IEEE Transactions on Big Data最新文献_第9页

Spatiotemporal Learning With Decoupled Causal Attention for Multivariate Time Series 多元时间序列的解耦因果注意时空学习

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-11-15 DOI: 10.1109/TBDATA.2024.3499312

Xin Bi;Qinghan Jin;Meiling Song;Xin Yao;Xiangguo Zhao;Ye Yuan;Guoren Wang

{"title":"Spatiotemporal Learning With Decoupled Causal Attention for Multivariate Time Series","authors":"Xin Bi;Qinghan Jin;Meiling Song;Xin Yao;Xiangguo Zhao;Ye Yuan;Guoren Wang","doi":"10.1109/TBDATA.2024.3499312","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3499312","url":null,"abstract":"In multivariate time series prediction tasks, the inter- and intra-variable relations have significant influence on prediction outcomes. In many engineering and industrial scenarios, the multivariate time series also contain a large number of subjective influencing factors, such as settings and behaviors of users. Existing learning methods neglect the interactions of these subjective factors among variables. This leads to the learning of incorrect inter-variable influences, consequently yielding inaccurate prediction results. To address this challenge, we propose a Decoupled Casal Attention Network (DECA) for multivariate time series prediction from a spatiotemporal learning perspective. multivariate time series prediction. The causality decoupling module, based on the captured causal relations among variables, disentangles the subjective factors from the objective factors. Then the objective learning module utilizes an objective causal attention to capture objective cross-variable dependencies; while the subjective learning module utilizes a subjective causal graph attention to capture subjective influences. Finally, the prediction module fuses the multi-scale features of subjective and objective factors to produce predictions. The performance is evaluated using three benchmark datasets. Results indicate that, compared to state-of-the-art methods, DECA exhibits superior accuracy in multivariate time series prediction and can be effectively used for recommendations.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 4","pages":"1589-1599"},"PeriodicalIF":7.5,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Parallel Graph Learning With Temporal Stamp Encoding for Fraudulent Transactions Detections 基于时间戳编码的并行图学习欺诈交易检测

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-11-15 DOI: 10.1109/TBDATA.2024.3499338

Jiacheng Ma;Sheng Xiang;Qiang Li;Liangyu Yuan;Dawei Cheng;Changjun Jiang

{"title":"Parallel Graph Learning With Temporal Stamp Encoding for Fraudulent Transactions Detections","authors":"Jiacheng Ma;Sheng Xiang;Qiang Li;Liangyu Yuan;Dawei Cheng;Changjun Jiang","doi":"10.1109/TBDATA.2024.3499338","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3499338","url":null,"abstract":"Financial transaction systems have become the critical backbone of modern society, and the sharp increase in fraudulent transactions has become an unavoidable significant topic. Their presence poses a severe threat to financial markets, impacting the health of the economic and social welfare systems of various countries. However, most existing fraud detection methods are limited to detecting individual fraudulent entities within static transaction networks, which are neither suitable for continuously changing dynamic transaction networks nor capable of detecting the increasingly prevalent organized fraud crimes. This paper introduces a novel approach, Parallel Graph Learning with Temporal Stamp Encoding (PGLTSE). On the one hand, it designs a history information module to perform temporal dimension feature learning to adapt to the continuous changes in transaction information in Continuous-Time Dynamic Graphs (CTDG). On the other hand, it designs a gang-aware risk propagation algorithm to infer the risk of organized fraudulent activities in the global transaction relation graph. By simultaneously conducting parallel graph representation learning in both homogeneous global transaction relation graphs and heterogeneous local entity interaction graphs, it aggregates local interaction and global association information for end-to-end training. Extensive experiments on diverse real-world datasets substantiate the superior performance of PGLTSE over existing methods, demonstrating its practical efficacy in detecting complex and evolving fraudulent behaviors in financial networks.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 4","pages":"1945-1958"},"PeriodicalIF":7.5,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Many Hands Make Light Work: Group Influence Maximization in Evolving Social Networks 人多力量大：不断发展的社会网络中的群体影响力最大化

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-11-15 DOI: 10.1109/TBDATA.2024.3499345

Yuliang Ma;Yu Chen;Peng Wei;Ye Yuan;Guoren Wang;Zhong-Zhong Jiang

{"title":"Many Hands Make Light Work: Group Influence Maximization in Evolving Social Networks","authors":"Yuliang Ma;Yu Chen;Peng Wei;Ye Yuan;Guoren Wang;Zhong-Zhong Jiang","doi":"10.1109/TBDATA.2024.3499345","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3499345","url":null,"abstract":"As the adage “many hands make light work” suggests, collaborative influence often surpasses individual influence. Inspired by this insight, we undertook a study on group influence maximization in evolving social networks, which is applicable to domains such as social media marketing and financial risk management. Our goal is to reveal how collaborative influence propagates in dynamic settings. Existing research concentrates predominantly on static networks and overlooks the dynamics of evolving social structures. Recognizing the limitations of current influence propagation models for our specific issue, we introduce an innovative model rooted in user behaviors. It considers temporal aspects, and we also suggest a methodology for assessing influence propagation probabilities based on both user behaviors and duration. We introduce an algorithm for extracting user groups using community search, improving efficiency through supergraph construction. Additionally, we present an influence maximization algorithm based on group dynamics with a 3-degree propagation framework. Recognizing diminishing influence, a 3-degree truncation strategy effectively enhances the group influence propagation efficiency. This approach efficiently captures the influence spread and accelerates convergence, boosting the search efficiency. Finally, we conducted comprehensive experiments on real-world and synthetic datasets. The results distinctly highlight the superiority of the proposed algorithms.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 4","pages":"1600-1613"},"PeriodicalIF":7.5,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144598077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Where Shall We Go: Point-of-Interest Group Recommendation With User Preference Embedding 我们何去何从：兴趣点群组推荐与用户偏好嵌入

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-11-14 DOI: 10.1109/TBDATA.2024.3499356

Yuliang Ma;Zhong-Zhong Jiang;Mingyang Sun;Ye Yuan;Guoren Wang

{"title":"Where Shall We Go: Point-of-Interest Group Recommendation With User Preference Embedding","authors":"Yuliang Ma;Zhong-Zhong Jiang;Mingyang Sun;Ye Yuan;Guoren Wang","doi":"10.1109/TBDATA.2024.3499356","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3499356","url":null,"abstract":"In the context of geo-social networks, the objective of Point-of-Interest (POI) group recommendation is to propose POIs that align with the preferences of all members within a specific temporal group. POI group recommendation is significant in enhancing user experience, promoting social interaction, and providing convenient access to information. It also aids in community building and business promotion in real-life scenarios. However, existing studies fail to capture user preferences accurately and reach consensus with respect to preferences for POIs, which leads to the recommendation of POIs with low accuracy. To tackle this issue, we propose a Point-of-Interest (POI) group recommendation model, named <italic>PGR-PM</i>, leveraging user preference embedding. Specifically, we first propose a strategy for representing user preferences dynamically by means of POI embedding. Subsequently, we propose a hybrid weight fusion strategy that utilizes an attention mechanism to aggregate the preferences of members within a temporal group. Furthermore, we design a three-layer perceptron structure to recommend POIs for the group. Finally, we conduct comprehensive experiments across four extensively employed real-world datasets, with the findings affirming the efficacy of our proposed approach.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 4","pages":"1614-1627"},"PeriodicalIF":7.5,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Guest Editorial TBD Special Issue on Graph Machine Learning for Recommender Systems 特约编辑 TBD 特刊：面向推荐系统的图式机器学习

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-11-12 DOI: 10.1109/TBDATA.2024.3452328

Senzhang Wang;Changdong Wang;Di Jin;Shirui Pan;Philip S. Yu

引用次数: 0

An Enhanced and Robust Data Publishing Scheme for Private and Useful 1:M Microdata 私有和有用的1:M微数据的增强和健壮的数据发布方案

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-11-11 DOI: 10.1109/TBDATA.2024.3495497

Muhammad Rizwan;Ammar Hawbani;Xingfu Wang;Adeel Anjum;Pelin Angin;Yigit Sever;Sanchuan Chen;Liang Zhao;Ahmed Al-Dubai

{"title":"An Enhanced and Robust Data Publishing Scheme for Private and Useful 1:M Microdata","authors":"Muhammad Rizwan;Ammar Hawbani;Xingfu Wang;Adeel Anjum;Pelin Angin;Yigit Sever;Sanchuan Chen;Liang Zhao;Ahmed Al-Dubai","doi":"10.1109/TBDATA.2024.3495497","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3495497","url":null,"abstract":"A data publishing deal conducted with anonymous microdata can preserve the privacy of people. However, anonymizing data with multiple records of an individual (1:M dataset) is still a challenging problem. After anonymizing the 1:M microdata, the vertical correlation can be exploited to launch privacy attacks. In this paper, a novel privacy preserving model <inline-formula><tex-math>$l_{c}, l_{s}$</tex-math></inline-formula>-ANGEL is proposed. To validate the new model, two privacy attacks are presented, namely, a Vertical correlation attack (<inline-formula><tex-math>$V_{c0}$</tex-math></inline-formula>) and a Vulnerable sensitive attribute attack (<inline-formula><tex-math>$V_{sa}$</tex-math></inline-formula>) on 1:M datasets, which breach the privacy of individuals. Furthermore, the proposed model is examined through High-Level Petri Nets (HLPNs). Our experiments on three real-world datasets;“INFORMS”,“YOUTUBE”, and “IMDb” demonstrate that the proposed model outperforms the state-of-the-art models. Our practices and lessons learned in this work can direct future concrete steps towards Multiple Sensitive Attributes, where we can expand the proposed model to dynamic datasets.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 4","pages":"1932-1944"},"PeriodicalIF":7.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

GeoVoronoi: A Voronoi Diagram Generation System for Large-Scale Geographical Point Data via Spatial Attribute Association 基于空间属性关联的大规模地理点数据Voronoi图生成系统

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-11-11 DOI: 10.1109/TBDATA.2024.3495499

Zhiguang Zhou;Haoxuan Wang;Zhendong Yang;Yuanyuan Chen;Xiaohui Chen;Ying Lai;Wei Chen;Yuwei Meng

{"title":"GeoVoronoi: A Voronoi Diagram Generation System for Large-Scale Geographical Point Data via Spatial Attribute Association","authors":"Zhiguang Zhou;Haoxuan Wang;Zhendong Yang;Yuanyuan Chen;Xiaohui Chen;Ying Lai;Wei Chen;Yuwei Meng","doi":"10.1109/TBDATA.2024.3495499","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3495499","url":null,"abstract":"Voronoi diagram is commonly used to visualize geographical point dataset with a collection of plane-partitioned facets. As the size of the geographical point dataset increases, facets are densely distributed, and present different sizes and irregular shapes, leading to overdrawing and confusion problems, and hampering the visual perception of Voronoi diagram and insightful exploration of geographical point data. In this paper, we propose a novel Voronoi diagram generation framework to visualize and explore large-scale geographical point datasets. Firstly, an attribute-based blue noise sampling model is designed to select a subset of points to generate the simplified Voronoi diagram, retaining both the spatial distribution and attribute relationship of the original large-scale geographical points. Then a couple of optimization schemes are integrated into the sampling model to replace the representative points, aiming to enhance the visual perception of Voronoi diagram, such as shape balance and color characterization. Furthermore, we implement an interactive online Voronoi diagram generation tool, GeoVoronoi, enabling users to generate meaningful facets according to their requirements. Quantitative comparisons, case studies and user studies based on real-world datasets have demonstrated the effectiveness of our proposed method in the generation of credible Voronoi diagram and in-depth exploration of geographical point datasets.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 4","pages":"1918-1931"},"PeriodicalIF":7.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Ranking Neighborhood and Class Prototype Contrastive Learning for Time Series 时间序列邻域排序与类原型对比学习

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-11-08 DOI: 10.1109/TBDATA.2024.3495509

Chixuan Wei;Jidong Yuan;Yi Zhang;Zhongyang Yu;Yanze Liu;Haiyang Liu

{"title":"Ranking Neighborhood and Class Prototype Contrastive Learning for Time Series","authors":"Chixuan Wei;Jidong Yuan;Yi Zhang;Zhongyang Yu;Yanze Liu;Haiyang Liu","doi":"10.1109/TBDATA.2024.3495509","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3495509","url":null,"abstract":"Time series are often complex and rich in information but sparsely labeled and therefore challenging to model. Existing contrastive learning methods conduct augmentations and maximize their similarity. However, they ignore the similarity of adjacent timestamps and suffer from the problem of sampling bias. In this paper, we propose a self-supervised framework for learning generalizable representations of time series, called <inline-formula><tex-math>$mathbf {R}$</tex-math></inline-formula>anking n<inline-formula><tex-math>$mathbf {E}$</tex-math></inline-formula> ighborhood and cla<inline-formula><tex-math>$mathbf {S}$</tex-math></inline-formula>s prototyp<inline-formula><tex-math>$mathbf {E}$</tex-math></inline-formula> contr<inline-formula><tex-math>$mathbf {A}$</tex-math></inline-formula>stive <inline-formula><tex-math>$mathbf {L}$</tex-math></inline-formula>earning (RESEAL). It exploits information about similarity ranking to learn an embedding space, ensuring that positive samples are ranked according to their temporal order. Additionally, RESEAL introduces a class prototype contrastive learning module. It contrasts time series representations and their corresponding centroids as positives against truly negative pairs from different clusters, mitigating the sampling bias issue. Extensive experiments conducted on several multivariate and univariate time series tasks (i.e., classification, anomaly detection, and forecasting) demonstrate that our representation framework achieves significant improvement over existing baselines of self-supervised time series representation.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 4","pages":"1907-1917"},"PeriodicalIF":7.5,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144598076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Multiscale Feature-Guided Adversarial Examples Quality Assessment via Hierarchical Perception of Human Visual System 基于人类视觉系统层次感知的多尺度特征引导对抗样例质量评估

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-11-08 DOI: 10.1109/TBDATA.2024.3495515

Wenying Wen;Minghui Huang;Li Dong;Yushu Zhang;Yuming Fang

{"title":"Multiscale Feature-Guided Adversarial Examples Quality Assessment via Hierarchical Perception of Human Visual System","authors":"Wenying Wen;Minghui Huang;Li Dong;Yushu Zhang;Yuming Fang","doi":"10.1109/TBDATA.2024.3495515","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3495515","url":null,"abstract":"Deep neural networks (DNNs) reveal significant robustness deficiencies due to their susceptibility to being misled by small and imperceptible adversarial examples, thus it is crucial to improve the robustness of DNNs against such harmful perturbations. The current <inline-formula><tex-math>$L_{p}$</tex-math></inline-formula> specification ignores differences in human visual perception when measuring similarity, and most existing image quality assessment (IQA) methods and adversarial example datasets lack subjective scores for evaluation. In this paper, we construct a new database of adversarial examples, called the AED, which contains 35 original images, 1050 adversarial examples, and the corresponding subjective scores of adversarial examples. Then, a novel full-reference IQA model for the quality evaluation of the adversarial examples is proposed by taking into full consideration the hierarchical perception of human visual system (HVS) and the outstanding capabilities of the multi-scale feature extraction network in feature extraction. Specifically, a feature encoding network that uses continuous convolution layers to pre-extract features and expand the receptive field of the image is employed. To simulate the HVS hierarchical perception, the features of different scales are further obtained by designing a multi-scale feature extraction network. The structural similarity scores of the feature maps at different scales are calculated for jointly arriving at the final IQA score of the adversarial examples. Experimental results have demonstrated that our proposed model is closer to the perception of HVS in small imperceptible distortions evaluation of adversarial examples compared with other classical and state-of-the-art models.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 4","pages":"1894-1906"},"PeriodicalIF":7.5,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Large Language Models for Link Stealing Attacks Against Graph Neural Networks 面向图神经网络的链接窃取攻击的大型语言模型

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-11-07 DOI: 10.1109/TBDATA.2024.3489427

Faqian Guan;Tianqing Zhu;Hui Sun;Wanlei Zhou;Philip S. Yu

{"title":"Large Language Models for Link Stealing Attacks Against Graph Neural Networks","authors":"Faqian Guan;Tianqing Zhu;Hui Sun;Wanlei Zhou;Philip S. Yu","doi":"10.1109/TBDATA.2024.3489427","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3489427","url":null,"abstract":"Graph data contains rich node features and unique edge information, which have been applied across various domains, such as citation networks or recommendation systems. Graph Neural Networks (GNNs) are specialized for handling such data and have shown impressive performance in many applications. However, GNNs may contain of sensitive information and susceptible to privacy attacks. For example, link stealing is a type of attack in which attackers infer whether two nodes are linked or not. Previous link stealing attacks primarily relied on posterior probabilities from the target GNN model, neglecting the significance of node features. Additionally, variations in node classes across different datasets lead to different dimensions of posterior probabilities. The handling of these varying data dimensions posed a challenge in using a single model to effectively conduct link stealing attacks on different datasets. To address these challenges, we introduce Large Language Models (LLMs) to perform link stealing attacks on GNNs. LLMs can effectively integrate textual features and exhibit strong generalizability, enabling attacks to handle diverse data dimensions across various datasets. We design two distinct LLM prompts to effectively combine textual features and posterior probabilities of graph nodes. Through these designed prompts, we fine-tune the LLM to adapt to the link stealing attack task. Furthermore, we fine-tune the LLM using multiple datasets and enable the LLM to learn features from different datasets simultaneously. Experimental results show that our approach significantly enhances the performance of existing link stealing attack tasks in both white-box and black-box scenarios. Our method can execute link stealing attacks across different datasets using only a single model, making link stealing attacks more applicable to real-world scenarios.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 4","pages":"1879-1893"},"PeriodicalIF":7.5,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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