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	1. Location Query System Based On Google Map
	Sheng Yadong,Wang Xiaojie
	Computer Science and Technology 12 December 2011
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	Abstract:With the popularity of GPS, Location-Based Service has been developed widely and applied in many fields such as location query service、point of interest search service、self-funded travel service and so on. To differentiate many similar locations, sentence similarity is introduced which is a very important research topic in the field of NLP, and has been widely used in the fields such as text classification, information processing and so on. In recent years, a great many methods have been proposed to measure the similarity of sentences, but these methods for computing sentence similarity have almost derived from approaches used for long text documents, they are not suitable for some applications. So this paper mainly focuses on very short sentence similarity computation, especially the similarity between Chinese and English addresses. In the process of computation, the sentence similarity is calculated with the information of both structure and semantic information. Experiments on the similarity calculation show that this proposed method has higher accuracy.
	TO cite this article:Sheng Yadong,Wang Xiaojie. Location Query System Based On Google Map[OL].[12 December 2011] http://en.paper.edu.cn/en_releasepaper/content/4455267


	2. A Bayesian Network for Automatic Term Recognition
	GUI Yaocheng,GAO Zhiqiang
	Computer Science and Technology 30 November 2011
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	Abstract:Terms with explicit meanings are used in the academic semantic search system to represent specific research domains.The major works of Automatic Term Recognition (ATR) focus on measuring the relationship between term and paper as the feature of term.The academic semantic search system does not provide full papers, and the short-text-corpus constructed by titles and abstracts of papers reduces the influence of the feature.This paper proposes a novel ATR approach.Firstly, new types of features are provided by measuring the relationships between term and other entities.Secondly, based on the relations between the features of term, the TRBN (term recognition bayesian network) model which is represented by Bayesian Network is proposed to integrate the features.The results of experiments, which are implemented on the corpus containing 7,750,000 titles and 4,500,000 abstracts from the domain of telecommunication and computer science, illustrate the good performance of this new approach that is 10 percent of precision outperforms the baseline method.
	TO cite this article:GUI Yaocheng,GAO Zhiqiang. A Bayesian Network for Automatic Term Recognition[OL].[30 November 2011] http://en.paper.edu.cn/en_releasepaper/content/4452995

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	3. ENTITY RANKING BASED ON DOCUMENT CENTERED MODEL
	Ouyang Haoyi,Xu Weiran
	Computer Science and Technology 29 November 2011
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	Abstract:The traditional search engines answer user's question by returning a collection of documents relevant to their query. However many user information needs would be better answered by specific entities instead of just any type of documents. This paper introduces a method that ranks a given list of entities according to their relevance to query. We use a method called Document Centered Model. Research models including BM25, KL-divergence have been tested to make the result better. Our criteria are from TREC Entity 2010.
	TO cite this article:Ouyang Haoyi,Xu Weiran. ENTITY RANKING BASED ON DOCUMENT CENTERED MODEL[OL].[29 November 2011] http://en.paper.edu.cn/en_releasepaper/content/4452555


	4. A Multi-feature Descriptor for E-Commodity Image Retrieval
	Yu Jie,Zhang Honggang,Li Qiaohong,Chai Lunshao,Qi Yonggang
	Computer Science and Technology 13 October 2011
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	Abstract:With the development of the 3G network and Electronic Commerce, more and more people prefer to shop online, especially via the Cell phone equipped with camera. In order to meet the requirements of the consumers to easily retrieve similar commodity images, we propose a Mobile Image-to-Search system. In this system a content-based retrieval algorithm combining color and shape features for electronic commerce has been investigated, which uses the Normalized Fourier Descriptor and a fuzzy-linking method of color histogram based on the HSV color space. In order to reduce the interference of background, we also use the method of improved canny descriptor before extracting the shape features and the approach of ROI (region of interest) weighted in the color histogram. The system has been tested on Corel data set and our own commodity library, a large number of images selected from Taobao, one of the largest Electronic Commerce web in China. The experimental results indicate that our method has a better retrieval performance.
	TO cite this article:Yu Jie,Zhang Honggang,Li Qiaohong, et al. A Multi-feature Descriptor for E-Commodity Image Retrieval[OL].[13 October 2011] http://en.paper.edu.cn/en_releasepaper/content/4444419


	5. Multi-Dimension Sandpile Space: a new theory of representation and reasoning for experience knowledge
	JIN Yu,HOU Wenjun,LIU Changhua
	Computer Science and Technology 10 October 2011
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	Abstract:Fuzzy experience knowledge cannot be explained quantitatively with mathematical formula or certain rules. In order to solve this problem, a new theory of Multi- Dimension Sandpile Space (MDSS) was proposed in this paper with analogy to the Sandpile Model in self-organization theory. MDSS theory has the ability of dealing with uncertain or fuzzy knowledge by combining the virtues of fuzzy logic theory and neural networks, the characteristic of continual learning of case-based reasoning (CBR), and avoided the unceasing inflation of the size of information in database when using CBR. Finally, the simulation results of an example are given to show the change of the size of database by using MDSS theory.
	TO cite this article:JIN Yu,HOU Wenjun,LIU Changhua. Multi-Dimension Sandpile Space: a new theory of representation and reasoning for experience knowledge[OL].[10 October 2011] http://en.paper.edu.cn/en_releasepaper/content/4445179


	6. Alternative Stream Clustering
	Zhang Jingyuan,Jiang He
	Computer Science and Technology 08 September 2011
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	Abstract:In this paper, we issue the new problem of alternative stream clustering, which aims to find two high quality and dissimilar macro-clusterings in a given data stream. To tackle this new task, we propose a new algorithm named AltStream consisting of two components. The online component of AltStream simultaneously maintains two alternative groups of micro-clusters which are used to record the statistical information about the evolving stream. These micro-clusters are periodically stored as snapshots by following the pyramidal time frame. When the users request to find two alternative macro-clusterings, the offline component is then invoked. After the two sets of micro-clusters are returned with respect to the specified time horizon and the number of clusters, an unsupervised alternative clustering algorithm, namely dec-kmeans, is then employed in the offline component to find two alternative macro-clusterings over one set of micro-clusters. Within the two macro-clusterings, the one with better quality is outputted as the first resulting macro-clustering, whereas the centroids of the other macro-clustering are extracted as the semi-supervised information. Under the guideline of these centroids, the second resulting macro-clustering is created by a weighted k-means algorithm. In such a way, the two resulting macro-clusterings may contain distinct centroids as well as non-overlapped data points. Experimental results on both real-world and synthetic streams illustrate that our new algorithm performs better than some comparative methods, in terms of both quality and dissimilarity.
	TO cite this article:Zhang Jingyuan,Jiang He. Alternative Stream Clustering[OL].[ 8 September 2011] http://en.paper.edu.cn/en_releasepaper/content/4442488


	7. Head Pose Estimation based on the Mahanalobis Sparse Representation Classifier
	MA Bingpeng,PANG Xiumei,HU Weijun
	Computer Science and Technology 05 August 2011
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	Abstract:In this paper, a novel method is presented to improve the accuracy of head pose estimation. For the classify in head pose, the Mahanalobis Sparse Representation Classifier~(MSRC) method is proposed to improve the ability of classify. By using the Mahanalobis distance of samples belongs to the different classes, MSRC improve the classify ability from reducing the reconstructed error of the testing samples. The combination of MSRC can improve the accuracy of head pose estimation greatly. To show the effectiveness of MSRC, we compared them with other methods under two different databases. The results of the experiments show the proposed methods can improve the accuracy of head pose estimation.
	TO cite this article:MA Bingpeng,PANG Xiumei,HU Weijun. Head Pose Estimation based on the Mahanalobis Sparse Representation Classifier[OL].[ 5 August 2011] http://en.paper.edu.cn/en_releasepaper/content/4437887


	8. A Hybrid Ant Colony Optimization for Continuous Domains
	XIAO Jing,LI LiangPing
	Computer Science and Technology 11 July 2011
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	Abstract:Research on optimization in continuous domains gains much of focus in swarm computation recently. A hybrid ant colony optimization approach which combines with the continuous population-based incremental learning and the differential evolution for continuous domains is proposed in this paper. It utilizes the ant population distribution and combines the continuous population-based incremental learning to dynamically generate the Gaussian probability density functions during evolution. To alleviate the less diversity problem in traditional population-based ant colony algorithms, differential evolution is employed to calculate Gaussian mean values for the next generation in the proposed method. Experimental results on a large set of test functions show that the new approach is promising and performs better than most of the state-of-the-art ACO algorithms do in continuous domains.
	TO cite this article:XIAO Jing,LI LiangPing. A Hybrid Ant Colony Optimization for Continuous Domains[J].


	9. Research on Fault Diagnosis System of Mine Ventilator Based on Elman Neural Network
	Ren Zihui,Li Jiangang,Liu Yanxia
	Computer Science and Technology 28 June 2011
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	Abstract:This paper introduced the theory, learning algorithm and technical route of Elman neural. Though acquainting fault signals on-site and normalizing characteristic data, this method realized intelligent diagnosis of ventilator by constructing optimum structure and parameters based on Elman neural network. Compared with the traditional BP neural network, Elman network had a better comprehensive performance in diagnosis of ventilator. The result for the fault diagnosis of a ventilator showed that the Elman network improves the study speed, represses the network to sink local minimum, shortens the study time, and Elman neural is a effective method for the fault diagnosis of ventilator.
	TO cite this article:Ren Zihui,Li Jiangang,Liu Yanxia. Research on Fault Diagnosis System of Mine Ventilator Based on Elman Neural Network[OL].[28 June 2011] http://en.paper.edu.cn/en_releasepaper/content/4433667


	10. Face Extraction from Video Sequences by K-means Clustering and Fusion
	JI Luping
	Computer Science and Technology 25 May 2011
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	Abstract:Face segmentation is an important processing step in a typically face-based person identification system. This paper presents a modified K-means clustering and fusion approach for face region extraction from colorful images of video sequences. It is based on Lab* color space analysis and consists of three processing steps: color number estimation, color region clustering, and face region post-processing. Moreover, experimental results on CIPR public video sequences are also exhibited to verify the feasibility, high efficiency and accuracy.
	TO cite this article:JI Luping. Face Extraction from Video Sequences by K-means Clustering and Fusion[OL].[25 May 2011] http://en.paper.edu.cn/en_releasepaper/content/4429836

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