IEEE Transactions on Big Data最新文献_第8页

Symbolic Knowledge Reasoning on Hyper-Relational Knowledge Graphs 基于超关系知识图的符号知识推理

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-07-05 DOI: 10.1109/TBDATA.2024.3423670

Zikang Wang;Linjing Li;Daniel Dajun Zeng

{"title":"Symbolic Knowledge Reasoning on Hyper-Relational Knowledge Graphs","authors":"Zikang Wang;Linjing Li;Daniel Dajun Zeng","doi":"10.1109/TBDATA.2024.3423670","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3423670","url":null,"abstract":"Knowledge reasoning has been widely researched in knowledge graphs (KGs), but there has been relatively less research on hyper-relational KGs, which also plays an important role in downstream tasks. Existing reasoning methods on hyper-relational KGs are based on representation learning. Though this approach is effective, it lacks interpretability and ignores the graph structure information. In this paper, we make the first attempt at symbolic reasoning on hyper-relational KGs. We introduce rule extraction methods based on both individual facts and paths, and propose a rule-based symbolic reasoning approach, HyperPath. This approach is simple and interpretable, it can serve as a baseline model for symbolic reasoning in hyper-relational KGs. We provide experimental results on almost all datasets, including five large-scale datasets and seven sub-datasets of them. Experiments show that the expressive power of the proposed model is similar to simple neural networks like convolutional networks, but not as advanced as more complex networks such as Transformer and graph convolutional networks, which is consistent with the performance of symbolic methods on KGs. Furthermore, we also analyze the impact of rule length and hyperparameters on the model's performance, which can provide insights for future research in hypergraph symbolic reasoning.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 2","pages":"578-590"},"PeriodicalIF":7.5,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611889","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

DCLCSE: Dynamic Curriculum Learning Based Contrastive Learning of Sentence Embeddings 基于动态课程学习的句子嵌入对比学习

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-07-05 DOI: 10.1109/TBDATA.2024.3423650

Chang Liu;Dacao Zhang;Meng Wang

{"title":"DCLCSE: Dynamic Curriculum Learning Based Contrastive Learning of Sentence Embeddings","authors":"Chang Liu;Dacao Zhang;Meng Wang","doi":"10.1109/TBDATA.2024.3423650","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3423650","url":null,"abstract":"Recently, Contrastive Learning (CL) has made impressive progress in natural language processing, especially in sentence representation learning. Plenty of data augmentation methods have been proposed for the generation of positive samples. However, due to the highly abstract nature of natural language, these augmentations cannot maintain the quality of generated positive samples, e.g., too easy or hard samples. To this end, we propose to improve the quality of positive examples from a data arrangement perspective and develop a novel model-agnostic approach: <italic>Dynamic Curriculum Learning based Contrastive Sentence Embedding framework</i> (<italic>DCLCSE</i>) for sentence embeddings. Specifically, we propose to incorporate a curriculum learning strategy to control the positive example usage. At the early learning stage, easy samples are selected to optimize the CL-based model. As the model's capability increases, we gradually select harder samples for model training, ensuring the learning efficiency of the model. Furthermore, we design a novel difficulty measurement module to calculate the difficulty of generated positives, in which the model's capability is considered for the accurate sample difficulty measurement. Based on this, we develop multiple arrangement strategies to facilitate the model learning process based on learned difficulties. Finally, extensive experiments over multiple representative models demonstrate the superiority of <italic>DCLCSE</i>. As a byproduct, we have released the codes to facilitate other researchers.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 2","pages":"635-647"},"PeriodicalIF":7.5,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611762","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

Dual Graph Convolutional Networks for Social Network Alignment 社会网络对齐的对偶图卷积网络

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-07-05 DOI: 10.1109/TBDATA.2024.3423699

Xiaoyu Guo;Yan Liu;Daofu Gong;Fenlin Liu

{"title":"Dual Graph Convolutional Networks for Social Network Alignment","authors":"Xiaoyu Guo;Yan Liu;Daofu Gong;Fenlin Liu","doi":"10.1109/TBDATA.2024.3423699","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3423699","url":null,"abstract":"Social network alignment aims to discover the potential correspondence between users across different social platforms. Recent advances in graph representation learning have brought a new upsurge to network alignment. Most existing representation-based methods extract local structural information of social networks from users’ neighborhoods, but the global structural information has not been fully exploited. Therefore, this manuscript proposes a dual graph convolutional networks-based method (DualNA) for social network alignment, which combines user representation learning and user alignment in a unified framework. Specifically, we design dual graph convolutional networks as feature extractors to capture the local and global structural information of social networks, and apply a two-part constraint mechanism, including reconstruction loss and contrastive loss, to jointly optimize the graph representation learning process. As a result, the learned user representations can not only preserve the local and global features of original networks, but also be distinguishable and suitable for the downstream task of social network alignment. Extensive experiments on three real-world datasets show that our proposed method outperforms all baselines. The ablation studies further illustrate the rationality and effectiveness of our method.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 2","pages":"684-695"},"PeriodicalIF":7.5,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143627828","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

Denoising Implicit Feedback for Graph Collaborative Filtering via Causal Intervention 基于因果干预的图协同滤波隐式反馈去噪

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-07-05 DOI: 10.1109/TBDATA.2024.3423727

Huiting Liu;Huaxiu Zhang;Peipei Li;Peng Zhao;Xindong Wu

{"title":"Denoising Implicit Feedback for Graph Collaborative Filtering via Causal Intervention","authors":"Huiting Liu;Huaxiu Zhang;Peipei Li;Peng Zhao;Xindong Wu","doi":"10.1109/TBDATA.2024.3423727","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3423727","url":null,"abstract":"The performance of graph collaborative filtering (GCF) models could be affected by noisy user-item interactions. Existing studies on data denoising either ignore the nature of noise in implicit feedback or seldom consider the long-tail distribution of historical interaction data. For the first challenge, we analyze the role of noise from a causal perspective: noise is an unobservable confounder. Therefore, we use the instrumental variable for causal intervention without requiring confounder observation. For the second challenge, we consider degree distribution of nodes in the course of causal intervention. And then we propose a model named causal graph collaborative filtering (CausalGCF) to denoise implicit feedback for GCF. Specifically, we design a degree augmentation strategy as the instrumental variable. First, we divide nodes into head and tail nodes according to their degree. Then, we purify the interactions of the head nodes and enrich those of the tail nodes based on similarity. We perform degree augmentation strategy from the user and item sides to obtain two different graph structures, which are trained together with self-supervised learning. Empirical studies on four real and four synthetic datasets demonstrate the effectiveness of CausalGCF, which is more robust against noisy interactions in implicit feedback than the baselines.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 2","pages":"696-709"},"PeriodicalIF":7.5,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629654","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

HEART: Historically Information Embedding and Subspace Re-Weighting Transformer-Based Tracking 基于历史信息嵌入和子空间重加权的变压器跟踪

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-07-05 DOI: 10.1109/TBDATA.2024.3423672

Tianpeng Liu;Jing Li;Amin Beheshti;Jia Wu;Jun Chang;Beihang Song;Lezhi Lian

{"title":"HEART: Historically Information Embedding and Subspace Re-Weighting Transformer-Based Tracking","authors":"Tianpeng Liu;Jing Li;Amin Beheshti;Jia Wu;Jun Chang;Beihang Song;Lezhi Lian","doi":"10.1109/TBDATA.2024.3423672","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3423672","url":null,"abstract":"Transformers-based trackers offer significant potential for integrating semantic interdependence between template and search features in tracking tasks. Transformers possess inherent capabilities for processing long sequences and extracting correlations within them. Several researchers have explored the feasibility of incorporating Transformers to model continuously changing search areas in tracking tasks. However, their approach has substantially increased the computational cost of an already resource-intensive Transformer. Additionally, existing Transformers-based trackers rely solely on mechanically employing multi-head attention to obtain representations in different subspaces, without any inherent bias. To address these challenges, we propose HEART (Historical Information Embedding And Subspace Re-weighting Tracker). Our method embeds historical information into the queries in a lightweight and Markovian manner to extract discriminative attention maps for robust tracking. Furthermore, we develop a multi-head attention distribution mechanism to retrieve the most promising subspace weights for tracking tasks. HEART has demonstrated its effectiveness on five datasets, including OTB-100, LaSOT, UAV123, TrackingNet, and GOT-10k.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 2","pages":"566-577"},"PeriodicalIF":7.5,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611925","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

DBNetVizor: Visual Analysis of Dynamic Basketball Player Networks 动态篮球运动员网络的可视化分析

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-07-05 DOI: 10.1109/TBDATA.2024.3423721

Baofeng Chang;Guodao Sun;Sujia Zhu;Qi Jiang;Wang Xia;Jingwei Tang;Ronghua Liang

{"title":"DBNetVizor: Visual Analysis of Dynamic Basketball Player Networks","authors":"Baofeng Chang;Guodao Sun;Sujia Zhu;Qi Jiang;Wang Xia;Jingwei Tang;Ronghua Liang","doi":"10.1109/TBDATA.2024.3423721","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3423721","url":null,"abstract":"Visual analysis has been increasingly integrated into the exploration of temporal networks, as visualization methods have the capability to present time-varying attributes and relationships of entities in an easy-to-read manner. Visualization techniques have been employed in a variety of dynamic network datasets, including social media networks, academic citation networks, and financial transaction networks. However, effectively visualizing dynamic basketball player network data, which consists of numerical networks, intensive timestamps, and subtle changes, remains a challenge for analysts. To address this issue, we propose a snapshot extraction algorithm that involves human-in-the-loop methodology to help users divide a series of networks into hierarchical snapshots for subsequent network analysis tasks, such as node exploration and network pattern analysis. Furthermore, we design and implement a prototype system, called DBNetVizor, for dynamic basketball player network data visualization. DBNetVizor integrates a graphical user interface to help users extract snapshots visually and interactively, as well as multiple linked visualization charts to display macro- and micro-level information of dynamic basketball player network data. To demonstrate the usability and efficiency of our proposed methods, we present two case studies based on dynamic basketball player network data in a competition. Additionally, we conduct an evaluation and receive positive feedback.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 2","pages":"591-605"},"PeriodicalIF":7.5,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611823","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

AGE: Age-Gender Effect on Faculty Career Progression in American Universities 年龄：年龄性别对美国大学教师职业发展的影响

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-07-05 DOI: 10.1109/TBDATA.2024.3423726

H. Rahmani;Anthony J. Olejniczak;Gary R. Weckman

{"title":"AGE: Age-Gender Effect on Faculty Career Progression in American Universities","authors":"H. Rahmani;Anthony J. Olejniczak;Gary R. Weckman","doi":"10.1109/TBDATA.2024.3423726","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3423726","url":null,"abstract":"This study was undertaken to examine the impact of age and gender on faculty career progression in academia and to identify key performance indicators leading to attaining promotion. To explore any evidence of age-gender effect on faculty career progression, gender compositions, promotion rates, and appointment lengths at the assistant and associate professor levels are investigated. Furthermore, the underlying factors influencing faculty performance evaluation decisions are analyzed using the commercial data provided by Academic Analytics, LLC, which comprises the scholarly records of 336 793 faculty members from 472 Ph.D.-granting universities in the United States during 2011-2020. Various machine learning techniques, including ensemble learning and association rule mining, are performed to determine the important features that provide the most significant insights into academic career growth. Our results indicate strong evidence of age-gender effect on faculty career advancement and underscore the significance of journal article and citation counts for career progression in higher education.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 2","pages":"606-619"},"PeriodicalIF":7.5,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10587099","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Reliable Federated Disentangling Network for Non-IID Domain Feature 非iid域特征的可靠联邦解纠缠网络

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-07-05 DOI: 10.1109/TBDATA.2024.3423694

Meng Wang;Kai Yu;Chun-Mei Feng;Yiming Qian;Ke Zou;Lianyu Wang;Rick Siow Mong Goh;Xinxing Xu;Yong Liu;Huazhu Fu

{"title":"Reliable Federated Disentangling Network for Non-IID Domain Feature","authors":"Meng Wang;Kai Yu;Chun-Mei Feng;Yiming Qian;Ke Zou;Lianyu Wang;Rick Siow Mong Goh;Xinxing Xu;Yong Liu;Huazhu Fu","doi":"10.1109/TBDATA.2024.3423694","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3423694","url":null,"abstract":"Federated Learning (FL), as an efficient decentralized distributed learning approach, enables multiple institutions to collaboratively train a model without sharing their local data. Despite its advantages, the performance of FL models is substantially impacted by the domain feature shift arising from different acquisition devices/clients. Moreover, existing FL methods often prioritize accuracy without considering reliability factors such as confidence or uncertainty, leading to unreliable predictions in safety-critical applications. Thus, our goal is to enhance FL performance by addressing non-domain feature issues and ensuring model reliability. In this study, we introduce a novel approach named RFedDis (Reliable Federated Disentangling Network). RFedDis leverages feature disentangling to capture a global domain-invariant cross-client representation while preserving local client-specific feature learning. Additionally, we incorporate an uncertainty-aware decision fusion mechanism to effectively integrate the decoupled features. This ensures dynamic integration at the evidence level, producing reliable predictions accompanied by estimated uncertainties. Therefore, RFedDis is the FL approach to combine evidential uncertainty with feature disentangling, enhancing both performance and reliability in handling non-IID domain features. Extensive experimental results demonstrate that RFedDis outperforms other state-of-the-art FL approaches, providing outstanding performance coupled with a high degree of reliability.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 2","pages":"648-658"},"PeriodicalIF":7.5,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629649","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

On the Convergence of Federated Learning Algorithms Without Data Similarity 无数据相似度的联邦学习算法的收敛性

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-07-05 DOI: 10.1109/TBDATA.2024.3423693

Ali Beikmohammadi;Sarit Khirirat;Sindri Magnússon

{"title":"On the Convergence of Federated Learning Algorithms Without Data Similarity","authors":"Ali Beikmohammadi;Sarit Khirirat;Sindri Magnússon","doi":"10.1109/TBDATA.2024.3423693","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3423693","url":null,"abstract":"Data similarity assumptions have traditionally been relied upon to understand the convergence behaviors of federated learning methods. Unfortunately, this approach often demands fine-tuning step sizes based on the level of data similarity. When data similarity is low, these small step sizes result in an unacceptably slow convergence speed for federated methods. In this paper, we present a novel and unified framework for analyzing the convergence of federated learning algorithms without the need for data similarity conditions. Our analysis centers on an inequality that captures the influence of step sizes on algorithmic convergence performance. By applying our theorems to well-known federated algorithms, we derive precise expressions for three widely used step size schedules: fixed, diminishing, and step-decay step sizes, which are independent of data similarity conditions. Finally, we conduct comprehensive evaluations of the performance of these federated learning algorithms, employing the proposed step size strategies to train deep neural network models on benchmark datasets under varying data similarity conditions. Our findings demonstrate significant improvements in convergence speed and overall performance, marking a substantial advancement in federated learning research.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 2","pages":"659-668"},"PeriodicalIF":7.5,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629651","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

Minerva: Decentralized Collaborative Query Processing Over InterPlanetary File System Minerva：星际文件系统上分散的协同查询处理

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-07-05 DOI: 10.1109/TBDATA.2024.3423729

Zhiyi Yao;Bowen Ding;Qianlan Bai;Yuedong Xu

{"title":"Minerva: Decentralized Collaborative Query Processing Over InterPlanetary File System","authors":"Zhiyi Yao;Bowen Ding;Qianlan Bai;Yuedong Xu","doi":"10.1109/TBDATA.2024.3423729","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3423729","url":null,"abstract":"Data silos create barriers to accessing and utilizing data dispersed over networks. Directly sharing data easily suffers from the long downloading time, the single point failure and the untraceable data usage. In this paper, we present Minerva, a peer-to-peer cross-cluster data query system based on the InterPlanetary File System (IPFS). Minerva makes use of the distributed Hash table (DHT) lookup to pinpoint the locations that store content chunks. We theoretically model the DHT query delay and introduce a fat Merkle tree structure as well as the DHT caching to reduce it. We design the query plan for read and write operations on top of Apache Drill that enables the collaborative query with decentralized workers. We conduct comprehensive experiments on Minerva, and the results show that Minerva achieves up to <inline-formula><tex-math>$2.08 times$</tex-math></inline-formula> query performance acceleration compared to the original IPFS data query, and can complete data analysis queries on the Internet-like environments within an average latency of 0.615 second. With a collaborative query, Minerva could perform up to <inline-formula><tex-math>$1.39 times$</tex-math></inline-formula> performance acceleration than the centralized query with raw data shipment.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 2","pages":"669-683"},"PeriodicalIF":7.5,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629656","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