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

DETECTING OPERATIONS THAT POTENTIALLY VIOLATE WELL-FORMEDNES...

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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Parallel Outlier Detection Using KD-Tree Based on MapReduce

Parallel Outlier Detection Using KD-Tree Based on MapReduce

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IEEE International Conference on Cloud computing technology and Science (CloudCom)

作者： Qing He Yunlong Ma Qun Wang Fuzhen Zhuang Zhongzhi Shi The Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China Chinese Academy of Sciences Beijing China

Distributed and Parallel algorithms have attracted a vast amount of interest and research in recent decades, to handle large-scale data set in real-world applications. In this paper, we focus on a parallel implementation of KD-Tree based outlier detection method to deal with large-scale data set. As one of the state-of-the-art outlier detection methods, KD-Tree based has been approved to be an effective algorithm. However, it still cannot process large-scale data set efficiently due to its serial implementation. Based on the current and powerful parallel programming framework -- MapReduce, we propose to implement the parallel KD-Tree based outlier detection algorithm (e.g., PKDTree for short). Experimental results demonstrate the efficiency of PKDTree according to the evaluation criterions of scale up, speedup and size up.

关键词： Smoothing methods Parallel algorithms Computers Arrays Detection algorithms Algorithm design and analysis Runtime

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The Estimation of Winter Wheat Yield Based on MODIS Remote Sensing Data

The Estimation of Winter Wheat Yield Based on MODIS Remote S...

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Computer and computing Technologies in Agriculture V——5th IFIP TC 5/SIG 5.1 Conference(CCTA2011) Part II

作者： Linsheng Huang Qinying Yang Dong Liang Yansheng Dong Xingang Xu Wenjiang Huang Key Laboratory of Intelligent Computing & Signal Processing Ministry of EducationAnhui University Beijing Research Center for Information Technology in Agriculture

ISBN: (纸本)9783642272776

A yield estimation method by remote sensing was used to estimate the yield of winter wheat in Jiangsu province,*** first step of this study was to extract the planting area of winter wheat from environmental satellite images and land-use map of Jiangsu province,meanwhile,correlation analyses were performed by using 8-day of composite Leaf Area Index(LAI)data from Moderate Resolution Imaging Spectroradiometer(MODIS)and statistical yield of corresponding ***,the average LAI was calculated at the optimal growth period,and the statistical yields of wheat for all counties were collected,in which the former was chosen as the independent variable and the latter was the dependent variable,and the regression model was ***,the accuracy and stability of the regression model were validated using the data of another *** results indicated that the yield estimation model at provincial level was reliable,the Root Mean Square Error(RMSE)and the Mean Absolute Error(MAE)of the model was 12.1%and 9.7%,*** addition,the yield estimation system of winter wheat in Jiangsu province was constructed and published based on ArcMap and ArcGIS Server.

关键词： LAI remote sensing yield estimation regression model ArcGIS Server

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Adaptive discriminant analysis for face recognition from single sample per person

Adaptive discriminant analysis for face recognition from sin...

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IEEE International Conference on Automatic Face and Gesture Recognition

作者： Kan, Meina Shan, Shiguang Su, Yu Chen, Xilin Gao, Wen Digital Media Research Center Institute of Computing Technology CAS Beijing China Key Laboratory of Intelligent Information Processing Chinese Academy of Sciences Beijing China Department of Computer Science University of Caen France Institute for Digital Media Peking University Beijing China School of Computer Science and Technology Harbin Institute of Technology Beijing China

ISBN: (纸本)9781424491407

Discriminant analysis, especially Fisherface and its numerous variants, have achieved great success in face recognition. However, these methods fail to work for face recognition from Single Sample per Person (SSPP), since they need more than one sample per person to estimate the within-class scatter matrix. To break this inability of traditional discriminant analysis, our paper proposes Adaptive Discriminant Analysis (ADA). In our method, the within-class scatter matrix of each enrolled subject is estimated from his/her single sample, by inferring from a generic training set with multiple samples per person. The inference is inspired by a simple intuition that similar person follows similar within-class variations. Specifically, both kNN regression and Lasso regression are explored for this purpose. We evaluate our method on FERET database and a large real-world face database. The results are very impressive compared with dominant traditional solutions to SSPP problem. © 2011 IEEE.

关键词： Discriminant analysis

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