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

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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Facilitating Developer-User Interactions with Mobile App Review Digests 31

Facilitating Developer-User Interactions with Mobile App Rev...

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31st Annual CHI Conference on Human Factors in Computing Systems:, CHI EA 2013

作者： Oh, Jeungmin Kim, Daehoon Lee, Uichin Lee, Jae-Gil Song, Junehwa Department of Knowledge Service Engineering KAIST Daejeon Korea Republic of Department of Knowledge Service Engineering KAIST Daejeon Korea Republic of Department of Knowledge Service Engineering KAIST Daejeon Korea Republic of Department of Knowledge Service Engineering KAIST Daejeon Korea Republic of Department of Computer Science KAIST Daejeon Korea Republic of

ISBN: (纸本)9781450318990

As users are interacting with a large of mobile apps under various usage contexts, user involvements in an app design process has become a critical issue. Despite this fact, existing apps or app store platforms only provide a limited form of user involvements such as posting app reviews and sending email reports. While building a unified platform for facilitating user involvements with various apps is our ultimate goal, we present our preliminary work on handling developers’ information overload attributed to a large number of app comments. To address this issue, we first perform a simple content analysis on app reviews from the developer’s standpoint. We then propose an algorithm that automatically identifies informative reviews reflecting user involvements. The preliminary evaluation results document the efficiency of our algorithm.

关键词： User centered design

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Prediction of multiple sequence alignment based on an improved particle swarm optimization

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Journal of Bionanoscience 2013年第1期7卷 92-96页

作者： Gao, Yu-Xi Wang, Yan-Min Pei, Zhi-Li Department of Computer Science Jilin Business and Technology College China Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University China College of Computer Science and Technology Inner Mongolia University for the Nationalities Tongliao 028000 China

In this paper we use an improved Particle Swarm Optimization algorithm to solve Multiple Sequence Alignment (MSA). MSA is a key problem in bioinformatics. The thesis starts with the theory of Particle swarm optimization algorithm and the design an improved algorithm for Multiple Sequence Alignment. The simulated experiments show that the improved algorithm surpasses existing algorithms in performance. Copyright © 2013 American Scientific Publishers.

关键词： Bioinformatics

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A Seed-Based Inter-Domain Supervised Framework to Cluster Mixed Data Types

A Seed-Based Inter-Domain Supervised Framework to Cluster Mi...

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4th International Joint Conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2012

作者： Abdullin, Artur Nasraoui, Olfa Knowledge Discovery and Web Mining Lab Department of Computer Engineering and Computer Science University of Louisville Louisville KY United States

ISBN: (纸本)9783642541049

We propose a Seed-based Inter-Domain Supervised (IDS) framework to handle possibly diverse data formats, mixed-type attributes and different sources of data. This approach can be used for combining diverse representations of the data, in particular where data comes from different sources, some of which may be unreliable or uncertain, or for exploiting optional external concept set labels to guide the clustering of the main data set in its original domain. Unlike semi-supervised clustering, our approach exploits the synergy between different domains instead of external labels. Also ensemble clustering and our proposed IDS clustering are different mechanisms with distinct goals: (i) the former aims to combine independent clustering results from many independent subsets sampled from the same data set or from different clustering algorithm results on the same data set, into one consensus, (ii) while the latter (IDS) aims to exploit a mutual synergy between different data domains or sources to guide the clustering in these different domains or sources. Our preliminary results in clustering data with mixed numerical and categorical attributes show that the proposed IDS framework gives better clustering results in the categorical domain. Thus the seeds or the constraints obtained from clustering the numerical domain give an additional knowledge to the categorical clustering algorithm. Additional results show that our approach outperforms clustering either domain on its own or clustering both domains converted to the same target domain. © Springer-Verlag Berlin Heidelberg 2013.

关键词： Clustering algorithms

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Distance-based feature selection from probabilistic data

Distance-based feature selection from probabilistic data

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14th International Conference on Web-Age Information Management, WAIM 2013

作者： Zhao, Tingting Pei, Bin Zhao, Suyun Chen, Hong Li, Cuiping Key Lab of Data Engineering and Knowledge Engineering Ministry of Education China Department of Computer Science Renmin University of China China

ISBN: (纸本)9783642385612

Feature selection is a powerful tool of dimension reduction from datasets. In the last decade, more and more researchers have paid attentions on feature selection. Further, some researchers begin to focus on feature selection from probabilistic datasets. However, in the existing method of feature selection from probabilistic data, the distance hidden in probabilistic data is neglected. In this paper, we design a new distance measure to select informative feature from probabilistic databases, in which both the distance and randomness in the data are considered. And then, we propose a feature selection algorithm based on the new distance and develop two accelerative algorithms to boost the computation. Furthermore, we introduce a parameter into the distance to reduce the sensitivity to noise. Finally, the experimental results verify the effectiveness of our algorithms. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Random processes

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