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2011 AAAI Workshop

作者： Xu, Yang Wu, Lei Guo, Liying Chen, Zheng Yang, Lai Shi, Zhongzhi Agent-based Cooperative System Lab School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu SC 611731 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781577355243

MapReduce provided a novel computing model for complex job decomposition and sub-tasks management to support cloud computing with large distributed data sets. However, its performance is significantly influenced by the working data distributions over those data sets. In this paper, we put forward a novel model to balance data distribution to improve cloud computing performance in data-intensive applications, such as distributed data mining. By extending the classic MapReduce model with an agent-aid layer and abstracting working load requests for data blocks as tokens, the agents can reason from previously received tokens about where to send other tokens in order to balance the working tasks and improve system performance. Our key contribution lies in building an efficient token routing algorithm in spite of agents' unknowing to the global state of data distribution in cloud. We also built a prototype of our system, and the experimental results show that our approach can significantly improve the efficiency of cloud computing. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： MapReduce

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Visual saliency detection by spatially weighted dissimilarity

Visual saliency detection by spatially weighted dissimilarit...

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作者： Duan, Lijuan Wu, Chunpeng Miao, Jun Qing, Laiyun Fu, Yu College of Computer Science and Technology Beijing University of Technology Beijing 100124 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China School of Information Science and Engineering Graduate University Chinese Academy of Sciences Beijing 100049 China Department of Computing University of Surrey Guildford Surrey GU2 7XH United Kingdom

ISBN: (纸本)9781457703942

In this paper, a new visual saliency detection method is proposed based on the spatially weighted dissimilarity. We measured the saliency by integrating three elements as follows: the dissimilarities between image patches, which were evaluated in the reduced dimensional space, the spatial distance between image patches and the central bias. The dissimilarities were inversely weighted based on the corresponding spatial distance. A weighting mechanism, indicating a bias for human fixations to the center of the image, was employed. The principal component analysis (PCA) was the dimension reducing method used in our system. We extracted the principal components (PCs) by sampling the patches from the current image. Our method was compared with four saliency detection approaches using three image datasets. Experimental results show that our method outperforms current state-of-the-art methods on predicting human fixations. © 2011 IEEE.

关键词： Principal component analysis

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Dual form back propagation on the EM algorithm

Dual form back propagation on the EM algorithm

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IEEE International Conference on Granular computing (GRC)

作者： Hong Hu Zhongzhi Shi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

Vladimir N. Vapnik. (1998) pointed out that maxlikelihood functions in EM algorithms are just a special risk function. Based on this opinion, a novel EM algorithm uses a risk function differ with maxlikelihood functions, in stead, a risk formula based on the least square method is used. The gradient descending approach should be used in such kind approaches. Such kind EM algorithms can estimate the parameters of a random model from both labeled and unlabeled samples, and are suitable for semi-supervised learning.

关键词： Gaussian distribution Error analysis Maximum likelihood estimation Least squares methods information processing Laboratories

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DETECTING OPERATIONS THAT POTENTIALLY VIOLATE WELL-FORMEDNESS CONSTRAINTS FOR METADATA REPOSITORY SYSTEMS

DETECTING OPERATIONS THAT POTENTIALLY VIOLATE WELL-FORMEDNES...

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2011 IEEE International Conference on Cloud computing and Intelligence Systems(2011年第一届IEEE云计算与智能系统国际会议 IEEE CCIS2011)

作者： Xiaofei Zhao Zhongzhi Shi The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

The organization of the metadata in repository systems exhibits a complex structure which is layered, multi-level and dynamically adaptable;the validating of well-formedness constraints is insufficiently specified in existing repository system standard, the above two reasons make it becomes a major challenge how to validate well-formedness constraints for MOF-based metadata repository systems. In this paper we propose a method to automatically detect the operations that may potentially violate a well-formedness constraint in the meta-level. Our approach can detect potentially violating operations by determining the construction actions that may violate the constraint and checking whether those actions appear in the operation specification. Our approach helps to improve efficiency of well-formedness constraint checking since its results can be used to discard many irrelevant tests.

关键词： Repository System Meta Object Facility(MOF) Well-Formedness Constraint Construction Action Internal Action

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An Orientation Model for Hierarchical Phrase-Based Translation

An Orientation Model for Hierarchical Phrase-Based Translati...

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International Conference on Asian Language Processing (IALP)

作者： Xinyan Xiao Jinsong Su Yang Liu Qun Liu Shouxun Lin Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

The hierarchical phrase-based (HPB) translation exploits the power of grammar to perform long distance reorderings, without specifying nonterminal orientations against adjacent blocks or considering the lexical information covered by nonterminals. In this paper, we borrow from phrase-based system the idea of orientation model to enhance the reordering ability of HPB translation. We distinguish three orientations (monotone, swap, discontinuous) of a nonterminal based on the alignment of grammar, and select the appropriate orientation of nonterminal using lexical information covered by it. By incorporating the orientation model, our approach significantly outperforms a standard HPB system up to 1.02 Bleu on large scale NIST Chinese-English translation task, and 0.51 Bleu on WMT German-English translation task.

关键词： Training Mathematical model Grammar Frequency estimation Probability Equations Entropy

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Multi-kernel Co-SVM with a new strategy using unlabeled data for image retrieval

Multi-kernel Co-SVM with a new strategy using unlabeled data...

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2011 6th International Conference on Pervasive computing and Applications, ICPCA 2011

作者： Zongmin, Li Yang, Liu Hua, Li School of Computer and Communication Engineering China University 0f Petroleum Shandong 257061 China Key Laboratory Intelligent Information Processing Institute of Computing Technology Chinese Academy of Science Beijing l0080 China

ISBN: (纸本)9781457702082

Relevance feedback based on SVM classifier shows a good performance recently but the finite feedback counts limited by user's patience and the small sample size problem are not solved well, Co-SVM does a good job in solving these problems but still has some flaws. We propose three strategies to try to improve this algorithm: (1) different kernel functions are used to characterize the color and texture visual similarities;(2) a new method is proposed to caculate the confident scores of the contention samples;(3) a bunch of the most irrelevant images with the highest confident score are added into the labeled images to extend the size of labeled data while choosing a bunch of images for user labeling. Experimental results verify the superiority of our method over Co-SVM. © 2011 IEEE.

关键词： Image enhancement

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Robust and discriminative image authentication based on sparse coding

Robust and discriminative image authentication based on spar...

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2011 IEEE Consumer Communications and Networking Conference, CCNC'2011

作者： Mou, Luntian Huang, Tiejun Tian, Yonghong Lian, Shiguo Chen, Xilin Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China Graduate University of Chinese Academy of Sciences Beijing China Institute of Digital Media Peking University Beijing China France Telecom RandD Beijing China

ISBN: (纸本)9781424487905

Image authentication is usually approached by checking the preservation of some invariant features, which are expected to be both robust and discriminative so that content-preserving operations are accepted while content-altering manipulations are rejected. However, most of existing features have not obtained convincing performance due to insufficiency of experiments and over biasing of robustness. Motivated by the sparse coding strategy discovered in primary visual cortex, we explore the possibility of using sparse coding coefficients for image authentication. Through extensive experiments, we discover that the proposed feature bears great discrimination as well as robustness, which indicates the effectiveness of sparse coding as a new invariant feature for image authentication. © 2011 IEEE.

关键词： Authentication

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Particle swarm optimization for automatic parameters determination of pulse coupled neural network

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Journal of Computers 2011年第8期6卷 1546-1553页

作者： Xinzheng, Xu Shifei, Ding Zhongzhi, Shi Zuopeng, Zhao Hong, Zhu School of Computer Science and Technology China University of Mining and Technology Xuzhou China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

Pulse coupled neural network (PCNN), a wellknown class of neural networks, has original advantage when applied to image processing because of its biological background. However, when PCNN is used, the main problem is that its parameters aren't self-adapting according to different image which limits the application range of PCNN. Considering that, this paper proposed a new method based on particle swarm optimization (PSO) to determine automatically the parameters of PCNN. In this method, the algorithm of PSO is applied to search automatically optimum in the solution space of PCNN's parameters until finding global optimal solution. Experimental results demonstrate that the proposed method is accurate and robust for image segmentation, and its performance is better than the methods of Otsu, manual adjustment of parameters when mutual information is adopted as evaluation criteria. © 2011 ACADEMY PUBLISHER.

关键词： Image segmentation

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A rough RBF neural networks optimized by the genetic algorithm

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Advances in information Sciences and Service Sciences 2011年第7期3卷 332-339页

作者： Ding, Shifei Ma, Gang Xu, Xinzheng School of Computer Science and Technology China University of Mining and Technology Xuzhou China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Science Beijing China

The large-scale data parallelism processing is an inherent characteristic of artificial neural networks, but the networks bring the efficiency problems of data processing. As one of the artificial neural networks, Radial Basis Function (RBF) neural networks have the same problem. Therefore, how to reduce the scale of data to improve the efficiency of data processing has been a hot issue among the artificial intelligence scholars. Based on the traditional RBF neural networks, this paper puts forward a method which determines the important degree of the sample attributes based on knowledge entropy of Rough set by analyzing the relationship between the knowledge entropy and the weight of the sample attributes, and assesses the importance of the sample attributes between the input layer and the hidden layer, namely the attribution reduction, so as to achieve reduce the scale of data processing. The ultimate aim of training RBF neural networks is to seek a set of suitable networks parameters which makes the sample output error achieve the minimum or required accuracy, while Genetic Algorithm (GA) has the properties of finding out the optimal solution through multiplepoint random search in the solution space, so Genetic Algorithm is used to optimize the centers, the widths and the weights between the hidden layer and the output layer of RBF neural networks in training the networks. Finally, a model about A Rough RBF Neural Networks Optimized by the Genetic Algorithm (GA-RS-RBF) is proposed in this paper. The simulation results show that the rough RBF neural network optimized by the Genetic Algorithm is better than the traditional RBF neural networks in classification about Iris datasets.

关键词： Genetic algorithms

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Fast intra mode selection for stereo video coding using epipolar constraint

Fast intra mode selection for stereo video coding using epip...

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IEEE Visual Communications and Image Processing (VCIP)

作者： Guolei Yang Luhong Liang Wen Gao Peking University Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China

Application of stereo video in TV industry and consumer electronics becomes very popular recently. Thus, fast algorithm for stereo video coding is highly desired because of its huge inter-view computational redundancy. In our previous work we proposed an epipolar constraint based fast inter mode selection for stereo video using motion vector of blocks as a indicator of similarity, nevertheless, intra mode selection is also highly complex in standards like H.264/AVC. In this paper, a practical fast intra mode selection is proposed to eliminate the computational redundancy by exploiting inter-view dependency based on epipolar constraint. The proposed method does not rely on disparity estimation. Instead, a sliding window is employed to generate an intra mode candidate pool from macro-blocks on the epipolar line. The candidate pool is then rectified to remove invalid modes and improve accuracy. Finally, optimal prediction mode is selected from the candidate pool. The proposed method can significantly reduce the number of mode candidates/prediction directions compared to exhaustive mode selection by 79%. Experiments on 5 HD video coded in 1-frame demonstrate the overall coding time of one view is saved by 56% on average, with slightly video quality loss less than 0.1 dB.

关键词： Encoding Prediction algorithms Redundancy Signal processing algorithms Video coding Estimation Complexity theory

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