发布求助
文献互助 智能选刊 最新文献

Software Impacts最新文献

筛选
英文 中文
EEG-learning: An integrated Python framework for experimental studies applying causal connectivity inference, preprocessing and spectral–temporal analysis 脑电图学习：一个集成的Python框架，用于应用因果连接推理，预处理和频谱时间分析的实验研究
IF 1.5
Software Impacts Pub Date : 2026-09-01 Epub Date: 2026-09-02 DOI: 10.1016/j.simpa.2026.100859
Paloma Alves, Antonio Neto, Melyssa Galdino, Lilia Costa, Diego C. Nascimento, Matheus Santos, Carla Minervino
{"title":"EEG-learning: An integrated Python framework for experimental studies applying causal connectivity inference, preprocessing and spectral–temporal analysis","authors":"Paloma Alves,&nbsp;Antonio Neto,&nbsp;Melyssa Galdino,&nbsp;Lilia Costa,&nbsp;Diego C. Nascimento,&nbsp;Matheus Santos,&nbsp;Carla Minervino","doi":"10.1016/j.simpa.2026.100859","DOIUrl":"10.1016/j.simpa.2026.100859","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"29 ","pages":"Article 100859"},"PeriodicalIF":1.5,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148934891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
PhishEvolve: Open-source research software for reproducible phishing URL detection studies PhishEvolve：用于可重复的网络钓鱼URL检测研究的开源研究软件
IF 1.5
Software Impacts Pub Date : 2026-09-01 Epub Date: 2026-08-31 DOI: 10.1016/j.simpa.2026.100858
Dam Minh Linh, Han Minh Chau, Huynh Trong Thua, Tran Cong Hung
{"title":"PhishEvolve: Open-source research software for reproducible phishing URL detection studies","authors":"Dam Minh Linh,&nbsp;Han Minh Chau,&nbsp;Huynh Trong Thua,&nbsp;Tran Cong Hung","doi":"10.1016/j.simpa.2026.100858","DOIUrl":"10.1016/j.simpa.2026.100858","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"29 ","pages":"Article 100858"},"PeriodicalIF":1.5,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148934890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
GenForge: A Multi-population Genetic Programming framework with Semantic-Preserving Feature Partitioning for classification and regression tasks GenForge：一个基于语义保持特征划分的多种群遗传规划框架
IF 1.2
Software Impacts Pub Date : 2026-04-01 Epub Date: 2026-01-28 DOI: 10.1016/j.simpa.2026.100812
Mohammad Sadegh Khorshidi , Navid Yazdanjue , Hassan Gharoun , Mohammad Reza Nikoo , Fang Chen , Amir H. Gandomi
{"title":"GenForge: A Multi-population Genetic Programming framework with Semantic-Preserving Feature Partitioning for classification and regression tasks","authors":"Mohammad Sadegh Khorshidi ,&nbsp;Navid Yazdanjue ,&nbsp;Hassan Gharoun ,&nbsp;Mohammad Reza Nikoo ,&nbsp;Fang Chen ,&nbsp;Amir H. Gandomi","doi":"10.1016/j.simpa.2026.100812","DOIUrl":"10.1016/j.simpa.2026.100812","url":null,"abstract":"<div><div>GenForge is an open-source Python package for interpretable symbolic modeling through multi-population genetic programming. It unifies regression, classification, and semantic feature partitioning into a single evolutionary learning framework. By integrating multi-gene symbolic regression, ensemble evolution, and Semantic-Preserving Feature Partitioning (SPFP), GenForge enables high-fidelity modeling while maintaining transparency and parsimony. The package provides modules for symbolic regression (gpregressor), classification (gpclassifier), and feature partitioning (SPFPPartitioner), each with reproducible example scripts and diagnostic visualization tools. GenForge supports reproducible research and educational use in explainable AI, symbolic learning, and multi-view ensemble modeling.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"27 ","pages":"Article 100812"},"PeriodicalIF":1.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Software architecture description in original software publications 原始软件出版物中的软件体系结构描述
IF 1.2
Software Impacts Pub Date : 2026-04-01 Epub Date: 2025-11-28 DOI: 10.1016/j.simpa.2025.100802
Tomasz Górski
{"title":"Software architecture description in original software publications","authors":"Tomasz Górski","doi":"10.1016/j.simpa.2025.100802","DOIUrl":"10.1016/j.simpa.2025.100802","url":null,"abstract":"<div><div>A software architecture description is a work product that reveals software architecture. An architecture view manifests the system architecture from a specific perspective. In publications presenting original software, it is crucial to introduce the functions implemented by the software and identify its users. The structure and operation of the software should also be depicted. However, many publications contain drawings that often combine content from several views. Therefore, the paper introduces a method for describing software architecture in Use Cases and Logical views of the 1+5 model. The method expresses the architecture of a new software package for real estate sales.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"27 ","pages":"Article 100802"},"PeriodicalIF":1.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145624935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
GO-HKP: A Gene Ontology hierarchy-driven framework for enzyme kcat prediction GO-HKP：用于酶kcat预测的基因本体层次驱动框架
IF 1.2
Software Impacts Pub Date : 2026-04-01 Epub Date: 2025-12-01 DOI: 10.1016/j.simpa.2025.100803
Qinghan Meng , Zhitao Mao , Hao Chen , Yuanyuan Huang , Hongwu Ma
{"title":"GO-HKP: A Gene Ontology hierarchy-driven framework for enzyme kcat prediction","authors":"Qinghan Meng ,&nbsp;Zhitao Mao ,&nbsp;Hao Chen ,&nbsp;Yuanyuan Huang ,&nbsp;Hongwu Ma","doi":"10.1016/j.simpa.2025.100803","DOIUrl":"10.1016/j.simpa.2025.100803","url":null,"abstract":"<div><div>GO-HKP is a Gene Ontology hierarchy-driven framework for predicting enzyme turnover numbers (<span><math><msub><mrow><mi>k</mi></mrow><mrow><mi>cat</mi></mrow></msub></math></span>) with improved coverage, generalizability, and interpretability. It integrates curated UniProt data, ontology-based <span><math><msub><mrow><mi>k</mi></mrow><mrow><mi>cat</mi></mrow></msub></math></span> propagation, and sequence-driven GO annotation (DeepGO-SE) to infer <span><math><msub><mrow><mi>k</mi></mrow><mrow><mi>cat</mi></mrow></msub></math></span> for both annotated and novel enzymes. Benchmarking across four genome-scale metabolic models demonstrated substantial improvements in reaction coverage — by 56.67%, 25.1%, 16.0%, and 14.5% — compared with existing methods, highlighting its strong gap-filling capability. GO-HKP offers a biologically grounded, scalable, and transparent approach, supporting applications in metabolic engineering, drug discovery, and systems biology. The framework and Python package are available via GitHub for broad usability and reproducibility.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"27 ","pages":"Article 100803"},"PeriodicalIF":1.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
SMCS: A lightweight MobileNet-based framework for skin cancer classification, segmentation, and explanation SMCS：一个轻量级的基于mobilenet的皮肤癌分类、分割和解释框架
IF 1.2
Software Impacts Pub Date : 2026-04-01 Epub Date: 2025-12-15 DOI: 10.1016/j.simpa.2025.100804
Thuan Van Tran, Triet Minh Nguyen, Quy Thanh Lu
{"title":"SMCS: A lightweight MobileNet-based framework for skin cancer classification, segmentation, and explanation","authors":"Thuan Van Tran,&nbsp;Triet Minh Nguyen,&nbsp;Quy Thanh Lu","doi":"10.1016/j.simpa.2025.100804","DOIUrl":"10.1016/j.simpa.2025.100804","url":null,"abstract":"<div><div>Skin is an important part of the guardian system, which helps to protect us from harmful factors such as physical impact, bacteria, viruses, and especially daily ultraviolet (UV) radiation. However, the changing of the environment in the present era creates prolonged exposure to UV, which can damage the skin and increase the risk of skin cancer. Thus, a skin cancer classification and detection framework called SMCS (Sampling in MobileNet for Skin Classification) was published by taking the power of artificial intelligence and deep learning. In this pipeline, skin illnesses can be discovered early, which aids doctors and patients in diagnosis and treatment while reducing both time and cost.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"27 ","pages":"Article 100804"},"PeriodicalIF":1.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
BEARING-FDD: An early detection and diagnosis tool for bearing faults in rotating machinery 轴承fdd：一种旋转机械轴承故障的早期检测和诊断工具
IF 1.2
Software Impacts Pub Date : 2026-04-01 Epub Date: 2026-01-03 DOI: 10.1016/j.simpa.2025.100810
L. Magadán , C. Ruiz-Cárcel , J.C. Granda , F.J. Suárez , A. Menéndez-González , A. Starr
{"title":"BEARING-FDD: An early detection and diagnosis tool for bearing faults in rotating machinery","authors":"L. Magadán ,&nbsp;C. Ruiz-Cárcel ,&nbsp;J.C. Granda ,&nbsp;F.J. Suárez ,&nbsp;A. Menéndez-González ,&nbsp;A. Starr","doi":"10.1016/j.simpa.2025.100810","DOIUrl":"10.1016/j.simpa.2025.100810","url":null,"abstract":"<div><div>This paper presents the design and implementation of a web tool offering an innovative method for detecting, diagnosing and classifying bearing faults in rotating machinery under limited data conditions, providing explainability and interpretability of the results obtained. The tool uses a machine learning model to detect and diagnose bearing faults. A monotonic smoothed stacked autoencoder builds a health indicator without requiring feature extraction, making the tool useful without the need for specialized staff. The tool generates explainability and interpretability reports with a correlation analysis between the health indicator and well-known engineering features and easily interpretable details on the diagnosed faults. The tool includes the option to use preloaded state-of-the-art datasets, while also allowing users to upload their own datasets to analyze vibration data from real industrial equipment.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"27 ","pages":"Article 100810"},"PeriodicalIF":1.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
AI-assisted issue report classification 人工智能辅助问题报告分类
IF 1.2
Software Impacts Pub Date : 2026-04-01 Epub Date: 2026-02-07 DOI: 10.1016/j.simpa.2026.100816
Muhammad Laiq
{"title":"AI-assisted issue report classification","authors":"Muhammad Laiq","doi":"10.1016/j.simpa.2026.100816","DOIUrl":"10.1016/j.simpa.2026.100816","url":null,"abstract":"<div><div>This paper introduces a tool for classifying software issue reports using machine learning techniques. The tool implements traditional machine learning techniques, AutoML, and more advanced large-language models to support automated categorization of issue reports. The tool has been evaluated on datasets from multiple open-source and closed-source software projects. The tool has also been evaluated in real industrial settings. The evaluation results and the feedback from practitioners indicate that the tool has the potential to assist practitioners in the early classification of issue reports.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"27 ","pages":"Article 100816"},"PeriodicalIF":1.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Rosetta-XAI: An automated evaluation and explainability framework for code translation models Rosetta-XAI：用于代码翻译模型的自动评估和可解释性框架
IF 1.2
Software Impacts Pub Date : 2026-04-01 Epub Date: 2026-01-19 DOI: 10.1016/j.simpa.2026.100811
Vishnu S. Pendyala, Neha Bais Thakur
{"title":"Rosetta-XAI: An automated evaluation and explainability framework for code translation models","authors":"Vishnu S. Pendyala,&nbsp;Neha Bais Thakur","doi":"10.1016/j.simpa.2026.100811","DOIUrl":"10.1016/j.simpa.2026.100811","url":null,"abstract":"<div><div>This paper presents Rosetta-XAI, a comprehensive software framework for evaluating and explaining Large Language Model (LLM) behavior in cross-language code conversion tasks. The system implements a four-stage automated pipeline: (1) code generation by LLMs accessed through the Ollama API inference service, (2) regex-based extraction of code blocks from markdown responses, (3) language-specific syntax and compilation validation with temporary artifact management, and (4) execution with timeout protections and CSV-based checkpoint recovery. The framework supports evaluation of 15 specialized code LLMs (1.3B–34B parameters), including DeepSeek Coder, Code Llama, CodeGemma, and Granite Code across 17 Rosetta Code programming tasks, generating 42 bidirectional conversion pairs among seven languages (C, C++, Go, Java, JavaScript, Python, Rust). Beyond traditional pass@1 accuracy metrics, the system incorporates explainability analysis through Shapley Value Sampling and Feature Ablation techniques implemented via Captum and PyTorch, enabling researchers to quantify token-level feature importance during translation. All pipeline components include XAI-enhanced variants supporting follow-up question analysis for interpretability studies. Built using Python with pandas for metrics aggregation and subprocess management for multi-language execution, the modular architecture separates extraction, validation, and execution concerns. Results are systematically organized into structured directories tracking accepted code, compilation failures, syntax errors, and execution outputs, with comprehensive metrics exported to CSVs for reproducible research and comparative model analysis.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"27 ","pages":"Article 100811"},"PeriodicalIF":1.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Lcpy: An open-source python package for parametric and dynamic life cycle assessment and life cycle costing analysis Lcpy：一个用于参数化和动态生命周期评估以及生命周期成本分析的开源python包
IF 1.2
Software Impacts Pub Date : 2026-04-01 Epub Date: 2025-12-05 DOI: 10.1016/j.simpa.2025.100805
Spiros Gkousis, Evina Katsou
{"title":"Lcpy: An open-source python package for parametric and dynamic life cycle assessment and life cycle costing analysis","authors":"Spiros Gkousis,&nbsp;Evina Katsou","doi":"10.1016/j.simpa.2025.100805","DOIUrl":"10.1016/j.simpa.2025.100805","url":null,"abstract":"<div><div>Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) are becoming key methods for sustainability analysis. Current software solutions usually focus on one method, omitting synergies and the provision of a holistic picture of system sustainability. Integrating LCA and LCC software with complex system models, uncertainty, and optimization tools remains a barrier for integrated techno-sustainability assessments. Lcpy is an open-source python package that enables using parametric or simulation process models, in-time projections, multiple scenarios, and flexible modelling for simple and dynamic LCA and LCC, uncertainty analysis and optimization. Visualization and storage functions are provided allowing end-to-end LCA and LCC analyses.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"27 ","pages":"Article 100805"},"PeriodicalIF":1.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信
小红书