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21st Workshop on Information Technologies and systems, WITS 2011

作者： Zhang, Jie Yang, Yanwu Qin, Rui Zeng, Daniel Li, Xin State Key Lab. of Intelligent Control and Management of Complex Systems Chinese Academy of Sciences Beijing 100190 China Department of Management Information Systems University of Arizona United States Department of Information Systems City University of Hong Kong Kowloon Tong Hong Kong

With serious advertising budget constraints, advertisers have to adjust their daily budget according to the performance of advertisements in real time. Thus we can leave precious budgets to better opportunities in the future, and avoid the surge of ineffective clicks for unnecessary costs. However, advertisers usually have no sufficient knowledge and time for real-time advertising operations in search auctions. We formulate the budget adjustment problem as a state-action decision process in the reinforcement learning (RL) framework. Considering dynamics of marketing environments and some distinctive features of search auctions, we extend continuous reinforcement learning to fit the budget decision scenarios. The market utility is defined as discounted total clicks to get during the remaining period of an advertising schedule. We conduct experiments to validate and evaluate our strategy of budget adjustment with real world data from search advertising campaigns. Experimental results showed that our strategy outperforms the two other baseline strategies.

关键词： Marketing

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LOCAL PECULIARITY ORIENTED DATA MINING AND ITS APPLICATION IN OUTLIER DETECTION

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2012年第6期11卷 1155-1181页

作者： Yang, Jian Zhong, Ning Yao, Yiyu Wang, Jue Beijing Univ Technol Int WIC Inst Beijing Peoples R China Maebashi Inst Technol Dept Life Sci & Informat Maebashi Gunma Japan Univ Regina Dept Comp Sci Regina SK S4S 0A2 Canada Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China

Peculiarity oriented mining (POM), aimed at discovering peculiarity rules hidden in a dataset, is a data mining method. Peculiarity factor (PF) is one of the most important concepts in POM. In this paper, it is proved that PF can accurately characterize the peculiarity of data sampled from a normal distribution. However, for a general one-dimensional distribution, it does not have the property. A local version of PF, called LPF, is proposed to solve the difficulty. LPF can effectively describe the peculiarity of data sampled from a continuous one-dimensional distribution. Based on LPF, a framework of local peculiarity oriented mining is presented, which consists of two steps, namely, peculiar data identification and peculiar data analysis. Two algorithms for peculiar data identification and a case study of peculiar data analysis are given to make the framework practical. Experiments on several benchmark datasets show their good performance.

关键词： Data mining peculiarity factor local peculiarity factor local peculiarity oriented mining outlier detection

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A closed-loop method to generate fast C-start for a robotic fish

A closed-loop method to generate fast C-start for a robotic ...

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2011 IEEE International Conference on Mechatronics and Automation, ICMA 2011

作者： Su, Zongshuai Yu, Junzhi Tan, Min Zhang, Jianwei State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Department of Informatics University of Hamburg Germany

ISBN: (纸本)9781424481149

This paper presents a novel closed-loop method for a multilink robotic fish to mimic the C-start maneuver, in which the turning speed and precision are emphasized. The turning speed is maximized by carefully designed preparatory stage, and the turning precision is achieved by the feedback of the turning angle from a gyroscope and a new design of the propulsive stage. Different types of C-starts are studied, due to the different sizes of caudal fins, in order to achieve the highest turning angular velocity. All the proposed types of C-starts are experimented and compared using a four-joint robotic fish. The experimental results show a fastest angular velocity up to 200°/s and the distinctions between all these different types. © 2011 IEEE.

关键词： Fish

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Vulnerability analysis of power grid based on multi-agent complex systems

Vulnerability analysis of power grid based on multi-agent co...

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

作者： Dong, Xisong Xiong, Gang Hou, Jiachen Dong, Fan Nyberg, Timo R. State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China BIT Research Centre Aalto University Aalto Finland

ISBN: (纸本)9781457705731

Modern power grid is a typical multi-level complex giant system. The conventional analytical methods based on reductionism can't provide sufficient guidance for its operation and management. complex system theory, based on holism, has its specific advantages in power grid's research. But, it has some limitations. In this article, we improve complex grid by introducing new parameters which can describe the grid's characters better and using multi-agent theory. As an application, the complex power grid constructed with actual data from North China grid is constructed and its vulnerability has been simulated and analyzed under different attacks. © 2011 IEEE.

关键词： complex networks

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Fast double-parallel image processing based on FPGA

Fast double-parallel image processing based on FPGA

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IEEE International Conference on Vehicular Electronics and Safety

作者： Li, Ye Yao, Qingming Tian, Bin Xu, Wencong State Key Lab. of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Mechanical Electrical and Information Engineering Shandong University Weihai 264209 China

ISBN: (纸本)9781457705762

Due to FPGA's flexibility and parallelism, it is popular for accelerating image processing. In this paper, a double-parallel architecture based on FPGA has been exploited to speed up median filter and edge detection tasks, which are essential steps during image processing. The double-parallel scheme includes an image-level parallel and an operation-level parallel. The image-level parallel is a high-level parallel which divides one image into different parts and processes them concurrently. The operation-level parallel, which is embedded in each image-level parallel thread, fully explores every parallel part inside the concrete algorithms. The corresponding design is based on a DE2 Development Board which contains a CYCLONE II FPGA device. Meanwhile, the same task has also been implemented on PC and DSP for performance comparison. Despite the fact that operating frequencies of used PC and DSP are much higher than FPGA's, FPGA costs less time per computed image than both of them. By taking advantage of the double-parallel technique, the speed/frequency ratio of FPGA is 202 times faster than PC and 147 times faster than DSP. Finally, a detailed discussion about different platforms is conducted, which analyzes advantages and disadvantages of used computing platforms. This paper reveals that the proposed double-parallel scheme can dramatically speed up image processing methods even on a low-cost FPGA platform with low frequency and limited resources, which is very meaningful for practical applications. © 2011 IEEE.

关键词： Field programmable gate arrays (FPGA)

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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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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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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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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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