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Constructing a facet-based and personalized just-in-time web information recommendation application in a multi-agent environment

Constructing a facet-based and personalized just-in-time web...

引用

IEEE 9th International Conference on Computer and information Technology, CIT 2009

作者： Kun, Yang Zhongzhi, Shi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100049 China

ISBN: (纸本)9780769538365

High precision is vital to the success of just-in-time information retrieval system. This paper attempts to improve it from two aspects: better understanding the user's current need and providing a highly relevant information source. For the former, an algorithm that can model a user's need in current context based on his behavior automatically is proposed, and for the latter, a mechanism is provided for the user to choose the information source he need by organizing all Web resources involved in a faceted way. All the algorithms and functions are encapsulated in several agents to constitute a multi-agent just-in-time Web information recommending application that help people write, and all these agent are constructed and managed by a multi-agent system middleware named "MAGE". © 2009 IEEE.

关键词： Middleware

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A novel rate estimation model for mode decision of H.264/AVC

A novel rate estimation model for mode decision of H.264/AVC

引用

Visual Communications and Image processing 2009

作者： Zhao, Xin Sun, Jun Gao, Wen Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Institute of Digital Media EECS Peking University Beijing 100871 China

To acquire the optimal coding mode of each macroblock, the H.264/AVC encoder exhaustively calculates the rate-distortion cost for all available modes and chooses the minimum one as the best mode. Therefore, the mode decision process is very computationally demanding. To reduce the computation complexity of the rate-distortion cost, in this paper, we propose a novel rate estimation model for the mode decision in H.264/AVC. By modeling the transform coefficients with Generalized Gaussian distributions (GGD), a direct relationship between the magnitude and the information bits of the quantized transform coefficients is deduced. Based on this deduction, the weighted sum of quantized transform coefficients is proposed as an efficient bit-rate estimator of the residual blocks. Extensive experiments show that the proposed algorithm can save up to 30% of total encoding time with ignorable degradation in coding performance for both inter- and intra-mode decision. © 2009 SPIE-IS&T.

关键词： Signal distortion

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Detection of front-view vehicle with occlusions using AdaBoost

Detection of front-view vehicle with occlusions using AdaBoo...

引用

2009 International Conference on information Engineering and Computer Science, ICIECS 2009

作者： Wu, Chunpeng Duan, Lijuan Miao, Jun Fang, Faming Wang, Xuebin College of Computer Sciences Beijing University of Technology Beijing 100124 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology China Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781424449941

In this paper, we propose a vehicle detection method based on AdaBoost. We focus on the detection of front-view car and bus with occlusions on highway. Samples with different occlusion situations are selected into the training set. By using basic and rotated Haar-like features extracted from the samples in the set, we train an AdaBoost-based cascade vehicle detector. The performance tests on static images and short time videos show that (1)our approach detects cars more effectively than buses (2)the real-time detection of our method on video proceeds at 30 frames per second. ©2009 IEEE.

关键词： Adaptive boosting

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Image classification using structural sparse coding model

Image classification using structural sparse coding model

引用

5th International Conference on Natural Computation, ICNC 2009

作者： Li, Zhiqing Shi, Zhiping Li, Zhixin Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University of Chinese Academy of Sciences Beijing 100049 China College of Information Engineering Xiangtan University Xiangtan 411105 China

ISBN: (纸本)9780769537368

Efficient coding hypothesis provides a quantitative relationship between environmental statistics and neural processing. In this paper, we put forward a novel sparse coding model based on structural similarity (SS-SC)for natural image feature extraction. The advantage for our model is to be able to preserve structural information from a scene, which human visual perception is highly adapted for. Using the proposed sparse coding model, the validity of image feature extraction is testified. Furthermore, inspired by Bayesian decision which is extensively used for classification, employing SS-SC we propose an algorithm for image classification. Compared with standard sparse coding (SC) model, the experimental results show that the quality of reconstructed images obtained by our method outperforms the SC method. Moreover, SS-SC model evidently enhances the classification accuracy. © 2009 IEEE.

关键词： Image classification

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FILTER OBJECT CATEGORIES USING COBOOST WITH 1ST AND 2ND ORDER FEATURES

FILTER OBJECT CATEGORIES USING COBOOST WITH 1ST AND 2ND ORDE...

引用

International Conference on Image processing

作者： Xi Liu Zhiping Shi Zhongzhi Shi The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences

ISBN: (纸本)9781424456536;9781424456543

We describe a method for filtering object category from a large number of noisy images. This problem is particularly difficult due to the greater variation within object categories and lack of labeled object images. Our method deals with it by combining a co-training algorithm CoBoost [7] with two features - 1st and 2nd order features, which define bag of words representation and spatial relationship between local features respectively. We iteratively train two boosting classifiers based on the 1st and 2nd order features, during which each classifier provides labeled data for the other classifier. It is effective because the 1st and 2nd order features make up an independent and redundant feature split. We evaluate our method on Berg dataset and demonstrate the precision comparative to the state-of-the-art.

关键词： Filter object category Co-training CoBoost 1st order feature 2nd order feature Bag of words Classifiers Filters Category dimensional relationship PURSE

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The ICT Statistical Machine Translation Systems for the IWSLT 2009 6

The ICT Statistical Machine Translation Systems for the IWSL...

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6th International Workshop on Spoken Language Translation, IWSLT 2009

作者： Mi, Haitao Liu, Yang Xia, Tian Xiao, Xinyan Feng, Yang Xie, Jun Xiong, Hao Tu, Zhaopeng Zheng, Daqi Lu, Yajuan Liu, Qun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences No.6 Kexueyuan South Road Haidian District P.O. Box 2704 Beijing100080 China

This paper describes the ICT Statistical Machine Translation systems that used in the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT) 2009. For this year's evaluation, we participated in the Challenge Task (Chinese-English and English-Chinese) and BTEC Task (Chinese-English). And we mainly focus on one new method to improve single system's translation quality. Specifically, we developed a sentence-similarity based development set selection technique. For each task, we finally submitted the single system who got the maximum BLEU scores on the selected development set. The four single translation systems are based on different techniques: a linguistically syntax-based system, two formally syntax-based systems and a phrase-based system. Typically, we didn't use any rescoring or system combination techniques in this year's evaluation. © IWSLT 2009. All Rights Reserved.

关键词： Computer aided language translation

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Machine learning as Granular computing

Machine learning as Granular Computing

引用

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 and Sciences China

Zadeh proposed that there are three basic concepts that underlie human cognition: granulation, organization and causation and a granule being a clump of points (objects) drawn together by indistinguishability, similarity, proximity or functionality. In this paper, we give out a novel definition of Granular computing which can be easily treated by neural network. Perception learning as granular computing tries to study the machine learning from perception information sampling to dimensional reduction and samples classification in a granular way, and can be summaries as two kind approaches:(1) covering learning, (2) svm kind learning. We proved that although there are tremendous algorithms for dimensional reduction and information transformation, their ability can't transcend wavelet kind nested layered granular computing which are very easy for neural network processing.

关键词： Machine learning Neural networks Cognition Computer networks information processing Humans Fuzzy logic Laboratories Learning systems Sampling methods

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WIRELESS INTRUSION DETECTION FOR DEFENDING AGAINST TCP SYN FLOODING ATTACK AND MAN-IN-THE-MIDDLE ATTACK

WIRELESS INTRUSION DETECTION FOR DEFENDING AGAINST TCP SYN F...

引用

2009 International Conference on Machine Learning and Cybernetics(2009机器学习与控制论国际会议)

作者： HUAN-RONG TANG ROU-LING SUN WEI-QIANG KONG Key Laboratory of Intelligent Computing & Information Processing (Xiangtan University) Ministry of Education China 411105

With the popularity of wireless communication application in a wide range of industry and daily life, it is more and more important to solve security problems of wireless local area network (WLAN). In this paper, we propose a new architecture of Wireless Intrusion Detection System (WIDS) for IEEE 802.11 wireless infrastructure networks. The WIDS detect man-in-the-middle-attacks by analyzing the channel gap. Moreover, it can defense the SYN flood attack. The results indicate that the WIDS proposed in this paper is superior at precisely detecting a man-in-thc-middle attack and successfully protecting AP from SYN Flood attack than other existing approaches.

关键词： WLAN WIDS Infrastructure Man-in-the-middle-attack SYN Flood Attack

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Text information Retrieval Based on Concept Semantic Similarity

Text Information Retrieval Based on Concept Semantic Similar...

引用

Fifth International Conference on Semantics,Knowledge and Grid(第五届语义、知识与网格国际会议 SKG 2009)

作者： Gang Lv Cheng Zheng Li Zhang Key Laboratory of Network and Intelligent Information Processing Hefei University Hefei China Educational Department Key Laboratory of Intelligent Computing & Signal Processing Anhui University Hefei China

In this paper, an improved method of calculating ontology semantic similarity is proposed to enhance the information retrieval recall and precision. To filter out the document which have smaller related degree with original query, the scores of search results document is re-calculated by use of ontology semantic similarity. A new definition of the iterative query expansion parameters is put forward which can reduce the number of expansion and further improve the efficiency of the query. The use of open source tools for text semantic retrieval test, i.e., Jena and Lucene, has verified the feasibility and effectiveness of the proposed method.

关键词： information Retrieval Ontology Semantic Similarity Query Expansion Document Scores

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Active Learning of Instance-Level Constraints for Semi-supervised Document Clustering

Active Learning of Instance-Level Constraints for Semi-super...

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Weizhong Zhao Qing He Huifang Ma Zhongzhi Shi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

This paper presents a framework that actively selects informative documents pairs for semi-supervised document clustering. The semi-supervised document clustering algorithm is a Constrained DBSCAN (Cons-DBSCAN), which incorporates instance-level constraints to guide the clustering process in DBSCAN. By obtaining user feedbacks, our proposed active learning algorithm can get informative instance level constraints to aid clustering process. Experimental results show that Cons-DBSCAN with the proposed active learning approach can provide an appealing clustering performance.

关键词： intelligent agent Clustering algorithms Feedback Machine learning Conferences information processing Computers Data mining Learning systems

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