Information Sciences最新文献_第4页

Learning to mine all minimal evidences for unverified claims 学习挖掘所有最小的证据，为未经证实的主张

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-07-14 DOI: 10.1016/j.ins.2025.122505

Yanan Liu , Hai Wan , Jianfeng Du , Yao Wang , Kunxun Qi , Weilin Luo

{"title":"Learning to mine all minimal evidences for unverified claims","authors":"Yanan Liu , Hai Wan , Jianfeng Du , Yao Wang , Kunxun Qi , Weilin Luo","doi":"10.1016/j.ins.2025.122505","DOIUrl":"10.1016/j.ins.2025.122505","url":null,"abstract":"<div><div>Mining evidences is crucial in checking unverified claims. Most existing methods usually find a single evidence expressed by a set of sentences to verify a given claim. However, treating a set of sentences as unique evidence is insufficient or even misleading, <em>e.g.</em>, when it involves both supporting and refuting information. Besides, gathering evidence from different perspectives helps us better analyze and understand the claim. In this article, we suggest mining <em>all</em> irreducible evidences for supporting or refuting a claim, where we treat a minimal set of sentences in the given text corpus for either the support or the refutation as an irreducible point of view and call it a <em>minimal evidence</em>. We develop a neural-symbolic framework to mine all minimal evidences. It exploits a logical algorithm to compute all minimal evidences one by one, where every minimal evidence is computed through two neural models <em>scorer</em> and <em>reasoner</em> learnt from the annotated minimal evidences. Experimental results demonstrate that our framework is effective in finding multiple minimal evidences for both textual and structural claims. Furthermore, we investigate several implementation combinations for scorers and reasoners so as to seek the best practice on our framework.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"720 ","pages":"Article 122505"},"PeriodicalIF":8.1,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Exploration of rough approximation operators with supervised justifiable granularity principle 基于监督合理粒度原理的粗糙逼近算子探索

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-07-14 DOI: 10.1016/j.ins.2025.122504

Lei-Jun Li , Mei-Zheng Li , Ju-Sheng Mi

{"title":"Exploration of rough approximation operators with supervised justifiable granularity principle","authors":"Lei-Jun Li , Mei-Zheng Li , Ju-Sheng Mi","doi":"10.1016/j.ins.2025.122504","DOIUrl":"10.1016/j.ins.2025.122504","url":null,"abstract":"<div><div>Rough set theory is a representative granular computing model that has received widespread attention. Rough approximation operators (RAOs) serve as foundational components in rough set models, leveraging information granules to approximate abstract concepts. The justifiable granularity principle (JGP) is one of the fundamentals in granular computing, and has achieved great success in the design and evaluation of information granules. Within this context, this study investigates RAOs based on the JGP in classification learning. First, the limitations of two types of popular RAOs, namely probabilistic and fuzzy RAOs, are analyzed from a classification learning perspective. It is concluded that different samples lack discrimination w.r.t. the decision classes in these RAOs. Subsequently, the supervised JGP (SJGP) is proposed. The relative coverage and relative specificity of information granules are formulated w.r.t. the decision classes. These are integrated into existing RAOs to address the challenges. Finally, a new type of reduct is introduced, and a unified framework for heuristic algorithms is also developed correspondingly. The proposed RAOs are applied to attribute reduction. Experimental results demonstrate the reasonableness and superiority of integrating SJGP into RAOs.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"720 ","pages":"Article 122504"},"PeriodicalIF":8.1,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144653826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Lightweight real-time discriminative Siamese deep coupling framework for robust aerial tracking 用于鲁棒空中跟踪的轻量级实时判别Siamese深度耦合框架

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-07-13 DOI: 10.1016/j.ins.2025.122510

Hanlin Huang , Guixi Liu , Ruke Xiong , Yinghao Li , Qian Lu , Zhiyu Wu

{"title":"Lightweight real-time discriminative Siamese deep coupling framework for robust aerial tracking","authors":"Hanlin Huang , Guixi Liu , Ruke Xiong , Yinghao Li , Qian Lu , Zhiyu Wu","doi":"10.1016/j.ins.2025.122510","DOIUrl":"10.1016/j.ins.2025.122510","url":null,"abstract":"<div><div>Recently, transformer-based Unmanned Aerial Vehicle (UAV) trackers have achieved notable success. However, the computationally intensive transformer model limits these trackers to static templates and shallow backbone networks, hampering their discriminative power and localization precision. Here, we propose a novel discriminative Siamese deep-coupling framework. This framework constructs a lightweight fine-grid anchor-free Siamese tracker with high spatial resolution specifically tailored for UAV scenarios, and complements its discriminative power with a targeted online discriminator. To achieve this, an efficient distractor detector is developed via knowledge transfer, enabling targeted detection of distractors that disturb the Siamese tracker. These distractors are utilized as training samples to construct a targeted online discriminator, which is deeply coupled with the Siamese tracker to enhance its discriminative power and specifically suppress hard distractors that hinder tracking performance. Additionally, a leading principal submatrix cluster sample space model and a scene-aware dynamic update strategy are developed to purify online samples and dynamically schedule the online discriminator update, significantly reducing the computational cost of the online discriminator optimization and boosting the tracker’s real-time performance. Finally, extensive experiments on eight UAV tracking benchmarks demonstrate that our tracker surpasses state-of-the-art transformer-based UAV trackers while achieving 70 FPS on CPU.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"719 ","pages":"Article 122510"},"PeriodicalIF":8.1,"publicationDate":"2025-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144613992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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-Branch CNN-Transformer network for robust Zero-Watermarking of medical images 医学图像鲁棒零水印的双支路CNN-Transformer网络

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-07-12 DOI: 10.1016/j.ins.2025.122511

Jingyou Li , Rongle Wei , Xiaotian Xi , Guangda Zhang , Zixin Yang , Fengshan Zhang

{"title":"Dual-Branch CNN-Transformer network for robust Zero-Watermarking of medical images","authors":"Jingyou Li , Rongle Wei , Xiaotian Xi , Guangda Zhang , Zixin Yang , Fengshan Zhang","doi":"10.1016/j.ins.2025.122511","DOIUrl":"10.1016/j.ins.2025.122511","url":null,"abstract":"<div><div>In this paper, we propose a novel zero-watermarking approach that utilizes deep learning to protect medical images from malicious attacks, including unauthorized copying, cropping, and other forms of tampering, during transmission. In this paper, we created a two-part system that uses a simple Convolutional Neural Network (CNN) and a transformer to effectively understand both small details and the overall context of medical images, called a dual-branch CNN-Transformer network. The CNN branch extracts the local details while the transformer branch captures the global contextual information. The Maximum Voting Adaptive Fusion Module (MVAM) integrates these features to generate robust medical images representations. Logistics map encryption is used to ensure the integrity of the watermark without altering the original image. Experimental results show that the proposed method is robust to various attacks (including noise, compression, filtering, rotation, scaling, translation, and cropping) and outperforms existing techniques. The ability to extract up to 4096 dimensional features greatly improves the characterization of medical images and helps to improve disease diagnosis.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"720 ","pages":"Article 122511"},"PeriodicalIF":8.1,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144653855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

An inverse graph model for conflict resolution under opinion dynamics with minimum cost 意见动态下最小成本冲突解决的逆图模型

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-07-11 DOI: 10.1016/j.ins.2025.122508

Jing Xiao , Fang Wang

{"title":"An inverse graph model for conflict resolution under opinion dynamics with minimum cost","authors":"Jing Xiao , Fang Wang","doi":"10.1016/j.ins.2025.122508","DOIUrl":"10.1016/j.ins.2025.122508","url":null,"abstract":"<div><div>Existing inverse graph model for conflict resolution (GMCR) primarily focuses on identifying opinions that can make the desired state an equilibrium, while overlooking the opinion dynamics among conflicting members and the transition costs involved in shifting from the current opinions to those that establish the desired equilibrium state. Accordingly, this study proposes an inverse GMCR with minimum cost in the framework of the social network DeGroot (SNDG) model. Recognizing the pivotal influence of leaders’ initial opinions and self-confidence levels on the formation of consensus under the SNDG model, this study introduces two inverse graph models. The first model aims to minimize the adjustment of leaders’ initial opinions, while the second seeks to minimize changes in their self-confidence levels. Both models incorporate bounded confidence constraints, capturing the limited extent to which leaders are willing to modify their initial opinions or self-confidence levels. Additionally, the models are designed to allow conflicting members to adopt conflict behaviors that best align with their individual circumstances. Finally, the practical application of the proposed method is demonstrated through the Elmira groundwater contamination conflict, accompanied by sensitivity and comparative analysis to validate its effectiveness and superiority.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"720 ","pages":"Article 122508"},"PeriodicalIF":8.1,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144653827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 multidimensional feature grouping sampling algorithm based on dynamic feedback of prior bias 基于先验偏差动态反馈的多维特征分组采样算法

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-07-11 DOI: 10.1016/j.ins.2025.122490

Yunwei Zhang , Zongkai Shen , Fang Wang , Jinguo You , Xiaoxia Zhao

{"title":"A multidimensional feature grouping sampling algorithm based on dynamic feedback of prior bias","authors":"Yunwei Zhang , Zongkai Shen , Fang Wang , Jinguo You , Xiaoxia Zhao","doi":"10.1016/j.ins.2025.122490","DOIUrl":"10.1016/j.ins.2025.122490","url":null,"abstract":"<div><div>The rapid development of information technology has led to the generation of massive amounts of large-scale discrete-variable data. However, processing the entire dataset will consume a lot of computing resources and be computationally inefficient. Sampling techniques provide a cost-effective solution to reduce the computational complexity while maintaining the original properties of the data. In pursuit of efficiency and effectiveness, this article proposes a multidimensional feature grouping sampling algorithm based on dynamic feedback of prior bias (MFGS) for sampling discrete-variable data. The basic idea is dynamic feedback iterative sampling. To this end, we established a dynamic feedback correction mechanism based on prior bias, which can accurately locate the sampling feature channel of each iteration, calculate the sampling size of each subgroup, and achieve accurate and targeted cyclic optimization sampling. Meanwhile, MFGS is introduced with the idea of smoothing filtering, which removes redundant samples in the oversampling area and can accurately limit the overall sample size. In addition, we use the multidimensional Manhattan distance to establish a sampling bias evaluation index, which provides a calculation basis for feedback and correction. Finally, we designed three experiments to verify the effectiveness of the feedback correction mechanism and smoothing filtering, and evaluate the sampling accuracy, computational efficiency, and sampling accuracy of the method under additional constraints. The experimental results show that the dynamic feedback correction mechanism and smoothing filter are effective, and MFGS outperforms the compared state-of-the-art methods in terms of sampling accuracy, and its computational efficiency is significantly improved compared with clustering-based sampling methods.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"719 ","pages":"Article 122490"},"PeriodicalIF":8.1,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144631469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Few-shot partial multi-label learning with credible non-candidate label 具有可信非候选标签的少射部分多标签学习

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-07-11 DOI: 10.1016/j.ins.2025.122485

Meng Wang , Yunfeng Zhao , Zhongmin Yan , Jinglin Zhang , Jun Wang , Guoxian Yu

{"title":"Few-shot partial multi-label learning with credible non-candidate label","authors":"Meng Wang , Yunfeng Zhao , Zhongmin Yan , Jinglin Zhang , Jun Wang , Guoxian Yu","doi":"10.1016/j.ins.2025.122485","DOIUrl":"10.1016/j.ins.2025.122485","url":null,"abstract":"<div><div>Partial multi-label learning (PML) addresses scenarios where each training sample is associated with multiple candidate labels, but only a subset are ground-truth labels. The primary difficulty in PML is to mitigate the negative impact of noisy labels. Most existing PML methods rely on sufficient samples to train a noise-robust multi-label classifier. However, in practical scenarios, such as privacy-sensitive domains or those with limited data, only a few training samples are typically available for the target task. In this paper, we propose an approach called <span>FsPML-CNL</span> (Few-shot Partial Multi-label Learning with Credible Non-candidate Label) to tackle the PML problem with few-shot training samples. Specifically, <span>FsPML-CNL</span> first utilizes the sample features and feature-prototype similarity in the embedding space to disambiguate candidate labels and to obtain label prototypes. Then, the credible non-candidate label is selected based on label correlation and confidence, and its prototype is incorporated into the training samples to generate new data for boosting supervised information. The noise-tolerant multi-label classifier is finally induced with the original and generated samples, along with the confidence-guided loss. Extensive experiments on public datasets demonstrate that <span>FsPML-CNL</span> outperforms competitive baselines across different settings.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"719 ","pages":"Article 122485"},"PeriodicalIF":8.1,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144613990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Adaptive structure learning for semi-supervised feature selection with binary single-label learning 基于二元单标签学习的半监督特征选择自适应结构学习

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-07-11 DOI: 10.1016/j.ins.2025.122498

Huming Liao , Hongmei Chen , Tengyu Yin , Zhong Yuan , Shi-Jinn Horng , Tianrui Li

{"title":"Adaptive structure learning for semi-supervised feature selection with binary single-label learning","authors":"Huming Liao , Hongmei Chen , Tengyu Yin , Zhong Yuan , Shi-Jinn Horng , Tianrui Li","doi":"10.1016/j.ins.2025.122498","DOIUrl":"10.1016/j.ins.2025.122498","url":null,"abstract":"<div><div>Learning pseudo-labels for unlabeled samples provides more helpful information in semi-supervised feature selection (SSFS), and the labels of unlabeled samples are learned as continuous values by most existing SSFS methods. Whereas the given labels of labeled samples are encoded in a one-hot encoding way, the two are not uniform in form and do not provide more explicit supervised information. So, this paper introduces binary single-label learning, which learns unlabeled sample labels into a uniform one-hot encoding form. Furthermore, this paper preserves the data's local and global structure by combining improved Euclidean distance-based adaptive graph learning with sparse representation learning. A novel SSFS model called Adaptive Structure Learning for Semi-supervised Feature Selection with Binary Single-label Learning (ASBLFS) is proposed, and an efficient optimization algorithm is derived. Finally, the following conclusions are observed through extensive experiments with several advanced SSFS models on 15 benchmark datasets: (1) Binary single labels achieve better performance than continuous labels on some datasets, suggesting that binary labels can provide more explicit supervisory information. (2) ASBLFS shows the second-best or best performance on most datasets, demonstrating the superiority of ASBLFS.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"720 ","pages":"Article 122498"},"PeriodicalIF":8.1,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 time series forecasting using a novel fuzzy regression approach based on kernel functions 基于核函数的模糊回归鲁棒时间序列预测

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-07-11 DOI: 10.1016/j.ins.2025.122496

Lingtao Kong, Jinyao Wang, Wei Lin

{"title":"Robust time series forecasting using a novel fuzzy regression approach based on kernel functions","authors":"Lingtao Kong, Jinyao Wang, Wei Lin","doi":"10.1016/j.ins.2025.122496","DOIUrl":"10.1016/j.ins.2025.122496","url":null,"abstract":"<div><div>In recent years, the use of fuzzy regression approaches in time series forecasting has increased notably. However, the influence of outliers in time series persists as a significant challenge. In this study, we propose a novel robust fuzzy regression functions approach, which can effectively address the issue of outliers. The proposed method incorporates robust techniques at both the clustering and inference stages. In particular, the fuzzy <em>c</em>-medoids clustering algorithm is employed in the initial stage, while a robust estimator based on kernel functions is utilised in the latter stage. To assess the forecasting performance of the proposed method, two financial time series datasets are considered, including Shanghai Stock Exchange Composite index time series and Taiwan Stock Exchange time series. Furthermore, to evaluate the robustness of the proposed method against outliers, four scenarios of contaminated data are examined. The experimental results demonstrate that the proposed method outperforms several popular methods in the majority of cases for both the original and contaminated datasets.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"719 ","pages":"Article 122496"},"PeriodicalIF":8.1,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Summarizing Boolean and fuzzy tensors with sub-tensors 总结布尔张量和模糊张量的子张量

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-07-11 DOI: 10.1016/j.ins.2025.122489

Victor Henrique Silva Ribeiro, Loïc Cerf

{"title":"Summarizing Boolean and fuzzy tensors with sub-tensors","authors":"Victor Henrique Silva Ribeiro, Loïc Cerf","doi":"10.1016/j.ins.2025.122489","DOIUrl":"10.1016/j.ins.2025.122489","url":null,"abstract":"<div><div>The disjunctive box cluster model summarizes an <em>n</em>-way Boolean tensor with some of its sub-tensors and their densities, <em>i.e.</em>, the arithmetic means of their values. Mirkin and Kramarenko proposed that easy-to-interpret regression model, for <span><math><mi>n</mi><mo>∈</mo><mo>{</mo><mn>2</mn><mo>,</mo><mn>3</mn><mo>}</mo></math></span>, and hill climbing to discover good sub-tensors, according to ordinary least squares. This article generalizes Mirkin and Kramarenko's work: <em>n</em>-way <em>fuzzy</em> tensors are summarized. They encode to what extent <em>n</em>-ary predicates are satisfied. The article also details significant performance improvements to the sequential execution, its parallelization, better starting points for hill climbing, a selection of the discovered sub-tensors, their ranking in order of contribution to the model, and the use of the elbow method to truncate the ordered list. In-depth experiments using synthetic and real-world tensors compare the proposed method, NclusterBox, to Mirkin and Kramarenko's and to the state-of-the-art algorithms for matrix factorization using the max (rather than +) operator and for Boolean tensor factorization. NclusterBox summarizes synthetic and real-world fuzzy tensors more efficiently and, most importantly, more accurately.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"719 ","pages":"Article 122489"},"PeriodicalIF":8.1,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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