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Seasonal and interannual variabilities of mean velocity of Kuroshio based on satellite data

Water Science and engineering 2012年第4期5卷 428-439页

作者： Jun-cheng ZUO Min ZHANG Qing XU Lin MU Juan LI Mei-xiang CHEN Key Laboratory of Coastal Disaster and Defence of Ministry of Education Hohai University College of Harbor Coastal and Offshore EngineeringHohai University South China Sea Prediction Center South China Sea BranchState Oceanic Administration National Marine Data and Information Service

Combining sea level anomalies with the mean dynamic topography derived from the geoid of the EGM08 global gravity field model and the CLS01 mean sea surface height, this study examined the characteristics of global geostrophic surface currents and the seasonal and interannual variabilities of the mean velocity of the Kuroshio （the Kuroshio source and Kuroshio extension）. The patterns of global geostrophic surface currents we derived and the actual ocean circulation are basically the same. The mean velocity of the Kuroshio source is high in winter and low in fall, and its seasonal variability accounts for 18% of its total change. The mean velocity of the Kuroshio extension is high in summer and low in winter, and its seasonal variability accounts for 25% of its total change. The interannual variabilities of the mean velocity of the Kuroshio source and Kuroshio extension are significant. The mean velocity of the Kuroshio source and ENSO index are inversely correlated. However, the relationship between the mean velocity of the Kuroshio extension and the ENSO index is not clear. Overall, the velocity of the Kuroshio increases when La Nina occurs and decreases when E1 Nino occurs.

关键词： seasonal and interannual variabilities global gravity field global geostrophicsurface current mean dynamic topography Kuroshio

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Bayesian network structure learning from attribute uncertain data

Bayesian network structure learning from attribute uncertain...

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13th International Conference on Web-Age Information Management, WAIM 2012

作者： Song, Wenting Yu, Jeffrey Xu Cheng, Hong Liu, Hongyan He, Jun Du, Xiaoyong Key Labs. of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China China Chinese University of Hong Kong Hong Kong Hong Kong Department of Manage. Sci. and Eng. Tsinghua University China

ISBN: (纸本)9783642322808

In recent years there has been a growing interest in Bayesian Network learning from uncertain data. While many researchers focus on Bayesian Network learning from data with tuple uncertainty, Bayesian Network structure learning from data with attribute uncertainty gets little attention. In this paper we make a clear definition of attribute uncertain data and Bayesian Network Learning problem from such data. We propose a structure learning method named DTAU based on information theory. The algorithm assumes that the structure of a Bayesian network is a tree. It avoids enumerating all possible worlds. The dependency tree is computed with polynomial time complexity. We conduct experiments to demonstrate the effectiveness and efficiency of our method. The experiments show the clustering results on uncertain dataset by our dependency tree are acceptable. © 2012 Springer-Verlag.

关键词： Bayesian networks

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Combining N-gram retrieval with weights propagation on massive RDF graphs

Combining N-gram retrieval with weights propagation on massi...

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International Conference on Fuzzy Systems and knowledge Discovery (FSKD)

作者： He Hu Xiaoyong Du School of Information Renmin University of China Beijing China Key Laboratories of Data Engineering and Knowledge Engineering Ministry of Education China

N-gram approach takes the position information into account additionally and thus can offer higher accuracy in query answering than keyword based approaches and is widely used in IR and NLP. However, in large-scale RDF graphs, URIs instead of documents are the ranking and querying units; URIs are usually much shorter than documents, and different URIs are interlinked into a massive network. One shot n-gram querying is usually not good for the RDF data in many cases. In this paper, we present a hybrid framework which combines the n-gram retrieval with link analysis based weight propagation. The idea is to exploit the link structures in the RDF data graphs and propagate the one shot n-gram score weights along with these links. Large scale experiments using MapReduce on Billion Triples Challenge dataset show the hybrid framework achieves an 80.3% improvement in relevance scores over mere n-gram retrieval.

关键词： Resource description framework Semantics Conferences educational institutions Legged locomotion Indexes USA Councils

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Radical features for Chinese text classification

Radical features for Chinese text classification

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International Conference on Fuzzy Systems and knowledge Discovery (FSKD)

作者： He Hu Xiaoyong Du Key Laboratories of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China Beijing China

Chinese radicals play important roles in forming Chinese character's semantic meaning. The semantic properties of radicals make them a promising source of information to be analyzed in text mining and content extraction. However, until recently there is little research work concentrating on using the radical set in text mining related tasks. We investigate the roles of radicals in Chinese text classification tasks. In the task, texts are transformed into vectors of radicals, characters and words. Radicals are further pruned by their semantic strengths and network traits. We carry out experiments with real data from Open Directory Project. The experiments results justify Chinese radicals as important features for semantic processing in Chinese text mining tasks.

关键词： Europe Positron emission tomography education Vectors

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Research of the timer granularity based on linux

Research of the timer granularity based on linux

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International Conference on Computer, Informatics, Cybernetics and Applications 2011, CICA 2011

作者： Qingtian, Geng Nan, Jiang Hongwei, Zhao Junling, Liu Department of Computer Science and Technology Jilin University Changchun 130012 China Changchun Normal University Changchun 130032 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun 130012 China

ISBN: (纸本)9789400718388

Time interrupts play an important role in the system. When time interrupts occur, system will inspect process running state, providing an opportunity to schedule, which is important for improving system real time performance. For the low system real-time ability of Linux, the paper adopt enhancing time granularity to improve system real time ability by means of one-shot triggering mode. The kernel of one-shot mode time interrupt is that: the system need not have to respond to any change every time, but only make response when having to respond. Experimental results show that the system real-time ability of Linux is improved through enhancing time granularity. © 2012 Springer Science+Business Media B.V.

关键词： Linux

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Detecting Compromised Kernel Hooks with Support of Hardware Debugging Features

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China Communications 2012年第10期9卷 78-90页

作者： Shi Wenchang Zhou HongWei Yuan JinHui Liang Bin School of Information Renmin University of China Beijing 100872 P. R. China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University of China Beijing 100872 P. R. China Information Engineering University of China Zhengzhou 450004 P. R. China

Although there exist a few good schemes to protect the kernel hooks of operating systems, attackers are still able to circumvent existing defense mechanisms with spurious context infonmtion. To address this challenge, this paper proposes a framework, called HooklMA, to detect compromised kernel hooks by using hardware debugging features. The key contribution of the work is that context information is captured from hardware instead of from relatively vulnerable kernel data. Using commodity hardware, a proof-of-concept pro- totype system of HooklMA has been developed. This prototype handles 3 082 dynamic control-flow transfers with related hooks in the kernel space. Experiments show that HooklMA is capable of detecting compomised kernel hooks caused by kernel rootkits. Performance evaluations with UnixBench indicate that runtirre overhead introduced by HooklMA is about 21.5%.

关键词： operating system kernel hook integrity hardware control flow

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A collective method to predict missing links in networks

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Journal of Computational Information Systems 2012年第13期8卷 5543-5552页

作者： Li, Lina Ouyang, Jihong Liu, Dayou Yang, Bo Chen, Huiling College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun China

Link prediction in networks has attracted increasing attention by researchers coming from different branches of science, and has been applied in many important domains, such as protein-protein interaction networks in biology, friend-of-friend network in sociology. Many algorithms have been presented to solve this problem, but we find that these methods are attempting to predict link between pair of nodes separately, they assume that the existence of one link is independent with other ones. However, in most real world network, links may be interrelated. So in this paper, we introduce the collective idea, and present a collective inference framework that extends current prediction methods to estimate interrelated links together. Then two new algorithms are proposed based on this framework. As will be shown in the experimental section, we compare our approach with several existence methods on five networks, the results indicate that our method achieve better performance especially when few links are known. © 2011 by Binary Information Press.

关键词： Iterative methods

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A novel method for computing the inverse of cardinal direction relations

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Journal of Convergence Information Technology 2012年第1期7卷 130-138页

作者： Wang, Fang Liu, Dayou Dong, Yiqun College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education

This paper focuses efforts on the problem of computing the inverse of cardinal direction relations (CRs), which is a fundamental problem in qualitative spatial reasoning. We study the quite expressive cardinal direction relation model for extended objects, known as cardinal direction calculus (CDC). We first concentrate on a set of a special type of CRs defined in CDC, named rectangle-CRs, and compute the inverse of rectangle-CRs by exploiting the evident connection between basic rectangle- CRs and interval relations. Then, we consider progressively the general cardinal direction relations in CDC, or called CDC relations for short, the inverse of which is computed by reducing to the computation of the inverse of rectangle-CRs. This simplifies the computations. Analyzing in theory and the final results both demonstrate that our algorithms are correct and complete and the time complexity is bounded by a constant number of operations.

关键词： Calculations

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An Approach for Personalized Tag Recommendation Based on Interest Transfer Model

An Approach for Personalized Tag Recommendation Based on Int...

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Web Information Systems and Applications Conference (WISA)

作者： Yue Liu Nan Yang Gang Yang The school of Information Renmin University of China Bejing China The school of Information Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China Bejing China

Recently, social tagging systems become more and more popular in many Web 2.0 applications. In such systems, Users are allowed to annotate a particular resource with a freely chosen a set of tags. These user-generated tags can represent users' interests more concise and closer to human understanding. Interests will change over time. Thus, how to describe users' interests and interests transfer path become a big challenge for personalized recommendation systems. In this approach, we propose a variable-length time interval division algorithm and user interest model based on time interval. Then, in order to draw users' interests transfer path over a specific time period, we suggest interest transfer model. After that, we apply a classical community partition algorithm in our approach to separate users into communities. Finally, we raise a novel method to measure users' similarities based on interest transfer model and provide personalized tag recommendation according to similar users' interests in their next time intervals. Experimental results demonstrate the higher precision and recall with our approach than classical user-based collaborative filtering methods.

关键词： Mathematical model Computational modeling Collaboration educational institutions Tagging Filtering Heuristic algorithms

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A new regularization classification method based on extreme learning machine in network data

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Journal of Information and Computational Science 2012年第12期9卷 3351-3363页

作者： Li, Lina Liu, Dayou Ouyang, Jihong Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun 130012 China

In this paper, we present a new regularization classification method based on extreme learning machine for within network node classification problem. In particular, we define a new objective function, which contains loss function, smoothness function, and intra-class regularization and inter-class regularization terms. In realization, we extend extreme learning machine to semi-supervised learning problem and deduce a new hidden layer weight, and then use the advanced extreme learning machine to optimize the objective function. Experiment results shows that our method can obtain higher accuracies than other methods when more than 30% nodes are labeled in the network. © 2012 Binary Information Press.

关键词： Supervised learning

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