Pub Date : 2024-11-15DOI: 10.1016/j.softx.2024.101960
Bartłomiej Kizielewicz, Wojciech Sałabun
The pymcdm-reidentify tool addresses the challenge of reconstructing multi-criteria decision analysis (MCDA) and decision-making (MCDM) models when original parameters are unavailable, but rankings are known. This Python package integrates with existing MCDA libraries and uses stochastic optimization to determine model parameters such as criterion weights and reference objects. Built on the pymcdm and Mealpy libraries, pymcdm-reidentify offers advanced methods for model re-identification, including visualization and fuzzy normalization. Its capabilities facilitate the update and adaptation of decision models, enhancing accuracy and efficiency in both academic and practical applications.
{"title":"The pymcdm-reidentify tool: Advanced methods for MCDA model re-identification","authors":"Bartłomiej Kizielewicz, Wojciech Sałabun","doi":"10.1016/j.softx.2024.101960","DOIUrl":"10.1016/j.softx.2024.101960","url":null,"abstract":"<div><div>The <span>pymcdm-reidentify</span> tool addresses the challenge of reconstructing multi-criteria decision analysis (MCDA) and decision-making (MCDM) models when original parameters are unavailable, but rankings are known. This Python package integrates with existing MCDA libraries and uses stochastic optimization to determine model parameters such as criterion weights and reference objects. Built on the <span>pymcdm</span> and <span>Mealpy</span> libraries, <span>pymcdm-reidentify</span> offers advanced methods for model re-identification, including visualization and fuzzy normalization. Its capabilities facilitate the update and adaptation of decision models, enhancing accuracy and efficiency in both academic and practical applications.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"28 ","pages":"Article 101960"},"PeriodicalIF":2.4,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659755","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 : 2024-11-15DOI: 10.1016/j.softx.2024.101963
Tímea Czvetkó, János Abonyi
HAT-VIS is a hypergraph visualization tool designed within the MATLAB environment, serving to depict the inherent relationships present within hypergraphs. The current scarcity of MATLAB tools dedicated to the analysis and visualization of hypergraphs necessitated the development of the HAT-VIS, which can be an independent, standalone tool or integrated within the HAT: Hypergraph Analysis Toolbox and other MATLAB libraries. HAT-VIS offers a valuable resource for visualizing hypergraphs by leveraging vertex similarities through multidimensional scaling, providing additional interpretable insights based on the location of vertices, in contrast to the predominantly employed forced layout techniques in existing hypergraph visualization tools. The proposed tool can be used to inform decision making by discovering relationships between vertices. The applicability of HAT-VIS is demonstrated through an illustrative case study on the development of electric vehicles.
{"title":"Version [1.0]- HAT-VIS — A MATLAB-based hypergraph visualization tool","authors":"Tímea Czvetkó, János Abonyi","doi":"10.1016/j.softx.2024.101963","DOIUrl":"10.1016/j.softx.2024.101963","url":null,"abstract":"<div><div>HAT-VIS is a hypergraph visualization tool designed within the MATLAB environment, serving to depict the inherent relationships present within hypergraphs. The current scarcity of MATLAB tools dedicated to the analysis and visualization of hypergraphs necessitated the development of the HAT-VIS, which can be an independent, standalone tool or integrated within the HAT: Hypergraph Analysis Toolbox and other MATLAB libraries. HAT-VIS offers a valuable resource for visualizing hypergraphs by leveraging vertex similarities through multidimensional scaling, providing additional interpretable insights based on the location of vertices, in contrast to the predominantly employed forced layout techniques in existing hypergraph visualization tools. The proposed tool can be used to inform decision making by discovering relationships between vertices. The applicability of HAT-VIS is demonstrated through an illustrative case study on the development of electric vehicles.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"28 ","pages":"Article 101963"},"PeriodicalIF":2.4,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659754","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 : 2024-11-15DOI: 10.1016/j.softx.2024.101969
Jorge Palomino Tamayo, Lucas Alves de Aguiar, Cristian de Campos, Daniel Barbosa Mapurunga Matos, Inácio Benvegnu Morsch
This paper presents a computer program named COMBEAMS written in Python and intended for the design verification of continuous steel-concrete composite beams under the ultimate limit state. Due to its friendly graphical interface the program allows the user to interact with the output results expressed in terms of computed shear and bending moment diagrams as well as computed shear connector distribution along the steel-concrete interface. Particularly, this last issue is important as connectors transfer the shear flow from the concrete slab to the steel profile to guarantee the desirable interaction between both members. The program can be also inserted into other methodologies. This tool will certainly aim engineers and researchers with their daily tasks.
{"title":"COMBEAMS: A numerical tool for the structural verification of steel-concrete composite beams","authors":"Jorge Palomino Tamayo, Lucas Alves de Aguiar, Cristian de Campos, Daniel Barbosa Mapurunga Matos, Inácio Benvegnu Morsch","doi":"10.1016/j.softx.2024.101969","DOIUrl":"10.1016/j.softx.2024.101969","url":null,"abstract":"<div><div>This paper presents a computer program named COMBEAMS written in Python and intended for the design verification of continuous steel-concrete composite beams under the ultimate limit state. Due to its friendly graphical interface the program allows the user to interact with the output results expressed in terms of computed shear and bending moment diagrams as well as computed shear connector distribution along the steel-concrete interface. Particularly, this last issue is important as connectors transfer the shear flow from the concrete slab to the steel profile to guarantee the desirable interaction between both members. The program can be also inserted into other methodologies. This tool will certainly aim engineers and researchers with their daily tasks.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"28 ","pages":"Article 101969"},"PeriodicalIF":2.4,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659756","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 : 2024-11-15DOI: 10.1016/j.softx.2024.101955
Ângelo Miguel Rodrigues Morgado , Nuno Gonçalo Coelho Costa Pombo
CARLA-GymDrive is a powerful framework designed to facilitate reinforcement learning experiments in autonomous driving using the Carla simulator. By providing a gymnasium-like environment, it offers an intuitive and efficient platform for training driving agents using reinforcement learning techniques. It includes features such as scenario configuration to ensure that the training/test suite is adequate without requiring any code. Additionally, it boasts other features such as custom sensor configuration and compatibility with training libraries like Stable-Baselines3. This tool aims to increase researchers’ productivity by abstracting them from the complex code of the simulator, allowing them to focus on their research.
{"title":"CARLA-GymDrive: Autonomous driving episode generation for the Carla simulator in a gym environment","authors":"Ângelo Miguel Rodrigues Morgado , Nuno Gonçalo Coelho Costa Pombo","doi":"10.1016/j.softx.2024.101955","DOIUrl":"10.1016/j.softx.2024.101955","url":null,"abstract":"<div><div>CARLA-GymDrive is a powerful framework designed to facilitate reinforcement learning experiments in autonomous driving using the Carla simulator. By providing a gymnasium-like environment, it offers an intuitive and efficient platform for training driving agents using reinforcement learning techniques. It includes features such as scenario configuration to ensure that the training/test suite is adequate without requiring any code. Additionally, it boasts other features such as custom sensor configuration and compatibility with training libraries like Stable-Baselines3. This tool aims to increase researchers’ productivity by abstracting them from the complex code of the simulator, allowing them to focus on their research.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"28 ","pages":"Article 101955"},"PeriodicalIF":2.4,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659753","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 : 2024-11-14DOI: 10.1016/j.softx.2024.101968
François Mauger , Cristel Chandre
The QMol-grid package provides a suite of routines for performing quantum-mechanical simulations in atomic and molecular systems, currently implemented in one spatial dimension. It supports ground- and excited-state calculations for the Schrödinger equation, density-functional theory, and Hartree–Fock levels of theory as well as propagators for field-free and field-driven time-dependent Schrödinger equation (TDSE) and real-time time-dependent density-functional theory (TDDFT), using symplectic-split schemes. The package is written using MATLAB’s object-oriented features and handle classes. It is designed to facilitate access to the wave function(s) (TDSE) and the Kohn–Sham orbitals (TDDFT) within MATLAB’s environment.
{"title":"QMol-grid : A MATLAB package for quantum-mechanical simulations in atomic and molecular systems","authors":"François Mauger , Cristel Chandre","doi":"10.1016/j.softx.2024.101968","DOIUrl":"10.1016/j.softx.2024.101968","url":null,"abstract":"<div><div>The <span>QMol-grid</span> package provides a suite of routines for performing quantum-mechanical simulations in atomic and molecular systems, currently implemented in one spatial dimension. It supports ground- and excited-state calculations for the Schrödinger equation, density-functional theory, and Hartree–Fock levels of theory as well as propagators for field-free and field-driven time-dependent Schrödinger equation (TDSE) and real-time time-dependent density-functional theory (TDDFT), using symplectic-split schemes. The package is written using MATLAB’s object-oriented features and handle classes. It is designed to facilitate access to the wave function(s) (TDSE) and the Kohn–Sham orbitals (TDDFT) within MATLAB’s environment.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"28 ","pages":"Article 101968"},"PeriodicalIF":2.4,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659752","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 : 2024-11-13DOI: 10.1016/j.softx.2024.101966
Saima Safdar , Nathaniel Barry , Michael Bynevelt , Suki Gill , Pejman Rowshan Farzad , Martin A Ebert
The SlicerBatchBrainMRTumorSegmentation is a graphical user interface (GUI) based Python scripted module within 3D Slicer. Its purpose is to perform automated brain tumour segmentation for numerous patients while preserving data integrity and organization. Through automation, manual intervention at each stage of the Brain Tumor Segmentation (BraTS) toolkit becomes unnecessary, resulting in efficient processing of multiple patient cases. Being an open-source software implementation, the SlicerBatchBrainMRTumorSegmentation is licensed under the BSD (Berkeley Source Distribution) 3-Clause License, facilitating its use by the broader research community. This tool empowers users to explore diverse segmentation approaches, fosters research advancements, and stimulates innovation in the field of brain tumour analysis.
{"title":"SlicerBatchBrainMRTumorSegmentation: Automating brain tumor segmentation in 3D slicer for improved efficiency and research support","authors":"Saima Safdar , Nathaniel Barry , Michael Bynevelt , Suki Gill , Pejman Rowshan Farzad , Martin A Ebert","doi":"10.1016/j.softx.2024.101966","DOIUrl":"10.1016/j.softx.2024.101966","url":null,"abstract":"<div><div>The SlicerBatchBrainMRTumorSegmentation is a graphical user interface (GUI) based Python scripted module within 3D Slicer. Its purpose is to perform automated brain tumour segmentation for numerous patients while preserving data integrity and organization. Through automation, manual intervention at each stage of the Brain Tumor Segmentation (BraTS) toolkit becomes unnecessary, resulting in efficient processing of multiple patient cases. Being an open-source software implementation, the SlicerBatchBrainMRTumorSegmentation is licensed under the BSD (Berkeley Source Distribution) 3-Clause License, facilitating its use by the broader research community. This tool empowers users to explore diverse segmentation approaches, fosters research advancements, and stimulates innovation in the field of brain tumour analysis.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"28 ","pages":"Article 101966"},"PeriodicalIF":2.4,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659750","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 : 2024-11-13DOI: 10.1016/j.softx.2024.101961
Jakub Stankowski, Adrian Dziembowski
This paper describes a new version of the IV-PSNR software, developed for the effective objective quality assessment of immersive video. Version 7.1 includes the calculation of structural similarity between compared sequences using the IV-SSIM metric, designed to properly handle the unique characteristics of immersive video, as well as the classic SSIM and MS-SSIM metrics. Moreover, by introducing new modes, IV-PSNR 7.1 is adapted to assess the quality of novel approaches to multiview video processing, based on radiance fields and implicit neural visual representations. Currently, this version of the software is used by the ISO/IEC MPEG VC standardization group for the evaluation of the second edition of the MIV coding standard, and in works aimed at the development of a future standard for radiance field representation and compression.
{"title":"Version [7.1] – [IV-PSNR: Software for immersive video objective quality evaluation]","authors":"Jakub Stankowski, Adrian Dziembowski","doi":"10.1016/j.softx.2024.101961","DOIUrl":"10.1016/j.softx.2024.101961","url":null,"abstract":"<div><div>This paper describes a new version of the IV-PSNR software, developed for the effective objective quality assessment of immersive video. Version 7.1 includes the calculation of structural similarity between compared sequences using the IV-SSIM metric, designed to properly handle the unique characteristics of immersive video, as well as the classic SSIM and MS-SSIM metrics. Moreover, by introducing new modes, IV-PSNR 7.1 is adapted to assess the quality of novel approaches to multiview video processing, based on radiance fields and implicit neural visual representations. Currently, this version of the software is used by the ISO/IEC MPEG VC standardization group for the evaluation of the second edition of the MIV coding standard, and in works aimed at the development of a future standard for radiance field representation and compression.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"28 ","pages":"Article 101961"},"PeriodicalIF":2.4,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659757","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 : 2024-11-13DOI: 10.1016/j.softx.2024.101964
Julian M. Bopp , Tim Schröder
Experiments in science and particularly quantum physics grow complex requiring sophisticated control software. Such software must provide a rigorous abstraction between hardware and measurement modules. Furthermore, it should provide networking functionality for accessing shared devices connected to a network and for publishing measured data to remote sites. However, to date there is no fast and easy-to-use experimental control software for this purpose written in C++. We introduce DynExp as a highly flexible laboratory automation software. It enables to assign physical devices to measurement modules at runtime and provides networking functionality. Its embedded Python interpreter allows processing measured data in realtime.
科学实验，尤其是量子物理实验越来越复杂，需要复杂的控制软件。这种软件必须在硬件和测量模块之间提供严格的抽象。此外，它还应提供联网功能，以访问连接到网络的共享设备，并将测量数据发布到远程站点。然而，迄今为止，还没有一款用 C++ 编写的快速易用的实验控制软件。我们介绍的 DynExp 是一款高度灵活的实验室自动化软件。它能在运行时为测量模块分配物理设备，并提供联网功能。其嵌入式 Python 解释器可实时处理测量数据。
{"title":"DynExp—Highly flexible laboratory automation for dynamically changing classical and quantum experiments","authors":"Julian M. Bopp , Tim Schröder","doi":"10.1016/j.softx.2024.101964","DOIUrl":"10.1016/j.softx.2024.101964","url":null,"abstract":"<div><div>Experiments in science and particularly quantum physics grow complex requiring sophisticated control software. Such software must provide a rigorous abstraction between hardware and measurement modules. Furthermore, it should provide networking functionality for accessing shared devices connected to a network and for publishing measured data to remote sites. However, to date there is no fast and easy-to-use experimental control software for this purpose written in C++. We introduce DynExp as a highly flexible laboratory automation software. It enables to assign physical devices to measurement modules at runtime and provides networking functionality. Its embedded Python interpreter allows processing measured data in realtime.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"28 ","pages":"Article 101964"},"PeriodicalIF":2.4,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659751","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 : 2024-11-12DOI: 10.1016/j.softx.2024.101959
Luigi Quaranta, Fabio Calefato, Filippo Lanubile
Jupyter Notebook is widely recognized as a crucial tool for data science professionals and students. Its interactive and self-documenting nature makes it particularly suitable for data-driven programming tasks. Nonetheless, it faces criticism for its limited support for software engineering best practices and its tendency to encourage bad programming habits, such as non-linear code execution. These issues often result in non-reproducible, poorly documented, and low-quality notebook code. In this paper, we introduce Pynblint, a static analyzer for Python Jupyter notebooks. Pynblint is designed to help data scientists write better notebooks, easy to understand and reproduce. We report on how we validated Pynblint with both professional data scientists and students, receiving overall positive feedback. Additionally, we discuss the potential of Pynblint to facilitate research inquiries into computational notebooks.
{"title":"Pynblint: A quality assurance tool to improve the quality of Python Jupyter notebooks","authors":"Luigi Quaranta, Fabio Calefato, Filippo Lanubile","doi":"10.1016/j.softx.2024.101959","DOIUrl":"10.1016/j.softx.2024.101959","url":null,"abstract":"<div><div>Jupyter Notebook is widely recognized as a crucial tool for data science professionals and students. Its interactive and self-documenting nature makes it particularly suitable for data-driven programming tasks. Nonetheless, it faces criticism for its limited support for software engineering best practices and its tendency to encourage bad programming habits, such as non-linear code execution. These issues often result in non-reproducible, poorly documented, and low-quality notebook code. In this paper, we introduce <span>Pynblint</span>, a static analyzer for Python Jupyter notebooks. <span>Pynblint</span> is designed to help data scientists write better notebooks, easy to understand and reproduce. We report on how we validated <span>Pynblint</span> with both professional data scientists and students, receiving overall positive feedback. Additionally, we discuss the potential of <span>Pynblint</span> to facilitate research inquiries into computational notebooks.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"28 ","pages":"Article 101959"},"PeriodicalIF":2.4,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659749","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 : 2024-11-11DOI: 10.1016/j.softx.2024.101962
Yuchan Lee , Sookwang Lee , Jaehwan Lee
We propose PyCAN, the first open-source Python implementation of N-dimensional Content-Addressable Network (CAN) with full feature sets to maintain peer-to-peer structure. Existing CAN implementations supports limited functions of Distributed Hash Table (DHT), so they cannot be used in practice. However, PyCAN offers full set of features such as N-dimension coordinates, node removal followed by node taking over, and a verification method to keep peer-to-peer structure. By extensive experiments, we confirm that PyCAN supports scalable overlay structures, so it is practically usable in peer-to-peer systems.
我们提出了 PyCAN，它是 N 维内容寻址网络（Content-Addressable Network，CAN）的第一个开源 Python 实现，具有维护点对点结构的完整功能集。现有的 CAN 实现支持分布式散列表（DHT）的有限功能，因此无法在实践中使用。然而，PyCAN 提供了全套功能，如 N 维坐标、节点移除后节点接管，以及保持点对点结构的验证方法。通过大量实验，我们证实 PyCAN 支持可扩展的叠加结构，因此它在点对点系统中实际上是可用的。
{"title":"PyCAN: Open-source Python software of N-dimensional Content-Addressable Network","authors":"Yuchan Lee , Sookwang Lee , Jaehwan Lee","doi":"10.1016/j.softx.2024.101962","DOIUrl":"10.1016/j.softx.2024.101962","url":null,"abstract":"<div><div>We propose PyCAN, the first open-source Python implementation of N-dimensional Content-Addressable Network (CAN) with full feature sets to maintain peer-to-peer structure. Existing CAN implementations supports limited functions of Distributed Hash Table (DHT), so they cannot be used in practice. However, PyCAN offers full set of features such as N-dimension coordinates, node removal followed by node taking over, and a verification method to keep peer-to-peer structure. By extensive experiments, we confirm that PyCAN supports scalable overlay structures, so it is practically usable in peer-to-peer systems.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"28 ","pages":"Article 101962"},"PeriodicalIF":2.4,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659747","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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