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DNA computing model based on lab-on-a-chip and its application on solving the timetabling problem

Progress in Natural Science:Materials International 2008年第5期18卷 633-637页

作者： Fengyue Zhang a,1, Bo Liu b,1, Wenbin Liu c, Qiang Zhang d a Department of Biomedical Engineering, School of Life Science and technology, Beijing institute of technology, Beijing 100081, China b Chinese Academy of Inspection and Quarantine, Beijing 100029, China c College of Computer Science and Engineering, Wenzhou University, Wenzhou, Zhejiang 325027, China d Liaoning key laboratory of intelligent information processing, Dalian University, Dalian, Liangning 116622, China Department of Biomedical Engineering School of Life Science and Technology Beijing Institute of Technology Beijing 100081 China Chinese Academy of Inspection and Quarantine Beijing 100029 China College of Computer Science and Engineering Wenzhou University Wenzhou Zhejiang 325027 China Liaoning Key Laboratory of Intelligent Information Processing Dalian University Dalian Liangning 116622 China

The essential characteristic of DNA computation is its massive parallelism in obtaining and managing information. With the develop- ment of molecular biology technique, the eld of DNA computation has made a great progress. By using an advanced biochip technique, laboratory-on-a-chip, a new DNA computing model is presented in the paper to solve a simple timetabling problem, which is a special ver- sion of the optimization problems. It also plays an important role in education and other industries. With a simulated biological experiment, the result suggested that DNA computation with lab-on-a-chip has the potential to solve a real complex timetabling problem.

关键词： DNA computing Timetabling problem Lab-on-a-chip

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Word lattice reranking for Chinese word segmentation and part-of-speech tagging

Word lattice reranking for Chinese word segmentation and par...

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22nd International Conference on Computational Linguistics, Coling 2008

作者： Jiang, Wenbin Mi, Haitao Liu, Qun Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing 100190 China Graduate University of Chinese Academy of Sciences Beijing 100049 China

ISBN: (纸本)9781905593446

In this paper, we describe a new reranking strategy named word lattice reranking, for the task of joint Chinese word segmentation and part-of-speech (POS) tagging. As a derivation of the forest reranking for parsing (Huang, 2008), this strategy reranks on the pruned word lattice, which potentially contains much more candidates while using less storage, compared with the traditional n-best list reranking. With a perceptron classifier trained with local features as the baseline, word lattice reranking performs reranking with non-local features that can't be easily incorporated into the perceptron baseline. Experimental results show that, this strategy achieves improvement on both segmentation and POS tagging, above the perceptron baseline and the n-best list reranking. © 2008. Licensed to the Coling.

关键词： Computational linguistics

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Hierarchical background subtraction using local pixel clustering

Hierarchical background subtraction using local pixel cluste...

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作者： Zhong, Bineng Yao, Hongxun Shan, Shiguang Chen, Xilin Gao, Wen Department of Computer Science and Engineering Harbin Institute of Technology China Digital Media Research Center Institute of Computing Tech. CAS Beijing China Key Laboratory of Intelligent Information Processing CAS Beijing China Digital Media Institute Peking University Beijing China

ISBN: (纸本)9781424421756

We propose a robust hierarchical background subtraction technique which takes the spatial relations of neighboring pixels in a local region into account to detect objects in difficult conditions. Our algorithm combines a per-pixel with a per-region background model in a hierarchical manner, which accentuates the advantages of each. This is a natural combination because the two models have complementary strengths. The per-pixel background model is achieved by mixture of Gaussians Models (GMM) with RGB feature. Although precisely describing background change in high resolution, it suffers from the sensitivity to quick variations in dynamic environment. To tolerate these quick variations, we further develop a novel GMM based per-region background model, which is updated by the cluster centers obtained from a k-means clustering of the pixels' RGB feature in the region. Numerical and qualitative experimental results on challenging videos demonstrate the robustness of the proposed method. © 2008 IEEE.

关键词： Pixels

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Parzen discriminant analysis

Parzen discriminant analysis

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作者： Fang, Youhan Shan, Shiguang Chang, Hong Chen, Xilin Gao, Wen Beijing China Digital Media Research Center Institute of Computing Tech. CAS Beijing China Key Laboratory of Intelligent Information Processing CAS Beijing China Digital Media Institute Peking University Beijing China

ISBN: (纸本)9781424421756

In this paper, we propose a non-parametric Discriminant Analysis method (no assumption on the distributions of classes), called Parzen Discriminant Analysis (PDA). Through a deep investigation on the non-parametric density estimation, we find that minimizing/maximizing the distances between each data sample and its nearby similar/dissimilar samples is equivalent to minimizing an upper bound of the Bayesian error rate. Based on this theoretical analysis, we define our criterion as maximizing the average local dissimilarity scatter with respect to a fixed average local similarity scatter. All local scatters are calculated in fixed size local regions, resembling the idea of Parzen estimation. Experiments in UCI machine learning database show that our method impressively outperforms other related neighbor based non-parametric methods. © 2008 IEEE.

关键词： Discriminant analysis

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A Hierarchical Scheme for Rapid Video Copy Detection

A Hierarchical Scheme for Rapid Video Copy Detection

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Xiao Wu Yongdong Zhang Sheng Tang Tian Xia Jintao Li Chinese Academy of Sciences China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

Today with the rapid increasing popularity of web video sharing, digital copyright protection encounters many troubles. Video copy detection schemes are emerging to cope with the digital video piracy and illegal distribution problems. But the large amount of video data and diversity of copy attacks pose difficulties on copy detection. This paper presents a hierarchical scheme to detect video copies, especially the temporal attacked and re-encoded ones. Our algorithm which is based on the ordinal signature of intra frames and effective R*-tree indexing structure archives real time performance. Comparison experiments are conducted on the benchmarked database of CIVR 2007 copy detection showcase and demonstrate the promising results of the proposed approach.

关键词： Video sharing Indexing Motion pictures Face detection Laboratories information processing Computers Copyright protection Data security Databases

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Multi-Agent based automatic evaluation system for classification algorithms

Multi-Agent based automatic evaluation system for classifica...

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International Conference on information and Automation (ICIA)

作者： Fuzhen Zhuang Qing He Zhongzhi Shi Chinese Academy of Sciences China China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences China

The current researches on classification usually focus on proposing novel algorithms and improving existent ones with better performances. However, although a classification algorithm is able to perform well for some given data sets, does it mean to any other data sets? And given several algorithm candidates, which one is the best for your classification problem? A practical solution to the above questions is to evaluate possible situations, which is extremely time-consuming and resource-consuming. In this paper we propose a distributed computing environment based on Multi-Agent technology to facilitate this evaluation process. In this computing environment we compare the performances of the same algorithm on different data sets, and different algorithms on the same data set. Experiments show that autonomic agents can run simultaneously and automatically on different computing hosts to achieve high availability, and this scheme can save the total evaluation time greatly. Therefore, this scheme will help us easily select the proper algorithm for a given classification problem according to different evaluation measures.

关键词： Classification algorithms Support vector machines Data mining Algorithm design and analysis Containers Training Testing

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Acquiring Commonsense Knowledge about Properties of Concepts from Text

Acquiring Commonsense Knowledge about Properties of Concepts...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Ya-nan Cao Cungen Cao Liangjun Zang Yao Zhu Shi Wang Dongsheng Wang Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

Commonsense knowledge plays an important role in various areas such as natural language understanding, information retrieval, etc. This paper presents a method for acquiring commonsense knowledge about properties of concepts by analyzing how adjectives are used with nouns in everyday language. We firstly mine a large scale corpus for potential concept-property pairs using lexico-syntactic patterns and then filter erroneously acquired ones based on heuristic rules and statistical approaches. For each concept, we automatically select the commonsensical properties and evaluate their applicability. Finally, we generate commonsense knowledge represented with explicit fuzzy quantifiers. Experimental results demonstrate the effectiveness of our approach.

关键词： Natural languages information retrieval Fuzzy systems Laboratories information processing Computers Large-scale systems Filters Text recognition Statistical distributions

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Web video recommendation and long tail discovering

Web video recommendation and long tail discovering

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiao Wu Yongdong Zhang Junbo Guo Jintao Li Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

Given countless web videos available online, one problem is how to help users find videos to their taste in an efficient way. In this paper, to facilitate userpsilas browsing we propose relevant and exploratory recommendation algorithms utilizing multimodal similarity and contextual network to organize web videos of various topics. Comparison experiments demonstrate proposed approach generates more accurate video relevancy. And our method is more flexible in discovering user latent interests in long tail videos.

关键词： YouTube Correlation Visualization Partitioning algorithms Clustering algorithms Construction industry History

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Geodesic distance based aproach for sentence similarity computation

Geodesic distance based aproach for sentence similarity comp...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Hui-Fang Ma Qing He Zhong-Zhi Shi Chinese Academy of Sciences Beijing China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

This paper presents a novel approach based on geodesic distance for sentence similarity computation, which can be used in a query-based information retrieval system. Unlike the traditional distance methods, geodesic distance takes into account the spatial relationships of sentences, which better reflects the intrinsic geometric structure of sentence manifold. Experiments demonstrate that the proposed method shows a better correlation to human intuition compared with traditional Euclidean method.

关键词： Distance measurement Manifolds Level measurement Euclidean distance Machine learning Correlation Helium

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Measuring Taxonomic Similarity between Words Using Restrictive Context Matrices

Measuring Taxonomic Similarity between Words Using Restricti...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Shi Wang Cungen Cao Ya-nan Cao Han Lu Xinyu Cao Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

Measuring taxonomic similarity between words plays an important role in many semantic-based applications but still remains a challenging task today. We propose a new method which utilizes restrictive context matrices for this problem. We learn a set of special lexico-syntactic patterns automatically and use them to extract taxonomic related contexts of words from raw text. These restrictive contexts are then transformed into real matrices and similarities between them are calculated to reflect the taxonomic similarities between words. The main contribution of our work is that taxonomic related context of words can be mined, evaluated, and used to measure taxonomic similarities between words. Experimental results on Miller-Charles benchmark dataset achieve a correlation coefficient of 0.856.

关键词： Web search Thesauri Fuzzy systems Laboratories information processing information retrieval Natural language processing Ontologies Machine learning Robustness

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