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

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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Through the looking glass: evaluating post hoc explanations using transparent models

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INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2023年 1-21页

作者： Velmurugan, Mythreyi Ouyang, Chun Sindhgatta, Renuka Moreira, Catarina Queensland Univ Technol Sch Informat Syst Brisbane Australia Queensland Univ Technol Ctr Data Sci Brisbane Australia IBM Res Bangalore India Univ Technol Sydney Human Technol Inst Sydney Australia

Modern machine learning methods allow for complex and in-depth analytics, but the predictive models generated by these methods are often highly complex and lack transparency. Explainable Artificial Intelligence (XAI) methods are used to improve the interpretability of these complex "black box" models, thereby increasing transparency and enabling informed decision-making. However, the inherent fitness of these explainable methods, particularly the faithfulness of explanations to the decision-making processes of the model, can be hard to evaluate. In this work, we examine and evaluate the explanations provided by four XAI methods, using fully transparent "glass box" models trained on tabular data. Our results suggest that the fidelity of explanations is determined by the types of variables used, as well as the linearity of the relationship between variables and model prediction. We find that each XAI method evaluated has its own strengths and weaknesses, determined by the assumptions inherent in the explanation mechanism. Thus, though such methods are model-agnostic, we find significant differences in explanation quality across different technical setups. Given the numerous factors that determine the quality of explanations, including the specific explanation-generation procedures implemented by XAI methods, we suggest that model-agnostic XAI methods may still require expert guidance for implementation.

关键词： Predictive models Explainable AI (XAI) performance evaluation of algorithms and systems Explanation fidelity

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

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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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Parallel Multiobjective Metaheuristics for Inferring Phylogenies on Multicore Clusters

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED systems 2015年第6期26卷 1678-1692页

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

The development of efficient parallel algorithms based on mixed mode programming represents one of the most popular lines of research in current bioinformatics. By exploiting hardware resources at inter-node/intra-node level, we can address grand computational challenges which involve the optimization of multiple objective functions simultaneously. In this sense, the inference of evolutionary trees represents one of the most difficult NP-hard problems in the field. Tackling such a problem requires efficient parallel designs to take advantage of the characteristics of modern multicore clusters. In this paper, we aim to solve the phylogenetic inference problem by applying MPI/OpenMP schemes to two multiobjective metaheuristics: fast non-dominated sorting genetic algorithm and multiobjective firefly algorithm. In order to assess the performance achieved by these proposals under different system and problem sizes, we have conducted experiments on six real nucleotide data sets according to a statistical methodology. Our parallel and multiobjective metrics point out the relevance of combining hybrid programming and novel bioinspired designs with regard to other parallel and biological approaches from the literature.

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

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Impact of the Accuracy of Automatic Segmentation of Cell Nuclei Clusters on Classification of Thyroid Follicular Lesions

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CYTOMETRY PART A 2014年第8期85A卷 709-718页

作者： Jung, Chanho Kim, Changick ETRI IT Convergence Technol Res Lab Taejon 305700 South Korea Korea Adv Inst Sci & Technol Dept Elect Engn Taejon 305732 South Korea

Automatic segmentation of cell nuclei clusters is a key building block in systems for quantitative analysis of microscopy cell images. For that reason, it has received a great attention over the last decade, and diverse automatic approaches to segment clustered nuclei with varying levels of performance under different test conditions have been proposed in literature. To the best of our knowledge, however, so far there is no comparative study on the methods. This study is a first attempt to fill this research gap. More precisely, the purpose of this study is to present an objective performance comparison of existing state-of-the-art segmentation methods. Particularly, the impact of their accuracy on classification of thyroid follicular lesions is also investigated "quantitatively" under the same experimental condition, to evaluate the applicability of the methods. Thirteen different segmentation approaches are compared in terms of not only errors in nuclei segmentation and delineation, but also their impact on the performance of system to classify thyroid follicular lesions using different metrics (e.g., diagnostic accuracy, sensitivity, specificity, etc.). Extensive experiments have been conducted on a total of 204 digitized thyroid biopsy specimens. Our study demonstrates that significant diagnostic errors can be avoided using more advanced segmentation approaches. We believe that this comprehensive comparative study serves as a reference point and guide for developers and practitioners in choosing an appropriate automatic segmentation technique adopted for building automated systems for specifically classifying follicular thyroid lesions. (C) 2014 International Society for Advancement of Cytometry

关键词： automatic segmentation cell nuclei clusters impact thyroid pathology performance evaluation of algorithms and systems survey

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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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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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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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