Abstract-Service provision networks are popular platforms for decentralized service management. eBay and Amazon are two representative examples of enabling and hosting service provision networks for their customers. Trust management is a critical component for scaling service provision networks to larger set of participants. This paper presents ServiceTrust, a quality sensitive and attack resilient trust management facility for service provision networks. ServiceTrust has three unique features. First, it encapsulates quality-sensitive feedbacks by multi-scale rating scheme and incorporates the variances of user's behaviors into the local trust algorithm. Second, ServiceTrust measures the similarity of two users' feedback behavior and aggregate the local trust values into the global trust algorithm by exploiting pairwise feedback similarity scores to weight the contributions of local trust values towards the global trust of a participant. Finally, pairwise feedback similarity weighted trust propagation is utilized to further strengthen the robustness of global trust computation against malicious or sparse feedbacks. Experimental evaluation with independent and colluding attack models show that ServiceTrust is highly resilient to various attacks and highly effective compared to EigenTrust, one of the most popular and representative trust models to date.

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Dental school graduates operating on patients without having had sufficient practice in school is potentially dangerous to the patients. In order to minimize this danger, it is necessary to establish a virtual learning environment for students. In this study, we incorporated DentSim, a clinical dentistry simulator, into an e-Learning platform. In addition to overcoming the time and space constraints on learning, DentSim can simulate clinical conditions. It also allows students to practice reading case histories and inspecting and diagnosing patients. To construct the research model for this study, we incorporated the four major factors for measuring e-Learner satisfaction-'learner interface', 'learning community', 'content' and 'personalization' with the variable of 'intention to use'. The subjects were 350 dental students studying at the College of Oral Medicine. The structural equation modeling (SEM) results showed that Factors that influenced 'intention to use' include 'learner interface', 'learning community' and 'personalization', and 'intention to use' affect 'e-Learner satisfaction' with the system.

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