Information Sciences最新文献

{"title":"Improving confidence intervals and central value estimation in small datasets through hybrid parametric bootstrapping","authors":"Victor V. Golovko","doi":"10.1016/j.ins.2025.122254","DOIUrl":"10.1016/j.ins.2025.122254","url":null,"abstract":"<div><div>We developed a hybrid parametric bootstrapping (HPB) method for analyzing small datasets with high precision. This method addresses the challenge of estimating confidence intervals (CI) and central values when traditional distribution assumptions do not apply. Our HPB is combined with Steiner's Most Frequent Value (MFV) technique. The MFV method minimizes the information loss associated with small datasets, while the HPB considers the uncertainty of each separate element. As a practical example, we applied this innovative and robust statistical methodology to refine prior measurements of the half-life of <span><math><mmultiscripts><mrow><mtext>Ru</mtext></mrow><mprescripts></mprescripts><none></none><mrow><mn>97</mn></mrow></mmultiscripts></math></span>. Using the MFV technique integrated with the HPB method, we obtained a significantly more precise half-life estimate, <span><math><msub><mrow><mi>T</mi></mrow><mrow><mn>1</mn><mo>/</mo><mn>2</mn><mo>,</mo><mtext>MFV(HPB)</mtext></mrow></msub><mo>=</mo><msubsup><mrow><mn>2.8385</mn></mrow><mrow><mo>−</mo><mn>0.0075</mn></mrow><mrow><mo>+</mo><mn>0.0022</mn></mrow></msubsup></math></span> days. This refined value features a 68.27% confidence interval from 2.8310 to 2.8407 days and a 95.45% confidence interval from 2.8036 to 2.8485 days, as calculated using the percentile method. Our analysis demonstrates a substantial reduction in uncertainty–over 30 times lower than that reported in nuclear data sheets–indicating the potential for widespread analytical impact. In addition, employing alternative minimization strategies can reduce the statistical uncertainty by a further 44%. The HPB method effectively addresses the uncertainties inherent in small datasets, as demonstrated by re-evaluating the specific activity measurements for <span><math><mmultiscripts><mrow><mtext>Ar</mtext></mrow><mprescripts></mprescripts><none></none><mrow><mn>39</mn></mrow></mmultiscripts></math></span> using underground data. We report <span><math><mi>S</mi><msub><mrow><mi>A</mi></mrow><mrow><mtext>MFV(HPB)</mtext></mrow></msub><mo>=</mo><msubsup><mrow><mn>0.966</mn></mrow><mrow><mo>−</mo><mn>0.020</mn></mrow><mrow><mo>+</mo><mn>0.027</mn></mrow></msubsup></math></span> Bq/kg_atmAr, with confidence intervals (68.27%: 0.946–0.993; 95.45%: 0.921–1.029) derived using the percentile method. Advances in statistical methods are important for making data analysis more accurate and reliable, especially when combining and interpreting information from different sources. The developed tools help handle complex data more effectively, thereby improving the process and understanding of information in real-world applications where precision is essential.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"716 ","pages":"Article 122254"},"PeriodicalIF":8.1,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143904501","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}

{"title":"Neural network compression using binarization and few full-precision weights","authors":"Franco Maria Nardini ,&nbsp;Cosimo Rulli ,&nbsp;Salvatore Trani ,&nbsp;Rossano Venturini","doi":"10.1016/j.ins.2025.122251","DOIUrl":"10.1016/j.ins.2025.122251","url":null,"abstract":"<div><div>Quantization and pruning are two effective Deep Neural Network model compression methods. In this paper, we propose <em>Automatic Prune Binarization</em> (<span>APB</span>), a novel compression technique combining quantization with pruning. <span>APB</span> enhances the representational capability of binary networks using a few full-precision weights. Our technique jointly maximizes the accuracy of the network while minimizing its memory impact by deciding whether each weight should be binarized or kept in full precision. We show how to efficiently perform a forward pass through layers compressed using <span>APB</span> by decomposing it into a binary and a sparse-dense matrix multiplication. Moreover, we design two novel efficient algorithms for extremely quantized matrix multiplication on CPU, leveraging highly efficient bitwise operations. The proposed algorithms are 6.9× and 1.5× faster than available state-of-the-art solutions. We extensively evaluate <span>APB</span> on two widely adopted model compression datasets, namely CIFAR-10 and ImageNet. <span>APB</span> shows to deliver better accuracy/memory trade-off compared to state-of-the-art methods based on i) quantization, ii) pruning, and iii) a combination of pruning and quantization. <span>APB</span> also outperforms quantization in the accuracy/efficiency trade-off, being up to 2× faster than the 2-bits quantized model with no loss in accuracy.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"716 ","pages":"Article 122251"},"PeriodicalIF":8.1,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143904499","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}

{"title":"Discovering optimal Markov blanket for high-dimensional streaming features","authors":"Waqar Khan ,&nbsp;Brekhna Brekhna ,&nbsp;Yajun Xie ,&nbsp;Muhammad Sadiq Hassan Zada ,&nbsp;Rasool Shah ,&nbsp;Yifan Zheng","doi":"10.1016/j.ins.2025.122240","DOIUrl":"10.1016/j.ins.2025.122240","url":null,"abstract":"<div><div>Conducting knowledge discovery on high-dimensional streaming features requires an online causal feature selection process that can significantly reduce the complexity of real-world feature spaces and enhance the learning process. This is achieved by mining online causal features to construct a Markov blanket (MB) for the class label, select highly relevant subsets, and minimize the numbers of irrelevant and redundant features within contained the streaming feature space. However, the prevailing MB algorithms (e.g., offline and online methods) often fall short in terms of discerning the causal relationship between a class label and the selected features, rendering them ineffective and inefficient for addressing high-dimensional streaming feature spaces. We propose a novel algorithm named <u>D</u>iscovering <u>O</u>ptimal - <u>M</u>arkov <u>b</u>lanket for high-dimensional <u>S</u>treaming <u>F</u>eatures (DO-MB<span><math><msub><mrow></mrow><mrow><mi>S</mi><mi>F</mi></mrow></msub></math></span>) to address these limitations, and this approach is tailored to optimally learn an MB online. First, DO-MB<span><math><msub><mrow></mrow><mrow><mi>S</mi><mi>F</mi></mrow></msub></math></span> dynamically learns the parents (Ps), children (Cs), and spouses of class labels, thereby distinguishing PC relationships from spouses and Ps from Cs during the MB learning procedure. Second, learning relevant PC and spousal relationships and accurately distinguishing them enables a balance to be struck between prediction accuracy and computational efficiency, ensuring a comprehensive online causal feature selection approach. An extensive experimental validation highlights the superiority of the DO-MB<span><math><msub><mrow></mrow><mrow><mi>S</mi><mi>F</mi></mrow></msub></math></span> algorithm in terms of accuracy and efficiency. By identifying powerfully relevant PC and spousal relationships and optimizing the tradeoff between accuracy and efficiency, DO-MB<span><math><msub><mrow></mrow><mrow><mi>S</mi><mi>F</mi></mrow></msub></math></span> is a promising solution for performing online causal feature selection in high-dimensional streaming feature spaces. The code has been released on <span><span>https://github.com/vickykhan89/DO-MBSF</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"716 ","pages":"Article 122240"},"PeriodicalIF":8.1,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143894990","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}

{"title":"The stability and oscillation analysis of fractional neural networks under periodically intermittent control from its response property","authors":"Jian-Bing Hu,&nbsp;Zhangrui Zheng,&nbsp;Chu-Teng Ying,&nbsp;Shu-Guang Li,&nbsp;Ping Tan","doi":"10.1016/j.ins.2025.122241","DOIUrl":"10.1016/j.ins.2025.122241","url":null,"abstract":"<div><div>The input switching must cause output oscillation as the input-output property of a system. However this important process has rarely been reported in many obtained achievements about neural networks under periodically intermittent control.</div><div>In this paper, we have studied the stability and oscillation of fractional neural networks under periodically intermittent control. Firstly, the relation between fractional derivative and the response property is studied. Secondly, the output is divided into the steady part and the transient part. The transient part and the steady-state part are discussed according to the historical inputs step by step. Then, the oscillation mechanism of fractional neural networks is elucidated. Lastly, a novel stability condition and an oscillation-analyzing approach are proposed. Our research shows that the steady part and the transient part are related to all historical processes and the input switching must cause the output oscillation, which can explain the learning speed and the divergence of neural networks very well. Some examples presented in this paper have verified our theoretical achievements.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"716 ","pages":"Article 122241"},"PeriodicalIF":8.1,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899360","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}

{"title":"Filter differentiation: An effective approach to interpret convolutional neural networks","authors":"Yongkai Fan,&nbsp;Hongxue Bao,&nbsp;Xia Lei","doi":"10.1016/j.ins.2025.122253","DOIUrl":"10.1016/j.ins.2025.122253","url":null,"abstract":"<div><div>The lack of interpretability in deep learning poses a major challenge for AI security, as it hinders the detection and prevention of potential vulnerabilities. Understanding black-box models, such as Convolutional Neural Networks (CNNs), is crucial for establishing trust in them. Currently, filter disentanglement is a mainstream approach for interpreting CNNs, but existing efforts still face the problem of reducing filter entanglement without compromising model accuracy. Inspired by bionic theory, we propose a filter differentiation method that disentangles filters while improving model accuracy by simulating the process of pluripotent to unipotent cell differentiation. Specifically, by using a differentiation matrix based on attention mechanisms and an activation matrix based on mutual information between filters and classes, the convolutional weights of filters can be adjusted, allowing general filters in CNNs to be differentiated into specialized filters that respond only to specific classes. Experiments on benchmark datasets, including CIFAR-10, CIFAR-100, and TinyImageNet, show that our method achieves consistent improvements in model performance. It improves accuracy by 0.5% to 2% across various architectures, including ResNet18 and MobileNetV2, while enhancing filter interpretability as measured by Mutual Information Scores (MIS). These results demonstrate that our method achieves an effective balance between interpretability and accuracy.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"716 ","pages":"Article 122253"},"PeriodicalIF":8.1,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899362","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}

{"title":"Sliding window-based high utility occupancy pattern mining for data streams","authors":"Seungwan Park ,&nbsp;Taewoong Ryu ,&nbsp;Doyoon Kim ,&nbsp;Doyoung Kim ,&nbsp;Hanju Kim ,&nbsp;Myungha Cho ,&nbsp;Unil Yun","doi":"10.1016/j.ins.2025.122243","DOIUrl":"10.1016/j.ins.2025.122243","url":null,"abstract":"<div><div>High utility-based pattern mining has been proposed to analyze information by considering not only the frequency of items but also their quantity and profit. Among these, studies on high utility occupancy-based patterns have emerged, which consider the occupancy measure reflecting the share of a pattern belonging to transactions. Furthermore, as the necessity to process real-time stream data has become more critical, a method to discover high utility occupancy-based patterns in stream information has been presented recently. However, this recent method handles all accumulated data on data stream environments. Since all previously accumulated data are processed, the volume of data to be processed steadily increases over time, leading to a decline in efficiency over time. In addition, it becomes difficult to give emphasis on recent data. Consequently, these methods become less suitable for practical applications. To surmount the drawbacks, we introduce a novel approach for mining high utility occupancy patterns, employing a sliding window technique to efficiently process stream data. By focusing on fixed-size, most recent data within the window, our method effectively reflects the trends in the latest data while exhibiting improved efficiency compared to previous approaches. Extensive performance evaluations demonstrate the efficacy of the proposed method against prior methods regarding runtime, memory usage, scalability, and sensitivity. Moreover, statistical tests confirm that our approach accurately extracts the exact number of patterns without pattern loss or duplication.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"716 ","pages":"Article 122243"},"PeriodicalIF":8.1,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143895091","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}

{"title":"Prompt-guided orthogonal multimodal fusion for cancer survival prediction","authors":"Lan Huang ,&nbsp;Shuyu Guo ,&nbsp;Tian Bai ,&nbsp;Ruihong Zhao ,&nbsp;Ke Tao","doi":"10.1016/j.ins.2025.122242","DOIUrl":"10.1016/j.ins.2025.122242","url":null,"abstract":"<div><div>Cancer survival prediction can assist clinicians in developing personalized treatment plans for patients. Comprehensive cancer diagnosis and treatment require integrating macroscopic and microscopic imaging. However, significant discrepancies in the spatial resolution and anatomical scale between imaging modalities hinder existing multimodal fusion methods from effectively learning correlated semantic features with limited datasets. In this work, we introduce a prompt-guided orthogonal multimodal fusion strategy (POMF) for fusing multimodal medical images across anatomical scales. POMF utilizes modality-specific prompts to fine-tune pretrained models, facilitating bias adaptation to medical imaging features while ensuring more computationally efficient training. A modality consistency-discrepancy prototype is designed as the modality-inherent prompt in POMF, disentangling the multimodal features and bridging the potential correlations across the orthogonal modalities. POMF is validated on a glioma survival prediction task using paired radiology and pathology images. The experiment results suggest that POMF achieves superior C-index with existing full-tuning and prompt-tuning methods. Additionally, the ablation studies demonstrate that POMF is adaptable to various architectures of pretrained encoders and multiple multimodal fusion strategies on cross-scale medical images.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"715 ","pages":"Article 122242"},"PeriodicalIF":8.1,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881409","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}

{"title":"Probability completion and consensus reaching based on kernel density estimation for incomplete probabilistic linguistic multi-attribute group decision making","authors":"Jinglin Xiao,&nbsp;Xinxin Wang,&nbsp;Ying Gao,&nbsp;Zeshui Xu","doi":"10.1016/j.ins.2025.122207","DOIUrl":"10.1016/j.ins.2025.122207","url":null,"abstract":"<div><div>Multi-attribute group decision-making is a hot topic in the study of uncertain decision-making processes, particularly when linguistic variables are employed to express evaluative information. However, incomplete information often arises due to cognitive disparities among decision-makers and their diverse evaluation preferences. To address these challenges, this paper proposes a novel multi-attribute group decision-making method that incorporates incomplete probabilistic linguistic term sets and considers nonlinear semantics. First, we introduce an innovative application of kernel density estimation to complete incomplete term sets, employing Gaussian kernel functions to model the nonlinear perceptual variations of decision-makers. The bandwidth and skewness parameters are utilized to reflect perceptual granularity and evaluation bias, respectively. Second, we modify the Kolmogorov-Smirnov distance measure and propose a novel comparison rule tailored to probabilistic linguistic term sets with semantic imbalance, enhancing the computational accuracy of attribute weight determination. Furthermore, two optimization models are developed to determine the bandwidths for completing incomplete information and aggregating individual evaluations. A dynamic adjustment mechanism is introduced to support decision-maker interaction in achieving consensus. The effectiveness of the proposed methods is demonstrated through a case study on gas meter selection. Sensitivity analysis and comparative experiments highlight its superior performance in handling incomplete information and managing uneven semantics.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"715 ","pages":"Article 122207"},"PeriodicalIF":8.1,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881408","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}

{"title":"Multimodal cross-domain contrastive learning: A self-supervised generative and geometric framework for visual perception","authors":"S. Muhammad Ahmed Hassan Shah ,&nbsp;Atif Rizwan ,&nbsp;Muhammad Sardaraz ,&nbsp;Muhammad Tahir ,&nbsp;Nagwan Abdel Samee ,&nbsp;Mona M. Jamjoom","doi":"10.1016/j.ins.2025.122239","DOIUrl":"10.1016/j.ins.2025.122239","url":null,"abstract":"<div><div>Self-Supervised Contrastive Representation Learning (SSCRL) has gained significant attention for its ability to learn meaningful representations from unlabeled data by leveraging contrastive learning principles. However, existing SSCRL approaches struggle with effectively handling heterogeneous data formats, particularly discrete and binary representations, limiting adaptability across multiple domains. This limitation hinders the generalization of learned representations, especially in applications requiring structured feature encoding and robust cross-domain adaptability. To address this, we propose the Modular QCB Learner, a novel algorithm designed to enhance representation learning for heterogeneous data types. This framework builds upon SSCRL by incorporating a Real Non-Volume Preserving transformation to optimize continuous representations, ensuring alignment with a Gaussian distribution. For discrete representation learning, vector quantization is utilized along with a Poisson distribution, while binary representations are modeled through nonlinear transformations and the Bernoulli distribution. Multi-Domain Mixture Optimization (MiDO) is introduced to facilitate joint optimization of different representation types by integrating multiple loss functions. To evaluate effectiveness, synthetic data generation is performed on extracted representations and compared with baselines. Experiments on CIFAR-10 confirm the Modular QCB Learner improves representation quality, demonstrating robustness across diverse data domains with applications in synthetic data generation, anomaly detection and multimodal learning.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"715 ","pages":"Article 122239"},"PeriodicalIF":8.1,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881411","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}

{"title":"Biometric untargeted attacks: A case study on near-collisions","authors":"Axel Durbet ,&nbsp;Paul-Marie Grollemund ,&nbsp;Kevin Thiry-Atighehchi","doi":"10.1016/j.ins.2025.122217","DOIUrl":"10.1016/j.ins.2025.122217","url":null,"abstract":"<div><div>Biometric recognition systems are now integral to many authentication and identification processes, prompting the need to understand their resilience under various attack scenarios. In this work, we analyze the security of such systems against <em>untargeted attacks</em>, where an adversary aims to impersonate any user without focusing on a specific target. Assuming a minimal leakage model—where only a binary acceptance or rejection is revealed—we derive upper and lower bounds on the attack complexity as functions of the template size, decision threshold, and database size. Our contributions apply to templates following a uniform distribution, such as randomized biometric templates or those derived from high-entropy secret sources. Many biometric template protection schemes, such as BioHashing or random projection-based transformations, combine biometric data with a high-entropy secret (e.g., a password or token). This combination is designed to produce pseudo-random outputs, making the uniform distribution a reasonable assumption for the transformed template space. As a result, our analysis covers two-factor authentication systems where biometrics are combined with a stored random secret or strong password. We use probabilistic modeling to assess the theoretical security limits of such systems. We investigate two practical attack scenarios: naive outsiders submitting random guesses, and multiple simultaneous attackers increasing the overall trial rate. We also introduce the notion of <em>weak near-collisions</em> to evaluate the risk of mutual impersonation due to close templates in the database. Our theoretical analysis is validated on real biometric datasets (LFW and FVC) using transformation schemes such as BioHashing. Finally, we provide practical recommendations for configuring system parameters to mitigate untargeted attacks and near-collision risks.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"716 ","pages":"Article 122217"},"PeriodicalIF":8.1,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918482","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}