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3rd International Conference on Advanced Cloud and Big Data, CBD 2015

作者： Zou, Yue Zhang, Sanfeng College of Software Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration in Southeast University Ministry of Education Southeast University Nanjing China

ISBN: (纸本)9781467385374

Opportunistic network mobile user-nodes location prediction can help to collect the data, improve the dynamic network and help to decrease the problems which include the delay, low success rate, high storage space energy consume and so on, which are the results of the blindness in the process of delivery. In this paper, a mobile node location prediction algorithm base on the Multi-Order Markov prediction model as the initial forecast, and then use social-relationship which is closely linked to the node to optimize the algorithm. Experimental results show that the proposed algorithm is better than the single Multi-Order Markov model. © 2015 IEEE.

关键词： Location

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Reliable task allocation with load balancing in multiplex networks

Reliable task allocation with load balancing in multiplex ne...

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作者： Jiang, Yichuan Zhou, Yifeng Li, Yunpeng School of Computer Science and Engineering Southeast University Nanjing211189 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing211189 China

In multiplex networks, agents are connected by multiple types of links;a multiplex network can be split into more than one network layer that is composed of the same type of links and involved agents. Each network link type has a bias for communicating different types of resources;thus, the task's access to the required resources in multiplex networks is strongly related to the network link types. However, traditional task allocation and load balancing methods only considered the situations of agents themselves and did not address the effects of network link types in multiplex networks. To solve this problem, this article considers both link types and agents, and substantially extends the existing work by highlighting the effect of network layers on task allocation and load balancing. Two multiplex network-adapted models of task allocation with load balancing are presented: network layer-oriented allocation and agent-oriented allocation. This article also addresses the unreliability in multiplex networks, which includes the unreliable links and agents, and implements a reliable task allocation based on a negotiation reputation and reward mechanism. Our findings show that both of our presented models can effectively and robustly satisfy the task allocation objectives in unreliable multiplex networks;the experiments prove that they can significantly reduce the time costs and improve the success rate of tasks for multiplex networks over the traditional simplex network-adapted task allocation model. Lastly, we find that our presented network layer-oriented allocation performs much better in terms of reliability and allocation time compared to our presented agent-oriented allocation, which further explains the importance of network layers in multiplex networks. © 2015 ACM.

关键词： network layers

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Gradient compared p-LMS algorithms for sparse system identification 27

Gradient compared p-LMS algorithms for sparse system identif...

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27th Chinese Control and Decision Conference, CCDC 2015

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

ISBN: (纸本)9781479970179

In this paper, we propose two novel p-norm penalty least mean square (p-LMS) algorithms as supplements of the conventional p-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 (pGC-LMS). We explain that the pGC-LMS can achieve lower mean square error than the standard p-LMS algorithm theoretically and experimentally. To further improve the performance of the filter, the pNGC-LMS algorithm is derived using a new gradient comparator which takes the sign-smoothed version of the previous one. The performance of the pNGC-LMS is superior to that of the pGC-LMS in theory and in simulations. Moreover, these two comparators can be easily applied to other norm constraint LMS algorithms to derive some new approaches for sparse adaptive filtering. The numerical simulation results show that the two proposed algorithms achieve better performance than the standard LMS algorithm and p-LMS algorithm in terms of convergence rate and steady-state behavior in sparse system identification settings. © 2015 IEEE.

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

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Temporally or spatially dispersed joint RFID estimation using snapshots of variable lengths 15

Temporally or spatially dispersed joint RFID estimation usin...

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16th ACM International Symposium on Mobile Ad Hoc networking and Computing, MobiHoc 2015

作者： Xiao, Qingjun Chen, Min Chen, Shigang Zhou, Yian Key Lab of Computer Network and Information Integration Southeast University Ministry of Education China Department of Computer and Information Science and Engineering University of Florida GainesvilleFL United States

ISBN: (纸本)9781450334891

Radio-frequency identification (RFID) technology has been widely used in applications such as inventory control, object tracking, supply chain management. An important research is to estimate the number of tags in a certain area covered by readers. This paper extends the research in both temporal and spatial dimensions to provide much richer information for monitoring the dynamics of distributed RFID systems. More specifically, we are interested in estimating the joint properties of any two snapshots taken at arbitrary locations and arbitrary times in a system. With many practical applications, there is however little prior work on this problem. We propose a joint RFID estimation protocol based on a simple yet versatile snapshot construction. Given the snapshots of any two tag sets, although their sizes may be very different, we design a way to combine their information and more importantly derive formulas to extract the joint properties of the two tag sets from the combined information, with an accuracy that can be arbitrarily set. Through formal analysis, we determine the optimal system parameters that minimize the execution time of taking snapshots, under the constraints of a given accuracy requirement. Our simulation results show that the proposed protocol can reduce the execution time by multifold when comparing with the best alternative approach in the literature. Copyright © 2015 ACM.

关键词： Inventory control

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Multi-threshold image segmentation through an improved quantum-behaved particle swarm optimization algorithm 1st

Multi-threshold image segmentation through an improved quant...

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1st International Conference on Cloud Computing and Security, ICCCS 2015

作者： Jiali, Wang Hongshen, Liu Yue, Ruan School of Computer Science Anhui University of Technology Maanshan243005 China School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University Nanjing210096 China

ISBN: (纸本)9783319270500

Multi-threshold segmentation is a basic and widely used technique in image segmentation. The key step of accomplishing this task is to find the optimal multi-threshold value, which in essence can be reduced to multi-objective optimization problem. The quantum particle-behaved swarm algorithm (QPSO) is an effective method to resolve the problem of this class. However in practice, we found the original QPSO has imperfections, such as the excessive dropping of the diversity of the population and trapping in local optimum. In order to improve the ability of searching the global optimum and accelerate the speed of convergence, we proposed an improved quantum-behaved particle swarm algorithm (IQPSO). The experiments showed that IQPSO was superior to PSO and QPSO on the searching of multi-threshold value in image segmentation under the premise of ensuring the accuracy of solutions. © Springer International Publishing Switzerland 2015.

关键词： Image segmentation

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A novel search engine-based method for discovering command and control server 15th

A novel search engine-based method for discovering command a...

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15th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2015

作者： Guo, Xiaojun Cheng, Guang Pan, Wubin Dinhtu, Truong Liang, Yixin School of Computer Science and Engineering Southeast University Nanjing211189 China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing211189 China School of Information Engineering Xizang Minzu University Xianyang712082 China XiZang Key Laboratory of Optical Information Processing and Visualization Technology Xizang Minzu University Xianyang712082 China

ISBN: (纸本)9783319271361

To solve the problem of getting command and control (C&C) server address covertly for malware of Botnet or advanced persistent threats, we propose a novel C&C-server address discovery scheme via search engine. This scheme is com-posed of five modules. The botmaster uses publish module to issue C&C-server IPs in diaries of several free blogs on Internet firstly. Then these diaries could be indexed by search engine (SE). When the infected terminal becomes a bot, it uses keyword production module to produce search keyword and submits some or all these keywords to SEs to obtain the search engine result pages (SERPs). For items in SERPs, the bot uses filtering algorithm to remove noise items and leave valid items whose abstract contain C&C-server IPs. Lastly the bot utilizes extraction and conversion module to extract these C&C-server IPs and translates them into binary format. The experimental results show that our proposed scheme is fully able to discover and obtain C&C-server IPs via various search engines. Furthermore, if we set proper threshold value for SE, it can extract C&C-server IPs accurately and efficiently. © Springer International Publishing Switzerland 2015.

关键词： Search engines

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Dust:Real-Time Code Offloading System for Wearable Computing

Dust:Real-Time Code Offloading System for Wearable Computing

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IEEE Global Communications Conference

作者： Di Huang Luning Yang Sanfeng Zhang Key Laboratory of Computer Network and Information Integration (Southeast University) Nanjing University

ISBN: (纸本)9781479959532

Runtime performance seriously bothers application developers and users for wearable devices such as Apple Watch and Google Glass. To ease such pains, one approach is to transplant the computation to the cloud, known as cloud offloading. This paradigm works well when the Internet is accessible. Another solution, known as device-to-device (D2D) offloading, is to utilize nearby devices for computation offloading. In this paper, propose the Dust, a D2D code offloading prototype for wearable computing. To our best knowledge, Dust is the first code offloading system for wearable computing with an implementation on Google Glass. Dust includes a programmer friendly framework based on Java annotation, a lightweight offloading service, and a runtime task scheduler to make offloading decisions. Extensive experiments demonstrate that Dust achieves real-time performance with speedup of 6.1X.

关键词： Wearable computers UNLOADING CLOUDS Clouds DIRT application developer Speeding

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Motiviting Others Drives Jamshidi [SMC Innovators]

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IEEE Systems, Man, and Cybernetics Magazine 2015年第3期1卷 67-72页

作者： M.E. El-Hawary School of Computer Science and Engineering Southeast University China Ministry of Education Key Laboratory of Computer Network and Information Integration China

Presents an interview conducted with Dr. Jamshidi.

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Leveraging Implicit Relative Labeling-Importance information for Effective Multi-label Learning

Leveraging Implicit Relative Labeling-Importance Information...

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IEEE International Conference on Data Mining (ICDM)

作者： Yu-Kun Li Min-Ling Zhang Xin Geng Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education China

ISBN: (纸本)9781467395052

In multi-label learning, each training example is represented by a single instance while associated with multiple labels, and the task is to predict a set of relevant labels for the unseen instance. Existing approaches learn from multi-label data by assuming equal labeling-importance, i.e. all the associated labels are regarded to be relevant while their relative importance for the training example are not differentiated. Nonetheless, this assumption fails to reflect the fact that the importance degree of each associated label is generally different, though the importance information is not explicitly accessible from the training examples. In this paper, we show that effective multi-label learning can be achieved by leveraging the implicit relative labeling-importance (RLI) information. Specifically, RLI degrees are formalized as multinomial distribution over the label space, which are estimated by adapting an iterative label propagation procedure. After that, the multi-label prediction model is learned by fitting the estimated multinomial distribution as regularized with popular multi-label empirical loss. Comprehensive experiments clearly validate the usefulness of leveraging implicit RLI information to learn from multi-label data.

关键词： Yttrium Training Reliability Predictive models Symmetric matrices Semantics Estimation

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Hyper-compact virtual estimators for big network data based on register sharing

Hyper-compact virtual estimators for big network data based ...

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ACM SIGMETRICS International Conference on Measurement and Modeling of computer Systems, SIGMETRICS 2015

作者： Xiao, Qingjun Chen, Shigang Chen, Min Ling, Yibei Department of Computer and Information Science and Engineering University of Florida GainesvilleFL United States Key Lab. of Computer Network and Information Integration Southeast University Ministry of Education China Telcordia Technologies and Applied Research Ericsson United States

ISBN: (纸本)9781450334860

Cardinality estimation over big network data consisting of numerous flows is a fundamental problem with many practical applications. Traditionally the research on this problem focused on using a small amount of memory to estimate each flow's cardinality from a large range (up to 109). However, although the memory needed for each flow has been greatly compressed, when there is an extremely large number of flows, the overall memory demand can still be very high, exceeding the availability under some important scenarios, such as implementing online measurement modules in network processors using only on-chip cache memory. In this paper, instead of allocating a separated data structure (called estimator) for each flow, we take a different path by viewing all the flows together as a whole: Each flow is allocated with a virtual estimator, and these virtual estimators share a common memory space. We discover that sharing at the register (multi-bit) level is superior than sharing at the bit level. We propose a framework of virtual estimators that allows us to apply the idea of sharing to an array of cardinality estimation solutions, achieving far better memory efficiency than the best existing work. Our experiment shows that the new solution can work in a tight memory space of less than 1 bit per flow or even one tenth of a bit per flow-a quest that has never been realized before. © Copyright 2015 ACM.

关键词： Cache memory

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