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2011 International Conference on Asian Language Processing, IALP 2011

作者： Zhang, Xiaoqian Li, Shoushan Zhou, Guodong Zhao, Hongxia Natural Language Processing Lab. Soochow University Suzhou China State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9780769545547

Polarity shifting has been a challenge to automatic sentiment classification. In this paper, we create a corpus which consists of polarity-shifted sentences in various kinds of product reviews. In the corpus, both the sentimental words and shifting trigger words are annotated. Furthermore, we analyze all the polarity shifted sentences and categorize them into five categories: opinion-itself, holder, target, time and hypothesis. Experimental study shows the agreement of annotation and the distribution of the five categories of polarity shifting. © 2011 IEEE.

关键词： corpus annotation polarity shifting sentiment classification

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An optimized modeling method based on BPEL4WS

An optimized modeling method based on BPEL4WS

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2011 IEEE International Conference on Service Operations, Logistics and Informatics, SOLI 2011

作者： Liu, Sheng Xiong, Gang Fan, Dong Yao, Jianshi State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Science Beijing 100190 China Chinese People's Liberation Army Beijing 100094 China

ISBN: (纸本)9781457705731

As an efficient business process execution language which supports web services, BPEL4WS is widely supported by the academic and the industrial circles. According to the shortcomings such as number of computer terms, abstract model definition and the complex description of people activity, this paper presents easy-to-use BPEL4WS modeling method and tool which encapsulates computer terms and convert business models to BPEL4WS models directly. In comparison to the other modeling methods, our method cut down the number of modeling elements by more than 85 percent and save the modeling time by 80 percent and accelerate model running by more than 40 percent. Thus it is more appropriate for popularization. © 2011 IEEE.

关键词： Web services

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Bidding behavior and equilibrium on competitive keyword advertising markets

Bidding behavior and equilibrium on competitive keyword adve...

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21st Workshop on Information Technologies and systems, WITS 2011

作者： Yuan, Yong Zeng, Daniel Li, Linjing Bao, Ning State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Department of Management Information Systems University of Arizona AZ United States School of Technical Physics Xidian University Xi'an China

Online advertisers bidding in keyword auctions through Web search engines are experiencing fierce competition. Based on a model of advertisers' rational competitive preference, we propose a novel solution concept called the Upper Bound Nash Equilibrium(UBNE) targeting at modeling the competitive bidding dynamics. UBNE yields the best outcome for search engines, and provides a rational explanation of the bid inflation dynamics on keyword markets.

关键词： Search engines

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Quadratic-Chi Similarity Metric Learning for Histogram Feature

Quadratic-Chi Similarity Metric Learning for Histogram Featu...

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第一届亚洲模式识别会议

作者： Rongguo Zhang Xinyuan Cai Chunheng Wang Baihua Xiao State Key Laboratory of Intelligent Control and Management of Complex SystemsInstitute of Automation State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automatio

ISBN: (纸本)9781457701221

Histogram features, such as SIFT, HOG, LBP et al, are widely used in modern computer vision algorithms. According to [18], chi-square distance is an effective measure for comparing histogram features. In this paper, we propose a new method, named the Quadric-chi similarity metric learning (QCSML) for histogram features. The main contribution of this paper is that we propose a new metric learning method based on chi-square distance, in contrast with traditional Mahalanobis distance metric learning methods. The use of quadric-chi similarity in our method leads to an effective learning algorithm. Our method is tested on SIFT features for face identification, and compared with the state-of-art metric learning method (LDML) on the benchmark dataset, the Labeled Faces in the Wild (LFW). Experimental results show that our method can achieve clear performance gains over LDML.

关键词： histogram feature metric learning Quardic-chi similarity face identification

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Background Modeling by Exploring Multi-scale Fusion of Texture and Intensity in complex Scenes

Background Modeling by Exploring Multi-scale Fusion of Textu...

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第一届亚洲模式识别会议

作者： Zhong Zhang Wen Zhou Chunheng Wang Baihua Xiao Shuang Liu State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automatio State Key Laboratory of Intelligent Control and Management of Complex SystemsInstitute of Automation

ISBN: (纸本)9781457701221

Background modeling is a fundamental yet challenging issue in video surveillance. Traditional methods usually adopt single feature type to solve the problem, while the performance is usually unsatisfactory when handling complex scenes. In this paper, we propose a multi-scale framework, which combines both texture and intensity feature, to achieve a robust and accurate solution. Our contributions are three folds: first, we provide a multi-scale analysis for the issue;second, for texture feature we propose a novel texture operator named Scale-invariant Centersymmetric Local Ternary Pattern, and a corresponding Pattern Adaptive Kernel Density Estimation technique for its probability estimation;third, we design a Simplified Gaussian Mixture Models for intensity feature. Our method is tested on several complex real world videos with illumination variation, soft shadows and dynamic backgrounds. The experimental results clearly demonstrate that our method is superior to the previous methods.

关键词： background modeling multi-scale fusion texture intensity

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A Local Learning Based Image-To-Class Distance for Image Classification

A Local Learning Based Image-To-Class Distance for Image Cla...

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第一届亚洲模式识别会议

作者： Xinyuan Cai Rongguo Zhang Baihua Xiao Chunheng Wang State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automatio State Key Laboratory of Intelligent Control and Management of Complex SystemsInstitute of Automation

ISBN: (纸本)9781457701221

Image-To-Class distance is first proposed in Naive- Bayes Nearest-Neighbor. NBNN is a feature-based image classifier, and can achieve impressive classification accuracy. However, the performance of NBNN relies heavily on the large number of training samples. If using small number of training samples, the performance will degrade. The goal of this paper is to address this issue. The main contribution of this paper is that we propose a robust Image-to-Class distance by local learning. We define the patch-to-class distance as the distance between the input patch to its nearest neighbor in one class, which is reconstructed in the local manifold space;and then our image-toclass distance is the sum of patch-to-class distance. Furthermore, we take advantage of large-margin metric learning framework to obtain a proper Mahalanobis metric for each class. We evaluate the proposed method on four benchmark datasets: Caltech, Corel, Scene13, and Graz. The results show that our defined Image-To-Class Distance is more robust than NBNN and Optimal-NBNN, and by combining with the learned metric for each class, our method can achieve significant improvement over previous reported results on these datasets.

关键词： local learning image-to-class distance large margin metric learning Na(i)ve Bayes Nearest-Neighbor image classification

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Contextual Constrained Independent Component Analysis Based Foreground Detection for Indoor Surveillance

Contextual Constrained Independent Component Analysis Based ...

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第一届亚洲模式识别会议

作者： Zhong Zhang Wen Zhou Baihua Xiao Shuang Liu Chunheng Wang State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automatio State Key Laboratory of Intelligent Control and Management of Complex SystemsInstitute of Automation

ISBN: (纸本)9781457701221

Recently, Independent Component Analysis based foreground detection has been proposed for indoor surveillance applications where the foreground tends to move slowly or remain still. Yet such a method often causes discrete segmented foreground objects. In this paper, we propose a novel foreground detection method named Contextual Constrained Independent Component Analysis (CCICA) to tackle this problem. In our method, the contextual constraints are explicitly added to the optimization objective function, which indicate the similarity relationship among neighboring pixels. In this way, the obtained de-mixing matrix can produce the complete foreground compared with the previous ICA model. In addition, our method performs robust to the indoor illumination changes and features a high processing speed. Two sets of image sequences involving room lights switching on/of and door opening/closing are tested. The experimental results clearly demonstrate an improvement over the basic ICA model and the image difference method.

关键词： foreground detection contextual constraints indoor surveillance independent component analysis

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Mean-square exponential stability for stochastic time-varying delay systems with Markovian jumping parameters:a delay decomposition approach

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Journal of systems Engineering and Electronics 2011年第5期22卷 816-824页

作者： Li Ma Feipeng Da Key Laboratory of Measurement and Control for Complex Systems of Engineering Ministry of Education Research Institute of Automation Southeast University Nanjing 210096 E R. China College of Electronic and Information Engineering Jiangsu University Zhenjiang 212013 P. R. China

The mean-square exponential stability problem is investigated for a class of stochastic time-varying delay systems with Markovian jumping parameters. By decomposing the delay interval into multiple equidistant subintervals, a new delay-dependent and decay-rate-dependent criterion is presented based on constructing a novel Lyapunov functional and employing stochastic analysis technique. Besides, the decay rate has no conventional constraint and can be selected according to different practical conditions. Finally, two numerical examples are provided to show that the obtained result has less conservatism than some existing ones in the literature.

关键词： stochastic systems time-varying delay Markov chain linear matrix inequality （LMI）.

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Analysis of neural interaction during adaptation of reach-to-grasp task under perturbation with Bayesian networks

Analysis of neural interaction during adaptation of reach-to...

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2011 4th International Conference on Biomedical Engineering and Informatics, BMEI 2011

作者： Sang, Dong Lv, Bin He, Huiguang Wang, Feiyue He, Jiping State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Harrington Departemnt of Bioengineering Center for Neural Interface Design Arizona State University Tempe United States

ISBN: (纸本)9781424493524

In this work, we took the analysis of neural interactions change in M1 of a monkey during the adaptation process for it to complete reach-to-grasp tasks with external perturbation across days. BN model was applied to model and evaluate neural interaction networks from recorded neural spike trains data of each set. Our results showed that for delay period across sets, interaction level of neural network tended to be higher during later stage of adaptation than during begin stage, which indicated the monkey performed more fully preparation through adaptation. In addition, for both delay period and peri-movement period, the neural interaction networks tended to change more stably from one set to the next as the monkey adapted to the perturbation experiment better. © 2011 IEEE.

关键词： Bayesian networks

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The application and issuse of linear wireless sensor networks

The application and issuse of linear wireless sensor network...

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2011 International Conference on System Science, Engineering Design and Manufacturing Informatization, ICSEM 2011

作者： Wang, Zhengjie Zhao, Xiaoguang Qian, Xu College of Information and Electrical Engineering Shandong University of Science and Technology Qingdao China State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing China

ISBN: (纸本)9781457702471

Wireless sensor networks consist of a large number of sensor nodes that have low power and limited transmission range and can be used in various scenario. The nodes can be deployed in the long and narrow region, such as road, bridge, tunnel and pipeline, to get some interesting information. The linear topology of these network application is different other application and have special feature, such as multi-hop, long delay, long distance and low reliability. This paper introduces the concept of linear wireless sensor networks and discusses the classification of topology and key issue of this network. The application of the linear wireless sensor network, such as road, bridge, tunnel and pipeline is presented. The research challenges are discussed at last in this paper. © 2011 IEEE.

关键词： Topology

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