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Recommender Systems Based on Tensor Decomposition

Computers, Materials & Continua 2021年第1期66卷 621-630页

作者： Zhoubao Sun Xiaodong Zhang Haoyuan Li Yan Xiao Haifeng Guo School of Information Engineering Nanjing Audit UniversityNanjing211815China School of Computing National University of Singapore117417Singapore Jiangsu Key Laboratory of Data Science and Smart Software Jinling Institute of TechnologyNanjing211169China

Recommender system is an effective tool to solve the problems of information *** traditional recommender systems,especially the collaborative filtering ones,only consider the two factors of users and *** social networks contain abundant social information,such as tags,places and *** show that the social information has a great impact on recommendation *** not only describe the characteristics of items,but also reflect the interests and characteristics of *** the traditional recommender systems cannot parse multi-dimensional information,in this paper,a tensor decomposition model based on tag regularization is proposed which incorporates social information to benefit recommender *** original Singular Value Decomposition(SVD)model is optimized by mining the co-occurrence and mutual exclusion of tags,and their features are constrained by the relationship between *** on real dataset show that the proposed algorithm achieves superior performance to existing algorithms.

关键词： Recommender system social information tensor decomposition tag

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Enhanced Spatial and Temporal Feature Integration for fMRI-Based ASD Classification

Enhanced Spatial and Temporal Feature Integration for fMRI-B...

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IEEE Conference on Cybernetics and Intelligent Systems

作者： Minghui Tan Xin Deng Lianhua Zhang Hao Zhang Daijiang Xu The Key Laboratory of Data Engineering and Visual Computing Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

Autism Spectrum Disorder (ASD) poses diagnostic challenges due to its varied manifestations and the absence of the specific biological test. To improve the auxiliary diagnosis of ASD, this work proposes a novel deep learning framework called BWA-GCN, which capitalizes on the strengths of Graph Convolutional Networks (GCN), Bidirectional Long Short-Term Memory (BiLSTM) networks, and attention mechanisms. The advantage of this model is by incorporating BiLSTM for the significant temporal feature extraction and GCN for the spatial information aggregation. The attention mechanisms are adopted in this model to enhance the precision of ASD classification. Extensive experiments based on the ABIDE dataset with CC200 and AAL brain atlas prove the good performance of this model, with the accuracy of 70.92% on the CC200 atlas and 68.72% on the AAL atlas. This research contributes a new method to the research of ASD classification, shedding light on the spatial-temporal dynamics of fMRI time-series data in ASD diagnosis.

关键词： Recurrent neural networks Attention mechanisms Mechatronics Random access memory Functional magnetic resonance imaging Brain modeling Reliability

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Residual Dense Information Distillation Network for Single Image Super-Resolution

Residual Dense Information Distillation Network for Single I...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Qiaosong Chen Jinxin Li Bolin Duan Liu Pu Xin Deng Jin Wang Key Laboratory of Data Engineering and Visual Computing Chongqing University of Posts and Telecommunications Chongqing China

In recent years, Convolutional Neural Networks (CNNs) have achieved excellent results in the study of single image super-resolution. However, super-resolution algorithms based on CNNs still face serious challenges, such as poor detail reconstruction, numerous parameters, and difficulty of training. A Residual Dense Information Distillation Network (RD-IDN) is proposed in this paper which uses dense skip connections and residual structure to solve the problems of difficult training and low utilization of features in Information Distillation Network. Experimental results show that the proposed method is superior to many other Super Resolution algorithms in terms of reconstruction performance and computational comsumption.

关键词： Training Feature extraction Image reconstruction Convolution Graphics processing units Interpolation

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Review of computational neuroaesthetics: bridging the gap between neuroaesthetics and computer science

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Brain Informatics 2020年第1期7卷 16页

作者： Li, Rui Zhang, Junsong National Engineering Laboratory for Educational Big Data Central China Normal University WuhanHubei China Fujian Key Laboratory of Brain-Inspired Computing Technique and Applications Department of Artificial Intelligence School of Informatics Xiamen University Xiamen China

The mystery of aesthetics attracts scientists from various research fields. The topic of aesthetics, in combination with other disciplines such as neuroscience and computer science, has brought out the burgeoning fields of neuroaesthetics and computational aesthetics within less than two decades. Despite profound findings are carried out by experimental approaches in neuroaesthetics and by machine learning algorithms in computational neuroaesthetics, these two fields cannot be easily combined to benefit from each other and findings from each field are isolated. Computational neuroaesthetics, which inherits computational approaches from computational aesthetics and experimental approaches from neuroaesthetics, seems to be promising to bridge the gap between neuroaesthetics and computational aesthetics. Here, we review theoretical models and neuroimaging findings about brain activity in neuroaesthetics. Then machine learning algorithms and computational models in computational aesthetics are enumerated. Finally, we introduce studies in computational neuroaesthetics which combine computational models with neuroimaging data to analyze brain connectivity during aesthetic appreciation or give a prediction on aesthetic preference. This paper outlines the rich potential for computational neuroaesthetics to take advantages from both neuroaesthetics and computational aesthetics. We conclude by discussing some of the challenges and potential prospects in computational neuroaesthetics, and highlight issues for future consideration. © 2020, The Author(s).

关键词： Brain

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Gradient-Based Differential kWTA Network With Application to Competitive Coordination of Multiple Robots

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IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1452-1463页

作者： Mei Liu Xiaoyan Zhang Mingsheng Shang Long Jin School of Information Science and Engineering Lanzhou UniversityLanzhou 730000 State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining 810016China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Shenzhen Research Institute Lanzhou UniversityShenzhen 518063China

Aiming at the k-winners-take-all(kWTA)operation,this paper proposes a gradient-based differential kWTA(GDk WTA)*** obtaining the network,theorems and related proofs are provided to guarantee the exponential convergence and noise resistance of the proposed GD-kWTA ***,numerical simulations are conducted to substantiate the preferable performance of the proposed network as compared with the traditional ***,the GD-k WTA network,backed with a consensus filter,is utilized as a robust control scheme for modeling the competition behavior in the multi-robot coordination,thereby further demonstrating its effectiveness and feasibility.

关键词： Consensus filter k-winners-take-all(kWTA) multirobot coordination noise resistance

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Accumulative Time Based Ranking Method to Reputation Evaluation in Information Networks

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Journal of Computer Science & Technology 2022年第4期37卷 960-974页

作者： Hao Liao Qi-Xin Liu Ze-Cheng Huang Ke-Zhong Lu Chi Ho Yeung Yi-Cheng Zhang National Engineering Laboratory on Big Data System Computing Technology College of Computer Science and SoftwareEngineeringShenzhen UniversityShenzhen 518060China Guangdong Province Key Laboratory of Popular High Performance Computers Shenzhen UniversityShenzhen 518060China Guangdong Province Engineering Center of China-Made High Performance Data Computing System College of ComputerScience and Software EngineeringShenzhen UniversityShenzhen 518060China Institute of Big Data Intelligent Management and Decision Shenzhen UniversityShenzhen 518060China Department of Science and Environmental Studies The Education University of Hong KongHong Kong 999077China Department of Physics University of FribourgFribourg 1700Switzerland

Due to over-abundant information on the Web, information filtering becomes a key task for online users to obtain relevant suggestions and how to extract the most related item is always a key topic for researchers in various fields. In this paper, we adopt tools used to analyze complex networks to evaluate user reputation and item quality. In our proposed Accumulative Time Based Ranking (ATR) algorithm, we take into account the growth record of the network to identify the evolution of the reputation of users and the quality of items, by incorporating two behavior weighting factors which can capture the hidden facts on reputation and quality dynamics for each user and item respectively. Our proposed ATR algorithm mainly combines the iterative approach to rank user reputation and item quality with temporal dependence compared with other reputation evaluation methods. We show that our algorithm outperforms other benchmark ranking algorithms in terms of precision and robustness on empirical datasets from various online retailers and the citation datasets among research publications. Therefore, our proposed method has the capability to effectively evaluate user reputation and item quality.

关键词： temporal network behavior dynamics reputation evaluation ranking algorithm

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A threshold effect of coupling delays on intra-layer synchronization in duplex networks

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Science China(Technological Sciences) 2018年第12期61卷 1907-1914页

作者： TANG LongKun LU JunAn Lü JinHu Fujian Province University Key Laboratory of Computation Science School of Mathematical Sciences Huaqiao University Department of Mathematics & Statistics Georgia State University School of Mathematics and Statistics Wuhan University School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

This paper investigates the impact of inter-layer coupling functions and intra-layer coupling delays on intra-layer synchronization regions and sychronizability. It is found that the inter-layer coupling functions have great influence on intra-layer synchronization regions, as well as on the intra-layer synchronizability. In particular, there exists an inter-layer coupling function such that the inter-layer coupling strength neither improves nor weakens the intra-layer synchronizability. Furthermore, no matter which one of three inter-layer coupling functions is chosen, a small intra-layer delay always keeps the intra-layer synchronized regions almost unchanged, implying that the small delay neither enhances nor suppresses the intra-layer synchronizability. At the same time the delay greatly frustrates the synchronizability in each layer when it is greater than some threshold. Our results may have potential applications for interconnected technological networks where communication delays are inevitably present.

关键词： multiplex network master stability function intra-layer synchronization synchronized region time delay

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EPLA: Efficient personal location anonymity

EPLA: Efficient personal location anonymity

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Lecture Notes in Computer Science

作者： Zhao, Dapeng Zhang, Kai Jin, Yuanyuan Wang, Xiaoling Hung, Patrick C. K. Ji, Wendi Shanghai Key Laboratory of Trustworthy Computing Institute for Data Science and Engineering East China Normal University Shanghai China OshawaON Canada

ISBN: (纸本)9783319458168

A lot of researchers utilize side-information, such as map which is likely to be exploited by some attackers, to protect users’ location privacy in location-based service (LBS). However, current technologies universally model the side-information for all users. We argue that the side-information is personal for every user. In this paper, we propose an efficient method, namely EPLA, to protect the users’ privacy using visit probability. We selected the dummy locations to achieve k-anonymity according to personal visit probability for users’ queries. AKDE greatly reduces the computational complexity compared with KDE approach. We conduct comprehensive experimental study on the realistic Gowalla data sets and the experimental results show that EPLA obtains fine privacy performance and efficiency. © Springer International Publishing Switzerland 2016.

关键词： Location based services

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A Fine-Grained Anomaly Detection Method Fusing Isolation Forest and Knowledge Graph Reasoning 19th

A Fine-Grained Anomaly Detection Method Fusing Isolation For...

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19th International Conference on Web Information Systems and Applications, WISA 2022

作者： Xu, Jie Zhou, Jiantao College of Computer Science Engineering Research Center of Ecological Big Data Ministry of Education National and Local Joint Engineering Research Center of Mongolian Intelligent Information Processing Technology Inner Mongolia Cloud Computing and Service Software Engineering Laboratory Inner Mongolia Social Computing and Data Processing Key Laboratory Inner Mongolia Discipline Inspection and Supervision Big Data Key Laboratory Inner Mongolia Big Data Analysis Technology Engineering Laboratory Inner Mongolia University Hohhot China

ISBN: (纸本)9783031203084

Anomaly detection aims to find outliers data that do not conform to expected behaviors in a specific scenario, which is indispensable and critical in current safety environments related studies. However, when performing outlier detection on a large-scale multidimensional dataset, most of the traditional methods make no distinctions between classes of outliers and lack the reasoning ability and the explainability of the analysis results, which leads to the low accuracy of global outlier detection and the inability to effectively identify local outliers. In this paper, we propose a fine-grained anomaly detection method which well utilizes isolation forest and knowledge graph reasoning tactics. First, a better extracting data feature method and a more reasonable weighted strategy are used to find global outliers based on the traditional isolation forest algorithm, and then we construct a custom rule base according to the in-depth research and analysis of global abnormal data, and at last the ontology knowledge is reasoned based on such rule base to achieve the detection of local outliers. Through analysis of extensive experiment results, our method could effectively discover more abnormal data without big loss of time costs, has strong generalization, and correspondingly improves the performance for abnormal detection. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Anomaly detection

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NERank+: a graph-based approach for entity ranking in document collections

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计算机科学前沿 2018年第3期12卷 504-517页

作者： Chengyu WANG Guomin ZHOU Xiaofeng HE Aoying ZHOU Shanghai Key Laboratory of Trustworthy Computing School of Computer Science and Software EngineeringEast China Normal University Shanghai 200062 China Department of Computer and Information Technology Zhejiang Police College Hangzhou 310053 China School of Data Science and Engineering East China Normal University Shanghai 200062 China

Most entity ranking research aims to retrieve a ranked list of entities from a Web corpus given a user *** rank order of entities is determined by the relevance between the query and contexts of ***,entities can be ranked directly based on their relative importance in a document collection,independent of any *** this paper,we introduce an entity ranking algorithm named NERank+.Given a document collection,NERank+ first constructs a graph model called Topical Tripartite Graph,consisting of document,topic and entity *** design separate ranking functions to compute the prior ranks of entities and topics,respectively.A meta-path constrained random walk algorithm is proposed to propagate prior entity and topic ranks based on the graph *** evaluate NERank+ over real-life datasets and compare it with *** results illustrate the effectiveness of our approach.

关键词： entity ranking Topical Tripartite Graph prior rank estimation meta-path constrained random walk

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