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Version [1.0]- [SAMbA-RaP is music to scientists’ ears: Adding provenance support to spark-based scientific workflows] 版本[1.0]- [SAMbA-RaP是科学家们耳熟能详的音乐：为基于火花的科学工作流添加出处支持］
IF 2.4 4区 计算机科学
SoftwareX Pub Date : 2024-10-17 DOI: 10.1016/j.softx.2024.101927
{"title":"Version [1.0]- [SAMbA-RaP is music to scientists’ ears: Adding provenance support to spark-based scientific workflows]","authors":"","doi":"10.1016/j.softx.2024.101927","DOIUrl":"10.1016/j.softx.2024.101927","url":null,"abstract":"<div><div>While researchers benefit from Apache Spark for executing scientific workflows at scale, they often lack provenance support due to the framework’s design limitations. This paper presents <span>SAMbA-RaP</span>, a provenance extension for Apache Spark. It focuses on: <em>(i)</em> Executing external, black-box applications with intensive I/O operations within the workflow while leveraging Spark’s in-memory data structures, <em>(ii)</em> Extracting domain-specific data from in-memory data structures and <em>(iii)</em> Implementing data versioning and capturing the provenance graph in a workflow execution. <span>SAMbA-RaP</span> also provides real-time reports via a web interface, enabling scientists to explore dataflow transformations and content evolution as they run workflows.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
GWAI: Artificial intelligence platform for enhanced gravitational wave data analysis GWAI：用于增强引力波数据分析的人工智能平台
IF 2.4 4区 计算机科学
SoftwareX Pub Date : 2024-10-17 DOI: 10.1016/j.softx.2024.101930
{"title":"GWAI: Artificial intelligence platform for enhanced gravitational wave data analysis","authors":"","doi":"10.1016/j.softx.2024.101930","DOIUrl":"10.1016/j.softx.2024.101930","url":null,"abstract":"<div><div>Gravitational wave (GW) astronomy has opened new frontiers in understanding the cosmos, while the integration of artificial intelligence (AI) in science promises to revolutionize data analysis methodologies. However, a significant gap exists, as there is currently no dedicated platform that enables scientists to develop, test, and evaluate AI algorithms efficiently for GW data analysis. To address this gap, we introduce GWAI, a pioneering AI-centered software platform designed for GW data analysis. GWAI contains a three-layered architecture that emphasizes simplicity, modularity, and flexibility, covering the entire analysis pipeline. GWAI aims to accelerate scientific discoveries, bridging the gap between advanced AI techniques and astrophysical research.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
clusEvol: An R package for Cluster Evolution Analytics clusEvol：用于集群演化分析的 R 软件包
IF 2.4 4区 计算机科学
SoftwareX Pub Date : 2024-10-17 DOI: 10.1016/j.softx.2024.101921
{"title":"clusEvol: An R package for Cluster Evolution Analytics","authors":"","doi":"10.1016/j.softx.2024.101921","DOIUrl":"10.1016/j.softx.2024.101921","url":null,"abstract":"<div><div>The paper proposes a new R package, named <em>clusEvol</em>, that introduces Cluster Evolution Analytics (CEA), a framework for advanced Exploratory Data Analysis and Unsupervised Learning. CEA studies the evolution of an object and its neighbors, identified via clustering algorithms, over time. It combines leave-one-out and plug-in principles, enabling “what if” scenarios by integrating current data into past datasets to explore temporal changes. The framework is demonstrated with a real dataset employing various clustering algorithms.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Wordless: An integrated corpus tool with multilingual support for the study of language, literature, and translation 无字：为语言、文学和翻译研究提供多语种支持的综合语料库工具
IF 2.4 4区 计算机科学
SoftwareX Pub Date : 2024-10-15 DOI: 10.1016/j.softx.2024.101931
{"title":"Wordless: An integrated corpus tool with multilingual support for the study of language, literature, and translation","authors":"","doi":"10.1016/j.softx.2024.101931","DOIUrl":"10.1016/j.softx.2024.101931","url":null,"abstract":"<div><div>This paper presents <em>Wordless</em>, an integrated corpus tool with multilingual support for the study of language, literature, and translation. It is a free, cross-platform, and open-source desktop application with a user-friendly graphical interface which is specially designed to cater the needs of non-technical users. Its ultimate goal is to remove all unnecessary technological barriers to the utilization of cutting-edge technologies by researchers in the field of corpus-based studies.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142437855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
eCOALIA: Neocortical neural mass model for simulating electroencephalographic signals eCOALIA：模拟脑电信号的新皮质神经块模型
IF 2.4 4区 计算机科学
SoftwareX Pub Date : 2024-10-14 DOI: 10.1016/j.softx.2024.101924
{"title":"eCOALIA: Neocortical neural mass model for simulating electroencephalographic signals","authors":"","doi":"10.1016/j.softx.2024.101924","DOIUrl":"10.1016/j.softx.2024.101924","url":null,"abstract":"<div><div>This paper introduces eCOALIA, a Python-based environment for simulating intracranial local field potentials and scalp electroencephalography (EEG) signals with neural mass models. The source activity is modeled by a novel neural mass model respecting the layered structure of the neocortex. The whole-brain model is composed of coupled neural masses, each representing a brain region at the mesoscale and connected through the human connectome matrix. The forward solution on the electrode contracts is computed using biophysical modeling. eCOALIA allows parameter evolution during a simulation time course and visualizes the local field potential at the level of cortex and EEG electrodes. Advantaged with the neurophysiological modeling, eCOALIA advances the <em>in silico</em> modeling of physiological and pathological brain activity.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
AdDownloader: Automating the retrieval of advertisements and their media content from the Meta Online Ad Library AdDownloader：从 Meta 在线广告库自动检索广告及其媒体内容
IF 2.4 4区 计算机科学
SoftwareX Pub Date : 2024-10-13 DOI: 10.1016/j.softx.2024.101919
{"title":"AdDownloader: Automating the retrieval of advertisements and their media content from the Meta Online Ad Library","authors":"","doi":"10.1016/j.softx.2024.101919","DOIUrl":"10.1016/j.softx.2024.101919","url":null,"abstract":"<div><div>AdDownloader is a Python package for downloading advertisements and their media content from the Meta Online Ad Library. With a valid Meta developer access token, AdDownloader automates the process of downloading relevant ads data and storing it in a user-friendly format. Additionally, AdDownloader uses individual ad links from the downloaded data to access each ad's media content (i.e. images and videos) and stores it locally. The package also offers various analytical functionalities, such as topic modelling of ad text and image captioning using AI, embedded in a Dashboard. AdDownloader can be run as a Command-Line Interface or imported as a Python package, providing a flexible and intuitive user experience. Applications range from understanding the effectiveness and transparency of online political campaigns to monitoring the exposure of different population groups to the marketing of harmful substances.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
PV2DOC: Converting the presentation video into the summarized document PV2DOC：将演示视频转换为摘要文档
IF 2.4 4区 计算机科学
SoftwareX Pub Date : 2024-10-11 DOI: 10.1016/j.softx.2024.101922
{"title":"PV2DOC: Converting the presentation video into the summarized document","authors":"","doi":"10.1016/j.softx.2024.101922","DOIUrl":"10.1016/j.softx.2024.101922","url":null,"abstract":"<div><div>The presentation video is an effective way to convey information, but it has the disadvantage of requiring a lot of time and effort to consume, as one needs to grasp both the visual and auditory information in the video to understand it. In this study, we propose PV2DOC, which transforms presentation videos into a document using the visual and audio data from the presentation video. PV2DOC utilizes both visual and auditory information to enable viewers to understand the presentation video effectively. This software simplifies data storage and facilitates data analysis for presentation videos by transforming unstructured data into a structured format, thus offering significant potential from the perspectives of information accessibility and data management. It provides a foundation for more efficient utilization of presentation videos.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
vegspec: A compilation of spectral vegetation indices and transformations in Python vegspec：用 Python 编写的光谱植被指数和转换汇编
IF 2.4 4区 计算机科学
SoftwareX Pub Date : 2024-10-09 DOI: 10.1016/j.softx.2024.101928
{"title":"vegspec: A compilation of spectral vegetation indices and transformations in Python","authors":"","doi":"10.1016/j.softx.2024.101928","DOIUrl":"10.1016/j.softx.2024.101928","url":null,"abstract":"<div><div>The vegspec software package is a Python-based compilation of 1) more than 145 spectral vegetation indices from refereed literature over the past half century and 2) algorithms for several common spectral transformations, including first and second derivatives of reflectance, the logarithm of inverse reflectance and its derivatives, and continuum removal. The software was developed to support analyses of spectral reflectance data from field spectroradiometers and hyperspectral imagers. The outputs are useful for data mining or machine learning studies that relate plant biophysical variables (e.g., leaf chlorophyll content) with vegetative spectral properties.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
PyDEMATEL: A Python-based tool implementing DEMATEL and fuzzy DEMATEL methods for improved decision making PyDEMATEL：基于 Python 的工具，用于实施 DEMATEL 和模糊 DEMATEL 方法以改进决策制定
IF 2.4 4区 计算机科学
SoftwareX Pub Date : 2024-10-09 DOI: 10.1016/j.softx.2024.101889
{"title":"PyDEMATEL: A Python-based tool implementing DEMATEL and fuzzy DEMATEL methods for improved decision making","authors":"","doi":"10.1016/j.softx.2024.101889","DOIUrl":"10.1016/j.softx.2024.101889","url":null,"abstract":"<div><div>In the context of decision-making, the DEMATEL (Decision Making Trial and Evaluation Laboratory) method stands out for its systematic approach to complex systems. By incorporating fuzzy logic, the DEMATEL fuzzy method takes traditional techniques a step further, effectively managing the uncertainties and imprecision inherent in expert assessments. This hybrid method has proved useful in a variety of fields, including business, engineering, healthcare, environmental management, and education. Its ability to refine subjective judgments into actionable information enables decision-makers to improve organizational performance, optimize resource allocation, and achieve more accurate results. The development of software tools for these methods makes them more accessible and practical, enabling more effective analysis and application. In this paper, we propose a flexible implementation that integrates seamlessly into Python-based applications, offering full access to all parameters, matrices, and intermediary calculations of the method. Additionally, the tool also provides a user-friendly graphical interface.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
AISLEX: Approximate individual sample learning entropy with JAX AISLEX：使用 JAX 的近似个体样本学习熵
IF 2.4 4区 计算机科学
SoftwareX Pub Date : 2024-10-07 DOI: 10.1016/j.softx.2024.101915
{"title":"AISLEX: Approximate individual sample learning entropy with JAX","authors":"","doi":"10.1016/j.softx.2024.101915","DOIUrl":"10.1016/j.softx.2024.101915","url":null,"abstract":"<div><div>We present AISLEX, an online anomaly detection module based on the Learning Entropy algorithm, a novel machine learning-based information measure that quantifies the learning effort of neural networks. AISLEX detects anomalous data samples when the learning entropy value is high. The module is designed to be readily usable, with both NumPy and JAX backends, making it suitable for various application fields. The NumPy backend is optimized for devices running Python3, prioritizing limited memory and CPU usage. In contrast, the JAX backend is optimized for fast execution on CPUs, GPUs, and TPUs but requires more computational resources. AISLEX also provides extensive implementation examples in Jupyter notebooks, utilizing in-parameter-linear-nonlinear neural architectures selected for their low data requirements, computational simplicity, convergence analyzability, and dynamical stability.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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