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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Rui Zeng Jiasong Wu L. Senhadji Huazhong Shu LIST Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education Nanjing 210096 China

ISBN: (纸本)9781467369985

This paper proposes an multilinear discriminant analysis network (MLDANet) for the recognition of multidimensional objects, knows as tensor objects. The MLDANet is a variation of linear discriminant analysis network (LDANet) and principal component analysis network (PCANet), both of which are the recently proposed deep learning algorithms. The MLDANet consists of three parts: 1) The encoder learned by MLDA from tensor data. 2) Features maps obtained from decoder. 3) The use of binary hashing and histogram for feature pooling. A learning algorithm for MLDANet is described. Evaluations on UCF11 database indicate that the proposed MLDANet outperforms the PCANet, LDANet, MPCA+LDA, and MLDA in terms of classification for tensor objects.

关键词： Deep learning LDANet MLDANet PCANet tensor object classification

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A novel community detection method based on discrete particle swarm optimization algorithms in complex networks

A novel community detection method based on discrete particl...

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Congress on Evolutionary Computation

作者： Cen Cao Qingjian Ni Yuqing Zhai School of Computer Science and Engineering Nanjing China Key Lab of Computer Network & Information Integration MOE P.R.China

ISBN: (纸本)9781479974931

The community structure is one of the most common and important attributes in complex networks. Community detection in complex networks has attracted much attention in recent years. As an effective evolutionary computation technique, particle swarm optimization (PSO) algorithm has become a candidate for many optimization applications. However, PSO algorithm was originally designed for continuous optimization. In this paper, an improved simple discrete particle swarm optimization (ISPSO) algorithm and a discrete particle swarm optimization with redefined operator (IDPSO-RO) algorithm are proposed in the discrete context of community detection problem. Furthermore, a community correcting strategy is used to optimize the results. The performance of the two algorithms is tested on three real networks with known community structures. The experiment results show that ISPSO and IDPSO-RO algorithms using community correcting strategy can detect community structures more efficiently without prior knowledge about the size of communities and the number of communities.

关键词： Particle swarm optimization Complex networks Sociology Statistics Algorithm design and analysis Optimization Image edge detection

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Tensor object classification via multilinear discriminant analysis network 40

Tensor object classification via multilinear discriminant an...

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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Zeng, Rui Wu, Jiasong Senhadji, Lotfi Shu, Huazhong LIST Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China INSERM U 1099 Rennes France Laboratoire Traitement du Signal et de l'Image Universit de Rennes 1 Rennes France France

ISBN: (纸本)9781467369978

关键词： Deep learning LDANet MLDANet PCANet tensor object classification

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Carriers Selection for Content Dissemination in Social Opportunistic networks

Carriers Selection for Content Dissemination in Social Oppor...

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International Conference on Collaborative Computing: networking, Applications and Worksharing (CollaborateCom)

作者： Yongqiang Dong Wang Yang Taieb Znati Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China Department of Computer Science School of Arts and Sciences Pittsburgh PA USA

Content dissemination in social opportunistic networks has attracted great interests in the field. Most of the researches in literature focused on the data forwarding and routing mechanisms in such network scenarios. There were few studies on the assignment of content originators. This paper is concerned about how to identify a small subset of users as content carriers through which the content can be disseminated more efficiently. The problem is modeled as a dominating set problem in bipartite graph, in which a minimum number of carriers are selected to cover all the venues with required probability, by using available location affinity information. The NP-complete property of the problem is pointed out, along with an exact exponential exhaustive algorithm. Then three polynomial heuristic algorithms are presented to solve the venue coverage problem. Among them, the adaptive metric-based algorithm chooses carriers based on dynamically calculated metric values. Several user-centric and expected contribution based metrics are discussed and examined. The submodular algorithm selects carriers greedily based on the estimation of marginal coverage gain from each user. The reduced-union algorithm asks each venue to select a candidate set separately based on its own individual affinity information. Then the candidate union set is reduced gradually by reasonable greedy method. Synthetized data and real trace experiment results show that the submodular algorithm can be applied in scenarios where coverage threshold is low and small number of carriers is required. In most cases we can rely on metric-based algorithm to solve the carriers selection problem. Among all the metrics, total affinity and proportional contribution perform more valuable than other metrics. Meanwhile, the adaptive operation in metric-based algorithm plays more important role than the metrics in seeking the most qualified carriers. When complete affinity information is not available, the reduced-union algo

关键词： Bipartite graph Algorithm design and analysis Routing Heuristic algorithms Delays network topology

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A novel PSO for portfolio optimization based on heterogeneous multiple population strategy

A novel PSO for portfolio optimization based on heterogeneou...

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Congress on Evolutionary Computation

作者： Xushan Yin Qingjian Ni Yuqing Zhai College of Software Engineering Southeast University Nanjing China MOE Key Lab of Computer Network & Information Integration P.R.China

ISBN: (纸本)9781479974931

The problem of portfolio selection in the field of financial engineering has received more attention in recent years. This paper presents a novel heterogeneous multiple population particle swarm optimization algorithm (HMPPSO) for solving a generalized Markowitz mean-variance portfolio selection model. The proposed HMPPSO is based on heterogeneous multiple population strategy, in which the whole population is divided into several sub-populations and all the sub-populations evolve with different PSO variants. The communication between the sub-populations is executed at regular intervals to maintain the information exchange inside the entire population and coordinate exploration and exploitation according to certain migration rules. The generalized portfolio selection model is classified as a quadratic mixed-integer programming model for which no computational efficient algorithms have been proposed. We employ the proposed HMPPSO to find the solution for the model and compare the performance of HMPPSO with several classic PSO variants. The test data set is the weekly prices from March, 1992 to September, 1997 including the following indices: Hang Seng in Hong Kong, DAX 100 in Germany, FTSE 100 in UK, S&P 100 in USA and Nikkei 225 in Japan. The computational results demonstrate that HMPPSO is much effective and robust, especially for problems with high dimensions, thus provides an effective solution for the portfolio optimization problem.

关键词： Portfolios Sociology Statistics Optimization Computational modeling Standards Particle swarm optimization

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A Generalized Weighted Closed Sequential Pattern Mining Algorithm with Item Interval

A Generalized Weighted Closed Sequential Pattern Mining Algo...

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2015 International Conference on Automation,Mechanical Control and Computational Engineering(AMCCE 2015)

作者： Haitao Lu Shuo Li College of Information Science and Engineering Yanshan University The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Library of Yanshan University Yanshan University

ISBN: (纸本)9781510803084

The algorithm of this paper inserts pseudo items which are converted from item interval to obtain equal extended sequence database;it defines item-interval constraints, which are relative to the item weight, to prune the mining patterns. Through doing this, the algorithm avoids mining the patterns which users are not interested in and shortens the running time. It adopts histogram statistic pattern to get the standardization description to item interval of the mining patterns, making the mining sequences include the item interval information which is valuable to user decision.

关键词： weighted sequential patterns closed sequential patterns item interval

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Enhancing binary relevance for multi-label learning with controlled label correlations exploitation

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8862卷 91-103页

作者： Li, Yu-Kun Zhang, Min-Ling School of Computer Science and Engineering Southeast University China Key Laboratory of Computer Network and Information Integration Southeast University China

Binary relevance (BR) is regarded as the most intuitive solution to learn from multi-label data, which decomposes the multi-label learning task into a number of independent binary learning tasks (one per class label). To amend its potential weakness of ignoring label correlations, many correlation-enabling extensions to BR have been proposed based on two major strategies, i.e. assuming random correlations with chaining structure or taking full-order correlations with stacking structure. However, in both strategies label correlations are only exploited in an uncontrolled manner, which may be problematic when error-prone and uncorrelated class labels arise. In this paper, to fulfill controlled label correlations exploitation, a novel enhancement to BR is proposed based on a two-stage filtering procedure. In the first stage, error-prone class labels are pruned from the label space based on holdout validation. In the second stage, closely-related class labels are identified based on supervised feature selection by viewing unpruned labels as features. Extensive experiments across fourteen multi-label data sets confirm the superiority of controlled label correlations exploitation, especially when large number class labels exist in the label space. © Springer International Publishing Switzerland 2014.

关键词： Learning systems

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Gradient Compared lp-LMS Algorithms for Sparse System Identification

Gradient Compared lp-LMS Algorithms for Sparse System Identi...

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第27届中国控制与决策会议

作者： Yong Feng Jiasong Wu Rui Zeng Limin Luo Huazhong Shu School of Biological Science and Medical Engineering Southeast University Laboratory of Image Science and Technology the Key Laboratory of Computer Network and Information IntegrationSoutheast University

ISBN: (纸本)9781479970186

In this paper,we propose two novel p-norm penalty least mean square（l-LMS） algorithms as supplements of the conventional l-LMS algorithm established for sparse adaptive filtering recently.A gradient comparator is employed to selectively apply the zero attractor of p-norm constraint for only those taps that have the same polarity as that of the gradient of the squared instantaneous error,which leads to the new proposed gradient compared p-norm constraint LMS algorithm（lGC-LMS）.We explain that the lGC-LMS can achieve lower mean square error than the standard l-LMS algorithm theoretically and *** further improve the performance of the filter,the lNGC-LMS algorithm is derived using a new gradient comparator which takes the sign-smoothed version of the previous *** performance of the l NGC-LMS is superior to that of the lGC-LMS in theory and in ***,these two comparators can be easily applied to other norm constraint LMS algorithms to derive some new approaches for sparse adaptive *** numerical simulation results show that the two proposed algorithms achieve better performance than the standard LMS algorithm and l-LMS algorithm in terms of convergence rate and steady-state behavior in sparse system identification settings.

关键词： Least Mean Square Algorithm p Norm Constraint Sparse System Identification New Gradient Comparator

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Detecting bot-infected machines based on analyzing the similar periodic DNS queries

Detecting bot-infected machines based on analyzing the simil...

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International Conference on Computing, Management and Telecommunications (ComManTel)

作者： Truong Dinh Tu Cheng Guang Liang Yi Xin School of Computer Science and Engineering Southeast University Nanjing China Department of Information Technologies Tuyhoa Industrial College Phuyen Vietnam Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing China

Modern botnets such as Zeus and Conficker commonly utilize a technique called domain fluxing or a Domain Generation Algorithm (DGA) to generate a large number of pseudo-random domain names dynamically for botnet operators to control their bots. These botnets are becoming one of the most serious threats to the Internet security on a global scale. In this paper, we present a method based on analyzing the similar periodic time intervals series of DNS queries to identify DGA-bot infected machines. This method passively captures all DNS traffic from the gateway of monitor network. Firstly, we group queries of the same domain name that is requested by hosts, and then extracts time interval series between adjacent queries. Secondly, we measure the similar periodicity of DNS queries by calculating the squared Euclidean distance between each pair of their time interval series. Finally, we apply a hierarchical clustering algorithm to cluster high similar domain names. The experiment results show that the domain names are generated by the same botnet or DGA would be grouped into the same cluster, thus all of the hosts that query to these clusters are marked as compromised hosts running a domain-flux botnet within monitor network.

关键词： Correlation Servers Monitoring Time measurement Clustering algorithms Time series analysis Euclidean distance

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Combo-Pre: A Combination Link Prediction Method in Opportunistic networks

Combo-Pre: A Combination Link Prediction Method in Opportuni...

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International Conference on computer Communications and networks (ICCCN)

作者： Yin Li Sanfeng Zhang College of Software Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education Southeast University Nanjing China

Opportunistic networks have emerged as prospective network architecture for smart mobile devices related applications. The main obstacle for designing routing protocol in opportunistic networks is their inherent dynamic nature. Unknown future link patterns lead to blind and inefficient packet forwarding behavior. Lots of efforts have focused on the future link prediction problem, but contact patterns among node pairs are too different to be predicted by one single method. To this end, we propose a combination link prediction method for opportunistic network routing named Combo-Pre. Combo-Pre applies periodic pattern mining methods to predict frequent and periodic contacts, decision tree method to predict frequent but non-periodic contacts and Adamic-Adar methods in complex networks to predict infrequent contacts. Experimental results show that Combo-Pre outperforms state-of-the-art link prediction methods in opportunistic networks. These results can be applied in routing protocol design to decrease routing cost and promote delivery rate.

关键词： Prediction methods Accuracy Routing Complex networks Jitter Decision trees History

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