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6th International Conference on Information Systems, Management and Technology, ICISTM 2012

作者： LeDoux, Charles Walenstein, Andrew Lakhotia, Arun Center for Advanced Computer Studies University of Louisiana at Lafayette Lafayette LA United States School of Computing and Informatics Computer Science Program University of Louisiana at Lafayette Lafayette LA United States

ISBN: (纸本)9783642291654

In classifying malware, an open research question is how to combine similar extracted data from program analyzers in such a way that the advantages of the analyzers accrue and the errors are minimized. We propose an approach to fusing multiple program analysis outputs by abstracting the features to a common form and utilizing a disjoint union fusion function. The approach is evaluated in an experiment measuring classification accuracy on fused dynamic trace data on over 18,000 malware files. The results indicate that a naïve fusion approach can yield improvements over non-fused results, but the disjoint union fusion function outperforms naïve union by a statistically significant amount in three of four classification methods applied. © 2012 Springer-Verlag.

关键词： Malware

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From laptops to supercomputers: a single highly scalable code base for spiking neuronal network simulations

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BMC Neuroscience 2013年第1期14卷 1-2页

作者： Susanne Kunkel Abigail Morrison Markus Diesmann Maximilian Schmidt Jochen M Eppler Moritz Helias Hans E Plesser Jun Igarashi Tomoki Fukai Gen Masumoto Shin Ishii Simulation Laboratory Neuroscience - Bernstein Facility Simulation and Database Technology Institute for Advanced Simulation Jülich Aachen Research Alliance Jülich Research Centre Germany Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) Jülich Research Centre and JARA Germany Bernstein Center Freiburg Albert-Ludwig University of Freiburg Germany Institute of Cognitive Neuroscience Faculty of Psychology Ruhr-University Bochum Germany Medical Faculty RWTH University Aachen Germany Laboratory for Neural Circuit Theory RIKEN Brain Science Institute Wako Japan Department of Mathematical Sciences and Technology Norwegian University of Life Sciences Aas Norway High-Performance Computing Team RIKEN Computational Science Research Program Kobe Japan Integrated Systems Biology Laboratory Department of Systems Science Graduate School of Informatics Kyoto University Japan

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Learning non-visual graphical information using a touch-based vibro-audio interface 12

Learning non-visual graphical information using a touch-base...

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14th International ACM SIGACCESS Conference on Computers and Accessibility, ASSETS 2012

作者： Giudice, Nicholas A. Palani, Hari Prasath Brenner, Eric Kramer, Kevin M. Spatial Informatics Program School of Computing and Information Science University of Maine 5711 Boardman Hall Orono ME 04469 United States Advanced Medical Electronics 6901 East Fish Lake Road Maple Grove MN 55368 United States

ISBN: (纸本)9781450313216

This paper evaluates an inexpensive and intuitive approach for providing non-visual access to graphic material, called a vibro-audio interface. The system works by allowing users to freely explore graphical information on the touchscreen of a commercially available tablet and synchronously triggering vibration patterns and auditory information whenever an on-screen visual element is touched. Three studies were conducted that assessed legibility and comprehension of the relative relations and global structure of a bar graph Exp 1 Pattern recognition via a letter identification task Exp 2 and orientation discrimination of geometric shapes Exp 3). Performance with the touch-based device was compared to the same tasks performed using standard hardcopy tactile graphics. Results showed similar error performance between modes for all measures, indicating that the vibro-audio interface is a viable multimodal solution for providing access to dynamic visual information and supporting accurate spatial learning and the development of mental representations of graphical material.

关键词： Pattern recognition

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On region-based fault tolerant design of distributed file storage in networks

On region-based fault tolerant design of distributed file st...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Sujogya Banerjee Shahrzad Shirazipourazad Arunabha Sen Computer Science and Engineering Program School of Computing Informatics and Decision System Engineering Arizona State University Tempe AZ USA

ISBN: (纸本)9781467307734;9781467307758

Distributed storage of data files in different nodes of a network enhances the reliability of the data by offering protection against node failure. In the (N,K),N ≥ K file distribution scheme, from a file F of size |F|, N segments of size |F|/K are created in such a way that it is possible to reconstruct the entire file, just by accessing any K segments. For the reconstruction scheme to work it is essential that the K segments of the file are stored in nodes that are connected in the network. However in case of node failures the network might become disconnected (i.e., split into several connected components). We focus on node failures that are spatially-correlated or region-based. Such failures are often encountered in disaster situations or natural calamities where only the nodes in the disaster zone are affected. The goal of this research is to devise a file segment distribution scheme so that, even if the network becomes disconnected due to any region fault, at least one of the largest connected components will have at least K distinct file segments with which to reconstruct the entire file. The distribution scheme will also ensure that the total storage requirement is minimized. We provide an optimal solution through Integer Linear programming and an approximation solution with a guaranteed performance bound of O(ln n) to solve the problem for any arbitrary network. The performance of the approximation algorithm is evaluated by simulation on two real networks.

关键词： Image color analysis Color Approximation algorithms Robustness Approximation methods Layout Encoding

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A novel spinal vertebrae segmentation framework combining geometric flow and shape prior with level set method

A novel spinal vertebrae segmentation framework combining ge...

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IEEE International Symposium on Biomedical Imaging

作者： Poay Hoon Lim Ulas Bagci Omer Aras Yan Wang Li Bai School of Computer Science University of Nottingham Nottingham UK Radiology and Imaging Sciences National Institutes of Health DHHS Bethesda MD USA Molecular Imaging Program National Cancer Institute Bethesda MD USA School of Computing Informatics and Media University of Bradford Bradford UK

ISBN: (纸本)9781457718571

Segmentation of spinal vertebrae is extremely important in the study of spinal related disease or disorders. However, limited work has been done on precise segmentation of spinal vertebrae. The complexity of vertebrae shapes, with gaps in the cortical bone, internal boundaries, as well as the noisy, incomplete or missing information from the images have undoubtedly increased the challenge for image analysis. In this paper, we introduce a novel level set segmentation framework that integrates shape prior and the Willmore flow to drive the level set evolution. While the shape energy draws the level set function towards a range of possible prior shapes, the edge-mounted Willmore energy captures the localized geometry information and smooths the surface during the level set evolution. Experimental results on segmentation of spinal vertebrae from CT images demonstrate the powerful combination of prior knowledge and geometrical flow.

关键词： Shape Level set Image segmentation Kernel Estimation Image edge detection Mathematical model

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Reports of the AAAI 2011 conference workshops

Reports of the AAAI 2011 conference workshops

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作者： Agmon, Noa Agrawal, Vikas Aha, David W. Aloimonos, Yiannis Buckley, Donagh Doshi, Prashant Geib, Christopher Grasso, Floriana Green, Nancy Johnston, Benjamin Kaliski, Burt Kiekintveld, Christopher Law, Edith Lieberman, Henry Mengshoel, Ole J. Metzler, Ted Modayil, Joseph Oard, Douglas W. Onder, Nilufer O'Sullivan, Barry Pastra, Katerina Precup, Doina Ramachandran, Sowmya Reed, Chris Sariel-Talay, Sanem Selker, Ted Shastri, Lokendra Singh, Satinder Smith, Stephen F. Srivastava, Siddharth Sukthankar, Gita Uthus, David C. Williams, Mary-Anne Department of Computer Science University of Texas at Austin United States Center for Knowledge Driven Information Systems Infosys Labs India Adaptive Systems Section Naval Research Laboratory Washington DC United States Department of Computer Science University of Maryland College Park United States EMC's Center of Excellence in Ireland Research Europe at EMC Ireland Department of Computer Science University of Georgia United States School of Informatics University of Edinburgh United Kingdom Department of Computer Science University of Liverpool United Kingdom Department of Computer Science University of North Carolina Greensboro United States Faculty of Engineering and Information Technology University of Technology Sydney Australia VeriSign Inc. United States Department of Computer Science University of Texas at El Paso United States Machine Learning Department Carnegie Mellon University United States Massachusetts Institute of Technology Media Laboratory United States Carnegie Mellon University's Silicon Valley Campus United States Darrell W. Hughes Program for Religion and Science Dialogue Oklahoma City University United States Department of Computing Science University of Alberta Canada College of Information Studies Institute for Advanced Computer Studies University of Maryland College Park United States Department of Computer Science Michigan Technological University United States Department of Computer Science University College Cork Ireland Cognitive Systems Research Insitute Athens Greece School of Computer Science McGill University Canada Stottler Henke Inc. United States School of Computing University of Dundee United Kingdom Department of Computer Engineering Istanbul Technical University Turkey Convergence Lab Infosys Technologies Ltd. United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States Robotics Institute Carnegie Mellon University United States University of Wisconsin

The AAAI-11 workshop program was held Sunday and Monday, August 7-18, 2011, at the Hyatt Regency San Francisco in San Francisco, California USA. The AAAI-11 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages;Analyzing Microtext;Applied Adversarial Reasoning and Risk Modeling;Artificial Intelligence and Smarter Living: The Conquest of Complexity;Artifiicial Intelligence for Data Center Management and Cloud computing;Automated Action Planning for Autonomous Mobile Robots;Computational Models of Natural Argument;Generalized Planning;Human Computation;Human-Robot Interaction in Elder Care;Interactive Decision Theory and Game Theory, 2010;Language-Action Tools for cognitive Artificial Agents: Integrating Vision, Action, and Language;Lifelong Learning from Sensorimotor Experience;Plan, Activity, and Intent Recognition;and Scalable Integration of Analytics and Visualization. This article presents short summaries of those events. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Human robot interaction

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Beyond connectivity-new metrics to evaluate robustness of networks

Beyond connectivity-new metrics to evaluate robustness of ne...

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International Conference on High Performance Switching and Routing

作者： Banerjee, Sujogya Shirazipourazad, Shahrzad Ghosh, Pavel Sen, Arunabha Computer Science and Engineering Program School of Computing Informatics and Decision System Engineering Arizona State University Tempe AZ 85281 United States

ISBN: (纸本)9781424484560

Robustness or fault-tolerance capability of a network is an important design parameter in both wired and wireless networks. Connectivity of a network is traditionally considered to be the primary metric for evaluation of its fault-tolerance capability. However, connectivity (G) (for random faults) or region-based connectivity R(G) (for spatially correlated or region-based faults, where the faults are confined to a region R) of a network G, does not provide any information about the network state, (i.e., whether the network is connected or not) once the number of faults exceeds (G) or R(G). If the number of faults exceeds (G) or R(G), one would like to know, (i) the number of connected components into which G decomposes, (ii) the size of the largest connected component, (iii) the size of the smallest connected component. In this paper, we introduce a set of new metrics that computes these values. We focus on one particular metric called region-based component decomposition number (RBCDN), that measures the number of connected components in which the network decomposes once all the nodes of a region fail. We study the computational complexity of finding RBCDN of a network. In addition, we study the problem of least cost design of a network with a target value of RBCDN. We show that the optimal design problem is NP-complete and present an approximation algorithm with a performance bound of O(log K + 4log n), where n denotes the number of nodes in the graph and K denotes a target value of RBCDN. We evaluate the performance of our algorithm by comparing it with the performance of the optimal solution. Experimental results demonstrate that our algorithm produces near optimal solution in a fraction of time needed to find an optimal solution. © 2011 IEEE.

关键词： Approximation algorithms

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Topology preserving domain adaptation for addressing subject based variability in SEMG signal

Topology preserving domain adaptation for addressing subject...

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2011 AAAI Spring Symposium

作者： Chattopadhyay, Rita Krishnan, Narayanan C. Panchanathan, Sethuraman Center for Cognitive Ubiquitous Computing School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ 85287 United States Center for Advanced Studies in Adaptive Systems School of Electrical Engineering and Computer Science Washington State University Pullman WA 99163 United States

ISBN: (纸本)9781577354963

A subject independent computational framework is one which does not require to be calibrated by the specific subject data to be ready to be used on the subject. The greatest challenge in developing such a framework is the variation in parameters across subjects which is termed as subject based variability. Spectral and amplitude variations in surface myoelectric signals (SEMG) are analyzed to determine the fatigue state of a muscle. But variations in the spectrum and magnitude of myoelectric signals across subjects cause variations in both marginal and conditional probability distributions in the features extracted across subjects, making it difficult to model the signal for any automated signal classification. However we observe that the manifold of the multidimensional SEMG data have an inherent similarity as the physiological state moves from no fatigue to fatigue state. In this paper we exploit this specific feature of the SEMG data and propose a domain adaptation technique that is based on intrinsic manifold of the data preserved in a low dimensional space, thus reducing the marginal probability differences between the subjects, followed by an instance selection methodology, based on similar conditional probabilities in the mapped domain. The proposed method provides significant improvement in subject independent accuracies compared to cases without any domain adaptation methods and also compared to other state-of-the-art domain adaptation methodologies. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Probability distributions

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Minimum maximum-degree publish-subscribe overlay network design

Minimum maximum-degree publish-subscribe overlay network des...

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作者： Onus, Melih Richa, Andréa W. Department of Computer Engineering Bilkent University Ankara 06560 Turkey Computer Science and Engineering Program School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ 85281 United States

Designing an overlay network for publish/subscribe communication in a system where nodes may subscribe to many different topics of interest is of fundamental importance. For scalability and efficiency, it is important to keep the degree of the nodes in the publish/subscribe system low. It is only natural then to formalize the following problem: Given a collection of nodes and their topic subscriptions, connect the nodes into a graph that has least possible maximum degree in such a way that for each topic t, the graph induced by the nodes interested in t is connected. We present the first polynomial-time logarithmic approximation algorithm for this problem and prove an almost tight lower bound on the approximation ratio. Our experimental results show that our algorithm drastically improves the maximum degree of publish/subscribe overlay systems. We also propose a variation of the problem by enforcing that each topic-connected overlay network be of constant diameter while keeping the average degree low. We present three heuristics for this problem that guarantee that each topic-connected overlay network will be of diameter 2 and that aim at keeping the overall average node degree low. Our experimental results validate our algorithms, showing that our algorithms are able to achieve very low diameter without increasing the average degree by much. © 2011 IEEE.

关键词： Overlay networks

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The third ACM international workshop on multimedia technologies for distance learning (MTDL 2011) 11

The third ACM international workshop on multimedia technolog...

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19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11

作者： Lau, Rynson W.H. Shih, Timothy K. Li, Frederick Yen, Neil Y. Department of Computer Science City University of Hong Kong Hong Kong Hong Kong Dept. of Computer Science and Info Engineering National Central University Taiwan School of Engineering and Computing Sciences University of Durham United Kingdom Dept. of Human Informatics and Cognitive Sciences Waseda University Japan

ISBN: (纸本)9781450306164

The MTDL 2011 workshop in its third edition aims to continue in the contribution and evaluation of the impact of multimedia technologies to e-Learning. This workshop is held in conjunction with the ACM Multimedia 2011 Conference in Scottsdale, Arizona, U.S.A.. As a cover paper of this workshop, we briefly summarize important issues to be addressed in e-learning in the first section, followed by a discussion of important issues proposed in the 5 papers accepted to the workshop (among the 9 submissions), plus 3 invited papers. © 2011 ACM.

关键词： E-learning

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