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International Wireless Communications and Mobile Computing Conference

作者： Khaled Alhazmi Abdallah Moubayed Abdallah Shami National Center for Artificial Intelligence and Big Data Technology Communication and Information Technology Research Institute King Abdulaziz City for Science and Technology (KACST) Riyadh Saudi Arabia Department of Electrical and Computer Engineering Western University London ON Canada

Cloud computing has become a business reality that impacts technology users globally. It has become a cornerstone for emerging technologies and an enabler of the future Internet services. This has been coupled with emerging trend of adopting software-based network infrastructure. Paradigms such as software-defined networks (SDNs) have gained more attention for large scale networks due to the flexibility and agility they offer to the network. Parallel to this emergence, the power consumption has rapidly increased. Hence, developing green and sustainable solutions has become a prime concern for the cloud providers. In this paper, a novel power aware algorithim titled "Green Distributed Cloud Services Provisioning" to provision the tenants' cloud services requests on an SDN-enabled cloud environment is presented. The role of the cloud and SDN controllers within the considered environment is illustrated to better highlight the interactions with the underlaying infrastructure. Furthermore, a brief review of well known cloud simulators is given. The conducted simulations show that the proposed algorithm significantly reduces the power consumption while maintaining high admission rates when compared to other work from the literature. Moreover, the average number of hops needed for a tenant to reach its requested VM was shown to remain stable with the increase in network load.

关键词： Cloud Computing Distributed data Centers SDN VM and Network Provisioning Power Efficiency

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Cooperative big data processing engine for fast reaction in internet of things environment: Greater than the sum of its parts

Cooperative big data processing engine for fast reaction in ...

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International Conference on Mobile and Wireless Technology, ICMWT 2016

作者： Lee, Yong-Ju Park, Hwin Dol Min, Okgee Big Data Intelligence Research Department Software & Contents Research Laboratory Electronics and Telecommunications Research Institute Daejeon Korea Republic of Department of Computer Software University of Science and Technology Daejeon Korea Republic of

ISBN: (纸本)9789811014086

This paper outlines cooperative big data processing engine in Internet of Things environment. Our platform splits an analytical job into two meaningful sub-jobs. First one of two sub-jobs push away from centralized points (e.g., analysis server) to the physical Internet of Things devices (e.g., embedded devices) for filtering out the inconsequential messages and achieving the best message-response possible. Second one of two sub-jobs conducts remaining parts of complicated analytic mission in traditional servers. This approach significantly decreases the data volume that must be moved, the consequent traffic. Furthermore, it can provide faster reaction in most instances. © Springer science+Business Media Singapore 2016.

关键词： Internet of things

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Robust frequent directions with application in online learning

arXiv

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arXiv 2017年

作者： Luo, Luo Chen, Cheng Zhang, Zhihua Li, Wu-Jun Zhang, Tong Department of Computer Science and Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China National Engineering Lab for Big Data Analysis and Applications School of Mathematical Sciences Peking University 5 Yiheyuan Road Beijing100871 China National Key Laboratory for Novel Software Technology Collaborative Innovation Center of Novel Software Technology and Industrialization Department of Computer Science and Technology Nanjing University 163 Xianlin Avenue Nanjing210023 China Computer Science & Mathematics Hong Kong University of Science and Technology Hong Kong

The frequent directions (FD) technique is a deterministic approach for online sketching that has many applications in machine learning. The conventional FD is a heuristic procedure that often outputs rank deficient matrices. To overcome the rank deficiency problem, we propose a new sketching strategy called robust frequent directions (RFD) by introducing a regularization term. RFD can be derived from an optimization problem. It updates the sketch matrix and the regularization term adaptively and jointly. RFD reduces the approximation error of FD without increasing the computational cost. We also apply RFD to online learning and propose an effective hyperparameter-free online Newton algorithm. We derive a regret bound for our online Newton algorithm based on RFD, which guarantees the robustness of the algorithm. The experimental studies demonstrate that the proposed method outperforms state-of-the-art second order online learning algorithms. Copyright © 2017, The Authors. All rights reserved.

关键词： Heuristic methods

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Author Correction: Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery

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Nature neuroscience 2025年第1期28卷 216页

作者： Boyan Bonev Gonçalo Castelo-Branco Fei Chen Simone Codeluppi M Ryan Corces Jean Fan Myriam Heiman Kenneth Harris Fumitaka Inoue Manolis Kellis Ariel Levine Mo Lotfollahi Chongyuan Luo Kristen R Maynard Mor Nitzan Vijay Ramani Rahul Satijia Lucas Schirmer Yin Shen Na Sun Gilad S Green Fabian Theis Xiao Wang Joshua D Welch Ozgun Gokce Genevieve Konopka Shane Liddelow Evan Macosko Omer Ali Bayraktar Naomi Habib Tomasz J Nowakowski Helmholtz Pioneer Campus Helmholtz Zentrum München Neuherberg Germany. Physiological Genomics Biomedical Center Ludwig-Maximilians-Universität München Munich Germany. Laboratory of Molecular Neurobiology Department of Medical Biochemistry and Biophysics Karolinska Institutet Stockholm Sweden. The Broad Institute of MIT and Harvard Cambridge MA USA. Moleculent Stockholm Sweden. Gladstone Institute of Neurological Disease San Francisco CA USA. Gladstone Institute of Data Science and Biotechnology San Francisco CA USA. Department of Neurology University of California San Francisco San Francisco CA USA. Department of Biomedical Engineering Johns Hopkins University Baltimore MD USA. Department of Brain and Cognitive Sciences MIT Cambridge MA USA. The Picower Institute for Learning and Memory MIT Cambridge MA USA. UCL Queen Square Institute of Neurology University College London London UK. Institute for the Advanced Study of Human Biology (WPI-ASHBi) Kyoto University Kyoto Japan. Computer Science and Artificial Intelligence Lab Massachusetts Institute of Technology Cambridge MA USA. Spinal Circuits and Plasticity Unit National Institute of Neurological Disorders and Stroke Bethesda MD USA. Institute of Computational Biology Helmholtz Center Munich - German Research Center for Environmental Health Neuherberg Germany. Wellcome Sanger Institute Wellcome Genome Campus Cambridge UK. Department of Human Genetics University of California Los Angeles Los Angeles CA USA. Lieber Institute for Brain Development Baltimore MD USA. Department of Psychiatry Johns Hopkins School of Medicine Baltimore MD USA. Department of Neuroscience Johns Hopkins School of Medicine Baltimore MD USA. School of Computer Science and Engineering The Hebrew University of Jerusalem Jerusalem Israel. Racah Institute of Physics The Hebrew University of Jerusalem Jerusalem Israel. Faculty of Medicine The Hebrew University of Jerusalem Jerusalem Israel. Department of Biochemis

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An Automated Approach for Recommending When to Stop Performance Tests

An Automated Approach for Recommending When to Stop Performa...

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International Conference on software Maintenance (ICSM)

作者： Hammam M. Alghmadi Mark D. Syer Weiyi Shang Ahmed E. Hassan Software Analysis and Intelligence Lab (SAIL) Queen's University Canada Department of Computer Science and Software Engineering Concordia University Canada

ISBN: (纸本)9781509038077

Performance issues are often the cause of failures in today's large-scale software systems. These issues make performance testing essential during software maintenance. However, performance testing is faced with many challenges. One challenge is determining how long a performance test must run. Although performance tests often run for hours or days to uncover performance issues (e.g., memory leaks), much of the data that is generated during a performance test is repetitive. Performance analysts can stop their performance tests (to reduce the time to market and the costs of performance testing) if they know that continuing the test will not provide any new information about the system's performance. To assist performance analysts in deciding when to stop a performance test, we propose an automated approach that measures how much of the data that is generated during a performance test is repetitive. Our approach then provides a recommendation to stop the test when the data becomes highly repetitive and the repetitiveness has stabilized (i.e., little new information about the systems' performance is generated). We performed a case study on three open source systems (i.e., CloudStore, PetClinic and Dell DVD Store). Our case study shows that our approach reduces the duration of 24-hour performance tests by 75% while preserving more than 91.9% of the information about the system's performance. In addition, our approach recommends a stopping time that is close to the most cost-effective stopping time (i.e., the stopping time that minimize the duration of the test and maximizes the amount of information about the system's performance provided by performance testing).

关键词： Radiation detectors Testing System performance Market research software systems software maintenance Time factors

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Image segmentation using membrane computing: A literature survey 11th

Image segmentation using membrane computing: A literature su...

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11th International Conference on Bio-inspired Computing – Theories and Applications, BIC-TA 2016

作者： Yahya, Rafaa I. Shamsuddin, Siti Mariyam Yahya, Salah I. Hasan, Shafatnnur Al-Salibi, Bisan Al-Khafaji, Ghada Faculty of Computing Universiti Teknologi Malaysia UTM SkudaiJohor81310 Malaysia Department of Computer College of Science University of Al-Mustansiriyah Baghdad Iraq UTM Big Data Center Ibnu Sina Institute for Scientific and Industrial Research Universiti Teknologi Malaysia UTM SkudaiJohor81310 Malaysia Department of Software Engineering Koya University Iraq School of Computer Sciences Universiti Sains Malaysia USM GelugorPenang11800 Malaysia Department of Computer Science College of Science Baghdad University Baghdad Iraq

ISBN: (纸本)9789811036101

Membrane computing, a recent branch of natural computing, has been gaining momentum attention in the last few decades due to its massive parallelism and efficient computation. Many researchers in the field of membrane computing have proposed sophisticated techniques inspired by cell biology for computer science applications, especially when they considered cell organization in tissues, organs, and most recently, from the organization of neurons. The interdisciplinary applications of membrane computing include, but not limited to computer science, biology, biomedicine, bioinformatics and several other fields such as mathematics, artificial intelligence, automation, economics, to name but a few. Their applications are pertaining to computer graphics, approximate optimization, cryptography, parallel computing and image processing. Hence, in this paper we present an up to date comprehensive literature review pertaining to the application of membrane computing in the area of digital image analysis, especially image segmentation, comprehensively and systematically. We thoroughly investigate the recent advancement in the field of image segmentation using membrane system. Furthermore, we highlight the merits and demerits of various software tools and methods. Finally, we suggest some intuitive future directions in light of the current limitations. © Springer Nature Singapore Pte Ltd. 2016.

关键词： Image segmentation

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Logging Library Migrations: A Case Study for the Apache software Foundation Projects

Logging Library Migrations: A Case Study for the Apache Soft...

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International Mining software Repositories, MSR

作者： Suhas Kabinna Cor-Paul Bezemer Weiyi Shang Ahmed E. Hassan Software Analysis and Intelligence Lab (SAIL) Queen's University Kingston Canada Department of Computer Science and Software Engineering Concordia University Montreal Canada

ISBN: (纸本)9781509022427

Developers leverage logs for debugging, performance monitoring and load testing. The increased dependence on logs has lead to the development of numerous logging libraries which help developers in logging their code. As new libraries emerge and current ones evolve, projects often migrate from an older library to another *** this paper we study logging library migrations within Apache software Foundation (ASF) projects. From our manual analysis of JIRA issues, we find that 33 out of 223 (i.e., 14%) ASF projects have undergone at least one logging library migration. We find that the five main drivers for logging library migration are: 1) to increase flexibility (i.e., the ability to use different logging libraries within a project) 2) to improve performance, 3) to reduce effort spent on code maintenance, 4) to reduce dependence on other libraries and 5) to obtain specific features from the new logging library. We find that over 70% of the migrated projects encounter on average two post-migration bugs due to the new logging library. Furthermore, our findings suggest that performance (traditionally one of the primary drivers for migrations) is rarely improved after a migration.

关键词： data mining software

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Examining the Stability of Logging Statements

Examining the Stability of Logging Statements

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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Suhas Kabinna Weiyi Shang Cor-Paul Bezemer Ahmed E. Hassan Software Analysis and Intelligence Lab (SAIL) Queen's University Kingston Ontario Department of Computer Science and Software Engineering Concordia University Montreal QC Canada

Logging statements produce logs that assist in understanding system behavior, monitoring choke-points and debugging. Prior research demonstrated the importance of logging statements in operating, understanding and improving software systems. The importance of logs has lead to a new market of log management and processing tools. However, logs are often unstable, i.e., the logging statements that generate logs are often changed without the consideration of other stakeholders, causing misleading results and failures of log processing tools. In order to proactively mitigate such issues that are caused by unstable logging statements, in this paper we empirically study the stability of logging statements in four open source applications namely:Liferay, ActiveMQ, Camel and Cloud Stack. We find that 20-45% of the logging statements in our studied applications change throughout their lifetime. The median number of days between the introduction of a logging statement and the first change to that statement is between 1 and 17 in our studied applications. These numbers show that in order to reduce maintenance effort, developers of log processing tools must be careful when selecting the logging statements on which they will let their tools depend. In this paper, we make an important first step towards assisting developers of log processing tools in determining whether a logging statement is likely to remain unchanged in the future. Using random forest classifiers, we examine which metrics are important for understanding whether a logging statement will change. We show that our classifiers achieve 83%-91% precision and 65%-85% recall in the four studied applications. We find that file ownership, developer experience, log density and SLOC are important metrics for determining whether a logging statement will change in the future. Developers can use this knowledge to build more robust log processing tools, by making those tools depend on logs that are generated by logging statement

关键词： Java Libraries History data mining Monitoring Measurement Context

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Studying the Effectiveness of Application Performance Management (APM) Tools for Detecting Performance Regressions for Web Applications: An Experience Report

Studying the Effectiveness of Application Performance Manage...

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International Mining software Repositories, MSR

作者： Tarek M. Ahmed Cor-Paul Bezemer Tse-Hsun Chen Ahmed E. Hassan Weiyi Shang Software Analysis and Intelligence Lab (SAIL) Queen's University Kingston Ontario Canada Department of Computer Science and Software Engineering Concordia University Montreal QC Canada

ISBN: (纸本)9781509022427

Performance regressions, such as a higher CPU utilization than in the previous version of an application, are caused by software application updates that negatively affect the performance of an *** a plethora of mining software repository research has been done to detect such regressions, research tools are generally not readily available to practitioners. Application Performance Management (APM) tools are commonly used in practice for detecting performance issues in the field by mining operational *** contrast to performance regression detection tools that assume a changing code base and a stable workload, APM tools mine operational data to detect performance anomalies caused by a changing workload in an otherwise stable code base. Although APM tools are widely used in practice, no research has been done to understand 1) whether APM tools can identify performance regressions caused by code changes and 2) how well these APM tools support diagnosing the root-cause of these *** this paper, we explore if the readily accessible APM tools can help practitioners detect performance regressions. We perform a case study using three commercial (AppDynamics, New Relic and Dynatrace) and one open source (Pinpoint) APM tools. In particular, we examine the effectiveness of leveraging these APM tools in detecting and diagnosing injected performance regressions (excessive memory usage, high CPU utilization and inefficient database queries) in three open source applications. We find that APM tools can detect most of the injected performance regressions, making them good candidates to detect performance regressions in practice. However, there is a gap between mining approaches that are proposed in state-of-the-art performance regression detection research and the ones used by APM tools. In addition, APM tools lack the ability to be extended, which makes it hard to enhance them when exploring novel mining approaches for detecting performance regressions

关键词： software data mining Monitoring Measurement Testing Production data models

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