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Spectrally regularised LVMs: A spectral regularisation framework for latent variable models designed for single-channel applications 频谱正则化lvm：为单通道应用设计的潜在变量模型的频谱正则化框架

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2025-05-22 DOI: 10.1016/j.softx.2025.102187

Ryan Balshaw , P. Stephan Heyns , Daniel N. Wilke , Stephan Schmidt

{"title":"Spectrally regularised LVMs: A spectral regularisation framework for latent variable models designed for single-channel applications","authors":"Ryan Balshaw , P. Stephan Heyns , Daniel N. Wilke , Stephan Schmidt","doi":"10.1016/j.softx.2025.102187","DOIUrl":"10.1016/j.softx.2025.102187","url":null,"abstract":"<div><div>Latent variable models (LVMs) are commonly used to capture the underlying dependencies, patterns, and hidden structures in observed data. Source duplication is a by-product of the data Hankelisation pre-processing step common to single-channel LVM applications, which hinders practical LVM utilisation. In this article, a Python package titled <span>spectrally-regularised-LVMs</span> is presented. The proposed package addresses the source duplication issue by adding a novel spectral regularisation term. This package provides a framework for spectral regularisation in single-channel LVM applications, thereby making it easier to investigate and utilise LVMs with spectral regularisation. This is achieved via symbolic or explicit representations of potential LVM objective functions, which are incorporated into a framework that uses spectral regularisation during the LVM parameter estimation process. This package aims to provide a consistent linear LVM optimisation framework incorporating spectral regularisation and caters to single-channel time-series applications.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"31 ","pages":"Article 102187"},"PeriodicalIF":2.4,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107092","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

TFM: An R package for truncated factor model TFM：截断因子模型的R包

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2025-05-21 DOI: 10.1016/j.softx.2025.102204

Beibei Wu, Guangbao Guo

引用次数: 0

MultiCoCoA: Multimodal data collector from collocated collaborative activities MultiCoCoA：来自并置协作活动的多模式数据收集器

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2025-05-20 DOI: 10.1016/j.softx.2025.102202

Diego Miranda , Jaime Godoy , Rene Noel , Cristian Cechinel , Roberto Munoz

{"title":"MultiCoCoA: Multimodal data collector from collocated collaborative activities","authors":"Diego Miranda , Jaime Godoy , Rene Noel , Cristian Cechinel , Roberto Munoz","doi":"10.1016/j.softx.2025.102202","DOIUrl":"10.1016/j.softx.2025.102202","url":null,"abstract":"<div><div>Collaborative work requires developing and applying soft skills that can influence the formation of social, emotional, and professional skills. Nevertheless, assessing the effectiveness of teamwork, collaboration, and communication is challenging and is commonly addressed by qualitative research approaches. Although through isolated initiatives, Multimodal Learning Analytics (MMLA) techniques have successfully addressed the challenge of measuring different communication features. This work presents MultiCoCoA, a multimodal analytics framework to facilitate data collection in collaborative activities. MultiCoCoA integrates state-of-the-art MMLA techniques and machine learning techniques to analyze audio and video data, and it can help identify areas to improve communication skills. MultiCoCoA allows data to be uploaded and analyzed intuitively, presenting the results through data visualization features and downloadable CSV files for its use with data analysis tools. To evaluate MultiCoCoA’s performance, we conducted both technical validation and user feedback analysis. In terms of accuracy, the system was tested using over 5700 manually labeled video frames from two sessions of collaborative software planning, achieving 92.85% precision in detecting spoken interventions, 85.59% in direction-of-arrival estimation, and 74.88% for identifying observer–observed gaze pairs. To assess usability, we applied the System Usability Scale with five professionals in software development roles, obtaining a favorable usability perception and highlighting ease of use and functional integration, alongside contextual suggestions for deployment in dynamic work environments. The expected outcome of MultiCoCoA is to support research in communication and collaboration, providing quantitative insights to complement existing research methods.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"31 ","pages":"Article 102202"},"PeriodicalIF":2.4,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144088677","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 v1.3.6 - PeriLab - Peridynamic Laboratory 版本v1.3.6 - peridydynamiclaboratory

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2025-05-19 DOI: 10.1016/j.softx.2025.102168

Christian Willberg , Jan-Timo Hesse , Anna Pernatii

引用次数: 0

QuadratiK: A Python and R package for clustering on the sphere and goodness-of-fit tests QuadratiK：用于在球体和拟合优度测试上聚类的Python和R包

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2025-05-19 DOI: 10.1016/j.softx.2025.102155

Giovanni Saraceno , Raktim Mukhopadhyay , Marianthi Markatou

{"title":"QuadratiK: A Python and R package for clustering on the sphere and goodness-of-fit tests","authors":"Giovanni Saraceno , Raktim Mukhopadhyay , Marianthi Markatou","doi":"10.1016/j.softx.2025.102155","DOIUrl":"10.1016/j.softx.2025.102155","url":null,"abstract":"<div><div>We introduce <span>QuadratiK</span>, an open-source software, implemented in <span>R</span> and <span>Python</span>. <span>QuadratiK</span> supports normality tests, and two and <span><math><mi>k</mi></math></span>-sample tests, using kernel-based quadratic distances. The software also includes tests for uniformity on the <span><math><mi>d</mi></math></span>-dimensional sphere and a clustering algorithm using the Poisson kernel-based densities. Functions for generating random samples from these densities are included. These methods are encoded via object-oriented and extensively unit-tested implementations. <span>QuadratiK</span> offers graphical functions that enhance user experience by facilitating the validation, visualization, and interpretation of clustering results. We compare <span>QuadratiK</span> with related available libraries and provide illustrative code examples. In summary, <span>QuadratiK</span> offers a powerful suite of tools in <span>R</span> and <span>Python</span>, enabling researchers and practitioners to perform meaningful analyses and derive valid and reproducible inference across a wide range of fields. The <span>R</span><span><span><sup>3</sup></span></span> and <span>Python</span><span><span><sup>4</sup></span></span> codes are available under the GPL-3.0 license. Finally, we propose a dashboard application, a graphical user interface to the implemented methods, with the aim to facilitate the usage of the software among practitioners from different domains.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"31 ","pages":"Article 102155"},"PeriodicalIF":2.4,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084064","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

A new drone chasing drone approach based on deep reinforcement learning with accelerated rewards 一种基于加速奖励的深度强化学习的无人机追踪方法

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2025-05-17 DOI: 10.1016/j.softx.2025.102201

Ziya Tan , Mehmet Karaköse

{"title":"A new drone chasing drone approach based on deep reinforcement learning with accelerated rewards","authors":"Ziya Tan , Mehmet Karaköse","doi":"10.1016/j.softx.2025.102201","DOIUrl":"10.1016/j.softx.2025.102201","url":null,"abstract":"<div><div>In this paper, we propose a deep reinforcement learning-based approach that uses the drone camera as the only input source for a drone to track another drone in real-time autonomously. Deep learning and deep reinforcement learning algorithms are developed for this proposal. First, one of the object detection algorithms, YOLO, was trained with a dataset of different drone images to instantaneously detect drones in the images taken from the camera of the following drone. The detected drone was enclosed in a box and positioned on the screen. The size information of the box was sent to the agent trained with the Deep deterministic policy gradient (DDPG) deep reinforcement learning algorithm to determine the action of the following drone. This approach is tested in five different following scenarios. These two scenarios were also tested in environments with different light levels. According to the results of the scenarios, the following accuracy rate is calculated to be at most 99 %, and the drone response accuracy rate is calculated to be at most 95 %. In addition, the OpenAI Gym simulator is designed according to our problem, the DDPG agent is trained, the performance of the developed system is tested in a real-world environment, and the results are observed.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"31 ","pages":"Article 102201"},"PeriodicalIF":2.4,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144071349","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

ALBA: Agile library for biomedical applications ALBA：用于生物医学应用的敏捷库

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2025-05-16 DOI: 10.1016/j.softx.2025.102188

Gianluigi Crimi , Nicola Vanella , Enrico Schileo , Giordano Valente , Giulia Fraterrigo , Fulvia Taddei

{"title":"ALBA: Agile library for biomedical applications","authors":"Gianluigi Crimi , Nicola Vanella , Enrico Schileo , Giordano Valente , Giulia Fraterrigo , Fulvia Taddei","doi":"10.1016/j.softx.2025.102188","DOIUrl":"10.1016/j.softx.2025.102188","url":null,"abstract":"<div><div>Efficient and unified software tools to manage the complex imaging and modelling workflows needed for computational biomechanics research are lacking. The Agile Library for Biomedical Applications (ALBA) is an open-source C++ versatile framework that handles various data types (e.g. 2D and 3D images, surfaces, finite element models, point clouds, motion analysis data), and offers an easily extensible platform for the rapid development of specialised applications that can manage, visualise, and manipulate biomedical data. The already available functionalities have been developed in the context of computational biomechanics, quantitative image analysis and pre-operative planning in orthopaedics. Software applications built with ALBA attracted the interest of the scientific community and are currently used, both inside and outside the original research group, in finite element modelling of bones and musculoskeletal modelling. The further ALBA adoption by other centres might increase the spread of a positive attitude towards open and reproducible research in the biomechanical community and increase the sharing of algorithms and data.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"31 ","pages":"Article 102188"},"PeriodicalIF":2.4,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144069250","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

PEAK: Python-based framework for heterogeneous agent communities PEAK：异构代理社区的基于python的框架

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2025-05-01 DOI: 10.1016/j.softx.2025.102190

Bruno Ribeiro, David Dias, Luis Gomes, Zita Vale

引用次数: 0

SPBView: An extendable data analysis and combined visualization tool for saccadic eye-movement, pupil size, and blink detection SPBView：一个可扩展的数据分析和组合可视化工具，用于眼动、瞳孔大小和眨眼检测

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2025-05-01 DOI: 10.1016/j.softx.2025.102171

Marcel Ritter , Alexandra Hoffmann , Nikolaus Rauch , Pierre Sachse , Atbin Djamshidian , Matthias Harders , Philipp Ellmerer

引用次数: 0

Compression of triangulated solids’ surfaces by Decimating Reconstructable Triangles 通过抽取可重构三角形压缩三角化固体的表面