Python scripts and notebooks by Ismail Belgacem (20845811)

Secure Python Code Manager: A Tool for Protected Python Code Distribution and Management by Pavel Izosimov (20096259)

“…A license key is generated, allowing authorized users to access the code.bash<pre><pre>python secure_python_code_manager.py --upload -f your_script.py<br></pre></pre></li><li><b>Updating Previously Uploaded Code</b>: With the <code>--update</code> function, update your code in the cloud without needing to redistribute new files to clients, ensuring seamless code maintenance and updates.bash<pre><pre>python secure_python_code_manager.py --update -f your_script.py -l your_license_key<br></pre></pre></li><li><b>Retrieving License Information</b>: The <code>--license-info</code> function lets you retrieve detailed information about your licenses, including status, usage data, and limits.bash<pre><pre>python secure_python_code_manager.py --license-info -l your_license_key<br></pre></pre></li><li><b>Service Usage Monitoring</b>: Use the <code>--service-usage</code> function to monitor your service usage, including uploaded scripts and associated licenses, helping you keep track of your code deployment.bash<pre><pre>python secure_python_code_manager.py --service-usage<br></pre></pre></li></ol><h2>Use Cases</h2><ul><li><b>Protect Python Code</b>: If you're looking to <b>protect Python code</b> from unauthorized use, this tool provides robust protection mechanisms.…”

Online Resource 3: Word Cloud Dataset and Code by Jessica Zylla (17692920)

“…<p dir="ltr">Online Resource 3: Word Cloud Dataset and Code. This set of files are part of Online Resource 3, which allows readers to implement a Jupyter Notebook Python program to create a word cloud based on survey responses. …”

This package of Python scripts implements the proposed unsupervised sentiment analysis approach.... by Xiaohan Yu (7525940)

“…<p>This package of Python scripts implements the proposed unsupervised sentiment analysis approach. …”

Artifact for the IJCAI 2024 paper "Solving Long-run Average Reward Robust MDPs via Stochastic Games" by Krishnendu Chatterjee (15367413)

“…<br>- `matplotlib` library should be installed. <br></pre></pre><h2>Structure and How to run</h2><p dir="ltr">There are four Python files in the repository.…”

The codes and data for "Lane Extraction from Trajectories at Road Intersections Based on Graph Transformer Network" by Chongshan Wan (19247614)

“…</li></ul><h2>Running the Code</h2><h3><b>Data processing and feature extraction</b></h3><pre>python run_process.py</pre><p dir="ltr">This step processes trajectory data, extracts graph node features and edge features, and saves them as CSV files in the `processed_data` folder.…”

<b>Data Availability</b> by Bonyad Ahmadi (20750327)

“…</p><p dir="ltr">Model Outputs & Validation:</p><p dir="ltr">Pre-trained MDN model weights and architecture files.…”

<b>Data Availability</b> by Bonyad Ahmadi (20750327)

“…</p><p dir="ltr">Model Outputs & Validation:</p><p dir="ltr">Pre-trained MDN model weights and architecture files.…”

Gene Editing using Transformer Architecture by Rishabh Garg (5261744)

“…</p><p dir="ltr">Once TASAG detects a deviation from a reference sequence (e.g., the H-Bot sequence), it facilitates on-screen gene editing, enabling targeted mutations or the insertion of desired genes. Implementation requires Python and deep learning frameworks like TensorFlow or PyTorch, with optional use of Biopython for genetic sequence handling. …”

RealBench: A Repo-Level Code Generation Benchmark Aligned with Real-World Software Development Practices by RealBench RealBench (22275393)

“…<br>The supplemented materials for Review #2570C is in the ''Response to Review #2570C.md''.<br><br><br></pre><p dir="ltr">This repository contains the implementation of **RealBench**, a comprehensive benchmark and evaluation framework for repository-level code aligned with real-world software development practices.…”

Data and some code used in the paper:<b>Expansion quantization network: A micro-emotion detection and annotation framework</b> by Zhou (20184816)

“…</p><p dir="ltr">GPU：NVIDIA GeForce RTX 3090 GPU</p><p dir="ltr">Bert-base-cased pre-trained model: https://huggingface.co/google-bert/bert-base-cased</p><p dir="ltr">python=3.7，pytorch=1.9.0，cudatoolkit=11.3.1，cudnn=8.9.7.29.…”

The artifacts and data for the paper "DD4AV: Detecting Atomicity Violations in Interrupt-Driven Programs with Guided Concolic Execution and Filtering" (OOPSLA 2025) by zixuan yuan (17602152)

“…</p><h3><b>Installation</b></h3><h4><b>install Dependencies</b></h4><p dir="ltr">Our artifact depends on several packages, please run the following command to install all necessary dependencies.</p><pre><pre>sudo apt-get install -y wget git build-essential python3 python python-pip python3-pip tmux cmake libtool libtool-bin automake autoconf autotools-dev m4 autopoint libboost-dev help2man gnulib bison flex texinfo zlib1g-dev libexpat1-dev libfreetype6 libfreetype6-dev libbz2-dev liblzo2-dev libtinfo-dev libssl-dev pkg-config libswscale-dev libarchive-dev liblzma-dev liblz4-dev doxygen libncurses5 vim intltool gcc-multilib sudo --fix-missing<br></pre></pre><pre><pre>pip install numpy && pip3 install numpy && pip3 install sysv_ipc<br></pre></pre><h4><b>Download the Code</b></h4><p dir="ltr">Download <b>DD4AV</b> from the Figshare website to your local machine and navigate to the project directory:</p><pre><pre>cd DD4AV<br></pre></pre><h4><b>Configure Environment and Install the Tool</b></h4><p dir="ltr">For convenience, we provide shell scripts to automate the installation process. …”

Data and code for: Automatic fish scale analysis by Christian Vogelmann (21646472)

“…</i></li></ul></li><li><b>README.txt</b> – detailed file explanations and usage instructions</li></ul><p dir="ltr">The full statistical analysis and visualization pipeline is implemented in R and hosted on GitHub:<br>https://github.com/Birdy332/Automatic-fish-scale-analysis-r-scripts</p><p dir="ltr"><br></p><p dir="ltr">All figures shown in the manuscript can be reproduced using these scripts and the datasets provided here.…”

Ambient Air Pollutant Dynamics (2010–2025) and the Exceptional Winter 2016–17 Pollution Episode: Implications for a Uranium/Arsenic Exposure Event by Thomas Clemens Carmine (19756929)

“…The full implementation is detailed in the accompanying Python script (Imputation_Air_Pollutants_NABEL.py). …”

IGD-cyberbullying-detection-AI by Bryan James (19921044)

“…</p><h2>Requirements</h2><p dir="ltr">To run this code, you'll need the following dependencies:</p><ul><li>Python 3.x</li><li>TensorFlow</li><li>scikit-learn</li><li>pandas</li><li>numpy</li><li>matplotlib</li><li>imbalanced-learn</li></ul><p dir="ltr">You can install the required dependencies using the provided <code>requirements.txt</code> file.…”

Mean Annual Habitat Quality and Its Driving Variables in China (1990–2018) by ChenXi Zhu (21374876)

“…</p><p dir="ltr">(HQ: Habitat Quality; CZ: Climate Zone; FFI: Forest Fragmentation Index; GPP: Gross Primary Productivity; Light: Nighttime Lights; PRE: Mean Annual Precipitation Sum; ASP: Aspect; RAD: Solar Radiation; SLOPE: Slope; TEMP: Mean Annual Temperature; SM: Soil Moisture)</p><p dir="ltr"><br>A Python script used for modeling habitat quality, including mean encoding of the categorical variable climate zone (CZ), multicollinearity testing using Variance Inflation Factor (VIF), and implementation of four machine learning models to predict habitat quality.…”