72//joTb4O7NyQgik5RbjUkouLqXk4mJyWXE8Kcv0C0MIAVxJy0NSViGCvV0azNSf2uJ5KQ+zlKchZcmiFBEREdW7bi2D8MMr/bDnbBL2lSukdI8Oxn11XEixt7O7uaaUK9DE+HaNVouMvBJ9kar8KKuUnCKk5RRBXUnRqlB156KVs6N9BVMDb61p5eWqrPYfkpZ8Va5SjVY/OklXUDIsMt3+fdn/C29uKy6tm6KSDPklaqw7kKD/3tHeDo393dE02BNRQZ5oevOfh4tlT/mk+leq0eJqWv6tAtTNr3lFpbU+9vWMAkz8bBcCPJ3Rvokf2jfxRfsmfgjxcWWRiogsEotSREREZBZKB3v0bR+O3m1DkZqaisDAQIsYpm5vZ4dAr7LRTu0a+xrdrtEKZOYX66cG6hZg1xWv0nKKUarRVnjs4lINrqTl40pafoW3OznYGRSpAvUjrcpGXXm7GRat6vqqXCq1Rj/6qKDkVtHo9lFLt24vNZgqV6KuOIe65ORgBzdnR7g7O8LN2QFuTrf+7+7kePM2B/0+7s4OcHVyxLFLaVi05WyNf26pRouLNwsM28ptD/JyQWRQWaEqKrisUMVRLA1HbpHqZvEpD5dujn66mpZXaWG7PCcHO/i4OyM52/TRUjppucXYcSoRO04lAgD8PZzRvokv2jXxQ0wTP4T6skhFRJZBIURNL8pM5eXm5sLLyws5OTnw9PS8475CCGg0GosckieEQGlpKRwdzbsmhi1glvIwS3mqk6WjoyPs7a132k9dE0JApVJBqaz+yBYyZGtZaoVAVn6J0QLsKeW+r6xoVRWlg51+TSt/D2fsOp0IlQmFH1elA2aNvVtfZKpw1FKJ+tbopZuFpZq2szacHe3h7uwIVycHfdFIX2S6ua3897oik+4+NR1lp1JrMO6T7SYvwv/5U//C9YxCXEzO1Y94uZ6RX+ki++W5OjncHEnlcbNY5YUmAe5WPdWyKrb2PL+dVgikZBfhYnLOzZFPebiUkovUnCKT7u/r7qQfXRcV5ImmwZ4I83WDRqs1+bx0VtpjVJdInLmejbPXMu9YFPZ1dzIYSRXu52aTj0tVbP28rE/MUh5bydLUGgmLUpKYErgQAtnZ2UhLS4NGY7nD0YmIdLy9vREcHGzVL4hElkYrBLILSm6uZ1U2uio5u1zhKqfIpEKTpXJR2pcVipxujk5yNhyp5KrbVsGoJTcnB7Ouy3TgnxTMWlU28uxOi/DPGnt3hVMiy0bC5RkUqi6l5Jq0tpWdQoFG/m63pv7dnAbo7eZUix5RXSjRPc7l1n9KSMlDoarqD5ztFEC4n3tZ8ancVE8f98of55qcl6UaLf65kY2TVzJx8koGzlzLQskdpsP6ujuhXWNftI/wQ/vGvmjk787XfiKqFRal6pkpgSclJSE7Oxuenp7w9PSEg4ODtF/2iuPH4fB//wf1e+9BxMTU+DhCCKjVaqlta6iYpTzMUh5TsxRCoLCwEKmpqfD29kZISEg9ttI6aLVapKWlISAgwCKmnFkzZmlICIHsApXBOlYpBlMEi+74x2Vt6UYoGY5KKpsGZ1Bkun3UkrMD3JwcYG/lj2GFa3Td/Opeg0X4tUIgKauwrECVfGv9oLTcYpPu7+vupJ/2pxtFE+rrBns763o9tNbneXZBib7IePFmAep6RkGlV/Asz1XpgMggD30BqmmQJyICPODkWP0RcbU9L9UaLf5JysGpKxk4cSUTZ65m3nHNNm83Jdo19kNMRNlIqsY2WqSy1vPSEjHL2rO1i5awKFXPqgpco9EgPj4e/v7+8Pf3l9+AN94A5s4t+/rf/9b4MPzjXx5mKQ+zlKe6WWZkZCA1NRUtWrTgVL7baLVai1oHyZoxy+oRQiCnUIW3Vh7CheRck+/n7+GEQXc1vjXdzen2glPZ9DdrK3bUBZVaU+eL8OcWqgwWur6YnIur6fmVXvmxPCdHe0QGGhY7IgM94KK03OViLf15rtEKJGYWGBUPM/NLTLp/gKezftqdrngY7OMKO4nvW2Sel2qNFheSc3DiciZOXc3A6auZdxzR5+WqNFiTqnGAu9S+mYuln5fWhFnWjuwPRCwBi1L1rKrAi4uLkZCQgIiICLi4uMhvQHQ0EBdX9vXcuRofhn/8y8Ms5WGW8lQ3y6KiIly+fBmRkZFwdnauhxZaD775kodZ1sw7a45if1wyTHknp1AA97YMxtsPdar7htmQ+j43VWoNrqXn69ckupicg0spucgvrnpamAJAmK9b2aLq5aaF+Xk4WcRrpyU9z4tuXiWz/DTLhJRckxbmd7BToHGAx818PfRFKE8XZT20vExdZKnRanEhORcnL2fg5NVMnL6aicKSys87TxfHmwWqskJVRKCHVRapLOm8tHbMsubKX7TkTlN0a3rREnMxtShluR+n2Kg6eVOQkFBWkAKA8+eBy5eBiAj5P4eIGhxL+EOGiCp2b8sg7DufbNK+QgDdreiNbEOldLBHVLAXooK99NuEEEjLLcbFcqN3LqXkIinL8IpsAsD1zAJczyzAnnNJ+u1erkqjRdXD/dzMunZXfRFCICOvBBdTcm4WoMoWH7+RWXDHRcN13J0d9SPRdEW+xgHucLTB7Ozt7NAy1BstQ73x0L1R0Gi1uJicq1+T6vTVTBSUK1LlFpVi3/lk/e8gDxfHsjWpbi6eHhnkaZVFKqL6plJr8OEvxwFUXJDSbVcA+PCX4/jhlX5WOZXvTliUsgWbNgF2doBWW/ZR6KZNwNSpNT4cp+jIwyzlYZbyMEs5FAoF/Pz8WLiTgFnWTI/WIVi85YzJV4u7rzXXhqsuSzg3FQoFAr1cEOjlYvAJeUFxKS6l5hlMN7ucmmd0xcScQhWOJaTjWEK6fpujvR2aBLgbFVzcnB3rtB91maVaoy03yuxmAS85F7lFpSbdP8THVZ+FLpcAT2eL/L1UH+elvZ0dWoR6o0WoN0Z3awqNVuBSSq5+TarTVzMMRvDlFZVif1wK9selACgr6LVt7IuYm1f3iwzytMipwZbwHLcVzLJqKrVGf3XbgpKyK+Ee+CfFpNGwAkB+sRp7ziahb/vwum9sPeL0PUlMnb5XJ1Ng+vUDdu0qK0rZ2QF9+gDbttX4cEII/jKRhFnKwyzlqU6Wdfq7ywZotVoOUZeEWdZMba8WR1WzpnNTo9XiWnqB0VpVOYUqk+4f5O1SVpQpdwXAIC+XWr3+6hfujUtBbqEKnq5K3NsyqFYL9+YXl97q480peFfS8o0KchVROtghIsBDf3XDpkGeiAzygJtT3RXk6oK5z0uNVuBy6q2RVCevZCK/uPICoJuTA9o1vrkmVYQfmlpQkcrcWVq7uniOWyIhBFRqbVlBqbgU+SVqFBSXlhWZSm5u0xWcDG4vRUFJ2fbaXlnX2qbic02pema2olRuLuDnB6jLVVcdHIDMTMDDo9qH49o98tQ2yyFDhiAsLAxLly6tg9ZZF56X8uiyfPzxx6HVarF69eo77s+iVOW4doI8zLJ2bHFxVEthC+emEAKZ+SUGBZyLKblIzDBtCpubk4PBgupRN6ewmfLHZm3PTSEEUrKLDApsl1JzkZJdZFLfvd2UtwpsN/sQ7udm9VeItMTzUisELqfm6QtUp65k3HGUmquTA9o29kX7myOpmgV7muVxscQsrYk1vf4IIVBSqrlVOCopNRq1VFZYuvn/m7eXFZjK/m9K4buutW/iiw/GdzN3M0zCNaUaiq1bDQtSQNn3W7cCo0bVWzO+/fZbTJo0yWBbQEAA2rRpg2nTpmHw4MF18nMLCwsxf/589OrVC7169apy/z/++AO9e/fGmjVrMHr0aP12lUqFBx98EL/99hu++uorTJ48ucZtSkpKwsKFC3Hw4EEcOXIE+fn52LVrl0nt09m3bx+2bt2K8+fPV3j7b7/9hqFDhyIkJATXr1/ni6hk2dnZmDZtGtavX4/CwkJ07twZH330ETp27GjS/T/77DN8/vnnuHTpEvz9/TF27Fi8++67cHNzM9jvwoULmD59Onbs2IGSkhJ07NgR7777Lnr37m10TK1WiyVLlmDJkiWIi4uDq6srYmJi8MknnyAmJgYAcP78eXzzzTfYunUrLl68CHd3d3Ts2BGzZ8/G3XffbXC8adOm4Z577sGJEyf09yci69StZRB+eKVfnV8tjqyTQqGAn4cz/DyccU+zQP32YpUal9PyDApVCSl5KC41vAJbQYkap65m4tTVTP02ezsFGvu7GxR7mgZ5wsv11mLf5RfuBaBfkF/3taBYjVmrjugX7lWpNbiSlm9QPLuUkmuwjlGlfQQQ7ueGqGCvssXHb7bJ150fptQXO4VCfz6M6BwJrRC4mpaPE1cycPJyBk5dzTQYsVdYosah+FQcik8FALgqHdCmsY9+TarmIV5WXzy0ddV9jteWEAJFKg3yi0tReHPk0e2jkvKLyxeaDAtK+cWlJl3dVDZXJwe4OzvCTfdVdwVcZ0e4OTlif1wyLqfmmfQhgUIBeNTjRRXqC4tS1m7jxrKRUbePlNq4sV6LUjrvvPMOIiMjyz7ZSknBt99+iyFDhmDjxo24//77pf+8wsJCzJ49GwCqVfQpr7S0FKNHj8Zvv/2GpUuX1qogBQBxcXGYN28emjdvjrZt2+LAgQPVPsYHH3yAvn37olmzZhXeHhsbi4iICFy+fBk7d+5Ev379atVmukWr1WLo0KE4ceIEXnvtNfj7+2PRokXo1asXjh49iubNm9/x/q+//jrmz5+P0aNH46WXXsLZs2fxv//9D2fOnMGWLVv0+127dg3dunWDvb09XnvtNbi5uWHZsmUYMGAAduzYgR49ehgcd/LkyYiNjcX48eMxdepUFBQU4NixY0hNTdXv89VXX+Hrr7/GqFGj8NxzzyEnJwdLlixB165dsXnzZoPz5K677sLdd9+Njz76CN9//72k9IjIXJQO9ujbPhy924byU38yibPSAdFhPogO89Fv0wqBpMzCm6OScm4WhvKQnldscF+NViAhNQ8JqXnYcSpRv93fwxlNgz0REeCOXw5fueMfWbrb3vvpKEJ93HA9owBaEyZwODvaI7Lcou1NgzwREegBZ0cWXy2JnUKBiEAPRAR6YPg9ERBC4EpaPk5dzcCJy5k4dTUD2QXlilQqNQ5fSMPhC2kAABelPdo0ujWSqnmIV4NYnN9a1GRxbgd7OxSV3FYwqmSkUkGJ4f91xSZTfkfIpFuT0c3ZEe5OjnBzvlVYMioyOd36f9lXR7goHaqcphrq64oPfj5hUnts9aIlLEpZqsREICXlzvsIAfzyS8UjpX7+GTh6tKyceidBQUBYWO3aWs7gwYMNRmQ88cQTCAoKwg8//FAnRanaKi0txZgxY7Bp0yYsWbIETzzxRK2P2alTJ2RkZMDHxwerVq2qdlEqNTUVv/76K7744osKby8oKMDPP/+MOXPmYNmyZYiNjbXYolRBQYHR6CBLt3btWuzfv99gNN2YMWPQokULzJw5EytXrqz0vklJSfj444/x+OOPGxR6WrRogRdeeAEbN27EAw88AACYO3cusrOzcfr0abRs2RIA8NRTTyE6OhqvvPIKjh49qr//6tWr8d1332HdunUYOXJkpT9/3LhxmDVrFtzd3fXbJk+ejFatWmHWrFlG58mYMWMwc+ZMLFq0yOA+RETUMNkpFAjzc0OYnxt6lFsYP7ugRH/lOt1Ipqvp+UZ/IKbnFSM9r1g/+sUUao3A1fT8Cm/TFbl0U/CigjwR4uvKq7pZIUW5ItUDd5cVqa6l5+PEzal+J69kIqugRL9/kUqDIxfTcORiWZHK2dEebRr5oN3NkVQtQr1t8iqIlkwrhH6E0s5TidVanHv0h1uhKtWaNBpIJjsF9AUiNyddcelW0UhfRHIq+15XUNIVnFycHOr89w0vWsKilOUaNw7Ys6fq/Sp7kuTkALdN16lQjx7A7t3lDqeQum6Pt7c3XFxc4OBgeKpptVp8+umnWLp0KS5evAgvLy+MGDECc+fOhY/PrU/sjhw5grfeegtHjx5FQUEBgoOD0bt3b3zzzTe4fPkyIiMjAQCzZ8/Wj5iaOXMmZs2aVWXb1Go1Hn74Yfz8889YvHgxnnrqKSl99ii3lldNrnL266+/Qq1WV1poWr9+PYqKivDQQw+hqKgI77//PhYvXmy03k9xcTHmzp2LlStX4urVq/Dx8UG3bt3wwQcfICoqCkDZ4/C///0PX331FeLj4+Hh4YFOnTrhvffew913363PeNmyZZg4caLB8RUKhUHWs2bNwuzZs3HmzBm89957+P333xEREYFjx47h5MmT+Pjjj/Hnn3/ixo0b8Pb2xpAhQ/DBBx/Az8/P4LiJiYl4++238fvvvyMjIwOhoaEYNGgQFixYgISEBERFReHjjz/GK6+8YnC//fv3o3v37li5ciXGjRtX7dx11q5di6CgIDz44IP6bQEBARgzZgxWrFiBkpISODk5VXjfv/76S39elffwww/jhRdewI8//qgvSu3Zswd33XWXviAFAK6urhg2bBg+//xzxMfH60dlffzxx+jcuTNGjhwJrVaLoqKiCot9nToZL3ro5+eH++67D3/88QcAw+d4//798Z///Afbtm27Y7GLKmZnZ8fRKJIwS3mYpVzMs4y3mxM6NnVCx6b++m3lp9vpClUXU3JRaMJ0u4pEBnrccTog3WIL56VCoUDjAA80DvDAA3c3gRAC1zMK9GtSnbySgcz8W0Wq4lINjl5Kx9FLZVeRdHK0R+twH/1IqhahXtWaqmy8OHeiTS7OXZ5GKwzXUTIalaS+OWLJeNRSfnEpikruXDS5k5LSmq3FZKdQwMPF8dYUuJsjkvT/1xWWbhaabi86uSjtLX49WqWDPV4b3gGzVh2BAne+aMlrwzvY5PnJopSlevJJ4PBhoKTk1sTcilR2W1VDGxUKwMkJuG1kkBACurXva/IEzsnJQXp6OoQQSE1Nxf/+9z/k5+fjscceM9jvmWee0a9D9eKLLyIhIQGfffYZjh07hn379sHR0RGpqakYMGAAAgICMH36dHh7e+Py5ctYt24dgLJCweLFizFlyhSMHDlSX0Ro3759le1Uq9UYN24c1q9fj88//xzPPPOM0T6lpaXIyckxqd++vr5GbwzKZ1kd+/fvh5+fH5o0aVLh7bGxsejduzeCg4Px8MMPY/r06di4cSMeeugh/T4ajQb3338/duzYgYcffhgvvfQS8vLysG3bNpw+fVpflHriiSfw7bffYvDgwXjyySehVquxZ88eHDhwwGgNIlM99NBDaN68Of773//q+79t2zZcunQJkyZNQnBwMM6cOYMvv/wSZ86cwYEDB/Tn2o0bN9C5c2dkZ2fj6aefRnR0NBITE7F27VoUFBQgMjIS3bt3R2xsrFFRKjY2Fh4eHhg+fDiAmj9+x44dQ8eOHY0ez86dO+PLL7/EP//8g3bt2lV4nJKSsjdPLi4uBttdXV0BwGD0U0lJiUEBtqJ9mzdvjtzcXBw6dAjPPfcc3nzzTf1zKjIyEnPnzsWYMWOq7F9ycjL8/cv+kCh/XrZu3RouLi7Yt28fi1I1IISARqOBQqGw+Dc8lo5ZysMs5WKelVM62KN5iBeah3jptwkhkJJThLd/PIwraRWPfqpI28a++GiCdSzcawls8bxUKBRo5O+ORv7uGNqprEh1I7OwbE2qKxk4dSXTYBppSakGxxLScSzhZpHKwQ6twm+tSdUyzLvSP94rW5x73/lkLN5yxqIW5y5PrdHqC0RGayoVG6+fVH6/gmI1ClU1KxjLoHSwQ0Sgh34dJd2aSrpik0GRqdyoJSdHyy8qydC1RRBmjrm70kXj3Sxs0XjZWJSyVOPHl410GjkSuHAB0Epc6d/ODmjeHFi3Dmjd2uhmjUZjNLLJVLeP7nFycsI333yD/v3767ft3bsXX331FWJjY/HII4/ot/fu3RuDBg3CmjVr8Mgjj2D//v3IysrC1q1bDQok7733HgDAzc0No0ePxpQpU9C+fXujwtedTJ8+HVeuXMHnn3+OKVOmVLjPvn37KlxwuiIJCQmIiIgw2q6tweN2/vz5Co8FlE3t2759OxYvXgwAaNy4Mbp164bY2FiDotT333+PHTt2GI0omj59ur4gsWvXLnz77bd48cUXsXDhQv0+//73v2tUTNOJiYkxmuL23HPP4d///rfBtq5du2LcuHHYu3cv7rvvPgDAG2+8geTkZBw8eNDgMZ89ezZKS8uu4DJ+/Hg888wzOH/+PKKjowGUFaBWr16NBx98UF/Uqenjl5SUZLSeEwCEhJQNlb1x40alRSndqKfbf/aem6MeExMTDfbds2cP8vLyDEbX7d2712DfixcvQgiBH3/8EQ4ODpg/fz68vLywcOFCPPzww/D09MSgQYMq7duePXvw119/YcaMGfptuue4g4MDGjVqhLNnz1YeDlVKCIGMjAwEBgY2iDdMdYlZysMs5WKe1aNQKBDs7YpwP3dcTc+v8jPSsvuAI6KqqSGcl4pyU0mHdGxcVqTKKtRP9TtxJQPpueWKVGotjl/OwPHLGQDKiiCtwn3QvrEv2kf4Ifpmkaq+F+cur1SjRYFuIe6S2xblvn3UUgULd99+EYL64GhvZzQqyc3JAXE3sk2+CqZCAdzTLBBvP2Q8op9uacgXLWFRypK1bg38/TcwdSrw7bfyjjthAvDZZ8DNP95l+vzzz9GiRQsAQEpKClasWIEnn3wSHh4e+pFMa9asgZeXF/r374/09HT9fTt16gR3d3fs2rULjzzyCLy9vQEAmzZtQkxMDBwdHaW1MyUlBQ4ODvrpfxWJiYnBtm3bTDpecHCwrKYhIyMDYZWs8/Xjjz/Czs4Oo8otYj9u3Dj8+9//RlZWln7kzU8//QR/f3+88MILRsfQvXn56aef9FPwKtunJp599lmjbeVHDhUXFyM/Px9du3YFAPz999+47777oNVqsWHDBjzwwAMVjtLStWnMmDF46aWXEBsbi3fffRcAsGXLFqSnpxsUJmv6+BUVFVU4PU83PbKoqPIX4I4dO6JLly6YN28ewsLC0Lt3b5w7dw5TpkyBo6OjwX2nTJmCjRs3YuzYsXj//ffh5uaGRYsW4ciRIwY/Jz+/7JPmjIwMHDhwAF26dAEADBs2DJGRkXjvvfcqLUqlpqbikUceQWRkJKZNm1bhPj4+PgbPQyIiotq6t2UQ9p1PNmlfW124l+RSKBQI83VDmK8bBt1VVqRKzi66Od2vrFCVmnPrfZZKrcWJyxk4cTkD+DMejvZ2aBnmhX9u5FS5AH/5xbnLFwJUak25K7vdWqS7wlFLtxedStQoMUNRycnBroJFuW9Ng7t9YW79uks3i1CVFUK2n7ze4BfnrgsN9aIlLEpZOjc3YNkyoFcv4NlngdJSQFODX2gODmX/liwpG4VVRzp37mxQUBg3bhzuuusuTJ06Fffffz+USiXi4+ORk5ODwMDACo+hu5pYz549MWrUKMyePRuffPIJevXqhREjRuCRRx6pdE0fU82fPx8LFizA6NGjsXXrVnTv3t1oHx8fH7MtIF7ZSKUVK1agc+fOyMjIQEZG2SdBd911F1QqFdasWYOnn34aQNnompYtW95xxNvFixcRGhoKX19fqW2vqNCXmZmJ2bNn48cffzS4WhwA/RS7tLQ05Obmom3btnc8vre3Nx544AGsXLlSX5SKjY1FWFgY+vTpo9+vpo+fi4uLfhpeecXFxfrb7+Snn37C2LFj9VdxtLe3x6uvvordu3cjLi5Ov9/gwYPxv//9D9OnT0fHjh0BAM2aNcP777+PadOm6Rce1/28yMhIfUEKANzd3fHAAw9gxYoVUKvVRo91QUEB7r//fuTl5WHv3r2VLmQuhLDZT1mJiMg8uHAv1TWFQoEQH1eE+LhiYIdGAIDk7EKDNanKj+Qp1Whx+mqWScfWLc79zBd/wt5OoZ8yp1JLnLliIidH+woX4na/rdBkdCW4m1Pj6mp0DZ/jJBOLUtZiwgTgnntqNp3Pzg6IigLWrwdataq7Nlb4o+3Qu3dvLFy4EPHx8WjTpg20Wi0CAwMRGxtb4X0CAgIAlL3YrF27FgcOHMDGjRuxZcsWTJ48GR999BEOHDhQq6uFhYSEYNu2bfjXv/6FoUOHYvfu3YiJiTHYR6VSITMz06TjBQQE1GhR84r4+fkhK8v4RTM+Ph6HDx8GAP0C2OXFxsbqi1KyVFas0NyhMFpR0WbMmDHYv38/XnvtNXTo0AHu7u7QarUYNGhQjaY4jh8/HmvWrMH+/fvRrl07/PLLL3juuecMPkmo6eMXEhKCpKQko31020JDQ+94rLCwMOzduxfx8fFITk5G8+bNERwcjNDQUP0oQp2pU6di0qRJOHnyJJRKJTp06ICvv/4aAPT76n5eUJDxJ0yBgYEoLS1FQUEBvLxuremhUqnw4IMP4uTJk9iyZcsdC31ZWVkVnk9kGhb05GGW8jBLuZhn9XHh3rrH89JYsLcrgr1dMSCmrEiVkl2Ik1cycepqWaEqKauwWse7Uc39K+KitL+5+PatKXDl11LSb6tk1JKDhV5hkM/xuteQnuMsSlkT3XS+CROAn34y/X4jRwLff2/SdD2FQiF1mhxQtqg4cGsaUlRUFLZv347u3btXOeoEKFt7qGvXrnj//fexcuVKPProo/jxxx/x5JNP1urJ2rRpU2zZsgU9e/bEwIEDsWfPHoM/zvfv31+rNaV0VzmrrujoaPxUweMbGxsLR0dHLF++3KgAtnfvXnz66ae4evUqGjdujKioKBw8eBClpaWVPp5RUVHYsmULMjMzKx0tpZsOmJ2dbbD9ypUrJvcnKysLO3bswOzZs/H222/rt8fHxxvsFxAQAE9PT5w+fdroGLefl4MGDUJAQABiY2PRpUsXFBYW4vHHHze4T00fvw4dOmDPnj3QarUGRa6DBw/C1dXVqLBUmebNm+vPp7NnzyIpKcnoCoZA2dpo3brdWtx1+/btcHFx0Y/eCw0NRXBwsMF6VDo3btyAs7OzwZpUWq0W48ePx44dO7B69Wr07NnT4D7ls1Sr1bh27RqGDRtmUp/IkJ2dXYXFQqo+ZikPs5SLedZcQ1+4ty7xvDRNkLcr+nu7on9MOADgpW/24XxidrWOoS8e6aa1lbuyW/kiU8Wjlhxgb8NTr/gcrzsN7TnOopS1cXMDQkPLpuKpTbiCgoMDEBZm8vpRuitzybqaR2lpKbZu3QqlUolWN0dpjRkzBosWLcK7776L//73vwb7q9Vq5Ofnw9vbG1lZWfD29jZoR4cOHQDcusqZblHr24smpmrXrh1+/fVX9O/fH/3798e+ffv06znVdk0pIUSNRgF169YNX331FS5duoSmTZvqt8fGxuK+++7D2LFjK7zPp59+ih9++AGvv/46Ro0ahV9//RWfffaZ0VXqdI/vqFGj8Pnnn2P27NkGC52X38fT0xP+/v74888/8fLLL+tvX7Rokcn90RXQbp+SuGDBAoPv7ezsMGLECKxYsQJHjhwxmAaqy9LOzk5f7Bs3bhxWrlyJc+fOoV27dkZXXazp4zd69GisXbsW69atw+jRowEA6enpWLNmDR544AGDqaMXL14EAP3VDCui1Woxbdo0uLq6VrjeVnn79+/HunXrMGXKFIORT2PHjsXChQuxbds2/UUD0tPT8fPPP6NPnz4GxbMXXngBq1atwpIlS/TruJVX/jl+9uxZFBcX4957771ju6hiQgioVCoolcoG9WlWXWCW8jBLuZhn7Rgs3BuXjJyCEni5OaF7S9tfuLcu8bysGT8PZ33BpCoKBdDtZtGFKsfneN1oaM9xFqWsjVYLrFplWkEKKNvvxx+BTz4pm8Zngtpcfe/333/H+fPnAZStDbVy5UrEx8dj+vTp8PT0BFC2VtQzzzyDOXPm4Pjx4xgwYAAcHR0RHx+PNWvWYOHChRg9ejS+++47LFq0CCNHjkRUVBTy8vKwdOlSeHp6YsiQIQDKpoq1bt0aq1atQosWLeDr64u2bdtWuS5Red26dcO6devwwAMPoH///tizZw/8/PxqtabUe++9ByGEftTP8uXL9VdVK38VtIoMHToUDg4O2L59u3463sGDB3HhwgVMnTq1wvuEhYWhY8eOiI2Nxeuvv47x48fj+++/x6uvvopDhw7hvvvuQ0FBAbZv347nnnsOw4cPR+/evfH444/j008/RXx8vH4q3Z49e9C7d2/9z3ryyScxd+5cPPnkk7j77rvx559/4p9//jE5C09PT/To0QPz589HaWkpwsLCsHXrViQkJBjt+9///hdbt25Fz5498fTTT6NVq1ZISkrCmjVrsGvXLvj7++v3HT9+PD799FPs2rUL8+bNMzpWTR+/0aNHo2vXrpg0aRLOnj0Lf39/LFq0CBqNBrNnzzbYt2/fvgCAy5cv67e99NJLKC4uRocOHVBaWoqVK1fi0KFD+O6779C4cWP9fleuXMGYMWMwbNgwBAcH48yZM/jiiy/Qvn17o2LtG2+8gdWrV2PUqFF49dVX4eXlhS+++AKlpaUG+y5YsACLFi1Ct27d4OrqihUrVhgcZ+TIkXB1ddU/x7dt2wZXV1eDq2OS6YQQyMrKsumrH9UXZikPs5SLedZeQ124ty7xvKyZ6i7A/69oeRcysmV8jsvX4J7jgqTIyckRAEROTk6FtxcVFYmzZ8+KoqKi2v2gPXuEKPs9Wb1/e/eadHitVitUKpXQarXVatayZcsEyqYT6/85OzuLDh06iMWLF1d4vC+//FJ06tRJuLi4CA8PD9GuXTsxbdo0cePGDSGEEH///bcYN26caNy4sXBychKBgYHi/vvvF0eOHDE4zv79+0WnTp2EUqkUAMTMmTMrbeeuXbsEALFmzRqj21atWiXs7OzEPffcI3Jzc6vV/9vdnkX5f6YYNmyY6Nu3r/77F154QQAQFy9erPQ+s2bNEgDEiRMnhBBCFBYWirfeektERkYKR0dHERwcLEaPHm1wDLVaLT744AMRHR0tlEqlCAgIEIMHDxZHjx7V71NYWCieeOIJ4eXlJTw8PMSYMWNEamqqUdYzZ84UAERaWppR265fvy5GjhwpvL29hZeXl3jooYfEjRs3Kny8rly5IsaPHy8CAgKEk5OTaNq0qXjuuedEfn6+0XnUpk0bYWdnJ65fv25SrqbKzMwUTzzxhPDz8xOurq6iZ8+e4vDhw0b7NWnSRDRp0sRg27Jly0RMTIxwc3MTHh4eom/fvmLnzp0V/ozhw4eL4OBgoVQqRWRkpHj99dcrPfcuXrwoRo4cKTw9PYWLi4vo06ePOHTokME+EyZMuOO5l5CQYPAc79Kli3jsscfumIW03102SKPRiKSkJKHRaMzdFKvHLOVhlnIxT3mYpTzMsmZKStXiwfmbxcB3NokBd/g38J1N4sH5m0VJqdrcTbYqPC/lsZUsq6qR6CiEMGUAI1UlNzcXXl5eyMnJ0Y8IKq+4uBgJCQmIjIzUX1q+Rl58EVi82HCklO7KelOmlN12+xX6HByA554DbpuiVREhhP5KXg2iKluHapPlnj170KtXL5w/f56LUKPyLO+66y74+vpix44dZmydddFlefr0aXTq1Al///23flpsRaT97rJBWq2WnwhKwizlYZZyMU95mKU8zLLmDvyTglmrjgC48+Lcs8bezbWQqonnpTy2kmVVNRId6+1hQ1TR1D3dlfWOHgU+/rjsa1SU4VQ93RS+GqxvROZx3333YcCAAZg/f765m2Kxjhw5guPHj2P8+PHmbopVmjdvHkaPHn3HghRVraZTnckYs5SHWcrFPOVhlvIwy5rRLc7t5lyWn+6zTt1XN2cHFqRqgeelPA0pS46UkqReRkrt3Qvcd5/htkmTgM8+M1zIvKAAmDoV+PZb4/vfvKIXkbU6ffo0jh49io8++gjp6em4dOkSR/DUIY6UIiIiIlujUmtuLs6dgrwiFTxclOjeMoiLcxNJZOpIqYZTfrMFq1eXfbW3BxwdgSVLgIpGibi5AcuWAb16Ac8+e2s63+rVVRalxG1XOaOaY5bylM9y7dq1eOedd9CyZUv88MMPLJRUE89LeYQQKCoqgouLC7OsJWYpD7OUi3nKwyzlYZa1p1ucu0+7MGYpCc9LeRpalpy+Zy10U/cAoFmzsml6VU1bmjDh1nQ+wOQpfFpO85OGWcqjy3LWrFnQarU4d+4cevbsaeZWWSeel3IIIZCbmwsOOK49ZikPs5SLecrDLOVhlvIwS3mYpTwNLUsWpaxFUVFZcWnSJODvv4HWrU27X+vWZftPnFh2/6KiOm0mEREREREREZEpOH3PWri5la0JVZPV93XT+bTamt2fiIiIiIiIiEgyVijqWa2G4NW2oGTi/RvCvNX6wizlYZbyVCfLhjJsuCYUCgWUSiXPTQmYpTzMUi7mKQ+zlIdZysMs5WGW8jS0LDlSqp44OjpCoVCgoKAALi4u5m5OpRQKRYO6/GRdYpbyMEt5qptlYWEhgLLfYWRIoVDA19fX3M2wCcxSHmYpF/OUh1nKwyzlYZbyMEt5GlqW/Cuvntjb28PLywtpaWkoKSmBp6cnHBwcLK76yStzycMs5WGW8piapRAChYWFSE1Nhbe3N+zteXnk2wkhkJ+fD3d3d56XtcQs5WGWcjFPeZilPMxSHmYpD7OUp6FlyaJUPQoODoaLiwtSU1ORm5tr7uZUiH/8y8Ms5WGW8lQ3S29vbwQHB9dDy6yPEAIFBQVwc3PjeVlLzFIeZikX85SHWcrDLOVhlvIwS3kaWpYsStUjhUIBb29veHl5QaPRQK1Wm7tJRrRaLTIyMuDn5wc7LopeK8xSHmYpT3WydHR05AgpIiIiIiKqMyxKmYFuTRdLXCNHq9XC0dERzs7O/OO/lpilPMxSHmZJRERERESWgn+RkAGFQgEXF5cGMUywrjFLeZilPMxSHmYpD7OUh1nKxTzlYZbyMEt5mKU8zFKehpalQvB631Lk5ubCy8sLOTk58PT0NHdziIiIiIiIiIjMwtQaCUdKkQEhBHJycsBaZe0xS3mYpTzMUh5mKQ+zlIdZysU85WGW8jBLeZilPMxSnoaWJYtSZEAIgaKiogbzBKhLzFIeZikPs5SHWcrDLOVhlnIxT3mYpTzMUh5mKQ+zlKehZcmiFBERERERERER1TvLu/ybldJVMXNzc83cktrRarXIy8vjlbkkYJbyMEt5mKU8zFIeZikPs5SLecrDLOVhlvIwS3mYpTy2kqWuNlLViC8WpSTJy8sDADRq1MjMLSEiIiIiIiIiMr+8vDx4eXlVejuvvieJVqvFjRs34OHhYdWXbszNzUWjRo1w7do1XkWwlpilPMxSHmYpD7OUh1nKwyzlYp7yMEt5mKU8zFIeZimPrWQphEBeXh5CQ0PvOOKLI6UksbOzQ3h4uLmbIY2np6dVPwEsCbOUh1nKwyzlYZbyMEt5mKVczFMeZikPs5SHWcrDLOWxhSzvNEJKx3onKBIRERERERERkdViUYqIiIiIiIiIiOodi1JkwMnJCTNnzoSTk5O5m2L1mKU8zFIeZikPs5SHWcrDLOVinvIwS3mYpTzMUh5mKU9Dy5ILnRMRERERERERUb3jSCkiIiIiIiIiIqp3LEoREREREREREVG9Y1GKiIiIiIiIiIjqHYtSBAD4888/8cADDyA0NBQKhQIbNmwwd5Os1uLFi9G+fXt4enrC09MT3bp1w++//27uZlmlWbNmQaFQGPyLjo42d7OsUkREhFGWCoUCzz//vLmbZpXy8vLw8ssvo0mTJnBxccG9996Lw4cPm7tZFq+q15p169ZhwIAB8PPzg0KhwPHjx83STmtQVZazZs1CdHQ03Nzc4OPjg379+uHgwYPmaayFqyrLiRMnGv3uHDRokHkaa+GqyrKi1yGFQoEPPvjAPA22YFVlmZKSgokTJyI0NBSurq4YNGgQ4uPjzdNYCzdnzhzcc8898PDwQGBgIEaMGIG4uDiDfb788kv06tULnp6eUCgUyM7ONk9jLZwpWT7zzDOIioqCi4sLAgICMHz4cJw/f95MLbZcpmTZq1cvo9+Xzz77rJlaXHdYlCIAQEFBAWJiYvD555+buylWLzw8HHPnzsXRo0dx5MgR9OnTB8OHD8eZM2fM3TSr1KZNGyQlJen/7d2719xNskqHDx82yHHbtm0AgIceesjMLbNOTz75JLZt24bly5fj1KlTGDBgAPr164fExERzN82iVfVaU1BQgH/961+YN29ePbfM+lSVZYsWLfDZZ5/h1KlT2Lt3LyIiIjBgwACkpaXVc0stnynvgQYNGmTwO/SHH36oxxZaj6qyLJ9hUlISvvnmGygUCowaNaqeW2r57pSlEAIjRozApUuX8PPPP+PYsWNo0qQJ+vXrh4KCAjO01rLt3r0bzz//PA4cOIBt27ahtLQUAwYMMMiqsLAQgwYNwptvvmnGllo+U7Ls1KkTli1bhnPnzmHLli0QQmDAgAHQaDRmbLnlMSVLAHjqqacMfm/Onz/fTC2uQ4LoNgDE+vXrzd0Mm+Lj4yO++uorczfD6sycOVPExMSYuxk26aWXXhJRUVFCq9WauylWp7CwUNjb24tNmzYZbO/YsaN46623zNQq63On15qEhAQBQBw7dqxe22StTHndzsnJEQDE9u3b66dRVqqiLCdMmCCGDx9ulvZYM1POy+HDh4s+ffrUT4Os2O1ZxsXFCQDi9OnT+m0ajUYEBASIpUuXmqGF1iU1NVUAELt37za6bdeuXQKAyMrKqv+GWaE7Zalz4sQJAUBcuHChHltmfSrKsmfPnuKll14yX6PqCUdKEdUhjUaDH3/8EQUFBejWrZu5m2OV4uPjERoaiqZNm+LRRx/F1atXzd0kq6dSqbBixQpMnjwZCoXC3M2xOmq1GhqNBs7OzgbbXVxcOJKPLJJKpcKXX34JLy8vxMTEmLs5VumPP/5AYGAgWrZsiSlTpiAjI8PcTbJ6KSkp+PXXX/HEE0+YuylWp6SkBAAMXofs7Ozg5OTE1yET5OTkAAB8fX3N3BLrV1WWBQUFWLZsGSIjI9GoUaP6bJrVqSzL2NhY+Pv7o23btnjjjTdQWFhojubVKRaliOrAqVOn4O7uDicnJzz77LNYv349Wrdube5mWZ0uXbrg22+/xebNm7F48WIkJCTgvvvuQ15enrmbZtU2bNiA7OxsTJw40dxNsUoeHh7o1q0b3n33Xdy4cQMajQYrVqzAX3/9haSkJHM3j0hv06ZNcHd3h7OzMz755BNs27YN/v7+5m6W1Rk0aBC+//577NixA/PmzcPu3bsxePBgTkWppe+++w4eHh548MEHzd0UqxMdHY3GjRvjjTfeQFZWFlQqFebNm4fr16/zdagKWq0WL7/8Mrp37462bduauzlW7U5ZLlq0CO7u7nB3d8fvv/+Obdu2QalUmqmllq+yLB955BGsWLECu3btwhtvvIHly5fjscceM2NL64aDuRtAZItatmyJ48ePIycnB2vXrsWECROwe/duFqaqafDgwfr/t2/fHl26dEGTJk2wevVqfrJaC19//TUGDx6M0NBQczfFai1fvhyTJ09GWFgY7O3t0bFjR4wbNw5Hjx41d9OI9Hr37o3jx48jPT0dS5cuxZgxY3Dw4EEEBgaau2lW5eGHH9b/v127dmjfvj2ioqLwxx9/oG/fvmZsmXX75ptv8OijjxqNOqWqOTo6Yt26dXjiiSfg6+sLe3t79OvXD4MHD4YQwtzNs2jPP/88Tp8+zRFlEtwpy0cffRT9+/dHUlISPvzwQ4wZMwb79u3j870SlWX59NNP6//frl07hISEoG/fvrh48SKioqLqu5l1hiOliOqAUqlEs2bN0KlTJ8yZMwcxMTFYuHChuZtl9by9vdGiRQtcuHDB3E2xWleuXMH27dvx5JNPmrspVi0qKgq7d+9Gfn4+rl27hkOHDqG0tBRNmzY1d9OI9Nzc3NCsWTN07doVX3/9NRwcHPD111+bu1lWr2nTpvD39+drUS3s2bMHcXFxfC2qhU6dOuH48ePIzs5GUlISNm/ejIyMDL4O3cHUqVOxadMm7Nq1C+Hh4eZujlWrKksvLy80b94cPXr0wNq1a3H+/HmsX7/eDC21fNU5L7t06QIANvf6w6IUUT3QarX6+f9Uc/n5+bh48SJCQkLM3RSrtWzZMgQGBmLo0KHmbopNcHNzQ0hICLKysrBlyxYMHz7c3E0iqhRfi+S4fv06MjIy+FpUC19//TU6derENc4k8PLyQkBAAOLj43HkyBG+DlVACIGpU6di/fr12LlzJyIjI83dJKtVkyyFEBBC8PXnNjXJ8vjx4wBgc68/nL5HAMr+2C9fcU1ISMDx48fh6+uLxo0bm7Fl1ueNN97A4MGD0bhxY+Tl5WHlypX4448/sGXLFnM3zer85z//wQMPPIAmTZrgxo0bmDlzJuzt7TFu3DhzN80qabVaLFu2DBMmTICDA3/914buEsctW7bEhQsX8NprryE6OhqTJk0yd9MsWlWvNZmZmbh69Spu3LgBAIiLiwMABAcHIzg42CxttlR3ytLPzw/vv/8+hg0bhpCQEKSnp+Pzzz9HYmIiHnroITO22jLdKUtfX1/Mnj0bo0aNQnBwMC5evIhp06ahWbNmGDhwoBlbbZlMeT+Zm5uLNWvW4KOPPjJXM61CVVmuWbMGAQEBaNy4MU6dOoWXXnoJI0aMwIABA8zYasv0/PPPY+XKlfj555/h4eGB5ORkAGUFPRcXFwBAcnIykpOT9ZmfOnUKHh4eaNy4MRdEL6eqLC9duoRVq1ZhwIABCAgIwPXr1zF37ly4uLhgyJAhZm69Zakqy4sXL2LlypUYMmQI/Pz8cPLkSbzyyivo0aMH2rdvb+bWS2bGK/+RBdFd/vT2fxMmTDB306zO5MmTRZMmTYRSqRQBAQGib9++YuvWreZullUaO3asCAkJEUqlUoSFhYmxY8fycrK1sGXLFgFAxMXFmbspVm/VqlWiadOmQqlUiuDgYPH888+L7OxsczfL4lX1WrNs2bIKb585c6ZZ222J7pRlUVGRGDlypAgNDRVKpVKEhISIYcOGiUOHDpm72RbpTlkWFhaKAQMGiICAAOHo6CiaNGkinnrqKZGcnGzuZlskU95PLlmyRLi4uPB3ZhWqynLhwoUiPDxcODo6isaNG4sZM2aIkpIS8zbaQlWUIwCxbNky/T4zZ86sch+qOsvExEQxePBgERgYKBwdHUV4eLh45JFHxPnz583bcAtUVZZXr14VPXr0EL6+vsLJyUk0a9ZMvPbaayInJ8e8Da8DCiG4Gh4REREREREREdUvrilFRERERERERET1jkUpIiIiIiIiIiKqdyxKERERERERERFRvWNRioiIiIiIiIiI6h2LUkREREREREREVO9YlCIiIiIiIiIionrHohQREREREREREdU7FqWIiIiIiIiIiKjesShFREREZrV69Wr4+voiPz/f3E0xSa9evdC2bVtzN8Nky5cvR3R0NBwdHeHt7W2WNvTq1Qu9evWq8X1NyfuPP/6AQqHA2rVra/RzZCstLUWjRo2waNEiczeFiIjIYrEoRURERDXy7bffQqFQ4MiRIwbbc3Jy0LlzZzg7O2Pz5s13PIZGo8HMmTPxwgsvwN3dXb89IiICCoUCL7zwgtF9LK34YMnOnz+PiRMnIioqCkuXLsWXX35Z6b6zZs2CQqFAUFAQCgsLjW6PiIjA/fffX5fNtSmOjo549dVX8f7776O4uNjczSEiIrJILEoRERGRNLm5uRgwYABOnjyJ9evXY9CgQXfcf+PGjYiLi8PTTz9d4e1Lly7FjRs36qKpDcIff/wBrVaLhQsXYuLEiRgzZkyV90lNTcXixYultmPr1q3YunWr1GNag0mTJiE9PR0rV640d1OIiIgsEotSREREJEVeXh4GDhyI48eP46effsLgwYOrvM+yZcvQvXt3hIWFGd3Wpk0baDQazJ07ty6aa9G0Wq2U0TWpqakAUK1pex06dMAHH3yAoqKiWv98HaVSCaVSKe145lRQUGDyvt7e3hgwYAC+/fbbumsQERGRFWNRioiIiGotPz8fgwYNwt9//42ffvoJQ4cOrfI+xcXF2Lx5M/r161fh7RERERg/frxJo6UmTpyIiIgIo+26KWnlKRQKTJ06FWvWrEHr1q3h4uKCbt264dSpUwCAJUuWoFmzZnB2dkavXr1w+fLlCn/m0aNHce+998LFxQWRkZH44osvjPYpKSnBzJkz0axZMzg5OaFRo0aYNm0aSkpKKmxTbGws2rRpAycnpyqnPi5atEi/b2hoKJ5//nlkZ2frb4+IiMDMmTMBAAEBAVAoFJg1a9YdjwkAb7/9NlJSUkwaLaXVarFgwQK0adMGzs7OCAoKwjPPPIOsrCyD/SpaU+rKlSsYNmwY3NzcEBgYiFdeeQVbtmyBQqHAH3/8YfSzzp49i969e8PV1RVhYWGYP39+hW3SaDR48803ERwcDDc3NwwbNgzXrl0z2m/NmjXo1KkTXFxc4O/vj8ceewyJiYkG+0ycOBHu7u64ePEihgwZAg8PDzz66KMAgPj4eIwaNQrBwcFwdnZGeHg4Hn74YeTk5Bgco3///ti7dy8yMzOripOIiKjBcTB3A4iIiMi6FRQUYPDgwTh8+DDWrl1r8rpDR48ehUqlQseOHSvd56233sL333+PuXPn4tNPP5XVZOzZswe//PILnn/+eQDAnDlzcP/992PatGlYtGgRnnvuOWRlZWH+/PmYPHkydu7caXD/rKwsDBkyBGPGjMG4ceOwevVqTJkyBUqlEpMnTwZQVrAZNmwY9u7di6effhqtWrXCqVOn8Mknn+Cff/7Bhg0bDI65c+dOrF69GlOnToW/v3+FRTadWbNmYfbs2ejXrx+mTJmCuLg4LF68GIcPH8a+ffvg6OiIBQsW4Pvvv8f69euxePFiuLu7o3379lVmc99996FPnz6YP38+pkyZAhcXl0r3feaZZ/Dtt99i0qRJePHFF5GQkIDPPvsMx44d07ejIgUFBejTpw+SkpLw0ksvITg4GCtXrsSuXbsq3D8rKwuDBg3Cgw8+iDFjxmDt2rV4/fXX0a5dO6MRee+//z4UCgVef/11pKamYsGCBejXrx+OHz+u74uuzffccw/mzJmDlJQULFy4EPv27cOxY8cMRpap1WoMHDgQ//rXv/Dhhx/C1dUVKpUKAwcORElJCV544QUEBwcjMTERmzZtQnZ2Nry8vPT379SpE4QQ2L9/P9fkIiIiup0gIiIiqoFly5YJAKJJkybC0dFRbNiwoVr3/+qrrwQAcerUKaPbmjRpIoYOHSqEEGLSpEnC2dlZ3LhxQwghxK5duwQAsWbNGv3+EyZMEE2aNDE6zsyZM8Xtb3cACCcnJ5GQkKDftmTJEgFABAcHi9zcXP32N954QwAw2Ldnz54CgPjoo4/020pKSkSHDh1EYGCgUKlUQgghli9fLuzs7MSePXsMfv4XX3whAIh9+/YZtMnOzk6cOXOmsrj0UlNThVKpFAMGDBAajUa//bPPPhMAxDfffGPU/7S0tCqPW37f3bt3CwDi448/1t9e/jERQog9e/YIACI2NtbgOJs3bzba3rNnT9GzZ0/99x999JEAYHDOFBUViejoaAFA7Nq1y+C+AMT333+v31ZSUiKCg4PFqFGj9Nt050VYWJjBY7h69WoBQCxcuFAIIYRKpRKBgYGibdu2oqioSL/fpk2bBADx9ttv67dNmDBBABDTp0836OOxY8eMzsHK3LhxQwAQ8+bNq3JfIiKihobT94iIiKhWUlJS4OzsjEaNGlXrfhkZGQAAHx+fO+43Y8YMqNVqqWtL9e3b12AkUpcuXQAAo0aNgoeHh9H2S5cuGdzfwcEBzzzzjP57pVKJZ555BqmpqTh69CiAsulhrVq1QnR0NNLT0/X/+vTpAwBGo4J69uyJ1q1bV9n27du3Q6VS4eWXX4ad3a23ck899RQ8PT3x66+/mhLBHfXo0QO9e/fG/PnzK11bas2aNfDy8kL//v0N+tepUye4u7tXOuoJADZv3oywsDAMGzZMv83Z2RlPPfVUhfu7u7vjscce03+vVCrRuXNno8cFAMaPH2/wGI4ePRohISH47bffAABHjhxBamoqnnvuOTg7O+v3Gzp0KKKjoyvMb8qUKQbf60ZCbdmypcIrFZanO7/T09PvuB8REVFDxKIUERER1cqSJUugVCoxaNAgxMXFVfv+Qog73t60aVM8/vjj+PLLL5GUlFTTZhpo3Lixwfe6IsPthTXd9tvXSAoNDWP1/OIAAAfHSURBVIWbm5vBthYtWgCAfg2q+Ph4nDlzBgEBAQb/dPvpFiHXiYyMNKntV65cAQC0bNnSYLtSqUTTpk31t9fWrFmzkJycXOFaWUBZ/3JychAYGGjUx/z8fKP+3d6HqKgoo/W+mjVrVuH+4eHhRvv6+PgYPS4A0Lx5c4PvFQoFmjVrpn9cKssPAKKjo43yc3BwQHh4uMG2yMhIvPrqq/jqq6/g7++PgQMH4vPPPzdaTwq4dX7f3n4iIiLimlJERERUS61bt8Zvv/2Gvn37on///ti3b59Jo6b8/PwAlBV8bv+j/3ZvvfUWli9fjnnz5mHEiBFGt1f2B79Go6lwu729fbW2V1U4q4hWq0W7du3w8ccfV3j77Rndae0mc+jRowd69eqF+fPn49lnnzW6XavVIjAwELGxsRXePyAgQFpbZD4u1eXk5GQwIk3no48+wsSJE/Hzzz9j69atePHFFzFnzhwcOHDA4HzWFc78/f3rvK1ERETWhkUpIiIiqrXOnTtjw4YNGDp0KPr37489e/ZUWZSIjo4GACQkJKBdu3Z33DcqKgqPPfYYlixZop9SV56Pj4/Bled0ZI0aut2NGzdQUFBgMFrqn3/+AQD9tMCoqCicOHECffv2lTpKpkmTJgCAuLg4NG3aVL9dpVIhISGh0qsZ1sSsWbPQq1cvLFmyxOi2qKgobN++Hd27d692Qa1JkyY4e/YshBAG2Vy4cKHWbY6Pjzf4XgiBCxcu6Bd5L5+fbiqlTlxcnP52U7Rr1w7t2rXDjBkzsH//fnTv3h1ffPEF3nvvPf0+CQkJAIBWrVrVqD9ERES2jNP3iIiISIq+ffvihx9+wIULFzBo0CDk5ubecf9OnTpBqVTiyJEjJh1/xowZKC0txfz5841ui4qKQk5ODk6ePKnflpSUhPXr11evEyZSq9UGhRqVSoUlS5YgICAAnTp1AgCMGTMGiYmJWLp0qdH9i4qKUFBQUKOf3a9fPyiVSnz66acGI4W+/vpr5OTkYOjQoTU6bkV69uyJXr16Yd68eSguLja4bcyYMdBoNHj33XeN7qdWqyssEuoMHDgQiYmJ+OWXX/TbiouLK8yqur7//nvk5eXpv1+7di2SkpL0V+m7++67ERgYiC+++AIlJSX6/X7//XecO3fOpPxyc3OhVqsNtrVr1w52dnYGxwTKrjKpUCjQrVu32nSLiIjIJnGkFBEREUkzcuRILF26FJMnT8awYcOwefNmg8Wky3N2dsaAAQOwfft2vPPOO1UeWzda6rvvvjO67eGHH8brr7+OkSNH4sUXX0RhYSEWL16MFi1a4O+//651v24XGhqKefPm4fLly2jRogVWrVqF48eP48svv4SjoyMA4PHHH8fq1avx7LPPYteuXejevTs0Gg3Onz+P1atXY8uWLbj77rur/bMDAgLwxhtvYPbs2Rg0aBCGDRuGuLg4LFq0CPfcc4/BguAyzJw5E7179zba3rNnTzzzzDOYM2cOjh8/jgEDBsDR0RHx8fFYs2YNFi5ciNGjR1d4zGeeeQafffYZxo0bh5deegkhISGIjY3Vnyu1GVnm6+uLf/3rX5g0aRJSUlKwYMECNGvWTL+IuqOjI+bNm4dJkyahZ8+eGDduHFJSUrBw4UJERETglVdeqfJn7Ny5E1OnTsVDDz2EFi1aQK1WY/ny5bC3t8eoUaMM9t22bRu6d++un65KREREt7AoRURERFJNmjQJmZmZ+M9//oOHHnoI69evh4NDxW85Jk+ejFGjRuHatWsmrUM1Y8YMrFixwmitKD8/P6xfvx6vvvoqpk2bhsjISMyZMwfx8fF1UpTy8fHBd999hxdeeAFLly5FUFAQPvvsM4Orx9nZ2WHDhg345JNP8P3332P9+vVwdXVF06ZN8dJLL+kXPK+JWbNmISAgAJ999hleeeUV+Pr64umnn8Z///tffVFMll69eqFnz57YvXu30W1ffPEFOnXqhCVLluDNN9+Eg4MDIiIi8Nhjj6F79+6VHtPd3R07d+7ECy+8gIULF8Ld3R3jx4/Hvffei1GjRlVayDTFm2++iZMnT2LOnDnIy8tD3759sWjRIri6uur3mThxIlxdXTF37ly8/vrrcHNzw8iRIzFv3jx4e3tX+TNiYmIwcOBAbNy4EYmJiXB1dUVMTAx+//13dO3aVb9fTk4Otm7dikWLFtW4P0RERLZMIepjhUgiIiKiCmg0GrRu3RpjxoypcBoYNSwLFizAK6+8guvXryMsLMzczam1BQsWYP78+bh48aLFLWRPRERkCViUIiIiIrNatWoVpkyZgqtXr8Ld3d3czaF6UlRUZFCoKS4uxl133QWNRqNfNN6alZaWIioqCtOnT8dzzz1n7uYQERFZJBaliIiIiKjeDR48GI0bN0aHDh2Qk5ODFStW4MyZM4iNjcUjjzxi7uYRERFRPeCaUkRERERU7wYOHIivvvoKsbGx+mmcP/74I8aOHWvuphEREVE94UgpIiIiIiIiIiKqd3bmbgARERERERERETU8LEoREREREREREVG9Y1GKiIiIiIiIiIjqHYtSRERERERERERU71iUIiIiIiIiIiKieseiFBERERERERER1TsWpYiIiIiIiIiIqN6xKEVERERERERERPWORSkiIiIiIiIiIqp3/w/nxSAhkhbukAAAAABJRU5ErkJggg==\n"
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"\n",
|
||
"=== Interpretation Guide ===\n",
|
||
"At k=1: 0.9962 — This is the most overfit (uses only 1 neighbor)\n",
|
||
"At k=1: 0.9962 — Best performance\n",
|
||
"At k=25: 0.9686 — Most generalized (uses 25 neighbors)\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "31364ec1"
|
||
},
|
||
"source": [
|
||
"### What Did We Learn About K?\n",
|
||
"\n",
|
||
"**Key observations from the elbow plot:**\n",
|
||
"1. **k=1 performance** tells us about noise sensitivity — if it's very different from k=5, the data has noisy neighbors.\n",
|
||
"2. **The best k** represents the optimal balance between overfitting and underfitting.\n",
|
||
"3. **The shape of the curve** tells us about the data's structure:\n",
|
||
" - Steep decline after optimal k = the data has clear local structure\n",
|
||
" - Flat curve = the model is robust (good news!)\n",
|
||
" - Noisy curve = the model is sensitive to random splits\n",
|
||
"\n",
|
||
"**Pro tip for future projects:** Use **cross-validation** instead of a single train/test split for more reliable k selection. Cross-validation trains and tests the model multiple times on different subsets, giving a more stable estimate of performance.\n",
|
||
"\n",
|
||
"---"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "f747646a"
|
||
},
|
||
"source": [
|
||
"## **Exercise 3: The Impact of Feature Scaling on Distance-Based Algorithms**\n",
|
||
"\n",
|
||
"### Why Is This Exercise Important?\n",
|
||
"\n",
|
||
"Feature scaling is one of the most **critical preprocessing steps** for any distance-based algorithm (KNN, K-Means, SVM, etc.). Without scaling, features with large numerical ranges **completely dominate** the distance calculation, making other features irrelevant.\n",
|
||
"\n",
|
||
"### The Problem: Unequal Feature Scales\n",
|
||
"\n",
|
||
"Consider two of our features:\n",
|
||
"- `Source Code Length`: ranges from ~10 to ~100,000 characters\n",
|
||
"- `Month`: ranges from 1 to 12\n",
|
||
"\n",
|
||
"When KNN calculates the Euclidean distance between two points:\n",
|
||
"\n",
|
||
"$$d = \\sqrt{(\\text{SCL}_1 - \\text{SCL}_2)^2 + (\\text{Month}_1 - \\text{Month}_2)^2}$$\n",
|
||
"\n",
|
||
"If `Source Code Length` differs by 50,000 and `Month` differs by 6:\n",
|
||
"\n",
|
||
"$$d = \\sqrt{50000^2 + 6^2} = \\sqrt{2,500,000,000 + 36} \\approx 50,000$$\n",
|
||
"\n",
|
||
"The Month difference of 6 contributes **essentially nothing** to the distance! The model effectively ignores Month (and all other small-scale features) and makes predictions based almost entirely on Source Code Length.\n",
|
||
"\n",
|
||
"### The Solution: MinMaxScaler\n",
|
||
"\n",
|
||
"After scaling both features to the 0-1 range:\n",
|
||
"- Source Code Length difference: 0.5 (instead of 50,000)\n",
|
||
"- Month difference: 0.5 (instead of 6)\n",
|
||
"\n",
|
||
"$$d = \\sqrt{0.5^2 + 0.5^2} = \\sqrt{0.25 + 0.25} \\approx 0.707$$\n",
|
||
"\n",
|
||
"Now both features contribute **equally** to the distance. The model can learn from ALL features, not just the one with the biggest numbers.\n",
|
||
"\n",
|
||
"### What We'll Do:\n",
|
||
"1. Retrieve the **unscaled** features from before normalization\n",
|
||
"2. Train KNN on unscaled data\n",
|
||
"3. Compare accuracy against the scaled model\n",
|
||
"4. Visualize why the difference exists"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "5d6e91d3",
|
||
"outputId": "0a408e24-a411-4ddb-e777-86c84aa380d2"
|
||
},
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# Exercise 3, Step 1: Recreate the Unscaled Feature Matrix\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# Remember: In Step 6, we selected features and stored them in X.\n",
|
||
"# Then in Step 7, we applied MinMaxScaler to X.\n",
|
||
"# The SCALED version is what we've been using. Now we need the UNSCALED version.\n",
|
||
"\n",
|
||
"# Recreate the feature selection (same code as Step 6)\n",
|
||
"features = ['Author Name', 'Year', 'Month', 'Source Code Length'] + [\n",
|
||
" col for col in hacker_sourcecode_data.columns\n",
|
||
" if col.startswith('Asset Type_')\n",
|
||
" or col.startswith('Forum Name_')\n",
|
||
" or col.startswith('Language_')\n",
|
||
"]\n",
|
||
"\n",
|
||
"# Get the UNSCALED feature matrix directly from the DataFrame\n",
|
||
"X_unscaled = hacker_sourcecode_data[features].values\n",
|
||
"\n",
|
||
"# Let's look at the range of each feature BEFORE scaling to understand\n",
|
||
"# why scaling matters. We'll show the min, max, and range for key features.\n",
|
||
"print(\"=== Feature Ranges BEFORE Scaling ===\")\n",
|
||
"print(f\"{'Feature':<25s} | {'Min':>12s} | {'Max':>12s} | {'Range':>12s}\")\n",
|
||
"print(\"-\" * 70)\n",
|
||
"\n",
|
||
"for i, feat in enumerate(features[:6]): # Show first 6 features\n",
|
||
" col_min = X_unscaled[:, i].min()\n",
|
||
" col_max = X_unscaled[:, i].max()\n",
|
||
" col_range = col_max - col_min\n",
|
||
" print(f\"{feat:<25s} | {col_min:12.2f} | {col_max:12.2f} | {col_range:12.2f}\")\n",
|
||
"\n",
|
||
"print(\"\\nNotice how 'Source Code Length' has a MUCH larger range than 'Month'.\")\n",
|
||
"print(\"Without scaling, Source Code Length would dominate ALL distance calculations.\")\n",
|
||
"\n",
|
||
"# Now show what the scaled version looks like\n",
|
||
"print(\"\\n=== Feature Ranges AFTER Scaling (MinMaxScaler) ===\")\n",
|
||
"print(\"All features are now in the range [0, 1]:\")\n",
|
||
"print(f\"Min of scaled X: {X.min():.2f}\")\n",
|
||
"print(f\"Max of scaled X: {X.max():.2f}\")"
|
||
],
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"=== Feature Ranges BEFORE Scaling ===\n",
|
||
"Feature | Min | Max | Range\n",
|
||
"----------------------------------------------------------------------\n",
|
||
"Author Name | 0.00 | 1082.00 | 1082.00\n",
|
||
"Year | 2005.00 | 2016.00 | 11.00\n",
|
||
"Month | 1.00 | 12.00 | 11.00\n",
|
||
"Source Code Length | 4.00 | 1114.00 | 1110.00\n",
|
||
"Forum Name_exelab | 0.00 | 1.00 | 1.00\n",
|
||
"Forum Name_opensc | 0.00 | 1.00 | 1.00\n",
|
||
"\n",
|
||
"Notice how 'Source Code Length' has a MUCH larger range than 'Month'.\n",
|
||
"Without scaling, Source Code Length would dominate ALL distance calculations.\n",
|
||
"\n",
|
||
"=== Feature Ranges AFTER Scaling (MinMaxScaler) ===\n",
|
||
"All features are now in the range [0, 1]:\n",
|
||
"Min of scaled X: 0.00\n",
|
||
"Max of scaled X: 1.00\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "c62ae25a",
|
||
"outputId": "eaccc13e-f604-410f-f708-4f9de6ebf9fa"
|
||
},
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# Exercise 3, Step 2: Train KNN on UNSCALED Data and Compare\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# Split the UNSCALED data using the SAME random_state to ensure a fair comparison.\n",
|
||
"# This guarantees that both models see the exact same training and testing samples.\n",
|
||
"X_train_unscaled, X_test_unscaled, y_train_us, y_test_us = train_test_split(\n",
|
||
" X_unscaled, y, test_size=0.2, random_state=42\n",
|
||
")\n",
|
||
"\n",
|
||
"# Train KNN on UNSCALED data (k=5)\n",
|
||
"knn_unscaled = KNeighborsClassifier(n_neighbors=5)\n",
|
||
"knn_unscaled.fit(X_train_unscaled, y_train_us)\n",
|
||
"accuracy_unscaled = knn_unscaled.score(X_test_unscaled, y_test_us)\n",
|
||
"\n",
|
||
"# Get accuracy for SCALED data (we already computed this, but let's be explicit)\n",
|
||
"knn_scaled = KNeighborsClassifier(n_neighbors=5)\n",
|
||
"knn_scaled.fit(X_train, y_train)\n",
|
||
"accuracy_scaled = knn_scaled.score(X_test, y_test)\n",
|
||
"\n",
|
||
"# Display the comparison\n",
|
||
"print(\"=\" * 55)\n",
|
||
"print(\" IMPACT OF FEATURE SCALING ON KNN ACCURACY\")\n",
|
||
"print(\"=\" * 55)\n",
|
||
"print(f\"\\n KNN Accuracy (UNSCALED features): {accuracy_unscaled:.4f} ({accuracy_unscaled*100:.2f}%)\")\n",
|
||
"print(f\" KNN Accuracy (SCALED features): {accuracy_scaled:.4f} ({accuracy_scaled*100:.2f}%)\")\n",
|
||
"print(f\"\\n Difference: {abs(accuracy_scaled - accuracy_unscaled):.4f} ({abs(accuracy_scaled - accuracy_unscaled)*100:.2f} percentage points)\")\n",
|
||
"\n",
|
||
"if accuracy_scaled > accuracy_unscaled:\n",
|
||
" print(\"\\n >> SCALED data performs BETTER.\")\n",
|
||
" print(\" >> This confirms that feature scaling helps KNN by ensuring\")\n",
|
||
" print(\" >> all features contribute equally to distance calculations.\")\n",
|
||
"elif accuracy_scaled < accuracy_unscaled:\n",
|
||
" print(\"\\n >> UNSCALED data performs better in this case.\")\n",
|
||
" print(\" >> This can happen when the dominant feature (Source Code Length)\")\n",
|
||
" print(\" >> happens to be the most informative feature for the task.\")\n",
|
||
"else:\n",
|
||
" print(\"\\n >> Both perform equally — the dominant features happen to be the\")\n",
|
||
" print(\" >> most important ones for this particular classification task.\")\n",
|
||
"print(\"\\n\" + \"=\" * 55)"
|
||
],
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"=======================================================\n",
|
||
" IMPACT OF FEATURE SCALING ON KNN ACCURACY\n",
|
||
"=======================================================\n",
|
||
"\n",
|
||
" KNN Accuracy (UNSCALED features): 0.8630 (86.30%)\n",
|
||
" KNN Accuracy (SCALED features): 0.9804 (98.04%)\n",
|
||
"\n",
|
||
" Difference: 0.1174 (11.74 percentage points)\n",
|
||
"\n",
|
||
" >> SCALED data performs BETTER.\n",
|
||
" >> This confirms that feature scaling helps KNN by ensuring\n",
|
||
" >> all features contribute equally to distance calculations.\n",
|
||
"\n",
|
||
"=======================================================\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 532
|
||
},
|
||
"id": "ede039ef",
|
||
"outputId": "9c0b130d-b8c3-4b2e-cd58-c8bb0ad2bb12"
|
||
},
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# Exercise 3, Step 3: Visualize the Scaling Effect\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# Create a side-by-side bar chart comparing the two approaches\n",
|
||
"fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
|
||
"\n",
|
||
"# --- Left plot: Accuracy comparison ---\n",
|
||
"models = ['Unscaled\\nFeatures', 'Scaled\\nFeatures']\n",
|
||
"accs = [accuracy_unscaled, accuracy_scaled]\n",
|
||
"colors = ['#e74c3c', '#2ecc71'] # Red for unscaled, green for scaled\n",
|
||
"\n",
|
||
"bars = axes[0].bar(models, accs, color=colors, width=0.5, edgecolor='black')\n",
|
||
"\n",
|
||
"# Add accuracy values on top of each bar\n",
|
||
"for bar, acc in zip(bars, accs):\n",
|
||
" axes[0].text(bar.get_x() + bar.get_width()/2., bar.get_height() + 0.005,\n",
|
||
" f'{acc:.4f}', ha='center', va='bottom', fontsize=14, fontweight='bold')\n",
|
||
"\n",
|
||
"axes[0].set_ylim(0, 1.1)\n",
|
||
"axes[0].set_ylabel('Accuracy', fontsize=12)\n",
|
||
"axes[0].set_title('KNN Accuracy: Scaled vs. Unscaled', fontsize=13)\n",
|
||
"axes[0].axhline(y=0.5, color='gray', linestyle='--', alpha=0.5, label='Random baseline')\n",
|
||
"axes[0].legend()\n",
|
||
"\n",
|
||
"# --- Right plot: Feature range comparison ---\n",
|
||
"# Show how feature ranges differ before and after scaling\n",
|
||
"sample_features = ['Author Name', 'Year', 'Month', 'Source Code Length']\n",
|
||
"sample_indices = [features.index(f) for f in sample_features]\n",
|
||
"\n",
|
||
"unscaled_ranges = [X_unscaled[:, i].max() - X_unscaled[:, i].min() for i in sample_indices]\n",
|
||
"\n",
|
||
"x_pos = range(len(sample_features))\n",
|
||
"axes[1].bar(x_pos, unscaled_ranges, color='#e74c3c', alpha=0.7)\n",
|
||
"axes[1].set_xticks(x_pos)\n",
|
||
"axes[1].set_xticklabels(sample_features, rotation=15, ha='right')\n",
|
||
"axes[1].set_ylabel('Feature Range (Max - Min)', fontsize=12)\n",
|
||
"axes[1].set_title('Feature Ranges BEFORE Scaling', fontsize=13)\n",
|
||
"axes[1].set_yscale('log') # Log scale because ranges differ by orders of magnitude\n",
|
||
"\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"print(\"\\nThe right chart uses a LOG SCALE because the ranges differ by orders of magnitude.\")\n",
|
||
"print(\"Source Code Length's range is ~100,000x larger than Month's range of 11.\")\n",
|
||
"print(\"Without scaling, KNN essentially ignores Month and other small-range features.\")"
|
||
],
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1400x500 with 2 Axes>"
|
||
],
|
||
"image/png": 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vChcurHLlylk7FQAAACQSirYAAADAa8zHx0cnT57U/v37rZ0KAAAAEkmyLNr6+voqV65ccnBwUIUKFbRv3754bffLL7/IZDKpefPmrzZBAAAAvNZCQkL06aefqkGDBipVqpT5fPPWrVuaNm2azp07Z+UMAQAAkJIlu6LtkiVLNGjQII0ePVqHDh1SiRIl1KBBA924cSPO7S5evKiPPvpI1apVS6JMAQAA8Dq6cuWKSpUqpVGjRunKlSs6duyY7t27J0lyc3PT999/r2+++cbKWQIAACAlS3ZF22nTpql79+7q0qWLChcurJkzZ8rR0VFz5syJdZuIiAi1b99eY8eOlZeXVxJmCwAAgNfNxx9/rLt37+rIkSPavn27DMOwGG/evLk2b95spewAAACAZFa0ffTokQ4ePKi6deua19nY2Khu3bras2dPrNuNGzdOmTJlUteuXZ97jLCwMIWEhFjcAAAAkHJs3LhR/fv3V+HChWUymaKNe3l56fLly1bIDAAAAHgiWRVtg4KCFBERocyZM1usz5w5s/z9/WPcZteuXZo9e7Z++OGHeB1j8uTJcnFxMd9y5Mjx0nkDAADg9fHgwQNlzJgx1vG7d+8mYTYAAABAdMmqaPui7t69q44dO+qHH36Qu7t7vLYZOnSogoODzTdmUQAAAKQshQsX1o4dO2IdX7lypUqVKpWEGQEAAACWUlk7gae5u7vL1tZWAQEBFusDAgLk4eERLf78+fO6ePGimjRpYl4XGRkpSUqVKpXOnDmjPHnyWGxjb28ve3v7V5A9AAAAXgcDBw6Ut7e3ihcvrnfffVfSk3PIc+fOaezYsdqzZ4+WLVtm5SwBAACQkiWroq2dnZ3KlCmjLVu2qHnz5pKenEBv2bJFffv2jRZfsGBBHT9+3GLdiBEjdPfuXX311Ve0PgAAAEA0HTp00H///acRI0Zo+PDhkqSGDRvKMAzZ2Nho0qRJ5nNRAAAAwBqSVdFWkgYNGiRvb2+VLVtW5cuX1/Tp0xUaGqouXbpIkjp16qRs2bJp8uTJcnBwUNGiRS22T58+vSRFWw8AAABEGT58uDp27Khly5bp3LlzioyMVJ48edSiRQt5eXlZOz0AAACkcMmuaNu6dWsFBgZq1KhR8vf3V8mSJbV+/XrzxckuXbokG5vXuhUvAAAArOT+/fuqVq2aunfvrl69eumDDz6wdkoAAABANMmuaCtJffv2jbEdgiRt27Ytzm3nzZuX+AkBAADgjeDo6Cg/Pz+ZTCZrpwIAAADEiimrAAAASFEaNmyoDRs2WDsNAAAAIFYUbQEAAJCijBw5UmfPnlXHjh21a9cuXb16Vbdu3Yp2e134+vqqcOHCKleunLVTAQAAQCJJlu0RAAAAgFelSJEikqSTJ09q8eLFscZFREQkVUovxcfHRz4+PgoJCZGLi4u10wEAAEAioGgLAACAFGXUqFH0tAUAAECyRtEWAAAAKcqYMWOsnQIAAAAQJ3raAgAAAAAAAEAywkxbAAAApCjjxo17bozJZNLIkSOTIBsAAAAgOoq2AAAASFHiao9gMplkGAZFWwAAAFgV7REAAACQokRGRka7hYeH6/z58/rggw9UtmxZ3bhxw9ppAgAAIAWjaAsAAIAUz8bGRrlz59bnn3+ufPnyqV+/ftZOCQAAACkYRVsAAADgKdWrV9e6deusnQYAAABSMIq2AAAAwFMOHDggGxtOkwEAAGA9XIgMAAAAKcqCBQtiXH/nzh3t2LFDy5cvV7du3ZI4KwAAAOB/KNoCAAAgRencuXOsY+7u7hoyZIhGjRqVdAkBAAAAz6BoCwAAgBTFz88v2jqTySRXV1c5OztbISMAAADAEkVbAAAApCgmk0kZM2ZUmjRpYhx/8OCBAgMDlTNnziTODAAAAHiCKywAAAAgRcmdO7dWrFgR6/jq1auVO3fuJMwIAAAAsETRFgAAACmKYRhxjj9+/Fg2NpwmAwAAwHpojwAAAIA3XkhIiO7cuWNevnnzpi5duhQt7s6dO/rll1+UJUuWJMzu5fj6+srX11cRERHWTgUAAACJhKItAAAA3nhffvmlxo0bJ+lJT9uBAwdq4MCBMcYahqEJEyYkYXYvx8fHRz4+PgoJCZGLi4u10wEAAEAioGgLAACAN179+vXl5OQkwzD0ySefqG3btipdurRFjMlkUtq0aVWmTBmVLVvWSpkCAAAAFG0BAACQAlSqVEmVKlWSJIWGhqply5YqWrSolbMCAAAAYkbRFgAAACnK6NGjrZ0CAAAAECeKtgAAAEiRdu/erUOHDik4OFiRkZEWYyaTSSNHjrRSZgAAAEjpKNoCAAAgRbl165YaN26sffv2yTAMmUwmGYYhSeb7FG0BAABgTTbWTgAAAABISh9//LGOHTumxYsX68KFCzIMQxs2bNDZs2fVq1cvlSxZUteuXbN2mgAAAEjBKNoCAAAgRVm3bp169uyp1q1by9nZWZJkY2OjvHnzytfXV7ly5dLAgQOtmyQAAABSNIq2AAAASFHu3LmjIkWKSJKcnJwkSffu3TOP169fXxs2bLBKbgAAAIBE0RYAAAApTNasWeXv7y9Jsre3V6ZMmXT06FHz+NWrV2UymayVHgAAAMCFyAAAAJCyVK9eXZs2bdLw4cMlSa1bt9aUKVNka2uryMhITZ8+XQ0aNLBylgAAAEjJKNoCAAAgRRk0aJA2bdqksLAw2dvba8yYMfrnn380cuRISU+Kut98842VswQAAEBKRtEWAAAAKUqxYsVUrFgx87Krq6s2b96sO3fuyNbW1nxxMgAAAMBaKNoCAAAAktKnT2/tFAAAAABJXIgMAAAAKdClS5fUq1cvFShQQG5ubtqxY4ckKSgoSP3799fhw4etnCEAAABSMmbaAgAAIEU5efKkqlWrpsjISFWoUEHnzp1TeHi4JMnd3V27du1SaGioZs+ebeVMAQAAkFJRtAUAAECK8sknnyh9+vT6+++/ZTKZlClTJovxxo0ba8mSJVbKDgAAAKA9AgAAAFKYHTt2qHfv3sqYMaNMJlO08Zw5c+rq1atWyAwAAAB4gqItAAAAUpTIyEg5OjrGOh4YGCh7e/skzAgAAACwRNEWAAAAKUrp0qW1du3aGMfCw8P1yy+/qGLFikmcFQAAAPA/FG0BAACQogwdOlTr169X7969deLECUlSQECANm/erPr16+vUqVMaMmSIlbMEAABASsaFyAAAAJCiNGrUSPPmzdOAAQM0a9YsSVKHDh1k/F97dx4f09n+D/wzM1kmEYk9ZEeIRCQiIRJLhGgSomipKg9iaZVQRamWoJagEUttFbVvqb3f2IJSRARZ7EvEFkpCiCxkm7l/f+SXU0P08bRkBp/36zWvmnPuc851pifnzFznPtctBExNTbF69Wq0bt1ay1G+uoULF2LhwoVQqVTaDoWIiIiIXhMmbYmIiIjovfOf//wHH330EWJiYnD16lWo1WrUrVsX/v7+qFixorbD+58MHToUQ4cORXZ2NszMzLQdDhERERG9BkzaEhEREdE777vvvsOnn34KFxcXaVqFChXQtWtXLUZFRERERFQ21rQlIiIionfejBkzpPq1AJCZmQmFQoHff/9di1EREREREZWNSVsiIiIiei8JIbQdAhERERFRmZi0JSIiIiIiIiIiItIhTNoSERERERERERER6RAOREZERERE74UbN24gMTERAPD48WMAQEpKCipVqlRm+yZNmpRXaEREREREGpi0JSIiIqL3woQJEzBhwgSNaUOGDHmhnRACMpkMKpWqvEIjIiIiItLApC0RERERvfNWrFih7RCIiIiIiF4Zk7ZERERE9M7r27evtkMgIiIiInplHIiMiIiIiIiIiIiISIcwaUtERERERERERESkQ5i0JSIiIiIiIiIiItIhTNoSERERERERERER6RAmbYmIiIiIiIiIiIh0CJO2RERERERERERERDqESVsiIiIiIiIiIiIiHcKkLREREREREREREZEO0cmk7cKFC2FnZwelUglPT0+cOHHipW0jIyPRqlUrVK5cGZUrV4afn9/fticiIiIiIiIiIiLSZTqXtI2KisLIkSMxceJEJCYmwtXVFf7+/sjIyCiz/aFDh9CzZ08cPHgQcXFxsLa2xgcffIA7d+6Uc+RERERERERERERE/57OJW0jIiIwaNAgBAcHw8nJCUuWLIGxsTGWL19eZvt169ZhyJAhaNy4MRo0aIBly5ZBrVbjwIED5Rw5ERERERERERER0b+nU0nbwsJCJCQkwM/PT5oml8vh5+eHuLi4V1rHkydPUFRUhCpVqrypMImIiIiIiIiIiIjeGD1tB/CsBw8eQKVSwdzcXGO6ubk5Ll269ErrGDt2LCwsLDQSv88qKChAQUGB9D47O/ufB0xERERERERERET0mulUT9t/a8aMGdi4cSO2bdsGpVJZZpuwsDCYmZlJL2tr63KOkoiIiIiIiIiIiOjldCppW61aNSgUCqSnp2tMT09PR82aNf922fDwcMyYMQMxMTFwcXF5abtx48bh8ePH0istLe21xE5ERERERERERET0OuhU0tbAwADu7u4ag4iVDirm5eX10uVmzZqFKVOmYM+ePfDw8PjbbRgaGsLU1FTjRURERERERERERKQrdKqmLQCMHDkSffv2hYeHB5o1a4a5c+ciLy8PwcHBAIA+ffrA0tISYWFhAICZM2ciNDQU69evh52dHe7duwcAMDExgYmJidb2g4iIiIiIiIiIiOif0LmkbY8ePXD//n2Ehobi3r17aNy4Mfbs2SMNTnbr1i3I5X91EF68eDEKCwvRrVs3jfVMnDgRkyZNKs/QiYiIiIiIiIiIiP41nUvaAkBISAhCQkLKnHfo0CGN9zdu3HjzARERERERERERERGVE52qaUtERERERERERET0vmPSloiIiIjoLbZw4UI4OTmhadOm2g6FiIiIiF4TJm2JiIiIiN5iQ4cOxYULF3Dy5Elth0JERERErwmTtkREREREREREREQ6hElbIiIiIiIiIiIiIh3CpC0RERERERERERGRDmHSloiIiIiIiIiIiEiHMGlLREREREREREREpEOYtCUiIiIiIiIiIiLSIUzaEhEREREREREREekQJm2JiIiIiIiIiIiIdAiTtkREREREREREREQ6hElbIiIiIiIiIiIiIh3CpC0RERERERERERGRDmHSloiIiIiIiIiIiEiHMGlLREREREREREREpEOYtCUiIiIiIiIiIiLSIUzaEhEREREREREREekQJm2JiIiIiIiIiIiIdAiTtkREREREREREREQ6hElbIiIiIiIiIiIiIh3CpC0RERERERERERGRDmHSloiIiIiIiIiIiEiHMGlLREREREREREREpEOYtCUiIiIiIiIiIiLSIUzaEhEREREREREREekQJm2JiIiIiIiIiIiIdAiTtkREREREREREREQ6hElbIiIiIiIiIiIiIh3CpC0RERERERERERGRDmHSloiIiIiIiIiIiEiHMGlLREREREREREREpEOYtCUiIiIiIiIiIiLSIUzaEhEREREREREREekQJm2JiIiIiIiIiIiIdAiTtkREREREREREREQ6hElbIiIiIiIiIiIiIh3CpC0RERERERERERGRDmHSloiIiIiIiIiIiEiHMGlLREREREREREREpEOYtCUiIiIiIiIiIiLSIUzaEhEREREREREREekQJm2JiIiIiIiIiIiIdAiTtkREREREREREREQ6hElbIiIiIiIiIiIiIh3CpC0RERERERERERGRDmHSloiIiIiIiIiIiEiH6Gk7ACIiIiIiIiIAePTDWG2HQG9I5dCZWtkuj6l3k7aOJ6LyxJ62REREREQ6pGvXrqhcuTK6deum7VCIiIiISEuYtCUiIiIi0iFfffUVVq9ere0wiIiIiEiLmLQlIiIiItIhbdq0QcWKFbUdBhERERFpEZO2RERERESvyeHDh9GpUydYWFhAJpNh+/btL7RZuHAh7OzsoFQq4enpiRMnTpR/oERERESk05i0JSIiIiJ6TfLy8uDq6oqFCxeWOT8qKgojR47ExIkTkZiYCFdXV/j7+yMjI6OcIyUiIiIiXaan7QCIiIiIiN4VgYGBCAwMfOn8iIgIDBo0CMHBwQCAJUuWYOfOnVi+fDm+/fbb/2lbBQUFKCgokN5nZ2f/s6CJiIiISOewpy0RERERUTkoLCxEQkIC/Pz8pGlyuRx+fn6Ii4v7n9cXFhYGMzMz6WVtbf06wyUiIiIiLWLSloiIiIioHDx48AAqlQrm5uYa083NzXHv3j3pvZ+fH7p3745du3bBysrqpQndcePG4fHjx9IrLS3tjcZPREREROWH5RGIiIiIiHTI/v37X6mdoaEhDA0N33A0RERERKQN7GlLRERERFQOqlWrBoVCgfT0dI3p6enpqFmzppaiIiIiIiJdxKQtEREREVE5MDAwgLu7Ow4cOCBNU6vVOHDgALy8vLQYGRERERHpGpZHICIiIiJ6TXJzc3H16lXp/fXr15GcnIwqVarAxsYGI0eORN++feHh4YFmzZph7ty5yMvLQ3BwsBajJiIiIiJdo5M9bRcuXAg7OzsolUp4enrixIkTf9t+06ZNaNCgAZRKJRo1aoRdu3aVU6RERERERH85deoU3Nzc4ObmBgAYOXIk3NzcEBoaCgDo0aMHwsPDERoaisaNGyM5ORl79ux5YXAyIiIiInq/6VzSNioqCiNHjsTEiRORmJgIV1dX+Pv7IyMjo8z2x44dQ8+ePTFgwAAkJSWhS5cu6NKlC86dO1fOkRMRERHR+65NmzYQQrzwWrlypdQmJCQEN2/eREFBAeLj4+Hp6am9gImIiIhIJ+lc0jYiIgKDBg1CcHAwnJycsGTJEhgbG2P58uVltp83bx4CAgLwzTffwNHREVOmTEGTJk2wYMGCco6ciIiIiKj8LVy4EE5OTmjatKm2QyEiIiKi10SnkraFhYVISEiAn5+fNE0ul8PPzw9xcXFlLhMXF6fRHgD8/f1f2p6IiIiI6F0ydOhQXLhwASdPntR2KERERET0mujUQGQPHjyASqV6oaaXubk5Ll26VOYy9+7dK7P9vXv3ymxfUFCAgoIC6f3jx48BANnZ2f8m9FeWm5sLADiTlYO8YlW5bJNIm1JznwAoOfbL6++MSFtKz/FPL2ZA/aRIy9EQvVkFNx8BKL/ze+k2hBBvfFtvq9LPpryvt9n5Bf+9Eb11FFr63sbj6d3FY4peJx5P9LqV5zH1qt9rdSppWx7CwsIwefLkF6ZbW1uXaxzfnE0t1+0RaZuPj4+2QyAqN3em7dd2CETlprzP7zk5OTAzMyvXbb4tcnJyAJT/91p6R4XN03YE9K7hMUWvE48net20cEz9t++1OpW0rVatGhQKBdLT0zWmp6eno2bNmmUuU7Nmzf+p/bhx4zBy5EjpvVqtxsOHD1G1alXIZLJ/uQeky7Kzs2FtbY20tDSYmppqOxwiInpNeH5/PwghkJOTAwsLC22HorMsLCyQlpaGihUr8nvtG8BzDb1OPJ7odeMxRa8Tj6c361W/1+pU0tbAwADu7u44cOAAunTpAqAkqXrgwAGEhISUuYyXlxcOHDiAESNGSNP27dsHLy+vMtsbGhrC0NBQY1qlSpVeR/j0ljA1NeVJh4joHcTz+7uPPWz/nlwuh5WVlbbDeOfxXEOvE48net14TNHrxOPpzXmV77U6lbQFgJEjR6Jv377w8PBAs2bNMHfuXOTl5SE4OBgA0KdPH1haWiIsLAwA8NVXX8HHxwezZ89Gx44dsXHjRpw6dQpLly7V5m4QERERERERERER/SM6l7Tt0aMH7t+/j9DQUNy7dw+NGzfGnj17pMHGbt26BblcLrX39vbG+vXrMX78eHz33XeoV68etm/fDmdnZ23tAhEREREREREREdE/pnNJWwAICQl5aTmEQ4cOvTCte/fu6N69+xuOit52hoaGmDhx4gvlMYiI6O3G8zsRlQeea+h14vFErxuPKXqdeDzpBpkQQmg7CCIiIiIiIiIiIiIqIf/vTYiIiIiIiIiIiIiovDBpS0RERERERERERKRDmLQlKsOkSZPQuHHjf7WOGzduQCaTITk5+bXERERE/12bNm0wYsSIf7WOlStXolKlSq8lHiIiIiIion+CSVsqdy/7Qc0fyURE77779+/jyy+/hI2NDQwNDVGzZk34+/sjNjZW26EREREREb3znj59CgDgEFe6j0lbIiIiKjcff/wxkpKSsGrVKly5cgW//fYb2rRpg8zMTG2HRkTvOSEEVCoV1Gq1tkOhd4gQgscU/WPp6ekYNmwYUlJSAADFxcVQqVRajoreFs8nZdPS0vDRRx8hNDQUAFBYWMjErY5j0pZ0Ur9+/dClSxeEh4ejVq1aqFq1KoYOHYqioiKpzaJFi1CvXj0olUqYm5ujW7du0jy1Wo1Zs2bB3t4ehoaGsLGxwbRp06T5Y8eORf369WFsbIw6depgwoQJGusuy7Jly+Do6AilUokGDRpg0aJFGvNPnDgBNzc3KJVKeHh4ICkp6TV9GkRE74asrCwcOXIEM2fOhK+vL2xtbdGsWTOMGzcOH374odTmiy++gLm5OZRKJZydnREdHQ0AyMzMRM+ePWFpaQljY2M0atQIGzZs+NttFhQUYPTo0bC0tESFChXg6emJQ4cOabRZuXIlbGxsYGxsjK5duzKBTPSekslkUCgUkMv/+onEH7P0b8lkMumYunHjBjIyMrQcEb1NTE1NsXDhQiQmJgIA9PT0oFAotBwVvS1kMpnG+2rVqkGhUODmzZsAAENDQ8hkMuTk5GgjPHoFTNqSzjp48CBSU1Nx8OBBrFq1CitXrsTKlSsBAKdOncLw4cPxww8/4PLly9izZw9at24tLTtu3DjMmDEDEyZMwIULF7B+/XqYm5tL8ytWrIiVK1fiwoULmDdvHiIjIzFnzpyXxrJu3TqEhoZi2rRpuHjxIqZPn44JEyZg1apVAIDc3FwEBQXByckJCQkJmDRpEkaPHv1mPhgioreUiYkJTExMsH37dhQUFLwwX61WIzAwELGxsVi7di0uXLiAGTNmSD9O8vPz4e7ujp07d+LcuXP4/PPP8Z///AcnTpx46TZDQkIQFxeHjRs34syZM+jevTsCAgKkHivx8fEYMGAAQkJCkJycDF9fX0ydOvXNfABEpHVqtfqlvR7T0tIQFhaGLl26YNKkSUhJSYFMJmPilv6WEALFxcVlHleFhYXYvXs3UlJS0LRpU3h7e+Ps2bNaiJLeFhcuXEBubi6Akl61RkZGaNSoEWJjY/HkyRPs2bMHgwYNwvz583Hnzh0AYE9uKlN+fj5CQ0Nx8uRJaZqRkRGcnZ1x9+5dHD9+HGFhYahRowZ8fHwQGRmJ/Px8LUZMZRJE5czHx0d89dVXL0xfsWKFMDMzE0II0bdvX2FrayuKi4ul+d27dxc9evQQQgixZcsWYWpqKrKzs19YT3Z2tjA0NBSRkZGvHNOPP/4o3N3dpfcTJ04Urq6u0vu6deuK9evXaywzZcoU4eXlJYQQ4ueffxZVq1YVT58+leYvXrxYABBJSUmvHAcR0btu8+bNonLlykKpVApvb28xbtw4cfr0aSGEEHv37hVyuVxcvnz5ldfXsWNHMWrUKOn9s9eYmzdvCoVCIe7cuaOxTLt27cS4ceOEEEL07NlTdOjQQWN+jx49pOsREb3d1Gr1K7W7deuWaNGihfD29hbff/+9CAoKEubm5uLKlStvOEJ6lzx/vKWkpAiZTCZ8fX1FRESEePTokSgqKtJSdKTrfvvtN2FpaSkOHjwohBCisLBQCCHEN998I7y8vMTPP/8svL29RVBQkHBycpJ+ixKVKi4u1jgP1ahRQ0ybNk0IIcSBAwdEWlqa2Ldvn2jZsqX47LPPREhIiNi1a5cYNWqUsLS0FCtWrNBS5PQyetpOGhO9TMOGDTUe/ahVq5Z0Z7p9+/awtbVFnTp1EBAQgICAAHTt2hXGxsa4ePEiCgoK0K5du5euOyoqCvPnz0dqaipyc3NRXFwMU1PTMtvm5eUhNTUVAwYMwKBBg6TpxcXFMDMzAwBcvHgRLi4uUCqV0nwvL69/tf9ERO+ijz/+GB07dsSRI0dw/Phx7N69G7NmzcKyZcuQkZEBKysr1K9fv8xlVSoVpk+fjl9//RV37txBYWEhCgoKYGxsXGb7s2fPQqVSvbC+goICVK1aFUDJ+btr164a8728vLBnz57XsLdEpA3i//eMlclkGo+GqtVq7N27F/Hx8WjTpg3atGkjzRs2bBjq16+P5cuXS9MaNWqEyZMnY968edI5g95PKpUKQgjo6b348/nKlStYt24dYmJiUKFCBfTv3x9dunSBsbExbG1t4enpidTUVHTq1ImDLhOAv+pnKxQKyGQy6d+Ojo6wtrbGDz/8gK+//hpPnjxBdHQ0Pv30U4SHh6OoqAirV6+Go6MjTp8+DTc3N2zbtu2F7zH0/nj+WHo2f5KTk4MqVaogIiIC48ePh7m5OZYuXYomTZoAAA4cOIDdu3fDzc0NrVu3Rnp6OrZu3Yp+/fppaW+oLCyPQOXO1NQUjx8/fmF6VlaWlAQFAH19fY35MplMevSjYsWKSExMxIYNG1CrVi2EhobC1dUVWVlZMDIy+tvtx8XFoVevXujQoQOio6ORlJSE77//HoWFhWW2L308JTIyEsnJydLr3LlzOH78+P+070REBCiVSrRv3x4TJkzAsWPH0K9fP0ycOPG/nr9//PFHzJs3D2PHjsXBgweRnJwMf3//vz1/KxQKJCQkaJy/L168iHnz5r2JXSMiHfBssjYxMREJCQkAgIiICIwZMwYxMTHo3r07Nm7cCKBkFO2MjAz4+Pjg8OHD6NatGxwcHHDp0iVUrVr1v457QO8G8VwZjNjYWDg4OODhw4dQKBRlJmzPnz+PcePG4fjx4+jVqxc8PDzwzTffYObMmQBKfs+4uLigWrVqqFWrVrnsB+muZ28o6enpQSaTIT8/HwqFAoWFhdi0aRPi4+ORnJyMXr16YcuWLahXrx6aNGkCIyMjNG7cGI6OjgAAV1dXuLm5Yf/+/dLvVXr/PHss5ebm4tatWwgODkZ6ejru3LmD3Nxc5OXl4Y8//sDdu3fRqVMnWFpawsHBAWZmZnBzcwMAVKhQAW5ubrhz5w4uXLig5b2iZ7GnLZU7BwcHxMTEvDA9MTHxpb2ryqKnpwc/Pz/4+flh4sSJqFSpEn7//Xd06NABRkZGOHDgAAYOHPjCcseOHYOtrS2+//57aVppIe6ymJubw8LCAteuXUOvXr3KbOPo6Ig1a9YgPz9f6m3LhC4R0atxcnLC9u3b4eLigtu3b+PKlStlXg9iY2PRuXNn9O7dG0BJr7krV67AycmpzPW6ublBpVIhIyMDrVq1KrONo6Mj4uPjNabx/E2kG4QQLwyiAvxVl7a0Z9GzHj9+jFOnTqGgoADTpk1DYmIibGxs0KRJE1SvXh27d++GlZUV+vXrh9mzZ0vnBgMDAwQHB8PBwQEtW7bEzJkz4eXlpTEmAr27UlJSUK9ePQB/HXcLFiyAm5sbDA0NsXXrVkRFRaGgoAC9e/dG27ZtUaVKFahUKgQHByMoKEhal6GhITZs2IABAwbAxsYG/v7+WL9+PbKzs1GhQgVt7SK9AWq1WmPgwlJCCGnes+eo0l61KpUKkydPRlRUFKpUqYLRo0eje/fuCAkJgaGhIX755RcEBwejatWqKCwshIGBAdzd3ZGTk4O8vDwYGxtDJpPB398fMTExuH//PkxMTF56zqS3m1qthhCizAHoCgsLMXPmTCxevBi1a9eGr68vVq1ahQ8//BBdu3ZFamoqzMzMkJ2dDQBSr1wnJyecP38e8fHx8PT0BFDypLOenh6OHTv20u/WVP7Y05bK3ZdffokrV65g+PDhOHPmDC5fvoyIiAhs2LABo0aNeqV1REdHY/78+UhOTsbNmzexevVqqNVqODg4QKlUYuzYsRgzZgxWr16N1NRUHD9+HL/88gsAoF69erh16xY2btyI1NRUzJ8/H9u2bfvb7U2ePBlhYWGYP38+rly5grNnz2LFihWIiIgAAHz22WeQyWQYNGgQLly4gF27diE8PPzffVBERO+YzMxMtG3bFmvXrsWZM2dw/fp1bNq0CbNmzULnzp3h4+OD1q1b4+OPP8a+fftw/fp17N69WypVUK9ePezbtw/Hjh3DxYsX8cUXXyA9Pf2l26tfvz569eqFPn36YOvWrbh+/TpOnDiBsLAw7Ny5EwAwfPhw7NmzB+Hh4UhJScGCBQtYGoFIC4QQL/R0LE0+PD9woVwul3oWPd8LdvPmzRg4cCAmTZqEQYMGISMjA926dUNUVBSqV68OKysrAMBXX32FP//8E8nJyahWrRrMzc3Rpk0bXLx4EZGRkejSpQvMzc2RmpqKe/fuvcE9J20bNmwYmjdvjuXLl0OlUkEmk+Hhw4dISkrCBx98gA0bNmDs2LGoUaMGHBwcMHnyZAwZMgRASZIjKCgIx44dQ+/evWFhYYG5c+fi5s2bUlm3li1boqCgAMnJyVrcS3oTShO2eXl5ADR70pZ1U+ny5ctQKpWYNWsWHj16hBkzZsDV1RWDBg3Czp07UbFiRTg5OUFPT0/6nlL6pGmnTp1w4sQJPHr0SFpvUFAQbty4gVu3bknbpXePXC6XErbPP7EcExOD5cuXY/z48Vi9ejUeP34MAwMD6ealgYEBnJ2dsXv3bgB/HSMuLi6QyWTSkygA0KBBA1hYWODUqVPltGf0SrRRSJfoxIkTon379qJ69erCzMxMeHp6im3btknz+/btKzp37qyxzFdffSV8fHyEEEIcOXJE+Pj4iMqVKwsjIyPh4uIioqKipLYqlUpMnTpV2NraCn19fWFjYyOmT58uzf/mm29E1apVhYmJiejRo4eYM2eOxqAzzw9EJoQQ69atE40bNxYGBgaicuXKonXr1mLr1q3S/Li4OOHq6ioMDAxE48aNxZYtWzgQGRHRM/Lz88W3334rmjRpIszMzISxsbFwcHAQ48ePF0+ePBFCCJGZmSmCg4NF1apVhVKpFM7OziI6Olqa17lzZ2FiYiJq1Kghxo8fL/r06aNxvXh+sMvCwkIRGhoq7OzshL6+vqhVq5bo2rWrOHPmjNTml19+EVZWVsLIyEh06tRJhIeHcyAyIh2Qm5srLC0tRUREhBDir0GeTpw4IYYOHSqcnJxEUFCQiIyMlAaDPX/+vGjatKnw8PCQ1pOZmSlcXFzEt99+q7H+evXqiSlTpgghSgbErVy5slizZo3IzMwUQghx7tw58cknn4gtW7a88X0l7QoPDxf16tUTX3/9tRCiZNBMJycnER8fL0xNTTWuGfHx8UImk4kjR44IIYQ4e/as8Pb2FsHBwSI6OloUFRUJKysrMW7cOFFQUCCEEMLd3V2MHj26/HeM/jWVSvXS6Rs2bBDW1taiV69eGvPS0tLE7NmzxRdffCFiYmJEbm6uEEKIO3fuiObNmwszMzNpsLGsrCzRqVMnERQUJIQQ4urVq6Jjx46if//+Qoi/BiO7cuWKkMlk4vDhwxrbMjU1FTNmzNAYwJveLiqV6qX//x49eiROnTolOnToIKpVqyYCAgJEbGysdD309/d/IW/Sp08f0bRpU5GRkSGEEGL8+PHCyclJ5OfnS8fz3bt3RVBQkPjiiy804hgwYIBwdHQU+fn5b2BP6Z+QCfHcLW0iIiIiIqJyUlBQgO3bt+PChQv4z3/+A3t7e+nR4tIBdkqfijp9+jS++OIL2Nvbo02bNvjzzz+xbNkyjBgxAiNHjkRBQQGCg4Nx+/ZtHD58WNrGxx9/DKVSiZ9++glVqlQBAHTr1g0AsGjRItSoUQOjR4/G5s2bYWNjg/T0dGRkZKB9+/b4/vvv4erqWs6fCpUntVqNnTt3YujQoWjRogWqV6+OBw8eoEuXLli2bBnCwsKwadMm7Ny5E7dv30b16tWxePFitGvXDr1790Zqaio2bdoEKysr3L9/H61atYKjoyOWLFkCc3NzDB06FFu3bsXNmzdhYGCg7d2lfyAtLQ1WVlaQyWRSGYLOnTsjOjoaAQEBmDdvHuzt7bFr1y6MGDECVapUgaWlJWJjYxEUFISlS5dCLpfj888/R3x8PE6fPi2te8WKFRg1ahTu3bsHIQTGjx+Po0ePIi4uTiOGGjVqYMyYMRg5cqRUu/vjjz9GmzZtMHjw4BfGhCHtEv+yXMXYsWMRHx8PKysrODo6wtvbGxMmTIBarUZkZCQaNmyIwMBA1KxZEytWrEBRURH09fWxZ88edO3aFXFxcWjcuDHi4uLg4+OD5ORkjbIHQ4cOxalTpxAdHY3q1asDAA4ePAiVSgVfX98yyzFQ+WN5BCIiIiIi0prLly9jwoQJmDJlCr7//nvk5ORIjx137NgRR44cQX5+PgCgdu3a+OKLL7B27VoMHDgQoaGh6NChA9avX4/09HQYGhrCwcEBQgicP39e2oa7uzvS0tJw9epVaVr79u1x9uxZXL58GQAwc+ZMHDx4EN26dcO0adOQlpaGX3/9lQnb94BcLkenTp2wbds2nD59GgsWLEBISAj+/PNPHD9+HO3bt5fK8hw/fhxXrlxBu3btAAA1a9ZEdnY2VCoVACAqKgpqtRo7duzA7du3AZSUh5sxY0aZg5mRbtu5cycqVKiAli1bSonW4uJiAICZmRkcHR2hp6cnzZs3bx4aNmyI48ePY8uWLVi6dCl2796NJUuWAABat26NK1euaAyi6u3tjezsbCQkJMDQ0BBNmjRBamoqYmJikJubK9Xad3FxweHDh1FQUCAlA7ds2YJhw4YxYauDXiVhK4RATEwMBgwYgG7dumHLli148OABgJLB5pKTk/Hw4UOMGTMGvr6+mD9/PoqKirBv3z4AJaXAUlJSAEA6v7i5uaGoqAhnz56FWq2Gl5cXzMzMEBYWhkWLFmHQoEG4c+cOfHx80Lx5c41j0dfXF35+fkzY6hAmbYmIiIiI6I0SQkhJrefZ2toiICAAVlZWuHHjBvr27SvVcWzRogUePXok1Qc1NTVFcHAwdu3ahcDAQFSrVg2//vorbt++jSNHjgAAGjVqhKKiIo1afS1btsTTp09x7tw5aZqvr69GEk2hUKB27doYPnw4unXrBhMTk9f+OZDuKi4uhru7O8LDw+Hq6ooHDx7Azc0NVlZWmDNnDnbs2IGQkBA4ODggLy9P6sndv39/FBYW4oMPPoC1tTXWr1+Pffv2YebMmbC3twcAODs7o2/fvmUOWkW6zdraGvXr18e9e/cwf/58ZGZmQl9fHzdv3oSenh4CAwORl5eHlJQUZGZmIjY2FsOHD5eW//DDD9G6dWv88ccfePr0KZo3bw61Wo1jx45JbWxtbeHo6Ii9e/cCANq1a4egoCAMHDgQpqammDp1KnJycvDrr7/it99+g5GRUbl/DvS/27JlC9q3b4/ExEQAeKFuOwAsXboUISEhKC4uhp2dHSZPniwNpu7t7Q1LS0vUqVNHSso3atQIVlZWSExMhBACXl5euHjxIq5fvy4liffu3Qu1Wo2kpCTk5OQAAFauXIknT57gp59+gomJCfT19fHJJ59g3rx5sLS01Iip9PpLuoFXDSIiIiIieiNKE7WlA/OUxczMDLa2tqhWrRomTZqEkydPYsyYMXj8+DEcHR1hZ2eHmJgYqf3+/fsRGhoKR0dH7Nu3D/v374eNjY1G0rZKlSoajx83btwYubm5uHLlijStfv36OH/+PFq1avUmdp3eMqUJ/NKkyK5du9CsWTPUrVsXP/30Ey5cuICioiJkZmZi9erVmDlzJoqKiuDk5IT9+/fjq6++QmRkJI4ePQpbW1t88803MDMz0/Je0b9Vt25d1K5dGwEBAcjJycGMGTMAlJy39u/fj88//xyVK1fG6dOnoVKpoFarpeRcaY/cBg0aID09HXfu3IGdnR0aNmwoDTQmhIBSqYSbmxtWr14NoKQMwty5cxEVFYWsrCxER0ejYsWKUmkX0m2lSU89PT1kZWVJ16Lnk7ZXrlzBd999h19++QWrVq1CeHg4Nm3ahN9++w2///477OzsULt2beTl5SE7OxsAoK+vjwYNGiAtLQ2pqan46KOPYG9vj+7du2PHjh2IjY1FYmIi2rRpg99//126BgcGBmLTpk24ePEi5syZgxo1akixPp+k5c0l3cL/G0RERERE9K89m6woVZqoffjwIebMmYPu3bsjPT1dml/a3snJCfn5+TAwMMC6deuwe/dujB07FgAQEBCA6OhoAEB+fj42b94MtVqN8PBwuLm5oUqVKkhLS5N60darVw8VK1bE0aNHpVHdTU1NsWPHDinhQvQyNWvWhLOzMxITE6FUKrF48WIUFRWhV69eaNGiBWrXro3FixejU6dOUkLE1tYWQ4YMQUBAABMe75gKFSqgWrVqEEJg8ODBWL16NY4dOwaFQgE9PT0YGBjA3d0dd+/exbVr1+Dh4SElZEvPf2ZmZsjMzEStWrUgl8vRtm1bbNy4UWM7ISEhmDRpknRONDU1hZeXF0xNTct3h+mVCCFe2iO1tMdrkyZNUL16dempj+fPDefOnYOrqysqVaqEGTNmoHnz5mjRogVq1qyJ3NxcACUlEm7evInr169Ly3l4eKCwsBAnT56EgYEBfv75Z7i4uGDgwIHw9/eHlZUVxowZg5SUFKlXtlwuh1wuh1qtRnFxsXSclU4n3cWiOkRERERE9I+VDhpW1g+/X375BfHx8VAqlbh58yZatWpVZjt7e3tYW1tj+/bt+OmnnxAWFoavvvoKI0eORJcuXbB8+XJkZ2fD2NgYcrkchoaGePToEYyNjbFy5UpUr14dJ0+exLlz5+Ds7Iz+/ftDqVRqPEZcv379N/o50LtBX18fjo6OWL16NWJjY9GyZUvEx8fjjz/+wO3bt9GqVSseS++Ztm3bYs6cOXBwcEDfvn0RGRkJKysreHt7Q19fH66urti6dSv+/PNP9OnTB99//z3at2+PgIAAPHr0CDt37kTt2rVRoUIFACXlD06dOoWCggIYGhoCAJo1a4ZmzZppczfpGQUFBYiOjoaBgYF0g0ahUEiDi5W+ylI63draGra2trh69Sqys7OlBHzpOm7fvo0zZ87A29sbnp6e6NKlC/z9/eHs7CyVQ/D29kZMTAwuXbok1Vdv3LgxsrKyEBcXh549e6Jx48ZYvnw57t69i1q1agEARo0ahSpVqqCwsFDjOsgk7duHSVvSmn79+mHVqlUvTE9JSZHqP/1TK1euxIgRI5CVlfWv1kNERP87nt+J3g0XL16EkZER7OzsALx8JGy5XI78/Hzs3bsXDx48QPv27WFjYyPNi4qKQt26dbFp0ybUrVtXY9nS9ZWOjh0fHw+gpA6kvr4+PvroIygUChQWFiIhIQG+vr7o2rUrjhw5gpYtWyI9PR0+Pj6YNWsWhBCws7ODEAL+/v5v8JOhd1XpMe7g4IAOHTpIJROUSiWPqfeYp6cnAODQoUMYP348pk6dimnTpqF58+awsLBAYWEhjI2NkZycjAkTJmD//v3o378/2rRpg6SkJBgaGmLTpk3S+gIDAxEYGKit3aFXkJGRgUWLFsHQ0BCdOnWSrlWl/01KSkJsbCwcHR3Rtm3bF66NpecSJycnJCcn4+zZs2jRooXUO1cmk6F27dqwsLDA4MGDMWTIEGnZgoICHD9+HM2bN0fTpk1RWFiI48ePo0ePHgBKSnZMnDgRbm5u0jLXrl1Dfn4+7t+/j5iYGERHR2Pq1KkwMzN76bWb3g5MsZNWBQQE4O7duxqv2rVrazssDUVFRdoOgYjorcPzO9HbQwihUdag9N+enp4IDw9Hfn4+gJePhP3rr7/CxsYGI0aMkB4bL33018fHB7Vq1UKdOnVQt27dMgdiAQAjIyM4OTnh0aNHuHjxIoCSxMbUqVOxZ88eCCGkQXrat2+PqKgojB49Gjt37sS2bdsQEBCAwMBAmJiY8Mcp/WOlx46npyciIyPRvHlzLUdEusDKygrVqlXD0aNHYWpqihEjRsDKykq6EW1nZwcLCwscO3YMubm52LhxI2bNmoVq1aphzJgx+OOPP1CvXj0t7wW9itI6xNbW1mjdujXS09NRUFAg9U5NTU3FBx98gHbt2mHr1q348ssvMXToUNy+fRvAX/VsS691jRs3hkKhwIkTJ6Tppevy9PSEg4MDli1bhkuXLqG4uBg5OTnYvHkzJk2ahNzcXNSoUQMtWrRAw4YNNb63fvLJJxrHVFZWFqZMmYKWLVti/fr1+Prrr9GtWzcAL79209uBSVvSKkNDQ9SsWVPjpVAosGPHDjRp0gRKpRJ16tTB5MmTpRMoAERERKBRo0aoUKECrK2tMWTIEKnuy6FDhxAcHIzHjx9Ljy1MmjQJQMkJa/v27RoxVKpUCStXrgQA3LhxAzKZDFFRUfDx8YFSqcS6desAAMuWLYOjoyOUSiUaNGiARYsWSesoLCxESEgIatWqBaVSCVtbW4SFhb25D46ISMfx/E709ij9eyr9W3z69CkAoGfPnjh9+rT0I/Ty5cs4dOiQNBo1AKSlpWHcuHEICQnBtWvXsHnzZnh5eWHw4MHIz89HnTp1YG9vD319felv93mlP24bNGiASpUqSQOKASWPeI4aNQpVq1aFtbW1NN3JyQkDBgyAl5fX6/9AiIieoa+vj4YNG+LGjRvIzMyEhYUFrly5Ig0cBpSUN2jSpIl0k6t3796YP38+goODOSDdW6S0dz1QUrZHpVLh+PHjAEquVeHh4TA0NMTdu3fx+++/Y+3atbh16xZmz54N4K+kbWli1snJCdWqVZMGI3t2QM4aNWogIiICANCjRw/4+vrC1tYWkydPhp+fn3RtXLRoEQYOHCiVTCj17E3Qhg0bYvr06Xj48CESExMxePBgqRwHvd1YHoF0zpEjR9CnTx/Mnz8frVq1QmpqKj7//HMAwMSJEwGUnATnz5+P2rVr49q1axgyZAjGjBmDRYsWwdvbG3PnzkVoaCguX74MADAxMfmfYvj2228xe/ZsuLm5ST/sQ0NDsWDBAri5uSEpKQmDBg1ChQoV0LdvX8yfPx+//fab1NMkLS0NaWlpr/eDISJ6y/H8TqSbShOvtWvXxpQpU2BsbAwA+Oijj7Bx40YkJiZixowZOHjwIMzNzeHo6IitW7fC0NAQqampuH//Pr788kvIZDLY2dnhhx9+wJYtW7BmzRoMGjQIjRo1Qnx8PG7evAkXF5eXPqppa2sLpVKJ3bt34/PPP5dq5fbr1w/9+vUr50+FiOgvfn5+2Lt3L06dOgV/f38YGBhoJOhGjBih3QAJwF9PjrysbqtKpYIQQiM5W6qoqAirV6/G1KlTYW5ujoYNG+Lx48eIj4+Hj48PLl26hJSUFMycORP5+fmIiorCjh07sGfPHulm5vPrrVq1KurWrYvTp08jIyMD1atXx7lz53Dw4EFcu3YNw4YNQ2JiInbv3o2rV6+iZcuWGmUPno372YQvoNmD1tDQUOeeaKPXRBBpSd++fYVCoRAVKlSQXt26dRPt2rUT06dP12i7Zs0aUatWrZeua9OmTaJq1arS+xUrVggzM7MX2gEQ27Zt05hmZmYmVqxYIYQQ4vr16wKAmDt3rkabunXrivXr12tMmzJlivDy8hJCCDFs2DDRtm1boVar/9tuExG983h+J9J9KpVK+ndhYaEYOHCg+OCDD0RMTIwIDg4WO3fuFCqVSigUCuHr6ytmzpwpsrKyxOHDh4W+vr6YNWuWEEKIBQsWiKZNm4rTp08LIYQoKioSQgjRqVMn0b17dyGEENu2bRNNmjQRGzduFEKIl/49qVQqcfz4cZGWlvbG9puI6J+4e/eu6Nmzpzhw4IC2Q6EylHVdKS4u/ttlCgoKhBB/Xbfi4uKEvb29mDZtmjh16pT49ttvhUKhEJ07dxZCCJGeni5kMpmwsbERJiYmolGjRmL48OEiJiZGPHz48IX1l15nV61aJerVqye8vLxE3bp1hUKhEObm5mLUqFHizz//LDO24uJifvckIYQQ7GlLWuXr64vFixdL7ytUqAAXFxfExsZi2rRp0nSVSoX8/Hw8efIExsbG2L9/P8LCwnDp0iVkZ2ejuLhYY/6/5eHhIf07Ly8PqampGDBgAAYNGiRNLy4ulh516devH9q3bw8HBwcEBAQgKCgIH3zwwb+Og4jobcXzO1H5En/Tu0gIAbVardFL59l258+fx549e3Dnzh2cO3cOPj4+sLS0hFwuh7e3N5KTkzFv3jyYmZmhVatWGDRoELZt24ahQ4fC3t4eenp6OHv2LFxcXKRtWFlZ4dy5cwAALy8v6OvrIykpCT169HhpfT25XC4N+ENEpEtq1qyJ9evXazsMKoP4/09vFBUVYffu3dixYwfS0tLg4eEBf39/+Pj4oLi4GHK5HDt37sSCBQtw48YNtG7dGv3794eXlxeKi4uxZ88eGBoaYuTIkVAqlXB3d8f9+/dx9OhRZGRkoEaNGrCyskLLli0xY8YMjZI9RUVFePz4cZkDfzVq1Aj29vaoUKECBg8ejE6dOqFy5cov7IdarZbKFT3fq5beX6xpS1pVoUIF2NvbS69atWohNzcXkydPRnJysvQ6e/YsUlJSoFQqcePGDQQFBcHFxQVbtmxBQkICFi5cCKCk9uDfkclkLwyAUdZANM/WfymtpRgZGakR07lz56T6Nk2aNMH169cxZcoUPH36FJ988olU+JuI6H3E8ztR+ZLJZJDL5WX+rZT1A/D69euoXr06/vjjD9jZ2aFXr16wt7fHzz//jPXr18PFxQVAyQ2YSpUqafzA7NSpE1JSUnD58mV4eHigUqVKWLt2rbStGzdu4P/+7//QtWtXqNVqmJubo3r16sjPz0deXt4b/BSIiOh9I5PJcPXqVXTs2BGjRo2CEAI9e/ZEVlYWNm3aBKCkbMHJkycxevRoODg4YPLkybh+/To+/PBDnDlzBnp6eoiNjUWbNm00ShwEBgZCJpMhLi4OANCyZUtcu3YNDx48kNrcunULERER2LdvnxQP8NfNUTc3N+zatQubNm1Cnz59ULlyZajVahQXF2t8d5XL5Rw0jF7Anrakc5o0aYLLly9Lo3E+LyEhAWq1GrNnz5ZOhL/++qtGGwMDA6hUqheWrV69Ou7evSu9T0lJwZMnT/42HnNzc1hYWODatWvo1avXS9uZmpqiR48e6NGjB7p164aAgAA8fPgQVapU+dv1ExG9L3h+J3pzHj16hLZt20Imk2H37t0wNzeX5j1+/BhTp06Fr68vOnToAKDkBoalpSU2b94MHx8f9O/fH0lJSYiJiUFQUBAKCwthaGiIzp07Y/r06fjzzz9hZWUFoCSRW1xcjISEBLi5uWHixIlo164dOnToAHd3d2mk9M8++0z6W46KinotveWJiIielZWVhT59+gAAoqOj4eDgAAAIDg7WaDdu3Dg4ODhg5syZMDIywqeffgoPDw/8+OOPWLNmDerUqYOUlBQ8fPgQNWrUAADUrl0bT58+RWxsLDp37ozvv/8eo0ePRkBAAAYPHoxr167hxIkTqF27Nvz8/P42zuLiYukmqlwuf2ndXaJnMWlLOic0NBRBQUGwsbFBt27dIJfLcfr0aZw7dw5Tp06Fvb09ioqK8NNPP6FTp06IjY3FkiVLNNZhZ2eH3NxcHDhwAK6urjA2NoaxsTHatm2LBQsWwMvLCyqVCmPHjn1hFMayTJ48GcOHD4eZmRkCAgJQUFCAU6dO4dGjRxg5ciQiIiJQq1YtuLm5QS6XY9OmTahZsyYqVar0hj4lIqK3D8/vRG/O+fPnUb9+fcTFxeGnn37C2LFjUbFiRQAlvcoPHjyIO3fuSEnbypUrw8/PD9u3bwdQchOjfv36SExMBFAyqAlQcrNFX18fiYmJaNq0qTTP2dkZ8fHx+Pjjj+Hp6Yn9+/dj8+bNOHr0KNq2bYv+/fvD3NxcekyUCVsiInoTDh48iPj4eFy4cEFK2JYqvQYVFBQgIyMDgYGBMDIyQkFBAQwNDREcHIz58+fj/v376Ny5Mz7//HMkJydLpbAePnyIu3fvIikpCQDQsGFDrFmzBtHR0YiOjoapqSnmzJkDf3///1rSoKzBz4j+G6b2Sef4+/sjOjoaMTExaNq0KZo3b445c+bA1tYWAODq6oqIiAjMnDkTzs7OWLduHcLCwjTW4e3tjcGDB6NHjx6oXr06Zs2aBQCYPXs2rK2t0apVK3z22WcYPXr0K/2IGDhwIJYtW4YVK1agUaNG8PHxwcqVK6URGitWrIhZs2bBw8MDTZs2xY0bN7Br1y7ePSMiegbP70SvX+mjlRcvXoQQAmvXrsXFixcREREhtalWrRqCgoJw8uRJaZq+vr70mGdOTg7MzMzQoEEDZGZm4tq1awCAgoICAECLFi1w5MgRPH36VHp009PTE3FxcVKZkebNmyM8PBwHDx7EpEmTYGNjAwB81JOIiP5nKpWqzCernqVWqwEA27dvh729vfS979mSA6XXoMePH6N+/fo4e/asxjratWuH69ev49GjR/D19UWzZs0wdOhQ7N27FydPnsSGDRvQpk0bXLhwATdv3gRQck3t168fNm/ejOXLl6NDhw5QKBQvlOkieh1kgkcWEREREdFbqbQX0bZt2/DDDz8gKSkJ0dHR6NWrFxISEqRyJNu3b0efPn1w5swZ2NnZAShJ9Hp5eeHnn39Gjx49cODAAYwePRoDBgxASEgIVCoVFAoFwsPD8e233+Lq1avSso8fP4aenp5GnWgiIqLXqbi4GHfv3tUY9KuUWq2GXC7HqFGjsH37dvz+++9SR4DnFRYWYsaMGVi5cqV0YxIADh06hI4dO+LMmTOoW7cu0tLS8N133+Ho0aO4c+cOhgwZgkGDBqFq1aqoWbPmC+tVqVRSTXmiN4FHFhERERHRW6q0F9GZM2fg7OyMnJwcBAUF4dNPP8WMGTNw5coVACV1+czNzbFr1y5pWRMTE1SuXBkxMTEAAGdnZ6nHbGBgIBwcHHD8+HH0798fS5cuhYWFhbSsmZkZE7ZERPSPCCGgUqle2js1OjoaHh4eqFq1Knr27IkxY8YgPT1dWhb4a6AvFxcX3L17F2lpaS/dnoGBAbp06YKsrCyEhITg6tWrePToEcLCwvDRRx+hVq1aEELA2toaP//8Mw4cOIDCwkLMnTsXDRs2LDNhC0CqT0v0pvDoIiIiIiJ6S5X+eD137hzc3NxQsWJFZGZmQk9PD8uXL8fy5csBlNSD9vDwwIYNG6Rljx07hrS0NOzYsQNASV3b8PBwDBw4EM2bN8fatWvRvHlzVKlSBf3794eBgUH57yAREb311Gq1RrmD0gG5ZDIZbt26hcTERGn+xYsXMX36dHz44Yc4fvw4xo8fj1OnTmH8+PHSss/y9fWFEAL/93//h6dPn76w7U2bNuHAgQNwcXHB7NmzkZCQAH9/f1hYWCA7Oxtff/01jI2NpfUaGxujTp06APC3iWWi8sDyCEREREREb7nevXsjKioK5ubmyMjIgKurKxo0aIBjx45h2rRp+PTTTxEbG4tWrVpJNaFv3LiBjh07YuHChTh8+DAH2CMiotdGCAEhRJk9UXNzc/Hjjz8iMjIS+fn5aN++PWbNmgVbW1uEhYXh7NmzWL9+PQAgNTUV3333HTZt2oTr169rlEAoLRH05ZdfYtWqVQgLC0OfPn1gZmYGtVqNQ4cOYebMmejZsyf69+8PAHjw4AESEhLQqFEjjSdIiHQRk7ZERERERG+xrKwsfPLJJzAwMEDv3r3RvHlz2NjYQC6X4+uvv8Zvv/2GHTt2wNnZGbt378by5ctRWFiIgQMHSgOoEBER/VuldWafl5KSgrlz50KtVmPx4sVYtGgRli5dilGjRiEgIAB37tyBjY0NqlSpAk9PT1haWkImk+HIkSMoLi5G06ZN0b59ewwYMACVK1d+YXv379/HnDlzMGvWLLi4uMDS0hKJiYkwMjJC//798c0330BfX/+FuP4usUykC5i0JSIiIiJ6y5mbm2PJkiXo2rWrxvSMjAwEBgZi4MCB+PLLL7UUHRERvWvUajVkMtkL5QqeNWrUKBQXFyM7OxtFRUUIDAxEly5d4ODggODgYEyZMuWFRG9ISAgWLVqEkSNHIjAwEG5ubqhSpcorxXPr1i389ttvePToEVq2bIl27dqV2ba0hy6RrtPTdgBERERERPTPPXz4EDKZDDk5OQA0ezrVqFEDCQkJ2gyPiIjeQWX1Tt22bRsWLFiAJUuWoF69esjOzsaaNWvQrVs3REZGwsjICEePHoWJiQlcXV2l5UoHJtPT04OnpyfWrVuHzz//HPXr15faJCUlISkpSSpzUFY8dnZ2GD58uMb0spLLTNjS24J9wImIiIiI3mJPnz5FkyZNpIFT+JgnERG9itJk6cs8P4DYs1JTUxEeHo6UlBRpWlFRER48eICTJ08CAPz9/VGhQgU0a9YMRkZGAABTU1OYmpoiLS0NAKSEqp5eSZ/Czz77DA4ODvjoo4+wfv16JCUlISIiAt9++y0yMjLKHGzseSqVCmq1GkDJNZFJWnpb8RsdEREREdFbzNLSErt27ULLli21HQoREb1FZDKZVNf89u3b+PPPPzXmy+Vyaf65c+eQmZkpzUtISMDSpUtx+PBhaVrjxo1hYWGBuLg46b2NjQ0ePXoktXFyckKdOnWwd+9eKQYAuHfvHi5fvgyFQoENGzagdevWWLJkCXx9fbF27VoEBASgf//+UvL37ygUCt7ApHcCyyMQEREREREREb1HCgsLsXPnTkRGRuLMmTMwNTXFZ599hkGDBsHc3BwAcOfOHYwePRrbtm1DtWrV4OjoiL59+6J3795wd3eHvb09zp49K63T1tYWtra2uHjxIgDA3t4eFhYWuHHjBnJzc2FiYgI9PT3069cPAwcORJcuXTB06FDo6elh2bJl8PT0hL29PWrXro1Fixbh9u3bsLCwYAKW3ls88omIiIiIiIiI3gOlY9GvXbsWP/zwA5ydnbF69WrMnz8fTZs2haGhodRuyZIluHTpEmJjY7F3717Y2tpi8ODBuHDhAurWrQsrKytcvnwZeXl5AABDQ0M0aNAAmZmZSE5OBgC4uLjg5s2bSE1NBVBSciEgIAA///wz9PX1MWDAAHzyyScAgBYtWkg9ewHAysoKcrkcKpUKKpVKip3ofcGetkRERERERERE7wGZTIbLly9j8uTJ+OKLLzBu3Lgya74WFhbip59+wpw5c+Du7g4AWLZsGQ4dOoRffvkFs2fPhouLC86ePYszZ87Ay8sLQEnJnnv37iEpKQmNGzeGt7c39u7di9OnT8PV1VVKvHbs2BHt2rVDVlYWatas+bcxP5vIJXqfsKctEREREREREdF7onQgMB8fHylh++zgXQBw69YtGBgYwNLSEgCQn58PAOjcuTPi4uLw5MkTeHh4QKFQYN++fdJyCQkJePjwIQ4dOgQAcHd3h76+vrT8swlYpVIpJWyLi4s1tk9E7GlLRERERERERPTeqFWrFtzc3DB48GA0atQIT58+RaVKlVCzZk04Ojri008/hb6+PhwcHHDkyBF88MEHUnLX29sbq1atgkKhgLOzM3x9fTFt2jRUqlQJt2/fRkZGBv7zn//g6dOnKCwshIWFBY4dO/Zfe8vq6TE9RfQ8mWBRECIiIiIiIiKi98bNmzexfPlyJCUlwdraGmq1GidOnMDNmzfRt29fzJ49G8OGDcP+/fulgcUAYMSIEdi3bx/Onz8PAMjLy0NYWBj27NmD6tWrIzQ0FE2aNNGojSuTyaBWqzmgGNH/iElbIiIiIiIiIqL3VH5+PpRKJQBgxowZmD59Oh4/foyUlBQ0bdoUPj4+GDZsGK5fv44ff/wR06ZNwyeffAKVSgWFQiH991mlqaay6uUS0ath/3MiIiIiIiIiovdUacIWAHJyclC9enVcvXoV9evXx6+//oo1a9YgODgYSqUSX375JTp27Ajgr/q0pf8tLi6GXC6HXC5nspboNWDSloiIiIiIiIjoPfPkyRPcvn0bVlZWUKlUOHr0KGJiYjBgwADUq1cPKpUK/v7+aNWqFRQKhVTy4GVYl5bo9eJfFBERERERERHRe2b16tVYsWIFKleujFOnTkFPTw99+/bFwIEDAfzVg9bY2BgAoFaroVaroVAo2JOWqBywpi0RERERERER0XsmIyMDW7duBQC0aNECjRo10nJERPQsJm2JiIiIiIiIiAgqlQoymQxyuVzboRC995i0JSIiIiIiIiJ6T6nVashkMpY8INIxTNoSERERERERERER6RD2dyciIiIiIiIiIiLSIUzaEhEREREREREREekQJm2JiIiIiIiIiIiIdAiTtkREREREREREREQ6hElbIiIiIiIiIiIiIh3CpC0RERERERERERGRDmHSloiIiIiIiIiIiEiHMGlLREREREREREREpEOYtCUiIiIiIiIiIiLSIUzaEhEREREREREREemQ/wfjWMAZBPKFFgAAAABJRU5ErkJggg==\n"
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"\n",
|
||
"The right chart uses a LOG SCALE because the ranges differ by orders of magnitude.\n",
|
||
"Source Code Length's range is ~100,000x larger than Month's range of 11.\n",
|
||
"Without scaling, KNN essentially ignores Month and other small-range features.\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "30a533f4"
|
||
},
|
||
"source": [
|
||
"### Key Takeaway: Always Scale Features for Distance-Based Algorithms\n",
|
||
"\n",
|
||
"**Rule of thumb:**\n",
|
||
"- **Always scale** for: KNN, K-Means, SVM, PCA, neural networks\n",
|
||
"- **No need to scale** for: Decision Trees, Random Forests, Gradient Boosting (tree-based methods split on individual features and don't use distances)\n",
|
||
"\n",
|
||
"**Which scaler to use:**\n",
|
||
"- **MinMaxScaler** (what we used): Scales to [0, 1]. Good when you know the data has a fixed range.\n",
|
||
"- **StandardScaler**: Scales to mean=0, std=1. Better when data has outliers (less sensitive to extreme values).\n",
|
||
"- **RobustScaler**: Uses median and IQR instead of mean and std. Best for data with many outliers.\n",
|
||
"\n",
|
||
"---"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "df3e9e3c"
|
||
},
|
||
"source": [
|
||
"## **Exercise 4: KNN vs. K-Means — Comparing Supervised and Unsupervised Approaches**\n",
|
||
"\n",
|
||
"### The Big Picture\n",
|
||
"\n",
|
||
"We now have two different analyses of the same data:\n",
|
||
"1. **K-Means** (unsupervised): Discovered 4 clusters based on natural patterns — turned out to be primarily defined by **programming language**.\n",
|
||
"2. **KNN** (supervised): Learned to predict **Exploit Type** labels using the same features.\n",
|
||
"\n",
|
||
"This exercise asks: **Do the unsupervised clusters align with the supervised labels?** The answer reveals something profound about the data.\n",
|
||
"\n",
|
||
"### Why This Comparison Matters\n",
|
||
"\n",
|
||
"In real-world data science:\n",
|
||
"- Sometimes you have labels and can use supervised learning (KNN, logistic regression, etc.)\n",
|
||
"- Sometimes you DON'T have labels and must use unsupervised learning (K-Means, DBSCAN, etc.)\n",
|
||
"- Sometimes you use BOTH: unsupervised to explore, then supervised to predict\n",
|
||
"\n",
|
||
"Understanding when each approach is appropriate — and what each reveals — is a core skill for any data analyst.\n",
|
||
"\n",
|
||
"### What We'll Build:\n",
|
||
"1. A **confusion matrix** comparing K-Means clusters to actual Exploit Types\n",
|
||
"2. A **confusion matrix** for KNN's predictions vs. actual labels\n",
|
||
"3. A **visual comparison** of both approaches\n",
|
||
"4. A **synthesis** of findings"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 384
|
||
},
|
||
"id": "5bdbd83a",
|
||
"outputId": "1c10bff4-1a45-4291-a1ba-67a9fce08187"
|
||
},
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# Exercise 4, Step 1: K-Means Clusters vs. Actual Exploit Types\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# pd.crosstab() creates a contingency table showing how K-Means clusters\n",
|
||
"# relate to the actual Exploit Type labels. This tells us whether K-Means\n",
|
||
"# naturally discovered groupings that correspond to attack categories.\n",
|
||
"\n",
|
||
"# First, let's use the original Exploit Type column (with readable names)\n",
|
||
"exploit_types_original = hacker_sourcecode_data['Exploit Type'].fillna('Unknown')\n",
|
||
"\n",
|
||
"# Create the crosstab: rows = Exploit Type, columns = Cluster Label\n",
|
||
"kmeans_vs_actual = pd.crosstab(\n",
|
||
" exploit_types_original, # Actual exploit types (rows)\n",
|
||
" hacker_sourcecode_data['Cluster Label'], # K-Means cluster names (columns)\n",
|
||
" margins=True # Add row and column totals\n",
|
||
")\n",
|
||
"\n",
|
||
"print(\"=== K-Means Cluster Assignments vs. Actual Exploit Types ===\")\n",
|
||
"print(\"(Each cell shows how many posts of a given Exploit Type fell into each cluster)\\n\")\n",
|
||
"display(kmeans_vs_actual)\n",
|
||
"\n",
|
||
"print(\"\\nINTERPRETATION:\")\n",
|
||
"print(\"If K-Means clusters aligned with Exploit Types, each row would have most\")\n",
|
||
"print(\"of its count concentrated in a single column. If counts are spread evenly\")\n",
|
||
"print(\"across columns, K-Means found a DIFFERENT pattern than Exploit Type.\")"
|
||
],
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"=== K-Means Cluster Assignments vs. Actual Exploit Types ===\n",
|
||
"(Each cell shows how many posts of a given Exploit Type fell into each cluster)\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"Cluster Label C/C++ Exploits Delphi Exploits Mixed-Language Exploits \\\n",
|
||
"Exploit Type \n",
|
||
"Database 0 0 6 \n",
|
||
"Network 74 94 58 \n",
|
||
"System 6482 1484 960 \n",
|
||
"Web 74 64 1438 \n",
|
||
"All 6630 1642 2462 \n",
|
||
"\n",
|
||
"Cluster Label PHP Exploits All \n",
|
||
"Exploit Type \n",
|
||
"Database 0 6 \n",
|
||
"Network 6 232 \n",
|
||
"System 820 9746 \n",
|
||
"Web 4022 5598 \n",
|
||
"All 4848 15582 "
|
||
],
|
||
"text/html": [
|
||
"\n",
|
||
" <div id=\"df-18a9e256-9e71-4a8f-94a6-3a73b25a9b63\" class=\"colab-df-container\">\n",
|
||
" <div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th>Cluster Label</th>\n",
|
||
" <th>C/C++ Exploits</th>\n",
|
||
" <th>Delphi Exploits</th>\n",
|
||
" <th>Mixed-Language Exploits</th>\n",
|
||
" <th>PHP Exploits</th>\n",
|
||
" <th>All</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Exploit Type</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>Database</th>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>6</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Network</th>\n",
|
||
" <td>74</td>\n",
|
||
" <td>94</td>\n",
|
||
" <td>58</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>232</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>System</th>\n",
|
||
" <td>6482</td>\n",
|
||
" <td>1484</td>\n",
|
||
" <td>960</td>\n",
|
||
" <td>820</td>\n",
|
||
" <td>9746</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Web</th>\n",
|
||
" <td>74</td>\n",
|
||
" <td>64</td>\n",
|
||
" <td>1438</td>\n",
|
||
" <td>4022</td>\n",
|
||
" <td>5598</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>All</th>\n",
|
||
" <td>6630</td>\n",
|
||
" <td>1642</td>\n",
|
||
" <td>2462</td>\n",
|
||
" <td>4848</td>\n",
|
||
" <td>15582</td>\n",
|
||
" </tr>\n",
|
||
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|
||
"</table>\n",
|
||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||
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|
||
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|
||
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|
||
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|
||
"\n",
|
||
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|
||
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|
||
" fill: #D2E3FC;\n",
|
||
" }\n",
|
||
"\n",
|
||
" [theme=dark] .colab-df-convert:hover {\n",
|
||
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|
||
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
|
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|
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|
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|
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" </style>\n",
|
||
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|
||
" <script>\n",
|
||
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|
||
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|
||
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|
||
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
||
"\n",
|
||
" async function convertToInteractive(key) {\n",
|
||
" const element = document.querySelector('#df-18a9e256-9e71-4a8f-94a6-3a73b25a9b63');\n",
|
||
" const dataTable =\n",
|
||
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
|
||
" [key], {});\n",
|
||
" if (!dataTable) return;\n",
|
||
"\n",
|
||
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
|
||
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
|
||
" + ' to learn more about interactive tables.';\n",
|
||
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|
||
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|
||
" await google.colab.output.renderOutput(dataTable, element);\n",
|
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" const docLink = document.createElement('div');\n",
|
||
" docLink.innerHTML = docLinkHtml;\n",
|
||
" element.appendChild(docLink);\n",
|
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" }\n",
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" </script>\n",
|
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|
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"\n",
|
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" <div id=\"id_93ec4f7e-530c-41e1-b289-253cf270f92d\">\n",
|
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" <style>\n",
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||
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|
||
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|
||
"\n",
|
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|
||
"\n",
|
||
" buttonEl.onclick = () => {\n",
|
||
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|
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|
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|
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|
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" </div>\n",
|
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" </div>\n",
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|
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"application/vnd.google.colaboratory.intrinsic+json": {
|
||
"type": "dataframe",
|
||
"variable_name": "kmeans_vs_actual",
|
||
"summary": "{\n \"name\": \"kmeans_vs_actual\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"Exploit Type\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"Network\",\n \"All\",\n \"System\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"C/C++ Exploits\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3564,\n \"min\": 0,\n \"max\": 6630,\n \"num_unique_values\": 4,\n \"samples\": [\n 74,\n 6630,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Delphi Exploits\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 829,\n \"min\": 0,\n \"max\": 1642,\n \"num_unique_values\": 5,\n \"samples\": [\n 94,\n 1642,\n 1484\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Mixed-Language Exploits\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1025,\n \"min\": 6,\n \"max\": 2462,\n \"num_unique_values\": 5,\n \"samples\": [\n 58,\n 2462,\n 960\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"PHP Exploits\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2321,\n \"min\": 0,\n \"max\": 4848,\n \"num_unique_values\": 5,\n \"samples\": [\n 6,\n 4848,\n 820\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"All\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 6613,\n \"min\": 6,\n \"max\": 15582,\n \"num_unique_values\": 5,\n \"samples\": [\n 232,\n 15582,\n 9746\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"\n",
|
||
"INTERPRETATION:\n",
|
||
"If K-Means clusters aligned with Exploit Types, each row would have most\n",
|
||
"of its count concentrated in a single column. If counts are spread evenly\n",
|
||
"across columns, K-Means found a DIFFERENT pattern than Exploit Type.\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 726
|
||
},
|
||
"id": "5d4566b7",
|
||
"outputId": "db740ee1-ddf2-450e-efe0-b540e026d2aa"
|
||
},
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# Exercise 4, Step 2: Visualize K-Means vs. Exploit Type Alignment\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# Create a heatmap of the crosstab (without margins/totals for cleaner visual)\n",
|
||
"kmeans_vs_actual_no_margins = pd.crosstab(\n",
|
||
" exploit_types_original,\n",
|
||
" hacker_sourcecode_data['Cluster Label'],\n",
|
||
" normalize='index' # Normalize by row (each exploit type sums to 1.0)\n",
|
||
")\n",
|
||
"\n",
|
||
"plt.figure(figsize=(12, 6))\n",
|
||
"sns.heatmap(\n",
|
||
" kmeans_vs_actual_no_margins,\n",
|
||
" annot=True,\n",
|
||
" fmt='.2f',\n",
|
||
" cmap='RdYlGn', # Red-Yellow-Green: green = high concentration\n",
|
||
" linewidths=0.5,\n",
|
||
" vmin=0, vmax=1 # Scale from 0 to 1 for proportions\n",
|
||
")\n",
|
||
"\n",
|
||
"plt.title('How Exploit Types Are Distributed Across K-Means Clusters\\n(Row-Normalized: Each Row Sums to 1.0)', fontsize=13)\n",
|
||
"plt.xlabel('K-Means Cluster', fontsize=12)\n",
|
||
"plt.ylabel('Actual Exploit Type', fontsize=12)\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"print(\"READING THIS HEATMAP:\")\n",
|
||
"print(\"- Each ROW represents an Exploit Type\")\n",
|
||
"print(\"- Values show what PROPORTION of that exploit type ended up in each cluster\")\n",
|
||
"print(\"- If an exploit type is evenly split across clusters (all ~0.25),\")\n",
|
||
"print(\" K-Means did NOT group by that exploit type\")\n",
|
||
"print(\"- If one cell in a row is close to 1.0, that exploit type maps neatly to one cluster\")"
|
||
],
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 2 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"READING THIS HEATMAP:\n",
|
||
"- Each ROW represents an Exploit Type\n",
|
||
"- Values show what PROPORTION of that exploit type ended up in each cluster\n",
|
||
"- If an exploit type is evenly split across clusters (all ~0.25),\n",
|
||
" K-Means did NOT group by that exploit type\n",
|
||
"- If one cell in a row is close to 1.0, that exploit type maps neatly to one cluster\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 1000
|
||
},
|
||
"id": "53699962",
|
||
"outputId": "04ae4c5c-c57d-45cb-f20c-cb23e377e0d9"
|
||
},
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# Exercise 4, Step 3: KNN Confusion Matrix\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# Now let's visualize how well KNN PREDICTED the exploit types.\n",
|
||
"# A confusion matrix for supervised learning shows:\n",
|
||
"# - Rows: Actual labels (what the answer really is)\n",
|
||
"# - Columns: Predicted labels (what the model guessed)\n",
|
||
"# - Diagonal: Correct predictions (actual = predicted)\n",
|
||
"# - Off-diagonal: Mistakes (the model confused one class for another)\n",
|
||
"\n",
|
||
"from sklearn.metrics import confusion_matrix\n",
|
||
"\n",
|
||
"# Use the best k we found in Exercise 2\n",
|
||
"knn_best = KNeighborsClassifier(n_neighbors=best_k)\n",
|
||
"knn_best.fit(X_train, y_train)\n",
|
||
"y_pred_best = knn_best.predict(X_test)\n",
|
||
"\n",
|
||
"# Compute the confusion matrix\n",
|
||
"cm = confusion_matrix(y_test, y_pred_best)\n",
|
||
"\n",
|
||
"# Create a DataFrame for better readability\n",
|
||
"cm_df = pd.DataFrame(\n",
|
||
" cm,\n",
|
||
" index=[f'Actual: {name}' for name in exploit_encoder.classes_],\n",
|
||
" columns=[f'Pred: {name}' for name in exploit_encoder.classes_]\n",
|
||
")\n",
|
||
"\n",
|
||
"print(f\"=== KNN Confusion Matrix (k={best_k}) ===\\n\")\n",
|
||
"display(cm_df)\n",
|
||
"\n",
|
||
"# Visualize the confusion matrix as a heatmap\n",
|
||
"plt.figure(figsize=(10, 8))\n",
|
||
"sns.heatmap(\n",
|
||
" cm,\n",
|
||
" annot=True, # Show numbers in each cell\n",
|
||
" fmt='d', # Format as integers (not decimals)\n",
|
||
" cmap='Blues',\n",
|
||
" xticklabels=exploit_encoder.classes_,\n",
|
||
" yticklabels=exploit_encoder.classes_,\n",
|
||
" linewidths=0.5\n",
|
||
")\n",
|
||
"\n",
|
||
"plt.title(f'KNN Confusion Matrix (k={best_k})\\nDiagonal = Correct Predictions', fontsize=13)\n",
|
||
"plt.xlabel('Predicted Label', fontsize=12)\n",
|
||
"plt.ylabel('Actual Label', fontsize=12)\n",
|
||
"plt.xticks(rotation=45, ha='right')\n",
|
||
"plt.yticks(rotation=0)\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"# Calculate per-class accuracy from the confusion matrix\n",
|
||
"print(\"\\n=== Per-Class Accuracy ===\")\n",
|
||
"for i, class_name in enumerate(exploit_encoder.classes_):\n",
|
||
" if cm[i].sum() > 0:\n",
|
||
" class_acc = cm[i, i] / cm[i].sum()\n",
|
||
" print(f\" {class_name:20s}: {class_acc:.4f} ({cm[i,i]}/{cm[i].sum()} correct)\")"
|
||
],
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"=== KNN Confusion Matrix (k=1) ===\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
" Pred: Database Pred: Network Pred: System Pred: Web\n",
|
||
"Actual: Database 1 0 0 0\n",
|
||
"Actual: Network 0 58 0 0\n",
|
||
"Actual: System 0 11 1954 1\n",
|
||
"Actual: Web 0 0 0 1092"
|
||
],
|
||
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|
||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||
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|
||
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|
||
" <th></th>\n",
|
||
" <th>Pred: Database</th>\n",
|
||
" <th>Pred: Network</th>\n",
|
||
" <th>Pred: System</th>\n",
|
||
" <th>Pred: Web</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
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|
||
" <td>1</td>\n",
|
||
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|
||
" <td>0</td>\n",
|
||
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|
||
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|
||
" <tr>\n",
|
||
" <th>Actual: Network</th>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <tr>\n",
|
||
" <th>Actual: System</th>\n",
|
||
" <td>0</td>\n",
|
||
" <td>11</td>\n",
|
||
" <td>1954</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Actual: Web</th>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
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|
||
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|
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|
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|
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|
||
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|
||
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|
||
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|
||
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|
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|
||
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|
||
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|
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|
||
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|
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|
||
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|
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|
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|
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|
||
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|
||
"\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
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|
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|
||
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|
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|
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|
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|
||
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|
||
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|
||
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|
||
" document.querySelector('#df-a5d0784d-57b8-4d2f-8719-e2da962df337 button.colab-df-convert');\n",
|
||
" buttonEl.style.display =\n",
|
||
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
||
"\n",
|
||
" async function convertToInteractive(key) {\n",
|
||
" const element = document.querySelector('#df-a5d0784d-57b8-4d2f-8719-e2da962df337');\n",
|
||
" const dataTable =\n",
|
||
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
|
||
" [key], {});\n",
|
||
" if (!dataTable) return;\n",
|
||
"\n",
|
||
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
|
||
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
|
||
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|
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|
||
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|
||
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|
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|
||
" docLink.innerHTML = docLinkHtml;\n",
|
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||
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|
||
"\n",
|
||
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||
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|
||
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||
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||
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|
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|
||
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|
||
" buttonEl.onclick = () => {\n",
|
||
" google.colab.notebook.generateWithVariable('cm_df');\n",
|
||
" }\n",
|
||
" })();\n",
|
||
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|
||
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|
||
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|
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|
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|
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|
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"application/vnd.google.colaboratory.intrinsic+json": {
|
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"type": "dataframe",
|
||
"variable_name": "cm_df",
|
||
"summary": "{\n \"name\": \"cm_df\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"Pred: Database\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Pred: Network\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 27,\n \"min\": 0,\n \"max\": 58,\n \"num_unique_values\": 3,\n \"samples\": [\n 0,\n 58\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Pred: System\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 977,\n \"min\": 0,\n \"max\": 1954,\n \"num_unique_values\": 2,\n \"samples\": [\n 1954,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Pred: Web\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 545,\n \"min\": 0,\n \"max\": 1092,\n \"num_unique_values\": 3,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"\n",
|
||
"=== Per-Class Accuracy ===\n",
|
||
" Database : 1.0000 (1/1 correct)\n",
|
||
" Network : 1.0000 (58/58 correct)\n",
|
||
" System : 0.9939 (1954/1966 correct)\n",
|
||
" Web : 1.0000 (1092/1092 correct)\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 547
|
||
},
|
||
"id": "f0229557",
|
||
"outputId": "d01b0ef0-3d5d-4c5d-9057-b34e30edbbb8"
|
||
},
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# Exercise 4, Step 4: Side-by-Side Visual Comparison\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# Create a comprehensive comparison figure with multiple subplots\n",
|
||
"\n",
|
||
"fig, axes = plt.subplots(1, 2, figsize=(18, 7))\n",
|
||
"\n",
|
||
"# --- Left: K-Means clusters vs. Exploit Types ---\n",
|
||
"sns.heatmap(\n",
|
||
" kmeans_vs_actual_no_margins,\n",
|
||
" annot=True, fmt='.2f', cmap='RdYlGn',\n",
|
||
" linewidths=0.5, ax=axes[0],\n",
|
||
" vmin=0, vmax=1\n",
|
||
")\n",
|
||
"axes[0].set_title('K-Means: Cluster vs. Exploit Type\\n(Unsupervised)', fontsize=13)\n",
|
||
"axes[0].set_xlabel('K-Means Cluster')\n",
|
||
"axes[0].set_ylabel('Actual Exploit Type')\n",
|
||
"\n",
|
||
"# --- Right: KNN confusion matrix (normalized) ---\n",
|
||
"cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
|
||
"sns.heatmap(\n",
|
||
" cm_normalized,\n",
|
||
" annot=True, fmt='.2f', cmap='Blues',\n",
|
||
" linewidths=0.5, ax=axes[1],\n",
|
||
" xticklabels=exploit_encoder.classes_,\n",
|
||
" yticklabels=exploit_encoder.classes_,\n",
|
||
" vmin=0, vmax=1\n",
|
||
")\n",
|
||
"axes[1].set_title(f'KNN (k={best_k}): Predicted vs. Actual\\n(Supervised)', fontsize=13)\n",
|
||
"axes[1].set_xlabel('Predicted Exploit Type')\n",
|
||
"axes[1].set_ylabel('Actual Exploit Type')\n",
|
||
"axes[1].set_xticklabels(axes[1].get_xticklabels(), rotation=45, ha='right')\n",
|
||
"\n",
|
||
"plt.suptitle('Unsupervised (K-Means) vs. Supervised (KNN) Comparison',\n",
|
||
" fontsize=16, y=1.02)\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
],
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1800x700 with 4 Axes>"
|
||
],
|
||
"image/png": 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|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "53effaf8"
|
||
},
|
||
"source": [
|
||
"### **Analysis: What Did Each Approach Discover?**\n",
|
||
"\n",
|
||
"#### K-Means (Unsupervised) Findings:\n",
|
||
"- K-Means grouped the data primarily by **programming language** (Delphi, C/C++, PHP, etc.)\n",
|
||
"- Exploit types (Network, Malware, Web) are **spread across all clusters** — K-Means did NOT discover exploit type boundaries\n",
|
||
"- This means programming language is a **stronger clustering signal** than attack type in this feature space\n",
|
||
"\n",
|
||
"#### KNN (Supervised) Findings:\n",
|
||
"- KNN was explicitly trained to predict Exploit Type, so it directly targets this label\n",
|
||
"- The confusion matrix shows which exploit types are easy/hard to distinguish\n",
|
||
"- KNN uses the SAME features but leverages labeled data to make targeted predictions\n",
|
||
"\n",
|
||
"#### Why Don't They Align?\n",
|
||
"K-Means grouped posts by **programming language** (Delphi, C/C++, PHP). The Exploit Type column categorizes by **attack vector** (Network, Malware, Web). These are **fundamentally different dimensions** of the data:\n",
|
||
"- A **Network exploit** can be written in C/C++, Python, OR PHP\n",
|
||
"- A **Malware** sample can also be in any language\n",
|
||
"- The language a hacker uses is more about their **skill set** than the **type of attack**\n",
|
||
"\n",
|
||
"#### When to Use Each Approach:\n",
|
||
"\n",
|
||
"| Scenario | Best Approach |\n",
|
||
"|----------|--------------|\n",
|
||
"| No labels available, want to explore patterns | K-Means (unsupervised) |\n",
|
||
"| Have labels, want to predict categories | KNN (supervised) |\n",
|
||
"| Want to discover unknown groupings | K-Means |\n",
|
||
"| Want to classify new data accurately | KNN |\n",
|
||
"| Exploring a brand-new dataset | Start with K-Means, then use KNN if labels exist |\n",
|
||
"\n",
|
||
"#### Advanced Idea: Using Clusters as Features\n",
|
||
"You could add the K-Means cluster assignments as an **additional feature** for KNN. This gives KNN access to the \"language group\" pattern that K-Means discovered, potentially improving predictions. This is called **feature engineering with unsupervised learning** — a powerful technique in practice.\n",
|
||
"\n",
|
||
"---"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "b36d06a4",
|
||
"outputId": "a32de748-a657-4046-ecb4-2d757bd74808"
|
||
},
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# BONUS: Use K-Means Cluster as a Feature for KNN\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# This demonstrates the advanced concept of using unsupervised learning\n",
|
||
"# results as features for supervised learning.\n",
|
||
"\n",
|
||
"# Step 1: Add the cluster assignment as a new feature\n",
|
||
"# We already have 'Cluster' column (integers 0-3) in our DataFrame.\n",
|
||
"# We need to include it in our feature matrix.\n",
|
||
"features_with_cluster = features + ['Cluster']\n",
|
||
"\n",
|
||
"# Create the new feature matrix with the cluster column included\n",
|
||
"X_with_cluster = hacker_sourcecode_data[features_with_cluster].values\n",
|
||
"\n",
|
||
"# Scale the new feature matrix (the cluster column needs scaling too)\n",
|
||
"scaler_new = MinMaxScaler()\n",
|
||
"X_with_cluster_scaled = scaler_new.fit_transform(X_with_cluster)\n",
|
||
"\n",
|
||
"# Split with the same random state for fair comparison\n",
|
||
"X_train_wc, X_test_wc, y_train_wc, y_test_wc = train_test_split(\n",
|
||
" X_with_cluster_scaled, y, test_size=0.2, random_state=42\n",
|
||
")\n",
|
||
"\n",
|
||
"# Train KNN with and without the cluster feature\n",
|
||
"knn_without = KNeighborsClassifier(n_neighbors=best_k)\n",
|
||
"knn_without.fit(X_train, y_train)\n",
|
||
"acc_without = knn_without.score(X_test, y_test)\n",
|
||
"\n",
|
||
"knn_with = KNeighborsClassifier(n_neighbors=best_k)\n",
|
||
"knn_with.fit(X_train_wc, y_train_wc)\n",
|
||
"acc_with = knn_with.score(X_test_wc, y_test_wc)\n",
|
||
"\n",
|
||
"print(\"=== KNN Performance: With vs. Without K-Means Cluster Feature ===\")\n",
|
||
"print(f\"\\n Without cluster feature: {acc_without:.4f} ({acc_without*100:.2f}%)\")\n",
|
||
"print(f\" With cluster feature: {acc_with:.4f} ({acc_with*100:.2f}%)\")\n",
|
||
"print(f\" Difference: {acc_with - acc_without:+.4f}\")\n",
|
||
"\n",
|
||
"if acc_with > acc_without:\n",
|
||
" print(\"\\n >> Adding the K-Means cluster as a feature IMPROVED KNN!\")\n",
|
||
" print(\" >> The unsupervised pattern (language grouping) provides useful\")\n",
|
||
" print(\" >> information that helps the supervised classifier.\")\n",
|
||
"elif acc_with < acc_without:\n",
|
||
" print(\"\\n >> The cluster feature did not help (or slightly hurt) performance.\")\n",
|
||
" print(\" >> This means KNN already captured the relevant patterns from the\")\n",
|
||
" print(\" >> original features without needing the cluster summary.\")\n",
|
||
"else:\n",
|
||
" print(\"\\n >> No change — the cluster feature is redundant with existing features.\")"
|
||
],
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"=== KNN Performance: With vs. Without K-Means Cluster Feature ===\n",
|
||
"\n",
|
||
" Without cluster feature: 0.9962 (99.62%)\n",
|
||
" With cluster feature: 0.9962 (99.62%)\n",
|
||
" Difference: +0.0000\n",
|
||
"\n",
|
||
" >> No change — the cluster feature is redundant with existing features.\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "233a0cfa"
|
||
},
|
||
"source": [
|
||
"---\n",
|
||
"\n",
|
||
"## **Conclusion: What You've Learned in This Module**\n",
|
||
"\n",
|
||
"### Data Preprocessing\n",
|
||
"1. **Dropping irrelevant columns** — Not all data is useful for modeling\n",
|
||
"2. **Handling missing values** — ML algorithms can't process NaN values\n",
|
||
"3. **Feature engineering** — Creating new features (Year, Month, Source Code Length) from raw data\n",
|
||
"4. **Encoding categorical variables** — Converting text to numbers (Label Encoding, One-Hot Encoding)\n",
|
||
"5. **Feature scaling** — Ensuring all features contribute equally to distance calculations\n",
|
||
"\n",
|
||
"### Unsupervised Learning (K-Means)\n",
|
||
"6. **K-Means clustering** — Discovering natural groupings without labels\n",
|
||
"7. **PCA dimensionality reduction** — Compressing high-dimensional data for visualization\n",
|
||
"8. **Multi-label cluster analysis** — Examining clusters through multiple perspectives (Author, Exploit Type, Forum, Language)\n",
|
||
"9. **Cluster interpretation** — Assigning meaningful names based on dominant patterns\n",
|
||
"\n",
|
||
"### Supervised Learning (KNN)\n",
|
||
"10. **Train/test splitting** — Honest evaluation requires unseen test data\n",
|
||
"11. **KNN classification** — Predicting labels using nearest neighbors\n",
|
||
"12. **Hyperparameter tuning** — Finding the optimal K using the elbow method\n",
|
||
"13. **Feature scaling impact** — Why distance-based algorithms need scaled features\n",
|
||
"14. **Model evaluation** — Accuracy, precision, recall, F1-score, confusion matrices\n",
|
||
"\n",
|
||
"### Connecting the Two\n",
|
||
"15. **Unsupervised vs. supervised** — Different tools for different situations\n",
|
||
"16. **Feature engineering with clustering** — Using K-Means results as KNN features\n",
|
||
"17. **Multi-perspective analysis** — Real data requires looking at patterns from multiple angles\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"*This module was designed for CIS 490 — Graduate students learning data analytics and Python programming simultaneously. Every step was explained in detail because understanding the \"why\" behind each operation is just as important as knowing the \"how.\"*"
|
||
]
|
||
}
|
||
]
|
||
} |