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Ensemble feature selection integrating elitist roles and quantum game model

Journal of Systems Engineering and Electronics 2015年第3期26卷 584-594页

作者： Weiping Ding Jiandong Wang Zhijin Guan Quan Shi College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics School of Computer Science and Technology Nantong University Provincial Key Laboratory for Computer Information Processing Technology Soochow University

To accelerate the selection process of feature subsets in the rough set theory （RST）, an ensemble elitist roles based quantum game （EERQG） algorithm is proposed for feature selec- tion. Firstly, the multilevel elitist roles based dynamics equilibrium strategy is established, and both immigration and emigration of elitists are able to be self-adaptive to balance between exploration and exploitation for feature selection. Secondly, the utility matrix of trust margins is introduced to the model of multilevel elitist roles to enhance various elitist roles＇ performance of searching the optimal feature subsets, and the win-win utility solutions for feature selec- tion can be attained. Meanwhile, a novel ensemble quantum game strategy is designed as an intriguing exhibiting structure to perfect the dynamics equilibrium of multilevel elitist roles. Finally, the en- semble manner of multilevel elitist roles is employed to achieve the global minimal feature subset, which will greatly improve the fea- sibility and effectiveness. Experiment results show the proposed EERQG algorithm has superiority compared to the existing feature selection algorithms.

关键词： ensemble quantum game utility matrix of trust mar-gin dynamics equilibrium strategy multilevel elitist role feature selection and classification.

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A hierarchical image matting model for blood vessel segmentation in fundus images

arXiv

引用

arXiv 2017年

作者： Fan, Zhun Lu, Jiewei Li, Wenji Wei, Caimin Huang, Han Cai, Xinye Chen, Xinjian Guangdong Provincial Key Laboratory of Digital Signal and Image Processing College of Engineering Shantou University Shan'tou515063 China Department of Mathematics Shantou University Shan'tou515063 China School of Software Engineering South China University of Technology Guang'zhou510006 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiang'su210016 China Medical Image Processing and Analysis Lab School of Electronics and Information Engineering Soochow University Su'zhou215006 China

In this paper, a hierarchical image matting model is proposed to extract blood vessels from fundus images. More specifically, a hierarchical strategy utilizing the continuity and extendibility of retinal blood vessels is integrated into the image matting model for blood vessel segmentation. Normally the matting models require the user specified trimap, which separates the input image into three regions manually: the foreground, background and unknown regions. However, since creating a user specified trimap is a tedious and time-consuming task, region features of blood vessels are used to generate the trimap automatically in this paper. The proposed model has low computational complexity and outperforms many other state-of-art supervised and unsupervised methods in terms of accuracy, which achieves a vessel segmentation accuracy of 96.0%, 95.7% and 95.1% in an average time of 10.72s, 15.74s and 50.71s on images from three publicly available fundus image datasets DRIVE, STARE, and CHASE_DB1, respectively. Copyright © 2017, The Authors. All rights reserved.

关键词： Blood vessels

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Learning robust and discriminative low-rank representations for face recognition with occlusion

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Pattern Recognition 2017年 66卷 129-143页

作者： Guangwei Gao Jian Yang Xiao-Yuan Jing Fumin Shen Wankou Yang Dong Yue Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Engineering Laboratory of Big Data Analysis and Control for active distribution network Nanjing University of Posts and Telecommunications Nanjing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) Fuzhou China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Automation Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Automation Southeast University Nanjing China

For robust face recognition tasks, we particularly focus on the ubiquitous scenarios where both training and testing images are corrupted due to occlusions. Previous low-rank based methods stacked each error image into a vector and then used L 1 or L 2 norm to measure the error matrix. However, in the stacking step, the structure information of the error image can be lost. Depart from the previous methods, in this paper, we propose a novel method by exploiting the low-rankness of both the data representation and each occlusion-induced error image simultaneously, by which the global structure of data together with the error images can be well captured. In order to learn more discriminative low-rank representations, we formulate our objective such that the learned representations are optimal for classification with the available supervised information and close to an ideal-code regularization term. With strong structure information preserving and discrimination capabilities, the learned robust and discriminative low-rank representation (RDLRR) works very well on face recognition problems, especially with face images corrupted by continuous occlusions. Together with a simple linear classifier, the proposed approach is shown to outperform several other state-of-the-art face recognition methods on databases with a variety of face variations.

关键词： Face recognition Low-rank matrix recovery Nuclear norm

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Multiway Dynamic Trust Chain Model on Virtual Machine for Cloud Computing

引用

China Communications 2016年第7期13卷 83-91页

作者： Jie Zhu Guoyuan Lin Fucheng You Huaqun Liu Chunru Zhou School of Computer Science and Technology China University of Mining&TechnologyXuzhou 221116China State Key Laboratory for Novel Software Technology hosted at Nanjing University Nanjing 210093China Information Engineering School of Beijing Institute of Graphic Communication Beijing 102600China Beijing Key Laboratory of Signal and Information Processing for High-end Printing Equipment Beijing Institute of Graphic CommunicationBeijing 100026China Information Security Center Beijing University of Posts and TelecommunicationsBeijing 100876China Nanjing University of Information Science&Technology Nanjing 210044China

This paper sums up four security factors after analyzing co-residency threats caused by the special multitenant environment in the *** secure the factors,a multiway dynamic trust chain transfer model was proposed on the basis of a measurement interactive virtual machine and current behavior to protect the integrity of the system.A trust chain construction module is designed in a virtual machine *** dynamic monitoring,it achieves the purpose of transferring integrity between virtual machine.A cloud system with a trust authentication function is implemented on the basis of the model,and its practicability is shown.

关键词： cloud computing virtual machine trustworthiness measurement dynamic trust transfer

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A constrained sparse representation approach for video anomaly detection

A constrained sparse representation approach for video anoma...

引用

2016 IEEE Advanced information Management,Communicates,Electronic and Automation Control Conference(IMCEC 2016)

作者： Yugen Yi Xiaohui Li Rui Zhao Chao Bi Jianzhong Wang Hui Sun School of Software Jiangxi Normal University College of Computer Science and Information Technology Key Laboratory of Intelligent Information Processing of Jilin UniversitiesNortheast Normal University College of Humanities and Sciences Northeast Normal University

ISBN: (纸本)9781509001668

Sparse representation based anomaly detection algorithms have received a widely interest in recent ***,most of the existing approaches fail to pay attention to the manifold structure of the video data,which has been pointed to be important for data *** overcome this limitation,we develop a new sparse coding algorithm named constrained sparse representation(CSR) for video anomaly detection,which explicitly takes the manifold structure of data into *** assumes that each sample's sparse coding coefficient can be linearly reconstructed by the coding coefficients of its ***,CSR algorithm can obtain relatively smoothly sparse representations along the manifold of *** apply the proposed CSR model to video abnormal event detection task and conduct extensive experiments on the UMN *** experimental results demonstrate that our proposed CSR model performed better than some related algorithms.

关键词： local geometrical structure sparse representation abnormal event detection

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Consensus algorithms for biased labeling in crowdsourcing

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information Sciences 2017年 382-383卷 254-273页

作者： Jing Zhang Victor S. Sheng Qianmu Li Jian Wu Xindong Wu School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 P. R. China Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing 210094 P. R. China Department of Computer Science University of Central Arkansas Conway AR 72035 USA Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing 210044 P. R. China Institute of Intelligent Information Processing and Application Soochow University Suzhou 215006 P. R. China School of Computing and Informatics University of Louisiana at Lafayette LA 70504 USA

Although it has become an accepted lay view that when labeling objects through crowdsourcing systems, non-expert annotators often exhibit biases, this argument lacks sufficient evidential observation and systematic empirical study. This paper initially analyzes eight real-world datasets from different domains whose class labels were collected from crowdsourcing systems. Our analyses show that biased labeling is a systematic tendency for binary categorization; in other words, for a large number of annotators, their labeling qualities on the negative class (supposed to be the majority) are significantly greater than are those on the positive class (minority). Therefore, the paper empirically studies the performance of four existing EM-based consensus algorithms , DS, GLAD, RY, and ZenCrowd, on these datasets. Our investigation shows that all of these state-of-the-art algorithms ignore the potential bias characteristics of datasets and perform badly although they model the complexity of the systems. To address the issue of handling biased labeling, the paper further proposes a novel consensus algorithm, namely adaptive weighted majority voting (AWMV), based on the statistical difference between the labeling qualities of the two classes. AWMV utilizes the frequency of positive labels in the multiple noisy label set of each example to obtain a bias rate and then assigns weights derived from the bias rate to negative and positive labels. Comparison results among the five consensus algorithms (AWMV and the four existing) show that the proposed AWMV algorithm has the best overall performance. Finally, this paper notes some potential related topics for future study.

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A Novel User Behavior Prediction and Optimization Algorithm for Single-User Multi-terminal Scenario

A Novel User Behavior Prediction and Optimization Algorithm ...

引用

International Symposium on Computational Intelligence and Design, ISCID

作者： Hui Zhang Juan Chen Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China Jiangsu Key Lab of Wireless Communications Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781509035595

For the single-user multi-terminal scenario, a novel user service behavior prediction and optimization algorithm is proposed in this paper, which can realize the accurate prediction of service requirements and optimal selection of service terminals, so as to improve user QoE effectively. Firstly, by using an adaptive feedback based weight correction method, a modified entropy weighted Markov model is formulated to accurately predict user future service states. Secondly, according to the above prediction results, the corresponding heavy-tailed model is used to estimate the service duration. Moreover, by integrating service preferences and terminal attributes, a matching metric of services and terminals is established, and then a terminal selection model is proposed for recommending the optimal service terminal to each user. Simulation results show the effectiveness of proposed algorithm.

关键词： Predictive models Markov processes Optimization Entropy Adaptation models Prediction algorithms Measurement

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A novel image tag completion method based on convolutional neural transformation

arXiv

引用

arXiv 2017年

作者： Geng, Yanyan Zhang, Guohui Li, Weizhi Gu, Yi Liang, Ru-Ze Liang, Gaoyuan Wang, Jingbin Wu, Yanbin Patil, Nitin Wang, Jing-Yan Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China Huawei Technologies Co. Ltd. Shanghai China Suning Commerce R&D Center USA Inc. Palo AltoCA94304 United States Analytics & Research Travelers HartfordCT06183 United States King Abdullah University of Science and Technology Saudi Arabia Jiangsu University of Technology Jiangsu213001 China Information Technology Service Center Intermediate People’s Court of Linyi City Linyi China Hebei University of Economics and Business Shijiazhuang050061 China Savitribai Phule Pune University Pune Maharashtra411007 India

In the problems of image retrieval and annotation, complete textual tag lists of images play critical roles. However, in real-world applications, the image tags are usually incomplete, thus it is important to learn the complete tags for images. In this paper, we study the problem of image tag complete and proposed a novel method for this problem based on a popular image representation method, convolutional neural network (CNN). The method estimates the complete tags from the convolutional filtering outputs of images based on a linear predictor. The CNN parameters, linear predictor, and the complete tags are learned jointly by our method. We build a minimization problem to encourage the consistency between the complete tags and the available incomplete tags, reduce the estimation error, and reduce the model complexity. An iterative algorithm is developed to solve the minimization problem. Experiments over benchmark image data sets show its effectiveness. Copyright © 2017, The Authors. All rights reserved.

关键词： Convolution

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ERA-RJN: A SPARQL-rank based top-k join query optimization 5th

ERA-RJN: A SPARQL-rank based top-k join query optimization

引用

5th Joint International Conference on Semantic technology, JIST 2015

作者： Xiao, Zhengrong Chen, Fengjiao Xu, Fangfang Gu, Jinguang College of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan430065 China

ISBN: (纸本)9783319316758

With the wide use of RDF data, searching and ranking semantic data with SPARQL has become a research hot-spot. While there is no much research work on top-k join queries optimization in RDF native stores. This paper proposes a new rank-join operator algorithm ERA-RJN on the basis of SPARQL-RANK algebra, making use of the advantage of random access availability in RDF native storage. This paper implements the ERA-RJN operator on the ARQ-RANK platform, and performs experiments, verifies the high efficiency of ERA-RJN algorithm dealing with SPARQL top-k join query in RDF native storage. © Springer International Publishing Switzerland 2016.

关键词： Database systems

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Analysis of smart glucose meter stability from the economic perspective

Boletin Tecnico/Technical Bulletin

引用

Boletin Tecnico/Technical Bulletin 2017年第9期55卷 457-462页

作者： Lin, Zhongyan Song, Libin Zheng, Dachuan Li, Zuoyong Internet Innovation Research Center College's Research Base of Humanities and Social Science in Fujian Province Minjiang University Fuzhou Fujian350108 China Fuzhou Digital Health Industry Technology Innovation Center Minjiang University Fuzhou Fujian350108 China Fuzhou Comvee Network Technology Co. Ltd. Fuzhou Fujian350001 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou Fujian350108 China

Objective: to explore and interpret the model of diabetes mobile online management from the perspective of web model. Methods: from the interdisciplinary perspective, combined with economics, management science and computer science, with the only dynamic model in microeconomics—the cobweb model, from chronic disease management, Internet + medical application, analyze the application of Internet technology in diabetes management. Results: through mobile Internet technology, optimized the unit of diabetes management and realized the "monitoring - assessment - intervention" closed-loop management. Conclusion: explore the digital medical health industry in periodic production commodity price fluctuations and other activities, can establish early warning system and mechanism, to improve patients and their families to the cognitive level of diabetes, according to the medical behavior and self management ability.

关键词： Medical applications

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