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25th International Conference on Neural Information Processing, ICONIP 2018

作者： Hu, Zhihui Jiang, Yubin Ling, Xinghong Liu, Quan School of Computer Science and Technology Soochow University SuzhouJiangsu215006 China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing210000 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

ISBN: (纸本)9783030041816

In order to solve the problem that Q-learning can suffer from large overestimations in some stochastic environments, we first propose a new form of Q-learning, which proves that it is equivalent to the incremental form and analyze the reasons why the convergence rate of Q-learning will be affected by positive bias. We generalize the new form for the purpose of easy adaptations. By using the current value instead of the bias term, we present an accurate Q-learning algorithm and show that the new algorithm converges to an optimal policy. Experimentally, the new algorithm can avoid the effect of positive bias and the convergence rate is faster than Q-learning and its variants on several MDP problems. © Springer Nature Switzerland AG 2018.

关键词： Reinforcement learning

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A Layered Co-evolution Based Rough Feature Selection Using Adaptive Neighborhood Radius Hierarchy and Its Application in 3D-MRI

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Chinese Journal of Electronics 2017年第6期26卷 1168-1176页

作者： DING Weiping GUAN Zhijin WANG Jiehua TIAN Di School of Computer Science and Technology Nantong University State Key Laboratory for Novel Software Technology Nanjing University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology

As the conventional feature selection algorithms are prone to the poor running efficiency in largescale datasets with interacting features, this paper aims at proposing a novel rough feature selection algorithm whose innovation centers on the layered co-evolutionary strategy with neighborhood radius hierarchy. This hierarchy can adapt the rough feature scales among different layers as well as produce the reasonable decompositions through exploiting any correlation and interdependency among feature subsets. Both neighborhood interaction within layer and neighborhood cascade between layers are adopted to implement the interactive optimization of neighborhood radius matrix, so that both the optimal rough feature selection subsets and their global optimal set are obtained efficiently. Our experimental results substantiate the proposed algorithm can achieve better effectiveness, accuracy and applicability than some traditional feature selection algorithms.

关键词： Layered co-evolution Rough feature se lection Adaptive neighborhood radius hierarchy Longitudinal cortical surface labeling

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Hyperbolic node embedding for signed networks

arXiv

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arXiv 2019年

作者： Song, Wenzhuo Chen, Hongxu Liu, Xueyan Jiang, Hongzhe Wang, Shengsheng College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Advanced Analytics Institute School of Computer Science Faculty of Engineering and IT University of Technology Sydney NSW2007 Australia College of Mechanical and Electronic Engineering Nanjing Forestry University Nanjing210037 China

Signed network embedding methods aim to learn vector representations of nodes in signed networks. However, existing algorithms only managed to embed networks into low-dimensional Euclidean spaces whereas many intrinsic features of signed networks are reported more suitable for non-Euclidean spaces. For instance, previous works did not consider the hierarchical structures of networks, which is widely witnessed in real-world networks. In this work, we answer an open question that whether the hyperbolic space is a better choice to accommodate signed networks and learn embeddings that can preserve the corresponding special characteristics. We also propose a non-Euclidean signed network embedding method based on structural balance theory and Riemannian optimization, which embeds signed networks into a Poincar ball in a hyperbolic space. This space enables our approach to capture underlying hierarchy of nodes in signed networks because it can be seen as a continuous tree. We empirically compare our method against six Euclidean-based baselines in three tasks on seven real-world datasets, and the results show the effectiveness of our method. Copyright © 2019, The Authors. All rights reserved.

关键词： Geometry

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A Cascaded Co-evolutionary Model for Attribute Reduction and Classification Based on Coordinating Architecture with Bidirectional Elitist Optimization

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Chinese Journal of Electronics 2017年第1期26卷 13-21页

作者： DING Weiping WANG Jiandong LI Yuehua CHENG Xueyun School of Computer Science and Technology Nantong University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Provincial Key Laboratory for Computer Information Processing Technology Soochow University College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics

A cascaded co-evolutionary model for Attribute reduction and classification based on Coordinating architecture with bidirectional elitist optimization(ARC-CABEO) is proposed for the more practical applications. The regrouping and merging coordinating strategy of ordinary-elitist-role-based population is introduced to represent a more holistic cooperative co-evolutionary framework of different populations for attribute reduction. The master-slave-elitist-based subpopulations are constructed to coordinate the behaviors of different elitists, and meanwhile the elitist optimization vector with the strongest balancing between exploration and exploitation is selected out to expedite the bidirectional attribute co-evolutionary reduction process. In addition, two coupled coordinating architectures and the elitist optimization vector are tightly cascaded to perform the co-evolutionary classification of reduction subsets. Hence the preferring classification optimization goal can be achieved better. Some experimental results verify that the proposed ARC-CABEO model has the better feasibility and more superior classification accuracy on different UCI datasets, compared with representative algorithms.

关键词： Attribute reduction for classification Bidirectional elitist optimization Coordinating architecture Cascaded co-evolutionary model

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Adversarial sampling and training for semi-supervised information retrieval

arXiv

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arXiv 2018年

作者： Park, Dae Hoon Chang, Yi Huawei Research America California School of Artificial Intelligence Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education China

Ad-hoc retrieval models with implicit feedback often have problems, e.g., the imbalanced classes in the data set. Too few clicked documents may hurt generalization ability of the models, whereas too many non-clicked documents may harm effectiveness of the models and efficiency of training. In addition, recent neural networkbased models are vulnerable to adversarial examples due to the linear nature in them. To solve the problems at the same time, we propose an adversarial sampling and training framework to learn ad-hoc retrieval models with implicit feedback. Our key idea is (i) to augment clicked examples by adversarial training for better generalization and (ii) to obtain very informational non-clicked examples by adversarial sampling and training. Experiments are performed on benchmark data sets for common ad-hoc retrieval tasks such as Web search, item recommendation, and question answering. Experimental results indicate that the proposed approaches significantly outperformstrong baselines especially for high-ranked documents, and they outperform IRGAN in NDCG@5 using only 5% of labeled data for the Web search task. Copyright © 2018, The Authors. All rights reserved.

关键词： Information retrieval

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A Simplified Rational Representation for Positive-Dimensional Polynomial Systems and SHEPWM Equations Solving

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Journal of Systems Science & Complexity 2017年第6期30卷 1470-1482页

作者： SHANG Baoxin ZHANG Shugong TAN Chang XIA Peng School of Mathematics Key Laboratory of Symbolic Computation and Knowledge Engineering (Ministry of Education) Jilin University College of Science Northeast Electric Power University College of Science Northeast Forestry University School of Mathematics Liaoning University

The paper is concerned with the improvement of the rational representation theory for solving positive-dimensional polynomial systems. The authors simplify the expression of rational representation set proposed by Tan and Zhang(2010), obtain the simplified rational representation with less rational representation sets, and hence reduce the complexity for representing the variety of a positive-dimensional ideal. As an application, the authors compute a "nearly" parametric solution for the SHEPWM problem with a fixed number of switching angles.

关键词： Positive-dimensional polynomial system solving rational univariate representation SHEPWM simplified rational representation

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A cloud-based evaluation system for science-and-engineering students 4th

A cloud-based evaluation system for science-and-engineering ...

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4th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2018

作者： Huang, Qian Ye, Feng Chen, Yong Xu, Peiling College of Computer and Information Hohai University Nanjing211100 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Postdoctoral Centre Nanjing Longyuan Micro-Electronic Company Nanjing211106 China Suzhou College of Information Technology Suzhou215200 China

ISBN: (纸本)9789811322051

With the burgeoning of IT industry, more and more companies and universities concentrate on the scientific evaluation of science-and-engineering students. Existing evaluation strategies typically lie on grades or scores of the courses taken by students, which have obvious drawbacks nowadays and cannot lead to a proper improvement of education management. This paper proposes an overall student evaluation system architecture that includes three levels, i.e. data collection infrastructure which comprises student data collection and student data transmission, data center which is composed of four sub-levels, and unified portal which defines unified applications and classes of visiting terminals. In the proposed architecture, the main contribution lie in the upper most sub-level of data center, i.e. evaluation services. Four categories of student evaluation services, i.e. moral trait, civic literacy, knowledge level and comprehensive ability, are defined. Furthermore, in order to have a satisfactory feedback in the practical teaching process, the most important comprehensive ability for science-and-engineering students is fractionized into four sub-categories and visualized from several different aspects for a good feedback in practical teaching process. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Students

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Approximate Model Counting by Partial knowledge Compilation

arXiv

引用

arXiv 2018年

作者： Lai, Yong College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education Changchun China

Model counting is the problem of computing the number of satisfying assignments of a given propositional formula. Although exact model counters can be naturally furnished by most of the knowledge compilation (KC) methods, in practice, they fail to generate the compiled results for the exact counting of models for certain formulas due to the explosion in sizes. Decision-DNNF is an important KC language that captures most of the practical compilers. We propose a generalized Decision-DNNF (referred to as partial Decision-DNNF) via introducing a class of new leaf vertices (called unknown vertices), and then propose an algorithm called PARTIALKC to generate randomly partial Decision-DNNF formulas from the given formulas. An unbiased estimate of the model number can be computed via a randomly partial Decision-DNNF formula. Each calling of PARTIALKC consists of multiple callings of MICROKC, while each of the latter callings is a process of importance sampling equipped with KC technologies. The experimental results show that PARTIALKC is more accurate than both SampleSearch and SearchTreeSampler, PARTIALKC scales better than SearchTreeSampler, and the KC technologies can obviously accelerate sampling. Copyright © 2018, The Authors. All rights reserved.

关键词： Importance sampling

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Image clustering segmentation based on SLIC superpixel and transfer learning

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Pattern Recognition and Image Analysis 2017年第4期27卷 838-845页

作者： Li, X.X. Shen, X.J. Chen, H.P. Feng, Y.C. College of Computer Science and Technology Jilin University ChangchunJilin130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University ChangchunJilin130012 China

Traditional fuzzy C-means clustering algorithm has poor noise immunity and clustering results in image segmentation. To overcome this problem, a novel image clustering algorithm based on SLIC superpixel and transfer learning is proposed in this paper. In the proposed algorithm, SLIC superpixel method is used to improve the edge matching degree of image segmentation and enhances the robustness to noise. Transfer learning is adopted to correct the image segmentation result and further improve the accuracy of image segmentation. In addition, the proposed algorithm improves the original SLIC superpixel algorithm and makes the edge of the superpixel more accurate. Experimental results show that the proposed algorithm can obtain better segmentation results. © 2017, Pleiades Publishing, Ltd.

关键词： Image segmentation

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Using improved particle swarm optimization to tune PID controllers in cooperative collision avoidance systems

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Frontiers of Information Technology & Electronic engineering 2017年第9期18卷 1385-1395页

作者： Xing-chen WU Gui-he QIN Ming-hui SUN He YU Qian-yi XU College of Computer Science and Technology Jilin University Changchun 130012 China MOE Key Laboratory of Symbol Computation and Knowledge Engineering Changchun 130012 China Department of Measurement and Controlling Engineering Changchun University Changchun 130012 China

The introduction ofproportional-integral-dorivative （PID） controllers into cooperative collision avoidance systems （CCASs） has been hindered by difficulties in their optimization and by a lack of study of their effects on vehicle driving stability, comfort, and fuel economy. In this paper, we propose a method to optimize PID controllers using an improved particle swarm optimization （PSO） algorithm, and to bettor manipulate cooperative collision avoidance with other vehicles. First, we use PRESCAN and MATLAB/Simulink to conduct a united simulation, which constructs a CCAS composed of a PID controller, maneuver strategy judging modules, and a path planning module. Then we apply the improved PSO algorithm to optimize the PID controller based on the dynamic vehicle data obtained. Finally, we perform a simulation test of performance before and after the optimization of the PID controller, in which vehicles equipped with a CCAS undertake deceleration driving and steering under the two states of low speed （≤50 km/h） and high speed （≥100 km/h） cruising. The results show that the PID controller optimized using the proposed method can achieve not only the basic functions of a CCAS, but also improvements in vehicle dynamic stability, riding comfort, and fuel economy.

关键词： Cooperative collision avoidance system （CCAS） Improved particle swarm optimization （PSO） PID controller Vehicle comfort Fuel economy

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