Python Implementation of HSGAdviser Chatbot: AI model for Sustainable Education by Suha Assayed (22454038)

“…<p dir="ltr">This repository contains the Python source code and model implementation for HSGAdviser, an AI speech assistant designed to provide personalized college and career guidance for high school students through conversational AI. …”

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

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

“…</li><li>`test_GTN.py`: Contains the code for model inference and lane extraction.</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.…”

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

“…<br>│ │ └── uml_dag.py # UML dependency graph analysis.<br>│ ├── model_gen/ # Code generation using various LLMs.<br>│ │ ├── generate/ # LLM inference implementations.…”

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

“…</p><h3>Includeed in this repository:</h3><ul><li><b>Raw data files:</b></li><li><code>comparison_all_scales.csv</code> – comparison_all_scales.csv - manually verified vs. automated measurements of 1095 coregonid scales</li></ul><ul><li><ul><li><code>Validation_data.csv</code> – manually measured scale data under binocular</li><li><code>Parameter_correction_numeric.csv</code> – calibration data (scale radius vs. fish length/weight)</li></ul></li><li><b>Statistical results:</b></li><li><ul><li><code>comparison_stats_core_variables.csv</code> – verification statistics (bias, relative error, limits of agreement)</li><li><code>Validation_statistics.csv</code> – summary statistics and model fits (manual vs. automated)</li></ul></li><li><b>Executable script (not GUI):</b></li><li><ul><li><code>Algorithm.py</code> – core processing module for scale feature extraction<br>→ <i>Note: The complete Coregon Analyzer application (incl. …”

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.…”

Void-Center Galaxies and the Gravity of Probability Framework: Pre-DESI Consistency with VGS 12 and NGC 6789 by Jordan Waters (21620558)

“…<br><br><br><b>ORCID ID: https://orcid.org/0009-0009-0793-8089</b><br></p><p dir="ltr"><b>Code Availability:</b></p><p dir="ltr"><b>All Python tools used for GoP simulations and predictions are available at:</b></p><p dir="ltr"><b>https://github.com/Jwaters290/GoP-Probabilistic-Curvature</b><br><br>The Gravity of Probability framework is implemented in this public Python codebase that reproduces all published GoP predictions from preexisting DESI data, using a single fixed set of global parameters. …”

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

“…</p><h2>Dependencies</h2><p dir="ltr">In order to run the code the following dependencies must be met:</p><pre><pre>- Python 3 should be installed. …”

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

“…</li></ul><h2>Installation</h2><ol><li>Clone the repository:<pre>git clone https://github.com/BryanSJamesDev/IGD-cyberbullying-detection-AI<br>cd IGD-cyberbullying-detection-AI</pre></li><li>Install the required dependencies:<pre>pip install -r requirements.txt</pre></li></ol><h2>Running the Code</h2><ol><li><b>Cyberbullying Prediction</b>:</li><li><ul><li>Open the <code>Cyberbullying.ipynb</code> notebook and run the cells in order to train and evaluate the deep learning models on the provided datasets.…”

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)

“…Includes imputation statistics, data dictionary, and the Python imputation code (Imputation_Air_Pollutants_NABEL.py). …”