IEEE Transactions on Big Data最新文献_第10页

Enhancing Vulnerability Reports With Automated and Augmented Description Summarization 使用自动和增强的描述摘要增强漏洞报告

IF 8.1 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-12-01 Epub Date: 2025-03-02 DOI: 10.1109/TBDATA.2025.3566618

Hattan Althebeiti;Mohammed Alkinoon;Manar Mohaisen;Saeed Salem;DaeHun Nyang;David Mohaisen

引用次数: 0

Disentangled Graph Contrastive Learning for Socially-Aware Next-Item Recommendation 社会意识下一项推荐的解纠缠图对比学习

IF 8.1 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-12-01 Epub Date: 2025-07-10 DOI: 10.1109/TBDATA.2025.3588077

Bin Wu;Xun Su;Long Chen;Jing Liang;Yangdong Ye

{"title":"Disentangled Graph Contrastive Learning for Socially-Aware Next-Item Recommendation","authors":"Bin Wu;Xun Su;Long Chen;Jing Liang;Yangdong Ye","doi":"10.1109/TBDATA.2025.3588077","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3588077","url":null,"abstract":"Next-item recommendation has received considerable attention in academia and industry, which aims to predict the next desired item for each user based on his historical behaviors. It has been validated that user behaviors are driven by two key factors: <italic>social influence, which leverages social relationships to better infer user preference, and <italic>sequential influence, which captures item transition patterns to model the dynamic evolution of user interest. While previous next-item recommenders have made great progress, we argue that there still exist three critical limitations: (1) they always follow the paradigm of entangling social and sequential influences, resulting in poor interpretability; (2) they fail to explicitly capture high-order influences at social-level and sequential-level, resulting in the suboptimal performance; (3) they fail to dynamically distinguish the importance of each influence factor when predicting user preference. To settle these three defects, we contribute a novel solution for socially-aware next-item recommendation, namely Disentangled Graph Contrastive Learning (DGCL) method, which explicitly disentangles social and sequential influences on user behavior data. Specifically, we first reorganize social relationships and all users’ behavior sequences as two separate graphs. Then, a disentangled graph propagation module is developed on these two graphs to independently capture high-order influences at social-level and sequential-level. Furthermore, we formalize a dual contrastive learning paradigm as an auxiliary task to supervise the thorough disentanglement. Finally, we devise a user-specific attention mechanism to adaptively differentiate the importance of each influence factor for model prediction. Empirical results on four benchmark datasets demonstrate the superiority of DGCL over recent state-of-the-art recommenders. Further analysis verifies the rationality and necessity of each part in our solution.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 6","pages":"3197-3211"},"PeriodicalIF":8.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148480045","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

Federated Average Clustering Learning Based on Time-Asynchronous Similarity 基于时间-异步相似度的联邦平均聚类学习

IF 8.1 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-12-01 Epub Date: 2025-03-02 DOI: 10.1109/TBDATA.2025.3566605

Xiaoyao Zheng;Haonan Li;Di Wu;Tianran Bu;Peng Hu;Ji Zhang

{"title":"Federated Average Clustering Learning Based on Time-Asynchronous Similarity","authors":"Xiaoyao Zheng;Haonan Li;Di Wu;Tianran Bu;Peng Hu;Ji Zhang","doi":"10.1109/TBDATA.2025.3566605","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3566605","url":null,"abstract":"To address the challenge of non-independent and identically distributed data in federated learning, clustering federated learning extracts local model features from clients and groups clients with high local model similarity into the same cluster to optimize the global model for the corresponding data distribution. However, current federated clustering algorithms bring additional communication cost for the accuracy of cluster division, which increases the communication burden of some performance-limited edge devices. To address the communication efficiency issue of clustering federated learning, we proposed an optimized communication efficiency federated average clustering learning that uses time weights to select partial clients to participate in training randomly. At the same time, a time-asynchronous similarity calculation method is proposed to improve the accuracy of local model similarity calculation for randomly selected clients. Extensive experimental evaluations show that our federated average clustering learning can achieve or even surpass the model accuracy of existing federated clustering algorithms. In the communication evaluation experiment, we can achieve the specified model accuracy using 5% to 30% of the communication of existing federated clustering algorithms.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 6","pages":"2958-2971"},"PeriodicalIF":8.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148480215","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

Knowledge Aggregation Transformer Network for Multivariate Time Series Classification 多元时间序列分类的知识聚合变压器网络

IF 8.1 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-12-01 Epub Date: 2025-07-31 DOI: 10.1109/TBDATA.2025.3594294

Zhiwen Xiao;Huanlai Xing;Rong Qu;Hui Li;Huagang Tong;Shouxi Luo;Jing Song;Li Feng;Qian Wan

{"title":"Knowledge Aggregation Transformer Network for Multivariate Time Series Classification","authors":"Zhiwen Xiao;Huanlai Xing;Rong Qu;Hui Li;Huagang Tong;Shouxi Luo;Jing Song;Li Feng;Qian Wan","doi":"10.1109/TBDATA.2025.3594294","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3594294","url":null,"abstract":"Over the years, various sophisticated deep learning algorithms have surfaced for multivariate time series classification (MTSC), notably the dual-network-based model. This model comprises two parallel networks tailored to time series data: one for local feature extraction and the other for global relation extraction. However, effectively integrating these dual networks poses a significant challenge. To address this, we propose a knowledge aggregation transformer network (KATN) for MTSC. KATN, composed of four aggregation transformer blocks, extracts abundant regularizations and connections hidden within the data. Each block incorporates a modified residual network (MResNet) for local feature extraction and a multi-head attention network for global relation extraction. Initially, the block merges MResNet’s output feature with that of the multi-head attention network through an additive operation. Subsequently, it aligns features with a fully connected (i.e., dense) layer and activates neural units using the Gaussian error linear unit function. This strategic feature aggregation allows for capturing long-range dependencies among multiple variables in multivariate time series data. Experimental results demonstrate that KATN significantly outperforms 6 state-of-the-art transformer variants, achieving a ‘win’/‘tie’/‘lose’ record of 9/6/15 and securing the lowest AVG_rank score. Furthermore, when evaluated against 18 existing MTSC algorithms across 13 UEA datasets, KATN consistently delivers superior performance, attaining the lowest AVG_rank score among all compared methods.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 6","pages":"3413-3429"},"PeriodicalIF":8.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148481127","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 Multi-Scale Deep Supervision Net for Hand Key Point Detection 手部关键点检测的并行多尺度深度监督网络

IF 8.1 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-12-01 Epub Date: 2025-03-02 DOI: 10.1109/TBDATA.2025.3566535

Renjie Li;Son N. Tran;Saurabh Garg;Katherine Lawler;Jane Alty;Quan Bai

{"title":"Parallel Multi-Scale Deep Supervision Net for Hand Key Point Detection","authors":"Renjie Li;Son N. Tran;Saurabh Garg;Katherine Lawler;Jane Alty;Quan Bai","doi":"10.1109/TBDATA.2025.3566535","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3566535","url":null,"abstract":"Key point detection plays an important role in a wide range of applications. However, predicting key points of small objects such as human hands is a challenging problem. Recent works fuse feature maps of deep Convolutional Neural Networks (CNNs), either via multi-level feature integration or multi-resolution aggregation. Despite achieving some success, the feature fusion approaches increase the complexity and the opacity of CNNs. To address this issue, we propose a novel CNN model named Parallel Multi-Scale Deep Supervision Network (P-MSDSNet) that learns feature maps at different scales in parallel with deep supervisions to produce spatial attention maps for adaptive feature propagation from layer to layer. P-MSDSNet has a multi-stage with a parallel structure that fuses multi-scale features from both the same and different depth levels. The deep supervision with spatial attention would enhance relevant features and help improve the transparency of the feature learning at each stage. In the experiment, we show that P-MSDSNet outperforms the state-of-the-art approaches on benchmark datasets while requiring fewer parameters. We also demonstrate the applicability of P-MSDSNet to quantifying finger-tapping hand movements in a neuroscience study.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 6","pages":"2870-2883"},"PeriodicalIF":8.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148479040","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

Graph-Enhanced Multi-Scale Contrastive Learning for Graph Anomaly Detection With Adaptive Diffusion Models 基于自适应扩散模型的图增强多尺度对比学习图异常检测

IF 8.1 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-12-01 Epub Date: 2025-03-02 DOI: 10.1109/TBDATA.2025.3566619

Chunjing Xiao;Yao Tong;Jiahui Lu;Yuxia Xue;Wenxin Tai;Zhangtao Cheng;Fan Zhou

{"title":"Graph-Enhanced Multi-Scale Contrastive Learning for Graph Anomaly Detection With Adaptive Diffusion Models","authors":"Chunjing Xiao;Yao Tong;Jiahui Lu;Yuxia Xue;Wenxin Tai;Zhangtao Cheng;Fan Zhou","doi":"10.1109/TBDATA.2025.3566619","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3566619","url":null,"abstract":"Graph anomaly detection has gained significant research interest across various domains. Due to the lack of labeled data, contrastive learning has been applied in detecting anomalies and various contrastive strategies have been initiated. However, these methods might force two instances (e.g., node-level and subgraph-level representations) with different category labels to be consistent during model training, which can adversely impact the model robustness. Also, they extract node-level representations only based on node attributes, which are inadequate in reflecting the information of the structural anomaly. To tackle this problem, we present a Graph-enhanced multi-scale Contrastive Learning framework for Anomaly Detection, GCLAD. In this framework, we design a diffusion probabilistic model-based graph enhancement module to adaptively manipulate neighbors to generate enhanced graphs, which can efficiently enhance subgraph-level representations and alleviate the inconsistent problem. Further, we present a multi-scale contrastive module where we introduce meta-paths to exploit a few relevant neighbors to boost node-level representations, and build the multi-scale contrastive losses to promote anomaly detection performance. Experimental results demonstrate the superiority of GCLAD compared with state-of-the-art baselines.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 6","pages":"2946-2957"},"PeriodicalIF":8.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148479678","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

Determination of the Number of Clusters in High-Dimensional Data With Subspace Clusters 用子空间聚类确定高维数据中的聚类数

IF 8.1 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-12-01 Epub Date: 2025-07-10 DOI: 10.1109/TBDATA.2025.3588027

Mohammad Sultan Mahmud;Joshua Zhexue Huang;Germán González-Almagro;Salvador García

{"title":"Determination of the Number of Clusters in High-Dimensional Data With Subspace Clusters","authors":"Mohammad Sultan Mahmud;Joshua Zhexue Huang;Germán González-Almagro;Salvador García","doi":"10.1109/TBDATA.2025.3588027","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3588027","url":null,"abstract":"Current big data clustering methods pose a challenge to efficiently estimate the number of clusters when dealing with a large number of instances and dimensions. However, determining the large number of clusters in a high-dimensional dataset is difficult due to the conflicting nature of its subspaces. In this paper, we propose a new distributed clustering ensemble method called <bold>sub<bold>space <bold>cluster <bold>ensemble (SSCE) to find the number of clusters in big high-dimensional datasets with informative subspace clusters. We represent the high-dimensional dataset as a set of random subspaces, and then in each subspace view, we investigate the number of clusters and initial cluster centers with the I-niceDP clustering scheme. To efficiently handle big data (i.e., a large number of instances with high dimensions), we use multiple random samples of a big dataset to estimate the number of clusters. With this aim, we first adopt the random sample partitioning (RSP) data model to facilitate random sample generation and then apply the subspace cluster estimation scheme. To do so, each random sample (i.e., RSP data block) is processed independently in parallel, and subspace clustering components are generated. A novel two-stage clustering ensemble, ball fusion (BF), is proposed to estimate the final result from the subspace clustering components and multiple RSP sample outcomes. Subspace and full-space clusters induce key connections within subspaces to generate the final ensemble outcome. Results from experiments on synthetic and real-world datasets demonstrated the effectiveness of the proposed method, which outperformed the state-of-the-art baselines.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 6","pages":"3240-3254"},"PeriodicalIF":8.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148480825","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

A Comprehensive Image Protection Framework Based on High-Capacity Adversarial Data Hiding 基于大容量对抗数据隐藏的综合图像保护框架

IF 8.1 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-12-01 Epub Date: 2025-07-28 DOI: 10.1109/TBDATA.2025.3593381

Ming Li;Wenwen Zhou;Tao Wang;Yushu Zhang;Wenying Wen

{"title":"A Comprehensive Image Protection Framework Based on High-Capacity Adversarial Data Hiding","authors":"Ming Li;Wenwen Zhou;Tao Wang;Yushu Zhang;Wenying Wen","doi":"10.1109/TBDATA.2025.3593381","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3593381","url":null,"abstract":"In recent years, a large number of personal images have been uploaded to social network platforms, contributing to the formation of image Big Data. These images are vulnerable to security threats, e.g., privacy inference, copyright infringement, and malicious tampering. Many image protection methods have been proposed, e.g., privacy protection methods based on adversarial perturbations, and copyright protection methods based on data hiding. However, these methods can only deal with a single security threat causing the image to suffer from residual threats. Therefore, this paper proposes a comprehensive image protection framework, which generates adversarial examples by embedding meaningful perturbations, achieving image privacy protection while protecting copyright and integrity by data hiding. In this framework, we design a novel high-capacity adversarial data hiding model (HADH) to support adversarially embedding of adequate robust watermarking for copyright protection and fragile watermarking for integrity protection. Experimental results show that HADH achieves a high embedding rate of 3.21 Reed-Solomon bits per pixel (RS-bpp), providing sufficient capacity for copyright and identity verification information. The capacity is even higher than other pure robust steganography schemes. In addition, the privacy protection performance is better than the existing adversarial attack-based privacy protection methods.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 6","pages":"3314-3329"},"PeriodicalIF":8.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148480882","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

Robustness Benchmarking of Convolutional and Transformer Architectures for Image Classification 图像分类中卷积和变换结构的鲁棒性基准测试

IF 8.1 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-12-01 Epub Date: 2025-07-28 DOI: 10.1109/TBDATA.2025.3593385

Vishesh Kumar;Shivam Shukla;Akshay Agarwal

{"title":"Robustness Benchmarking of Convolutional and Transformer Architectures for Image Classification","authors":"Vishesh Kumar;Shivam Shukla;Akshay Agarwal","doi":"10.1109/TBDATA.2025.3593385","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3593385","url":null,"abstract":"Amidst the burgeoning landscape of thousands of deep neural networks (DNNs), selecting a robust architecture for image classification poses a formidable challenge. The prime reason is the vulnerability of these DNNs to image corruption. Surprisingly, the literature still does not understand which network is sensitive to which kind of corruption and to what extent. Our study rigorously analyzes DNNs across the classification spectrum, from pure convolutional neural networks (CNNs: without attention layers) to state-of-the-art Vision Transformers (ViTs). To reach a robust conclusion, we have performed extensive experiments using 18 DNNs, 5 diverse datasets, and 15 corruption types of varying severity. Our analysis uncovers insightful and surprising findings concerning the robustness of different DNNs across corruption types. For example, it is observed that the ViT trained on CIFAR10 is found to be highly robust in handling noise corruptions, even of considerable severity (say, severity 4), but is found vulnerable to environmental corruptions of the same severity. At the same time, while the performance of pure CNNs is lower than that of ViT, they can handle environmental factors better than noise corruption. Another interesting observation showcased that these corruptions can even fool one of the popular network explainability algorithms, Grad-CAM. The heat map highlights the same region of interest on the noisy image, similar to clean images, but the class label differs. Shedding light on the vulnerabilities of deep learning models and revealing their vulnerability against particular corruption ensures the deployment of a correct network in the real world that deals with specific corruption frequently.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 6","pages":"3330-3341"},"PeriodicalIF":8.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148479350","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

Asymmetric Dual-Encoder Network With Clustering and Mutual Contrast Loss for the Semantic Segmentation of Remote-Sensing Images 基于聚类和相互对比度损失的非对称双编码器网络遥感图像语义分割

IF 8.1 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-12-01 Epub Date: 2025-06-02 DOI: 10.1109/TBDATA.2025.3575945

Wujie Zhou;Yangzhen Li;Yuanyuan Liu

{"title":"Asymmetric Dual-Encoder Network With Clustering and Mutual Contrast Loss for the Semantic Segmentation of Remote-Sensing Images","authors":"Wujie Zhou;Yangzhen Li;Yuanyuan Liu","doi":"10.1109/TBDATA.2025.3575945","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3575945","url":null,"abstract":"In recent years, the semantic segmentation of multimodal remote-sensing images using convolutional methods has received significant attention. Owing to the localized nature of convolutional operations, existing methods use the attention method to obtain global relations. However, effectively complementing and eliminating the global and local relations between two modalities has become a major challenge. In addition, category imbalance often affects the model performance as a result of the high resolution of remote-sensing images. In this study, we propose an asymmetric dual encoder network with clustering mutual contrast loss. Specifically, we use a convolutional neural network and Transformer as the backbone to extract two modal features in parallel to learn the local and global information, respectively. Next, our proposed multimodal hierarchical interaction module and dynamic weight inspired block efficiently fuse the multimodal features to complement the local and global information. The fused features are fed into our proposed local context extraction module and global context extraction module. Furthermore, to address the challenge presented by feature class imbalances, we apply a clustering algorithm to classify each pixel, which is subsequently inter-supervised using the inter-contrast loss. Extensive experiments on benchmark datasets show that the proposed model is extremely effective in the semantic segmentation of remotely sensed images and that it outperforms current state-of-the-art networks both quantitatively and qualitatively.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 6","pages":"3103-3115"},"PeriodicalIF":8.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148478889","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