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8th Asia information Retrieval Societies Conference, AIRS 2012

作者： Li, Long Song, Dandan Liao, Lejian Beijing Eng. Research Center of High Volume Language Information Processing and Cloud Computing Beijing Lab of Intelligent Information Technology School of Computer Science Beijing Institute of Technology Beijing China

ISBN: (纸本)9783642353406

We propose a general hierarchical vertical classification framework, which can automatically discover the inherent hierarchical structure of relationships among verticals based on flat datasets, and then build a hierarchical classifier. We conducted a set of comparison experiments to verify the performance of it, such as with flat vs hierarchical structure of relationships, as well as among different feature selection and classification methods. Experimental results show that the hierarchical classifiers built on the basis of the proposed framework make big improvements over the flat classifiers when classifying unseen web pages. Among them, the Support Vector Machine using Odds Ratio to select discriminative features performs best. © Springer-Verlag 2012.

关键词： Classification (of information)

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Spectral matching algorithm based on nonsubsampled contourlet transform and scale-invariant feature transform

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Journal of Systems Engineering and Electronics 2012年第3期23卷 453-459页

作者： Dong Liang Pu Yan Ming Zhu Yizheng Fan Kui Wang Key Lab of Intelligent Computing ＆ Signal Processing Ministry of Education Anhui University Hefei 230039 E R. China School of Electronics and Information Engineering Anhui University Hefei 230039 P. R. China School of Mathematics and Computation Sciences Anhui University Hefei 230039 P. R. China

A new spectral matching algorithm is proposed by us- ing nonsubsampled contourlet transform and scale-invariant fea- ture transform. The nonsubsampled contourlet transform is used to decompose an image into a low frequency image and several high frequency images, and the scale-invariant feature transform is employed to extract feature points from the low frequency im- age. A proximity matrix is constructed for the feature points of two related images. By singular value decomposition of the proximity matrix, a matching matrix （or matching result） reflecting the match- ing degree among feature points is obtained. Experimental results indicate that the proposed algorithm can reduce time complexity and possess a higher accuracy.

关键词： point pattern matching nonsubsampled contourlet transform scale-invariant feature transform spectral algorithm.

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Energy-efficient network routing with discrete cost functions

Energy-efficient network routing with discrete cost function...

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9th Annual Conference on Theory and Applications of Models of Computation, TAMC 2012

作者： Wang, Lin Fernández Anta, Antonio Zhang, Fa Hou, Chenying Liu, Zhiyong Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China Institute IMDEA Networks Spain China State Key Lab. for Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China Graduate University of Chinese Academy of Sciences China

ISBN: (纸本)9783642299513

Energy consumption is an important issue in the design and use of networks. In this paper, we explore energy savings in networks via a rate adaptation model. This model can be represented by a cost-minimization network routing problem with discrete cost functions. We formulate this problem as an integer program, which is proved to be NP-hard. Then a constant approximation algorithm is developed. In our proposed method, we first transform the program into a continuous-cost network routing problem, and then we approximate the optimal solution by a two-step rounding process. We show by analysis that, for uniform demands, our method provides a constant approximation for the uniform network routing problem with discrete costs. A bicriteria network routing problem is also developed so that a trade-off can be made between energy consumption and network delay. Analytical results for this latter model are also presented. © 2012 Springer-Verlag.

关键词： Network routing

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CloudLCA: finding the lowest common ancestor in metagenome analysis using cloud computing

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Protein & Cell 2012年第2期3卷 148-152页

作者： Guoguang Zhao Dechao Bu Changning Liu Jing Li Jian Yang Zhiyong Liu Yi Zhao Runsheng Chen Bioinformatics Research Group Key Laboratory of Intelligent Information ProcessingAdvanced Computing Research LaboratoryInstitute of Computing TechnologyChinese Academy of SciencesBeijing 100190China Bioinformatics Laboratory and National Laboratory of Biomacromolecules Institute of BiophysicsChinese Academy of SciencesBeijing 100101China State Key Laboratory for Molecular Virology and Genetic Engineering National Institute for Viral Disease Control and PreventionChinese Center for Disease Control and PreventionBeijing 100176China Graduate School of the Chinese Academy of Sciences Beijing 100190China

Estimating taxonomic content constitutes a key problem in metagenomic sequencing data ***,extracting such content from high-throughput data of next-generation sequencing is very time-consuming with the currently available ***,we present CloudLCA,a parallel LCA algorithm that significantly improves the efficiency of determining taxonomic composition in metagenomic data *** show that CloudLCA(1)has a running time nearly linear with the increase of dataset magnitude,(2)displays linear speedup as the number of processors grows,especially for large datasets,and(3)reaches a speed of nearly 215 million reads each minute on a cluster with ten thin *** comparison with MEGAN,a well-known metagenome analyzer,the speed of CloudLCA is up to 5 more times faster,and its peak memory usage is approximately 18.5%that of MEGAN,running on a fat *** can be run on one multiprocessor node or a *** is expected to be part of MEGAN to accelerate analyzing reads,with the same output generated as MEGAN,which can be import into MEGAN in a direct way to finish the following ***,CloudLCA is a universal solution for finding the lowest common ancestor,and it can be applied in other fields requiring an LCA algorithm.

关键词： CloudLCA metagenome analysis cloud computing

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Density based Collective Spatial keyword Query

Density based Collective Spatial Keyword Query

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第8届语义知识与网络国际会议(2012 Eighth International Conference on Semanties,Knowledge and Grids )(SKG2012)

作者： Li Zhang Xiaoping Sun Hai Zhuge Knowledge Grid Lab Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China

Geographic objects with descriptive text are gaining in prevalence in many web services such as Google *** keyword query which combines both the location information and textual description stands out in recent *** works mainly focus on finding top-k Nearest Neighbours where each node has to match the whole querying keywords.A collective query has been proposed to retrieve a group of objects nearest to the query object such that the group's keywords cover query's keywords and has the shortest inner-object *** the previous method does not consider the density of data objects in the spatial *** practice,a group of dense data objects around a query point will be more interesting than those sparse data *** distance of data objects of a group cannot reflect the density of the *** overcome this shortage,we proposed an approximate algorithm to process the collective spatial keyword query based on density and inner *** empirical study shows that our algorithm can effectively retrieve the data objects in dense areas.

关键词： Indexes Spatial databases Approximation algorithms Clustering algorithms Communities Query processing Semantics

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Evaluating Structure of Complex Networks by Navigation Entropy

Evaluating Structure of Complex Networks by Navigation Entro...

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第8届语义知识与网络国际会议(2012 Eighth International Conference on Semanties,Knowledge and Grids )(SKG2012)

作者： Xiaoping Sun Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

Complex networks are extensively studied in various areas such as social networks,biological networks,Internet and networks have many characters such as small-diameter,higher cluster and power-law degree ***-world is evolved for efficient information transformation and ***,navigation is an important functional character of *** researches mostly focus on understanding the navigability of small-world networks by analyzing the diameter and the routing *** this paper,we use the navigability to model the basic structural complexity of a *** is,given a network topology,we need a model to evaluate how complex the topology *** network complexity models have been proposed but none of them consider the navigability factor of the network *** believe that using the navigability factor to evaluate the network structural complexity is a feasible and reasonable *** use the adjacent matrix to build a navigation transition matrix and evaluate the randomness of random walks on the transition matrix by defining navigation entropy on *** use the iteration of the random walk matrix to evaluate the navigability of a network and the complexity of *** is,the higher the navigation entropy,the higher the randomness of a *** lower the navigation entropy,the higher the structure of a *** apply the navigation entropy model on a set of structural and random network topologies to show how the model can show the different complexity of networks.

关键词： navigation entropy structure complexity complex network

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Faceted Navigation on Text

Faceted Navigation on Text

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第8届语义知识与网络国际会议(2012 Eighth International Conference on Semanties,Knowledge and Grids )(SKG2012)

作者： Bei Xu Key Lab of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of Sciences Beijing 100080 China

This paper proposes a method which is not for summarization but for extracting multiple facets from a text according to the keyword sets representing readers’ interests,so that readers can obtain the interested facets and carry out faceted navigation on text.A facet is a meaningful combination of the subsets of the *** text process technologies are mostly based on text features such as word frequency,sentence location,syntax analysis and discourse *** approaches neglect the cognition process of human *** proposed method considers human reading *** show that the facet extraction is effective and robust.

关键词： Semantics Humans Robustness Navigation Feature extraction Drugs information processing

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An efficient two-stage level set segmentation framework for overlapping plant leaf image

An efficient two-stage level set segmentation framework for ...

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8th International Conference on intelligent computing Technology, ICIC 2012

作者： Wang, Xiao-Feng Min, Hai Key Lab. of Network and Intelligent Information Processing Department of Computer Science and Technology Hefei University Hefei Anhui 230022 China Intelligent Computing Lab. Hefei Institute of Intelligent Machines Chinese Academy of Sciences P.O.Box 1130 Hefei Anhui 230031 China Department of Automation University of Science and Technology of China Hefei Anhui 230027 China

ISBN: (纸本)9783642315879

In this paper, an efficient two-stage segmentation framework was proposed to address the plant leaf image with overlapping phenomenon, which is built based on the leaf approximate symmetry and level set evolution theory. In the pre-segmentation stage, a straight line was manually set on the target leaf to approximate the principal leaf vein and the Local Chan-Vese (LCV) model was used on the global image region to help searching the so-called un-overlapping contour in target leaf. In the formal segmentation stage, the symmetry detection was performed based on the pre-defined approximated principal vein to obtain the narrow-band evolution region and the second initial contour. Next, the LCV model was once again used to find the complete target leaf contour in the narrow-band evolution region. Finally, experiments on some real leaf images with overlapping phenomenon have demonstrated the efficiency and robustness of the proposed segmentation framework. © 2012 Springer-Verlag.

关键词： Image segmentation

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Reducing blocking artifacts in compressed images via transform-domain non-local coefficients estimation

Reducing blocking artifacts in compressed images via transfo...

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2012 13th IEEE International Conference on Multimedia and Expo, ICME 2012

作者： Zhang, Xinfeng Xiong, Ruiqin Ma, Siwei Gao, Wen Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100049 China Institute of Digital Media Peking University Beijing 100871 China

ISBN: (纸本)9781467316590

Block transform coding using discrete cosine transform is the most popular approach for image compression. However, many annoying blocking artifacts are generated due to coarse quantization on transform coefficients independently. This paper proposes an effective blocking artifacts reduction method by estimating the transform coefficients from their quantized version. In the proposed scheme, we estimate the transform coefficients based on an image statistic model and non-local similarity among blocks in transform domain. The parameters used in our proposed scheme are discussed and adaptively selected. Extensive experimental results show that our proposed method significantly reduces blocking artifacts and improves the subjective and the objective quality of block transform coded images. © 2012 IEEE.

关键词： Discrete cosine transforms

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PPLSA: Parallel probabilistic latent semantic analysis based on MapReduce

PPLSA: Parallel probabilistic latent semantic analysis based...

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7th IFIP International Conference on intelligent information processing, IIP 2012

作者： Li, Ning Zhuang, Fuzhen He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Graduate University Chinese Academy of Sciences Beijing China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

ISBN: (纸本)9783642328909

PLSA(Probabilistic Latent Semantic Analysis) is a popular topic modeling technique for exploring document collections. Due to the increasing prevalence of large datasets, there is a need to improve the scalability of computation in PLSA. In this paper, we propose a parallel PLSA algorithm called PPLSA to accommodate large corpus collections in the MapReduce framework. Our solution efficiently distributes computation and is relatively simple to implement. © 2012 IFIP International Federation for information processing.

关键词： MapReduce

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