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.…”

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. …”

Things to do after installing mdciao. by Guillermo Pérez-Hernández (21156182)

Overview of the MD datasets used in S2 Fig. by Guillermo Pérez-Hernández (21156182)

“…<div><p>We present mdciao, an open-source command line tool and Python Application-Programming-Interface (API) for easy, one-shot analysis and representation of molecular dynamics (MD) simulation data. …”

Expanding Access to Advanced Scientific Workflows: The NSF Unidata Science Gateway Leverages Innovative Capabilities of the NSF Jetstream2 Cloud for Atmospheric Science Education by Julien Chastang (640650)

“…</p><p><br></p><p dir="ltr">Since 2020, the NSF Unidata Science Gateway has hosted pre-configured PyAOS (Python for Atmosphere and Ocean Science) JupyterHub servers to facilitate the learning experiences of over 1,830 students across 24 universities, regional workshops, and AMS workshops. …”

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

“…The package includes the unified parameter set {κA, E0, f_ent, A_CP}, the GoP decoherence kernel, ΛCDM baselines, and standardized analysis tools. Source code is available at:</p><p dir="ltr"><b>https://github.com/Jwaters290/GoP-Void-Galaxy-Testbed</b></p><p dir="ltr"><br>_____________________________________________________<br><br>This preprint examines two newly released observational studies of isolated void-center galaxies—<b>VGS 12</b> (arXiv:2511.00235) and <b>NGC 6789</b> (arXiv:2511.07041)—and evaluates them against the <i>a priori</i>, pre-DESI predictions of the <b>Gravity of Probability (GoP)</b> framework. …”

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. …”

Trustworthy and Ethical AI for Intrusion Detection in Healthcare IoT (IoMT) Systems: An Agentic Decision Loop Framework by ibrahim adabara (22107287)

“…</p><h2>️ Repository Structure</h2><pre><pre>agentic-ethical-ids-healthcare/<br>│<br>├── src/ # Source code for model, rule engine, and agent<br>│ ├── train_agent.py<br>│ ├── ethical_engine.py<br>│ ├── detector_model.py<br>│ └── utils/<br>│<br>├── data/ # Links or sample data subsets<br>│ ├── CIC-IoMT-2024/ <br>│ └── CSE-CIC-IDS2018/<br>│<br>├── notebooks/ # Jupyter notebooks for training and analysis<br>│<br>├── models/ # Pretrained model checkpoints (.pth, .pkl)<br>│<br>├── results/ # Evaluation outputs and figures<br>│<br>├── requirements.txt # Python dependencies<br>├── LICENSE # MIT License for open research use<br>└── README.md # Project documentation<br></pre></pre><h2>⚙️ Setup and Installation</h2><p dir="ltr">Clone the repository and set up your environment:</p><pre><pre>git clone https://github.com/ibrahimadabara01/agentic-ethical-ids-healthcare.git<br>cd agentic-ethical-ids-healthcare<br>python -m venv venv<br>source venv/bin/activate # On Windows: venv\Scripts\activate<br>pip install -r requirements.txt<br></pre></pre><h2> Datasets</h2><p dir="ltr">This project uses three datasets:</p><table><tr><th><p dir="ltr">Dataset</p></th><th><p dir="ltr">Purpose</p></th><th><p dir="ltr">Source</p></th></tr><tr><td><b>CIC-IoMT 2024</b></td><td><p dir="ltr">Primary IoMT intrusion detection dataset</p></td><td><a href="https://www.unb.ca/cic/datasets/index.html" rel="noopener" target="_new">Canadian Institute for Cybersecurity</a></td></tr><tr><td><b>CSE-CIC-IDS2018</b></td><td><p dir="ltr">Domain-shift evaluation</p></td><td><a href="https://www.unb.ca/cic/datasets/ids-2018.html" rel="noopener" target="_new">CIC Dataset Portal</a></td></tr><tr><td><b>MIMIC-IV (Demo)</b></td><td><p dir="ltr">Clinical context signals</p></td><td><a href="https://physionet.org/content/mimic-iv-demo/2.2/" rel="noopener" target="_new">PhysioNet</a></td></tr></table><blockquote><p dir="ltr">⚠️ Note: All datasets are publicly available. …”

Not All local LLMs Are Equal: A Benchmark of Energy and Performance by Simão Cunha (19712455)

“…This CLI application also evaluates the quality and functionality of the generated code by benchmarking LLMs such as HumanEval-X and MBPP+.<br><br><br>#### Pre-requisites<br><br>- **Python 3.9** or newer (mandatory)<br>- **Docker** (mandatory)<br>- **CMake** (mandatory)<br>- **Debian-based Linux distributions**, such as Ubuntu (recommended)<br>- **Intel CPU** (highly recommended)<br><br>To facilitate the installation of the pre-requisites, you can execute the following script. …”

Sample subject data. by Julia Jurkowska (22192327)

“…<p dir="ltr">Sample subject data used as a use-case in "Multi-Source Neural Activity Indices and Spatial Filters for EEG/MEG Inverse Problem: An Extension to MNE-Python". …”

<b>Rethinking neighbourhood boundaries for urban planning: A data-driven framework for perception-based delineation</b> by Shubham Pawar (22471285)

“…</p><p dir="ltr"><b>Input:</b></p><ul><li><code>perception_module/output_data/glasgow_perception.nc</code> - Perception scores from Step 2</li></ul><p dir="ltr"><b>Command:</b></p><pre><pre>python cluster_module/cluster_perceptions.py<br></pre></pre><p dir="ltr"><b>Output:</b></p><ul><li><code>cluster_module/output_data/clusters.shp</code> - Shapefile of neighbourhood boundaries</li></ul><h2>Quick Test</h2><p dir="ltr">To test the clustering module with demo data:</p><pre><pre>python cluster_module/cluster_perceptions.py<br></pre></pre><p dir="ltr">This uses the pre-computed embeddings from <code>perception_module/pred_output/20250626_*_embedding_efficientnet.nc</code>.…”

<b>Tau Degradome Foundation Atlas</b> by Axel Petzold (7076261)

“…</li><li><b>Compression format:</b> Datasets are distributed in <code>.tar.gz</code> format and can be extracted with:</li></ul><pre></pre><pre><code>tar -xvzf FILENAME.tar.gz</code><br><br></pre><p dir="ltr"><b>Reproducibility and Methods</b><br>All datasets can be fully regenerated using open-source software tools, including <b>Python</b> and <b>SAS</b>. …”

A Human Brain Map of Mitochondrial Respiratory Capacity and Diversity by Eugene V. Mosharov (20543317)

“…Supplementary File 1 | Extended discussion of the loadings for MRI-based metrics and mitochondria biology from Extended Table 1. Source Data 1 | Analysis of snRNAseq data. Detailed markdown of the single nucleus RNA sequencing (snRNAseq) analysis, including the code for data pre-processing, visualization, cluster identification and mitochondrial analysis. …”

Scripts_scParadise_article by Vadim Chechekhin (20569496)

“…To address these challenges, we introduce scParadise, an open-source and flexible Python package comprising three integrated tools: scAdam for tissue-specific, multi-task cell type annotation; scEve for cross-tissue, cross-species modality imputation; and scNoah for benchmarking cell type annotation and modality imputation methods. …”

Wolframin Degradome Foundation Atlas by Axel Petzold (7076261)

“…To extract, use the following command in a bash terminal:</p><pre><pre>tar -xvJf Wolframin_Degradome_Foundation_Atlas_v3.tar.gz<br></pre></pre><p dir="ltr"><b>Codes</b><br>Dataset generation is fully reproducible using three open-source tools: <b>Python, BLAST, and SAS</b>. …”

ImproBR Replication Package by Anonymus (18533633)

“…**Setup Virtual Environment**<br> Create virtual environment:<br> ```bash<br> python -m venv improbr_env<br> ```<br> <br> Activate virtual environment:<br> ```bash<br> source improbr_env/bin/activate # Linux/Mac<br> # improbr_env\Scripts\activate # Windows<br> ```<br><br>3. …”

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

“…</p><pre>pip install -r requirements.txt</pre><h2>Datasets</h2><p dir="ltr">The repository contains preprocessed datasets for both <b>Cyberbullying detection</b> and <b>IGD</b>. …”

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

“…<pre>We greatly appreciate your thoughtful feedback on our work! …”

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)

“…</p><p dir="ltr"><br><b>Data Source:</b><br>All primary air quality data originates from the NABEL network, Bundesamt für Umwelt (BAFU), Switzerland ⁵<br></p><p dir="ltr"><br><b>Files Included (6 total)</b><br></p><p><br></p><ul><li><b>AP_NABEL_Zurich_2010–2025.xlsx</b> (Microsoft Excel Spreadsheet)</li><li><ul><li>Raw_AP_Data_Zurich_2010-25: Unaltered daily measurements and derived ratios (Nov 2009-mid 2025), contains original missing values including blanks for PM₂.₅ pre-Jan 2016.…”

AMBILE_Shah_Jo_Risalo_Labeled by Abdul Majid Bhurgri Institute of Language Engineering Hyderabad (22197295)

“…</li><li>Open the CSV file using <b>Python</b> or <b>Excel</b>:</li></ol><pre><pre>import pandas as pd <br>df = pd.read_csv("Bhittaipedia Risalo -(25-08-25).csv") <br>print(df.head()) <br></pre></pre><p dir="ltr">The dataset is sourced from the <a href="https://bhittaipedia.org/sur-kalyan/d-1/1" rel="noopener" target="_new"><b>AMBILE Bhittaipedia project</b></a>.…”