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The AAAI-13 conference workshops

The AAAI-13 conference workshops

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作者： Agrawal, Vikas Archibald, Christopher Bhatt, Mehul Bui, Hung Cook, Diane J. Cortés, Juan Geib, Christopher Gogate, Vibhav Guesgen, Hans W. Jannach, Dietmar Johanson, Michael Kersting, Kristian Konidaris, George Kotthoff, Lars Michalowski, Martin Natarajan, Sriraam O'Sullivan, Barry Pickett, Marc Podobnik, Vedran Poole, David Shastri, Lokendra Shehu, Amarda Sukthankar, Gita IBM Research India Mississippi State University United States University of Bremen Germany Laboratory for Natural Language Understanding Sunnyvale CA United States Washington State University United States University of Toulouse France Drexel University United States Department of Computer Science University of Texas Dallas United States Massey University New Zealand TU Dortmund Germany University of Alberta in Edmonton Canada Fraunhofer IAIS and the University of Bonn Germany MIT United States INSIGHT Centre for Data Analytics University College Cork Ireland Adventium Labs Minneapolis MN United States Indiana University Bloomington IN United States Naval Research Laboratory Washington DC United States Telecommunication Department of the Faculty of Electrical Engineering and Computing University of Zagreb Croatia Department of Computer Science University of British Columbia Vancouver Canada Infosys India George Mason University United States University of Central Florida United States

The AAAI-13 Workshop Program, a part of the 27th AAAI Conference on Artificial Intelligence, was held Sunday and Monday, July 14-15, 2013, at the Hyatt Regency Bellevue Hotel in Bellevue, Washington, USA. The program included 12 workshops covering a wide range of topics in artificial intelligence, including Activity Context-Aware System Architectures (WS-13-05);Artificial Intelligence and Robotics Methods in Computational Biology (WS-13-06);Combining Constraint Solving with Mining and Learning (WS-13-07);computer Poker and Imperfect Information (WS-13-08);Expanding the Boundaries of Health Informatics Using Artificial Intelligence (WS-13-09);Intelligent Robotic Systems (WS-13-10);Intelligent Techniques for Web Personalization and Recommendation (WS- 13-11);Learning Rich Representations from Low-Level Sensors (WS-13-12);Plan, Activity, and Intent Recognition (WS-13-13);Space, Time, and Ambient Intelligence (WS-13-14);Trading Agent Design and Analysis (WS-13-15);and Statistical Relational Artificial Intelligence (WS-13-16). Copyright © 2013, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： data mining

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Towards understanding dominant processes in complex dynamical systems: Case of precipitation extremes

Towards understanding dominant processes in complex dynamica...

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6th International Workshop on Knowledge Discovery from Sensor data, SensorKDD'12 - Held in Conjunction with the 18th ACM SIGKDD Conference on Knowledge Discovery and data Mining, KDD 2012

作者： Das, Debasish Ganguly, Auroop Banerjee, Arindam Obradovic, Zoran Department of Civil and Environmental Engineering Northeastern University Boston MA United States Center for Data Analytics and Biomedical Informatics Temple University Philadelphia PA United States Computer Science and Engineering University of Minnesota Minneapolis MN United States

ISBN: (纸本)9781450315548

Complex dynamical systems like precipitation extremes under climate variability or change are typically governed by multiple processes at multiple scales. The processes themselves may be manifested at multiple scales and would need to be captured through key indicator variables, which in turn may be better projected by physical models than the variables of interest. We posit that hybrid approaches based on physically-motivated approaches and data-driven methods, which in turn are conditioned on both observations and simulations from large-scale physics-based models, may offer novel and quantifiable insights. The data-driven approaches may need to extend and adapt methods developed and tested in statistics, data mining and machine learning to the concept of dominant processes. In this paper, we performed some exploratory data analysis to characterize the effect of dominant processes on precipitation extremes, annually and seasonally, and from global and century scale to regional and decadal scale, and found some interesting insights that pointed towards need of improved understanding. We identified the gaps in understanding the regional drivers where data-driven methods can make useful improvements and eventually lead to a predictive model for precipitation extremes. Although we do not propose any specific method for solving the problem, we realize that any successful data mining solution should include all or a subset of tools like dimensionality reduction, grouped variable selection, non-linear regression and graphical models. The concepts of dominant processes proposed here would likely generalize broadly to climate extremes while the solution frameworks themselves may generalize beyond climate. Copyright 2012 ACM.

关键词： data mining

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Online robust image alignment via iterative convex optimization

Online robust image alignment via iterative convex optimizat...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yi Wu Bin Shen Haibin Ling Center for Data Analytics & Biomedical Informatics Computer & Information Science Department Temple University Philadelphia PA USA Department of Computer Science Purdue University West Lafayette IN USA

In this paper we study the problem of online aligning a newly arrived image to previously well-aligned images. Inspired by recent advances in batch image alignment using low rank decomposition [16], we treat the newly arrived image, after alignment, as being linearly and sparsely reconstructed by the well-aligned ones. The task is accomplished by a sequence of convex optimization that minimizes the l\-norm. After that, online basis updating is pursued in two different ways: (1) a two-stage incremental alignment for joint registration of a large image dataset which is known a prior, and (2) a greedy online alignment of dynamically increasing image sequences, such as in the tracking scenario. In (1), we first sequentially collect basis images that are easily aligned by checking their reconstruction residuals, followed by the second stage where all images are re-aligned one-by-one using the collected basis set. In (2), during the tracking process, we dynamically enrich the image basis set by the new target if it significantly distinguishes itself from existing basis images. While inheriting the benefits of sparsity, our method enjoys the great time efficiency and therefore be capable of dealing with large image set and real time tasks such as visual tracking. The efficacy of the proposed online robust alignment algorithm is verified with extensive experiments on image set alignment and visual tracking, in reference with state-of-the-art methods.

关键词： Target tracking Visualization Face Robustness Joints Image reconstruction Convex functions

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Categorization of computing education resources with utilization of crowdsourcing 12

Categorization of computing education resources with utiliza...

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12th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL '12

作者： Chen, Yinlin Bogen II, Paul Logasa Hsieh, Haowei Fox, Edward A. Cassel, Lillian N. Department of Computer Science Virginia Tech. Blacksburg VA United States Intelligent Computing Research Team Computational Data Analytics Group Oak Ridge National Laboratory Oak Ridge TN 37931 United States School of Library and Information Science University of Iowa Iowa City IA 52242 United States Department of Computing Sciences Villanova University Villanova PA 19085 United States

ISBN: (纸本)9781450311540

The Ensemble Portal harvests resources from multiple heterogeneous federated collections. Managing these dynamically increasing collections requires an automatic mechanism to categorize records in to corresponding topics. We propose an approach to use existing ACM DL metadata to build classifiers for harvested resources in the Ensemble project. We also present our experience with utilizing the Amazon Mechanical Turk platform to build ground truth training data sets from Ensemble collections. © 2012 ACM.

关键词： Digital libraries

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Collaborative authoring of Walden's paths

Collaborative authoring of Walden's paths

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2nd International Conference on Theory and Practice of Digital Libraries, TPDL 2012

作者： Li, Yuanling Bogen II, Paul Logasa Pogue, Daniel Furuta, Richard Shipman, Frank Center for the Study of Digital Libraries Department of Computer Science and Engineering Texas A and M University College Station TX United States Intelligent Computing Research Team Computational Data Analytics Group Oak Ridge National Laboratory Oak Ridge United States User Experience and Interaction Design Team Production Enhancement Halliburton Energy Services Houston TX United States

ISBN: (纸本)9783642332890

This paper presents a prototype of an authoring tool to allow users to collaboratively build, annotate, manage, share and reuse collections of distributed resources from the World Wide Web. This extends on the Walden's Path project's work to help educators bring resources found on the World Wide Web into a linear contextualized structure. The introduction of collaborative authoring feature fosters collaborative learning activities through social interaction among participants, where participants can coauthor paths in groups. Besides, the prototype supports path sharing, branching and reusing;specifically, individual participant can contribute to the group with private collections of knowledge resources;paths completed by group can be shared among group members, such that participants can tailor, extend, reorder and/or replace nodes to have sub versions of shared paths for different information needs. © 2012 Springer-Verlag.

关键词： World Wide Web

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Learning-based automatic breast tumor detection and segmentation in ultrasound images

Learning-based automatic breast tumor detection and segmenta...

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IEEE International Symposium on Biomedical Imaging

作者： Peng Jiang Jingliang Peng Guoquan Zhang Erkang Cheng Vasileios Megalooikonomou Haibin Ling School of Computer Science and Technology Shandong Provincial Key Laboratory of Software Engineering P.R.China Department of Ultrasound Shandong Provincial Hospital Affiliated to Shandong University P.R. China Center for Data Analytics & Biomedical Informatics Temple University Philadelphia PA USA

ISBN: (纸本)9781457718571

Ultrasound (US) images have been widely used in the diagnosis of breast cancer in particular. While experienced doctors may locate the tumor regions in a US image manually, it is highly desirable to develop algorithms that automatically detect the tumor regions in order to assist medical diagnosis. In this paper, we propose a novel algorithm for automatic detection of breast tumors in US images. We formulate the tumor detection as a two step learning problem: tumor localization by bounding box and exact boundary delineation. Specifically, the proposed method uses an AdaBoost classifier on Harr-like features to detect a preliminary set of tumor regions. The preliminarily detected tumor regions are further screened with a support vector machine using quantized intensity features. Finally, the random walk segmentation algorithm is performed on the US image to retrieve the boundary of each detected tumor region. The proposed method has been evaluated on a data set containing 112 breast US images, including histologically confirmed 80 diseased ones and 32 normal ones. The data set contains one image from each patient and the patients are from 31 to 75 years old. Experiments demonstrate that the proposed algorithm can automatically detect breast tumors, with their locations and boundary shapes retrieved with high accuracy.

关键词： Image segmentation Support vector machines Breast tumors Ultrasonic imaging Feature extraction Accuracy

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Verification of adaptive systems

Verification of adaptive systems

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AIAA Infotech at Aerospace Conference and Exhibit 2012

作者： Pullum, Laura Cui, Xiaohui Vassev, Emil Hinchey, Mike Rouff, Christopher Buskens, Richard Computational Data Analytics Group Oak Ridge National Laboratory P.O. Box 2008 MS-6085 Oak Ridge TN 37830 United States Computer Science Department New York Institute of Technology 1855 Broadway Manhattan NY 10023 United States Lero - The Irish Software Engineering Research Centre University of Limerick Limerick Ireland Lockheed Martin Advanced Technology Laboratories Informatics Laboratory 3 Executive Campus Suite 600 Cherry Hill NJ 08002 United States Lockheed Martin Advanced Technology Laboratories Informatics Laboratory Applied Science Laboratory 3 Executive Campus Cherry Hill NJ 08002 United States

ISBN: (数字)9781600869396

ISBN: (纸本)9781600869396

Adaptive systems are critical for future space and other unmanned and intelligent systems. Verification of these systems is also critical for their use in systems with potential harm to human life or with large financial investments. Due to their nondeterministic nature and extremely large state space, current methods for verification of software systems are not adequate to provide a high level of assurance for them. The combination of stabilization science, high performance computing simulations, compositional verification and traditional verification techniques, plus operational monitors, provides a complete approach to verification and deployment of adaptive systems that has not been used before. This paper gives an overview of this approach. © 2012 by the American Institute of Aeronautics and Astronautics, Inc.

关键词： Adaptive systems

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Learning-based vessel segmentation in mammographic images

Learning-based vessel segmentation in mammographic images

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2011 1st IEEE International Conference on Healthcare Informatics, Imaging and Systems Biology, HISB 2011

作者： Cheng, Erkang McLaughlin, Shawn Megalooikonomou, Vasileios Bakic, Predrag R. Maidment, Andrew D.A. Ling, Haibin Center for Data Analytics and Biomedical Informatics Computer and Information Science Department Temple University Philadelphia PA United States Temple University Philadelphia PA United States Department of Radiology University of Pennsylvania Philadelphia PA United States

ISBN: (纸本)9780769544076

In this paper we propose using a learning-based method for vessel segmentation in mammographic images. To capture the large variation in vessel patterns not only across subjects, but also within a subject, we create a feature pool containing local, Gabor and Haar features extracted from mammographic images generating a feature space of very high dimension. We also employ a huge number of training samples, which essentially contains the pixels in the training images. To deal with the very high dimensional feature space and the huge number of training samples, we apply a forest with boosting trees for vessel segmentation. Specifically, we use the standard AdaBoost algorithm for each tree in the forest. The randomness is encoded, when training each AdaBoost tree, using randomly sampled training set (pixels) and randomly selected features from the whole feature pool. The proposed method is tested using a real dataset with 20 anonymous mammographic images. The effectiveness of the proposed features and classifiers is demonstrated in the experiments where we compare different approaches and feature combinations. In the paper, we also present full analysis of different types of features. © 2011 IEEE.

关键词： Pixels

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Visual tracking based on log-Euclidean Riemannian sparse representation

Visual tracking based on log-Euclidean Riemannian sparse rep...

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7th International Symposium on Visual Computing, ISVC 2011

作者： Wu, Yi Ling, Haibin Blasch, Erik Bai, Li Chen, Genshe Center for Data Analytics and Biomedical Informatics Computer and Information Science Department Temple University Philadelphia PA United States Air Force Research Lab. SNAA OH United States Electrical and Computer Engineering Department Temple University Philadelphia PA United States

ISBN: (纸本)9783642240270

Recently, sparse representation has been utilized in many computer vision tasks and adapted for visual tracking. Sparsity-based visual tracking is formulated as searching candidates with minimal reconstruction errors from a template subspace with sparsity constraints in the approximation coefficients. However, an intensity template is easily corrupted by noise and not robust for target tracking under a dynamic environment. The recently proposed covariance region descriptor has been proven robust and versatile for a modest computational cost. Further, the covariance matrix enables efficient fusion of different types of features, where the spatial and statistical properties as well as their correlation are characterized, and its dimension is small. Although the covariance matrix lies on Riemannian manifolds, its log-transformation can be measured on a Euclidean subspace. Based on the covariance region descriptor and using the sparse representation, we propose a novel tracking approach on the Log-Euclidean Riemannian subspace. Specifically, the target region is characterized by a covariance matrix which is then log-transformed from the Riemannian manifold to the Euclidean subspace. After that, the target tracking problem is integrated under a sparse approximation framework, where the sparsity is achieved by solving an 1- regularization problem. Then the candidate with the smallest approximation is taken as the tracked target. For target propagation, we use the Bayesian state inference framework, which propagates sample distributions over time using the particle filter algorithm. To evaluate our method, we have collected several video sequences and the experimental results show that our tracker can achieve robustly and reliably target tracking. © 2011 Springer-Verlag.

关键词： Target tracking

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Preservative License Plate De-identification for Privacy Protection

Preservative License Plate De-identification for Privacy Pro...

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International Conference on Document Analysis and Recognition

作者： Liang Du Haibin Ling Center for Data Analytics & Biomedical Informatics Department of Computer & Information Science Temple University Philadelphia PA USA

Advances in imaging devices and web technologies have brought dramatic improvements in collecting, storing, and sharing images. The leakage of privacy information in the process becomes an important issue that has started drawing attention from both academia and industry. In this work, we study the problem of privacy preserving with focus on license plate number protecting in imagery. Specifically, we present a novel method for de-identifying license plate images with the least degradation in image visual quality for privacy protection. Unlike previous de-identification methods that pay little attention to the image quality preservation, our method, named inhomogeneous principal component blur (IPCB), adaptively blurs different pixels of a license plate by taking into account the prior distribution of sensitive information. We tested the proposed method on a public dataset in comparison with several popular de-identification methods. The evaluation shows that our method successfully de-identified the privacy information with the least damage of image quality when compared with several other solutions.

关键词： Licenses Privacy Image quality Degradation data privacy Nonhomogeneous media Visualization

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