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Ad-Hoc and Sensor Networks Symposium

作者： Thuan Ngo Hiroki Nishiyama Nei Kato Satoshi Kotabe Hiroshi Tohjo Graduate School of Information Sciences Tohoku University NTT Network Innovation Laboratories NTT Corporation

ISBN: (纸本)9781479966653

Although the importance of Disaster Response Networks (DRNs) has been highlighted in many researches, the requirement of spectrum agility has not been well addressed. In this paper, we focus on using all-spectrum cognitive radio for DRNs to fulfill this requirement. We consider a DRN constructed by Cognitive Radio Base Stations (CRBSs), which are deployed in the disaster affected area. Each CRBS is equipped with multiple antennas to support different frequency bands available in the area. based on the considered DRN, we propose a graph-based Hybrid Adaptive Routing scheme, which we refer to as GHAR. There are two phases in GHAR, centralized phase for topology formation and distributed phase for adaptive routing. In the centralized phase, we propose an algorithm that unites k non-overlapping minimum spanning trees to construct the topology for the next phase. We provide an analysis on the relationship between k and the adaptability with cognitive radio as well as the complexity of routing process. We also provide an analysis on the optimality of k. Furthermore, extensive simulations are conducted to validate our analysis. Simulation results confirm the effectiveness of our proposal and the existence of the optimal value of k.

关键词： Cognitive radio Adaptive routing graph-based algorithm Disaster response network

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Towards Deployment of a User-Aware Tenancy Using Rich-Variant Components for SaaS Applications 4

Towards Deployment of a User-Aware Tenancy Using Rich-Varian...

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International Conference on Cloud Computing Technologies and Applications (CloudTech)

作者： Kriouile, Houda El Asri, Bouchra El Haloui, M'barek Benali, Asmae Mohammed V Univ ENSIAS SIME Lab IMS Team Rabat Morocco

ISBN: (纸本)9781467381499

The approach of User-Aware Tenancy integrates the high configurability of multi-tenant applications with the flexibility and the functional variability of Rich-Variant Component use. Multi-tenancy concept consists in sharing instances among a large group of customers, called tenants. Multi-tenancy is a tool to exploit economies of scale widely promoted by Software as a Service (SaaS) models. However, the ability of a SaaS application to be adapted to individual tenant's needs seem to be a major requirement. Thus, our approach focuses on more flexibility and more reusability for Multi-tenant SaaS application using the multiview notion of Rich-Variant Components. The approach consists in a user-aware tenancy for SaaS. In this paper, we provide an application of an algorithm deriving the necessary instances of Rich-Variant Components building the application in a scalable and performing manner. The algorithm is based on fundamental concepts from the graph theory, and is accompanied by a reduced school management application as an illustrating example.

关键词： SaaS application Rich-Variant Component graph-based algorithm

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Dynamic Clustering algorithm Design for Ultra Dense Small Cell Networks in 5G 10

Dynamic Clustering Algorithm Design for Ultra Dense Small Ce...

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10th International Conference on Communications and Networking in China (CHINACOM)

作者： Chen, Siyi Xing, Chengwen Fei, Zesong Wang, Hualei Pan, Zhengang Beijing Inst Technol Sch Informat & Elect Beijing Peoples R China China Mobile Res Inst Beijing Peoples R China

ISBN: (纸本)9781479987955

Ultra dense networks are a promising technology enabling high power and spectrum efficiencies in future wireless systems. It is well-known that for ultra dense networks inter-cell interference is one of the main bottlenecks prohibiting achieving the promised performance gains. In order to effectively coordinate or mitigate interference in paper, we propose a graph-based low complexity dynamic clustering algorithm. The key idea behind the proposed algorithm is that dividing the whole network into a number of clusters under size constraint and the maximum intra-cluster interference and minimum inter-cluster interference. The logic is maximum intra-cluster can be effectively controlled by the coordination within each cluster. Meanwhile, graph-based algorithm is exploited to further reduce implementation complexity and make the proposed algorithm suitable for practical implementation. Finally, simulation results numerically demonstrate that the proposed low complexity algorithm has almost the same performance compared to the existing high performance algorithm but the complexity is much lower.

关键词： Ultra dense networks clustering algorithms graph-based algorithm

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graph-based approach for outlier detection in sequential data and its application on stock market and weather data

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KNOWLEDGE-based SYSTEMS 2014年 61卷 89-97页

作者： Rahmani, Ali Afra, Salim Zarour, Omar Addam, Omar Koochakzadeh, Negar Kianmehr, Keivan Alhajj, Reda Rokne, Jon Global Univ Dept Comp Sci Beirut Lebanon Univ Calgary Dept Comp Sci Calgary AB T2N 1N4 Canada

Outlier detection has a large variety of applications ranging from detecting intrusion in a computer network, to forecasting hurricanes and tornados in weather data, to identifying indicators of potential crisis in stock market data, etc. The problem of finding outliers in sequential data has been widely studied in the data mining literature and many techniques have been developed to tackle the problem in various application domains. However, many of these techniques rely on the peculiar characteristics of a specific type of data to detect the outliers. As a result, they cannot be easily applied to different types of data in other application domains;they should at least be tuned and customized to adapt to the new domain. They also may need certain amount of training data to build their models. This makes them hard to apply especially when only a limited amount of data is available. The work described in this paper tackle the problem by proposing a graph-based approach for the discovery of contextual outliers in sequential data. The developed algorithm offers a higher degree of flexibility and requires less amount of information about the nature of the analyzed data compared to the previous approaches described in the literature. In order to validate our approach, we conducted experiments on stock market and weather data;we compared the results with the results from our previous work. Our analysis of the results demonstrate that the algorithm proposed in this paper is successful and effective in detecting outliers in data from different domains, one financial and the other meteorological. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Data mining graph-based algorithm Outlier detection Weather data Stock market

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Bidirectional Weight graph Transformation Matching algorithm 4

Bidirectional Weight Graph Transformation Matching Algorithm

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International Conference on Audio, Language and Image Processing

作者： Wang, Song Guo, Xin Mu, Xiaomin Qi, Lin Zhengzhou Univ Sch Informat Engn Zhengzhou Peoples R China

ISBN: (纸本)9781479939039

A stable and accurate point matching algorithm named Bidirectional Weight graph Transformation Matching (BWGTM) is proposed in this paper. The algorithm starts with a set of correspondences which contain a variable number of erroneous correspondences, or outliers, in addition to a fixed number of true correspondences (inliers). For each feature point and its K nearest neighbors (KNN), there are two set of graphs to be generated. Depending on the co-angular distances between edges that connect a feature point to its KNN in graphs, the vertices with maximum distance will be founded and deemed as candidate outliers. Considering that some inliers whose KNN consist of outliers are regarded as candidate outliers, a recovery strategy utilizes the addition of fresh vertices to regain these inliers. Experimental results demonstrate the superior performance of this algorithm under various conditions for images.

关键词： point matching algorithm graph-based algorithm image registration outlier removal

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Bidirectional Weight graph Transformation Matching algorithm

Bidirectional Weight Graph Transformation Matching Algorithm

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International Conference on Audio, Language and Image Processing

作者： Song Wang Xin Guo Xiaomin Mu Lin Qi School of Information Engineering Zhengzhou University

ISBN: (纸本)9781479939046

关键词： Point matching algorithm graph-based algorithm Image registration Outlier removal

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Robust Weighted graph Transformation Matching for Rigid and Nonrigid Image Registration

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2012年第10期21卷 4369-4382页

作者： Izadi, Mohammad Saeedi, Parvaneh Simon Fraser Univ Sch Engn Sci Lab Robot Vis Burnaby BC V5A 1S6 Canada

This paper presents an automatic point matching algorithm for establishing accurate match correspondences in two or more images. The proposed algorithm utilizes a group of feature points to explore their geometrical relationship in a graph arrangement. The algorithm starts with a set of matches (including outliers) between the two images. A set of nondirectional graphs is then generated for each feature and its K nearest matches (chosen from the initial set). Using the angular distances between edges that connect a feature point to its K nearest neighbors in the graph, the algorithm finds a graph in the second image that is similar to the first graph. In the case of a graph including outliers, the algorithm removes such outliers (one by one, according to their strength) from the graph and re-evaluates the angles until the two graphs are matched or discarded. This is a simple intuitive and robust algorithm that is inspired by a previous work. Experimental results demonstrate the superior performance of this algorithm under various conditions, such as rigid and nonrigid transformations, ambiguity due to partial occlusions or match correspondence multiplicity, scale, and larger view variation.

关键词： Feature-point matching graph-based algorithm image registration outlier detection structural similarity

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Efficient Manifold Ranking for Image Retrieval 11

Efficient Manifold Ranking for Image Retrieval

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34th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

作者： Xu, Bin Bu, Jiajun Chen, Chun Cai, Deng He, Xiaofei Liu, Wei Luo, Jiebo Zhejiang Univ Coll Comp Sci Zhejiang Prov Key Lab Serv Robot Hangzhou Peoples R China Zhejiang Univ Coll Comp Sci State Key Lab CAD&CG Hangzhou Peoples R China Columbia Univ New York NY USA Eastman Kodak Co Kodak Res Labs Rochester NY USA

ISBN: (纸本)9781450309349

Manifold Ranking (MR), a graph-based ranking algorithm, has been widely applied in information retrieval and shown to have excellent performance and feasibility on a variety of data types. Particularly, it has been successfully applied to content-based image retrieval, because of its outstanding ability to discover underlying geometrical structure of the given image database. However, manifold ranking is computationally very expensive, both in graph construction and ranking computation stages, which significantly limits its applicability to very large data sets. In this paper, we extend the original manifold ranking algorithm and propose a new framework named Efficient Manifold Ranking (EMR). We aim to address the shortcomings of MR from two perspectives: scalable graph construction and efficient computation. Specifically, we build an anchor graph on the data set instead of the traditional k-nearest neighbor graph, and design a new form of adjacency matrix utilized to speed up the ranking computation. The experimental results on a real world image database demonstrate the effectiveness and efficiency of our proposed method. With a comparable performance to the original manifold ranking, our method significantly reduces the computational time, makes it a promising method to large scale real world retrieval problems.

关键词： Efficient manifold ranking image retrieval graph-based algorithm out-of-sample

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An Efficient Combinatorial Approach for Solving the DNA Motif Finding Problem

An Efficient Combinatorial Approach for Solving the DNA Moti...

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9th International Conference on Intelligent Systems Design and Applications

作者： Geraci, Filippo Pellegrini, Marco Renda, M. Elena CNR Ist Informat & Telemat I-56124 Pisa Italy

ISBN: (纸本)9781424447350

The detection of an over-represented sub-sequence in a set of (carefully chosen) DNA sequences is often the main clue leading to the investigation of a possible functional role for such a subsequence. Over-represented substrings (with possibly local mutations) in a biological string are termed motifs. A typical functional unit that can be modeled by a motif is a Transcription Factor Binding Site (TFBS), a portion of the DNA sequence apt to the binding of a protein that participates in complex transcriptomic biochemical reactions. In the literature it has been proposed a simplified combinatorial problem called the planted (l-d)-motif problem (known also as the (l-d) Challenge Problem) that captures the essential combinatorial nature of the motif finding problem. In this paper we propose a novel graph-based algorithm for solving a refinement of the (l-d) Challenge Problem. Experimental results show that instances of the (l-d) Challenge Problem considered difficult for competing state of the art methods in literature can be solved efficiently in our framework.

关键词： motif finding problem TFBS detection (l-d) Challenge Problem graph-based algorithm

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