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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Cao, Yong Luo, Wenjian Nature Inspired Computation and Applications Laboratory School of Computer Science and Technology University of Science and Technology of China Hefei 230027 Anhui China Anhui Key Laboratory of Software in Computing and Communication University of Science and Technology of China Hefei 230027 Anhui China

ISBN: (纸本)9781424463343

Associative memory schemes have been developed for Evolutionary Algorithms (EAs) to solve Dynamic Optimization Problems (DOPs), and demonstrated powerful performance. In these schemes, how to update the memory could be important for their performance. However, little work has been done about the associative memory updating strategies. In this paper, a novel updating strategy is proposed for associative memory schemes. In this strategy, the memory point whose associated environmental information is most similar to the current environmental information is first picked out from the memory. Then, the selected memory individual is updated according to the fitness value, and the associated environmental information is updated according to the matching degree between environmental information and individuals. This updating strategy is embedded into a stateof-the-art algorithm, i.e. the MPBIL, and tested by experiments. Experimental results demonstrate that the proposed updating strategy is helpful for associative memory schemes to enhance their search ability in cyclic dynamic environments.

关键词： Associative processing

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Learning with only multiple instance positive bags

Learning with only multiple instance positive bags

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2016 International Joint Conference on Neural Networks, IJCNN 2016

作者： Wang, Zhigang Zhao, Zengshun Zhang, Changshui Computer and Communication Engineering School Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology China Key Laboratory of Computer Vision and System Tianjin University of Technology Ministry of Education China College of Electronics Communication and Physics Shandong University of Science and Technology China Automation Department Tsinghua University State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology China

ISBN: (纸本)9781509006199

In traditional multiple instance learning (MIL), both positive and negative bags are required to learn a prediction function. However, a high human cost is needed to know the label of each bag - positive or negative. Only positive bags contain our focus (positive instances) while negative bags consist of noise or background (negative instances). So we do not expect to spend too much to label the negative bags. Contrary to our expectation, nearly all existing MIL methods require enough negative bags besides positive ones. In this paper we propose an algorithm called 'Positive Multiple Instance' (PMI), which learns a classifier given only a set of positive bags. So the annotation of negative bags becomes unnecessary in our method. PMI is constructed based on the assumption that the unknown positive instances in positive bags be similar each other and constitute one compact cluster in feature space and the negative instances locate outside this cluster. The experimental results demonstrate that PMI achieves the performances close to or a little worse than those of the traditional MIL algorithms on benchmark and real data sets. However, the number of training bags in PMI is reduced significantly compared with traditional MIL algorithms. © 2016 IEEE.

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An adaptive strategy for updating the memory in evolutionary algorithms for dynamic optimization

An adaptive strategy for updating the memory in evolutionary...

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IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments

作者： Zhu, Tao Luo, Wenjian Li, Zhifang Nature Inspired Computation and Applications Laboratory School of Computer Science and Technology University of Science and Technology of China Hefei 230027 Anhui China Anhui Key Laboratory of Software Engineering in Computing and Communication University of Science and Technology of China Hefei 230027 China

ISBN: (纸本)9781424499311

The memory scheme is one of the most widely employed techniques in Evolutionary Algorithms for solving dynamic optimization problems. The updating strategy is a key concern for the memory scheme. Unfortunately, the existent memory updating strategies neglect the characteristics of the memory updating behaviors, and sometimes this could lead results against the original intention. In this paper, a novel updating strategy is proposed, which can adaptively update the memory according to the characteristics of the memory updating behaviors. Experiments are carried out in different kinds of dynamic environments, and the experimental results show that the proposed strategy is better than the traditional strategies. © 2011 IEEE.

关键词： Evolutionary algorithms

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On the self representation based on the adaptive self radius

On the self representation based on the adaptive self radius

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4th International Workshop on Advanced Computational Intelligence, IWACI 2011

作者： Zhang, Jie Luo, Wenjian School of Computer Science and Technology University of Science and Technology of China Hefei 230027 Anhui China Anhui Key Laboratory of Software Engineering in Computing and Communication University of Science and Technology of China Hefei 230027 Anhui China

ISBN: (纸本)9781612843735

Self representation problem is very important for the negative selection algorithms. In this paper, two methods, named as K-NN-ASR and B-NN-ASR, are proposed to calculate the self radius of a self sample in the continuous space. As for the K-NN-ASR, the self radius of a self sample is calculated according to the distances from itself to the surrounding self samples. In the B-NN-ASR, both the position of the self sample and the distances to the surrounding self samples are considered. Experiments are done to test the performance of the proposed methods. Experimental results demonstrate that the proposed methods are competitive, and the B-NN-ASR is better. © 2011 IEEE.

关键词： selection algorithm Self tuning CONTINUOUS SPACE

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Spike sorting based on radial basis function network with overlap decomposition

Spike sorting based on radial basis function network with ov...

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作者： Dai, Min Hu, Su-Rui Qi, Jia Tianjin Key Lab of Intelligent Computing and Novel Software Technology School of Computer and Communication Engineering Tianjin University of Technology Tianjin 300191 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin 300191 China

Spike sorting is the essential step in analyzing recording spike signals for studying information processing mechanisms within the nervous system. Overlapping is one of the most serious problems in the spike sorting for multi-channel recordings. In this paper, a modified radial basis function (RBF) network is proposed to decompose the overlapping signals and separate spikes within the same RBF network. A modified radial basis function based on the Gaussian function is employed in the method to improve the accuracy of overlap decomposition. In addition, the improved constructing algorithm reduces the calculation cost by taking advantage of the symmetry of the RBF network. The performance of the presented method is tested at various signal-to-noise ratio levels based on simulated data coming from the University of Leicester and Wave-clus software. Experiment results show that our method successfully solves the fully overlapping problem and has higher accuracy comparing with the Gaussian function. © 2011 Elsevier Ltd. All rights reserved.

关键词： Radial basis function networks

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A preliminary study on why using the nonself detector set for anomaly detection in artificial immune Systems

A preliminary study on why using the nonself detector set fo...

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2009 International Conference on Computational Intelligence and Security, CIS 2009

作者： Xu, Baoliang Luo, Wenjian Wang, Xufa Nature Inspired Computation and Applications Laboratory School of Computer Science and Technology University of Science and Technology of China Hefei 230027 Anhui China Anhui Key Laboratory of Software in Computing and Communication University of Science and Technology of China Hefei 230027 Anhui China

ISBN: (纸本)9780769539317

In artificial immune systems, the detectors in the nonself space are often adopted to detect anomaly changes, such as the negative selection algorithm and its improvements. Since the detectors in the self space can also be used to detect anomaly changes, a frequently asked question is which kind of detector sets is more efficient for a specific problem. In this paper, firstly, the advantages and disadvantages of the self detector set and the nonself detector set are briefly reviewed. Secondly, when the complete matching rule is adopted, the average time costs of employing the nonself detector set and the self detector set for anomaly detection are compared theoretically. Thirdly, simulated experiments are done, and experimental results demonstrate that the theoretical conclusion is essentially correct. © 2009 IEEE.

关键词： Immune system

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Linguistic Steganalysis Based on Meta Features and Immune Mechanism

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Chinese Journal of Electronics 2023年第4期19卷 661-666页

作者： Hao Yang Xianbin Cao Anhui Province Key Laboratory of Software in Computing and Communication School of Computer Science and Technology University of Science and Technology of China Hefei China

Linguistic steganalysis depends on efficient detection features due to the diversity of syntax and the polysemia of semantics in natural language processing. This paper presents a novel linguistics steganalysis approach based on meta features and immune clone mechanism. Firstly, meta features are used to represent texts. Then immune clone mechanism is exploited to select appropriate features so as to constitute effective detectors. Our approach employed meta features as detection features, which is an opposite view from the previous literatures. Moreover, the immune training process consists of two phases which can identify respectively two kinds of stego texts. The constituted detectors have the capable of blind steganalysis to a certain extent. Experiments show that the proposed approach gets better performance than typical existing methods, especially in detecting short texts. When sizes of texts are confined to 3kB, detection accuracies have exceeded 95%.

关键词： Training Integrated circuits Steganography Semantics Cloning Detectors Linguistics

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Modeling and analysis of collaboration network in large-scale complex software systems

Modeling and analysis of collaboration network in large-scal...

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作者： Sun, Shiwen Xia, Chengyi Wang, Li School of Computer and Communication Engineering Tianjin University of Technology Tianjin 300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin 300384 China

ISBN: (纸本)9789881725592

Large-scale software systems represent an important class of artificial complex systems, which can be investigated from the complex network perspective. Based on the collaboration network model, the collaboration relationships between header files from source code of large-scale computer software systems are explored. It has been found that the generated network displays a high degree of heterogeneity in connectivity. The structural heterogeneity also leads to network's another important feature - Robust yet fragile. That is the network displays an unexpected degree of robustness to random attack or disturbance, but is extremely vulnerable to intentional attack, i.e. to the removal of a few of highly connected nodes. These findings can help people to get a better understanding of the organizational principles and evolving mechanism of complex software systems. © 2011 Chinese Assoc of Automati.

关键词： Complex networks

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Directed differential evolution based on directional derivative for numerical optimization problems

Directed differential evolution based on directional derivat...

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2011 11th International Conference on Hybrid Intelligent Systems, HIS 2011

作者： Zhang, Jun Luo, Wenjian School of Computer Science and Technology University of Science and Technology of China Hefei 230027 Anhui China Anhui Province Key Laboratory of Software Engineering in Computing and Communication University of Science and Technology of China Hefei 230027 Anhui China

ISBN: (纸本)9781457721502

Differential Evolution is one kind of Evolutionary Algorithms, which has been successfully applied to solve many optimization problems. In this paper, a directed differential mutation (DDM), which utilizes the directional derivative to decide a suitable search direction and a proper mutation step size, is proposed. It is merged into the classical DE to form a new algorithm, named directed differential evolution (DDE). The performance of the DDE is tested on 23 classical problems for numerical optimization. The experimental results demonstrate that the performance of the DDE outperforms the classical DE on most functions. © 2011 IEEE.

关键词： Evolutionary algorithms

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SvdNPD: A negative data publication method based on the sensitive value distribution

SvdNPD: A negative data publication method based on the sens...

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International Workshop on Artificial Immune Systems, AIS 2015

作者： Wu, Linli Luo, Wenjian Zhao, Dongdong School of Computer Science and Technology University of Science and Technology of China Hefei Anhui230027 China Anhui Province Key Laboratory of Software Engineering in Computing and Communication University of Science and Technology of China Hefei Anhui230027 China

ISBN: (纸本)9781509002986

Existing data publication methods retain the relationship between the quasi-identifier attributes and sensitive attributes of published data. We call them "positive data publication". However, it will lead to potential risk that attackers could deduce the privacy of the corresponding individuals from the published data. Recently, by combining the negative representation with k-anonymity and l-diversity, (k, m)- anonNPD and (l, m)-divNPD algorithms have been proposed, which improve the degree of privacy because the sensitive value of each individual is transformed into a negative value randomly. However, to reduce the average error of the reconstructed distribution, the data published by (k, m)-anonNPD and (l, m)-divNPD are often much larger than the original data. In this paper, we take the Sensitive Value Distribution into consideration, and propose a novel Negative Data Publication approach (i.e. SvdNPD), which is based on l-diversity. According to the sensitive value distribution, the negative selection probability of the sensitive attribute in each record can be calculated. Then the data could be published by transforming the sensitive value to a negative one with this probability. Experimental results show that the effective usage of the individual sensitive value distribution could enhance the utility of published data. Meanwhile, compared with (l, 1)-divNPD, SvdNPD just sacrifices a tiny privacy degree which is acceptable. © 2015 IEEE.

关键词： Metadata

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