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作者： Burns, Gully A.P.C. Gil, Yolanda Villanueva-Rosales, Natalia Liu, Yan Risi, Sebastian Lehman, Joel Clune, Jeff Lebiere, Christian Rosenbloom, Paul S. Van Harmelen, Frank Hendler, James A. Hitzler, Pascal Janowicz, Krzysztof Swarup, Samarth Biomedical Knowledge Engineering Group Information Sciences Institute University of Southern California United States Interactive Knowledge Capture Research Group Information Sciences Institute United States Department of Computer Science University of Southern California United States Department of Computer Science and Cyber-ShARE Center of Excellence University of Texas El Paso United States Center for Computer Games Research University of Copenhagen Denmark Department of Computer Science University of Texas Austin United States University of Wyoming United States Department of Psychology Carnegie Mellon University United States Institute for Creative Technologies United States Vrije Universiteit Amsterdam Netherlands Rensselaer Polytechnic Institute United States Wright State University United States University of California Santa Barbara United States Virginia Bioinformatics Institute Virginia Tech. United States

The Association for the Advancement of Artificial Intelligence was pleased to present the 2013 Fall Symposium Series, held Friday through Sunday, November 15-17, at the Westin Arlington Gateway in Arlington, Virginia, near Washington, D.C., USA. The titles of the five symposia were Discovery Informatics: AI Takes a science- Centered View on Big Data (FS-13-01);How Should Intelligence Be Abstracted in AI Research: MDPs, Symbolic Representations, Artificial Neural Networks, or - ? (FS-13-02);Integrated Cognition (FS-13-03);Semantics for Big Data (FS- 13-04);and Social Networks and Social Contagion: Web Analytics and Computational Social science (FS-13-05). The highlights of each symposium are presented in this report. Copyright © 2014, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Big data

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Scalable Resolution-based Image Coding Algorithm

Scalable Resolution-based Image Coding Algorithm

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International Joint Conference on computer science and Software engineering

作者： Phakphoom Claiboon Sartra Wongthanavasu Cellular Automata and Knowledge Engineering (CAKE) Laboratory Machine Learning and Intelligent Systems (MLIS) Laboratory Department of Computer Science Faculty of Science Khon Kaen University

ISBN: (纸本)9781479958238

Currently, multimedia data has become increasingly more important with the growth of the Internet. Images are one example of sharing across the internet, especially through social media applications. More specifically, nearly every website consists of a considerable amount of image data. Some pictures are stored in multiple resolutions to be displayed as desired. When the same image is stored in different resolutions, it is difficult to manage. This paper presents an algorithm for image coding in scalable resolution. The proposed method consists of an intra-prediction and reversible cellular automata. Intra-predication aims to reduce the data to be compressed and reversible cellular automata allows image data to increase or decrease to the desired resolution. For the performance evaluation, an 8-bit grayscale was experimented with using the proposed method and then compared with JPEG2000 and RCAGIC using PSNR and SSIM statistics. In this regard, the proposed method provides more promising results than RCAGIC and JPEG2000.

关键词： Component Scalable image coding Intra-prediction Cellular automata (CA)

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Research on a dynamic integration method of isomerous multi-source data for the hot rolling process

Research on a dynamic integration method of isomerous multi-...

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2014 International Conference on Artificial Intelligence and Industrial Application (AIIA2014)

作者： D.Z.Zhang L.M.Zhao W.Adeli X.Y.Wang H.Li School of Computer and Communication Engineering University of Science and Technology Beijing Beijing Key Laboratory of Knowledge Engineering for Materials Science Department of Information Management China Petroleum Corporation Urumqi Petrochemical Corporation School of Automation and Electrical Engineering University of Science and Technology

ISBN: (纸本)9781784660529

In this paper, we describe the current situation of data integration and the feature of the traditional data integration method, we analyse the data characteristics of the hot rolling process of modern steel enterprises, and present a new dynamic data integration method, including the dynamic integration of isomerous multi-source data, the dynamic creation of a database along with a metadata management mechanism. Combined with features of data in the hot rolling process, this method could resolve the problem of the dynamic integration of isomerous multi-source data in the hot rolling process, and offer an opportunity for further comprehensive in-depth data analysis.

关键词： hot rolling process isomerous dynamic integration metadata management

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Learning from Imbalanced Data in Relational Domains: A Soft Margin Approach

Learning from Imbalanced Data in Relational Domains: A Soft ...

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IEEE International Conference on Data Mining (ICDM)

作者： Shuo Yang Tushar Khot Kristian Kersting Gautam Kunapuli Kris Hauser Sriraam Natarajan School of Informatics and Computing Indiana University-Bloomington USA Department of Computer Science University Of Wisconsin-Madison USA Department of Knowledge Discovery Technical University of Dortmund Germany UtopiaCompression Corp Department of Electrical and Computer Engineering Duke University USA

ISBN: (纸本)9781479943012

We consider the problem of learning probabilistic models from relational data. One of the key issues with relational data is class imbalance where the number of negative examples far outnumbers the number of positive examples. The common approach for dealing with this problem is the use of sub-sampling of negative examples. We, on the other hand, consider a soft margin approach that explicitly trades off between the false positives and false negatives. We apply this approach to the recently successful formalism of relational functional gradient boosting. Specifically, we modify the objective function of the learning problem to explicitly include the trade-off between false positives and negatives. We show empirically that this approach is more successful in handling the class imbalance problem than the original framework that weighed all the examples equally.

关键词： Boosting Standards Measurement Cost function Computational modeling Probabilistic logic Electronic mail

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An approach of VM image customized through Linux from scratch on cloud platform

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computer Modelling and New Technologies 2014年第4期18卷 62-67页

作者： Xu, Gaochao Dong, Yushuang Sun, Bingyi Fu, Xiaodong Zhao, Jia Department of Computer Science and Technology JiLin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China

The cloud platform provides abundant resources and services for users. More and more mobile users began to use the cloud services. They have higher real-time demands on service. The size of traditional virtual machine (VM) operating system is basically large. It will take many resources in deployment and communication process, and always affect the real-time performance of system. To reduce communication overhead and improve deployment speed of VMs, this paper proposes an approach of customized VM image with LFS. LFS can reduce the size of VM image efficiently and enable flexible customization of the VM image by incremental customization. The experimental results show us that the size of VM image generated by the proposed method is smaller than the one generated by kernel tailoring technology in system overhead. Meanwhile it is also faster in running speed.

关键词： Virtual machine

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Automated heterogeneous platoons in unstructured environment: Real time tracking of a preceding vehicle using video stream

Automated heterogeneous platoons in unstructured environment...

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International Conference on Information and Communication Systems (ICICS)

作者： Mohammad Alfraheed Alicia Dröge Daniel Schilberg Sabina Jeschke Department of Mathematics and Computer Science Tafila Technical University Tafila Jordan Institute of Information Management in Mechanical Engineering & Center of Learning and Knowledge Management RWTH-Aachen University Aachen Germany

In autonomous driving, object tracking is necessary to gather actual information about the object of interest. The longitudinal and lateral controls of automated highway systems need a target object not only to maintain the safety distance between vehicles but also to keep the following vehicle in the same track as the preceding vehicle. So far automated highway systems were only developed for urban and highway environment depending on lane markings. In future, their application should be extended to unstructured environments (e.g. desert) and be adapted for heterogeneous vehicles. In this paper an approach towards this is presented, where the back view of preceding vehicle is the target object. This solution is independent from the environmental structure as well as additional equipment like infrared emitters. In this paper, the tracking process of the back view is discussed using video streams recorded by a stereo vision system. For an accurate and fast tracking in unstructured environment and with heterogeneous platoons the proposed method is a supplement to the detection process. Therefore, the tracking process has to be a) applicable under real time constraints and b) adaptable in dynamic environments. Compared to other methods related to object detection and tracking, the proposed method reduces the running time for the tracking of the back view from reported 12 - 30 to 16 - 66 frame/s.

关键词： Vehicles Real-time systems Cameras Road transportation Vehicle dynamics Streaming media Automated highways

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Modeling and Mining Spatiotemporal Social Contact of Metapopulation from Heterogeneous Data

Modeling and Mining Spatiotemporal Social Contact of Metapop...

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IEEE International Conference on Data Mining (ICDM)

作者： Bo Yang Hongbin Pei Hechang Chen Jiming Liu Shang Xia School of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China Department of Computer Science Hong Kong Baptist University Hong Kong National Institute of Parasitic Diseases Chinese CDC Shanghai China

ISBN: (纸本)9781479943012

During an epidemic, the spatial, temporal and demographical patterns of disease transmission are determined by multiple factors. Besides the physiological properties of pathogenes and hosts, the social contacts of host population, which characterize individuals' reciprocal exposures of infection in view of demographical structures and various social activities, are also pivotal to understand and further predict the prevalence of infectious diseases. The means of measuring social contacts will dominate the extent how precisely we can forecast the dynamics of infections in the real world. Most current works focus their efforts on modeling the spatial patterns of static social contacts. In this work, we address the problem on how to characterize and measure dynamical social contacts during an epidemic from a novel perspective. We propose an epidemic-model-based tensor deconvolution framework to address this issue, in which the spatiotemporal patterns of social contacts are represented by the factors of tensors, which can be discovered by a tensor deconvolution procedure with an integration of epidemic models from rich types of data, mainly including heterogeneous outbreak surveillance, social-demographic census and physiological data from medical reports. Taking SIR model as a case study, the efficacy of the proposed method is theoretically analyzed and empirically validated through a set of rigorous experiments on both synthetic and real-world data.

关键词： Educational institutions Diseases Sociology Statistics Tensile stress Surveillance Data models

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Essential protein identification based on essential protein-protein interaction prediction by integrated edge weights

Essential protein identification based on essential protein-...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yuexu Jiang Yan Wang Wei Pang Liang Chen Huiyan Sun Yanchun Liang Enrico Blanzieri Key Laboratory of Symbolic Computation and Knowledge Jilin University Changchun China Department of Information Engineering and Computer Science University of Trento Povo Italy School of Natural and Computing Sciences University of Aberdeen Aberdeen UK

Essential proteins are crucial to cellular survival and development. Traditionally, essential proteins are identified by knock-out experiments, which are expensive and often fatal to the target organisms. Regarding this, an important approach to essential protein identification is through computational prediction. In this research, we present a novel computational method, Integrated Edge Weights (IEW), to innovatively predict proteins' essentiality based on essential protein-protein interactions. The experimental results on all three organisms: Saccharomyces cere-visiae (Yeast), Escherichia coli (E. coli), and Caenorhabditis ele-gans (C. elegans) show that IEW achieves better performance than the state-of-the-art methods in terms of precision-recall. Furthermore, we have demonstrated that the highly-ranked protein-protein interactions predicted by our approach tend to be biologically significant in Yeast, E. coli, and C. elegans protein-protein interaction (PPI) networks.

关键词： Proteins Protein engineering Gene expression Organisms Databases Vectors

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Cross-participant modelling based on joint or disjoint feature selection: an fMRI conceptual decoding study

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Applied Informatics 2014年第1期1卷 1-21页

作者： Hiroyuki Akama Brian Murphy Miao Mei Lei Massimo Poesio Graduate School of Decision Science and Technology Tokyo Institute of Technology Meguro-ku Tokyo Japan Knowledge & Data Engineering EEECS Queen's University Belfast Belfast Northern Ireland Machine Learning Department Carnegie Mellon University Pittsburgh USA Centre for Mind/Brain Sciences University of Trento Rovereto Italy School of Computer Science and Electronic Engineering University of Essex Colchester UK

Multivariate classification techniques have proven to be powerful tools for distinguishing experimental conditions in single sessions of functional magnetic resonance imaging (fMRI) data. But they are vulnerable to a considerable penalty in classification accuracy when applied across sessions or participants, calling into question the degree to which fine-grained encodings are shared across subjects. Here, we introduce joint learning techniques, where feature selection is carried out using a held-out subset of a target dataset, before training a linear classifier on a source dataset. Single trials of functional MRI data from a covert property generation task are classified with regularized regression techniques to predict the semantic class of stimuli. With our selection techniques (joint ranking feature selection (JRFS) and disjoint feature selection (DJFS)), classification performance during cross-session prediction improved greatly, relative to feature selection on the source session data only. Compared with JRFS, DJFS showed significant improvements for cross-participant classification. And when using a groupwise training, DJFS approached the accuracies seen for prediction across different sessions from the same participant. Comparing several feature selection strategies, we found that a simple univariate ANOVA selection technique or a minimal searchlight (one voxel in size) is appropriate, compared with larger searchlights.

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Interlinking Korean resources on the web

Interlinking Korean resources on the web

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2nd Joint International Conference on Semantic Technology, JIST 2012

作者： Hong, Soon Gill Jang, Saemi Chung, Young Ho Yi, Mun Yong Choi, Key-Sun Department of Knowledge Service Engineering KAIST Korea Republic of Department of Computer Science KAIST Korea Republic of

ISBN: (纸本)9783642379956

LOD (Linked Open Data) is an international endeavor to interlink structured data on the Web and create the Web of Data on a global level. In this paper, we report about our experience of applying existing LOD frameworks, most of which are designed to run only in European language environments, to Korean resources to build linked data. Through the localization of Silk, we identified localized similarity measures as essential for interlinking Korean resources. Specifically, we built new algorithms to measure distance between Korean strings and to measure distance between transliterated Korean strings. A series of empirical tests have found that the new measures substantially improve the performance of Silk with high precision for matching Korean strings and with high recall for matching transliterated Korean strings. We expect the localization issues described in this paper to be applicable to many non-Western countries. © Springer-Verlag 2013.

关键词： Linked data

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