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

MultiSEAO-Mix: A Multimodal Multitask Framework for Sentiment, Emotion, Support, and Offensive Analysis in Code-Mixed Setting 多seao - mix：语码混合环境下情感、情感、支持和攻击性分析的多模态多任务框架

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-10-18 DOI: 10.1109/TCSS.2024.3430821

Gopendra Vikram Singh;Mamta;Atul Verma;Asif Ekbal

{"title":"MultiSEAO-Mix: A Multimodal Multitask Framework for Sentiment, Emotion, Support, and Offensive Analysis in Code-Mixed Setting","authors":"Gopendra Vikram Singh;Mamta;Atul Verma;Asif Ekbal","doi":"10.1109/TCSS.2024.3430821","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3430821","url":null,"abstract":"Social media platforms have become an open door for users to share their views, resulting in a growing trend of offensive content being shared on social media. Detecting and addressing offensive content is crucial due to its significant impact on society. Although there has been extensive research on the detection of offensive content in the English language, there is a notable gap in detecting offensive content in multimodal settings involving code-mixed languages. In this article, we propose a large scale multimodal code-mixed dataset for Hinglish (Hindi+English) <italic>MultiSEAO-Mix</i> focusing on women and children. The <italic>MultiSEAO-Mix</i> is annotated with offensiveness, sentiment, emotion, and their respective intensities. Additionally, it is also annotated with author support. A multimodal, multitask framework is proposed that considers offensive detection, intensity prediction, and author support as the primary tasks and improves their performance using sentiment, emotion, and corresponding intensities as the auxiliary tasks. Further, we propose a fusion technique that captures the enhanced multimodal representation to improve the performance of our model. Experimental results demonstrate that the proposed multitask framework improves the model performance by more than 4.5 points compared to multitask system without sentiment and emotion as the auxiliary tasks.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 1","pages":"101-112"},"PeriodicalIF":4.5,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361494","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

Tradeoff Between Capacity and Cost: Maximizing User Recruitment Through Collaboration in Mobile Crowdsensing 容量和成本之间的权衡：通过移动众感合作最大化用户招募

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-10-17 DOI: 10.1109/TCSS.2024.3473297

Dingwen Chi;Jun Tao;Haotian Wang;Yifan Xu

{"title":"Tradeoff Between Capacity and Cost: Maximizing User Recruitment Through Collaboration in Mobile Crowdsensing","authors":"Dingwen Chi;Jun Tao;Haotian Wang;Yifan Xu","doi":"10.1109/TCSS.2024.3473297","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3473297","url":null,"abstract":"Utilizing mobile crowdsensing (MCS) for data collection and analysis has become a prominent paradigm in the Internet of Things (IoTs). However, the existing research predominantly focuses on platform-user interactions, often neglecting the potential for user collaboration, which is crucial for improving data quality and task efficiency. In practical applications, mobile users tend to cooperate with familiar individuals based on their preferences in sensing tasks. To tackle this issue, we introduce a novel MCS model that integrates user cooperation, significantly enhancing the system's overall effectiveness. Specifically, users’ capabilities and costs are synthesized and managed through a cooperation degree matrix. Additionally, cooperation is updated based on historical behaviors and user preferences. To incentivize user participation, currencies are employed for recruitment. Within this framework, we investigate the maximum collaborative user selection (MCUS) problem, which is dedicated to the problem of maximizing the amount of recruitment under user cooperation. The MCUS problem is proved to be an NP-hard problem and thus intractable. To address this, we propose the minimum weighted cost replacement (MWCR) algorithm. Experimental results demonstrate that the MWCR algorithm exhibits low complexity and high efficiency across various scales, making it an excellent solution for collaborative crowd recruitment.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 1","pages":"295-305"},"PeriodicalIF":4.5,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361061","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 Novel Retrospective-Reading Model for Detecting Chinese Sarcasm Comments of Online Social Network 一种基于回顾阅读的在线社交网络中文讽刺评论检测模型

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-10-17 DOI: 10.1109/TCSS.2024.3470317

Lei Zhang;Xiaoming Zhao;Qinfeng Mao;Yanbing Yang;Dong Li;Haizhou Wang

{"title":"A Novel Retrospective-Reading Model for Detecting Chinese Sarcasm Comments of Online Social Network","authors":"Lei Zhang;Xiaoming Zhao;Qinfeng Mao;Yanbing Yang;Dong Li;Haizhou Wang","doi":"10.1109/TCSS.2024.3470317","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3470317","url":null,"abstract":"Through the use of sarcastic sentences on social media, people can express their strong emotions. Therefore, the detection of sarcasm in social media has received more and more attention over the past years. Classifying a sentence as sarcastic or nonsarcastic heavily relies on the contextual information of the sentence. However, only focusing on the features of target text is the main solution of most existing research. Moreover, the scale of publicly available Chinese sarcasm dataset is very small and does not contain the contextual information. To address the issues mentioned above, we build a Chinese sarcasm dataset from Bilibili, which is one of the most widely used social network platforms in China and has a significant number of sarcastic comments and contextual information. As far as we know, our dataset is the first publicly available large-scale Chinese sarcasm dataset including contextual information. Additionally, we have proposed a novel retrospective reading method for detecting sarcasm that leverages contextual information to improve model's performance. The experimental results show the effectiveness of the proposed model and the significance of contextual information for Chinese sarcasm detection: achieving the highest F-score of 0.6942, outperforming existing state-of-the-art (SOTA) approaches. The study presented in this article offers approaches and ideas for future Chinese sarcasm detection studies.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"792-806"},"PeriodicalIF":4.5,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769472","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

Conversational Crowdsensing in the Age of Industry 5.0: A Parallel Intelligence and Large Models Powered Novel Sensing Approach 工业5.0时代的会话式群体感知：一种并行智能和大模型驱动的新型感知方法

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-10-15 DOI: 10.1109/TCSS.2024.3451649

Zhengqiu Zhu;Yong Zhao;Sihang Qiu;Kai Xu;Quanjun Yin;Jincai Huang;Zhong Liu;Fei-Yue Wang

{"title":"Conversational Crowdsensing in the Age of Industry 5.0: A Parallel Intelligence and Large Models Powered Novel Sensing Approach","authors":"Zhengqiu Zhu;Yong Zhao;Sihang Qiu;Kai Xu;Quanjun Yin;Jincai Huang;Zhong Liu;Fei-Yue Wang","doi":"10.1109/TCSS.2024.3451649","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3451649","url":null,"abstract":"The transition from cyber-physical-system-based (CPS-based) Industry 4.0 to cyber-physical-social-system-based (CPSS-based) Industry 5.0 brings new requirements and opportunities to current sensing approaches, especially in light of recent progress in large language models (LLMs) and retrieval augmented generation (RAG). Therefore, the advancement of parallel intelligence powered crowdsensing intelligence (CSI) is witnessed, which is currently advancing toward linguistic intelligence. In this article, we propose a novel sensing paradigm, namely conversational crowdsensing, for Industry 5.0 (especially for social manufacturing). It can alleviate workload and professional requirements of individuals and promote the organization and operation of diverse workforce, thereby facilitating faster response and wider popularization of crowdsensing systems. Specifically, we design the architecture of conversational crowdsensing to effectively organize three types of participants (biological, robotic, and digital) from diverse communities. Through three levels of effective conversation (i.e., interhuman, human–AI, and inter-AI), complex interactions and service functionalities of different workers can be achieved to accomplish various tasks across three sensing phases (i.e., requesting, scheduling, and executing). Moreover, we explore the foundational technologies for realizing conversational crowdsensing, encompassing LLM-based multiagent systems, scenarios engineering and conversational human–AI cooperation. Finally, we present potential applications of conversational crowdsensing and discuss its implications. We envision that conversations in natural language will become the primary communication channel during crowdsensing process, enabling richer information exchange and cooperative problem-solving among humans, robots, and AI.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"8046-8063"},"PeriodicalIF":4.5,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789123","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

Altruism in Fuzzy Reinforcement Learning 模糊强化学习中的利他主义

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-10-14 DOI: 10.1109/TCSS.2024.3460653

Rachel M. Haighton;Howard M. Schwartz;Sidney N. Givigi

引用次数: 0

Shilling Attacks and Fake Reviews Injection: Principles, Models, and Datasets 先令攻击和虚假评论注入：原理、模型和数据集

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-10-14 DOI: 10.1109/TCSS.2024.3465008

Dina Nawara;Ahmed Aly;Rasha Kashef

{"title":"Shilling Attacks and Fake Reviews Injection: Principles, Models, and Datasets","authors":"Dina Nawara;Ahmed Aly;Rasha Kashef","doi":"10.1109/TCSS.2024.3465008","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3465008","url":null,"abstract":"Recommendation systems have proved to be a compelling performance in overcoming the data overload problem in many domains, such as e-commerce, e-health, and transportation. Recommender systems guide users/clients to personalized recommendations based on their preferences. However, some recommendation systems are vulnerable to shilling attacks, which create rating biases or fake reviews that will eventually affect the authenticity and integrity of the generated recommendations. This survey comprehensively covers various shilling attack methods, including high-knowledge, low-knowledge attacks, and obfuscated attacks. It explores malicious review generators that generate fake text. In addition to that, this survey covers shilling attack detection methods such as supervised, unsupervised, semisupervised, and hybrid techniques. Natural Language Processing techniques are also thoroughly explored for fake text review detection using large language models (LLMs). A wide range of detection mechanisms incorporated in the literature is examined, such as convolutional neural network (CNN), long short term memory (LSTM)-based detectors for rating-based shilling attacks, and bidirectional encoder representation (BERT) and RoBERTa-based detectors for fake reviews that are accompanied by shilling attacks, aiming to offer insights into the evolving methods of shilling attack strategies and the corresponding advancements in the detection methods.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 1","pages":"362-375"},"PeriodicalIF":4.5,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10716004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Information Propagation Dynamic Model Based on Rumors, Antirumors, Prom-Rumors, and the Dynamic Game 基于谣言、反谣言、正谣言和动态博弈的信息传播动态模型

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-10-10 DOI: 10.1109/TCSS.2024.3465023

Qian Li;Long Gao;Xiaole Guo;Xinhong Wu;Rong Wang;Yunpeng Xiao

{"title":"Information Propagation Dynamic Model Based on Rumors, Antirumors, Prom-Rumors, and the Dynamic Game","authors":"Qian Li;Long Gao;Xiaole Guo;Xinhong Wu;Rong Wang;Yunpeng Xiao","doi":"10.1109/TCSS.2024.3465023","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3465023","url":null,"abstract":"The propagation of Internet rumors has complex dynamics. The traditional propagation model, based mainly on rumor and antirumor messages, provides a static analysis of the rumor propagation situation. This article introduces the concept of a prom-rumor (promoting rumor) message and thoroughly investigates competition and cooperation among multitype rumor messages. First, we employ a multiple linear regression method to construct the information influence mechanism by extracting network structure, topic content information, and historical behavior information, considering the complexity of spreading multitype compound rumor messages. Then, we introduce game theory and formulate a game strategy to capture the competition and cooperation among rumors, antirumors, and prom-rumors. Furthermore, we consider the interactivity in multitype compound rumor messages and combine the dynamic game to construct the driving mechanism of rumors, antirumors, and prom-rumors. Finally, we define the user-state transfer equation and propose a dynamic model of information propagation based on rumors, antirumors, prom-rumors, and the dynamic game while considering the multidimensionality and polymorphism of multitype compound rumor messages and combining mean-field theory and the infectious disease model. In validation experiments on real-life data, the model accurately captures the game propagation process of the multitype rumors and predicts the propagation trends in rumor networks.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 1","pages":"376-389"},"PeriodicalIF":4.5,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106591","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

Derivative Topic Dissemination Model Based on Multitopic Iterative Derivation and Social Psychology 基于多话题迭代衍生和社会心理学的衍生话题传播模型

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-10-10 DOI: 10.1109/TCSS.2024.3463428

Rong Wang;Kexin Ma;Xiaole Guo;Shihong Wei;Zhiwei Wang;Tun Li;Yunpeng Xiao

{"title":"Derivative Topic Dissemination Model Based on Multitopic Iterative Derivation and Social Psychology","authors":"Rong Wang;Kexin Ma;Xiaole Guo;Shihong Wei;Zhiwei Wang;Tun Li;Yunpeng Xiao","doi":"10.1109/TCSS.2024.3463428","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3463428","url":null,"abstract":"As topics evolve during the dissemination process, their derivative characteristics play an important role in revealing the mechanism of topic dissemination in social networks. Due to users’ cognitive inertia, followers of antecedent topics tend to pay more attention to derivative topics than ordinary users, and this cognitive inertia is directly related to the correlation between these two topics. Based on this finding, we propose a derivation topic dissemination model based on iterative multitopic derivation and social psychology, taking into full consideration users’ emotional accumulation of antecedent topics and repeated derivation of topics. First, a multiple linear regression model is used to construct a metric algorithm for user antecedent sentiment and to effectively analyze the dynamics of antecedent sentiment accumulation affecting the spread of derivative topics. Second, to analyze the interactions between and within multitopic layers in full, an iterative multitopic dissemination model iterative-susceptible infectious recovery (SIR) is proposed. In addition, the association degree is introduced to define the cross-model state transition equation by considering the association and differences among multitopics. Last, considering the influence of cognitive inertia and “continuous attention psychology” in social psychology, we construct a user psychology-based driving force model which can further improve the cross-model state transition equation. According to the experiments, the model can effectively reveal the influence of different factors on the dissemination trend of derivative topics in social networks, as well as depict the dissemination dynamics of derivative topics.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 1","pages":"390-403"},"PeriodicalIF":4.5,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106466","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

FedSC: Game-Theoretic Design of Sustainable Contracts for Unreliable Federated Edge Learning 不可靠联邦边缘学习可持续契约的博弈论设计

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-10-09 DOI: 10.1109/TCSS.2024.3465015

Jianfeng Lu;Wenxuan Yuan;Riheng Jia;Shuqin Cao;Chen Wang;Minglu Li

{"title":"FedSC: Game-Theoretic Design of Sustainable Contracts for Unreliable Federated Edge Learning","authors":"Jianfeng Lu;Wenxuan Yuan;Riheng Jia;Shuqin Cao;Chen Wang;Minglu Li","doi":"10.1109/TCSS.2024.3465015","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3465015","url":null,"abstract":"Although promising, federated edge learning (FEL) is being plagued by unreliable clients with low-quality parameters due to tight edge association and frequent edge aggregation. Existing efforts mainly focus on setting thresholds or identifying malicious behaviors to resist unreliable clients, which comes at the cost of losing their training samples and leads to unsustainable and collaborative inefficiencies. To tackle this issue, we propose the first sustainable contract, named FedSC, which allows for sustaining truthful contributions in more general conditions including clients’ multidimensional attributes and imperfect system monitoring. Specifically, by modeling the long-term strategic behaviors of self-interested clients as a Markov decision process, we quantify the impact of client behavior on their utilities and derive the critical conditions that make the rating-based contract sustainable, thereby promoting honest participation as the optimal choice for strategic clients. Since directly deriving the optimal design of FedSC under multiple constraints and nonlinear coupling of parameters is intractable, we characterize the impact of design parameters on objective function and analytically prove the existence of closed solution. Then, through a low-time-complexity greedy-based algorithm, the optimality of sustainable contracts under different system errors is guaranteed. Extensive experiments using both synthetic and real datasets demonstrate the effectiveness and superiority of FedSC compared to the state-of-the-art baselines. Excitingly, FedSC can reduce the number of free-riders up to 34.52% and improve the amount of contributed data and model performance up to 22.98% and 8.62%, respectively.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"764-776"},"PeriodicalIF":4.5,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769384","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

Diffusion of Ordinal Opinions in Social Networks: An Agent-Based Model and Heuristics for Campaigning 社会网络中有序意见的扩散：一个基于主体的竞选模型与启发式

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-10-09 DOI: 10.1109/TCSS.2024.3458950

Xiaoxue Liu;Shohei Kato;Wen Gu;Fenghui Ren;Guoxin Su;Minjie Zhang

{"title":"Diffusion of Ordinal Opinions in Social Networks: An Agent-Based Model and Heuristics for Campaigning","authors":"Xiaoxue Liu;Shohei Kato;Wen Gu;Fenghui Ren;Guoxin Su;Minjie Zhang","doi":"10.1109/TCSS.2024.3458950","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3458950","url":null,"abstract":"Most research investigating how social influence affects election results mainly uses diffusion models for binary opinions. However, these diffusion models are progressive and focus on the diffusion of one opinion. In this article, we introduce a general diffusion model for ordinal opinions expressed as linear orderings over a finite set of candidates. We employ agent-based modeling to simulate a nonprogressive diffusion process, allowing multiple types of opinion diffusion about different candidates. The proposed agent-based diffusion model can forecast long-term trends of opinion diffusion in social networks by capturing voters’ personalized features and incorporating dynamic social contexts. Furthermore, we examine the possibility of affecting election outcomes by externally changing the ordinal opinions of certain vertices, i.e., campaigning. Since finding influential voters from the social network is computationally challenging, we propose a heuristic approach, i.e., backward influence rank (BIR). Experimental results demonstrate that the proposed BIR approach is superior to the classic greedy approach for campaigning by achieving a similar margin of victory to that of the greedy approach but running two orders of magnitude faster than the greedy approach did.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 1","pages":"335-347"},"PeriodicalIF":4.5,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106588","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