SoftwareX最新文献

英文中文

BPMN graph transformation: A unified multi-format parser library for standardized graph-based business process model integration BPMN图转换：用于标准化的基于图的业务流程模型集成的统一多格式解析器库

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2026-02-01 DOI: 10.1016/j.softx.2026.102548

Kurnia Cahya Febryanto , Izzat Aji Androfaza , Lalu Aldo Wadagraprana , Riyanarto Sarno , Kelly Rossa Sungkono , Yeni Anistyasari , Joko Siswantoro , A Min Tjoa

Heterogeneous Business Process Model and Notation (BPMN) platforms present critical integration challenges, as approximately 75% of large enterprises employ multiple modeling tools lacking unified transformation capabilities. Existing solutions address only single-format conversions or provide limited cross-platform compatibility without comprehensive validation. This paper presents a production-ready multi-format BPMN parser library uniquely integrating intelligent format detection, dual-tier validation, and optimized graph transformation within a unified architecture. The library utilizes specialized parsers for BPMN 2.0 XML, XML Process Definition Language (XPDL) 2.2, native formats, and Microsoft Visio diagrams through a plugin-based architecture. Multi-criteria detection algorithms automatically identify source formats with 99.2% accuracy by analyzing file signatures, XML namespaces, structural patterns, and content heuristics. The dual-tier validation framework ensures structural BPMN 2.0 compliance through rule-based constraints derived from official OMG specifications and semantic consistency through metadata quality assessment based on established process modeling guidelines, surpassing existing tools that perform only syntactic validation. The transformation pipeline generates standardized Cypher queries optimized for process mining workflows. Evaluation across 127 real-world business process models demonstrates 98.7% overall parsing accuracy, with format-specific performance ranging from 97.2% (Visio) to 99.8% (BPMN XML), achieving 85% reduction in transformation time compared to manual approaches. Released as open-source software via the Python Package Index with complete documentation, the library establishes foundational infrastructure for cross-platform business process intelligence, enabling unified graph-based analytics across heterogeneous modeling ecosystems without format-specific preprocessing.

异构业务流程模型和符号（BPMN）平台提出了关键的集成挑战，因为大约75%的大型企业使用缺乏统一转换功能的多种建模工具。现有的解决方案只能处理单一格式的转换，或者在没有全面验证的情况下提供有限的跨平台兼容性。本文提出了一个生产就绪的多格式BPMN解析器库，该解析器库在统一的体系结构中独特地集成了智能格式检测、双层验证和优化的图转换。该库通过基于插件的体系结构，为BPMN 2.0 XML、XML过程定义语言（XML Process Definition Language， XPDL） 2.2、本机格式和Microsoft Visio图表使用专门的解析器。多标准检测算法通过分析文件签名、XML名称空间、结构模式和内容启发式，以99.2%的准确率自动识别源格式。双层验证框架通过派生自官方OMG规范的基于规则的约束确保结构化的BPMN 2.0遵从性，并通过基于已建立的流程建模指导方针的元数据质量评估确保语义一致性，超越了仅执行语法验证的现有工具。转换管道生成针对流程挖掘工作流优化的标准化Cypher查询。对127个实际业务流程模型的评估表明，总体解析准确率为98.7%，特定格式的性能范围从97.2% （Visio）到99.8% (BPMN XML)，与手动方法相比，转换时间减少了85%。该库通过Python Package Index作为开源软件发布，带有完整的文档，为跨平台业务流程智能建立了基础基础设施，支持跨异构建模生态系统的基于图形的统一分析，而无需特定格式的预处理。

{"title":"BPMN graph transformation: A unified multi-format parser library for standardized graph-based business process model integration","authors":"Kurnia Cahya Febryanto , Izzat Aji Androfaza , Lalu Aldo Wadagraprana , Riyanarto Sarno , Kelly Rossa Sungkono , Yeni Anistyasari , Joko Siswantoro , A Min Tjoa","doi":"10.1016/j.softx.2026.102548","DOIUrl":"10.1016/j.softx.2026.102548","url":null,"abstract":"<div><div>Heterogeneous Business Process Model and Notation (BPMN) platforms present critical integration challenges, as approximately 75% of large enterprises employ multiple modeling tools lacking unified transformation capabilities. Existing solutions address only single-format conversions or provide limited cross-platform compatibility without comprehensive validation. This paper presents a production-ready multi-format BPMN parser library uniquely integrating intelligent format detection, dual-tier validation, and optimized graph transformation within a unified architecture. The library utilizes specialized parsers for BPMN 2.0 XML, XML Process Definition Language (XPDL) 2.2, native formats, and Microsoft Visio diagrams through a plugin-based architecture. Multi-criteria detection algorithms automatically identify source formats with 99.2% accuracy by analyzing file signatures, XML namespaces, structural patterns, and content heuristics. The dual-tier validation framework ensures structural BPMN 2.0 compliance through rule-based constraints derived from official OMG specifications and semantic consistency through metadata quality assessment based on established process modeling guidelines, surpassing existing tools that perform only syntactic validation. The transformation pipeline generates standardized Cypher queries optimized for process mining workflows. Evaluation across 127 real-world business process models demonstrates 98.7% overall parsing accuracy, with format-specific performance ranging from 97.2% (Visio) to 99.8% (BPMN XML), achieving 85% reduction in transformation time compared to manual approaches. Released as open-source software via the Python Package Index with complete documentation, the library establishes foundational infrastructure for cross-platform business process intelligence, enabling unified graph-based analytics across heterogeneous modeling ecosystems without format-specific preprocessing.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102548"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Simet: Synthetic image metrics - a synthetic image evaluation framework Simet：合成图像度量-一个合成图像评估框架

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2026-02-01 DOI: 10.1016/j.softx.2026.102526

O. Agost , F. Aran , J. Rius , P. Fraile , I. Barri , J. Vilaplana , J. Mateo

Simet provides a modular framework designed for the rigorous evaluation of synthetic image datasets. The framework integrates data provisioning, preprocessing, feature extraction, and complementary metrics, including Fréchet Inception Distance (FID), generative Precision/Recall, and classifier two-sample area under the receiver operating characteristic curve (ROC-AUC), within a single GPU-accelerated pipeline. A restraint mechanism enables declarative pass or fail gating. YAML- and command-line (CLI)-driven orchestration, shared feature caches, and structured logs facilitate reproducible, continuous-integration (CI)-ready workflows. Extensible abstractions, including providers, transforms, feature extractors, and metrics, allow practitioners to add new data sources or tests with minimal code. Templates support downstream utility evaluations, such as training on synthetic data and testing on real data (TSTR). Simet is positioned relative to existing toolkits, and protocols are outlined to demonstrate scalable, multidimensional evaluation of synthetic image data.

Simet提供了一个模块化框架，设计用于合成图像数据集的严格评估。该框架集成了数据提供、预处理、特征提取和互补指标，包括fr起始距离（FID）、生成精度/召回率（Precision/Recall）和接收器工作特征曲线（ROC-AUC）下的分类器双样本区域，在单个gpu加速管道中。约束机制支持声明性的通过或失败控制。YAML和命令行（CLI）驱动的编排、共享特性缓存和结构化日志有助于实现可重复的、可持续集成（CI）的工作流。可扩展的抽象，包括提供者、转换、特征提取器和度量，允许从业者用最少的代码添加新的数据源或测试。模板支持下游效用评估，例如对合成数据的培训和对真实数据的测试（TSTR）。Simet相对于现有工具包定位，并概述了协议，以演示合成图像数据的可扩展、多维评估。

引用次数: 0

TaBSA – A framework for training and benchmarking algorithms for scheduling tasks for mobile robots working in dynamic environments TaBSA -一个用于训练和基准算法的框架，用于在动态环境中工作的移动机器人的任务调度

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2026-02-01 DOI: 10.1016/j.softx.2025.102489

Wojciech Dudek , Daniel Giełdowski , Kamil Młodzikowski , Dominik Belter , Tomasz Winiarski

This article introduces a software framework for benchmarking robot task scheduling algorithms in dynamic and uncertain service environments. The system provides standardised interfaces, configurable scenarios with movable objects, human agents, tools for automated test generation, and performance evaluation. It supports both classical and AI-based methods, enabling repeatable, comparable assessments across diverse tasks and configurations. The framework facilitates diagnosis of algorithm behaviour, identification of implementation flaws, and selection or tuning of strategies for specific applications. It includes a SysML-based domain-specific language for structured scenario modelling and integrates with the ROS-based system for runtime execution. Validated on patrol, fall assistance, and pick-and-place tasks, the open-source framework is suited for researchers and integrators developing and testing scheduling algorithms under real-world-inspired conditions.

本文介绍了一个在动态和不确定服务环境下对标机器人任务调度算法的软件框架。该系统提供了标准化的接口、可配置的带有可移动对象的场景、人工代理、自动化测试生成工具和性能评估。它既支持传统方法，也支持基于人工智能的方法，可以在不同的任务和配置中进行可重复、可比较的评估。该框架有助于诊断算法行为，识别实现缺陷，以及为特定应用程序选择或调整策略。它包括一种基于sysml的领域特定语言，用于结构化场景建模，并与基于ros的系统集成，用于运行时执行。该开源框架在巡逻、坠落辅助和拾取放置任务中得到了验证，适合研究人员和集成商在现实世界启发的条件下开发和测试调度算法。

引用次数: 0

FibreApp: Mobile machine learning tool for fruit and vegetable fiber content FibreApp：果蔬纤维含量的移动机器学习工具

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2026-01-23 DOI: 10.1016/j.softx.2026.102528

Vadym Chibrikov, Justyna Cybulska, Artur Zdunek

The issue of food quality and its control has become a daily routine for humanity, driven by both health and economic reasons. Among other components, plant cell wall components—such as cellulose, hemicellulose, and pectin—have several pro-health roles in the human organism that are barely discussed in public. To address this, there is a clear need for a portable digital framework built on accurate, accessible, and scientifically-proven data. Here, our commitment was the development of FibreApp, an Android/iOS mobile application that helps users obtain data on the chemical composition of common fruit and vegetable species available on the European market. FibreApp's architecture was designed as a hybrid local/offline system that integrates on-device machine learning for visual identification with a pre-loaded, unified database of fruit and vegetable compositional parameters. A machine learning-powered livestream tool for image classification of fruits and vegetables was included in the app by rigorously following a systematic image acquisition protocol, coupled with a transfer learning approach using pre-trained feature extractors to train the machine learning models. The latter performed well despite significant changes in lighting and diverse polar orientations, as well as during polyclass image classification. FibreApp was released and field-tested, positioning it to capture a niche in improving public awareness of fruits and vegetables as a source of functional polysaccharides.

由于健康和经济原因，食品质量及其控制问题已成为人类的日常事务。在其他成分中，植物细胞壁成分——如纤维素、半纤维素和果胶——对人体有几种有益健康的作用，但很少在公众场合讨论。为解决这一问题，显然需要建立在准确、可获取和经科学证明的数据基础上的便携式数字框架。在这里，我们的承诺是开发FibreApp，这是一个Android/iOS移动应用程序，可以帮助用户获取欧洲市场上常见水果和蔬菜物种的化学成分数据。FibreApp的架构被设计为一个本地/离线混合系统，将设备上的机器学习与预加载的水果和蔬菜成分参数统一数据库集成在一起，用于视觉识别。应用程序中包含了一个机器学习驱动的水果和蔬菜图像分类直播工具，严格遵循系统图像采集协议，再加上使用预训练的特征提取器来训练机器学习模型的迁移学习方法。尽管光照和不同的极性方向发生了显著变化，但后者在polyclass图像分类过程中表现良好。FibreApp发布并进行了实地测试，定位于在提高公众对水果和蔬菜作为功能性多糖来源的认识方面占有一席之地。

{"title":"FibreApp: Mobile machine learning tool for fruit and vegetable fiber content","authors":"Vadym Chibrikov, Justyna Cybulska, Artur Zdunek","doi":"10.1016/j.softx.2026.102528","DOIUrl":"10.1016/j.softx.2026.102528","url":null,"abstract":"<div><div>The issue of food quality and its control has become a daily routine for humanity, driven by both health and economic reasons. Among other components, plant cell wall components—such as cellulose, hemicellulose, and pectin—have several pro-health roles in the human organism that are barely discussed in public. To address this, there is a clear need for a portable digital framework built on accurate, accessible, and scientifically-proven data. Here, our commitment was the development of <em>FibreApp</em>, an Android/iOS mobile application that helps users obtain data on the chemical composition of common fruit and vegetable species available on the European market. <em>FibreApp</em>'s architecture was designed as a hybrid local/offline system that integrates on-device machine learning for visual identification with a pre-loaded, unified database of fruit and vegetable compositional parameters. A machine learning-powered livestream tool for image classification of fruits and vegetables was included in the app by rigorously following a systematic image acquisition protocol, coupled with a transfer learning approach using pre-trained feature extractors to train the machine learning models. The latter performed well despite significant changes in lighting and diverse polar orientations, as well as during polyclass image classification. <em>FibreApp</em> was released and field-tested, positioning it to capture a niche in improving public awareness of fruits and vegetables as a source of functional polysaccharides.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102528"},"PeriodicalIF":2.4,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LumiX: A type-safe, data-centric python library for modern mathematical optimization LumiX：用于现代数学优化的类型安全、以数据为中心的python库

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2026-01-23 DOI: 10.1016/j.softx.2026.102533

Beyzanur Siyah , Tolga Berber

This paper presents LumiX, an open-source Python library for mathematical optimization designed for data-intensive applications. LumiX employs a data-centric, type-safe modeling paradigm in which problem data, scenario parameters, and optimization models are managed within a unified framework. Key features include Object-Relational Mapping (ORM) integration for automatic variable generation, a solver-agnostic API supporting OR-Tools, Gurobi, CPLEX, and GLPK, automatic linearization of common non-linear expressions, native goal programming, and integrated analysis tools for sensitivity, scenario, and what-if analyses. We present a multi-stage timetabling case study and a quantitative benchmark comparing LumiX against Pyomo and PuLP. The evaluation demonstrates LumiX’s position as a framework for researchers and practitioners developing data-driven optimization solutions, addressing the gap between lightweight procedural libraries and traditional Algebraic Modeling Languages (AMLs). Current limitations, including Big-M parameter sensitivity and McCormick relaxation tightness, are discussed.

本文介绍了LumiX，一个为数据密集型应用程序设计的数学优化的开源Python库。LumiX采用以数据为中心、类型安全的建模范式，在该范式中，问题数据、场景参数和优化模型在统一框架中进行管理。关键特性包括用于自动变量生成的对象关系映射（ORM）集成，支持OR-Tools、Gurobi、CPLEX和GLPK的求解器不确定API，常见非线性表达式的自动线性化，本机目标规划，以及用于灵敏度、场景和假设分析的集成分析工具。我们提出了一个多阶段的时间表案例研究和定量基准比较LumiX与Pyomo和PuLP。该评估证明了LumiX作为研究人员和从业者开发数据驱动优化解决方案的框架的地位，解决了轻量级过程库与传统代数建模语言（AMLs）之间的差距。讨论了当前的限制，包括大m参数灵敏度和麦考密克弛豫紧度。

引用次数: 0

An-augmenter: A unified platform for efficient image annotation and data augmentation 增强器：用于高效图像注释和数据增强的统一平台

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2026-01-22 DOI: 10.1016/j.softx.2026.102516

Samriddha Das, C. Igathinathane, Xin Sun

The growing reliance on AI and deep learning in vision-based applications requires efficient dataset preparation tools, however, existing solutions are often commercially licensed or lack integrated, multi-format workflows. This study presents An-Augmenter, a cross-platform, open-source software that integrates image annotation and augmentation within an offline environment. It supports YOLO, XML, and JSON formats and ensures annotation-consistent augmentation for labeled and unlabeled datasets. Processing 1200 images with all possible augmentation techniques required 50 s on a standard CPU. Validation using YOLO11n object detection model improved [email protected] from 0.905 to 0.941 on a custom egg dataset and from 0.799 to 0.825 on a public apple dataset, demonstrating improved detection performance with augmented data.

基于视觉的应用越来越依赖人工智能和深度学习，这需要高效的数据集准备工具，然而，现有的解决方案通常是商业许可的，或者缺乏集成的多格式工作流程。本研究提出了一种跨平台的开源软件an - augmenter，它集成了离线环境中的图像注释和增强。它支持YOLO、XML和JSON格式，并确保标记和未标记数据集的注释一致的增强。使用所有可能的增强技术处理1200张图像在标准CPU上需要50秒。使用YOLO11n对象检测模型的验证将[email protected]在自定义鸡蛋数据集上从0.905提高到0.941，在公共苹果数据集上从0.799提高到0.825，展示了增强数据的改进检测性能。

引用次数: 0

DEXiWare: a software development framework for building cooperative decision support systems DEXiWare：用于构建协作决策支持系统的软件开发框架

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2026-01-22 DOI: 10.1016/j.softx.2026.102531

Bojan Blažica , Vladimir Kuzmanovski , Marko Bohanec , Tanja Dergan , Aneta Ivanovska , Jurij Marinko , Robert Modic , Matevž Ogrinc , Marko Debeljak

The development of decision support systems (DSS) for agriculture increasingly relies on complex decision models, yet transforming such models into operational, user-friendly software remains challenging. DEXiWare is a software framework designed to support the development of web-based, cooperative DSS based on decision models built with the DEX (Decision EXpert) method. The framework provides a standardized workflow for operationalizing decision models, including automated model import, data handling, assessment, and scenario analysis, within a reusable backend–frontend architecture. DEXiWare integrates backend services, a web-based user interface, and a decision engine supporting top-down (goal-seeking) and bottom-up (what-if) scenario exploration. The framework is evaluated through its application in multiple agricultural DSS and through usability testing with stakeholders, demonstrating its applicability for translating qualitative decision models into operational decision support tools for sustainability assessment in agricultural production systems.

农业决策支持系统（DSS）的发展越来越依赖于复杂的决策模型，然而将这些模型转化为可操作的、用户友好的软件仍然具有挑战性。DEXiWare是一个软件框架，旨在支持基于DEX （decision EXpert）方法构建的决策模型的基于web的协作式决策支持系统的开发。该框架在可重用的后端-前端体系结构中为操作决策模型提供了一个标准化的工作流，包括自动模型导入、数据处理、评估和场景分析。DEXiWare集成了后端服务、基于web的用户界面和支持自顶向下（目标搜索）和自底向上（假设）场景探索的决策引擎。该框架通过其在多个农业决策支持系统中的应用以及与利益相关者的可用性测试进行评估，证明了其将定性决策模型转化为农业生产系统可持续性评估的操作决策支持工具的适用性。

{"title":"DEXiWare: a software development framework for building cooperative decision support systems","authors":"Bojan Blažica , Vladimir Kuzmanovski , Marko Bohanec , Tanja Dergan , Aneta Ivanovska , Jurij Marinko , Robert Modic , Matevž Ogrinc , Marko Debeljak","doi":"10.1016/j.softx.2026.102531","DOIUrl":"10.1016/j.softx.2026.102531","url":null,"abstract":"<div><div>The development of decision support systems (DSS) for agriculture increasingly relies on complex decision models, yet transforming such models into operational, user-friendly software remains challenging. DEXiWare is a software framework designed to support the development of web-based, cooperative DSS based on decision models built with the DEX (Decision EXpert) method. The framework provides a standardized workflow for operationalizing decision models, including automated model import, data handling, assessment, and scenario analysis, within a reusable backend–frontend architecture. DEXiWare integrates backend services, a web-based user interface, and a decision engine supporting top-down (goal-seeking) and bottom-up (what-if) scenario exploration. The framework is evaluated through its application in multiple agricultural DSS and through usability testing with stakeholders, demonstrating its applicability for translating qualitative decision models into operational decision support tools for sustainability assessment in agricultural production systems.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102531"},"PeriodicalIF":2.4,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Version [1.2]-[AsymIntervals: A Python library for uncertainty modeling with asymmetric interval numbers] Version [1.2]-[AsymIntervals：用于不对称区间数的不确定性建模的Python库]

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2026-01-21 DOI: 10.1016/j.softx.2026.102518

Wojciech Sałabun , Damian Kedziora , Andrii Shekhovtsov

In this paper, we present an extension of the AsymIntervals library, designed to enhance the modelling and processing of uncertainty using Asymmetric Interval Numbers (AINs). In response to the growing demand for expressive and mathematically consistent tools for interval-based uncertainty representation, the library has been extended with a comprehensive set of interval characteristics, logical predicates, relational operators, and mathematical transformations implemented within a unified core class. The extension introduces support for advanced algebraic, trigonometric, as well as exponential and logarithmic operations, flexible construction of AIN objects from multiple input formats, sampling-based data generation, and normalization of AIN collections. Additionally, enhanced export and serialisation mechanisms enable seamless integration with numerical workflows and scientific applications. These improvements substantially broaden the applicability of AsymIntervals in decision analysis, uncertainty modelling, and computational research.

在本文中，我们提出了AsymIntervals库的扩展，旨在增强使用非对称区间数（ain）的不确定性建模和处理。为了响应对基于区间的不确定性表示的表达性和数学上一致的工具的不断增长的需求，该库已经扩展了一组全面的区间特征、逻辑谓词、关系运算符和在统一的核心类中实现的数学转换。该扩展引入了对高级代数、三角函数以及指数和对数运算的支持，从多种输入格式灵活地构建AIN对象，基于采样的数据生成，以及AIN集合的规范化。此外，增强的导出和序列化机制能够与数字工作流程和科学应用程序无缝集成。这些改进极大地扩展了AsymIntervals在决策分析、不确定性建模和计算研究中的适用性。

引用次数: 0

Dakar: A CoinJoin forensic software 达喀尔：CoinJoin的取证软件

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2026-01-21 DOI: 10.1016/j.softx.2026.102523

Michael Herbert Ziegler , Mariusz Nowostawski , Basel Katt

The tension between blockchain transparency and user privacy has driven innovation in mixing protocols creating a need for comprehensive analytical frameworks that can rigorously evaluate privacy properties across different implementations. Dakar is an open-source framework that unifies ingestion and provides reproducible classification and analysis of CoinJoin transactions on UTXO blockchains. Its graph database captures the relationships between mixing transactions while a web interface enables experimentation with built-in privacy tools such as CoinJoin transaction heuristics and similarity measures. By enabling researchers to compare and quantify CoinJoin activity across multiple protocols Dakar facilitates studies on privacy-enhancing techniques and supports the discovery and analysis of differences in CoinJoin implementations.

区块链透明度和用户隐私之间的紧张关系推动了混合协议的创新，从而需要能够严格评估不同实现之间隐私属性的综合分析框架。Dakar是一个开源框架，它统一了摄取，并在UTXO区块链上提供可重复的CoinJoin交易分类和分析。它的图形数据库捕获混合交易之间的关系，而web界面可以使用内置的隐私工具（如CoinJoin交易启发式和相似性度量）进行实验。通过使研究人员能够跨多个协议比较和量化CoinJoin活动，Dakar促进了对隐私增强技术的研究，并支持发现和分析CoinJoin实现中的差异。

引用次数: 0

CONNECT: find your dream team 联系：找到你的梦之队

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2026-01-21 DOI: 10.1016/j.softx.2026.102522

Gianluca Amato , Luca Di Vita , Paolo Melchiorre , Maria Chiara Meo , Francesca Scozzari , Matteo Vitali

CONNECT is an AI-powered tool designed to support the creation of research teams targeting competitive funding calls. The tool takes a short input text (for instance the scientific objectives of a specific call) and analyzes the metadata of scholarly publications (title and abstract) from a repository to suggest a list of potential collaborators, based on semantic similarity and scientific relevance. The current instance includes all the researchers from ten research institutions located across Europe.

CONNECT是一个人工智能驱动的工具，旨在支持创建针对竞争性资助电话的研究团队。该工具接受一个简短的输入文本（例如一个特定呼叫的科学目标），并从存储库中分析学术出版物的元数据（标题和摘要），以基于语义相似性和科学相关性建议潜在合作者的列表。目前的实例包括来自欧洲10个研究机构的所有研究人员。

引用次数: 0

首页上一页

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

SoftwareX

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀