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L-priorities Bloom Filter: A New Member of the Bloom Filter Family

International Journal of Automation and computing 2012年第2期9卷 171-176页

作者： Huang-Shui Hu Hong-Wei Zhao Fei Mi College of Computer Science and Technology Jilin University Changchun 130012 PRC Key Laboratory of Symbolic Computation and Knowledge Engineering for Ministry of Education Jilin University Changchun 130012 PRC

A Bloom filter is a space-efficient data structure used for concisely representing a set as well as membership queries at the expense of introducing false positive. In this paper, we propose the L-priorities Bloom filter （LPBF） as a new member of the Bloom filter （BF） family, it uses a limited multidimensional bit space matrix to replace the bit vector of standard bloom filters in order to support different priorities for the elements of a set. We demonstrate the time and space complexity, especially the false positive rate of LPBF. Furthermore, we also present a detailed practical evaluation of the false positive rate achieved by LPBF. The results show that LPBF performs better than standard BFs with respect to false positive rate.

关键词： Bloom filter bit space matrix false positive L-priorities time and space complexity.

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A parallel scheduling algorithm for reinforcement learning in large state space

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Frontiers of Computer Science 2012年第6期6卷 631-646页

作者： Quan LIU Xudong YANG Ling JING Jin LI Jiao LI Institute of Computer Science and Technology Soochow UniversitySuzhou 215006China Department of Computer Science and Technology Nanjing UniversityNanjing 210093China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun 130012China

The main challenge in the area of reinforcement learning is scaling up to larger and more complex problems. Aiming at the scaling problem of reinforcement learning, a scalable reinforcement learning method, DCS-SRL, is proposed on the basis of divide-and-conquer strategy, and its convergence is proved. In this method, the learning problem in large state space or continuous state space is decomposed into multiple smaller subproblems. Given a specific learning algorithm, each subproblem can be solved independently with limited available resources. In the end, component solutions can be recombined to obtain the desired result. To ad- dress the question of prioritizing subproblems in the scheduler, a weighted priority scheduling algorithm is proposed. This scheduling algorithm ensures that computation is focused on regions of the problem space which are expected to be maximally productive. To expedite the learning process, a new parallel method, called DCS-SPRL, is derived from combining DCS-SRL with a parallel scheduling architecture. In the DCS-SPRL method, the subproblems will be distributed among processors that have the capacity to work in parallel. The experimental results show that learning based on DCS-SPRL has fast convergence speed and good scalability.

关键词： divide-and-conquer strategy parallel schedule scalability large state space continuous state space

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Learning the structure of dynamic bayesian network with domain knowledge

Learning the structure of dynamic bayesian network with doma...

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2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012

作者： Chen, Juan Jia, Haiyang Huang, Yuxiao Liu, Dayou College of Computer Science and Technology Key Laboratory for Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China

ISBN: (纸本)9781467314855

Dynamic Bayesian Network (DBN) is a graphical model for representing temporal stochastic processes. Learning the structure of DBN is a fundamental step for parameter learning, inference and application. For large scale problem, the structure learning is intractable. In some domains the training data is very limited and noisy, so learning the DBN structure only with training data is impractical. Domain knowledge may improve both the efficiency and the accuracy of the learning algorithm. But usually, the domain knowledge is uncertainty, unclear and even with conflict. This paper presents a novel algorithm for learning the structure of DBN, which consider the data and domain knowledge simultaneously, empirical experiment shows that the proposed algorithm improved the efficiency and the accuracy of the DBN structure learning. © 2012 IEEE.

关键词： Bayesian networks

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Speech separation in the vehicle environment based on fastICA algorithm

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Journal of Multimedia 2012年第1期7卷 33-40页

作者： Zhang, Jindong Qin, Guihe Liu, Ye College of computer science and technology Jilin University Changchun China Key Laboratory of Symbol Computation Knowledge Engineering of the Ministry of Education Changchun China

The speech interaction in-vehicle was mainly realized by the speech recognition. The human-machine interaction around was usually disturbed by the noise, and the speech received by the receiver was not the original pure speech, so compared to the pure environment, the accuracy of the speech recognition declined so sharply that it could not meet the demand of the practical application of human- machine interaction. So the speech recognition was required to have the strong adaptability and processing capacity to the speech with noise. In this paper, the FastICA algorithm in signal process and statistics was studied and used to separate the driver's speech in the vehicle environment, and realizes the pretreatment in recognizing driver's speech. The effectiveness of the method had been validated in actual vehicle experimental configuration. © 2012 Academy Publisher.

关键词： Speech processing

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Algorithmic challenges in structure-based drug design and NMR structural biology

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Frontiers of Electrical and Electronic engineering in China 2012年第1期7卷 69-84页

作者： Lincong WANG Shuxue ZOU Yao WANG College of Computer Science and Technology The Joint Center for Systems Biology of Jilin University and the University of Georgia Jilin University Changchun 130012 China Key Laboratory of Symbol Computation and Knowledge Engineer-ing of Ministry of Education Jilin University Changchun 130012China

The three-dimensional structure of a biomolecule rather than its one-dimensionM sequence determines its biological function. At present, the most accurate structures are derived from experimental data measured mainly by two techniques： X-ray crystallog- raphy and nuclear magnetic resonance （NMR） spec- troscopy. Because neither X-ray crystallography nor NMR spectroscopy could directly measure the positions of atoms in a biomolecule, algorithms must be designed to compute atom coordinates from the data. One salient feature of most NMR structure computation algorithms is their reliance on stochastic search to find the lowest energy conformations that satisfy the experimentally- derived geometric restraints. However, neither the cor- rectness of the stochastic search has been established nor the errors in the output structures could be quantified. Though there exist exact algorithms to compute struc- tures from angular restraints, similar algorithms that use distance restraints remain to be developed. An important application of structures is rational drug design where protein-ligand docking plays a crit- ical role. In fact, various docking programs that place a compound into the binding site of a target protein have been used routinely by medicinal chemists for both lead identification and optimization. Unfortunately, de- spite ongoing methodological advances and some success stories, the performance of current docking algorithms is still data-dependent. These algorithms formulate the docking problem as a match of two sets of feature points. Both the selection of feature points and the search for the best poses with the minimum scores are accomplished through some stochastic search methods. Both the un- certainty in the scoring function and the limited sam- pling space attained by the stochastic search contribute to their failures. Recently, we have developed two novel docking algorithms： a data-driven docking algorithm and a general docking algorithm that does not re

关键词： structure-based drug design （SBDD） vir- tual screening （VC） protein-ligand docking scoring function molecular dynamics （MD） Monte Carlo （MC） simulated annealing （SA） Markov chain Monte Carlo （MCMC） nuclear magnetic resonance （NMR） nuclear Overhauser effect （NOE） residual dipolar couplings （RDCs） chemical shift （CS） inference structure deter- mination （ISD） Bayesian Gibbs sampling probabil- ity distribution functions （PDFs） degrees of freedom （DOF） van der Waals （VDW） root mean square devi- ation （RMSD） manifold Poisson-Boltzmann equation （PBE）

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Quasi-periodic Solutions of the General Nonlinear Beam Equations

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Communications in Mathematical Research 2012年第1期28卷 51-64页

作者： GAO YI-XIAN College of Mathematics and Statistics Northeast Normal University Changchun 130024 Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012

In this paper, one-dimensional （1D） nonlinear beam equations of the form utt - uxx ＋ uxxxx ＋ mu = f （u） with Dirichlet boundary conditions are considered, where the nonlinearity f is an analytic, odd function and f（u） = O（u3）. It is proved that for all m ∈ （0, M＊]  R （M＊ is a fixed large number）, but a set of small Lebesgue measure, the above equations admit small-amplitude quasi-periodic solutions corresponding to finite dimensional invariant tori for an associated infinite dimensional dynamical system. The proof is based on an infinite dimensional KAM theory and a partial Birkhoff normal form technique.

关键词： beam equation KAM theorem quasi-periodic solution partial Birkhoffnormal form

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Improving the Recommendation of Collaborative Filtering by Fusing Trust Network

Improving the Recommendation of Collaborative Filtering by F...

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International Conference on computational Intelligence and Security

作者： Bo Yang Pengfei Zhao Shuqiu Ping Jing Huang School of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun P.R.China

To accurately and actively provide users with their potentially interested information or services is the main task of a recommender system. Collaborative filtering is one of the most widely adopted recommender methods, whereas it is suffering the issue of sparse rating data that will severely degenerate the quality of recommendations. To address this issue, the article proposes a novel method, named the FTRA (Fusing Trust and Ratings), trying to improve the performance of collaborative filtering recommendation by means of elaborately integrating twofold sparse information, i.e., the conventional rating data given by users and the social trust network among the same users. The performance of FTRA is rigorously validated by comparing it with six representative methods on a real-world dataset. The experimental results show that the FTRA outperforms all other competitors in terms of both precision and recall. More importantly, our work suggests that the strategy of augmenting sparse rating data by fusing trust networks does significantly improve the quality of conventional collaborative filtering recommendation, and its quality could be further improved by means of designing more effective integrating schemes.

关键词： Collaboration Recommender systems Measurement Social network services Web sites Educational institutions

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A hybrid optimization algorithm for the multi-chromosomal reversal median problem

A hybrid optimization algorithm for the multi-chromosomal re...

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1st International Conference on engineering and Technology Innovation, ICETI 2011

作者： Zhang, Yi Zhang, Meng Department of Computer Science Jilin Business and Technology College Changchun 130062 China Military Simulation Technology Institute Air Force Aviation University Changchun130022 China Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun 130012 China

ISBN: (纸本)9783037853245

In this paper, we introduce a hybrid optimization algorithm with the Branch-and-Bound Method and the Ant Colony Optimization to solve the multi-chromosomal reversal median problem. We convert the large-scale genome into TSP maps at first. Then we use a hybrid optimization algorithm with the Branch-and-Bound Method and the Ant Colony Optimization to solve the problem. In our improved algorithm, we increase the search speed by implement multi-branch parallel search of ACO. Our extensive experiments on simulated datasets show that this median solver is efficient.

关键词： Ant colony optimization

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Conflict-based belief revision operators in possibilistic logic 26

Conflict-based belief revision operators in possibilistic lo...

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26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12

作者： Qi, Guilin Wang, Kewen Key Laboratory of Computer Network and Information Integration of State Education Ministry School of Computer Science and Engineering Southeast University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China School of Computing and Information Technology Griffith University Australia

ISBN: (纸本)9781577355687

In this paper, we investigate belief revision in possibilistic logic, which is a weighted logic proposed to deal with incomplete and uncertain information. Existing revision operators in possibilistic logic are restricted in the sense that the input information can only be a formula instead of a possibilistic knowledge base which is a set of weighted formulas. To break this restriction, we consider weighted prime implicants of a possibilistic knowledge base and use them to define novel revision operators in possibilistic logic. Intuitively, a weighted prime implicant of a possibilistic knowledge base is a logically weakest possibilistic term (i.e., a set of weighted literals) that can entail the knowledge base. We first show that the existing definition of a weighted prime implicant is problematic and need a modification. To define a revision operator using weighted prime implicants, we face two problems. The first problem is that we need to define the notion of a conflict set between two weighted prime implicants of two possibilistic knowledge bases to achieve minimal change. The second problem is that we need to define the disjunction of possibilistic terms. We solve these problems and define two conflict-based revision operators in possibilistic logic. We then adapt the well-known postulates for revision proposed by Katsuno and Mendelzon and show that our revision operators satisfy four of the basic adapted postulates and satisfy two others in some special cases. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Problem solving

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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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