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Integrated Computing Technology

First International Conference, INTECH 2011, Sao Carlos, Brazil, May 31 – June 2, 2011. Proceedings

herausgegeben von: Estevam Rafael Hruschka Jr., Junzo Watada, Maria do Carmo Nicoletti

Verlag: Springer Berlin Heidelberg

Buchreihe : Communications in Computer and Information Science

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

This book constitutes the refereed proceedings of the First International Conference on Integrated Computing Technology, INTECH 2011, held in Sao Carlos, Brazil, in May/ June 2011. The 14 revised full papers presented were carefully reviewed and selected from 103 submissions. The conference fosters discussions in integrating models, framework, designs, content,networks and the knowledge through more robust and high quality research.

Inhaltsverzeichnis

Frontmatter

Reputation Based Trust Model for Grid with Enhanced Reliabilty

Abstract

Grid computing is a next evolutionary level of distributed computing. It integrates the users and resources which are scattered in various domains. The Grid and its related technologies will be used only if the users and the providers mutually trust each other. The system must be as reliable and robust as of their own. The reliability can be defined as the probability of any process to complete it’s task successfully as the way it was expected. In grid the reliability of any transaction can be improved by considering trust and reputation. Trust depends on one’s own individual experiences and referrals from other entities. This paper proposes a model which improves reliability in grid by considering reputation and trust.

P. Vivekananth

Biomedical Resource Discovery Considering Semantic Heterogeneity in Data Grid Environments

Abstract

Informatics application in biomedicine accumulates large amount of data constituting a big data source network. These sources are highly heterogeneous, dynamic and distributed in large scale environments. Resource discovery in such environments presents an important step for the SQL-query processing. Many research studies have focused on this issue. However, structural heterogeneity problems have been more widely studied than semantic ones. In this paper, we deal with the resource discovery in large-scale environments (as data grid systems) considering data semantic heterogeneity of biomedical sources. The main advantages of the proposed resource discovery method are: (i) allowing a permanent access, through an addressing system, from any domain ontology DO_i to another DO_j (inter-domain discovery) despite peers’ dynamicity, (ii) reducing the maintenance cost and (iii) taking into account the semantic heterogeneity.

Imen Ketata, Riad Mokadem, Franck Morvan

On Combining Higher-Order MAP-MRF Based Classifiers for Image Labeling

Abstract

In this paper we present a framework for combining MAP-MRF based classifiers for solving image labeling problems, by deriving a classification rule that uses a Gaussian Markov Random Field to model the observed data and a higher-order Potts MRF model as prior knowledge. In this scenario, the Potts model parameter acts like a regularizarion parameter, controlling the tradeoff between data fidelity and smoothing. Maximum Pseudo-Likelihood equations are applied to automatically set this parameter value. The proposed methodology consists in using several initial conditions for the iterative combinatorial optimization algorithms in order to escape local maxima solutions. Experiments with NMR image data show, in quantitative terms, that the joint use of multiple initializations and higher-order neighborhood systems significantly improves the classification performance.

Alexandre L. M. Levada, Nelson D. A. Mascarenhas, Alberto Tannús

Integrated Cooperative Framework for Project Resources Allocation

Abstract

The present paper presents a generic, flexible and robust framework for decision support in cooperative systems in charge with human resources allocation for various project tasks. Considering a knowledge database where people and projects are characterized through the task and competences binomial, the particularity of this frameworks consists in integrating inside the cooperative systems the benefits of complex user modeling and of personalization handled by the adaptive hypermedia systems, as well as the benefits of knowledge management with the support of semantic Web technologies.

Mihaela Brut, Jean-Luc Soubie, Florence Sèdes

SNPs Classification: Building Biological High-Level Knowledge Using Genetic Algorithms

Abstract

Computational approaches have been applied in many different biology application domains. When such tools are based on conventional computation, their approach has shown limitations when dealing with complex biological problems. In the present study, a computational evolutionary environment (GASNP) is proposed as a tool to extract classification rules from biological dataset. The main goal of the proposed approach is to allow the discovery of concise, and accurate, high-level rules (from a biological database named dbSNP - Database Single Nucleotide Polymorphism) which can be used as a classification system. More than focusing only on the classification accuracy, the proposed GASNP model aims at balancing prediction precision, interpretability and comprehensibility. The obtained results show that the proposed GASNP has great potential and is capable of extracting useful high-level knowledge that could not be extracted by traditional classification methods such as Decision Trees, One R and the Single Conjunctive Rule Learner, among others, using the same dataset.

Andre Bevilaqua, Fabricio Alves Rodrigues, Laurence Rodrigues do Amaral

A Comparison of Clustering Algorithms for Data Streams

Abstract

In this paper we present a comparative study of three data stream clustering algorithms: STREAM, CluStream and MR-Stream. We used a total of 90 synthetic data sets generated from spatial point processes following Gaussian distributions or Mixtures of Gaussians. The algorithms were executed in three main scenarios: 1) low dimensional; 2) low dimensional with concept drift and 3) high dimensional with concept drift. In general, CluStream outperformed the other algorithms in terms of clustering quality at a higher execution time cost. Our results are analyzed with the non-parametric Friedman test and post-hoc Nemenyi test, both with α = 5%. Recommendations and future research directions are also explored.

Cássio M. M. Pereira, Rodrigo F. de Mello

Approach Space Framework for Image Database Classification

Abstract

This article considers the problem of how to formulate a framework for classifying digital images in large-scale image databases. The solution to this problem stems from recent work on near tolerance rough sets and from the realisation that collections of images can be viewed in the context of approach spaces. A nonempty set equipped with a distance function satisfying certain conditions is an example of an approach space. In approach spaces, the notion of distance is closely related to the notion of nearness. Approach merotopies provide a means of determining the similarity between a query image and a collection of images. An application of approach space-based image classification is given in terms of collections of hand-finger movement images captured during therapeutic gaming system exercises.

Sheela Ramanna, James F. Peters

Learning Temporal Interval Relations Using Inductive Logic Programming

Abstract

The notion of time permeates every single aspect of the world around us and, as such, it should be taken into account when developing automatic systems that implement many of its processes. In the literature several proposals for representing the notion of time can be found. One of the most popular is the Allen’s temporal interval, based on a set of 13 relations that may hold between two time intervals. The main goal of this work is to explore the automatic learning of several of temporal relations from data, using an inductive logic programming (ILP) system. The paper describes a set of automatic learning experiments whose main aims are (i) determining the impact of the negative training patterns on the induced relation (ii) evidencing the necessary background knowledge for inducing the exact expression of the target concept and (iii) investigate the viability of ILP as a learning mechanism in real-time systems.

Maria do Carmo Nicoletti, Flávia O. S. de Sá Lisboa, Estevam Rafael Hruschka Jr.

Power Spectral Density Technique for Fault Diagnosis of an Electromotor

Abstract

Developing a special method for maintenance of electrical equipments of industrial company is necessary for improving maintenance quality and reducing operating costs. The combination of corrective preventative and condition based maintenance will require applying for critical equipments of industrial company. This type of maintenance policy and strategy will improve performance of this equipment through availability of industrial equipment. Many vibration environments are not related to a specific driving frequency and may have input from multiple sources which may not be harmonically related. Examples may be excitation from turbulent flow as in air flow over a wing or past a car body, or acoustic input from jet engine exhaust, wheels running over a road, etc. With these types of vibration, it may be more accurate, or of more interest to analyze and test using random vibration. In this research we were calculated RMS and PSD (Power Spectral Density) of an electromotor in different faults situation. We were calculated Grms and PSD for different faults. The results showed that different faults were showed different PSD vs. frequency. The results showed that with calculating PSD we could find some fault and diagnosis of electromotor as soon as possible.

Hojjat Ahmadi, Zeinab Khaksar

Efficient ID-Based Multi-proxy Signature Scheme from Bilinear Pairing Based on k-plus Problem

Abstract

Proxy signatures are efficient alternatives to delegate the signing rights. In a proxy signature scheme, the original signer can delegate its signing rights to any other party, to make signature on its behalf. In a multi-proxy signature scheme, the original signer delegates its signing rights to a group of persons called proxy groups. With the exploit of bilinear pairing over elliptic curves, several ID-based multi-proxy signature schemes have been developed till now [5,9,17]. In this paper, we have proposed an ID-based multi-proxy signature, based on ‘k-plus problem’. The proposed scheme is secure under the INV-CDHP assumption. Moreover, our scheme is computationally more efficient than the schemes [5,9] and assure all the security requirements of a proxy signature, given in [11].

Shivendu Mishra, Rajeev Anand Sahu, Sahadeo Padhye, Rama Shankar Yadav

Decision Tree Technique for Customer Retention in Retail Sector

Abstract

Currently, data mining techniques are used in different areas, and numerous commercial data systems are available. The retail industry is a major application area for data mining techniques, since it collects large amount of data on sales, customer shopping history, goods transportation, consumption, and service. The quantity of data collected continues to expand rapidly, especially due to the increased ease, availability, and popularity of business conducted on the Web, or e-commerce. Therefore, most effective data mining technique must be identified from large number of the available data mining techniques. This paper explores four potential data mining techniques to the problem of customer retention in the retail sector and propose decision tree to be the most effective technique. The decision is made by considering the use and features of the retail datasets, such as size which include number of instances and number of attributes.

Rose Tinabo

Separation between Arabic and Latin Scripts from Bilingual Text Using Structural Features

Abstract

The identification of scripts is an important step in the characters recognition. In this work, we are interested in Arabic and Latin scripts. The identification is based on an approach to character recognition. The approach is structural feature. Selected characteristics of scripts are based on the geometric shape of the characters. Evaluations are conducted on a printed document Arabic and Latin, and with the neural networks functions. The results are interesting in the case of bilingual documents.

Sofiene Haboubi, Samia Snoussi Maddouri, Hamid Amiri

Introducing a New Agile Development for Web Applications Using a Groupware as Example

Abstract

The purpose of this paper is introduce a new agile methodology for Web development based on User Stories and that use some concepts of Scrum like Product Backlog and Sprint. The methodology is divided in three disciplines: Communication, Modeling and Construction; each one refining the User Stories, from requirements specification with the User and the use of the Navigation Model and Story Cards until the execution of these User Stories to guide the coding. Thus, the development team can use these User Stories as acceptance tests, which represent the User behavior when using the system. The code written to pass in those tests can generate, through reverse engineering, design for the team to evaluate how the Web application is being developed and evolved. In the end, the team has more guarantees that the Web application developed represents what the User wanted in the beginning.

Vinicius Pereira, Antonio Francisco do Prado

Hardware/Software Co-design for Image Cross-Correlation

Abstract

Cross-correlation is an important image processing algorithm for template matching widely used on computer vision based systems. This work follows a profile-based hardware/software co-design method to develop an architecture for normalized cross-correlation coefficient calculus using Nios II soft-processor. Results present comparisons between general purpose processor implementation and different customized softprocessor implementation considering execution time and the influence of image and sub-image size. Nios II soft-processors configured with floating-point hardware acceleration achieved a 8.31 speedup.

Mauricio A. Dias, Fernando S. Osorio

Backmatter

Titel: Integrated Computing Technology
herausgegeben von: Estevam Rafael Hruschka Jr.
Junzo Watada
Maria do Carmo Nicoletti
Verlag: Springer Berlin Heidelberg
Electronic ISBN: 978-3-642-22247-4
Print ISBN: 978-3-642-22246-7
DOI: https://doi.org/10.1007/978-3-642-22247-4

Springer Professional

Über dieses Buch

Inhaltsverzeichnis

Frontmatter

Reputation Based Trust Model for Grid with Enhanced Reliabilty

Biomedical Resource Discovery Considering Semantic Heterogeneity in Data Grid Environments

On Combining Higher-Order MAP-MRF Based Classifiers for Image Labeling

Integrated Cooperative Framework for Project Resources Allocation

SNPs Classification: Building Biological High-Level Knowledge Using Genetic Algorithms

A Comparison of Clustering Algorithms for Data Streams

Approach Space Framework for Image Database Classification

Learning Temporal Interval Relations Using Inductive Logic Programming

Power Spectral Density Technique for Fault Diagnosis of an Electromotor

Efficient ID-Based Multi-proxy Signature Scheme from Bilinear Pairing Based on k-plus Problem

Decision Tree Technique for Customer Retention in Retail Sector

Separation between Arabic and Latin Scripts from Bilingual Text Using Structural Features

Introducing a New Agile Development for Web Applications Using a Groupware as Example

Hardware/Software Co-design for Image Cross-Correlation

Backmatter

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