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International Asia-Pacific Web Conference (APWEB)

作者： Chenjing Zhang Xiaoling Wang Aoying Zhou Shanghai Key Laboratory of Intelligent Information Fudan University China College of Information Technology Shanghai Ocean University China Shanghai Key Laboratory of Trustworthy Computing East China Jiao Tong University China

Cloud provides dynamically computing services for large scales of data over the Internet. IaaS(information as a service) is one of utilities to provide information service in Cloud computing. Large scales of XML data are produced continually in Internet. Efficient information filtering services are needed. Previous XML filter approaches aim at XPath queries. However, many users tend to use keywords to describe requirements. SLCA (Smallest Lowest Common Ancestor)-based XML keyword search is one of the most important information retrieval approaches. Former approaches focus on building centralized index for a large scale of XML document collection and can't process continuous XML streams. This paper addresses SLCA computing service for continuous XML document. A novel SLCA computing service is designed, where SLCAs are obtained in one scan of XML stream. We demonstrate the efficiency of our algorithms analytically and experimentally.

关键词： XML information filtering Large-scale systems information filters Cloud computing Web and internet services Keyword search information retrieval Buildings Algorithm design and analysis

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Automatic video genre categorization and event detection techniques on large-scale sports data 10

Automatic video genre categorization and event detection tec...

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Proceedings of the 2010 Conference of the Center for Advanced Studies on Collaborative Research

作者： Zhang, Ning Duan, Ling-Yu Huang, Qingming Li, Lingfang Gao, Wen Guan, Ling Ryerson Multimedia Research Laboratory Ryerson University Toronto ON Canada Institute of Digital Media Peking University Beijing 100871 China Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China Graduate University of Chinese Academy of Sciences Beijing 100190 China

This paper presents an efficient and robust automatic process for large-scale sports video analysis. The proposed system firstly identifies the genre of the query video, and then accomplishes the interesting event detection task. The significance of this framework is its automatic characteristic in testing with minimum human involvement in training, as well as the scalability and expansibility in dealing with a large-scale dataset. Domain-knowledge independent local features are extracted from an input video sequence and a histogram based distribution representation is created using the bag-of-visual-words (BoW) model. In genre categorization, k-nearest neighbor (k-NN) classifiers with various dissimilarity measures are assessed and evaluated analytically. For the event detection, a hidden conditional random field (HCRF) structured prediction model is utilized. Overall, this framework demonstrates the efficiency and accuracy in processing voluminous data from sports collection and achieves various tasks in video analysis. It also demonstrates a potential technology transformation from the "laboratory bench" to commercial applications. © 2010 Ning Zhang, Ling-Yu Duan, Qing-ming Huang, Lingfang Li, Wen Gao and Ling Guan.

关键词： Sports

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D-LDA: A topic modeling approach without constraint generation for semi-defined classification

HP Laboratories Technical Report

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HP Laboratories Technical Report 2010年第162期

作者： Zhuang, Fuzhen Luo, Ping Shen, Zhiyong He, Qing Xiong, Yuhong Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS China Graduate University Chinese Academy of Sciences China Hewlett Packard Labs China China Innovation Works Australia

We study what we call semi-defined classification, which deals with the categorization tasks where the taxonomy of the data is not well defined in advance. It is motivated by the real-world applications, where the unlabeled data may also come from some other unknown classes besides the known classes for the labeled data. Given the unlabeled data, our goal is to not only identify the instances belonging to the known classes, but also cluster the remaining data into other meaningful groups. It differs from traditional semi-supervised clustering in the sense that in semi-supervised clustering the supervision knowledge is too far from being representative of a target classification, while in semi-defined classification the labeled data may be enough to supervise the learning on the known classes. In this paper we propose the model of Double-latent-layered LDA (D-LDA for short) for this problem. Compared with LDA with only one latent variable y for word topics, D-LDA contains another latent variable z for (known and unknown) document classes. With this double latent layers consisting of y and z and the dependency between them, D-LDA directly injects the class labels into z to supervise the exploiting of word topics in y. Thus, the semi-supervised learning in D-LDA does not need the generation of pairwise constraints, which is required in most of the previous semi-supervised clustering *** present the experimental results on ten different data sets for semi-defined classification. Our results are either comparable to (on one data sets), or significantly better (on the other nine data set) than the six compared methods, including the stateof- the-art semi-supervised clustering methods. © Copyright The Tenth IEEE International Conference on Data Mining, 2010.

关键词： Classification (of information)

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A general approach to extracting full names and abbreviations for Chinese entities from the web

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作者： Jiang, Guang Cungen, Cao Yuefei, Sui Lu, Han Wang, Shi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences No.6 Kexueyuan South Road Zhongguancun Beijing 100190 China Graduate University of Chinese Academy of Sciences No.19 Yuquan Road Shi Jing Shan Distinct Beijing 100049 China

ISBN: (纸本)3642163262

Identifying Full names/abbreviations for entities is a challenging problem in many applications, e.g. question answering and information retrieval. In this paper, we propose a general extraction method of extracting full names/abbreviations from Chinese Web corpora. For a given entity, we construct forward and backward query items and commit them to a search engine (e.g. Google), and utilize search results to extract full names and abbreviations for the entity. To verify the results, filtering and marking methods are used to sort all the results. Experiments show that our method achieves precision of 84.7% for abbreviations, and 77.0% for full names. © 2010 IFIP.

关键词： Search engines

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An intelligent Call Center Platform for Mobile Communication Enterprises

An Intelligent Call Center Platform for Mobile Communication...

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WRI International Conference on Communications and Mobile computing, CMC

作者： Weimin Wang Liangliang Liu Dongsheng Wang Yanan Cao Yuming Wu Baoyuan Qi Dongchun Qiao Jingyang Guo Yufei Zheng Cungen Cao Li Jianghua Shi Yuan Li Ruilin Sang Changqin Shen Nan The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Guangdong Communication Group Customer Service Center Guangzhou China

ISBN: (纸本)9781424463275

In this paper, we introduced an intelligent call center platform (ICCP) for communication enterprises. The platform is based on two broad suites of technologies: Knowledge management and natural language processing. First, we designed an enterprise knowledge modeling technology, and provided a knowledge management system for managing customer service knowledge. Then mobile customers are allowed to send Chinese queries, through either Internet tools (e.g. MSN, QQ, BBS and Fetion) or Wireless SM devices (e.g. mobile phones), to a multi-tier natural language understanding engine, and the engine retrieves the knowledge bases to find correct answers after understanding the queries. The ICCP has been successfully used in a domestic communication group since March 2009, and achieved a satisfactory performance.

关键词： Mobile communication Knowledge management Search engines Natural language processing Process design technology management Customer service Internet Samarium Mobile handsets

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On using Tabu Search for RNA secondary structure prediction

International Journal of Digital Content Technology and its ...

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International Journal of Digital Content technology and its Applications 2010年第9期4卷 252-262页

作者： Liu, Yongguo Hao, Jianrui School of Computer Science Engineering University of Electronic Science and Technology of China Chengdu 611731 China Key Laboratory of Intelligent Computing Information Processing of Ministry of Education Xiangtan University Xiangtan 411105 China

In this article, a novel approach is proposed to predict RNA secondary structure called RNA secondary structure prediction based on Tabu Search (RNATS). In the RNATS algorithm, two search models, intensification search and diversification search, are developed to exploit the local regions around the current solution and explore the unvisited solution space, respectively. Simulation experiments are conducted on eight RNA sequences to show that the proposed method is feasible and effective.

关键词： Tabu search

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A Sample Pre-mapping Method Enhancing Boosting for Object Detection

A Sample Pre-mapping Method Enhancing Boosting for Object De...

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International Conference on Pattern Recognition

作者： Haoyu Ren Xiaopeng Hong Cher-Keng Heng Luhong Liang Xilin Chen Institute of Computing Technology Key Laboratory of Intelligent Information Processing Chinese Academy and Sciences China School of Computer Science and Technology Harbin Institute of Technology China Panasonic Singapore Laboratories Private Limited Singapore

ISBN: (纸本)9781424475421

We propose a novel method to improve the training efficiency and accuracy of boosted classifiers for object detection. The key step of the proposed method is a sample pre-mapping on original space by referring to the selected `reference sample' before feeding into weak classifiers. The reference sample corresponds to an approximation of the optimal separating hyper-plane in an implicit high dimensional space, so that the resulting classifier could achieve the performance similar to kernel method, while spending the computation cost of linear classifier in both training and detection. We employ two different non-linear mappings to verify the proposed method under boosting framework. Experimental results show that the proposed approach achieves performance comparable with the common used methods on public datasets in both pedestrian detection and car detection.

关键词： Classification algorithms Training Boosting Kernel Support vector machine classification Accuracy Object detection

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Boosted Sigma Set for Pedestrian Detection

Boosted Sigma Set for Pedestrian Detection

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International Conference on Pattern Recognition

作者： Xiaopeng Hong Hong Chang Xilin Chen Wen Gao School of Computer Science and Technology Harbin Institute of Technology China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences China Institute of Digital Media Peking University China

ISBN: (纸本)9781424475421

This paper presents a new method to detect pedestrian in still image using Sigma sets as image region descriptors in the boosting framework. Sigma set encodes second order statistics of an image region implicitly in the form of a point set. Compared with the covariance matrix, the traditional second order statistics based region descriptor, which requires computationally demanding operations based on Riemannian manifold, Sigma set preserves similar robustness and discriminative power more efficiently because the classification on Sigma sets can be directly performed in vector space. Experimental results on the INRIA and the Daimler Chrysler pedestrian datasets show the effectiveness and efficiency of the proposed method.

关键词： Classification algorithms Manifolds Detectors Feature extraction Training Covariance matrix Support vector machine classification

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A HSC-based sample selection method for Support Vector Machine

A HSC-based sample selection method for Support Vector Machi...

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International Conference on Machine Learning and Cybernetics

作者： He, Qing Li, Ning Shi, Zhong-Zhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Hebei University Baoding 071002 Hebei China Graduate University of Chinese Academy of Sciences Hebei University Baoding 071002 Hebei China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

ISBN: (纸本)9781424465262

Support Vector Machine (SVM) is a classification technique of machine learning based on statistical learning theory. A quadratic optimization problem needs to be solved in the algorithm, and with the increase of the samples, the time complexity will also increase. So it is necessary to shrink training sets to reduce the time complexity. A sample selection method for SVM is proposed in this paper. It is inspired from the Hyper surface classification (HSC), which is a universal classification method based on Jordan Curve Theorem, and there is no need for mapping from lower-dimensional space to higher-dimensional space. The experiments show that the algorithm shrinks training sets keeping the accuracy for unseen vectors high. © 2010 IEEE.

关键词： Support vector machines

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Extracting comparative commonsense from the web

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作者： Cao, Yanan Cao, Cungen Zang, Liangjun Wang, Shi Wang, Dongsheng Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences No. 6 Kexueyuan South Road Zhongguancun Beijing 100190 China Graduate University of Chinese Academy of Sciences No. 19 Yu Quan Road Shi Jing Shan Distinct Beijing 100049 China

ISBN: (纸本)3642163262

Commonsense acquisition is one of the most important and challenging topics in Artificial Intelligence. Comparative commonsense, such as "In general, a man is stronger than a woman", denotes that one entity has a property or quality greater or less in extent than that of another. This paper presents an automatic method for acquiring comparative commonsense from the World Wide Web. We firstly extract potential comparative statements from related texts based on multiple lexico-syntactic patterns. Then, we assess the candidates using Web-scale statistical features. To evaluate this approach, we use three measures: coverage of the web corpora, precision and recall which achieved 79.2%, 76.4% and 83.3%, respectively in our experiments. And the experimental results show that this approach profits significantly when the semantic similarity relationships are involved in the commonsense assessment. © 2010 IFIP.

关键词： Semantics

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