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2nd International conference on Software and data Technologies/Evaluation of Novel Approaches to Software Engineering 2007, CSOFT/ENASE 2007

2nd International Conference on Software and Data Technologi...

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2nd International conference on Software and data Technologies, ICSOFT 2007

ISBN: (纸本)3540886540

The proceedings contain 31 papers. The special focus in this conference is Software and data Technologies. The topics include: benefits of enterprise ontology for the development of ICT-based value networks;SOA pragmatism;a simple language for novel visualizations of information;generic components for static operations at object level;a visual dataflow language for image segmentation and registration;a debugger for the interpreter design pattern;concepts for high-perfomance scientific computing;a model driven architecture approach to web development;reverse-architecting legacy software based on roles;a supporting tool for requirements elicitation using a domain ontology;pattern detection in object-oriented source code;testing the effectiveness of MBIUI life-cycle framework for the development of affective interfaces;an ontological SW architecture supporting agile development of semantic portals;the vcodex platform for data compression;classification of benchmarks for the evaluation of grid resource planning algorithms;a disconnection-aware mechanism to provide anonymity in two-level P2P systems;approximation and scoring for XML data management;quantitative analysis of the top ten Wikipedia's;a semantic web approach for ontological instances analysis;aspects based modeling of web applications to support co-evolution;recommending trustworthy knowledge in KMS by using agents;recent developments in automated inferencing of emotional state from face images;inconsistency-tolerant integrity checking for knowledge assimilation;improving cutting-stock plans with multi-objective genetic algorithm;knowledge purpose and visualization;empirical experimentation for validating the usability of knowledge;an ontological investigation in the field of computer programs;formal problem domain modeling within MDA;model based testing for agent systems and a metamodel for defining development methodologies.

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Proceedings of the 2000 Information Security Curriculum Development conference, lnfoSecCD'06

Proceedings of the 2000 Information Security Curriculum Deve...

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2006 Information Security Curriculum Development conference, InfoSecCD '06

ISBN: (纸本)1595934375

The proceedings contain 39 papers. The topics discussed include: campus-wide spyware and virus removal as a method of teaching information security;secure software engineering teaching modules;threading secure coding principles and risk analysis into the undergraduate computer science and information systems curriculum;incorporating certification and accreditation coursework into network security curriculum;knowledge flow with information assurance track;new challenges in teaching database security;some security alternatives for encrypting information on storage devices;combining an e-commerce simulation with a cyber-survivor exercise;visualization and management of digital forensics data;assessment of need and method of delivery for information security awareness program;encouraging good passwords;the effect of computer self-efficacy on security training effectiveness;and a review of spyware campaigns and strategies to combat them.

关键词： Security of data

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Parallel Rough Set: Dimensionality Reduction and Feature Discovery of Multi-dimensional data in visualization

Parallel Rough Set: Dimensionality Reduction and Feature Dis...

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18th International conference on Neural Information Processing (ICONIP)

作者： Huang, Tze-Haw Huang, Mao Lin Jin, Jesse S. Univ Technol Sydney Sch Software Sydney NSW 2007 Australia Univ Newcastle Sch Design Commun & Informat Technol Newcastle NSW 2308 Australia

ISBN: (纸本)9783642249570;9783642249587

Attempt to visualize high dimensional datasets typically encounter over plotting and decline in visual comprehension that makes the knowledge discovery and feature subset analysis difficult. Hence, reshaping the datasets using dimensionality reduction technique is paramount by removing the superfluous attributes to improve visual analytics. In this work, we applied rough set theory as dimensionality reduction and feature selection methods on visualization to facilitate knowledge discovery of multi-dimensional datasets. We provided the case study using real datasets and comparison against other methods to demonstrate the effectiveness of our approach.

关键词： Dimensionality Reduction Rough Set Theory Feature Selection Knowledge Discovery Parallel Coordinate Visual Analytics

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FocalNet - Foveal Attention for Post-processing DNN Outputs

FocalNet - Foveal Attention for Post-processing DNN Outputs

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International Joint conference on Neural Networks (IJCNN)

作者： Mudassar, Burhan A. Mukhopadhyay, Saibal Georgia Inst Technol Sch ECE Atlanta GA 30332 USA

ISBN: (纸本)9781728119854

This paper presents FocalNet - an iterative information extraction algorithm that uses the concept of foveal attention to post-process the outputs of Deep Neural Networks (DNNs) by performing variable sampling of the input/feature space. FocalNet is integrated into an existing task-driven deep learning model without modifying the weights of the network. Layers, at which to perform foveation are automatically selected using a data-driven approach. We apply FocalNet to the task of object detection using a state of the art convolutional detector, RFCN ResNet-101. On the PASCAL VOC 2007 dataset, we are able to achieve a mAP increase of 3.7%. On the MS COCO 2017 validation dataset we achieve an increase in mAP by 0.3%. Further, a higher increase in mAP is observed for a computationally efficient detector (1.7% for Faster R-CNN with ResNet5O). In additon to object detection, we show effectiveness of FocalNet to the problem of single object tracking with a 0.5% increase in average IoU on the MOT17 dataset over a simple tracking by detection approach using DNNs.

关键词： Object detection Detectors Task analysis Uncertainty Feature extraction visualization Clutter

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Organ analysis and classification using principal component and linear discriminant analysis

Organ analysis and classification using principal component ...

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Medical Imaging 2007 conference

作者： Horsthemke, William H. Raicu, Daniela S. Depaul Univ Chicago IL 60604 USA

ISBN: (纸本)9780819466303

Texture analysis and classification of soft tissues in Computed Tomography (CT) images recently advanced with a new approach that disambiguates the checkboard problem where two distinctly different patterns produce identical co-occurrence matrices, but this method quadruples the size of the feature space. The feature space size problem is exacerbated by the use of varying sized texture operators for improving boundary segmentation. Dimensionality reduction motivates this investigation into systematic analysis of the power of feature categories (Haralick descriptors, distance, and direction) to differentiate between soft tissues. The within-organ variance explained by the individual components of feature categories offers a ranking of their potential power for between-organ discrimination. This paper introduces a technique for combining the Principal Component analysis (PCA) results to compare and visualize the explanatory power of features with varying window sizes. We found that 1) the two Haralick features Cluster Tendency and Contrast contribute the most;2) as distance increases, its contribution to overall variance decreases;and 3) direction is unimportant. We also evaluated the proposed technique with respect to its classification power. Linear Discriminant analysis (LDA) and Decision Tree (DT) were used to produce two classification models based on the reduced data set. We found that using PCA either fails to improve or markedly degrades the classification performance of LDA as well as of the DT model. Though feature extraction for classification shows no promise, the proposed technique offers a systematic mechanism to compare feature reduction strategies for varying window sizes as well as other measurement techniques.

关键词： Principal component analysis Feature extraction Feature selection Lung Kidney Spleen data modeling Image classification Tissues visualization

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A data-Driven Approach for Discovering the Recent Research Status of Diabetes in China 1

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6th International conference on Health Information Science (HIS)

作者： Chen, Xieling Weng, Heng Hao, Tianyong Jinan Univ Sch Econ Guangzhou Peoples R China Guangzhou Univ Chinese Med Affiliated Hosp 2 Guangzhou Peoples R China Guangdong Univ Foreign Studies Sch Informat Sci & Technol Guangzhou Peoples R China

ISBN: (数字)9783319691824

ISBN: (纸本)9783319691824;9783319691817

This paper aims at discovering the recent research status of diabetes in China through a data-driven bibliometrics and knowledge mapping analysis method on diabetes-related literature. With the basis of 24,561 publication documents from CNKI during 2007-2016, the quantitative analysis are conducted in three aspects: (1) descriptive statistical method for acquiring literature distribution characteristics;(2) hierarchical clustering, k-means clustering analysis, and multidimensional scaling analysis based on a keyword co-occurrence matrix for discovering research hotspots;and (3) network analysis for revealing cooperation relationships among authors and affiliations. The result shows some findings about the recent diabetes research in China. It also demonstrates the close cooperation of diabetes research among productive authors and affiliations through network generation and visualization.

关键词： Diabetes Distribution characteristics Research hotspots Cooperation network

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Results from the wageningen 2007 workshop on propeller open water calculations including cavitation

Results from the wageningen 2007 workshop on propeller open ...

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RINA - International conference - Marine CFD 2008

作者： Streckwall, H. Salvatore, F. Hamburg Ship Model Basin Germany INSEAN Italy

ISBN: (纸本)9781905040452

Propeller cavitation is still mainly explored by testing and only to a small extent within the computational framework of RANS or Euler solvers. Contrary to potential flow methods, the latter approaches in principle allow resolving re-entrant jets and the shedding of cavities. At a workshop held within the VIRTUE project in 2007, propeller flow under non-cavitating and cavitating conditions was predicted for homogeneous inflow applying 9 different viscous codes. Various solutions for the propeller grid were presented. A comparative analysis of the results was performed and revealed that the ability to predict the fully wetted flow was well covered by all participants. A common visualization of results was carried out on the basis of field data that were transmitted by the participants in either TECPLOT or FIELDVIEW format. The comparison of the predicted cavitation showed a scatter in cavity shape and the associated pressure field. To give advice for future calculations it is concluded that the grid should provide sufficient resolution to resolve expected flow phenomena such as side jets that tend to detach from the trailing end of the cavity.

关键词： Cavitation

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Study on data mining technology in hyperspectral remote sensing

Study on data mining technology in hyperspectral remote sens...

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15th International conference on Geoinformatics

作者： Su, Hongjun Sheng, Yehua Wen, Yongning Tao, Hong Nanjing Normal Univ Key Lab Virtual Geog Environm Minist Educ Nanjing 210046 Jiangsu Prov Peoples R China

ISBN: (纸本)9780819469120

In this paper, the problems rise in hyperspectral data mining and some key issues should pay attention to were proposed based on the analysis on the state-of-art of hyperspectral data mining. The problems are as follows: data mining precision, mining algorithm efficiency, new hyperspectral data mining algorithms, the uncertainty in hyperspectral data mining, visualization in hyperspectral data mining process, and knowledge presentation, interpretation, estimation and management etc.. Some key issues should emphasize in the future are: systematic hyperspectral data mining theory, dimensionality reduction, mining spatial and temporal knowledge from images, and mining distributed data and mining multi-agent data. Also the framework and architecture of hyperspectral data mining were put forward in this paper. Hyperspectral data mining framework includes some subparts as follows: data selection, data preprocessing, data transfer, data mining and pattern estimation. And the architecture is composed of database, data warehouse, database management system, repository, mining process, user interface etc.. At last, an algorithm which named Relational perspective map (R-PM) was introduced into the field of hyperspectral data mining. By the experiment on the spectra data from USGS spectral library, it proves that this algorithm is suitable to discover those spectral features and to identify and discriminate object classes based on their spectra.

关键词： hyperspectral data mining framework relational perspective map information processing

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Applying Animation to the Visual analysis of Financial Time-Dependent data

Applying Animation to the Visual Analysis of Financial Time-...

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International conference on Information Visualisation (IV)

作者： Tatiana Tekusova Jorn Kohlhammer Fraunhofer IGD Darmstadt Technische Universität Darmstadt Germany

For decades, financial analysts have strived to use modern data visualization tools to improve the timeliness and quality of their analysis. As the amount of data to be processed increases rapidly and requirements on quality of financial analysis rise, the demand for analysis support systems grows. We present a system for the visual analysis of large amounts of time-dependent data using animation. For each data entity, indicators are presented in a scatter-plot framework, displaying the correlation between them. The design of the glyphs illustrates additional data dimensions. The system uses animation to handle the time-dimension of the data. It offers various features, such as focus, zoom, details on demand and time period selection to support the analysis. Financial indicators are used to demonstrate the usability of the system. The animation proves to be a powerful tool for analysing time-dependent processes in cross-sectional data sets and discovering patterns in the data.

关键词： Animation data visualization Scattering data analysis Stock markets Usability Pattern analysis Investments Pricing Government

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Everyday Life Discoveries: Mining and Visualizing Activity Patterns in Social Science Diary data

Everyday Life Discoveries: Mining and Visualizing Activity P...

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International conference on Information Visualisation (IV)

作者： Katerina Vrotsou Kajsa Ellegard Matthew Cooper Norrköping Visualization and Interaction Studio Linköping University Sweden TEMA-Technology and Social Change Linköping University Sweden

The ability to identify and examine patterns of activities is a key tool for social and behavioural science. In the past this has been done by statistical or purely visual methods but automated sequential pattern analysis through sophisticated data mining and visualization tools for pattern location and evaluation can open up new possibilities for interactive exploration of the data. This paper describes the addition of a sequential pattern identification method to the visual activity-analysis tool, VISUAL-TimePAcTS, and its effectiveness in the process of pattern analysis in social science diary data. The results have shown that the method correctly identifies patterns and conveys them effectively to the social scientist in a manner that allows them quick and easy understanding of the significance of the patterns.

关键词： data visualization Pattern analysis data mining Feature extraction Testing Visual databases Humans

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