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共 237 条记录，以下是121-130 订阅

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renmin University of China at ImageCLEF 2013 scalable concept image annotation

Renmin University of China at ImageCLEF 2013 scalable concep...

引用

2013 Cross Language Evaluation Forum Conference, CLEF 2013

作者： Li, Xirong Liao, Shuai Liu, Binbin Yang, Gang Jin, Qin Xu, Jieping Du, Xiaoyong Key Lab of Data Engineering and Knowledge Engineering MOE Japan Multimedia Computing Lab School of Information Renmin University of China Zhongguancun Street 59 Beijing100872 China

In this paper we describe our image annotation system par ticipated in the ImageCLEF 2013 scalable concept image annotation task. The system leverages multiple base classifiers, including single feature and multi-feature kNN classifiers and histogram intersection ker nel SVMs, all of which are learned from the provided 250K web images and provided features with no extra manual verification. These base clas sifiers are combined into a stacked model, with the combination weights optimized to maximize the geometric mean of F-samples, F-concepts, and AP-samples metrics on the provided development set. By varying the configuration of the system, we submitted five runs. Evaluation re sults show that for all of our runs, model stacking with optimized weights performs best. Our system can annotate diverse Internet images purely based on the visual content, at the following accuracy level: F-samples of 0.290, F-concepts of 0.304, and AP-samples of 0.380. What is more, a system-to-system comparison reveals that our system and the best sub mission this year are complementary with respect to the best annotated concepts, suggesting the potential for future improvement.

关键词： Image annotation

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FARP: Mining fuzzy association rules from a probabilistic quantitative database

FARP: Mining fuzzy association rules from a probabilistic qu...

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作者： Pei, Bin Zhao, Suyun Chen, Hong Zhou, Xuan Chen, Dingjie Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

Current studies on association rule mining focus on finding Boolean/quantitative association rules from certain databases or Boolean association rules from probabilistic databases. However, little work on mining association rules from probabilistic quantitative databases has been mentioned because the simultaneous measurement of quantitative information and probability is difficult. By introducing a novel Shannon-like Entropy, we aggregate and measure the information contained in an item with the coexistence of fuzzy uncertainty hidden in quantitative values and random uncertainty. We then propose Support and Confidence metrics for a fuzzy-probabilistic database to quantify association rules. Finally, we design an algorithm, called FARP (mining Fuzzy Association Rules from a Probabilistic quantitative data), to discover frequent fuzzy-probabilistic itemsets and fuzzy association rules using the proposed interest measures. The experimental results show the effectiveness of our method and its practicality in real-world applications. © 2013 Elsevier Inc. All rights reserved.

关键词： Entropy

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TYG: A tag-as-you-go online annotation tool for web browsing and navigation

TYG: A tag-as-you-go online annotation tool for web browsing...

引用

6th International Conference on knowledge Science, engineering and Management, KSEM 2013

作者： Hu, He Du, Xiaoyong School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing 100872 China

ISBN: (纸本)9783642397868

We present TYG (Tag-as-You-Go) in this paper, a chrome browser extension for personal knowledge annotation on standard web pages. We investigate an approach to combine a K-Medoid-style clustering algorithm with the user input to achieve semi-automatic web page annotation. The annotation process supports user-defined tagging schema and comprises an automatic mechanism that is built upon clustering techniques, which can group similar html nodes into clusters corresponding to the user specification. TYG is a prototype system illustrating the proposed approach. Experiments with TYG show that our approach can achieve both efficiency and effectiveness in real world annotation scenarios. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Websites

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Cross-codebook image classification

Cross-codebook image classification

引用

14th Pacific-Rim Conference on Multimedia, PCM 2013

作者： Liao, Shuai Li, Xirong Du, Xiaoyong Key Lab of Data Engineering and Knowledge Engineering MOE China School of Information Renmin University of China Beijing 100872 China State Key Lab of Software Development Environment Beijing 100191 China

ISBN: (纸本)9783319037301

Representing images by bag of visual codes (BoVC) features has been the cornerstone of state-of-the-art image classification system. Since the BoVC features depend on a precomputed codebook in use, when the codebook applied to test images differs from the codebook of an existing image classification system, the system becomes inapplicable. To resolve the codebook incompatibility problem, we propose in this paper cross-codebook image classification. This is achieved by transforming BoVC features derived from one codebook to make them compatible with another codebook. Two BoVC transform methods, i.e., code-reassignment and least squares, are studied. Experiments on a popular image classification benchmark set show that both methods are better than random guess when crossing the codebooks. In particular, when the BoVC features are transformed from a higher dimension to a relatively small dimension, cross-codebook image classification has a similar performance compared to within-codebook image classification, with a relative performance loss of 1.3% only. The results justify the feasibility of the proposed cross-codebook image classification. © Springer International Publishing Switzerland 2013.

关键词： Image classification

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ASAWA: An automatic partition key selection strategy

ASAWA: An automatic partition key selection strategy

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15th Asia-Pacific Web Conference on Web Technologies and Applications, APWeb 2013

作者： Wang, Xiaoyan Chen, Jinchuan Du, Xiaoyong School of Information Renmin University of China China School of Information and Electrical Engineering Ludong University China Key Laboratory of Data Engineering and Knowledge Engineering MOE China

ISBN: (纸本)9783642374005

With the rapid increase of data volume, more and more applications have to be implemented in a distributed environment. In order to obtain high performance, we need to carefully divide the whole dataset into multiple partitions and put them into distributed data nodes. During this process, the selection of partition key would greatly affect the overall performance. Nevertheless, there are few works addressing this topic. Most previous projects on data partitioning either utilize a simple strategy, or rely on a commercial database system, to choose partition keys. In this work, we present an automatic partition key selection strategy called ASAWA. It chooses partition keys according to the analysis on both dataset and workload schemas. In this way, intimate tuples, i.e. co-appearing in queries frequently, would be probably put into the same partition. Hence the cross-node joins could be greatly reduced and the system performance could be improved. We conduct a series of experiments over the TPC-H datasets to illustrate the effectiveness of the ASAWA strategy. © 2013 Springer-Verlag.

关键词： data handling

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Efficient querying of correlated uncertain data with cached results

Efficient querying of correlated uncertain data with cached ...

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18th International Conference on database Systems for Advanced Applications, DASFAA 2013

作者： Chen, Jinchuan Zhang, Min Xie, Xike Du, Xiaoyong Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE China School of Information Renmin University of China China Department of Computer Science Aalborg University Denmark

ISBN: (纸本)9783642374869

Although there have been many efforts for management of uncertain data, evaluating probabilistic inference queries, a known NP-hard problem, is still a big challenge, especially for querying data with highly correlations. The state-of-art exact algorithms for accelerating the evaluation of inference queries are based on special indices. Besides, with the observation of the existence of many frequent queries, some researchers try to improve efficiency by reusing previously queried results. Indexing depends on the static properties like data distributions, whereas caching is in favor of the dynamic features like query workload. In this paper we propose a new approach for speeding up the evaluation of inference queries by caching frequent results in a junction tree-based hierarchical index. To the best of our knowledge, this is the first effort on utilizing both the static (data) and dynamic (query workload) properties to efficiently evaluate probabilistic inference queries. Moreover, according to our experience, different caching strategies may significantly affect the query performance. Basically a good caching strategy needs to have high cache hit ratio with limited space *** on these considerations, we propose a novel caching approach, called FVEC, and present corresponding algorithms for efficiently querying correlated uncertain data. We further conduct a series of extensive experiments on large uncertain datasets in order to illustrate the effectiveness and efficiency of our proposed approaches. As illustrated by the results, compared with previous solutions, our method could greatly improve the query performance. © Springer-Verlag 2013.

关键词： Efficiency

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Classifying tag relevance with relevant positive and negative examples 13

Classifying tag relevance with relevant positive and negativ...

引用

21st ACM International Conference on Multimedia, MM 2013

作者： Li, Xirong Snoek, Cees G. M. Key Lab of Data Engineering and Knowledge Engineering Renmin University of China China Intelligent Systems Lab Amsterdam University of Amsterdam Netherlands

ISBN: (纸本)9781450324045

Image tag relevance estimation aims to automatically deter- mine what people label about images is factually present in the pictorial content. Different from previous works, which either use only positive examples of a given tag or use pos- itive and random negative examples, we argue the impor- Tance of relevant positive and relevant negative examples for tag relevance estimation. We propose a system that se- lects positive and negative examples, deemed most relevant with respect to the given tag from crowd-annotated images. While applying models for many tags could be cumbersome, our system trains efficient ensembles of Support Vector Ma- chines per tag, enabling fast classification. Experiments on two benchmark sets show that the proposed system com- pares favorably against five present day methods. Given extracted visual features, for each image our system can pro- cess up to 3,787 tags per second. The new system is both effective and efficient for tag relevance estimation. Copyright © 2013 ACM.

关键词： Image classification

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Distance-based feature selection from probabilistic data

Distance-based feature selection from probabilistic data

引用

14th International Conference on Web-Age Information Management, WAIM 2013

作者： Zhao, Tingting Pei, Bin Zhao, Suyun Chen, Hong Li, Cuiping Key Lab of Data Engineering and Knowledge Engineering Ministry of Education China Department of Computer Science Renmin University of China China

ISBN: (纸本)9783642385612

Feature selection is a powerful tool of dimension reduction from datasets. In the last decade, more and more researchers have paid attentions on feature selection. Further, some researchers begin to focus on feature selection from probabilistic datasets. However, in the existing method of feature selection from probabilistic data, the distance hidden in probabilistic data is neglected. In this paper, we design a new distance measure to select informative feature from probabilistic databases, in which both the distance and randomness in the data are considered. And then, we propose a feature selection algorithm based on the new distance and develop two accelerative algorithms to boost the computation. Furthermore, we introduce a parameter into the distance to reduce the sensitivity to noise. Finally, the experimental results verify the effectiveness of our algorithms. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Random processes

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Zask: A case study on online collaborative learning

Zask: A case study on online collaborative learning

引用

10th IASTED International Conference on Web-Based Education, WBE 2013

作者： Wang, Xiaoyan Chen, Jinchuan Du, Xiaoyong School of Information Renmin University of China Beijing China School of Information and Electrical Engineering Ludong University Shandong China Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing China

ISBN: (纸本)9780889869431

Modern educational theories, such as collaborative learning, constructivism and inquiry learning, have achieved many successes in real-world applications. Especially, with the development of information technologies, there have been several online collaborative learning platforms in practice. Unfortunately, as these platforms are either too complicated or too expensive, none of them are suitable for us in the practice of STEM+. Moreover, most of these platforms are in English, while we are using Chinese as our teaching language. Using an online collaborative learning platform (OCLP) named Zask, this paper reported our practice in online collaborative learning on course Introduction to database System. According to the data collected from the first round of our practice, it shows that users' active participations in Zask could benefit for both teaching and learning, and then provide positive effects in education.

关键词： Teaching

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Fast SimRank computation over disk-resident graphs

Fast SimRank computation over disk-resident graphs

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18th International Conference on database Systems for Advanced Applications, DASFAA 2013

作者： Zhang, Yinglong Li, Cuiping Chen, Hong Sheng, Likun Key Lab of Data Engineering and Knowledge Engineering of Ministry of Education And Department of Computer Science Renmin University of China China JiangXi Agricultural University China

ISBN: (纸本)9783642374494

There are many real-world applications based on similarity between objects, such as clustering, similarity query processing, information retrieval and recommendation systems. SimRank is a promising measure of similarity based on random surfers model. However, the computational complexity of SimRank is high and several optimization techniques have been proposed. In the paper optimization issue of Sim-Rank computation in disk-resident graphs is our primary focus. First we suggest a new approach to compute *** we propose optimization techniques that improve the time cost of the new approach from O (kN2 D2) to O (kNL), where k is the number of iteration, N is the number of nodes, L is the number of edges, and D is the average degree of nodes. Meanwhile, a threshold sieving method is presented to reduce storage and computational cost. On this basis, an external algorithm computing SimRank in disk-resident graphs is introduced. In the experiments, our algorithm outperforms its opponent whose computation complexity also is O (kNL). © Springer-Verlag 2013.

关键词： Search engines

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