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Asynchronous Non-Generational Model to Parallelize Metaheuristics: A Bioinformatics Case Study

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED systems 2017年第7期28卷 1825-1838页

作者： Santander-Jimenez, Sergio Vega-Rodriguez, Miguel A. Univ Extremadura Dept Comp & Commun Technol Escuela Politecn Campus Univ S-N Caceres 10003 Spain

The integration of parallel computing techniques into metaheuristics has traditionally represented a promising approach to tackle computationally demanding optimization problems. In the last years, metaheuristics have evolved by including more complex search mechanisms whose parallelization often leads to performance issues under classic parallel schemes. This work investigates the asynchronous non-generational parallelization model, which is aimed at dealing with performance pitfalls by allowing worker threads to behave as asynchronous independent agents. We incorporate asynchronous principles into a recently proposed metaheuristic for multiobjective optimization, the Indicator-Based Multiobjective Bat Algorithm, and apply the resulting approach to solve a real-world problem in the bioinformatics domain: the reconstruction of evolutionary histories. Experiments on multicore multiprocessor systems comprising up to 64 cores reveal the suitability of the model to address the main challenges of the metaheuristic design under study, outperforming other implementations and methods in terms of parallel performance while also achieving significant solution quality.

关键词： Parallel algorithms performance evaluation of algorithms and systems soft computing biology and genetics

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A Bidding Algorithm for Optimized Utility-Based Resource Allocation in Ad Hoc Networks

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2008年第12期7卷 1397-1414页

作者： Curescu, Calin Nadjm-Tehrani, Simin Ericsson Res S-16483 Stockholm Sweden Linkoping Univ Dept Comp & Informat Sci S-58183 Linkoping Sweden

This paper proposes a scheme for bandwidth allocation in wireless ad hoc networks. The quality-of-service (QoS) levels for each end-to-end flow are expressed using a resource-utility function, and our algorithms aim to maximize aggregated utility. The shared channel is modeled as a bandwidth resource defined by maximal cliques of mutual interfering links. We propose a novel resource allocation algorithm that employs an auction mechanism in which flows are bidding for resources. The bids depend both on the flow's utility function and the intrinsically derived shadow prices. We then combine the admission control scheme with a utility-aware on-demand shortest path routing algorithm where shadow prices are used as a natural distance metric. As a baseline for evaluation, we show that the problem can be formulated as a linear programming (LP) problem. Thus, we can compare the performance of our distributed scheme to the centralized LP solution, registering results very close to the optimum. Next, we isolate the performance of price-based routing and show its advantages in hotspot scenarios, and also propose an asynchronous version that is more feasible for ad hoc environments. Further experimental evaluation compares our scheme with the state of the art derived from Kelly's utility maximization framework and shows that our approach exhibits superior performance for networks with increased mobility or less frequent allocations.

关键词： Mobile computing pricing and resource allocation quality of service optimization performance evaluation of algorithms and systems

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Slicing Distributed systems

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IEEE TRANSACTIONS ON COMPUTERS 2009年第11期58卷 1444-1455页

作者： Gramoli, Vincent Vigfusson, Ymir Birman, Ken Kermarrec, Anne-Marie van Renesse, Robbert Ecole Polytech Fed Lausanne LPD CH-1015 Lausanne Switzerland Univ Neuchatel CH-1015 Lausanne Switzerland Cornell Univ Ithaca NY 14853 USA INRIA Rennes IRISA F-35042 Rennes France

Peer-to-peer (P2P) architectures are popular for tasks such as collaborative download, VoIP telephony, and backup. To maximize performance in the face of widely variable storage capacities and bandwidths, such systems typically need to shift work from poor nodes to richer ones. Similar requirements are seen in today's large data centers, where machines may have widely variable configurations, loads, and performance. In this paper, we consider the slicing problem, which involves partitioning the participating nodes into k subsets using a one-dimensional attribute, and updating the partition as the set of nodes and their associated attributes change. The mechanism thus facilitates the development of adaptive systems. We begin by motivating this problem statement and reviewing prior work. Existing algorithms are shown to have problems with convergence, manifesting as inaccurate slice assignments, and to adapt slowly as conditions change. Our protocol, Sliver, has provably rapid convergence, is robust under stress and is simple to implement. We present both theoretical and experimental evaluations of the protocol.

关键词： Distributed systems fault tolerance performance evaluation of algorithms and systems

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Esaliency (Extended Saliency): Meaningful Attention Using Stochastic Image Modeling

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2010年第4期32卷 693-708页

作者： Avraham, Tamar Lindenbaum, Michael Technion Israel Inst Technol Dept Comp Sci IL-32000 Haifa Israel

Computer vision attention processes assign variable-hypothesized importance to different parts of the visual input and direct the allocation of computational resources. This nonuniform allocation might help accelerate the image analysis process. This paper proposes a new bottom-up attention mechanism. Rather than taking the traditional approach, which tries to model human attention, we propose a validated stochastic model to estimate the probability that an image part is of interest. We refer to this probability as saliency and thus specify saliency in a mathematically well-defined sense. The model quantifies several intuitive observations, such as the greater likelihood of correspondence between visually similar image regions and the likelihood that only a few of interesting objects will be present in the scene. The latter observation, which implies that such objects are (relaxed) global exceptions, replaces the traditional preference for local contrast. The algorithm starts with a rough preattentive segmentation and then uses a graphical model approximation to efficiently reveal which segments are more likely to be of interest. Experiments on natural scenes containing a variety of objects demonstrate the proposed method and show its advantages over previous approaches.

关键词： Computer vision scene analysis similarity measures performance evaluation of algorithms and systems object recognition visual search attention

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Fusing face-verification algorithms and humans

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IEEE TRANSACTIONS ON systems MAN AND CYBERNETICS PART B-CYBERNETICS 2007年第5期37卷 1149-1155页

作者： O'Toole, Alice J. Abdi, Herve Jiang, Fang Phillips, P. Jonathon Univ Texas Dallas Sch Behav & Brain Sci Richardson TX 75083 USA Natl Inst Stand & Technol Gaithersburg MD 20899 USA

It has been demonstrated recently that state-of-the-art face-recognition algorithms can surpass human accuracy at matching faces over changes in illumination. The ranking of algorithms and humans by accuracy, however, does not provide information about whether algorithms and humans perform the task comparably or whether algorithms and humans can be fused to improve performance. In this paper, We fused humans and algorithms using partial least square regression (PLSR). In the first experiment, we applied PLSR to face-pair similarity scores generated by seven algorithms participating in the Face Recognition ion Grand Challenge. The PLSR produced an optimal weighting A the similarity scores, which we tested for generality with a jack-knife procedure. Fusing the algorithms similarity scores using he optimal weights produced a twofold reduction of error rate over the most accurate algorithm. Next, human-subject-generated similarity scores were added to the PLSR analysis. Fusing humans and algorithms increased the performance to near-perfect classification accuracy. These results are discussed in terms of maximizing face-verification accuracy with hybrid systems consisting A multiple algorithms and humans.

关键词： face and gesture recognition human information processing performance evaluation of algorithms and systems

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Review of Serial and Parallel Min-Cut/Max-Flow algorithms for Computer Vision

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第2期45卷 2310-2329页

作者： Jensen, Patrick M. M. Jeppesen, Niels Dahl, Anders B. B. Dahl, Vedrana A. A. Tech Univ Denmark Dept Appl Math & Comp Sci DK-2800 Lyngby Denmark

Minimum cut/maximum flow (min-cut/max-flow) algorithms solve a variety of problems in computer vision and thus significant effort has been put into developing fast min-cut/max-flow algorithms. As a result, it is difficult to choose an ideal algorithm for a given problem. Furthermore, parallel algorithms have not been thoroughly compared. In this paper, we evaluate the state-of-the-art serial and parallel min-cut/max-flow algorithms on the largest set of computer vision problems yet. We focus on generic algorithms, i.e., for unstructured graphs, but also compare with the specialized GridCut implementation. When applicable, GridCut performs best. Otherwise, the two pseudoflow algorithms, Hochbaum pseudoflow and excesses incremental breadth first search, achieves the overall best performance. The most memory efficient implementation tested is the Boykov-Kolmogorov algorithm. Amongst generic parallel algorithms, we find the bottom-up merging approach by Liu and Sun to be best, but no method is dominant. Of the generic parallel methods, only the parallel preflow push-relabel algorithm is able to efficiently scale with many processors across problem sizes, and no generic parallel method consistently outperforms serial algorithms. Finally, we provide and evaluate strategies for algorithm selection to obtain good expected performance. We make our dataset and implementations publicly available for further research.

关键词： Computer vision Parallel algorithms Benchmark testing Image segmentation Sun Partitioning algorithms Heuristic algorithms algorithms computer vision graph algorithms graph-theoretic methods parallel algorithms performance evaluation of algorithms and systems

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Attention-based dynamic visual search using inner-scene similarity: algorithms and bounds

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2006年第2期28卷 251-264页

作者： Avraham, T Lindenbaum, M Technion Israel Inst Technol Dept Comp Sci IL-32000 Haifa Israel

A visual search is required when applying a recognition process on a scene containing multiple objects. In such cases, we would like to avoid an exhaustive sequential search. This work proposes a dynamic visual search framework based mainly on inner-scene similarity. Given a number of candidates (e. g., subimages), we hypothesize is that more visually similar candidates are more likely to have the same identity. We use this assumption for determining the order of attention. Both deterministic and stochastic approaches, relying on this hypothesis, are considered. Under the deterministic approach, we suggest a measure similar to Kolmogorov's epsilon-covering that quantifies the difficulty of a search task. We show that this measure bounds the performance of all search algorithms and suggest a simple algorithm that meets this bound. Under the stochastic approach, we model the identity of the candidates as a set of correlated random variables and derive a search procedure based on linear estimation. Several experiments are presented in which the statistical characteristics, search algorithm, and bound are evaluated and verified.

关键词： computer vision scene analysis feature representation similarity measures performance evaluation of algorithms and systems object recognition visual search attention

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Sampling Online Social Networks

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2013年第3期25卷 662-676页

作者： Papagelis, Manos Das, Gautam Koudas, Nick Univ Toronto Dept Comp Sci Bahen Ctr Informat Technol Toronto ON M5S 2E4 Canada Univ Texas Arlington Comp Sci & Engn Dept Arlington TX 76019 USA

As online social networking emerges, there has been increased interest to utilize the underlying network structure as well as the available information on social peers to improve the information needs of a user. In this paper, we focus on improving the performance of information collection from the neighborhood of a user in a dynamic social network. We introduce sampling-based algorithms to efficiently explore a user's social network respecting its structure and to quickly approximate quantities of interest. We introduce and analyze variants of the basic sampling scheme exploring correlations across our samples. Models of centralized and distributed social networks are considered. We show that our algorithms can be utilized to rank items in the neighborhood of a user, assuming that information for each user in the network is available. Using real and synthetic data sets, we validate the results of our analysis and demonstrate the efficiency of our algorithms in approximating quantities of interest. The methods we describe are general and can probably be easily adopted in a variety of strategies aiming to efficiently collect information from a social graph.

关键词： Information networks search process query processing performance evaluation of algorithms and systems

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Investigation of Context Prediction Accuracy for Different Context Abstraction Levels

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2012年第6期11卷 1047-1059页

作者： Sigg, Stephan Gordon, Dawud von Zengen, Georg Beigl, Michael Haseloff, Sandra David, Klaus Tech Univ Carolo Wilhelmina Braunschweig D-38106 Braunschweig Germany Telecooperat Off TecO D-76131 Karlsruhe Germany Karlsruhe Inst Technol Pervas Comp Syst Karlsruhe Germany Alexander Von Humboldt Fdn D-53173 Bonn Germany Univ Kassel D-34121 Kassel Germany

Context prediction is the task of inferring information about the progression of an observed context time series based on its previous behaviour. Prediction methods can be applied at several abstraction levels in the context processing chain. In a theoretical analysis as well as by means of experiments we show that the nature of the input data, the quality of the output, and finally the flow of processing operations used to make a prediction, are correlated. A comprehensive discussion of basic concepts in context prediction domains and a study on the effects of the context abstraction level on the context prediction accuracy in context prediction scenarios is provided. We develop a set of formulae that link scenario-dependent parameters to a probability for the context prediction accuracy. It is demonstrated that the results achieved in our theoretical analysis can also be confirmed in simulations as well as in experimental studies.

关键词： Pervasive computing stochastic processes location-dependent and sensitive performance evaluation of algorithms and systems time series analysis

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Identification and Asymptotic Localization of Rumor Sources Using the Method of Types

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2020年第3期7卷 1145-1157页

作者： Kesavareddigari, Himaja Spencer, Sam Eryilmaz, Atilla Srikant, R. Ohio State Univ Elect & Comp Engn Dept Columbus OH 43210 USA Univ Illinois Coordinated Sci Lab Urbana IL 61801 USA Univ Illinois Elect & Comp Engn Dept Urbana IL 61801 USA

We are interested in identifying a rumor source on a tree network. We begin with extended star networks under the SI infection model with exponential waiting times. We present and analyze the types center, a highly tractable approximation of the ML source estimate, obtained using the method of types. We empirically show that this approximate ML estimator is exact for some small test cases. We prove that the approximation error is at most logarithmic in infection size on large networks, providing highly efficient source identification (especially compared to the accuracy in similar problems, such as the O(root n) best possible accuracy estimate in a line network). We also show that the qualitative properties of the types and rumor centers are different on extended star networks. We further propose a heuristic-based generalization of this approach to trees: the relative-leaf counting algorithm. In simulations on regular and nonregular trees, types center's performance is competitive with rumor centrality (which is optimal for d-regular trees), while requiring less computation time. In addition to providing a faster (and sometimes more accurate) alternative on its own, our approach could potentially be used with rumor centrality to improve results with less than twice the total computation time.

关键词： Maximum likelihood estimation Heuristic algorithms Computational modeling Partitioning algorithms Mathematical model Approximation algorithms Network problems probability and statistics symbolic and algebraic manipulation performance evaluation of algorithms and systems heuristic design graph and tree search strategies Trees

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