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Objectivity in Research: Challenges from the Evidence-Based Paradigm

Objectivity in Research: Challenges from the Evidence-Based ...

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Australian Conference on Software Engineering

作者： Stuart Charters David Budgen Mark Turner Barbara Kitchenham O. Pearl Brereton Stephen Linkman Department of Applied Computing Lincoln University Lincoln New Zealand Department of Computer Science Durham University Durham UK School of Computing and Mathematics Keele University Staffordshire UK

ISBN: (纸本)9781424437689

For other domains that have adopted the evidence-based paradigm, the impact has included research outcomes having greater influence in terms of informing and influencing practitioners and policy-makers. We examine how evidence-based practices are being adapted for use in software engineering and discuss how decision-making in our own discipline can be liberated from over-reliance on expert judgement. To support our arguments we discuss some outcomes from recent studies and present an example in which performing a systematic literature review demonstrates the unreliability of depending only upon the outcomes of individual studies. Finally, we identify six challenges that need to be addressed in order to provide software engineers with standards and practices that are underpinned by evidence.

关键词： Software engineering Biological system modeling Humans Predictive models computer science Mathematics Decision making Software standards Knowledge engineering Muscles

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Dynamic probabilistic counter-based broadcasting in mobile ad hoc networks

Dynamic probabilistic counter-based broadcasting in mobile a...

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International Conference on Adaptive science & Technology (ICAST)

作者： Aminu Mohammed Mohamed Ould-Khaoua Lewis M. Mackenzie Jamal-Deen Abdulai The Department of Mathematics Computer Science Unit Usmanu Danfodiyo University Sokoto Nigeria Department of Electrical & Computer Engineering Sultan Qaboos University Muscat Oman The Department of Computing Science University of Glasgow UK The Department of Applied Mathematics & Computer Science Faculty of Applied Sciences University of Development Studies Navrongo Ghana

In Mobile Ad hoc Network (MANETs), flooding is the simplest broadcasting mechanism where each node retransmits every uniquely received packet exactly once. Despite its simplicity it could potentially leads to high redundant retransmissions causing high channel contention and thus excessive packet collisions in the network. This phenomenon referred to as broadcast storm problem has been shown to greatly increase the network communication overhead and end-to-end delay. Numerous probabilistic approaches have been proposed to mitigate the impact of this inherent phenomenon. However, most of these techniques are inadequate in reducing the number of redundant retransmissions while still guaranteeing that all nodes receive the packet. Further, in most cases they use a predetermined forwarding probability value for all nodes in the network which is quite unlikely to be optimal in other network set up. In this paper, we propose a new dynamic probabilistic counter-based broadcast scheme that can dynamically compute the forwarding probability at a node based on its neighbourhood information. Simulation results show that the new broadcast scheme achieves superior performance in terms of retransmitting nodes, collision rate, and end-to-end delay without sacrificing reachability compared to the existing schemes.

关键词： Broadcasting Mobile ad hoc networks Spine Storms Computational modeling Routing protocols Mathematics computer science Bandwidth Batteries

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Machine learning based online performance prediction for runtime parallelization and task scheduling

Machine learning based online performance prediction for run...

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International Symposium on Performance Analysis of Systems and Software, ISPASS 2009

作者： Li, Jiangtian Ma, Xiaosong Singh, Karan Schulz, Martin De Supinski, Bronis McKee, Sally A. Dept. of Computer Science North Carolina State University Raleigh NC 27606 United States Computer Science and Mathematics Division Oak Ridge National Laboratory Oak Ridge TN 37831 United States Computer Systems Laboratory Cornell University Ithaca NY 14850 United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore CA 94550 United States Department of Computer Science and Engineering Chalmers University of Technology Göteborg Sweden

ISBN: (纸本)9781424441846

With the emerging many-core paradigm, parallel programming must extend beyond its traditional realm of scientific applications. Converting existing sequential applications as well as developing next-generation software requires assistance from hardware, compilers and runtime systems to exploit parallelism transparently within applications. These systems must decompose applications into tasks that can be executed in parallel and then schedule those tasks to minimize load imbalance. However, many systems lack a priori knowledge about the execution time of all tasks to perform effective load balancing with low scheduling overhead. In this paper, we approach this fundamental problem using machine learning techniques first to generate performance models for all tasks and then applying those models to perform automatic performance prediction across program executions. We also extend an existing scheduling algorithm to use generated task cost estimates for online task partitioning and scheduling. We implement the above techniques in the pR framework, which transparently parallelizes scripts in the popular R language, and evaluate their performance and overhead with both a realworld application and a large number of synthetic representative test scripts. Our experimental results show that our proposed approach significantly improves task partitioning and scheduling, with maximum improvements of 21.8%, 40.3% and 22.1% and average improvements of 15.9%, 16.9% and 4.2% for LMM (a real R application) and synthetic test cases with independent and dependent tasks, respectively. © 2009 IEEE.

关键词： Scheduling algorithms

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Image-based automatic object localisation in iSpace environment

Image-based automatic object localisation in iSpace environm...

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10th International Symposium of Hungarian Researchers on Computational Intelligence and Informatics, CINTI 2009

作者： Zanaty, Péter Korondi, Péter Sziebig, Gábor Jeni, László A. Department of Scientific Computing Narvik University College Electrical Engineering and Space technology Hungary Department of Mechatronics Budapest University of Technology and Economics Optics and Applied Informatics Hungary Computer and Automation Research Institute Japan Department of Industrial Engineering Narvik University College Norway Institute of Industrial Science University of Tokyo Japan

Localization is a fundamental task in mobile robotics and in indoor environments we can use various sensors to solve this problem. In the Intelligent Space environment we can use laser range finders or ultrasonic positioning systems to localize and track mobile robots. Nevertheless, our final goal is to substitute these sensors and accomplish this task using just cameras. In this paper, we show the feasibility of determining the robot's location based on the images received from a single camera. In our experimental room we used a surveillance camera, which can be controlled to pan/tilt in order to change the point of view. The camera had been mounted on the ceiling and six preset positions were selected to cover the whole area. The object recognition is based on colour space filtering and contour detection. Finally, the contours of the detected objects are transformed from the image space to the world coordinate system and the polygons are reduced to simpler ones. The system is able to detect not only the position of the objects, but their orientations.

关键词： Cameras

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Compression and occlusion culling for fast isosurface extraction from massive datasets

Compression and occlusion culling for fast isosurface extrac...

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Workshop on Mathematical Foundations of Scientific Visualization, computer Graphics, and Massive Data Exploration, 2004

作者： Gregorski, Benjamin Senecal, Joshua Duchaineau, Mark Joy, Kenneth I. Center for Applied Scientific Computing Lawrence Livermore National Laboratory 7000 East Avenue LivermoreCA94551 United States Institute for Data Analysis and Visualization Department of Computer Science University of California Davis One Shields Avenue DavisCA95616 United States

ISBN: (纸本)9783642543005

We present two algorithms for data compression and occlusion culling that improve interactive, adaptive isosurface extraction from large volume datasets. Our algorithm, based on hierarchical tetrahedral meshes defined by longest edge bisection, allows arbitrary isosurfaces to be adaptively extracted at interactive rates from losslessly compressed volumes where the region of interest, determined at runtime by user interaction, is decompressed on-the-fly. For interactive applications, we exploit frame-to-frame coherence between consecutive views to simplify the mesh structure in occluded regions and eliminate occluded triangles significantly reducing the complexity of the visualized surface and the underlying multiresolution volume representation. We extend the use of hardware accelerated occlusion queries to adaptive isosurface extraction applications where the surface geometry and topology change with the level-of-detail and view-point and the user can select an arbitrary isovalue for visualization. © 2009 Springer-Verlag Berlin Heidelberg.

关键词： Extraction

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The Mathematical Intelligencer 2009年第2期15卷 63-75页

作者： J. H. Loxton Robert Levinson Rodney Canfield School of Mathematics Physics Computing and Electronics Macquarie University North Ryde Australia Department of Computer and Information Sciences Applied Sciences Building University of California Santa Cruz USA Department of Computer Science University of Georgia Athens USA

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Spatiotemporal latent semantic cues for moving people tracking

Spatiotemporal latent semantic cues for moving people tracki...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Peng Zhang Sabu Emmanuel Pradeep K. Atrey Mohan S. Kankanhalli School of Computer Engineering Nanyang Technological University Singapore Department of Applied Computer Science University of Winnipeg Winnipeg Canada School of Computing National University of Singapore Singapore

Effective and robust visual tracking is one of the most important tasks for the intelligent visual surveillance. In this paper, we proposed a novel method for detecting and tracking moving people using the spatiotemporal latent semantic cues and the incremental eigenspace tracking techniques. During tracking process, the target appearance model is incrementally learned in low dimensional tensor eigenspace by adaptively updating the eigenbasis and sample mean. At the same time, the spatiotemporal latent semantic cues calibrate the estimation of tracking and detect new moving people coming in the same surveillance scene. Experiment results show that with the calibration based on spatiotemporal latent semantic cues, the proposed method can track the moving people automatically and effectively.

关键词： Spatiotemporal phenomena Target tracking Surveillance Lighting Noise robustness Object detection Cameras computer science Tensile stress Layout

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A novel Hebbian rules based method for computation of sparse coding basis vectors

A novel Hebbian rules based method for computation of sparse...

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5th International Conference on Natural Computation, ICNC 2009

作者： Zou, Baixian Miao, Jun Yang, Xiaoling Duan, Lijuan Qiao, Yuanhua Department of Information Science and Technology College of Arts and Science of Beijing Union University Beijing 100083 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Tianjin Academy of Agricultural Science Tianjin 300192 China College of Computer Science and Technology Beijing University of Technology Beijing 100022 China College of Applied Science Beijing University of Technology Beijing 100022 China

ISBN: (纸本)9780769537368

Sparse coding has high-performance encoding and ability to express images, sparse encoding basis vector plays a crucial role. The computational complexity of the most existing sparse coding basis vectors of is relatively large. In order to reduce the computational complexity and save the time to train basis vectors. A new Hebbian rules based method for computation of sparse coding basis vectors is proposed in this paper. A two-layer neural network is constructed to implement the task. The main idea of our work is to learn basis vectors by removing the redundancy of all initial vectors using Hebbian rules. The experiments on natural images prove that the proposed method is effective for sparse coding basis learning. It has the smaller computational complexity compared with the previous work. © 2009 IEEE.

关键词： Computational complexity

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AAAI 2008 workshop reports

AAAI 2008 workshop reports

引用

作者： Anand, Sarabjot Singh Bunescu, Razvan Carvcdho, Vitor Chomicki, Jan Conitzer, Vincent Cox, Michael T. Dignum, Virginia Dodds, Zachary Dredze, Mark Furcy, David Gabrilovich, Evgeniy Göker, Mehmet H. Guesgen, Hans Hirsh, Haym Jannach, Dietmar Junker, Ulrich Ketter, Wolfgang Kobsa, Alfred Koenig, Sven Lau, Tessa Lewis, Lundy Matson, Eric Metzler, Ted Mihalcea, Rada Mobasher, Bamshad Pineau, Joelle Poupart, Pascal Raja, Anita Ruml, Wheeler Sadeh, Norman Shani, Guy Shapiro, Daniel Smith, Trey Taylor, Matthew E. Wagstaff, Kiri Walsh, William Zhou, Rong Department of Computer Science University of Warwick United Kingdom School of Electrical Engineering and Computer Science Ohio University United States Microsoft Live Labs United States computer science and engineering University at Buffalo United States Computer science and economics Duke University United States Intelligent Computing group of BBN Technologies Utrecht University Netherlands Department of Computer science Harvey Mudd College United States University of Pennsylvania United States University of Wisconsin United States Yahoo Research PricewaterhouseCoopers Center for Advanced Research United States Department of Computer science School of Engineering and Advanced Technology Massey University New Zealand Departent of Computer science Rutgers University United States Department of Computer Science Dortmund University of Technology. Germany ILOG Rotterdam School of Management Erasmus University Netherlands Donald Bren School of Information and Computer Sciences University of California Irvine United States Depatrment of Computer science University of Southern California United States IBM Almaden Research Center United States Department of Computer Information Technology Southern New Hampshire University United States Computer and Information Technology Program College of Technology Purdue University United States Hughes Program for Religion and Science Dialogue Oklahoma City University United States Department of Computer Science and Engineering University of North Texas United States School of Computing DePaul University United States Department of Computer Science McGill University Canada School of Computer Science University of Waterloo Canada Department of Software and Information Systems University of North Carolina Charlotte United States University of New Hampshire United States Microsoft Research Institute for the Study of Learning and Expertise Applied Reactivity Inc. United States NASA Ames Research Center Carnegie Mellon University West

AAAI was pleased to present the AAAI-08 Workshop Program, held Sunday and Monday, July 13-14, in Chicago, Illinois, USA. The program included the following 15 workshops: Advancements in POMDP Solvers;AI Education Workshop Colloquium;Coordination, Organizations, Institutions, and Norms in Agent Systems, Enhanced Messaging;Human Implications of Human-Robot Interaction;Intelligent Techniques for Web Personalization and Recommender Systems;Metareasoning: Thinking about Thinking;Multidisciplinary Workshop on Advances in Preference Handling;Search in Artificial Intelligence and Robotics;Spatial and Temporal Reasoning;Trading Agent Design and Analysis;Transfer Learning for Complex Tasks;What Went Wrong and Why: Lessons from AI Research and Applications;and Wikipedia and Artificial Intelligence: An Evolving Synergy. Copyright © 2009, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Human robot interaction

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Adaptive Processor Allocation with Estimated Job Execution Time in Heterogeneous computing Grid

Adaptive Processor Allocation with Estimated Job Execution T...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Kuo-Chan Huang Kuan-Po Lai Hsi-Ya Chang Computer and Information Science Department National Taichung University Taichung Taiwan National Applied Research Laboratories National Center of High-performance Computing Hsinchu Taiwan

Many parallel computer systems installed in computing centers worldwide, which adopts backfilling based job scheduling policies, require that users should provide estimated job execution time when submitting a job to the system. This paper presents an approach, taking advantage of the estimated job execution time, to effectively allocating processors to jobs submitted to a heterogeneous computing grid. The proposed adaptive processor allocation approach was evaluated with simulation studies under various workload and processor speed conditions. The results indicate that the adaptive processor allocation approach can effectively improve the overall system performance, in terms of jobspsila average turnaround time, from two to four times under different conditions, compared to currently used methods.

关键词： Grid computing Concurrent computing Processor scheduling Distributed computing Computational modeling System performance Hypercubes Distributed processing Application software Laboratories

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