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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Wenjian Luo Yingying Qiao Xin Lin Peilan Xu Mike Preuss Anhui Province Key Laboratory of Software Engineering in Computing and Communication School of Computer Science and Technology University of Science and Technology of China Hefei Anhui China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

Evolutionary multimodal optimization has received considerable attention in the past decade. Most existing evolutionary multimodal optimization algorithms are designed to solve problems with relatively few global optima. However, in real-world applications, the problems can possess a lot of global optima (and sometimes acceptable local optima). Finding more global optima can help us learn more about their landscapes and distributions. However, solving these problems with limited computational resources is a challenge for current algorithms. In this paper, many-modal optimization problems are studied, and each of them has more than 100 global optima. We first present a benchmark with 10 many-modal problems based on the existing multimodal optimization benchmarks. The numbers of global optima of these 10 problems vary from 108 to 7776. Second, we propose the difficulty-based cooperative co-evolution (DBCC) strategy for solving many-modal optimization problems. DBCC comprises four primary steps: problem separation, resource allocation, optimization, and solution reconstruction. The clonal selection algorithm is selected as the optimizer in DBCC. Experimental results demonstrate that DBCC provides satisfactory performance.

关键词： Optimization Benchmark testing Sociology Statistics Heuristic algorithms Resource management Search problems

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Study on Parallel Computing

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Journal of computer Science & Technology 2006年第5期21卷 665-673页

作者：陈国良孙广中张云泉莫则尧 Anhui Province-MOST Key Co-Lab of High Performance Computing and Its Applications Department of Computer Science and Technology University of Science and Technology of China Hefei 230027 P.R. China Laboratory of Parallel Computing Institute of Software Chinese Academy of Sciences Beijing 100080 P.R. China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing 100088 P.R. China

In this paper, we present a general survey on parallel computing. The main contents include parallel computer system which is the hardware platform of parallel computing, parallel algorithm which is the theoretical base of parallel computing, parallel programming which is the software support of parallel computing. After that, we also introduce some parallel applications and enabling technologies. We argue that parallel computing research should form an integrated methodology of ＂architecture algorithm programming application＂. Only in this way, parallel computing research becomes continuous development and more realistic.

关键词： parallel computing parallel architecture parallel programming parallel algorithm parallel application

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Combining multipopulation evolutionary algorithms with memory for dynamic optimization problems

Combining multipopulation evolutionary algorithms with memor...

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Congress on Evolutionary Computation

作者： Tao Zhu Wenjian Luo Lihua Yue Anhui Province Key Laboratory of Software Engineering in Computing and Communication Hefei Anhui China School of Computer Science and Technology University of Science and Technology of China Hefei Anhui China

ISBN: (纸本)9781479914869

Both multipopulation and memory are widely used approaches in the field of evolutionary dynamic optimization. It would be interesting to examine the effect of the combinations of multipopulation algorithms (MPAs) and memory schemes. However, since most of the existing memory schemes are proposed with single population algorithms, straightforwardly applying them to MPAs may cause problems. By addressing the possible problems, a new memory scheme is proposed for MPAs in this paper. In the experiments, several existing memory schemes and the newly proposed scheme are combined with a MPA, i.e. the Species-based Particle Swarm Optimizer (SPSO), and these combinations are tested on cyclic and acyclic problems. The experimental results indicate that 1) straightforwardly using the existing memory schemes sometimes degrades the performance of SPSO even on cyclic problems; 2) the newly proposed memory scheme is very competitive.

关键词： Sociology Statistics Memory management Optimization Heuristic algorithms Benchmark testing Particle swarm optimization

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Random directional attack for fooling deep neural networks

arXiv

引用

arXiv 2019年

作者： Luo, Wenjian Wu, Chenwang Zhou, Nan Ni, Li Anhui Province Key Laboratory of Software Engineering in Computing and Communication School of Computer Science and Technology University of Science and Technology of China Hefei Anhui230027 China

Deep neural networks (DNNs) have been widely used in many fields such as images processing, speech recognition;however, they are vulnerable to adversarial examples, and this is a security issue worthy of attention. Because the training process of DNNs converge the loss by updating the weights along the gradient descent direction, many gradient-based methods attempt to destroy the DNN model by adding perturbations in the gradient direction. Unfortunately, as the model is nonlinear in most cases, the addition of perturbations in the gradient direction does not necessarily increase loss. Thus, we propose a random directed attack (RDA) for generating adversarial examples in this paper. Rather than limiting the gradient direction to generate an attack, RDA searches the attack direction based on hill climbing and uses multiple strategies to avoid local optima that cause attack failure. Compared with state-of-the-art gradient-based methods, the attack performance of RDA is very competitive. Moreover, RDA can attack without any internal knowledge of the model, and its performance under black-box attack is similar to that of the white-box attack in most cases, which is difficult to achieve using existing gradient-based attack methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Community-based 3-SAT formulas with a predefined solution

arXiv

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arXiv 2019年

作者： Hu, Yamin Luo, Wenjian Wang, Junteng Anhui Province Key Laboratory of Software Engineering in Computing and Communication School of Computer Science and Technology University of Science and Technology of China China

It is crucial to generate crafted SAT formulas with predefined solutions for the testing and development of SAT solvers since many SAT formulas from real-world applications have solutions. Although some generating algorithms have been proposed to generate SAT formulas with predefined solutions, community structures of SAT formulas are not considered in these algorithms. Consequently, we propose a 3-SAT formula generating algorithm that not only guarantees the existence of a predefined solution, but also simultaneously considers community structures and clause distributions. The proposed 3-SAT formula generating algorithm controls the quality of community structures through controlling (1) the number of clauses whose variables have a common community, which we call intra-community clauses, and (2) the number of variables that only belong to one community, which we call intra-community variables. For a SAT formula, more intra-community clauses and intracommunity variables, higher quality of community structures. To study the combined effect of community structures and clause distributions on the hardness of SAT formulas, we measure solving runtimes of two solvers, gluHack (a leading CDCL solver) and CPSparrow (a leading SLS solver), on the generated SAT formulas under different groups of parameter settings. Through extensive experiments, we obtain some noteworthy observations on the SAT formulas generated by the proposed algorithm: (1) The community structure has little or no effects on the hardness of SAT formulas with regard to CPSparrow but a strong effect with regard to gluHack. (2) Only when the proportion of true literals in a SAT formula in terms of the predefined solution is 0.5, SAT formulas are hard-to-solve with regard to gluHack;when this proportion is below 0.5, SAT formulas are hard-to-solve with regard to CPSparrow. (3) When the ratio of the number of clauses to that of variables is around 4.25, the SAT formulas are hard-to-solve with regard to both glu

关键词： Hardness

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FACIAL ACTION UNIT RECOGNITION BY RELATION MODELING FROM BOTH QUALITATIVE KNOWLEDGE AND QUANTITATIVE DATA

FACIAL ACTION UNIT RECOGNITION BY RELATION MODELING FROM BOT...

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IEEE International Conference on Multimedia and Expo Workshops

作者： Zhen Gao Shangfei Wang Chongliang Wu Jun Wang Qiang Ji Key Lab of Computing and Communication Software of Anhui Province School of Computer Science and Technology University of Science and Technology of China Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute

ISBN: (纸本)9781479947164

In this paper, we propose to capture Action Unit (AU) relations existing in both qualitative knowledge and quantitative data through Credal Networks (CN). Each node of the CN represents an AU label, and the links and probability intervals capture the probabilistic dependencies among multiple AUs. The structure of CN is designed based on prior knowledge. The parameters of CN are learned from both knowledge and ground-truth AU labels. The AU preliminary estimations are obtained by an existing image-driven recognition method. With the learned credal network, we infer the true AU labels by combining the relationships among labels with the previous obtained estimations. Experimental results on the CK+ database and MMI database demonstrate that with complete AU labels, our CN model is slightly better than the Bayesian Network (BN) model, demonstrating that credal sets learned from data can capture uncertainty more reliably;With incomplete and error-prone AU annotations, our CN model outperforms the BN model, indicating that credal sets can successfully capture qualitative knowledge.

关键词： Prior knowledge Credal network AU recognition

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Coverage-preserving Data Gathering in Wireless Sensor Networks

Coverage-preserving Data Gathering in Wireless Sensor Networ...

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ACIS International Conference on software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD)

作者： Hongli Xu Liusheng Huang Jumin Wu Yang Wang Depart. of Computer Science and Technology Anhui Province key Laboratory of Software in Computing and Communication University of Science and Technology Hefei China

The power is the most significant resource in wireless sensor nodes, so energy conservation is the most important factor in designing the protocol for sensor networks. In this paper, we study the problem of energy-efficient collection of the physical information from the target area. The coverage-preserving data gathering (CPDG) problem is to build up a tree, which minimizes the total energy consumption, and preserves the original coverage to ensure the result correctness. This problem can be formalized as 0-1 integer linear programming, and solved by an efficient heuristic algorithm. Furthermore, an improved algorithm is also proposed to balance the energy consumption among all the sensor nodes. The experimental results show that CPDG algorithm can conserve the energy about 30% of PEDAP protocol, which is regarded as one of the most efficient data gathering protocols as we know.

关键词： Wireless sensor networks Energy efficiency Energy consumption Heuristic algorithms Monitoring Distributed computing computer networks Energy conservation Wireless application protocol Integer linear programming

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A Hormone-based Clustering Algorithm in Wireless Sensor Networks

A Hormone-based Clustering Algorithm in Wireless Sensor Netw...

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2010 2nd International Conference on computer Engineering and Technology(2010年第二届计算机工程与技术国际会议 ICCET 2010)

作者： Dayuan Dong Haifeng You Yipeng Zhang Xufa Wang Key Laboratory of Software in Computing and Communication Anhui Province School of Computer Science and Technology University of Science and Technology of China HefeiChina

ISBN: (纸本)9781424463473;9781424463497;9781424463503

Clustering problem is one of the hottest issues in wireless sensor networks (WSNs). The strategy for selection of cluster head has not been sufficiently investigated. In this paper,we propose a hormone-based clustering algorithm (HCA) inspired by the communication mechanism of bioendocrine system to prolong the lifetime of the sensor nodes. Two kinds of hormones,cluster hormone and member hormone,are used in the algorithm. Hormone message can promote a node to become a cluster head or a member node. Comparative experiment shows that the proposed algorithm has effectively prolong the lifetime of WSN compared with LEACH[1] and HEED[2].

关键词： hormone:clustering:wireless sensor networks

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Accurate classification of time series based on a new robust similarity measure

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Journal of Computational Information Systems 2013年第9期9卷 3521-3528页

作者： He, Xiaoxu Shao, Chenxi Xiong, Yan School of Computer Science and Technology University of Science and Technology of China Hefei 230027 China Anhui Province Key Laboratory of Software in Computing and Communication Hefei 230027 China

Among different time series data mining, time series classification is one of the most important aspects. How to design a suitable similarity measure of similarity is a burning issue for accurate time series classification. In this paper, we propose a new similarity measure based on feature exaction of the original time series. The new similarity measure is used in classification with the nearest neighbor rule. In order to provide a comprehensive comparison, we conducted three sets of experiments, testing effectiveness on different time series datasets from a wide variety of application domains. Experimental evaluations show that the proposed similarity measure can tolerate the most distortions than other three typical similarity;besides, the new classifier is not only superior to other conventional classifier, but also more excellent in 1nn classifier than other similarity measure. © 2013 by Binary Information Press.

关键词： Data mining

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PN-HASH: An Immune-Inspired Scheme for Data Integrity Check

PN-HASH: An Immune-Inspired Scheme for Data Integrity Check

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Junteng Wang Wenjian Luo Yamin Hu Hao Jiang Anhui Province Key Laboratory of Software Engineering in Computing and Communication School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (数字)9781728142487

ISBN: (纸本)9781728142494

Cryptographic hash functions play significant roles in information security applications, including data integrity. Facing hash collision attacks, several prevalent cryptographic hash functions have been proved not safe enough. In this paper, we propose a simple scheme for data integrity check called PN-HASH (Positive hash and Negative hash). PN-HASH is inspired by the negative representation of information, which is a research branch in the field of immune computation. This scheme computes two hash values of the input message and its negative representation. The resulting hashes are called the positive hash and the negative hash, respectively. The proposed scheme is simple but can improve the resistance to hash collision attacks. Furthermore, for more flexibility and higher security, we propose three extensions of PN-HASH. Security and complexity analyses show that PN-HASH could improve the security of underlying cryptographic hash functions, with approximately twice cost of them. To give exact examples, we present PN-MD5, an implementation of PN-HASH based on MD5.

关键词： Cryptography Data integrity Databases Resistance Complexity theory Face

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