IEEE Transactions on Computational Social Systems最新文献_第9页

Incentivizing Socio-Ethical Integrity in Decentralized Machine Learning Ecosystems for Collaborative Knowledge Sharing 在分散的机器学习生态系统中激励社会伦理诚信以促进协作知识共享

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-17 DOI: 10.1109/TCSS.2024.3450494

Yuanfang Chi;Qiyue Zhang;Jiaxiang Sun;Wei Cai;Z. Jane Wang;Victor C. M. Leung

{"title":"Incentivizing Socio-Ethical Integrity in Decentralized Machine Learning Ecosystems for Collaborative Knowledge Sharing","authors":"Yuanfang Chi;Qiyue Zhang;Jiaxiang Sun;Wei Cai;Z. Jane Wang;Victor C. M. Leung","doi":"10.1109/TCSS.2024.3450494","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3450494","url":null,"abstract":"To broaden domain knowledge and enable advanced analytics, machine learning (ML) algorithms increasingly utilize comprehensive datasets across diverse sectors. However, these disparate datasets held by various stakeholders raise concerns over data heterogeneity, privacy, and security. Decentralized ML research aims to protect data privacy and integrate knowledge bases, especially knowledge graphs, to address data heterogeneity challenges. Yet, the question of how to foster trustworthy collaborations in decentralized ML ecosystems remains underexplored. This study pioneers two innovative socio-economic mechanisms designed to ensure dependable collaborations with socio-ethical integrity within a decentralized knowledge inference framework, enabling participants to share knowledge while maintaining data privacy and ethical standards. We employ an evolutionary game theory model to analyze the dynamic interactions between requestors and workers, focusing on achieving a stable equilibrium through theoretical and numerical evaluations. Furthermore, we explore how various critical factors, such as incentive schemes and the accuracy of identifying malicious workers, influence the system's equilibrium, providing insights into optimizing collaborative efforts in decentralized ML ecosystems.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"7857-7870"},"PeriodicalIF":4.5,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

ReOP: Generating Transferable Fake Users for Recommendation Systems via Reverse Optimization ReOP：通过反向优化为推荐系统生成可转移的假用户

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-17 DOI: 10.1109/TCSS.2024.3451452

Fulan Qian;Yan Cui;Hai Chen;Wenbin Chen;Yuanting Yan;Shu Zhao

{"title":"ReOP: Generating Transferable Fake Users for Recommendation Systems via Reverse Optimization","authors":"Fulan Qian;Yan Cui;Hai Chen;Wenbin Chen;Yuanting Yan;Shu Zhao","doi":"10.1109/TCSS.2024.3451452","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3451452","url":null,"abstract":"Recent research has demonstrated that recommendation systems exhibit vulnerability under data poisoning attacks. The primary process of data poisoning attacks involves generating malicious data (i.e., fake users) through surrogate models and injecting the malicious data into the target models’ datasets, thereby manipulating the output results of the target models. However, current methods generating fake users based on gradient descent may cause them to fall into undesired local minimum in the loss landscape and overfitting to the surrogate model, thus limiting the performance of attacking other recommendation models. To address this problem, we propose the reverse optimization algorithm (ReOP), which utilizes the reverse direction of optimization to update fake users, enabling them to steer clear of sharp local minimum in loss landscape and navigate towards the flat local minimum. ReOP makes fake users less sensitive to model changes, alleviates their overfitting to the surrogate model, and thus significantly improves the transferability of fake users. Experimental results demonstrate that ReOP surpasses the state-of-the-art baseline methods, effectively generating fake users with significant attack effects on various target models.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"7830-7845"},"PeriodicalIF":4.5,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Semantic Disentangling for Audiovisual Induced Emotion 视听诱发情绪的语义解纠结

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-16 DOI: 10.1109/TCSS.2024.3450717

Qunxi Dong;Wang Zheng;Fuze Tian;Lixian Zhu;Kun Qian;Jingyu Liu;Xuan Zhang

{"title":"Semantic Disentangling for Audiovisual Induced Emotion","authors":"Qunxi Dong;Wang Zheng;Fuze Tian;Lixian Zhu;Kun Qian;Jingyu Liu;Xuan Zhang","doi":"10.1109/TCSS.2024.3450717","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3450717","url":null,"abstract":"Emotions regulation play an important role in human behavior, but exhibit considerable heterogeneity among individuals, which attenuates the generalization ability of emotion models. In this work, we aim to achieve robust emotion prediction through efficient disentanglement of affective semantic representations. In detail, the data generation mechanism behind observations from different perspectives is causally set, where latent variables that relate to emotion are explicitly separate into three parts: the intrinsic-related part, the extrinsic-related part, and the spurious-related part. Affective semantic features consist of the first two parts, with the understanding that spurious latent variables generate the inherent biases in the data. Furthermore, a variational autoencoder with a reformulated objective function is proposed to learn such disentangled latent variables, and only adopts semantic representations to perform the final classification task, avoiding the interference of spurious variables. In addition, for electroencephalography (EEG) data used in this article, a space-frequency mapping method is introduced to improve information utilization. Comprehensive experiments on popular emotion datasets show that the proposed method can achieve competitive intersubject generalization performance. Our results highlight the potential of efficient latent representation disentanglement in addressing the complexity challenges of emotion recognition.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"928-936"},"PeriodicalIF":4.5,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

iCyberGuard: A FlipIt Game for Enhanced Cybersecurity in IIoT iCyberGuard：工业物联网中增强网络安全的FlipIt游戏

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-16 DOI: 10.1109/TCSS.2024.3443174

Xiaoguang Chen;Wenyuan Cao;Lili Chen;Jinpeng Han;Manzhi Yang;Zhen Wang;Fei-Yue Wang

{"title":"iCyberGuard: A FlipIt Game for Enhanced Cybersecurity in IIoT","authors":"Xiaoguang Chen;Wenyuan Cao;Lili Chen;Jinpeng Han;Manzhi Yang;Zhen Wang;Fei-Yue Wang","doi":"10.1109/TCSS.2024.3443174","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3443174","url":null,"abstract":"Social manufacturing has significantly advanced the industrial Internet of Things (IIoT), integrating information technology and operation technology to enhance production efficiency and quality, and to foster new business models. This integration, however, introduces novel risks, including advanced persistent threats, which demand robust security measures to safeguard IIoT systems. This article proposes an iCyberGuard game model, tailored for IIoT environments, designed to imitate the cyber and physical attacks for information and operation technologies. Then, we used a reinforcement learning algorithm to compute the optimal strategy. We conducted comprehensive simulation experiments, which demonstrate that our model the strategic interactions between attackers and defenders. Participants are enabled to learn adaptively, discerning optimal strategies based on the intelligence of their adversaries. Finally, we explain the practical significance of the best strategy of defenders or attackers, and how users can rely on these best strategies to strengthen the security performance of the network.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"8005-8014"},"PeriodicalIF":4.5,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Review on Machine Theory of Mind 机器心智理论研究综述

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-13 DOI: 10.1109/TCSS.2024.3416707

Yuanyuan Mao;Shuang Liu;Qin Ni;Xin Lin;Liang He

{"title":"A Review on Machine Theory of Mind","authors":"Yuanyuan Mao;Shuang Liu;Qin Ni;Xin Lin;Liang He","doi":"10.1109/TCSS.2024.3416707","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3416707","url":null,"abstract":"Theory of Mind (ToM) is the ability to attribute mental states to others, an important component of human cognition. At present, there has been growing interest in the artificial intelligence (AI) with cognitive abilities, for example in healthcare and the motoring industry. Research indicates that infants exhibit early signs in cognitive and social understanding, including some basic abilities related to beliefs, desires, and intentions (BDIs). Thus, the ability to attribute BDIs to others is also crucial for the development of machine ToM. In this article, we review recent progress in machine ToM on BDIs. And we shall introduce the experiments, datasets, and methods of machine ToM on these three aspects, summarize the development of different tasks and datasets in recent years, and compare well-behaved models in aspects of advantages, limitations, and applicable conditions, hoping that this study can guide researchers to quickly keep up with latest trend in this field. Unlike other domains with a specific task and resolution framework, machine ToM lacks a unified instruction and a series of standard evaluation tasks, which make it difficult to formally compare the proposed models. And the existing models still cannot exhibit the same ToM reasoning ability as real humans, lack of transferability, interpretability, few-shot learning, etc. We argue that, one method to address this difficulty is now to present a standard assessment criteria and dataset, better a large-scale dataset covered multiple aspects of ToM. Besides, for developing an AI of ToM, it requires the cooperation of experts from various domains.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"7114-7132"},"PeriodicalIF":4.5,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

An Approach Toward Stock Market Prediction and Portfolio Optimization in Indian Financial Sectors 印度金融部门股票市场预测与投资组合优化研究

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-12 DOI: 10.1109/TCSS.2024.3450291

Manali Patel;Krupa Jariwala;Chiranjoy Chattopadhyay

{"title":"An Approach Toward Stock Market Prediction and Portfolio Optimization in Indian Financial Sectors","authors":"Manali Patel;Krupa Jariwala;Chiranjoy Chattopadhyay","doi":"10.1109/TCSS.2024.3450291","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3450291","url":null,"abstract":"In this article, we aim at predicting future stock price movements and recommending a profitable portfolio for the NIFTY-50 stocks. Stock market prediction is a challenging task due to multiple influencing factors, its nonlinear and volatile nature, and complex interdependencies. Recent approaches have neglected the interconnections between stocks and relied on predefined static relationships. The collection of relational data is difficult to access due to confidentiality and privacy agreements for emerging economies. Moreover, these predefined relationships lack the ability to explain the latent interactions between stocks. This work proposes a data-driven end-to-end framework, dynamic relation aware relational temporal network (DR2TNet), that learns the hidden intra- and intersector associations between stock pairs and temporal patterns. A financial knowledge graph is built from historical data and is updated dynamically during the training process to reflect the interactions between the stocks according to the current market situation. We have proposed a new loss function that considers prediction loss and directional movement loss to train a model. The applicability of prediction results obtained by DR2TNet is demonstrated in the portfolio optimization problem. The results show a higher return compared to other existing baseline models.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 1","pages":"128-139"},"PeriodicalIF":4.5,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Real-Time Driver and Traffic Data Integration for Enhanced Road Safety 实时驾驶员和交通数据集成提高道路安全

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-11 DOI: 10.1109/TCSS.2024.3448400

Yufei Huang;Shan Jiang;Mohsen Jafari;Peter J. Jin

{"title":"Real-Time Driver and Traffic Data Integration for Enhanced Road Safety","authors":"Yufei Huang;Shan Jiang;Mohsen Jafari;Peter J. Jin","doi":"10.1109/TCSS.2024.3448400","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3448400","url":null,"abstract":"Traditional roadway safety assessment heavily relies on historical crash data, overlooking real-time factors such as driver behaviors and current traffic conditions and lacking forward-looking analysis for predicting future trends. This study introduces an enhanced innovative data fusion method based on the safe route mapping (SRM) methodology with combined use of historical crash data and real-time data, leveraging a custom-built Android app to amalgamate road and vehicle data effectively, showcasing notable advancements in real-time risk assessment. The enhanced safe route mapping (ESRM) framework monitors driver actions and road conditions meticulously. Data collected from drivers is analyzed on a central server using facial recognition algorithm to detect signs of fatigue and distractions, assessing overall driving competence. Simultaneously, roadside cameras capture live traffic data, analyzed using a specialized video analytics method to track vehicle speed and paths. The fusion of these data streams enables the introduction of a predictive model, Light gradient boosting machine (GBM), forecasting potential immediate issues for drivers. Predicted risk scores are integrated with historical crash data using a Fuzzy logic model, delineating risk levels for different road sections. The performance of ESRM model is tested using real-world data and a driving simulation, demonstrating remarkable accuracy, especially in accounting for real-time fusion of driver behavior and traffic conditions. The resultant visual risk heatmap aids authorities in identifying safer routes, proactive law enforcement deployment, and informed trip planning based on real-time risk levels. This study not only underscores the importance of real-time data in roadway safety but also paves the way for data-driven, dynamic risk assessment models, potentially reducing road accidents and fostering a safer driving environment.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"7711-7722"},"PeriodicalIF":4.5,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142761537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Empathetic Response Generation With Self and Other-Imagine Graph 用自我和他人想象图产生共情反应

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-11 DOI: 10.1109/TCSS.2024.3424424

Xun Wang;Zhen Liu;Tingting Liu;Zheng Fang

{"title":"Empathetic Response Generation With Self and Other-Imagine Graph","authors":"Xun Wang;Zhen Liu;Tingting Liu;Zheng Fang","doi":"10.1109/TCSS.2024.3424424","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3424424","url":null,"abstract":"Empathy, an essential quality in daily human conversations, plays a crucial role in dialogue systems. In recent years, there has been a surge of interest among researchers in developing empathetic response generation. However, existing methods often ignore the high-level process of generating empathy-imagination, which involves consciously putting oneself in the other person's shoes during a conversation. To solve this critical problem, we propose a novel approach called EmpSOI that adopts self and other-imagine to generate empathetic responses. Specifically, we design two heterogeneous graphs to incorporate the two different imaginative perspectives: the self-imagine perspective and the other-imagine perspective, which enables the model to empathize with the user from different perspectives. Besides, we incorporate a gating mechanism to regulate the contribution of imagination information from these two perspectives during the response generation stage. The mechanism enables the model to differentiate between two imaginative perspectives. The results of extensive automatic and manual experiments illustrate the advantages of our model compared to other comparative models in perceiving the user's emotional state and generating empathetic responses.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"7801-7813"},"PeriodicalIF":4.5,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

User Experience of Different Groups in Social VR Applications: An Empirical Study Based on User Reviews 社交VR应用中不同群体的用户体验：基于用户评论的实证研究

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-10 DOI: 10.1109/TCSS.2024.3416208

Jiong Dong;Kaoru Ota;Mianxiong Dong

{"title":"User Experience of Different Groups in Social VR Applications: An Empirical Study Based on User Reviews","authors":"Jiong Dong;Kaoru Ota;Mianxiong Dong","doi":"10.1109/TCSS.2024.3416208","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3416208","url":null,"abstract":"Social virtual reality (VR) applications provide a diverse and evolving ecosystem for different groups to socialize in VR. Understanding how people explore social VR applications is crucial for VR developers, such as designing social VR content. Previous work has focused on interviewing participants to study the user experience (UX) of social VR. However, the potential value of user reviews of social VR platforms is largely unexplored. In this article, we collect 105 757 user reviews of nine social VR applications from two digital distribution platforms (Steam and Oculus) to study the impact of social VR on people by in-depth analysis of reviews related to avatars, harassment, and physical reactions of different groups. We observe that players prefer avatar customization, and social VR applications are suitable places for some groups, such as lesbian, gay, bisexual, transgender, queer (LGBTQ). However, there are also many complaints from players about harassment and bullying in these social VR applications. Our findings highlight potential design implications of social VR applications for creating more friendly and fulfilling social VR experiences for users.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"7133-7145"},"PeriodicalIF":4.5,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Multigroup Multirole Assignment Multigroup多角色分配

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-10 DOI: 10.1109/TCSS.2024.3447902

Zhihang Yu;Cong Guo;Libo Zhang;Haibin Zhu;Bo Wang

{"title":"Multigroup Multirole Assignment","authors":"Zhihang Yu;Cong Guo;Libo Zhang;Haibin Zhu;Bo Wang","doi":"10.1109/TCSS.2024.3447902","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3447902","url":null,"abstract":"Role-based collaboration (RBC) theory is a promising paradigm for problem-solving in complex systems. Multigroup role assignment (MGRA) specifically tackles the task of assigning roles for multigroup collaboration. However, due to the constraint that an agent can only play a role in one group, the current MGRA models are incapable of handling when required agents outnumber the available supply. Group multirole assignment (GMRA) resolves the problem by permitting an agent to be assigned multiple roles, but it cannot address the assignment involving multiple environments-classes, agents, roles, groups, objects (E-CARGO) groups. Therefore, this article presents a comprehensive overview of the GMRA problem in multiple E-CARGO groups under various conditions, generalized as the multigroup multirole assignment (MGMRA) problem. The MGMRA problem primarily revolves around two key factors: the maximum number of roles that an agent can undertake within an E-CARGO group, and the maximum number of different roles across all E-CARGO groups, which have a significant impact on the sufficiency or necessity conditions of the algorithm as well as its performance. Therefore, a unified model and its special cases are proposed to solve the concrete assignment problems under different conditions. The effectiveness of models is verified through comprehensive experiments.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 1","pages":"259-273"},"PeriodicalIF":4.5,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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