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IEEE Conference on Wireless Communications and Networking

作者： Shiyou Qian Yanmin Zhu Minglu Li Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiaotong University China

Recommending good driving paths is valuable to taxi drivers for reducing unnecessary waste in fuel and increasing revenue. Driving only according to personal experience may lead to poor performance. With the availability of large-scale GPS traces collected from urban taxis, we have the curiosity about whether we can discover the hidden knowledge in the trace data for smart driving recommendation. This paper focuses on developing a smart recommender system based on mining large-scale GPS trace datasets from a large number of urban taxis. However, such the trace datasets are in nature complex, large-scale, and dynamic, which makes mining the datasets particularly challenging. We first extract vehicular mobility pattern from the large-scale GPS trace datasets. Then, the optimal driving process is modeled as a Markov Decision Process (MDP). Solving the MDP problem results in the optimal driving strategy that gives smart recommendation for taxi drivers. In essence, the most rewarding driving paths can be derived in the long run. We have conducted extensive trace driven simulations and conclusive results show that our recommendation algorithm can successfully find good driving paths and outperforms other alternative algorithms.

关键词： Vehicles Roads Data mining Computational modeling Complexity theory Global Positioning System Markov processes

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Accountable authority key policy attribute-based encryption

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Science China(Information Sciences) 2012年第7期55卷 1631-1638页

作者： WANG YongTao 1,2 ,CHEN KeFei 1,3 ,LONG Yu 1,4 & LIU ZhaoHui 2 1 Department of Computer Science and Engineering,shanghai Jiao Tong University,shanghai 200240,China 2 China Information Technology Security Evaluation Center,Beijing 100085,China 3 shanghai key laboratory of scalable computing and systems,shanghai Jiao Tong University,shanghai,200240,China 4 State key laboratory of Information Security,Graduate School of Chinese Academy of Sciences,100039,China 1. Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China2. China Information Technology Security Evaluation Center Beijing 100085 China3. Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University Shanghai 200240 China4. State Key Laboratory of Information Security Graduate School of Chinese Academy of Sciences Beijing 100039 China

We present an accountable authority key policy attribute-based encryption (A-KPABE) *** this paper,we extend Goyal's work to key policy attribute-based encryption *** first generalize the notion of accountable authority in key policy attribute-based encryption scenario,and then give a *** addition,our scheme is shown to be secure in the standard model under the modified Bilinear Decisional Diffie-Hellman (mBDDH) assumption.

关键词： accountable authority key policy attribute-based encryption standard model access structure

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EEG-based emotion recognition in listening music by using support vector machine and linear dynamic system

EEG-based emotion recognition in listening music by using su...

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19th International Conference on Neural Information Processing, ICONIP 2012

作者： Duan, Ruo-Nan Wang, Xiao-Wei Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai 200240 China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University 800 Dongchuan Road Shanghai 200240 China Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University 800 Dongchuan Road Shanghai 200240 China MOE Key Laboratory of Systems Biomedicine Shanghai Jiao Tong University 800 Dongchuan Road Shanghai 200240 China

ISBN: (纸本)9783642344770

This paper focuses on the variation of EEG at different emotional states. We use pure music segments as stimuli to evoke the exciting or relaxing emotions of subjects. EEG power spectrum is adopted to form features, power spectrum, differential asymmetry, and rational asymmetry. A linear dynamic system approach is applied to smooth the feature sequence. Minimal-redundancy-maximal-relevance algorithm and principal component analysis are used to reduce the dimension of features. We evaluate the performance of support vector machine, k-nearest neighbor classifiers and least-squares classifiers. The accuracy of our proposed method reaches 81.03% on average. And we show that the frequency bands, beta and theta, perform better than other frequency bands in the task of emotion recognition. © 2012 Springer-Verlag.

关键词： Emotion Recognition

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On guaranteed detectability for surveillance sensor networks

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International Journal of Distributed Sensor Networks 2012年第7期2012卷 1-15页

作者： Zhu, Yanmin Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China Shanghai Key Lab of Scalable Computing and Systems Shanghai 200240 China

Surveillance is an important class of applications for wireless senor networks (WSNs), whose central task is to detect events of interest. Existing approaches seriously suffer from blind spots and low energy efficiency. In this paper, we propose a fully distributed algorithm GAP for energy-efficient event detection for surveillance applications. Employing the probabilistic approach, GAP actively tunes the active probability and minimizes the energy consumption of each sensor. The unique features of GAP are threefold. First, it provides guaranteed detectability for any event occurring in the sensing field. Second, it exposes a convenient interface of the user to specify the desired detectability. Finally, it supports differentiated service to empower better surveillance for critical spots. Without relying on costly time synchronization, GAP is a lightweight distributed protocol and is truly scalable to network scale and sensor density. Theoretical analysis and comprehensive simulation experiments are conducted, which jointly demonstrate that GAP is able to provide guaranteed detectability while significantly prolonging the system lifetime compared with other schemes. Copyright © 2012 Yanmin Zhu.

关键词： Energy efficiency

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A Priority Based Scheduling Strategy for Virtual Machine Allocations in Cloud computing Environment

A Priority Based Scheduling Strategy for Virtual Machine All...

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International Conference on Cloud and Service computing (CSC)

作者： Jing Xiao Zhiyuan Wang Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University Shanghai China

Cloud computing is regarded as a revolution of the IT industry. It is also a business model, in which the service provider should try to make best use of resources, reduce energy consumption and earn profits as much as possible. Scheduling strategy plays an important role in service providing. A priority based algorithm for scheduling virtual machines on physical hosts in cloud computing environment is proposed. The target of this algorithm is to maximize the benefits of the service providers in the case of current resources are not enough to process all the requests in time. In this strategy, the requests are ranked according to the profits they can bring. Through the experiments, this approach has been proven it can increase the benefits than applying typical first come first serve strategy.

关键词： Virtual machining Cloud computing Hardware Computational modeling Job shop scheduling Processor scheduling Resource management

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Sparse probabilistic relational projection 26

Sparse probabilistic relational projection

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26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12

作者： Li, Wu-Jun Yeung, Dit-Yan Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong

ISBN: (纸本)9781577355687

Probabilistic relational PCA (PRPCA) can learn a projection matrix to perform dimensionality reduction for relational data. However, the results learned by PRPCA lack interpretability because each principal component is a linear combination of all the original variables. In this paper, we propose a novel model, called sparse probabilistic relational projection (SPRP), to learn a sparse projection matrix for relational dimensionality reduction. The sparsity in SPRP is achieved by imposing on the projection matrix a sparsity-inducing prior such as the Laplace prior or Jeffreys prior. We propose an expectation-maximization (EM) algorithm to learn the parameters of SPRP. Compared with PRPCA, the sparsity in SPRP not only makes the results more interpretable but also makes the projection operation much more efficient without compromising its accuracy. All these are verified by experiments conducted on several real applications. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Principal component analysis

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An ant colony system algorithm for the problem of server consolidation in virtualized data centers

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Journal of Computational Information systems 2012年第16期8卷 6631-6640页

作者： Gao, Yongqiang Guan, Haibing Qi, Zhengwei Wang, Bin Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China

The problem of server sprawl is common in today's data centers. Virtualization-based server consolidation is a vital mechanism to solve the server sprawl problem in modern data centers by consolidating multiple virtualized servers onto a few physical servers leading to improved resource utilization. In this paper we propose an ant colony optimization metaheuristic to solve the problem of server consolidation in virtualized data centers. For evaluating the proposed algorithm we apply it to ten classes of instances proposed in the literature, and compare the results to three competing algorithms including a first-fit decreasing (FFD) algorithm, a genetic algorithm, and a max-min ant system algorithm. The results show that the algorithm proposed in this paper is an effective and efficient methodology to solve the problem of server consolidation in virtualized data centers. © 2012 Binary Information Press.

关键词： Virtualization

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An Ant Colony System Algorithm for The Problem of Server Consolidation in Virtualized Data Centers

An Ant Colony System Algorithm for The Problem of Server Con...

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2012年全国理论计算机科学学术年会(NCTCS2012)

作者： Yongqiang Gao Haibing Guan Zhengwei Qi Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University

e problem of server sprawl is common in today's data ***-based server consolidation is a vital mechanism to solve the server sprawl problem in modern data centers by consolidating multiple virtualized servers ...

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EMP: Exploiting Mobility Patterns for Collaborative Localization in Sparse Mobile Networks

EMP: Exploiting Mobility Patterns for Collaborative Localiza...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Zhenzhen Tian Yanmin Zhu Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiaotong University China

ISBN: (纸本)9781467358088

Location awareness plays an indispensable role in a wide variety of application domains such as environment monitoring, and vehicle tracking. In this paper we focus on the localization of mobile users in sparse mobile networks which exist in many practical scenarios where users are distributed over a vast area. The unique characteristics of sparse mobile networks present several challenges for accurate localization, such as constant movement and little information from anchors. By analyzing five large datasets of real users traces with entropy analysis from five sites, we make an important observation that there is strong patterns with user mobility. Motivated by this observation, we propose a localization approach called EMP by exploiting mobility patterns of users for localization in sparse mobile networks. EMP implements a range-free distributed algorithm, with which each user collaboratively estimates its current location by fusing two localization sources, i.e., network connectivity with other nodes and mobility patterns. With trace driven simulations, we demonstrate that EMP significantly improves the localization accuracy, comparing with other existing localization approaches.

关键词： Mobility Patterns Collaborative Localization Sparse Mobile Networks electromagnetic pulses mobile networking Consumers Location awareness environmental monitoring Current Position Boat anchors

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Denoising Diffusion Tensor Images with Shearlet

Denoising Diffusion Tensor Images with Shearlet

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2012 IEEE 11th International Conference on Signal Processing(ICSP 2012)

作者： Xiangfen Zhang Bao-Liang Lu Yan Ma Xiaozhong Xu Fangfang Wei Wenjie Xu Institute of Intelligent Computing & Image Processing College of Mechanical and Electronic EngineeringShanghai Normal University Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and EngineeringShanghai Jiaotong University MOE-Microsoft Key Lab for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University

ISBN: (纸本)9781467321969

Diffusion tensor imaging （DTI） is known to be the best non-invasive imaging modality in providing anatomical information as white-matter fiber bundles. However, the Gaussian noise introduced into the diffusion tensor images can bring serious impacts on tensor calculation and fiber tracking. To decrease the effects of the Gaussian noise, many denoising methods have been presented. In this paper, a shearlet based denosing strategy is introduced. To evaluate the efficiency of the proposed shearlet based denoising method in accounting for the Gaussian noise introduced into the images, the peak to peak signal-to-noise ratio （PSNR）, signal-to-mean squared error ratio （SMSE） and edge keeping index （Beta） metrics are adopted. The experiment results acquired from both the synthetic and real data indicate the good performance of our proposed filter.

关键词： diffusion tensor imaging shearlet transform wavelet transform PSNR SMSE denoising

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