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Computational Science and Engineering (CSE), 2013 IEEE 16th International Conference on

Date 3-5 Dec. 2013

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Displaying Results 1 - 25 of 213
  • [Front cover]

    Page(s): C4
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  • [Title page i]

    Page(s): i
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  • [Title page iii]

    Page(s): iii
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  • [Copyright notice]

    Page(s): iv
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  • Table of contents

    Page(s): v - xviii
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  • Message from CSE2013 Chairs

    Page(s): xix
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  • CSE2013 Organizing and Program Committees

    Page(s): xx - xxii
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  • Message from CIT2013 Chairs

    Page(s): xxiii
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  • CIT2013 Organizing and Program Committees

    Page(s): xxiv - xxvi
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  • Message from ICESS2013 Chairs

    Page(s): xxvii
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  • ICESS2013 Organizing and Program Committees

    Page(s): xxviii - xxix
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  • Message from BDSE2013 Chairs

    Page(s): xxx
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  • BDSE2013 Organizing and Program Committees

    Page(s): xxxi - xxxiv
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  • Message from ACIT2013 Chairs

    Page(s): xxxv
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  • Message from MR.BDI2013 Chairs

    Page(s): xxxvi
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  • MR.BDI2013 Organizing and Program Committees

    Page(s): xxxvii
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  • Message from DCPIS2013 Chairs

    Page(s): xxxviii
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  • DCPIS2013 Organizing and Program Committees

    Page(s): xxxix
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  • Nonlinear Support Vector Machines for Solving the PMC-Based System-Level Fault Diagnosis Problem

    Page(s): 1 - 8
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (357 KB) |  | HTML iconHTML  

    This paper deals with the system-level fault diagnosis problem which main objective is to identify faults, in particular permanent ones, in diagnosable systems under the PMC model. The PMC model assumes that each system's node is tested by a subset of the other nodes, and that at most t of these nodes are permanently faulty. Tests performed by faulty nodes are unreliable, and hence, they can incorrectly diagnose fault-free nodes as faulty or faulty ones as fault-free. In this paper, we describe a new nonlinear support vector machines-based (SVMs) diagnosis algorithm, which exploits the off-line learning phase of SVMs to speed up the diagnosis algorithm. The novel diagnosis approach has been implemented and evaluated using randomly generated diagnosable systems. Results from the thorough simulation study demonstrate the effectiveness of the nonlinear SVM-based fault diagnosis algorithm, in terms of diagnosis correctness, latency, and scalability. In addition, extreme faulty situations, where the number of faults is around the bound t, and large diagnosable systems have been also experimented to show the efficiency of the new nonlinear SVM-based diagnosis algorithm. View full abstract»

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  • Secure Cloud Services: Matrix Multiplication Revisited

    Page(s): 9 - 14
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (250 KB) |  | HTML iconHTML  

    This paper revisits the issue of secure outsourcing of services. It proposes a data scrambling approach of transforming private data into public without public key encryption in the cloud computing context. The approach is applicable to non-ordering integer data type used in matrix multiplication. The paper applies row-column shuffling to matrices, and adds random noises to data in order to hide actual values and their sequence. The paper demonstrates the applicability of the approach with a running example. View full abstract»

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  • A Load-Balancing Force Decomposition Scheme for Parallel Simulation of Chemical Dynamics with Multiple Inter-atomic Force Models

    Page(s): 15 - 19
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (249 KB) |  | HTML iconHTML  

    Force evaluation is the most computationally intensive part in a chemical dynamics simulation, and hence most parallel simulation algorithms choose the force calculation as the main target for parallelization. The majority of existing parallel algorithms assume a uniform force-evaluation cost for all atom pairs. For dynamics with considerable bonded interactions, different evaluation formulas are usually used for forces between different atom pairs, and this complicates the load balancing for the simulation of chemical dynamics on parallel computers. In this paper, we present a load-balancing scheme that takes into account differences of inter-atomic force models for different atom pairs. By considering different force models, the load partitioning of our algorithm can effectively handle the differences in computation costs for calculating different inter-atomic interactions when atom-tailored force models are used for different atom pairs, which is usually the case for bonded interactions. A parallel simulation algorithm for bonded-interaction-dominated dynamics was developed that employs the load partitioning scheme, and the algorithm was implemented and tested on different ensembles of atoms, and produced good performances for the testing problems. View full abstract»

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  • Aircraft Landing Problem: An Efficient Algorithm for a Given Landing Sequence

    Page(s): 20 - 27
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (285 KB) |  | HTML iconHTML  

    In this paper, we investigate a special case of the static aircraft landing problem (ALP) with the objective to optimize landing sequences and landing times for a set of air planes. The problem is to land the planes on one or multiple runways within a time window as close as possible to the preferable target landing time, maintaining a safety distance constraint. The objective of this well-known NP-hard optimization problem is to minimize the sum of the total penalty incurred by all the aircraft for arriving earlier or later than their preferred landing times. For a problem variant that optimizes a given feasible landing sequence for the single runway case, we present an exact polynomial algorithm and prove the run-time complexity to lie in O(N^3), where N is the number of aircraft. The proposed algorithm returns the optimal solution for the ALP for a given feasible landing sequence on a single runway for a common practical case of the ALP described in the paper. Furthermore, we propose a strategy for the ALP with multiple runways and present our results for all the benchmark instances with single and multiple runways, while comparing them to previous results in the literature. View full abstract»

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  • Developing a Lean Mass Customization Based Manufacturing

    Page(s): 28 - 33
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (404 KB) |  | HTML iconHTML  

    We describe an experimental mass customization based manufacturing system which relies on a sophisticated IT infrastructure and CAE to produce novel wooden design products in a lean and flexible way. The main focus of this paper is on the IT infrastructure where several AI techniques for machine vision, search and planning are applied. The IT system has a service oriented architecture and is composed of heterogeneous distributed components communicating via custom web services. A key component of this system is an smart optimizer which helps to improve warehouse logistics, material utilization and speeds up manual creative work. View full abstract»

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  • Securing Mobile Devices from DoS Attacks

    Page(s): 34 - 41
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (763 KB) |  | HTML iconHTML  

    Today mobile devices are increasingly being used to access data services in addition to the voice communications. However such devices have limited resources to enforce strong security measures and hence they are easily vulnerable to attacks. In this paper we propose techniques for securing mobile devices from denial of service attacks. We will make use of the IPSec protocol for secure traceback and preventing the attack upstream. Finally we present the implementation and performance analysis of our model. View full abstract»

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  • What to Reuse?: A Probabilistic Model to Transfer User Annotations in a Surveillance Video

    Page(s): 42 - 49
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (623 KB) |  | HTML iconHTML  

    Techniques to extract or understand interactions between moving objects in video is becoming increasingly important as the amount of video increases. Applications in surveillance range from understanding traffic to studying fish schooling behaviour. Because of the massive amount of data, fast, approximate techniques based on statistical models are common. These models connect user annotations (labels) to scenes in a (short) video segment. The connection forms a domain, which associates information about moving objects in scenes with the labels, such as to indicate whether a user considers a particular traffic scene to be "dangerous." Unfortunately a statistical model trained in one domain often yields low precision and recall when applied to another domain because the random variables that explain video content exhibit changing marginal and conditional probability distributions over time (e.g., due to different backgrounds, changes in illumination, shading, and numbers of moving objects). This problem is exacerbated when new domains continuously arise (e.g., in the real-time processing of video) and user annotations are only limited to training data, a common scenario for surveillance video. In this paper, we propose a new, cross-domain technique that reuses labelled content from source domains to improve the prediction of user annotations in a target domain. Our model probabilistically learns how users annotate scenes based on the similarity of target to source domains. Two domains that are similar will share a large number of observable features. We encode the similarity in a covariance matrix, which flexibly allows allows users to set an arbitrary covariance structure between pairs of domains before training the model. Experiments show that our method improves state-of-the-art techniques (SVM and CF) in predicting dangerous scenes in real-world traffic surveillance videos. View full abstract»

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