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2nd IEEE/ACM International Workshop on Automated Program Repair (APR)

作者： Vu, Duc-Ly Pashchenko, Ivan Massacci, Fabio Univ Trento Trento Italy Vrije Univ Amsterdam Amsterdam Netherlands

ISBN: (纸本)9781665444729

Current evaluations of automatic program repair (APR) techniques focus on tools' effectiveness, while little is known about the practical aspects of using APR tools, such as how long one should wait for a tool to generate a bug fix. In this work, we empirically study whether APR tools are any time algorithms (e.g., the more time they have, the more fixes they generate, so it makes sense to trade off longer time for better quality). Our preliminary experiment shows that the amount of plausible patches, given exponentially greater time, only increases linearly or not at all.

关键词： Automated Program Repair anytime algorithms Empirical Software Engineering

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Sparsity-Exploiting anytime algorithms for Model Predictive Control: A Relaxed Barrier Approach

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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2020年第2期28卷 425-435页

作者： Feller, Christian Ebenbauer, Christian Univ Stuttgart Inst Syst Theory & Automat Control D-70550 Stuttgart Germany

We present and analyze a novel class of stabilizing and numerically efficient model predictive control (MPC) algorithms for discrete-time linear systems subject to polytopic input and state constraints. The proposed approach combines the previously presented concept of relaxed barrier function-based MPC with suitable warm-starting and sparsity-exploiting factorization techniques and allows to rigorously prove important stability and constraint satisfaction properties of the resulting closed-loop system independently of the number of performed Newton iterations. The effectiveness of the proposed approach is demonstrated by means of a numerical benchmark example.

关键词： Closed loop systems Prediction algorithms Stability analysis Optimal control Numerical stability Cost function anytime algorithms barrier functions model predictive control real-time optimization stability analysis

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anytime algorithms for the longest common palindromic subsequence problem

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COMPUTERS & OPERATIONS RESEARCH 2020年 114卷 104827-000页

作者： Djukanovic, Marko Raidl, Guenther R. Blum, Christian TU Wien Inst Log & Computat Vienna Austria CSIC Artificial Intelligence Res Inst IIIA Campus UAB Bellaterra Spain

The longest common palindromic subsequence (LCPS) problem aims at finding a longest string that appears as a subsequence in each of a set of input strings and is a palindrome at the same time. The problem is a special variant of the well known longest common subsequence problem and has applications in particular in genomics and biology, where strings correspond to DNA or protein sequences and similarities among them shall be detected or quantified. We first present a more traditional A* search that makes use of an advanced upper bound calculation for partial solutions. This exact approach works well for instances with two input strings and, as shown in experiments, outperforms several other exact methods from the literature. However, the A* search also has natural limitations when a larger number of strings shall be considered due to the problem's complexity. To effectively deal with this case in practice, anytime A* search variants are investigated, which are able to return a reasonable heuristic solution at almost any time and are expected to find better and better solutions until reaching a proven optimum when enough time given. In particular a novel approach is proposed in which anytime Column Search (ACS) is interleaved with traditional A* node expansions. The ACS iterations are guided by a new heuristic function that approximates the expected length of an LCPS in subproblems usually much better than the available upper bound calculation. This A*+ACS hybrid is able to solve small to medium-sized LCPS instances to proven optimality while returning good heuristic solutions together with upper bounds for large instances. In rigorous experimental evaluations we compare A*+ACS to several other anytime A* search variants and observe its superiority. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： The longest common palindromic subsequence problem anytime algorithms A* Search Beam search Hybrid algorithms

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Algorithm Selection of anytime algorithms

Algorithm Selection of Anytime Algorithms

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Jesus, Alexandre D. Liefooghe, Arnaud Derbel, Bilel Paquete, Luis Univ Lille INRIA Cent Lille CNRSUMR 9189 CRIStAL F-59000 Lille France Univ Tokyo JFLI CNRS IRL 3527 Tokyo Japan Univ Coimbra CISUC DEI Coimbra Portugal

ISBN: (纸本)9781450371285

anytime algorithms for optimization problems are of particular interest since they allow to trade off execution time with result quality. However, the selection of the best anytime algorithm for a given problem instance has been focused on a particular budget for execution time or particular target result quality. Moreover, it is often assumed that these anytime preferences are known when developing or training the algorithm selection methodology. In this work, we study the algorithm selection problem in a context where the decision maker's anytime preferences are defined by a general utility function, and only known at the time of selection. To this end, we first examine how to measure the performance of an anytime algorithm with respect to this utility function. Then, we discuss approaches for the development of selection methodologies that receive a utility function as an argument at the time of selection. Then, to illustrate one of the discussed approaches, we present a preliminary study on the selection between an exact and a heuristic algorithm for a bi-objective knapsack problem. The results show that the proposed methodology has an accuracy greater than 96% in the selected scenarios, but we identify room for improvement.

关键词： Algorithm Selection anytime algorithms anytime Performance Measure

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anytime algorithms for Recommendation Service Providers

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ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 2016年第3期7卷 43-43页

作者： Ben-Shimon, David Rokach, Lior Shani, Guy Shapira, Bracha Ben Gurion Univ Negev Informat Syst Engn Dept IL-84105 Beer Sheva Israel

Recommender systems (RS) can now be found in many commercial Web sites, often presenting customers with a short list of additional products that they might purchase. Many commercial sites do not typically have the ability and resources to develop their own system and may outsource the RS to a third party. This had led to the growth of a recommendation as a service industry, where companies, referred to as RS providers, provide recommendation services. These companies must carefully balance the cost of building recommendation models and the payment received from the e-business, as these payments are expected to be low. In such a setting, restricting the computational time required for model building is critical for the RS provider to be profitable. In this article, we propose anytime algorithms as an attractive method for balancing computational time and the recommendation model performance, thus tackling the RS provider problem. In an anytime setting, an algorithm can be stopped after any amount of computational time, always ensuring that a valid, although suboptimal, solution will be returned. Given sufficient time, however, the algorithm should converge to an optimal solution. In this setting, it is important to evaluate the quality of the returned solution over time, monitoring quality improvement. This is significantly different from traditional evaluation methods, which mostly estimate the performance of the algorithm only after its convergence is given sufficient time. We show that the popular item-item top-N recommendation approach can be brought into the anytime framework by smartly considering the order by which item pairs are being evaluated. We experimentally show that the time-accuracy trade-off can be significantly improved for this specific problem.

关键词： algorithms Recommenders Evaluation Performance Experimentation Recommender systems collaborative filtering anytime algorithms

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Efficient anytime Anywhere algorithms for Closeness Centrality in Large and Dynamic Graphs 30

Efficient Anytime Anywhere Algorithms for Closeness Centrali...

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30th IEEE International Parallel and Distributed Processing Symposium (IPDPS)

作者： Santos, Eunice E. Korah, John Murugappan, Vairavan Subramanian, Suresh IIT Dept Comp Sci Chicago IL 60616 USA

ISBN: (纸本)9781509036820

Recent advances in social network analysis methodologies for large (millions of nodes and billions of edges) and dynamic (evolving at different rates) networks have focused on leveraging new high performance architectures, parallel/distributed tools and novel data structures. However, there has been less focus on designing scalable and efficient algorithms to handle the challenges of dynamism in large-scale networks. In our previous work, we presented an overarching anytime anywhere framework for designing parallel and distributed social network analysis algorithms that are scalable to large network sizes and can handle dynamism. A key contribution of our work is to leverage the anytime and anywhere properties of graph analysis problems to design algorithms that can efficiently handle network dynamism by reusing partial results, and by reducing re-computations. In this paper, we present an algorithm for closeness centrality analysis that can handle changes in the network in the form of edge deletions. Using both theoretical analysis and experimental evaluations, we examine the performance of our algorithm with different network sizes and dynamism rates.

关键词： social network analysis parallel and distributed processing centrality analysis dynamic graphs anytime algorithms

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Matrix Profile XXX: MADRID: A Hyper -anytime and Parameter -Free Algorithm to Find Time Series Anomalies of all Lengths 23

Matrix Profile XXX: MADRID: A Hyper -Anytime and Parameter -...

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23rd IEEE International Conference on Data Mining (IEEE ICDM)

作者： Lu, Yue Srinivas, Thirumalai Vinjamoor Akhil Nakamura, Takaaki Imamura, Makoto Keogh, Eamonn Univ Calif Riverside Riverside CA 92521 USA Mitsubishi Electr Corp Kamakura Kanagawa Japan Tokai Univ Tokyo Japan

ISBN: (纸本)9798350307887

In recent years there has been increasing evidence that one of the simplest time series anomaly detection methods, time series discords, remains one of the most effective methods. However, time series discords have one notable issue;the anomalies discovered depend on the algorithm's only input parameter, the subsequence length. The obvious way to bypass this issue is to find anomalies at every possible length, however this seems to be untenably slow. In this work we introduce MADRID, an algorithm to efficiently solve the all-discords problem. We show that we can reduce the absolute time to compute all-discords, and that by using a novel computation ordering strategy, MADRID is a Hyper-anytime Algorithm. We will formally define this term later, but this refers to an anytime algorithm that converges exceptionally fast. In practice this means that for most real-world analytical tasks, the user can interact with their data in real-time. The ability to compute anomalies of all lengths produces the issue of ranking anomalies of different lengths. We further introduce novel algorithms for this task. We demonstrate the utility of MADRID in various domains and show that it allows us to find anomalies that would otherwise escape our attention.

关键词： Time series anomaly detection anytime algorithms

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Mobile ID Document Recognition-Coarse-to-Fine Approach

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PATTERN RECOGNITION AND IMAGE ANALYSIS 2022年第1期32卷 89-108页

作者： Arlazarov, V. L. Arlazarov, V. V. Bulatov, K. B. Chernov, T. S. Nikolaev, D. P. Polevoy, D., V Sheshkus, A., V Skoryukina, N. S. Slavin, O. A. Usilin, S. A. Russian Acad Sci Fed Res Ctr Comp Sci & Control Moscow 119333 Russia Russian Acad Sci Inst Informat Transmiss Problems Kharkevich Inst Moscow 127051 Russia Smart Engines Serv LLC Moscow 121205 Russia

Automatic optical recognition of documents is a traditional function of modern document processing systems. In this context, recognition represents a complex process which includes image processing, segmentation, classification, and linguistic analysis. Although the idea of using mobile devices for recognition of paper documents is not new, direct usage of existing software solutions for scanned images recognition yields low recognition precision on images obtained using a mobile device. This is due, first of all, to perspective distortions and lower effective resolution in the latter case. In this paper, we present an original approach and a set of algorithms for recognition of video frame sequence containing a document image, which is suitable for mobile implementation. It is based on a coarse-to-fine methodology, where template matching and fields localization are performed on the image with lowered resolution, followed by lazy processing of parts of the images only corresponding to the fields which are not recognized yet. Video stream is utilized as a source of noise reduction both in coordinates of the fields and optical character recognition classifiers outputs. The algorithm based on the proposed approach is suitable for running on the device itself and can operate even when none of the video frames contain a document image of sufficient quality by themselves.

关键词： optical character recognition optical document recognition coarse-to-fine recognition real-time video processing frame quality evaluation document identification and matching anytime algorithms neural networks optimization

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An Adaptive Hierarchical Method for anytime Set-wise Clustering of Variable and High-Speed Data Streams

An Adaptive Hierarchical Method for Anytime Set-wise Cluster...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Aarti Challa, Jagat Sesh Darolia, Utkarsh Chandak, Mridul Goyal, Poonam Goyal, Navneet Birla Institute of Technology & Science Adapt Lab Dept. of Computer Science Pilani Campus Pilani333031 India

ISBN: (纸本)9798350324457

Set-wise Clustering is a clustering technique for data streams that groups sets of objects based on distribution patterns, applicable in contexts like retail chain clustering, text-based community clustering, restaurant categorization, etc. The existing set-wise clustering method cannot handle variable and high-speed streams with reasonable accuracy. This paper presents an anytime Set-wise Clustering method for data streams known as ANYSETCLUS. The method handles the variable inter-arrival rates of stream objects using a proposed indexing structure called AnySetClusTree, which stores a hierarchy of micro-clusters of multi-set entities at varying granularity. ANYSETCLUS is highly adaptive as it supports incremental model updates, segregates outliers, enables outlier-to-concept transition, and captures concept drift. The method also enables anytime offline clustering wherein it can generate multiple clusterings of varying granularity and purity depending upon the available time allowance for final clustering. The experimental results affirm the superior efficacy of the proposed method in handling variable and high-speed streams compared to the state-of-the-art method. The experimental results also showcase its effectiveness in achieving significantly higher micro-cluster purity for low and high-speed streams. This contrasts with the state-of-the-art method, which is unable to generate valid clustering results for high-speed streams. The experiments further validate the proposed method's capability for anytime offline clustering. © 2023 IEEE.

关键词： anytime algorithms Data Clustering Data Streams

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An anytime Querying Algorithm for Predicting Cardiac Arrest in Children: Work-in-Progress 20th

An Anytime Querying Algorithm for Predicting Cardiac Arrest ...

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20th International Conference on Artificial Intelligence in Medicine (AIME)

作者： Skinner, Michael A. Yu, Priscilla Raman, Lakshmi Natarajan, Sriraam Univ Texas Dallas Dallas TX 75080 USA Univ Texas Southwestern Med Ctr Dallas Dallas TX 75390 USA

ISBN: (纸本)9783031093425;9783031093418

Cardiac arrest (CA) is a devastating complication for children in the cardiac intensive care unit (CICU). We developed an "anytime" algorithm to predict CA, using the first few hours of EHR data for initial approximation, and then using information from subsequent time periods to augment the predictive model, improving performance at each iteration. Our initial empirical evaluation on EHR CICU data shows that the model achieves significantly higher performance than learning with all the available data at each iteration when predicting CA inside CICU.

关键词： Gradient boosting anytime algorithms Cardiac arrest

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