Applied Soft Computing最新文献_第9页

Community detection by spectral methods in multi-layer networks

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-02-08 DOI: 10.1016/j.asoc.2025.112769

Huan Qing

{"title":"Community detection by spectral methods in multi-layer networks","authors":"Huan Qing","doi":"10.1016/j.asoc.2025.112769","DOIUrl":"10.1016/j.asoc.2025.112769","url":null,"abstract":"<div><div>Community detection in multi-layer networks is a crucial problem in network analysis. In this paper, we analyze the performance of two spectral clustering algorithms for community detection within the framework of the multi-layer degree-corrected stochastic block model (MLDCSBM) framework. One algorithm is based on the sum of adjacency matrices, while the other utilizes the debiased sum of squared adjacency matrices. We also provide their accelerated versions through subsampling to handle large-scale multi-layer networks. We establish consistency results for community detection of the two proposed methods under MLDCSBM as the size of the network and/or the number of layers increases. Our theorems demonstrate the advantages of utilizing multiple layers for community detection. Our analysis also indicates that spectral clustering with the debiased sum of squared adjacency matrices is generally superior to spectral clustering with the sum of adjacency matrices. Furthermore, we provide a strategy to estimate the number of communities in multi-layer networks by maximizing the averaged modularity. Substantial numerical simulations demonstrate the superiority of our algorithm employing the debiased sum of squared adjacency matrices over existing methods for community detection in multi-layer networks, the high computational efficiency of our accelerated algorithms for large-scale multi-layer networks, and the high accuracy of our strategy in estimating the number of communities. Finally, the analysis of several real-world multi-layer networks yields meaningful insights.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"171 ","pages":"Article 112769"},"PeriodicalIF":7.2,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Concurrent attentional reconstruction network for 3D point cloud reconstruction from single image

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-02-08 DOI: 10.1016/j.asoc.2025.112821

PremaLatha Velagapalli , Nikhat Parveen

{"title":"Concurrent attentional reconstruction network for 3D point cloud reconstruction from single image","authors":"PremaLatha Velagapalli , Nikhat Parveen","doi":"10.1016/j.asoc.2025.112821","DOIUrl":"10.1016/j.asoc.2025.112821","url":null,"abstract":"<div><div>The reconstruction of point clouds from a 2D single image is challenging due to the complex nature of images that cannot be projected easily. Also, the images include multiple objects that require huge concentration while transforming them into a point cloud representation. Deep learning-based methods are recently gaining attention due to the high-performance outcomes enlisted in the image processing domain. This paper introduces a two-tiered deep learning-based reconstruction model known as concurrent attentional reconstruction network (CARN) to better reconstruct a 3D point cloud from a 2D single image. The feature extraction and the point cloud prediction are the two modules employed in the proposed CARN model. Here, concurrent excited DenseNet (An important development in the field of computer vision is the use of dense convolutional neural networks (DenseNets) linked with concurrent squeeze-and-excitation (CSE) modules for feature extraction in 3D point cloud reconstruction from a single image. The attention-dense gated recurrent unit (AD-GRU) is the next module employed for point cloud reconstruction. The proposed CARN model is trained and tested with publicly available ShapeNet datasets. The Python platform is applied for implementation, and various performance metrics such as accuracy, Earth Mover's Distance (EMD), and Chamfer Distance (CD) are analyzed with existing methods. The proposed CARN model acquires an overall accuracy above 99 % and obtains minimum CD and EMD values for the ShapeNet dataset.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"172 ","pages":"Article 112821"},"PeriodicalIF":7.2,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Multiple unmanned surface vehicles pathfinding in dynamic environment

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-02-08 DOI: 10.1016/j.asoc.2025.112820

Yuqing Lin, Liang Du, Kum Fai Yuen

{"title":"Multiple unmanned surface vehicles pathfinding in dynamic environment","authors":"Yuqing Lin, Liang Du, Kum Fai Yuen","doi":"10.1016/j.asoc.2025.112820","DOIUrl":"10.1016/j.asoc.2025.112820","url":null,"abstract":"<div><div>In the burgeoning field of autonomous maritime operations, efficiently coordinating and navigating multiple unmanned surface vehicles (USVs) in dynamic environments is a significant challenge. This study presents an enhanced Q-learning algorithm designed to improve pathfinding for multiple USVs in such settings. The algorithm innovates on the traditional Q-learning framework by adjusting the learning rate, Epsilon-greedy strategy, and penalty and reward functions, integrating a collision avoidance mechanism specifically tailored for complex maritime navigation. Extensive simulations across six diverse scenarios – ranging from single to multiple USVs operations in both static and dynamic obstacle environments – demonstrate the algorithm’s superior adaptability and efficiency compared to existing methods. Notably, in single USV scenarios, the improved Q-learning algorithm not only plots more direct paths but also reduces computational demands significantly over traditional path planning methods such as the A<span><math><msup><mrow></mrow><mrow><mo>∗</mo></mrow></msup></math></span> and APF algorithms. In multi-USV scenarios, it demonstrates robust performance, reducing calculation times by an average of 55.51% compared to SARSA, 49.14% compared to the original Q-learning, and 45.26% compared to the Speedy Q-learning approach. These advancements underscore the algorithm’s potential to enhance autonomous maritime navigation, laying a strong foundation for future improvements in the safety and efficiency of USV operations.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"172 ","pages":"Article 112820"},"PeriodicalIF":7.2,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Taxonomy of metrics for effectively estimating quantum software projects: A fuzzy-AHP based analysis

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-02-08 DOI: 10.1016/j.asoc.2025.112816

Mohammad Shameem , Mohammad Nadeem , Mahmood Niazi , Sajjad Mahmood , Ankur Kumar

{"title":"Taxonomy of metrics for effectively estimating quantum software projects: A fuzzy-AHP based analysis","authors":"Mohammad Shameem , Mohammad Nadeem , Mahmood Niazi , Sajjad Mahmood , Ankur Kumar","doi":"10.1016/j.asoc.2025.112816","DOIUrl":"10.1016/j.asoc.2025.112816","url":null,"abstract":"<div><div>Quantum computing represents a revolutionary shift in computing, yet developing quantum software is significantly more complex than traditional software engineering. Existing research provides limited guidance on estimating costs, development efforts, and timelines within this emerging paradigm. This lack of guidance leaves a critical gap for software organizations aiming to manage quantum projects effectively. To address this gap, the proposed study investigates the key metrics influencing estimation in quantum software development. Through a comprehensive literature review, we identified 13 critical metrics categorized into four groups: technical complexity, resource availability, team expertise, and project environment. In the next phase, a survey-based empirical study was conducted to validate the identified metrics and their categories. Additionally, we applied the fuzzy-AHP method to determine the relative significance of each metric. Our results culminate in a prioritized taxonomical framework that provides a structured approach for managing quantum software development estimations. The findings suggest that adopting the proposed framework can significantly enhance overall project management within the quantum software engineering domain.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"172 ","pages":"Article 112816"},"PeriodicalIF":7.2,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Metaheuristic search algorithms in frequency constrained truss problems: Four improved evolutionary algorithms, optimal solutions and stability analysis

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-02-07 DOI: 10.1016/j.asoc.2025.112854

Hasan Tahsin Öztürk , Hamdi Tolga Kahraman

{"title":"Metaheuristic search algorithms in frequency constrained truss problems: Four improved evolutionary algorithms, optimal solutions and stability analysis","authors":"Hasan Tahsin Öztürk , Hamdi Tolga Kahraman","doi":"10.1016/j.asoc.2025.112854","DOIUrl":"10.1016/j.asoc.2025.112854","url":null,"abstract":"<div><div>Truss problems with <u>f</u>requency <u>c</u>onstraints (TPFCs) are among the most complex real-world engineering optimization problems in the literature due to the non-linearity of the objective and constraint functions and the geometric structure of the search spaces. These problems have many local solutions due to the irregular geometric structure of the search spaces. Therefore, it is a challenge for meta-heuristic search (MHS) algorithms to converge stably to the global optimum solution for TPFCs. To overcome this challenge, this paper presents four new algorithms with improved performance for the optimization of TPFCs. The methodology of the research and the contributions to the literature are as follows: (i) a TPFC benchmark suite consisting of five different problem types was presented, (ii) for each problem in the benchmark suite, 152 different MHS algorithms were tested and the ones with the best convergence performance were identified, (iii) the update mechanisms of these algorithms that perform competitively on TPFCs were redesigned using the Natural Survivor Method (NSM). Thus, four different MHS algorithms with improved performance were proposed for the optimization of TPFCs, (iv) the optimal solutions for TPFCs were presented, (v) the stability of the proposed algorithms for TPFCs was analyzed and the times and success rates of finding feasible solutions were presented. According to the results of the statistical analysis, the optimal and feasible solutions for the 10/37/52/72/200 bar truss problems were found by the NSM-MadDE, NSM-LSHADE-CnEpSin, NSM-LSHADE-SPACMA and NSM-BO algorithms introduced in this paper.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"171 ","pages":"Article 112854"},"PeriodicalIF":7.2,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Pythagorean cubic fuzzy multiple attributes group decision method for sustainable supply chain management

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-02-07 DOI: 10.1016/j.asoc.2025.112802

Fei Wang

{"title":"Pythagorean cubic fuzzy multiple attributes group decision method for sustainable supply chain management","authors":"Fei Wang","doi":"10.1016/j.asoc.2025.112802","DOIUrl":"10.1016/j.asoc.2025.112802","url":null,"abstract":"<div><div>A Pythagorean cubic fuzzy set (PCFS) is composed of Pythagorean fuzzy values and interval details. Unlike interval Pythagorean fuzzy sets, PCFS contains more data and can be valuable in complex multi-attribute group decision making (MAGDM). However, as a novel fuzzy set, certain essential principles of PCFS, such as the scoring function's implausibility and the absence of operations, require improvement. To address these concerns, we have refined the PCFS scoring function and introduced a new PCFS operation. Additionally, we have developed a PCFS reliability measure to account for uncertain expert opinions and attribute weights in MAGDM. Furthermore, overcoming the challenge of collecting PCFS evaluation data presents an obstacle. In the context of content distribution, the Heronian-mean (HM) operator tackles attribute association. While most existing Pythagorean-cubic fuzzy aggregation operators have an algebraic nature, we leverage the HM operator to establish a variety of Pythagorean cubic fuzzy aggregation operators. These operators showcase properties such as equivalence, monotonicity, boundedness, and commutative invariance. Finally, grounded in the Pythagorean cubic fuzzy HM aggregation operator, we introduce a MAGDM approach for sustainable supply chain management (SSCM). We conduct a practicality and superiority comparison with the existing Pythagorean cubic fuzzy aggregation operator. The primary contribution of this article is to enrich the research on aggregation operators of PCFS and expand their social applications in the realm of SSCM.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"172 ","pages":"Article 112802"},"PeriodicalIF":7.2,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Application of personalized individual semantics in MAGDM with preference information

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-02-07 DOI: 10.1016/j.asoc.2025.112822

Hongbin Liu , Zhuoyu Xu

{"title":"Application of personalized individual semantics in MAGDM with preference information","authors":"Hongbin Liu , Zhuoyu Xu","doi":"10.1016/j.asoc.2025.112822","DOIUrl":"10.1016/j.asoc.2025.112822","url":null,"abstract":"<div><div>Subjective and objective evaluations are often utilized simultaneously in Multi-Attribute Group Decision Making (MAGDM) problems. Decision makers’ different understanding of linguistic terms, i.e., personalized individual semantics, may influence decision making results. In this study, we propose a novel MAGDM model to deal with these problems based on two types of preference information: an objective multi-attribute linguistic decision matrix and subjective alternative rankings. In the first stage, we introduce a consistency-driven model to obtain the personalized interval numerical scales associated with each linguistic term and attribute weights. In the second stage, we introduce dynamic personalized individual semantics to maximize consensus by minimizing the discrepancy between individual opinions and collective opinion. The optimal alternative is then determined based on overall scores of the alternatives. We finally apply the proposed model in the selection of new energy vehicles, and conduct simulation experiments and comparative analysis. The results show that considering personalized individual semantics and weights of the decision makers brings much benefit for consensus reaching process in group decision making. A high group consensus level can be reached in this model, and the computational complexity of this model is acceptable.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"171 ","pages":"Article 112822"},"PeriodicalIF":7.2,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Deep neural network-based intelligent health monitoring system for oil and gas pipelines

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-02-06 DOI: 10.1016/j.asoc.2025.112827

Mohamed Almahakeri , Ahmad Jobran Al-Mahasneh , Mohammed Abu Mallouh , Basel Jouda

{"title":"Deep neural network-based intelligent health monitoring system for oil and gas pipelines","authors":"Mohamed Almahakeri , Ahmad Jobran Al-Mahasneh , Mohammed Abu Mallouh , Basel Jouda","doi":"10.1016/j.asoc.2025.112827","DOIUrl":"10.1016/j.asoc.2025.112827","url":null,"abstract":"<div><div>Oil and gas pipelines are critical infrastructures that require continuous monitoring to ensure public safety and prevent economic losses. This paper addresses the challenges associated with pipeline failures by proposing a Deep Neural Network (DNN)-based Structural Health Monitoring (SHM) system for real-time monitoring of oil and gas pipelines. The system utilizes installed transducers and ultrasound guided waves to collect data about the structural health without the need for pipeline shutdown. The DNN-based SHM system predicts three crucial crack parameters: crack location, width, and depth. The performance of the proposed system is compared with five commonly used Machine Learning (ML) approaches. The results demonstrate that the DNN-based SHM system outperforms the other ML-based systems, achieving 18 % less prediction error than the most accurate of the other ML approaches. Moreover, the average prediction accuracy with the proposed DNN approach for crack location, width, and depth were 97 %, 93 % and 96 %, respectively. The findings highlight the potential of DNNs for accurate and efficient pipeline health monitoring, contributing to improved decision-making and safe pipeline operations.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"171 ","pages":"Article 112827"},"PeriodicalIF":7.2,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Two-stage optimized unified adversarial patch for attacking visible-infrared cross-modal detectors in the physical world

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-02-06 DOI: 10.1016/j.asoc.2025.112818

Chengyin Hu , Weiwen Shi , Wen Yao , Tingsong Jiang , Ling Tian , Wen Li

{"title":"Two-stage optimized unified adversarial patch for attacking visible-infrared cross-modal detectors in the physical world","authors":"Chengyin Hu , Weiwen Shi , Wen Yao , Tingsong Jiang , Ling Tian , Wen Li","doi":"10.1016/j.asoc.2025.112818","DOIUrl":"10.1016/j.asoc.2025.112818","url":null,"abstract":"<div><div>Visible-infrared cross-modal object detectors, leveraging both visible and infrared imaging technologies, play a pivotal role in vision-based systems. However, they necessitate thorough security scrutiny due to the risks posed by physical adversarial attacks, which employ tailored physical inputs to deceive vision-based models, posing significant peril to the integrity of vision-based systems. While previous research has predominantly focused on the security of visible and infrared detectors individually, real-world scenarios often necessitate the use of visible-infrared cross-modal detectors with heightened reliability. However, there is a notable dearth of comprehensive security evaluations for these hybrid systems. Despite some efforts to explore attacks on cross-modal detectors, developing a robust and practical strategy remains a significant challenge. This study introduces TOUAP, a novel two-stage adversarial patch technique designed specifically for real-world, black-box visible-infrared detectors. TOUAP initiates with octagonal-shape optimization to create infrared adversarial samples, leveraging this to disrupt infrared detection. Subsequently, it generates visible patches resembling color QR codes while preserving the geometry of the infrared patch for precise cropping, thereby undermining the visible detector. The extensive experimental validation in both digital and physical domains emphatically underscores the superior effectiveness and robustness of TOUAP, outperforming conventional baseline methods convincingly.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"171 ","pages":"Article 112818"},"PeriodicalIF":7.2,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Small aerial object detection through GAN-integrated feature pyramid networks

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-02-05 DOI: 10.1016/j.asoc.2025.112834

Usman Ahmad , Jing Liang , Tianlei Ma , Kunjie Yu , Faisal Mehmood , Farhad Banoori

{"title":"Small aerial object detection through GAN-integrated feature pyramid networks","authors":"Usman Ahmad , Jing Liang , Tianlei Ma , Kunjie Yu , Faisal Mehmood , Farhad Banoori","doi":"10.1016/j.asoc.2025.112834","DOIUrl":"10.1016/j.asoc.2025.112834","url":null,"abstract":"<div><div>Small Aerial Object Detection (SAOD) is a pivotal research domain in computer vision, with significant applications in environmental regulation, intelligent surveillance, and autonomous vehicles. However, SAOD remains challenging due to low resolution, background noise, and variable object sizes. In this study, we propose a novel Feature Pyramid Generative Adversarial Network (FPGAN) to address these issues. FPGAN enhances feature extraction across multiple scales, improving precision, recall, and accuracy in detecting small aerial objects of diverse sizes. Furthermore, we integrate an Edge Sharpening Network (ESN) using the U-Net architecture to mitigate noise and distortions generated during adversarial learning, resulting in the FPGAN+ESN model. Extensive experiments on three SAOD datasets, namely DOTA, COWC, and OGST, demonstrate that our model outperforms state-of-the-art methods, showcasing remarkable improvements in detection accuracy. The proposed FPGAN+ESN approach enhances the resolution of small aerial objects and improves edge quality, leading to more robust and efficient SAOD. Our findings underscore the potential of the FPGAN+ESN model for tackling the complexities associated with SAOD tasks.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"171 ","pages":"Article 112834"},"PeriodicalIF":7.2,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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