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IEEE International Conference on systems, Man and Cybernetics

作者： Feng Gui Yunlong Ma Feng Zhang Min Liu Rong Yin Weiming Shen Tongji University School of Electronic and Information Engineering Shanghai China Tongji University The Key Laboratory of Embedded System and Service Computing Shanghai China

ISBN: (纸本)9781479986989

In the research area of community detection which aims at detecting some highly cohesive vertex subsets in social network, there mainly exist some problems, such as the algorithms with comparatively excellent quality of the final partitioning usually have high time complexity and some other fast algorithms often result in low quality of partitioning or other disadvantages. Nowadays, the increasing demands for community detection in large-scale social networks necessitate the use of distributed and scalable methods to detect communities in an effective and efficient manner. Label propagation algorithm (LPA), whose time complexity is O (m) on a network with m edges, is a near linear time algorithm to detect community effectively. Besides, owing to having good scalability, the parallel version of LPA (DLPA) is suitable for community detection in large-scale social networks. However, DLPA synchronously updates the vertices labels, which usually brings about label oscillations and results in low quality of partitioning. In this paper, we analyze the drawbacks of DLPA and propose a novel method C-DLPA, which combines DLPA with the notion of maximal cliques and at the same time utilizes a new updating mechanism that updating each node' label by probability of its adjacent nodes, to make final partitioning become more accurate and to avoid oscillations effectively. The experimental results show that C-DLPA has better performance is not only low time cost by as much to avoid oscillations but its community detection accuracy compared with DLPA.

关键词： Oscillators Image edge detection Time complexity Partitioning algorithms Algorithm design and analysis Social network services Transforms

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Tracking Salient keypoints for Human Action Recognition

Tracking Salient Keypoints for Human Action Recognition

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IEEE International Conference on systems, Man and Cybernetics

作者： Hanli Wang Yun Yi Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China Department of Mathematics and Computer Science Gannan Normal University Ganzhou China

ISBN: (纸本)9781479986989

It plays an important role to recognize human actions from realistic videos in multimedia event detection and understanding. To this aim, a novel human tracking approach is proposed in this paper. Firstly, salient key points trajectories are generated to track human actions at multiple spatial scales. Then, camera motion elimination is utilized to further improve the robustness of motion trajectories. To depict human motions accurately and efficiently, the Histogram of Oriented Gradient (HOG), Histogram of Optical Flow (HOF) and Motion Boundary Histogram (MBH) are employed with the Fisher vector model being utilized to aggregate these three features. Extensive experimental results on four challenging human action video datasets demonstrate that the proposed approach is able to achieve better recognition performances in a more computationally efficient manner as compared with a number of state-of-the-art approaches.

关键词： Trajectory Videos Tracking Cameras Robustness Feature extraction Optical saturation

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An efficient method for skyline recommendation in distributed networks

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Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology 2015年第5期37卷 1214-1219页

作者： Huang, Zhen-Hua Zhang, Bo School of Electronics and Information Tongji University Shanghai201804 China Key Laboratory of Embedded System and Service Computing Tongji University Shanghai201804 China College of Information Mechanical and Electrical Engineering Shanghai Normal University Shanghai200234 China

Based on distributed networks of the Super-Peer Architecture (SPA), this paper proposes Efficient Method for Skyline Recommendation in Distributed Networks (EMSRDN), to handle u skyline recommendation instructions by prestore w skyline snapshots. The EMSRDN method fully considers the characteristic of storage and communication of SPA networks, and uses the map/reduce distributed computation model. The EMSRDN algorithm can fast produce the optimal w skyline snapshots through the phase of heuristically constructing the initial set of snapshot. The detailed theoretical analyses and extensive experiments demonstrate that the proposed EMSRDN algorithm is both efficient and practical. ©, 2015, Science Press. All right reserved.

关键词： Information services

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Correlative Filters for Convolutional Neural Networks

Correlative Filters for Convolutional Neural Networks

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IEEE International Conference on systems, Man and Cybernetics

作者： Peiqiu Chen Hanli Wang Jun Wu Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Department of Computer Science and Technology Tongji University Shanghai China

ISBN: (纸本)9781479986989

This paper introduces a regularization method called Correlative Filter (CF) for Convolutional Neural Network (CNN), which takes advantage of the relevance between the convolutional kernels belonging to the same convolutional layer. During the process of training with the proposed CF method, several pairs of filters are designed in a manner of randomness to contain opposite weights in low-level layers. Regarding higher level layers where synthetical features are processed, the relation between correlative filters is explored as translation of various directions. The proposed CF method attempts to optimize the inner structure of convolutional layers and it can work jointly with other regularization techniques, such as stochastic pooling, Dropout, etc. The experimental results on the competitive image classification benchmark dataset CIFAR-10 demonstrates the performance of the proposed CF method, additionally, it is also verified that the proposed CF method is wonderful to be employed to enhance several state-of-the-art regularization models.

关键词： Feature extraction Neurons Training Stochastic processes Convolution Kernel Biological neural networks

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Identification of mild cognitive impairment using extreme learning machines model 11th

Identification of mild cognitive impairment using extreme le...

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11th International Conference on Intelligent computing, ICIC 2015

作者： Zhang, Wen Shen, Hao Ji, Zhiwei Meng, Guanmin Wang, Bing School of Electronics and Information Engineering Tongji University Shanghai201804 China The Advanced Research Institute of Intelligent Sensing Network Tongji University Shanghai201804 China The Key Laboratory of Embedded System and Service Computing Tongji University Shanghai201804 China Department of Clinical Laboratory Tongde Hospital of Zhejiang Province 234th Gucui Road Hangzhou310012 China

ISBN: (纸本)9783319221854

Alzheimer’s disease (AD) is very common disorder among the older people. Predicting mild cognitive impairment (MCI), an intermediate stage between normal cognition and dementia, in individuals with some symptoms of cognitive decline may have great influence on treatment choice and disease progression. In this work, a novel prediction model based on extreme learning machines algorithm was proposed to identify mild cognitive impairment using the information of some biomarkers, i.e., Position Emission Tomography (PET), Magnetic Resonance Imaging (MRI), and the cerebrospinal fluid (CSF). Firstly, each subject will be represented by three biomarkers using a vector with 201 dimensions after some essential pre-processing. Then, a prediction model based on extreme learning machine algorithm was trained to catch the difference between the patients and healthy persons. Finally, the model had been employed to identify identifying MCI participants from the normal people. Based on a dataset, including 99 MCI patients and 52 health controls, our proposed method achieved 70.86 % prediction accuracy with 67.68 % sensitivity at the precision of 76.92 %. Extensive experiments are performed to compare our method with state-of-the-art techniques, i.e., support vector machine (SVM) and SVM with fusion kernels. Experimental results demonstrate that proposed extreme learning machine is a powerful tool for predicting MCI with excellent performance and less time. © Springer International Publishing Switzerland 2015.

关键词： Support vector machines

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Petri net based behavior description of cross-organization workflow with synchronous interaction pattern 1

Petri net based behavior description of cross-organization w...

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1st International Workshop on Process-Aware systems, PAS 2014

作者： Liu, Cong Zeng, Qingtian Duan, Hua Lu, Faming The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai200092 China College of Information Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Electronic Communication Shandong University of Science and Technology Qingdao266590 China College of Mathematics Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9783662461693

Today’s workflow systems are crossing organizational boundaries and usually involve multiple organizations or partners, and the crossorganization workflow has received much public attention from both the academia and the industry. This way the property analysis as well as system control for a cross-organization workflow is very important. In this paper, we focus on a kind of loosely coupled workflow architecture with synchronous interaction pattern, i.e. each organization owns its private workflow process and can operate independently, and they need to synchronize through certain tasks. Its behavior description approach is obtained using the synchronized shuffle operation of Petri net language. It is proved that our approach benefits the behavior characterization of cross-organizational workflow with synchronous interaction pattern and can be further used to decide and control the fairness, liveness, deadlock and trap for the cross-organization workflow. A running case of cross-organizational medical diagnosis workflow is given to validate our approaches throughout the whole paper. © Springer-Verlag Berlin Heidelberg 2015.

关键词： Petri nets

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Null space based discriminant sparse representation large margin for face recognition

Null space based discriminant sparse representation large ma...

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International Joint Conference on Neural Networks (IJCNN)

作者： Ying Wen Lili Hou Lianghua He Department of Computer Science and Technology East China Normal University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

ISBN: (纸本)9781479919611

In this paper, we propose a novel subspace learning algorithm, termed as null space based discriminant sparse representation large margin (NDSLM). There are two contributions in the paper. First, we propose a new expectation to obtain the neighborhood information for large margin subspace learning, i.e., the within-neighborhood scatter and between-neighborhood scatter are modeled by the sparse reconstruction weights of the samples from the same class and different classes, respectively. Since the neighborhood information formed by sparse representation can capture non-linearities in the data, the proposed method possesses more discriminative information than the traditional large margin learning methods with the expectation using Euclidean distance, etc. Second, the large margin information integrated into the model of Fisher criterion makes the discriminating power of NDSLM further boosted. NDSLM addresses the small sample size problem by solving an eigenvalue problem in null space. Experiments on ORL, Yale, AR, Extended Yale B and CMU PIE five face databases are performed to evaluate the proposed algorithm and the results demonstrate the effectiveness of NDSLM.

关键词： Principal component analysis Silicon Pipelines Glass Databases

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Observer-based controller design for networked systems with multiple packet losses and mixed delay

Observer-based controller design for networked systems with ...

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第27届中国控制与决策会议

作者： Fengfeng Qian Huaicheng Yan Hongbo Shi Hao Zhang 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 EducationTongji University

The paper focuses on a stabilizing controller design problem for networked systems with quantization,mixed delays,and a series of packet losses due to the signal transmission through the unreliable communication *** of both sensor-to-controller and controller-to-actuator are taken into account for the networked systems,and the distributed time delay is also considered in the network *** conditions for designing the controller as well as the system parameters can be obtained by solving certain linear matrix ***,the effectiveness of the designed method is proved by a numerical example.

关键词： quantization,mixed delays,multiple packet losses

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An efficient algorithm of information recommendation between groups in social networks

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Tien Tzu Hsueh Pao/Acta Electronica Sinica 2015年第6期43卷 1090-1093页

作者： Huang, Zhen-Hua Zhang, Bo Fang, Qiang Xiang, Yang Department of Computer and Technology Tongji University Shanghai201804 China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China College of Information Mechanical and Electrical Engineering Shanghai Normal University Shanghai200234 China

Information recommendation between groups is one of the most important ways for information sharing and transmitting in social networks. However, it needs exponential time cost to achieve the exact optimal recommendation solution. Hence this paper proposed an efficient algorithm EAOORS (Efficient Algorithm for Obtaining Optimal Recommendation Solution) which equivalently reduced this exponential time cost problem to extended steiner tree problem, and fast achieved the approximate optimal recommendation solution in the polynomial time complexity. The experimental result shows that the EAOORS algorithm is more efficient than the existing ones. ©, 2015, Chinese Institute of Electronics. All right reserved.

关键词： Approximation algorithms

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New reachability trees for unbounded Petri nets

New reachability trees for unbounded Petri nets

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IEEE International Conference on Robotics and Automation (ICRA)

作者： ShouGuang Wang MengChu Zhou MengDi Gan Dan You Yue Li School of Information & Electronic Engineering Zhejiang Gongshang University Hangzhou China State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong University Xi'an China MoE Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (纸本)9781479969241

Reachability is an important dynamic property of Petri nets. Its determination given an unbounded net and initial marking has remained an open problem since 1960s. Due to its extreme difficulty, a great deal of research has been focused on some subclasses of unbounded nets. For arbitrary ones, this work intends to propose a new reachability tree (NRT), which consists of only but all reachable markings from its initial marking. Furthermore, an NRT-based method to decide deadlocks of unbounded nets is presented. An example is provided to show the new results.

关键词： system recovery Petri nets Manufacturing systems Solids Gallium nitride embedded systems

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