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2018 2nd International Conference on Artificial Intelligence: Technologies and Applications（ICAITA2018）

作者： Shaozhi Dai Yanchun Liang Shuyan Liu Ying Wang Wenle Shao Xixun Lin Xiaoyue Feng Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Zhuhai Laboratory of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Zhuhai College of Jilin University

With the growth of existing knowledge graph, the completion of knowledge graph has become a crucial problem. In this paper, we propose a novel model based on descriptionembodied knowledge representation learning framework, which is able to take advantages of both fact triples and entity description. Specifically, the relation projection is combined with description-embodied representation learning to learn entity and relation embeddings. Convolutional neural network and Trans R are adopted to get the description-based and structure-based representation of entity and relation, respectively. We employ FB15 K dataset generated from a large knowledge graph freebase, to evaluate the performances of the proposed model. Experimental results show that our proposed model greatly outperforms other existing baseline models.

关键词： knowledge graph completion entity description natural language processing

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A Task Allocation Method for Stream Processing with Recovery Latency Constraint

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Journal of Computer Science & Technology 2018年第6期33卷 1125-1139页

作者： Hong-Liang Li Jie Wu Zhen Jiang Xiang Li Xiao-Hui Wei College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Changchun 130012 China Department of Computer and Information Sciences Temple University Philadelphia PA 19122 U.S.A. Department of Computer Science West Chester University of Pennsylvania West Chester PA 19383 U.S.A.

Stream processing applications continuously process large amounts of online streaming data in real time or nearreal time. They have strict latency constraints. However, the continuous processing makes them vulnerable to any failures,and the recoveries may slow down the entire processing pipeline and break latency constraints. The upstream backupscheme is one of the most widely applied fault-tolerant schemes for stream processing systems. It introduces complexbackup dependencies to tasks, which increases the difficulty of controlling recovery latencies. Moreover, when dependenttasks are located on the same processor, they fail at the same time in processor-level failures, bringing extra recovery latencies that increase the impacts of failures. This paper studies the relationship between the task allocation and therecovery latency of a stream processing application. We present a correlated failure effect model to describe the recoverylatency of a stream topology in processor-level failures under a task allocation plan. We introduce a recovery-latency awaretask allocation problem （RTAP） that seeks task allocation plans for stream topologies that will achieve guaranteed recoverylatencies. We discuss the difference between RTAP and classic task allocation problems and present a heuristic algorithmwith a computational complexity of O（n log2 n） to solve the problem. Extensive experiments were conducted to verify thecorrectness and effectiveness of our approach. It improves the resource usage by 15%-20% on average.

关键词： stream processing task allocation fault-tolerance upstream backup, recovery latency

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Ordered proposition fusion based on consistency and uncertainty measurements

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Science China(Information Sciences) 2017年第8期60卷 117-135页

作者： Dayou LIU Yungang ZHU Ni NI Jie LIU College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Beijing Entry-Exit Inspection and Quarantine Bureau

The fusion of ordered propositions is an important and widespread problem in artificial intelligence,but existing fusion methods have difficulty handling the fusion of ordered propositions. In this paper, we propose a solution based on consistency and uncertainty measurements. The main contributions of this paper are as follows. First, we propose the concept of convexity degree, mean, and center of basic support function to comprehensively describe the basic support function of ordered propositions. Second, we introduce entropy as a measure of uncertainty in the basic support function of ordered propositions. Third, we generalize the indeterminacy of the basic support function and propose a novel method to measure the consistency between two basic support functions. Finally, based on the above researches, we propose a novel algorithm for fusing ordered propositions. Theoretical analysis and experimental results demonstrate that the proposed method outperforms state-of-the-art methods.

关键词： uncertainty processing ordered proposition evidence theory dempster rule of combination information fusion

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An improved fruit fly optimization algorithm for solving traveling salesman problem

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Frontiers of Information Technology & Electronic engineering 2017年第10期18卷 1525-1533页

作者： Lan HUANG Gui-chao WANG Tian BAI Zhe WANG College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering (Jilin University) Ministry of Education Changchun 130012 China

The traveling salesman problem(TSP), a typical non-deterministic polynomial(NP) hard problem, has been used in many engineering applications. As a new swarm-intelligence optimization algorithm, the fruit fly optimization algorithm(FOA) is used to solve TSP, since it has the advantages of being easy to understand and having a simple implementation. However, it has problems, including a slow convergence rate for the algorithm, easily falling into the local optimum, and an insufficient optimization precision. To address TSP effectively, three improvements are proposed in this paper to improve FOA. First, the vision search process is reinforced in the foraging behavior of fruit flies to improve the convergence rate of FOA. Second, an elimination mechanism is added to FOA to increase the diversity. Third, a reverse operator and a multiplication operator are proposed. They are performed on the solution sequence in the fruit fly's smell search and vision search processes, respectively. In the experiment, 10 benchmarks selected from TSPLIB are tested. The results show that the improved FOA outperforms other alternatives in terms of the convergence rate and precision.

关键词： Traveling salesman problem Fruit fly optimization algorithm Elimination mechanism Vision search Operator

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Topology-Aware Task Allocation for Distributed Stream Processing with Latency Guarantee

Topology-Aware Task Allocation for Distributed Stream Proces...

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作者： Xiaohui Wei Xun Wei Hongliang Li Yuan Zhuang Hengshan Yue College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education

Nowadays it becomes more and more critical to process the increasingly large amounts of data in timely *** order to meet this requirement and ensure the reliable processing of streaming data,a variety of distributed stream processing architectures and platforms have been developed,which handles the fundamental task of allocating processing tasks to the currently available physical resources and routing streaming data between these ***,many stream processing systems lack an intelligent scheduling mechanism,in which their default schedulers allocate tasks without taking resource demands and availability,or the transfer latency between resources into *** stream processing has a strict request for *** it's important to give latency guarantee for distributed stream *** this paper,we propose a new algorithm for stream processing with latency guarantee,the algorithm both consider transfer latency and resource demand in the process of task *** experiments verify the correctness and effectiveness of our *** the condition of satisfying the latency constraints,the heuristic algorithm AHA on average,reduce more than 21.3% and 58.9% resources compared with the greedy and the round-robin algorithms.

关键词： Distributed stream processing Task allocation Transfer latency

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A randomized diversification strategy for solving satisfiability problem with long clauses

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Science China(Information Sciences) 2017年第9期60卷 121-131页

作者： Jian GAO Ruizhi LI Minghao YIN College of Computer Science Northeast Normal University College of Information Science and Technology Dalian Maritime University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University

Satisfiability problem(SAT) is a central problem in artificial intelligence due to its computational complexity and usefulness in industrial applications. Stochastic local search(SLS) algorithms are powerful to solve hard instances of satisfiability problems, among which CScore SAT is proposed for solving SAT instances with long clauses by using greedy mode and diversification mode. In this paper, we present a randomized variable selection strategy to improve efficiency of the diversification mode, and thus propose a new SLS *** perform a number of experiments to evaluate the new algorithm comparing with the recently proposed algorithms, and show that our algorithm is comparative with others for solving random instances near the phase transition threshold.

关键词： SAT local search randomized diversification strategy phase transition long clause

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Accurate Q-learning 25th

Accurate Q-learning

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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 Cloud-Based Evaluation System for Science-and-engineering Students

A Cloud-Based Evaluation System for Science-and-Engineering ...

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2018国际计算机前沿大会（原国际青年计算机大会）

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

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-andengineering students is fractionized into four sub-categories and visualized from several different aspects for a good feedback in practical teaching process.

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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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