Communications in Computer and Information Science 165 Estevam Rafael Hruschka Jr. Junzo Watada Maria do Carmo Nicoletti (Eds.) Integrated Computing Technology First International Conference, INTECH 2011 Sao Carlos, Brazil, May 31 – June 2, 2011 Proceedings 1 3 VolumeEditors EstevamRafaelHruschkaJr. FederalUniversityofSaoCarlos,Brazil E-mail:[email protected] JunzoWatada WasedaUniversity Wakamatsu,Kita-Kyushu,Japan E-mail:[email protected] MariadoCarmoNicoletti FederalUniversityofSaoCarlos,Brazil E-mail:[email protected] ISSN1865-0929 e-ISSN1865-0937 ISBN978-3-642-22246-7 e-ISBN978-3-642-22247-4 DOI10.1007/978-3-642-22247-4 SpringerHeidelbergDordrechtLondonNewYork LibraryofCongressControlNumber:Appliedfor CRSubjectClassification(1998):H.3,F.1,H.4,J.3,H.2.8,I.4 ©Springer-VerlagBerlinHeidelberg2011 Thisworkissubjecttocopyright.Allrightsarereserved,whetherthewholeorpartofthematerialis concerned,specificallytherightsoftranslation,reprinting,re-useofillustrations,recitation,broadcasting, reproductiononmicrofilmsorinanyotherway,andstorageindatabanks.Duplicationofthispublication orpartsthereofispermittedonlyundertheprovisionsoftheGermanCopyrightLawofSeptember9,1965, initscurrentversion,andpermissionforusemustalwaysbeobtainedfromSpringer.Violationsareliable toprosecutionundertheGermanCopyrightLaw. Theuseofgeneraldescriptivenames,registerednames,trademarks,etc.inthispublicationdoesnotimply, evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevantprotectivelaws andregulationsandthereforefreeforgeneraluse. Typesetting:Camera-readybyauthor,dataconversionbyScientificPublishingServices,Chennai,India Printedonacid-freepaper SpringerispartofSpringerScience+BusinessMedia(www.springer.com) Message from the Chairs We are pleased to presentthe proceedings of the First International Conference onIntegratedComputingTechnology(INTECH2011).Thisscholarlyconference wasco-sponsoredbySpringerandorganizedandhostedbytheUniversityofSao Carlos in Brazil, during May 31- June 02, 2011 in association with the Digital Information Research Foundation, India. TheINTECHconferencebringsforthdiscussionsonintegratingmodels,frame- works,designs,content,networksandknowledgethroughmorerobustandhigh- qualityresearch.Allacceptedpapersareblindreviewedbeforetheyareincluded in the refereed conference proceedings. This scientific conference included guest lectures and the presentation of 16 researchpapersinthetechnicalsession.Thismeetingwasagreatopportunityto exchangeknowledgeandexperienceforalltheparticipantswhojoinedusfromall overtheworldandtodiscussnewideasintheareaofcomputingtechnology.We aregratefultothe UniversityofSaoCarlosinBrazilforhosting this conference. We use this occasion to express our thanks to the Technical Committee and to all the external reviewers. We are grateful to Springer for co-sponsoring the event. Finally, we would like to thank all the participants and sponsors. April 2011 Estevam Rafael Hruschka Junior Junzo Watada Maria do Carmo Nicoletti Preface On behalf of the INTECH 2011 ProgramCommittee and the University of Sao Carlos in Brazil, we welcome you to the proceedings of the First International ConferenceonIntegratedComputingTechnology(INTECH2011).TheINTECH 2011 conference explored new advances in computing technology and its ap- plications. It brought together researchers from various areas of computer and informationscienceswhoaddressedboththeoreticalandappliedaspectsofcom- puting technology applications. We hope that the discussions and exchange of ideas that took place will contribute to advancements in the technology in the near future. The conferencereceived103papers,outofwhich24wereaccepted,resulting inanacceptancerateof25%.Theseacceptedpapersareauthoredbyresearchers from many countries covering many significant areas of computing technology. Each paper was evaluated by a minimum of two reviewers. Finally, we believe thattheproceedingsdocumentthebestresearchinthestudiedareas.Weexpress our thanks to the University of Sao Carlos in Brazil, Springer, the authors, and the organizers of the conference. Estevam Rafael Hruschka Junior Junzo Watada Maria do Carmo Nicoletti Table of Contents Reputation Based Trust Model for Grid with Enhanced Reliabilty...... 1 P. Vivekananth Biomedical Resource Discovery Considering Semantic Heterogeneity in Data Grid Environments.......................................... 12 Imen Ketata, Riad Mokadem, and Franck Morvan On Combining Higher-Order MAP-MRF Based Classifiers for Image Labeling ........................................................ 25 Alexandre L.M. Levada, Nelson D.A. Mascarenhas, and Alberto Tannu´s Integrated Cooperative Framework for Project Resources Allocation.... 40 Mihaela Brut, Jean-Luc Soubie, and Florence S`edes SNPs Classification: Building Biological High-Level Knowledge Using Genetic Algorithms .............................................. 50 Andre Bevilaqua, Fabricio Alves Rodrigues, and Laurence Rodrigues do Amaral A Comparison of Clustering Algorithms for Data Streams............. 59 Ca´ssio M.M. Pereira and Rodrigo F. de Mello Approach Space Framework for Image Database Classification ......... 75 Sheela Ramanna and James F. Peters Learning Temporal Interval Relations Using Inductive Logic Programming.................................................... 90 Maria do Carmo Nicoletti, Fla´via O.S. de S´a Lisboa, and Estevam Rafael Hruschka Jr. Power Spectral Density Technique for Fault Diagnosis of an Electromotor .................................................... 105 Hojjat Ahmadi and Zeinab Khaksar Efficient ID-Based Multi-proxy Signature Scheme from Bilinear Pairing Based on k-plus Problem ......................................... 113 Shivendu Mishra, Rajeev Anand Sahu, Sahadeo Padhye, and Rama Shankar Yadav Decision Tree Technique for Customer Retention in Retail Sector ...... 123 Rose Tinabo VIII Table of Contents SeparationbetweenArabicandLatinScripts fromBilingualTextUsing Structural Features............................................... 132 Sofiene Haboubi, Samia Snoussi Maddouri, and Hamid Amiri Introducing a New Agile Development for Web Applications Using a Groupware as Example ........................................... 144 Vinicius Pereira and Antonio Francisco do Prado Hardware/SoftwareCo-design for Image Cross-Correlation ............ 161 Mauricio A. Dias and Fernando S. Osorio Author Index.................................................. 177 Reputation Based Trust Model for Grid with Enhanced Reliabilty P. Vivekananth Lecturer, BOTHO COLLEGE Gaborone Botswana [email protected] 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. Keywords: Trust, Reputation, Reliabilty. 1 Introduction A Grid integrates and coordinates resources and users within different domains. Grid computing is interconnected computer systems where the machines share the resources which are highly heterogeneous. To achieve reliable transactions mutual trust must be established between the initiator and the provider. Trust is measured by using reputation and reputation is the collective opinion of others. Trust can be defined as strong belief in an entity to act dependably, securely and reliably in a specific context. When we say that we trust someone or someone is trust worthy [1], we assume that the probability that he/she will perform an action that is beneficial to us is high. On the other hand when we say some one is un trust worthy we imply that the beneficial probability is very low and detrimental probability is high. According to Abdul-Rahman and Hailes [2], a reputation is the expectation about an entity’s behavior based on information about or observations of its past behavior. Reputation is what is generally said or believed about a person or thing’s character [3]. Therefore, reputation is a measure of trustworthiness, in the sense of reliability. Reputation can be the source of building trust. Reputation can be considered as a collective measure of trustworthiness (in the sense of reliability) based on the referrals or feed backs from members in the same community. An individual's subjective trust can be derived from a combination of received referrals and personal experience. E.R. Hruschka Junior et al. (Eds.): INTECH 2011, CCIS 165, pp. 1–11, 2011. © Springer-Verlag Berlin Heidelberg 2011 2 P. Vivekananth The main purpose of security mechanisms in any distributed environment such as grid is to provide protection against malicious parties. There is a whole range of security challenges that are yet to be met by traditional approaches. Traditional security mechanisms such as authentication and authorization will typically protect resources from malicious users, by restricting access to only authorized users. However, in many situations one has to protect themselves from those who offer resources so that the problem in fact is reversed. Information providers can deliberately mislead by providing false information, and traditional security mechanisms are unable to protect against this type of security threat. Trust and reputation systems on the other hand can very well provide protection against such threats. Reputation models can be modeled in such a way that could provide reliability for both users and providers. Reputation systems provide a way for building trust through social control by utilizing community based feedback about past experiences of peers to help making recommendation and judgment on quality and reliability of the transactions..Reputation and Trust systems are soft security mechanisms which can assure behavior conformity. The reliability of any transaction obviously increases when feed backs of past experience of same type of jobs are considered and given with more weightage. The rest of the sections are organized as follows. Section 2 analyzes the similar previous work. Section 3 discusses about the proposed model. Section 4 gives details of experiments and analysis of results and section 5 concludes. 2 Related Work The simplest form of computing reputation scores is proposed by Resnick and Zeckhauser [4] who simply measure the reputation by finding the sum of the number of positive ratings and negative ratings separately, and keep the total score as the positive score minus the negative score . The advantage is that it is very simple model where anyone can understand the principle behind the reputation score, while the disadvantage is that it is primitive and therefore gives a poor picture on participants’ reputation score. Advanced models in this category compute a weighted average of all the ratings, where the rating weight can be determined by factors such as the rater trustworthiness/reputation, the age of the rating, the distance between rating and current score, etc. Xiong and Liu in their paper [5] use an adjusted weighted average of amount of satisfaction that a user gets for each transaction. The parameters of the model include the feedback from transactions, the number of transactions, the credibility of feedbacks, the criticality of the transaction. Zacharia and Maes [6] review some systems in 2000 that address reputation management in e-commerce sites. Regarding on-line trading environments, Dellarocas [7] analyzes reputation mechanisms from a game-theoretical point of view. He allows opportunistic players to take part of the game and his analysis is fully based on mathematics developments. Reputation Based Trust Model for Grid with Enhanced Reliabilty 3 Probabilistic / Bayesian models directly model the statistical interaction between the consumers and the providers. Wang and Vassileva [8] use a naive Bayesian network which is generally used for representing and analyzing models involving uncertainty, to represent the trust of a user with a provider, the concept of trust being defined in terms of both the capability of the provider in providing services and the reliability of the user in providing recommendations about other users. Baolin Ma, Jizhou Sun [9] talk about trust model based on reputation. In this model both direct and indirect trust are calculated by using reputation. Direct trust is calculated and the value of direct trust is used to find the value of indirect trust. Gregor von laszewki [10] provide a way for efficient resource selection by considering Eigen trust algorithm. Their approach is similar to Azzedin approach [11] except for a new parameter context. Ayman Tajeddine et al. [12] propose an impressive reputation based trust model. In this approach the initiator host calculates reputation value of target host based on its previous experiences and gathered feedbacks from other hosts. The recommenders can be from the same administrative control (neighbor) or from different trusted domain (friends) or from a completely strange domain (stranger). Srivaramangai [13] talk about the trust system is made more robust by eliminating the unreliable feedbacks by using rank correlation method. The model is further improved in Srivaramangai [14] by adding two way test criteria. 3 Proposed Model The proposed model is further enhancement. In the last two models proposed, two types of trust have been taken, namely direct trust and indirect trust . Indirect trust is measured from the reputation score of other entities. In the first model the initiator eliminates the feed backs of entities whose evolution procedure are not correlated to that of its’ own. The second model is further enhanced by adding two way test criteria. In that model the transaction is allowed only when the user trust score as evaluated by the provider is greater than the pre defined threshold value and the provider trust score is greater than the threshold of the user. These two models and other existing models take the direct trust score from the table. There is no categorization of type of jobs. This model measures direct trust based upon different parameters such as context, size and complexity. It categorizes the jobs. The model assumes that the feedback value given by the user for one kind of job provided by one entity is different from another kind of job by the same entity. So the model uses three types of trust namely DT1, DT2 and indirect trust. DT1 represents trust of user on the provider as a result of same kind of transactions and DT2 for different type of transactions. Indirect trust is calculated by same expression as that of previous models. This model adheres to the fact that the reputation values are not always constant. When there is no transaction between two entities for a longer period of time