IFDD-Surf is software for generating surface meshes from the results of large-scale numerical simulations in distributed-memory parallel environments. It converts simulation results into scalar fields based on implicit representations, such as signed-distance fields and particle-number-density fields, and generates triangular meshes using an MPI-parallelized implementation of the marching cubes algorithm. Each process locally handles domain-decomposed distributed data, while communication with neighboring subdomains suppresses the formation of spurious facets at subdomain boundaries. The software can process results from both mesh-based and particle-based methods, and the generated surface meshes can be exported to rendering environments such as Unreal Engine 5.
UzbekTaggerBERT is an open-source Python library for word-level part-of-speech tagging of Uzbek text using a fine-tuned Tahrirchi-BERT model. The package normalizes common apostrophe variants, extracts word spans, aligns Transformer subword predictions through character-span overlap, aggregates overlapping logits, and deterministically labels punctuation. It provides Python and command-line interfaces with slash, JSON, and CoNLL-like outputs. Five-fold cross-validation on UzbekPOS yielded mean accuracy and weighted F1 of 0.9784 ± 0.0023 and 0.9784 ± 0.0023, respectively; the best validation fold (Fold 4) reached 0.9810 and 0.9811. The release includes documentation, tests, benchmarks, PyPI distribution, and a Zenodo archive.
The proposed Pyp-EEG package is a Python open-source pipeline that makes EEG preprocessing and analysis transparent, modular, and reproducible for practitioners. The pipeline integrates artifact removal using independent component analysis (ICA) with automatic classification via ICLabel, absolute and relative spectral power computation by frequency band and region of interest (ROI), temporal analysis based on fixed windows, and automated execution of basic statistical tests. Additionally, an optional module for directed acyclic network inference using the Multiregression Dynamic Model (MDM) enables effective connectivity learning between brain regions over time, based on the power time series generated.
PhishEvolve is an open-source software package for phishing URL inspection. The software integrates a browser extension, a cloud-hosted blacklist repository containing more than 7000 phishing URLs, and an ONNX-compatible inference service within a cloud–edge architecture. Source code, deployment documentation, software artifacts, and example configurations are publicly available through GitHub and Zenodo. The software has been applied in research on phishing URL detection and browser-assisted cybersecurity systems. PhishEvolve provides software components for URL inspection, blacklist-assisted verification, and ONNX-based URL analysis in cybersecurity research and software development activities.
This paper presents a browser-based software tool for post hoc kinematic analysis of upper-limb rehabilitation exercises performed in immersive virtual reality. The tool was conceived to transform raw telemetry logs into clinically interpretable visualizations and quantitative indicators without requiring ad hoc scripts, laboratory-grade motion-capture systems, or complex installation workflows. CSV files generated during therapy sessions are loaded directly into a client-side web interface, where the software supports temporal segmentation through a region-of-interest selection, interactive trajectory inspection in 2D and 3D, time-series exploration, and automatic computation of movement-quality descriptors such as functional range-of-motion volume, velocity-derived indicators, smoothness-related measures, tortuosity, and head–hand relationship metrics to estimate compensatory behavior. The software consolidates the analytical layer of the Rehab-Immersive ecosystem and supports the interpretation of datasets used in previously published studies on framework-based rehabilitation environments (Herrera et al., 2023) safe bimanual goalkeeper training (Herrera et al., 2024), and adaptive task execution in personalized virtual environments (Herrera et al., 2025). By converting VR telemetry into reusable quantitative evidence, the tool facilitates longitudinal follow-up, improves reproducibility across studies, and lowers the barrier for clinicians and researchers to exploit kinematic data in rehabilitation research.
