IEEE Transactions on Knowledge and Data Engineering最新文献_第7页

Discovering the Representation Bottleneck of Graph Neural Networks 发现图神经网络的表征瓶颈

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-08-20 DOI: 10.1109/TKDE.2024.3446584

Fang Wu;Siyuan Li;Stan Z. Li

{"title":"Discovering the Representation Bottleneck of Graph Neural Networks","authors":"Fang Wu;Siyuan Li;Stan Z. Li","doi":"10.1109/TKDE.2024.3446584","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3446584","url":null,"abstract":"Graph neural networks (GNNs) rely mainly on the message-passing paradigm to propagate node features and build interactions, and different graph learning problems require different ranges of node interactions. In this work, we explore the capacity of GNNs to capture node interactions under contexts of different complexities. We discover that \u0000<italic>GNNs usually fail to capture the most informative kinds of interaction styles for diverse graph learning tasks\u0000, and thus name this phenomenon as GNNs’ representation bottleneck. As a response, we demonstrate that the inductive bias introduced by existing graph construction mechanisms can result in this representation bottleneck, i.e., preventing GNNs from learning interactions of the most appropriate complexity. To address that limitation, we propose a novel graph rewiring approach based on interaction patterns learned by GNNs to adjust each node's receptive fields dynamically. Extensive experiments on both real-world and synthetic datasets prove the effectiveness of our algorithm in alleviating the representation bottleneck and its superiority in enhancing the performance of GNNs over state-of-the-art graph rewiring baselines.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"36 12","pages":"7998-8008"},"PeriodicalIF":8.9,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10640313","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636450","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

Budget-Constrained Ego Network Extraction With Maximized Willingness 预算受限的意愿最大化自我网络提取

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-08-20 DOI: 10.1109/TKDE.2024.3446169

Bay-Yuan Hsu;Chia-Hsun Lu;Ming-Yi Chang;Chih-Ying Tseng;Chih-Ya Shen

{"title":"Budget-Constrained Ego Network Extraction With Maximized Willingness","authors":"Bay-Yuan Hsu;Chia-Hsun Lu;Ming-Yi Chang;Chih-Ying Tseng;Chih-Ya Shen","doi":"10.1109/TKDE.2024.3446169","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3446169","url":null,"abstract":"Many large-scale machine learning approaches and graph algorithms are proposed recently to address a variety of problems in online social networks (OSNs). To evaluate and validate these algorithms and models, the data of ego-centric networks (ego networks) are widely adopted. Therefore, effectively extracting large-scale ego networks from OSNs becomes an important issue, particularly when privacy policies become increasingly strict nowadays. In this paper, we study the problem of extracting ego network data by considering jointly the user willingness, crawling cost, and structure of the network. We formulate a new research problem, named \u0000Structure and Willingness Aware Ego Network Extraction (SWAN)\u0000 and analyze its NP-hardness. We first propose a \u0000<inline-formula><tex-math>$(1-frac{1}{e})$</tex-math></inline-formula>\u0000-approximation algorithm, named \u0000Tristar-Optimized Ego Network Identification with Maximum Willingness (TOMW)\u0000. In addition to the deterministic approximation algorithm, we also propose to automatically \u0000learn\u0000 an effective heuristic approach with machine learning, to avoid the huge efforts for human to devise a good algorithm. The learning approach is named \u0000Willingness-maximized and Structure-aware Ego Network Extraction with Reinforcement Learning (WSRL)\u0000, in which we propose a novel constrastive learning strategy, named \u0000Contrastive Learning with Performance-boosting Graph Augmentation\u0000. We recruited 1,810 real-world participants and conducted an evaluation study to validate our problem formulation and proposed approaches. Moreover, experimental results on real social network datasets show that the proposed approaches outperform the other baselines significantly.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"36 12","pages":"7692-7707"},"PeriodicalIF":8.9,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645398","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

High-Capacity Framework for Reversible Data Hiding Using Asymmetric Numeral Systems 利用非对称数字系统实现可逆数据隐藏的高容量框架

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-08-19 DOI: 10.1109/TKDE.2024.3438943

Na Wang;Shuxi Xu;Chuan Qin;Sian-Jheng Lin;Shuo Shao;Yunghsiang S. Han

{"title":"High-Capacity Framework for Reversible Data Hiding Using Asymmetric Numeral Systems","authors":"Na Wang;Shuxi Xu;Chuan Qin;Sian-Jheng Lin;Shuo Shao;Yunghsiang S. Han","doi":"10.1109/TKDE.2024.3438943","DOIUrl":"10.1109/TKDE.2024.3438943","url":null,"abstract":"Reversible data hiding (RDH) has been extensively studied in the field of multimedia security. Embedding capacity is an important metric for RDH performance evaluation. However, the embedding capacity of existing methods for independent and identically distributed (i.i.d.) gray-scale signals is still not good enough. In this paper, we propose a high-capacity RDH code construction method that employs asymmetric numeral systems (ANS) coding as the underlying coding framework. Based on the proposed framework, two RDH methods are presented. First, we propose a static RDH method that takes the constant host probability mass function (PMF) as input parameters and offers high embedding performance. Then, we give a dynamic RDH method that can eliminate the need for transmitting the host PMF in advance by designing a reversible dynamic probability calculator. The simulation results on discrete normally distributed signals demonstrate that the performance of the proposed static method is very close to the expected rate-distortion bound, and the proposed dynamic method can achieve satisfactory embedding capacity without prior knowledge of host PMF at the cost of slightly sacrificing steganographic data quality. Moreover, the experimental results on gray-scale images show that the proposed static method provides higher peak signal-to-noise ratio (PSNR) values and larger embedding capacities than some state-of-the-art methods, e.g., the embedding capacity of image Lena is as high as 3.571 bits per pixel.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"36 12","pages":"8447-8461"},"PeriodicalIF":8.9,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220123","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

Self-Learning Symmetric Multi-View Probabilistic Clustering 自学习对称多视图概率聚类

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-08-19 DOI: 10.1109/tkde.2024.3440352

Junjie Liu, Junlong Liu, Rongxin Jiang, Yaowu Chen, Chen Shen, Jieping Ye

引用次数: 0

Towards Effective Top-N Hamming Search via Bipartite Graph Contrastive Hashing 通过双方图对比哈希算法实现有效的 Top-N Hamming 搜索

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-08-19 DOI: 10.1109/TKDE.2024.3425891

Yankai Chen;Yixiang Fang;Yifei Zhang;Chenhao Ma;Yang Hong;Irwin King

{"title":"Towards Effective Top-N Hamming Search via Bipartite Graph Contrastive Hashing","authors":"Yankai Chen;Yixiang Fang;Yifei Zhang;Chenhao Ma;Yang Hong;Irwin King","doi":"10.1109/TKDE.2024.3425891","DOIUrl":"10.1109/TKDE.2024.3425891","url":null,"abstract":"Searching on bipartite graphs serves as a fundamental task for various real-world applications, such as recommendation systems, database retrieval, and document querying. Conventional approaches rely on similarity matching in continuous euclidean space of vectorized node embeddings. To handle intensive similarity computation efficiently, hashing techniques for graph-structured data have emerged as a prominent research direction. However, despite the retrieval efficiency in Hamming space, previous studies have encountered \u0000<italic>catastrophic performance decay\u0000. To address this challenge, we investigate the problem of hashing with Graph Convolutional Network for effective Top-N search. Our findings indicate the learning effectiveness of incorporating hashing techniques within the exploration of bipartite graph reception fields, as opposed to simply treating hashing as post-processing to output embeddings. To further enhance the model performance, we advance upon these findings and propose \u0000<bold>B\u0000ipartite \u0000<bold>G\u0000raph \u0000<bold>C\u0000ontrastive \u0000<bold>H\u0000ashing (\u0000<bold>BGCH+\u0000). BGCH+ introduces a novel dual augmentation approach to both \u0000<italic>intermediate information\u0000 and \u0000<italic>hash code outputs\u0000 in the latent feature spaces, thereby producing more expressive and robust hash codes within a dual self-supervised learning paradigm. Comprehensive empirical analyses on six real-world benchmarks validate the effectiveness of our dual feature contrastive learning in boosting the performance of BGCH+ compared to existing approaches.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"36 12","pages":"9418-9432"},"PeriodicalIF":8.9,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10638796","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220124","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

A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects 即时配送中的服务路线和时间预测调查：分类、进展与前景

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-08-19 DOI: 10.1109/TKDE.2024.3441309

Haomin Wen;Youfang Lin;Lixia Wu;Xiaowei Mao;Tianyue Cai;Yunfeng Hou;Shengnan Guo;Yuxuan Liang;Guangyin Jin;Yiji Zhao;Roger Zimmermann;Jieping Ye;Huaiyu Wan

{"title":"A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects","authors":"Haomin Wen;Youfang Lin;Lixia Wu;Xiaowei Mao;Tianyue Cai;Yunfeng Hou;Shengnan Guo;Yuxuan Liang;Guangyin Jin;Yiji Zhao;Roger Zimmermann;Jieping Ye;Huaiyu Wan","doi":"10.1109/TKDE.2024.3441309","DOIUrl":"10.1109/TKDE.2024.3441309","url":null,"abstract":"Instant delivery services, such as food delivery and package delivery, have achieved explosive growth in recent years by providing customers with daily-life convenience. An emerging research area within these services is service Route&Time Prediction (RTP), which aims to estimate the future service route as well as the arrival time of a given worker. As one of the most crucial tasks in those service platforms, RTP stands central to enhancing user satisfaction and trimming operational expenditures on these platforms. Despite a plethora of algorithms developed to date, there is no systematic, comprehensive survey to guide researchers in this domain. To fill this gap, our work presents the first comprehensive survey that methodically categorizes recent advances in service route and time prediction. We start by defining the RTP challenge and then delve into the metrics that are often employed. Following that, we scrutinize the existing RTP methodologies, presenting a novel taxonomy of them. We categorize these methods based on three criteria: (i) type of task, subdivided into only-route prediction, only-time prediction, and joint route&time prediction; (ii) model architecture, which encompasses sequence-based and graph-based models; and (iii) learning paradigm, including Supervised Learning (SL) and Deep Reinforcement Learning (DRL). Conclusively, we highlight the limitations of current research and suggest prospective avenues. We believe that the taxonomy, progress, and prospects introduced in this paper can significantly promote the development of this field.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"36 12","pages":"7516-7535"},"PeriodicalIF":8.9,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227344","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

ZBTree: A Fast and Scalable B$^+$+-Tree for Persistent Memory ZBTree：用于持久内存的快速可扩展 B$^+$ 树

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-08-16 DOI: 10.1109/TKDE.2024.3421232

Wenkui Che;Zhiwen Chen;Daokun Hu;Jianhua Sun;Hao Chen

{"title":"ZBTree: A Fast and Scalable B$^+$+-Tree for Persistent Memory","authors":"Wenkui Che;Zhiwen Chen;Daokun Hu;Jianhua Sun;Hao Chen","doi":"10.1109/TKDE.2024.3421232","DOIUrl":"10.1109/TKDE.2024.3421232","url":null,"abstract":"In this paper, we present the design and implementation of ZBTree, a hotness-aware B\u0000<inline-formula><tex-math>$^+$</tex-math></inline-formula>\u0000-Tree for persistent memory (PMem). ZBTree leverages the PMem+DRAM architecture, which is featured with a volatile operation layer to accelerate data access and an order-preserving persistent layer to achieve fast recovery and low-overhead consistency and persistence guarantees. The operation layer contains inner nodes for indexing and compacted leaf nodes (DLeaves) that hold metadata. Based on leaf node compaction, we present a data lodging method, which supports to load hot data into fast DRAM dynamically, avoiding PMem accesses for subsequent reads of hot data and achieving improved read performance without incurring extra DRAM usage. In addition, we present a lightweight node splitting mechanism with constant persistence overhead that does not vary with node size. Our extensive evaluations show that ZBTree achieves higher throughput by a factor of 1.4x-6.3x compared to state-of-the-art tree indexes under a wide range of workloads. Meanwhile, ZBTree achieves comparable or faster recovery speed compared to existing designs.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"36 12","pages":"9547-9563"},"PeriodicalIF":8.9,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220129","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

Forecasting Turning Points in Stock Price by Integrating Chart Similarity and Multipersistence 综合图表相似性和多持续性预测股价转折点

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-08-16 DOI: 10.1109/TKDE.2024.3444814

Shangzhe Li;Yingke Liu;Xueyuan Chen;Junran Wu;Ke Xu

{"title":"Forecasting Turning Points in Stock Price by Integrating Chart Similarity and Multipersistence","authors":"Shangzhe Li;Yingke Liu;Xueyuan Chen;Junran Wu;Ke Xu","doi":"10.1109/TKDE.2024.3444814","DOIUrl":"10.1109/TKDE.2024.3444814","url":null,"abstract":"Forecasting financial data plays a crucial role in financial market. Relying solely on prices or price trends as prediction targets often leads to a vast of invalid transactions. As a result, researchers have increasingly turned their attention to turning points as the prediction target. Surprisingly, existing methods have largely overlooked the role of technical charts, despite turning points being closely related to the technical charts. Recently, several researchers have attempted to utilize chart information via converting price sequences into images for turning point forecasting, but robustness and convergence problems arise. To address these challenges and enhance the turning point predictions, this article introduces a new method known as MPCNet. Specifically, we first transform the price series into a graph structure using chart similarity to robustly extract valuable information from technical charts. Additionally, we introduce the multipersistence topology tool to accurately predict stock turning points and provide convergence guarantee. Experimental results demonstrate the significant superiority of our proposed model over existing methods. Furthermore, based on additional performance evaluations using real stock data, MPCNet consistently achieves the highest average return during the transaction backtesting period. Meanwhile, we provide empirical validation of robustness and theoretical analysis to confirm its convergence, establishing it as a superior tool for financial forecasting.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"36 12","pages":"8251-8266"},"PeriodicalIF":8.9,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220127","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

C2F-Explainer: Explaining Transformers Better Through a Coarse-to-Fine Strategy C2F-Explainer：通过从粗到细的策略更好地解释变压器

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-08-15 DOI: 10.1109/TKDE.2024.3443888

Weiping Ding;Xiaotian Cheng;Yu Geng;Jiashuang Huang;Hengrong Ju

{"title":"C2F-Explainer: Explaining Transformers Better Through a Coarse-to-Fine Strategy","authors":"Weiping Ding;Xiaotian Cheng;Yu Geng;Jiashuang Huang;Hengrong Ju","doi":"10.1109/TKDE.2024.3443888","DOIUrl":"10.1109/TKDE.2024.3443888","url":null,"abstract":"Transformer interpretability research is a hot topic in the area of deep learning. Traditional interpretation methods mostly use the final layer output of the Transformer encoder as masks to generate an explanation map. However, These approaches overlook two crucial aspects. At the coarse-grained level, the mask may contain uncertain information, including unreliable and incomplete object location data; at the fine-grained level, there is information loss on the mask, resulting in spatial noise and detail loss. To address these issues, in this paper, we propose a two-stage coarse-to-fine strategy (C2F-Explainer) for improving Transformer interpretability. Specifically, we first design a sequential three-way mask (S3WM) module to handle the problem of uncertain information at the coarse-grained level. This module uses sequential three-way decisions to process the mask, preventing uncertain information on the mask from impacting the interpretation results, thus obtaining coarse-grained interpretation results with accurate position. Second, to further reduce the impact of information loss at the fine-grained level, we devised an attention fusion (AF) module inspired by the fact that self-attention can capture global semantic information, AF aggregates the attention matrix to generate a cross-layer relation matrix, which is then used to optimize detailed information on the interpretation results and produce fine-grained interpretation results with clear and complete edges. Experimental results show that the proposed C2F-Explainer has good interpretation results on both natural and medical image datasets, and the mIoU is improved by 2.08% on the PASCAL VOC 2012 dataset.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"36 12","pages":"7708-7724"},"PeriodicalIF":8.9,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220130","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

Causal Discovery From Unknown Interventional Datasets Over Overlapping Variable Sets 从重叠变量集上的未知干预数据集中发现因果关系

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-08-15 DOI: 10.1109/TKDE.2024.3443997

Fuyuan Cao;Yunxia Wang;Kui Yu;Jiye Liang

{"title":"Causal Discovery From Unknown Interventional Datasets Over Overlapping Variable Sets","authors":"Fuyuan Cao;Yunxia Wang;Kui Yu;Jiye Liang","doi":"10.1109/TKDE.2024.3443997","DOIUrl":"10.1109/TKDE.2024.3443997","url":null,"abstract":"Inferring causal structures from experimentation is a challenging task in many fields. Most causal structure learning algorithms with unknown interventions are proposed to discover causal relationships over an identical variable set. However, often due to privacy, ethical, financial, and practical concerns, the variable sets observed by multiple sources or domains are not entirely identical. While a few algorithms are proposed to handle the partially overlapping variable sets, they focus on the case of known intervention targets. Therefore, to be close to the real-world environment, we consider discovering causal relationships over overlapping variable sets under the unknown intervention setting and exploring a scenario where a problem is studied across multiple domains. Here, we propose an algorithm for discovering the causal relationships over the integrated set of variables from unknown interventions, mainly handling the entangled inconsistencies caused by the incomplete observation of variables and unknown intervention targets. Specifically, we first distinguish two types of inconsistencies and then deal with respectively them by presenting some lemmas. Finally, we construct a fusion rule to combine learned structures of multiple domains, obtaining the final structures over the integrated set of variables. Theoretical analysis and experimental results on synthetic, benchmark, and real-world datasets have verified the effectiveness of the proposed algorithm.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"36 12","pages":"7725-7742"},"PeriodicalIF":8.9,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220128","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