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作者： Ghapani, Sheida Mei, Jie Ren, Wei Song, Yongduan Department of Electrical and Computer Engineering University of California Riverside United States School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Graduate School Shenzhen China Key Laboratory of Dependable Service Computing Cyber Physical Society Ministry of Education Chongqing University China School of Automation Chongqing University China

This paper addresses the leader-follower flocking problem with a moving leader for networked Lagrange systems with parametric uncertainties under a proximity graph. Here a group of followers move cohesively with the moving leader to maintain connectivity and avoid collisions for all time and also eventually achieve velocity matching. In the proximity graph, the neighbor relationship is defined according to the relative distance between each pair of agents. Each follower is able to obtain information from only the neighbors in its proximity, involving only local interaction. We consider two cases: (i) the leader moves with a constant velocity, and (ii) the leader moves with a varying velocity. In the first case, a distributed continuous adaptive control algorithm accounting for unknown parameters is proposed in combination with a distributed continuous estimator for each follower. In the second case, a distributed discontinuous adaptive control algorithm and estimator are proposed. Then the algorithm is extended to be fully distributed with the introduction of gain adaptation laws. In all proposed algorithms, only one-hop neighbors' information (e.g., the relative position and velocity measurements between the neighbors and the absolute position and velocity measurements) is required, and flocking is achieved as long as the connectivity and collision avoidance are ensured at the initial time and the control gains are designed properly. Numerical simulations are presented to illustrate the theoretical results. © 2016 Elsevier Ltd. All rights reserved.

关键词： Multi agent systems

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An efficient webpage classification algorithm based on LSH

An efficient webpage classification algorithm based on LSH

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International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2015

作者： Liu, Junjun Sun, Haichun Ding, Zhijun The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai200092 China

ISBN: (纸本)9783662462478

With the explosive growth of Internet information, it is more and more important to fetch real-time and related information. And it puts forward higher requirement on the speed of webpage classification which is one of common methods to retrieve and manage information. To get a more efficient classifier, this paper proposes a webpage classification method based on locality sensitive hash function. In which, three innovative modules including building feature dictionary, mapping feature vectors to fingerprints using Localitysensitive hashing, and extending webpage features are contained. The compare results show that the proposed algorithm has better performance in lower time than the naïve bayes one. © Springer-Verlag Berlin Heidelberg 2015.

关键词： Hash functions

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Fuzzy H∞ Filtering for Nonlinear Networked systems with Multiple Stochastic Time-Varying Delays, Packet Dropouts and Sensor Saturation 34

Fuzzy H∞ Filtering for Nonlinear Networked Systems with Mul...

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第三十四届中国控制会议

作者： XU Xiaoli YAN Huaicheng ZHANG Hao SHI Hongbo Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education School of information Science and Engineering East China University of Science and Technology Key Laboratory of Embedded System and Service Computing Ministry of Education

ISBN: (纸本)9781467374439

This paper investigates the problem of H∞ filter design for a class of nonlinear networked system based on T-S fuzzy model. Multiple stochastic time-varying delays and some incomplete information are considered simultaneously. Incomplete information includes randomly occurring sensor saturation and packet dropouts. Stochastic time-varying delays are depicted as a sequence of stochastic and independent variables, which take values on 0 and 1. Two sets of Bernoulli distributed white noises are introduced to describe randomly occurring sensor saturation and packet dropouts. system conservatism is reduced due to introduce an approach of piecewise quadratic Lyapunov function. By solving a set of linear matrix inequalities(LMIs), the filter parameters are obtained. Finally, a simulation example is provided to illustrate the effectiveness of the proposed filter design approach.

关键词： H ∞ filtering,multiple time-varying delays,randomly occurring sensor saturation,packet dropouts,piecewise quadratic Lyapunov function

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Exploiting structural similarity of log files in fault diagnosis for Web service composition

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CAAI Transactions on Intelligence technology 2016年第1期1卷 61-71页

作者： Xu Han Binyang Li Kam-Fai Wong Zhongzhi Shi Beijing Key Laboratory of Electronic System Reliability Technology Capital Normal University Beijing China University of International Relations Beijing China Department of Systems Engineering & Engineering Management The Chinese University of Hong Kong Hong Kong China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

With increasing deployment of Web services, the research on the dependability and availability of Web service composition becomes more and more active. Since unexpected faults of Web service composition may occur in different levels at runtime, log analysis as a typical data- driven approach for fault diagnosis is more applicable and scalable in various architectures. Considering the trend that more and more service logs are represented using XML or JSON format which has good flexibility and interoperability, fault classification problem of semi-structured logs is considered as a challenging issue in this area. However, most existing approaches focus on the log content analysis but ignore the structural information and lead to poor performance. To improve the accuracy of fault classification, we exploit structural similarity of log files and propose a similarity based Bayesian learning approach for semi-structured logs in this paper. Our solution estimates degrees of similarity among structural elements from heterogeneous log data, constructs combined Bayesian network （CBN）, uses similarity based learning algorithm to compute probabilities in CBN, and classifies test log data into most probable fault categories based on the generated CBN. Experimental results show that our approach outperforms other learning approaches on structural log datasets.

关键词： Web services composition Fault diagnosis Combined Bayesian network （CBN） Similarity Probability

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A New ETL Approach Based on Data Virtualization

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Journal of Computer Science & technology 2015年第2期30卷 311-323页

作者：郭树盛袁子牧孙傲冰岳强 State Key Laboratory of Computer System and Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100190 China Cloud Computing Center Chinese Academy of Sciences Dongguan 523808 China

ETL （Extract-Transform-Load） usually includes three phases： extraction, transformation, and loading. In building data warehouse, it plays the role of data injection and is the most time-consuming activity. Thus it is necessary to improve the performance of ETL. In this paper, a new ETL approach, TEL （Transform-Extract-Load） is proposed. The TEL approach applies virtual tables to realize the transformation stage before extraction stage and loading stage, without data staging area or staging database which stores raw data extracted from each of the disparate source data systems. The TEL approach reduces the data transmission load, and improves the performance of query from access layers. Experimental results based on our proposed benchmarks show that the TEL approach is feasible and practical.

关键词： cloud computing big data ETL heterogeneous database data virtualization

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Secure searchable encryption:a survey

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Journal of Communications and Information Networks 2016年第4期1卷 52-65页

作者： WANG Yunling WANG Jianfeng CHEN Xiaofeng State Key Laboratory of Integrated Service Networks(ISN) Xidian UniversityXian 710071China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic TechnologyGuilin 541004China

Cloud computing facilitates convenient and on-demand network access to a centralized pool of ***,many users prefer to outsource data to the cloud in order to mitigate the burden of local ***,storing sensitive data on remote servers poses privacy challenges and is currently a source of ***(Searchable Encryption)is a positive way to protect users sensitive data,while preserving search ability on the server *** allows the server to search encrypted data without leaking information in plaintext *** two main branches of SE are SSE(Searchable Symmetric Encryption)and PEKS(Public key Encryption with keyword Search).SSE allows only private key holders to produce ciphertexts and to create trapdoors for search,whereas PEKS enables a number of users who know the public key to produce ciphertexts but allows only the private key holder to create *** article surveys the two main techniques of SE:SSE and *** SE schemes are categorized and compared in terms of functionality,efficiency,and ***,we point out some valuable directions for future work on SE schemes.

关键词： cloud storage encrypted data searchable encryption searchable symmetric encryption public key encryption with keyword search.

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Erratum to: Bionic Optimization Design of Electronic Nose Chamber for Oil and Gas Detection

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Journal of Bionic Engineering 2018年第5期15卷 939-939页

作者： Chang, Zhiyong Sun, Youhong Zhang, Yuchen Gao, Yanli Weng, Xiaohui Chen, Donghui David, Liewe Xie, Jun College of Biological and Agricultural Engineering Jilin University Changchun China Key Laboratory of Bionic Engineering Ministry of Education Jilin University Changchun China National-Local Joint Engineering Laboratory of In-situ Conversion Drilling and Exploitation Technology for Oil Shale Jilin University Changchun China College of Construction Engineering Jilin University Changchun China Clinical Medicine Bethune First Hospital of Jilin University Changchun China College of Mechanical Science and Engineering Jilin University Changchun China School of Computing and Technology the University of Gloucestershire Cheltenham UK Air Combat Service Academy Air Force Aviation University Changchun China

The first affiliation of the first author was incorrect in the published paper and should be revised as “1. College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China”.

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Correction: Hypercrosslinked porous polymer materials: design, synthesis, and applications

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Chemical Society reviews 2017年第11期46卷 3481页

作者： Liangxiao Tan Bien Tan Key Laboratory for Large-Format Battery Materials and System Ministry of Education Hubei Key Laboratory of Material Chemistry and Service Failure School of Chemistry and Chemical Engineering Huazhong University of Science and Technology Wuhan 430074 China. bien.tan@***.

Correction for 'Hypercrosslinked porous polymer materials: design, synthesis, and applications' by Liangxiao Tan et al., Chem. Soc. Rev., 2017, DOI: .

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A User-Centric Approach to Activity Recognition and Guidance in Semantic Smart Home

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China Communications 2015年第S2期12卷 103-113页

作者： LI Haitao GUO Kun LU Yueming LI Yonghua South China University of Technology Guangdong Power Grid Co. Ltd Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education

Wireless smart home system is to facilitate people's lives and it trend to adopt a more intelligent way to provide services. It is very desirable in the recent SH market for the system to recognize users' behaviors and automatically response the corresponding activities to satisfy users' actual demands. However, activity models in the existing approaches are usually defined separately through knowledge-driven methods. These approaches cause that the activity models can't be matched with the services dynamically. To address the problem, we develop the semantic association model and a novel approach of activity recognition and guidance is presented. In our approach, the smart devices and users' requirements are described by semantic models. When the requirements are detected and understood, smart gateway can provide appropriate services, achieving activity assistance. The semantic association model allows all related elements in smart home connect with each other logically. The approach has been implemented and the results show that the success rate of the approach based on semantic association model is higher than 33% at average as compared to the approach based on predefined models. The proposed approach can effectively help people who are in trouble with learning or remembering in the common life.

关键词： Internet of Things smart home activity recognition activity guidance semantic model

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Shilling attack detection in recommender systems via selecting patterns analysis

Shilling attack detection in recommender systems via selecti...

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作者： Li, Wentao Gao, Min Li, Hua Zeng, Jun Xiong, Qingyu Hirokawa, Sachio Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education Chongqing400044 China School of Software Engineering Chongqing University Chongqing400044 China College of Computer Science Chongqing University Chongqing400044 China Research Institute for Information Technology Kyushu University Fukuoka-shi812-8581 Japan

Collaborative filtering (CF) has been widely used in recommender systems to generate personalized recommendations. However, recommender systems using CF are vulnerable to shilling attacks, in which attackers inject fake profiles to manipulate recommendation results. Thus, shilling attacks pose a threat to the credibility of recommender systems. Previous studies mainly derive features from characteristics of item ratings in user profiles to detect attackers, but the methods suffer from low accuracy when attackers adopt new rating patterns. To overcome this drawback, we derive features from properties of item popularity in user profiles, which are determined by users' different selecting patterns. This feature extraction method is based on the prior knowledge that attackers select items to rate with man-made rules while normal users do this according to their inner preferences. Then, machine learning classification approaches are exploited to make use of these features to detect and remove attackers. Experiment results on the MovieLens dataset and Amazon review dataset show that our proposed method improves detection performance. In addition, the results justify the practical value of features derived from selecting patterns. © 2016 The Institute of Electronics, Information and Communication Engineers.

关键词： Feature extraction

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