Pub Date : 2024-10-18DOI: 10.1016/j.softx.2024.101925
Ariadne A. Cruz, Kayol S. Mayer, Dalton S. Arantes
Deep learning is an essential artificial intelligence tool broadly used in engineering, physics, data science, biology, healthcare, agribusiness, finance, and many other areas. Current Python frameworks for deep learning, such as TensorFlow, Keras, PyTorch, and scikit-learn, only solve real-domain problems, representing a considerable part of real-world applications but not all. For instance, complex-valued signals are essential for current and future technologies in telecommunications. Thus far, numerous works employing real-valued neural networks adapted to complex-domain processing, end up generating sub-optimal results. To fulfill this demand, this article presents RosenPy, an open-source framework in Python for complex-valued neural networks.
{"title":"RosenPy: An open source Python framework for complex-valued neural networks","authors":"Ariadne A. Cruz, Kayol S. Mayer, Dalton S. Arantes","doi":"10.1016/j.softx.2024.101925","DOIUrl":"10.1016/j.softx.2024.101925","url":null,"abstract":"<div><div>Deep learning is an essential artificial intelligence tool broadly used in engineering, physics, data science, biology, healthcare, agribusiness, finance, and many other areas. Current Python frameworks for deep learning, such as TensorFlow, Keras, PyTorch, and scikit-learn, only solve real-domain problems, representing a considerable part of real-world applications but not all. For instance, complex-valued signals are essential for current and future technologies in telecommunications. Thus far, numerous works employing real-valued neural networks adapted to complex-domain processing, end up generating sub-optimal results. To fulfill this demand, this article presents RosenPy, an open-source framework in Python for complex-valued neural networks.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"28 ","pages":"Article 101925"},"PeriodicalIF":2.4,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532526","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}
Pub Date : 2024-10-17DOI: 10.1016/j.softx.2024.101927
Thaylon Guedes , Marta Mattoso , Marcos Bedo , Daniel de Oliveira
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 SAMbA-RaP, a provenance extension for Apache Spark. It focuses on: (i) Executing external, black-box applications with intensive I/O operations within the workflow while leveraging Spark’s in-memory data structures, (ii) Extracting domain-specific data from in-memory data structures and (iii) Implementing data versioning and capturing the provenance graph in a workflow execution. SAMbA-RaP also provides real-time reports via a web interface, enabling scientists to explore dataflow transformations and content evolution as they run workflows.
{"title":"Version [1.0]- [SAMbA-RaP is music to scientists’ ears: Adding provenance support to spark-based scientific workflows]","authors":"Thaylon Guedes , Marta Mattoso , Marcos Bedo , Daniel de Oliveira","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":"28 ","pages":"Article 101927"},"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}
Pub Date : 2024-10-17DOI: 10.1016/j.softx.2024.101930
Tianyu Zhao , Yue Zhou , Ruijun Shi , Zhoujian Cao , Zhixiang Ren
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.
{"title":"GWAI: Artificial intelligence platform for enhanced gravitational wave data analysis","authors":"Tianyu Zhao , Yue Zhou , Ruijun Shi , Zhoujian Cao , Zhixiang Ren","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":"28 ","pages":"Article 101930"},"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}
Pub Date : 2024-10-17DOI: 10.1016/j.softx.2024.101921
Víctor Morales-Oñate , Bolívar Morales-Oñate
The paper proposes a new R package, named clusEvol, 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.
{"title":"clusEvol: An R package for Cluster Evolution Analytics","authors":"Víctor Morales-Oñate , Bolívar Morales-Oñate","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":"28 ","pages":"Article 101921"},"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}
Pub Date : 2024-10-15DOI: 10.1016/j.softx.2024.101931
Lei Ye
This paper presents Wordless, 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.
{"title":"Wordless: An integrated corpus tool with multilingual support for the study of language, literature, and translation","authors":"Lei Ye","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":"28 ","pages":"Article 101931"},"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}
Pub Date : 2024-10-14DOI: 10.1016/j.softx.2024.101924
Elif Köksal-Ersöz , Maxime Yochum , Pascal Benquet, Fabrice Wendling
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 in silico modeling of physiological and pathological brain activity.
{"title":"eCOALIA: Neocortical neural mass model for simulating electroencephalographic signals","authors":"Elif Köksal-Ersöz , Maxime Yochum , Pascal Benquet, Fabrice Wendling","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":"28 ","pages":"Article 101924"},"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}
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.
AdDownloader 是一个 Python 软件包，用于从 Meta 在线广告库下载广告及其媒体内容。有了有效的 Meta 开发人员访问令牌，AdDownloader 就能自动下载相关广告数据，并以用户友好的格式进行存储。此外，AdDownloader 还使用下载数据中的单个广告链接来访问每个广告的媒体内容（如图片和视频），并将其存储在本地。该软件包还提供各种分析功能，例如使用人工智能对广告文本和图片标题进行主题建模，并将其嵌入仪表板中。AdDownloader 可作为命令行界面运行，也可作为 Python 软件包导入，提供灵活直观的用户体验。应用范围从了解在线政治活动的有效性和透明度，到监控不同人群接触有害物质营销的情况。
{"title":"AdDownloader: Automating the retrieval of advertisements and their media content from the Meta Online Ad Library","authors":"Paula-Alexandra Gitu , Roberto Cerina , Stefanie Vandevijvere , Roselinde Kessels","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":"28 ","pages":""},"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}
Pub Date : 2024-10-11DOI: 10.1016/j.softx.2024.101922
Won-Ryeol Jeong , Seung-Kyu Hong , Hyuk-Yoon Kwon
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.
{"title":"PV2DOC: Converting the presentation video into the summarized document","authors":"Won-Ryeol Jeong , Seung-Kyu Hong , Hyuk-Yoon Kwon","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":"28 ","pages":"Article 101922"},"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}
Pub Date : 2024-10-09DOI: 10.1016/j.softx.2024.101928
Kelly R. Thorp
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.
{"title":"vegspec: A compilation of spectral vegetation indices and transformations in Python","authors":"Kelly R. Thorp","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":"28 ","pages":"Article 101928"},"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}
Pub Date : 2024-10-09DOI: 10.1016/j.softx.2024.101889
Abderrahman Chekry , Jamal Bakkas , Mohamed Hanine , Elizabeth Caro Montero , Mirtha Silvana Garat de Marin , Imran Ashraf
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.
{"title":"PyDEMATEL: A Python-based tool implementing DEMATEL and fuzzy DEMATEL methods for improved decision making","authors":"Abderrahman Chekry , Jamal Bakkas , Mohamed Hanine , Elizabeth Caro Montero , Mirtha Silvana Garat de Marin , Imran Ashraf","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":"28 ","pages":"Article 101889"},"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}
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