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Peng Y.,Hangzhou Dianzi University | Peng Y.,Key Laboratory of Complex Systems Modeling and Simulation | Lu B.-L.,Shanghai JiaoTong University
Multimedia Tools and Applications | Year: 2016

By representing a test sample with a linear combination of training samples, sparse representation-based classification (SRC) has shown promising performance in many applications such as computer vision and signal processing. However, there are several shortcomings in SRC such as 1) the l2-norm employed by SRC to measure the reconstruction fidelity is noise sensitive and 2) the l1-norm induced sparsity does not consider the correlation among the training samples. Furthermore, in real applications, face images with similar variations, such as illumination or expression, often have higher correlation than those from the same subject. Therefore, we correspondingly propose to improve the performance of SRC from two aspects by: 1) replacing the noise-sensitive l2-norm with an M-estimator to enhance its robustness and 2) emphasizing the sparsity in terms of the number of classes instead of the number of training samples, which leads to the structured sparsity. The formulated robust structured sparse representation (RGSR) model can be efficiently optimized via alternating minimization method under the half-quadratic (HQ) optimization framework. Extensive experiments on representative face data sets show that RGSR can achieve competitive performance in face recognition and outperforms several state-of-the-art methods in dealing with various types of noise such as corruption, occlusion and disguise. © 2016 Springer Science+Business Media New York Source

Yu D.,Hangzhou Dianzi University | Wang R.,Key Laboratory of Complex Systems Modeling and Simulation | Wang J.,Key Laboratory of Complex Systems Modeling and Simulation | Li W.,Key Laboratory of Complex Systems Modeling and Simulation
Journal of Imaging Science and Technology | Year: 2016

City traffic often exhibits regional characteristics, such as large trucks frequently appearing in the suburbs, and the paths to playgrounds on weekends generally being congested. Discovering and visualizing these hidden traffic regions inside which roads share similar characteristics of traffic conditions simplifies the modeling complexities of whole city traffic conditions and therefore contributes significantly toward city planning. Unfortunately, such traffic regions always have irregular shapes and are time varying, which makes their discovery extremely complicated. In addition, establishing a method to visualize and explore the traffic regions interactively still remains challenging. In this article, the authors propose a latent Dirichlet allocation (LDA)-based approach to the discovery of underlying traffic regions (or region topics) from vehicle trajectories captured by surveillance devices installed along roadsides. They treat vehicle trajectories as documents and the values of different traffic features, such as locations, directions, speeds and vehicle types, as the corresponding words. After applying the LDA model, they obtain a list of region topics with combined feature values, in which the different feature values are clustered with probabilistic assignments. Meanwhile, they build a prototype system to explore the surveillance-device-based vehicle trajectories according to the discovered region topics. The prototype system, which consists of map view, cloud view, treemap view and matrix-table view, visualizes the feature values of hidden traffic regions. The authors finally research a real case based on the traffic data in Wenzhou City, a large city in eastern China with a population of more than nine million. They investigate approximately 157 surveillance devices and 750,000 moving vehicles. The case demonstrates the effectiveness of both their proposed approach and the prototype system. © 2016 Society for Imaging Science and Technology. Source

Yin Y.,Hangzhou Dianzi University | Yin Y.,Zhejiang University | Yin Y.,Key Laboratory of Complex Systems Modeling and Simulation | Aihua S.,Hangzhou Dianzi University | And 4 more authors.
International Journal of Software Engineering and Knowledge Engineering | Year: 2016

Web service recommendation is one of the key problems in service computing, especially in the case of a large number of service candidates. The QoS (quality of service) values are usually leveraged to recommend services that best satisfy a user's demand. There are many existing methods using collaborative filtering (CF) to predict QoS missing values, but very limited works can leverage the network location information in the user side and service side. In real-world service invocation scenario, the network location of a user or a service makes great impact on QoS. In this paper, we propose a novel collaborative recommendation framework containing three novel prediction models, which are based on two techniques, i.e. matrix factorization (MF) and network location-aware neighbor selection. We first propose two individual models that have the capability of using the user and service information, respectively. Then we propose a unified model that combines the results of the two individual models. We conduct sufficient experiments on a real-world dataset. The experimental results demonstrate that our models achieve higher prediction accuracy than baseline models, and are not sensitive to the parameters. © 2016 World Scientific Publishing Company. Source

You X.,Hangzhou Dianzi University | You X.,Key Laboratory of Complex Systems Modeling and Simulation | Zhou R.,Hangzhou Dianzi University | Zhou R.,Key Laboratory of Complex Systems Modeling and Simulation | Zhou R.,Zhejiang Provincial Engineering Center on Data Cloud Processing and Analysis
Advances in Condensed Matter Physics | Year: 2014

A first-principles study has been performed to investigate the structural and electronic properties of the GaAs1 x Bix system. The simulations are based upon the generalized gradient approximation (GGA) within the framework of density functional theory (DFT). Calculations are performed to different Bi concentrations. The lattice constant of GaAs 1 x Bi x increases with Bi concentration while the alloy remains in the zinc-blende structure. The band gap of GaAs 1 x Bix clearly shrinks with the Bi concentration. The optical transition of Bi dopant in GaAs exhibits a red shift. Besides, other important optical constants, such as the dielectric function, reflectivity, refractive index, and loss function also change significantly. © 2014 Xindong You and Renjie Zhou. Source

Ding H.,Hangzhou Dianzi University | Ding H.,Key Laboratory of Complex Systems Modeling and Simulation | Cao L.,Hangzhou Dianzi University | Qiu H.,Hangzhou Dianzi University | And 3 more authors.
Proceedings - 2015 International Conference on Identification, Information, and Knowledge in the Internet of Things, IIKI 2015 | Year: 2015

Most previous studies involving public goods games are investigated under a simplifying assumption that participators are compulsive in collective interactions and contribute unconditionally to the public pool. Nevertheless, how the conditional investment mechanism based on individual's reputation affects the evolution of cooperation in structured populations is still unclear. Here we introduce a reputation-based conditional investment rule for constituting participant groups into spatial threshold public goods game, where the public goods game can be conducted only if the participant number is not less than the threshold parameter. Interestingly, we find that large threshold parameter results in the optimal environment for cooperators' viability. © 2015 IEEE. Source

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