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检索条件"任意字段=Conference on Visualization and Data Analysis 2008"

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Locating and Understanding Features of Complex Software Systems by Synchronizing Time-, Collaboration- and Code-Focused Views on Execution Traces

Locating and Understanding Features of Complex Software Syst...

引用

IEEE Workshop on Program Comprehension

作者： Johannes Bohnet Stefan Voigt Juergen Dollner University of Potsdam Hasso-Plattner-Institute Potsdam Germany Hasso-Plattner-Inst. Potsdam Univ. Potsdam

Extending or modifying features of complex software systems is often a highly time-consuming and cost-intensive task as, beforehand, the features have to be located within the code and to be understood in detail. To support developers in performing this task, we propose a technique that takes execution traces and implementation unit structuring as input data and provides various views thereupon. Views focus on different trace characteristics, namely they are time-, collaboration-, and code-focused. Synchronizing the views creates a rich user interface that helps developers to effectively identify and understand feature relevant parts of the implementation.

关键词： Software systems Collaborative software User interfaces data mining International collaboration Information analysis Control systems Phase detection visualization Software prototyping

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An EM algorithm for robust Bayesian PCA with student’s t-distribution

An EM algorithm for robust Bayesian PCA with student’s t-di...

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IEEE International conference on Image Processing

作者： Jiading Gai Yong Li Robert L. Stevenson Department of Electrical Engineering University of Notre Dame Notre Dame IN USA

Principal component analysis (PCA) is a technique that is widely used for applications such as dimensionality reduction, image compression, feature extraction and data visualization. One of the key issues in the use of PCA for modelling is that it is very sensitive to outliers since its formulation is based on Gaussian density model. Lately, more heavy-tailed distribution (i.e., Student’s t-distribution) is introduced to increase the robustness of traditional PCA. But the robust version of PCA is expressed as the maximum likelihood solution of a probabilistic latent variable model. This reformulation raises the question of how to determine the optimal number of principal components to be retained. In this paper, we develop a Bayesian model selection approach to estimate the true dimensionality of the data. The proposed algorithm is based on a new Bayesian treatment of robust Student’s t-distribution PCA. A simple Expectation-Maximization (EM) solver is introduced to find approximate solutions for the model. Experiments show that the proposed model achieves simultaneous optimal dimensionality selection and accurate principal components recovery.

关键词： Bayesian methods Principal component analysis Gaussian distribution Noise robustness Maximum likelihood estimation Context modeling Algorithm design and analysis Tail Image coding Feature extraction

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Principles Emerging from the Design of Visual Search Algorithms for Practical Inspection Tasks

Principles Emerging from the Design of Visual Search Algorit...

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International Machine Vision and Image Processing conference

作者： E. R. Davies Machine Vision Group Department of Physics Royal Holloway University of London Egham Surrey TW20 0EX UK

ISBN: (纸本)9780769533322

This paper is targeted at the design of inspection algorithms involving fast visual search for features, products, and the artefacts that accompany them. The work is based on a number of cereal grain inspection tasks that have presented important problems relating to robustness, sensitivity, accuracy and speed of operation. Techniques covered include multi-level thresholding, morphological operators, mask design, multi-stage processing, and sampling procedures. While in some sense these techniques are simple, they are not trivial to apply, because of the difficulty of specifying algorithms in applications where the image data to be analysed is highly variable, fuzzy, and voluminous. Indeed the difficulty of describing the data sufficiently precisely is both here and in general a highly inhibiting obstacle on the path to specifying and producing optimal algorithms. Nevertheless, it has proved possible to identify a number of generic principles underlying the design of visual search algorithms.

关键词： Algorithm design and analysis Inspection Robustness visualization Machine vision Pixel Histograms

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A Blended Text Mining Method for Authorship Authentication analysis

A Blended Text Mining Method for Authorship Authentication A...

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Asia International conference on Modeling & Simulation

作者： Philip Sallis Subana Shanmuganathan Auckland University of Technology New Zealand

The paper elaborates upon the interim results achieved in resolving a few newly discovered 16th century letters now alleged to be written by Queen Mary of Scots (QMS). Despite the significant progress seen in stylometry and its role in authorship attribute analysis especially in disputed writings/ texts controversies over the authorship of Shakespeare's literary work still continue as does research into this corpus of letters. Using more sophisticated computational and mathematical modelling techniques than in previously published research, this study still employs the use of stylometic measures, to show a distinct variation between the authentic writings of QMS and the newly discovered letters, claimed by numerous enthusiasts to be of her authorship. Incorporating additional advanced statistical methods, such as principle component analysis (PCA) and artificial neural networks (ANNs), especially Kohonen's self-organising map (SOM) based visualisation technique, a text mining approach for this application has been developed. The similarities between different pairs of the new and authentic letters and in some cases within individual letters become apparent when using "cusum" analysis adding further complexity to the task of resolving the anomaly seen among QMS loyalists, archaeologists, linguists and the like. The reasons for the inconclusive results of this study are presented with suggestions for future work but in essence, the data mining method used is regarded as being unique in its blend of conventional and non-conventional statistics and useful for this class of text analysis problem

关键词： Text mining Authentication Writing Statistical analysis Mathematical model Principal component analysis Artificial neural networks visualization data mining Text analysis

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A novel information theoretic method for detecting gene-gene and gene-environment interactions in complex diseases

A novel information theoretic method for detecting gene-gene...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Pritam Chanda Aidong Zhang Murali Ramanthan Department of Computer Science and Engineering State University of New York Buffalo NY USA Faculty of Computer Science and Engineering State University of New York Buffalo NY USA Faculty of Pharmaceutical Sciences State University of New York Buffalo NY USA

Gene-gene and gene-environment interactions play important roles in the etiology of complex multi-factorial diseases. With the advancements in genotyping technology, large genetic association studies based on hundreds of thousands of single-nucleotide polymorphisms are a popular option for the study of complex diseases. In this paper we use information theoretic concepts to develop a novel method for detecting statistical gene-gene and gene-environment interactions in complex disease models. We explore the effectiveness of our method with extensive simulations using different gene-gene interaction models and the rheumatoid arthritis dataset from genetic analysis workshop-15. The performance of the method was compared to the well known multi-factor dimensionality reduction (MDR) and generalized MDR (GMDR) methods. We demonstrate that our method is capable of analyzing a diverse range of epidemiological data sets containing evidences for gene-gene interactions.

关键词： Genetics Analytical models data analysis Cardiovascular diseases data visualization Arthritis Cardiac disease Genomics Bioinformatics Computer science

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Automation of compressor test procedure using advantech data acquisition module

Automation of compressor test procedure using advantech data...

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International conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Abdul Md Mazid Ryan Martin Faculty of Sciences Engineering & Health Central Queensland University Rockhampton Australia Department of Mechanical Engineering Monash University Melbourne Australia

This article offers a newly design and built industrial automation technique for compressor testing procedures for their final tests of assembled compressors. Sequential total test procedure activities for a model of twin screw compressor tests have been developed. In this particular project it is developed how some of the activities can be automated using ADAM programmable data communication module communicating with a PC. For data listening such as pressure and temperature values of a compressor a pressure transducer of model Energair T-16 and a temperature sensor of model OMEGA TAC 80 B-K, respectively, have been successfully used at the experimental stage. The output derivatives were displayed using GUI on a computer screen for visualization as well as recorded in the computer memory for further uses. The GUI screen was developed using Java programming technique. The results of the research based experimental project could be valuable information and technology usable in compressor manufacturing industries as well as for compressor using industries.

关键词： Automatic testing data acquisition Sequential analysis Temperature sensors Graphical user interfaces Computer displays Manufacturing industries Design automation Assembly Fasteners

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*** Web Services: Enabling Microbiologic Workflows Such as Phylogenetic Tree Building and Biomarker Comparison

*** Web Services: Enabling Microbiologic Workflows Such as P...

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IEEE International conference on e-Science and Grid Computing

作者： Bert Verslyppe Bram Slabbinck Wim De Smet Paul De Vos Bernard De Baets Peter Dawyndt Department of Applied Mathematics and Computer Science Ghent University Ghent Belgium

In this paper we present novel web services offered by the *** bioportal. This portal integrates information in the domain of microbiology and offers a uniform web interface to a multitude of data providers. By providing web services, the integration results of *** become available for automated processing. Several classes of web services are implemented and some interesting uses are discussed in more detail. Combined with third-party services, the *** services can be integrated into workflows. We describe two example workflows: one basic workflow for the construction of a phylogenetic tree based on 16S rRNA gene sequences retrieved from the species of a given genus and a more advanced workflow to collect data of several biomarkers, to calculate the corresponding distance matrices, and to visualize the intra- and inter-species variation among the different biomarkers using the TaxonGap tool. Hereby, the tedious and manual work of collecting and analyzing data, and of visualizing the analysis results has become automated.

关键词： Web services Phylogeny Biomarkers Capacitive sensors Microorganisms Sequences Biological materials DNA data visualization Mathematics

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Integrating Audio Visual data for Human Action Detection

Integrating Audio Visual Data for Human Action Detection

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International conference on Computer Graphics, Imaging and visualization, CGIV

作者： Lili Nurliyana Abdullah Shahrul Azman Mohd Noah Department of Multimedia Faculty of Computer Science and Information Technology Serdang Selangor Darul Ehsan Malaysia Department of Information Science Bangi Selangor Darul Ehsan Malaysia

ISBN: (纸本)9780769533599;0769533590

This paper presents a method which able to integrate audio and visual information for action scene analysis in any movie. The approach is top-down for determining and extract action scenes in video by analyzing both audio and video data. In this paper, we directly modelled the hierarchy and shared structures of human behaviours, and we present a framework of the hidden Markov model based application for the problem of activity recognition. We proposed a framework for recognizing actions by measuring human action-based information from video with the following characteristics: the method deals with both visual and auditory information, and captures both spatial and temporal characteristics; and the extracted features are natural, in the sense that they are closely related to the human perceptual processing. Our effort was to implementing idea of action identification by extracting syntactic properties of a video such as edge feature extraction, colour distribution, audio and motion vectors. In this paper, we present a two layers hierarchical module for action recognition. The first one performs supervised learning to recognize individual actions of participants using low-level visual features. The second layer models actions, using the output of the first layer as observations, and fuse with the high level audio features. Both layers use hidden Markov model-based approaches for action recognition and clustering, respectively. Our proposed technique characterizes the scenes by integration cues obtained from both the video and audio tracks. We are sure that using joint audio and visual information can significantly improve the accuracy for action detection over using audio or visual information only. This is because multimodal features can resolve ambiguities that are present in a single modality. Besides, we modelled them into multidimensional form.

关键词： Hidden Markov models visualization Humans Feature extraction Image color analysis data mining Motion pictures

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A Grid Service based Parallel Molecular Surface Reconstruction System

A Grid Service based Parallel Molecular Surface Reconstructi...

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16th Euromicro conference on Parallel, Distributed and Network-Based Processing (PDP 2008)

作者： Daniele D'Agostino Andrea Clematis Ivan Merelli Luciano Milanesi Matteo Coloberti IMATI-CNR Genova Italy ITB-CNR Segrate MI Italy DICO-UNIMI Milano MI Italy

Surface characterization of molecules may provide useful information about their functionalities and potential interactions with other molecules. Even if many algorithms and tools for the protein surface modeling were proposed, presently a suitable and efficient system that enables wide range analysis of the protein external morphology does not exists. In this paper we present a parallel workflow for the reconstruction of molecular surfaces based on a set of Grid Services. The aim is to provide scientists to get easily and quickly an accurate surface representation of the molecules made available in the Protein data Bank. The system front end is represented by a Java application, that is responsible to manage the computation on the basis of the parameters specified by the user. Furthermore, the application permits to directly visualize the resulting molecular surface and to download it on the user machine. The modular approach we propose allows in perspective the extensibility of the system through the integration of further Grid Services, in order to perform even more complex surface analyses in the Grid environment.

关键词： Surface reconstruction Surface morphology Proteins Java Production data visualization Bioinformatics Nuclear magnetic resonance Atomic measurements Algorithm design and analysis

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Discovery, analysis and monitoring of hidden social networks and their evolution

Discovery, analysis and monitoring of hidden social networks...

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IEEE conference on Technologies for Homeland Security

作者： Mark Goldberg Mykola Hayvanovych Apirak Hoonlor Stephen Kelley Malik Magdon-Ismail Konstantin Mertsalov Boleslaw Szymanski William Wallace Rensselaer Polytechnic Institute Troy NY USA

Social networks that arise spontaneously and evolve over time have become an important component of ever growing global societies used for spreading ideas and indoctrinating people. Their loose membership and dynamics make them difficult to observe and monitor. We present a set of tools for discovery, analysis and monitoring evolution of hidden social groups on the internet and in cyberspace. Two complementary kinds of tools form a core of our approach. One is based on statistical analysis of communication network without considering communication content. The other focuses on communication content and analyzes recursive patterns arising in it. First, we present a software system SIGHTS (Statistical Identification of Groups Hidden in Time and Space), designed for the discovery, analysis, and knowledge visualization of social coalition in communication networks by analyzing communication patterns. We discuss how our algorithms extract groups and track their evolution in Enron-email dataset and in Blog data. The goal of SIGHTS is to assist an analyst in identifying relevant information. A complementary set of tools uses Recursive data Mining (RDM) to identify frequent patterns in communication content such as email, blog or chat-room sessions. Our approach enables discovery of patterns at varying degrees of abstraction, in a hierarchical fashion, and in language independent way. We use RDM to distinguish among different roles played by communicators in social networks (e.g., distinguishing between leaders and members). Experiments on the Enron dataset, which categorize members into organizational roles demonstrate that use of the RDM dominant patterns improves role detection.

关键词： Monitoring Social network services Internet Pattern analysis Communication networks data mining Information services Web sites Statistical analysis Software systems

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