IEEE Transactions on Emerging Topics in Computational Intelligence最新文献_第7页

Multi-View Clustering With Consistent Local Structure-Guided Graph Fusion 基于一致性局部结构引导图融合的多视图聚类

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-16 DOI: 10.1109/TETCI.2024.3423459

Naiyao Liang;Zuyuan Yang;Wei Han;Zhenni Li;Shengli Xie

引用次数: 0

A Co-Evolutionary Dual Niching Differential Evolution Algorithm for Nonlinear Equation Systems Optimization 非线性方程组优化的协同进化双小生境差分进化算法

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-16 DOI: 10.1109/TETCI.2024.3442867

Shuijia Li;Rui Wang;Wenyin Gong;Zuowen Liao;Ling Wang

{"title":"A Co-Evolutionary Dual Niching Differential Evolution Algorithm for Nonlinear Equation Systems Optimization","authors":"Shuijia Li;Rui Wang;Wenyin Gong;Zuowen Liao;Ling Wang","doi":"10.1109/TETCI.2024.3442867","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3442867","url":null,"abstract":"A nonlinear equation system often has multiple roots, while finding all roots simultaneously in one run remains a challenging work in numerical optimization. Although many methods have been proposed to solve the problem, few have utilised two algorithms with different characteristics to improve the root rate. To locate as many roots as possible of nonlinear equation systems, in this paper, a co-evolutionary dual niching differential evolution with information sharing and migration is developed. To be specific, firstly it utilizes a dual niching algorithm namely neighborhood-based crowding/speciation differential evolution co-evolutionary to search concurrently; secondly, a parameter adaptation strategy is employed to ameliorate the capability of the dual algorithm; finally, the dual niching differential evolution adaptively performs information sharing and migration according to the evolutionary experience, thereby balancing the population diversity and convergence. To investigate the performance of the proposed approach, thirty nonlinear equation systems with diverse characteristics and a more complex test set are used as the test suite. A comprehensive comparison shows that the proposed method performs well in terms of root rate and success rate when compared with other advanced algorithms.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"109-118"},"PeriodicalIF":5.3,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143107208","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 Completion Learning for Incomplete Multi-View Clustering 不完全多视图聚类的双完成学习

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-13 DOI: 10.1109/TETCI.2024.3451562

Qiangqiang Shen;Xuanqi Zhang;Shuqin Wang;Yuanman Li;Yongsheng Liang;Yongyong Chen

{"title":"Dual Completion Learning for Incomplete Multi-View Clustering","authors":"Qiangqiang Shen;Xuanqi Zhang;Shuqin Wang;Yuanman Li;Yongsheng Liang;Yongyong Chen","doi":"10.1109/TETCI.2024.3451562","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3451562","url":null,"abstract":"Incomplete Multi-View Clustering (IMVC) offers a way to analyze incomplete data, facilitating the inference of unobserved and missing data points through completion techniques. However, existing IMVC methods, predominantly depending on either data completion or similarity matrix completion, failed to uncover the inherent geometric structure and potential complementary information between intra- and inter-views, causing incomplete similarity matrices to further tear apart the connections between views. To address this problem, we propose Dual Completion Learning for Incomplete Multi-view Clustering (DCIMC), which elaborately designs data completion and similarity tensor completion, and fuses both of them into a unified model to effectively recover the missing samples and similarities. Concretely, in data completion, DCIMC utilizes subspace clustering to recover the missing and unknown instances directly. Meanwhile, in similarity tensor completion, DCIMC introduces the idea of tensor completion to make better use of the high-order complementary information from multi-view data. By fusing the dual completions, missing information and complementary information in each completion are fully explored by each other, reciprocally enhancing one another to boost the accuracy of our clustering algorithm. Experimental results on various datasets show the effectiveness of the proposed DCIMC. Moreover, our DCIMC also achieved superior or comparable performance in an extended comparison with recent deep learning-based multi-view clustering algorithms.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"455-467"},"PeriodicalIF":5.3,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361375","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

Towards Precise Weakly Supervised Object Detection via Interactive Contrastive Learning of Context Information 基于上下文信息交互对比学习的精确弱监督目标检测

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-13 DOI: 10.1109/TETCI.2024.3436853

Qi Lai;Chi-Man Vong;Sai-Qi Shi;C.L. Philip Chen

{"title":"Towards Precise Weakly Supervised Object Detection via Interactive Contrastive Learning of Context Information","authors":"Qi Lai;Chi-Man Vong;Sai-Qi Shi;C.L. Philip Chen","doi":"10.1109/TETCI.2024.3436853","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3436853","url":null,"abstract":"Weakly supervised object detection (WSOD) aims at learning precise object detectors with only image-level tags. In spite of intensive research on deep learning (DL) approaches over the past few years, there is still a significant performance gap between WSOD and fully supervised object detection. Existing WSOD methods only consider the visual appearance of each region proposal but ignore the useful context information in the image. This paper proposes an interactive end-to-end WSDO framework called JLWSOD with two innovations: i) two types of WSOD-specific context information (i.e., <italic>instance-wise correlation</i> and <italic>semantic-wise correlation</i>) are proposed and introduced into WSOD framework; ii) an <italic>interactive graph contrastive learning</i> (iGCL) mechanism is designed to jointly optimize the visual appearance and context information for better WSOD performance. Specifically, the iGCL mechanism takes full advantage of the complementary interpretations of the WSOD, namely instance-wise detection and semantic-wise prediction tasks, forming a more comprehensive solution. Extensive experiments on the widely used PASCAL VOC and MS COCO benchmarks verify the superiority of JLWSOD over alternative SOTA and baseline models (improvement of 3.0%<inline-formula><tex-math>$sim$</tex-math></inline-formula>23.3% on mAP and 3.1%<inline-formula><tex-math>$sim$</tex-math></inline-formula>19.7% on CorLoc, respectively).","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1795-1804"},"PeriodicalIF":5.3,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706607","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

Multi-Hop Reasoning With Relation Based Node Quality Evaluation for Sparse Medical Knowledge Graph 稀疏医学知识图的多跳推理及基于关系的节点质量评价

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-12 DOI: 10.1109/TETCI.2024.3452748

Tian Zhang;Jian Cheng;Lijie Miao;Hanning Chen;Qing Li;Qiang He;Jianhui Lyu;Lianbo Ma

{"title":"Multi-Hop Reasoning With Relation Based Node Quality Evaluation for Sparse Medical Knowledge Graph","authors":"Tian Zhang;Jian Cheng;Lijie Miao;Hanning Chen;Qing Li;Qiang He;Jianhui Lyu;Lianbo Ma","doi":"10.1109/TETCI.2024.3452748","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3452748","url":null,"abstract":"Medical knowledge graph (KG) is sparse KG that contains insufficient information and missing paths. Multi-hop reasoning is an effective approach of medical KG completion, since it offers logical insights of the underlying KG and shows more direct interpretability. However, existing methods based on reinforcement learning focus on the use of historical and current state information but ignore the importance of evaluating the quality of candidate nodes in sparse KGs. Especially, it is difficult for the agent to select the correct search actions in sparse KGs. Occasionally, the agent will be at a dilemma state (i.e., state trap), where few actions can be selected. To address the above issue, we propose an effective relation-based node quality evaluation (RNQE) model for multi-hop reasoning. This model has two merits: (1) it reduces the impact of insufficient information in sparse KGs by synthesizing the reasoning quality information (i.e., the potential reasoning contribution) of candidate nodes; (2) it avoids the state trap by encouraging the agents to explore the path along a set of nodes with more relations. Experiments on both benchmark and real-world medical knowledge graphs demonstrate the promising ability of our proposed method to improve the reasoning performance for KGs.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1805-1816"},"PeriodicalIF":5.3,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706565","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

MUSTER: A Multi-Scale Transformer-Based Decoder for Semantic Segmentation 基于多尺度变压器的语义分割解码器

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-09 DOI: 10.1109/TETCI.2024.3449911

Jing Xu;Wentao Shi;Pan Gao;Qizhu Li;Zhengwei Wang

{"title":"MUSTER: A Multi-Scale Transformer-Based Decoder for Semantic Segmentation","authors":"Jing Xu;Wentao Shi;Pan Gao;Qizhu Li;Zhengwei Wang","doi":"10.1109/TETCI.2024.3449911","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3449911","url":null,"abstract":"In recent works on semantic segmentation, there has been a significant focus on designing and integrating transformer-based encoders. However, less attention has been given to transformer-based decoders. We emphasize that the decoder stage is equally vital as the encoder in achieving superior segmentation performance. It disentangles and refines high-level cues, enabling precise object boundary delineation at the pixel level. In this paper, we introduce a novel transformer-based decoder called MUSTER, which seamlessly integrates with hierarchical encoders and consistently delivers high-quality segmentation results, regardless of the encoder architecture. Furthermore, we present a variant of MUSTER that reduces FLOPS while maintaining performance. MUSTER incorporates carefully designed multi-head skip attention (MSKA) units and introduces innovative upsampling operations. The MSKA units enable the fusion of multi-scale features from the encoder and decoder, facilitating comprehensive information integration. The upsampling operation leverages encoder features to enhance object localization and surpasses traditional upsampling methods, improving mIoU (mean Intersection over Union) by 0.4% to 3.2%. On the challenging ADE20K dataset, our best model achieves a single-scale mIoU of 50.23 and a multi-scale mIoU of 51.88, which is on-par with the current state-of-the-art model. Remarkably, we achieve this while significantly reducing the number of FLOPs by 61.3%.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"202-212"},"PeriodicalIF":5.3,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143107209","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 Collaborative Multi-Component Optimization Model Based on Pattern Sequence Similarity for Electricity Demand Prediction 基于模式序列相似性的电力需求预测协同多分量优化模型

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-09 DOI: 10.1109/TETCI.2024.3449881

Xiaoyong Tang;Juan Zhang;Ronghui Cao;Wenzheng Liu

{"title":"A Collaborative Multi-Component Optimization Model Based on Pattern Sequence Similarity for Electricity Demand Prediction","authors":"Xiaoyong Tang;Juan Zhang;Ronghui Cao;Wenzheng Liu","doi":"10.1109/TETCI.2024.3449881","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3449881","url":null,"abstract":"In the new electricity market, the accurate electricity demand prediction can make high possible profit. However, electricity consumption data exhibits nonlinearity, high volatility, and susceptibility to various factors. Most existing prediction schemes inadequately account for these traits, resulting in weak performance. In view of this, we propose a collaborative multi-component optimization model (MCO-BHPSF) to achieve high accuracy electricity demand prediction. For this model, the original data is first decomposed into linear trend components and nonlinear residual components using the Moving Average filter. Then, the enhanced Pattern Sequence-based Forecasting (PSF) algorithm that can effectively capture data patterns with obvious changes is used to accurately forecast the trend component and the embedded LightGBM for residual components. We further optimize the prediction results by using an error optimization scheme based on online sequence extreme learning machines to reduce prediction errors. The results of extensive experiments on four real-world datasets demonstrate that our proposed MCO-BHPSF model outperforms four advanced baseline models. In day-ahead prediction, our model is on average 31% better than PSF baselines. For long-term prediction, our proposed MCO-BHPSF model has an average improvement rate of 37% compared to PSF baselines.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"119-130"},"PeriodicalIF":5.3,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106801","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 Hub-Based Self-Organizing Algorithm for Feedforward Small-World Neural Network 基于hub的前馈小世界神经网络自组织算法

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-09 DOI: 10.1109/TETCI.2024.3451335

Wenjing Li;Can Chen;Junfei Qiao

{"title":"A Hub-Based Self-Organizing Algorithm for Feedforward Small-World Neural Network","authors":"Wenjing Li;Can Chen;Junfei Qiao","doi":"10.1109/TETCI.2024.3451335","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3451335","url":null,"abstract":"By integrating the small-world (SW) property into the design of feedforward neural networks, the network performance would be improved by well-documented evidence. To achieve the structural self-adaptation of the feedforward small-world neural networks (FSWNNs), a self-organizing FSWNN, namely SOFSWNN, is proposed based on a hub-based self-organizing algorithm in this paper. Firstly, an FSWNN is constructed according to Watts-Strogatz's rule. Derived from the graph theory, the hub centrality is calculated for each hidden neuron and then used as a measurement for its importance. The self-organizing algorithm is designed by splitting important neurons and merging unimportant neurons with their correlated neurons, and the convergence of this algorithm can be guaranteed theoretically. Extensive experiments are conducted to validate the effectiveness and superiority of SOFSWNN for both classification and regression problems. SOFSWNN achieves an improved generalization performance by SW property and the self-organizing structure. Besides, the hub-based self-organizing algorithm would determine a compact and stable network structure adaptively even from different initial structure.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"160-175"},"PeriodicalIF":5.3,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143107207","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

Robust Hypergraph Regularized Deep Non-Negative Matrix Factorization for Multi-View Clustering 多视图聚类的鲁棒超图正则化深度非负矩阵分解

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-09 DOI: 10.1109/TETCI.2024.3451352

Hangjun Che;Chenglu Li;Man-Fai Leung;Deqiang Ouyang;Xiangguang Dai;Shiping Wen

{"title":"Robust Hypergraph Regularized Deep Non-Negative Matrix Factorization for Multi-View Clustering","authors":"Hangjun Che;Chenglu Li;Man-Fai Leung;Deqiang Ouyang;Xiangguang Dai;Shiping Wen","doi":"10.1109/TETCI.2024.3451352","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3451352","url":null,"abstract":"As the increasing heterogeneous data, mining valuable information from various views is in demand. Currently, deep matrix factorization (DMF) receives extensive attention because of its ability to discover latent hierarchical semantics of the data. However, the existing multi-view DMF methods have the following shortcomings: (1). Most of multi-view DMF methods exploit Frobenius norm as the reconstruction error measure, which is easily affected by noises and outliers. (2). A <inline-formula><tex-math>$k$</tex-math></inline-formula>NN-based graph keeps the geometric structure of the representation similar to the raw data, which fails to consider the higher-order relationships between instances. To solve these issues, in this research, a novel robust multi-view hypergraph regularized deep non-negative matrix factorization is proposed. Concretely, <inline-formula><tex-math>$l_{2, 1}$</tex-math></inline-formula>-norm is adopted to measure the factorization error for enhancing the robustness. A hypergraph regularization is designed to discover the higher-order relationships between the instances. Additionally, a pair-wise consistency learning term is utilized to mine consistency information in multi-view data. An optimization algorithm based on iterative updating rules is developed for solving the proposed model, which makes the objective function value monotonically non-increase until convergence. Moreover, the convergence of the proposed optimization algorithm is validated theoretically and experimentally. Finally, abundant experiments are performed on six real world and two synthetic multi-view datasets to evaluate the performance of the proposed method and the comparison methods.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1817-1829"},"PeriodicalIF":5.3,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706661","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

Density Peaks Clustering Based on Label Propagation and K-Mutual-Nearest Neighbors 基于标签传播和k近邻的密度峰聚类

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-09 DOI: 10.1109/TETCI.2024.3452687

Liping Sun;Fan Huang;Xiaoyao Zheng;Liangmin Guo;Qingying Yu;Zhenghua Chen;Yonglong Luo

{"title":"Density Peaks Clustering Based on Label Propagation and K-Mutual-Nearest Neighbors","authors":"Liping Sun;Fan Huang;Xiaoyao Zheng;Liangmin Guo;Qingying Yu;Zhenghua Chen;Yonglong Luo","doi":"10.1109/TETCI.2024.3452687","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3452687","url":null,"abstract":"The density peaks clustering algorithm is one of the density-based clustering algorithms. This algorithm has several advantages, including not requiring a preset number of clusters, requiring fewer parameters, and being able to achieve clustering of any shape. However, it also has limitations, such as poor clustering performance on datasets with uneven density, the need to manually select cluster centers on the decision graph, and a chain reaction that can lead to a large number of point misallocations due to incorrect allocation of individual points. To overcome the shortcomings of the density peaks clustering algorithm, we propose a density peaks clustering algorithm based on label propagation and k-mutual-nearest neighbors. First, the local density and the distance are defined by incorporating the concept of k-mutual-nearest neighbors to enhance clustering performance on datasets with uneven-density clusters. Second, an adaptive method for selecting cluster centers is proposed to avoid the manual selection of cluster centers. Third, an improved label propagation algorithm is used to assign all remaining points to solve the chain reaction problem. The experimental results show that our algorithm can accurately identify cluster centers and obtain high-quality clustering results on synthetic datasets with different characteristics, including datasets with uneven cluster density, convex datasets, manifold datasets, and datasets with inter-cluster contact. On different types of UCI datasets, including small datasets, high-dimensional datasets, and large datasets, our algorithm outperforms other comparative algorithms.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1830-1842"},"PeriodicalIF":5.3,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706662","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