The increasing proliferation of Cloud Services (CSs) has made the reliable CS selection problem a major challenge. To tackle this problem, this article introduces an effective trust model called Chain Augmented Naïve Bayes-based Trust Model (CAN-TM). This model leverages the correlation that may exist among QoS attributes to solve many issues in reliable CS selection challenge such as predicting missing assessments, and improving accuracy of trust computing. This is achieved by combining both the n-gram Markov model and the Naïve Bayes model. Experiments are conducted to validate that our proposed CAN-TM outperforms state-of-the-art approaches.
In a crowdsourcing system, it is important for the crowdsourcer to engineer extrinsic rewards to incentivize the participants. In this paper, we incorporate network effects as a contributing factor to intrinsic rewards, and study its influence on the design of extrinsic rewards. We show that the number of participating users and their contributions to the crowdsourcing system evolve to a steady equilibrium. We design progressively more sophisticated extrinsic reward mechanisms, and propose new and optimal strategies for a crowdsourcer to obtain a higher utility. Through simulations, we demonstrate that with our new strategies, a crowdsourcer can attract more participants.
Consistent low response time is essential for e-commerce due to intense competitive pressure. However, practitioners of web applications have often encountered the long-tail response time problem as the system utilization reaches moderate levels (e.g., 50%). Our fine-grained measurements of an n-tier benchmark application (RUBBoS) show such long response times are often due to the synchronous RPC-style inter-tier communications, which create strong inter-tier dependencies. We gradually remove the dependencies in n-tier applications by replacing the classic synchronous servers with their corresponding event-driven asynchronous version. Our measurements show that only when all servers become asynchronous the long-tail response time problem is resolved.
Sentiment analysis is a powerful tool that uses social media information to predict target domains. However, social media information may not come from trustworthy users. To utilize this information, a very first critical problem to solve is to filter credible and trustworthy information from contaminated data, advertisements or scams. We investigate different aspects of a social media user to score trustworthiness and credibility. Furthermore, we provide suggestions on how to improve trustworthiness on social media by analyzing the contribution of each trust score. We apply trust scores to filter the tweets related to the stock market in this paper.
Online social network services (OSNSs) are changing the fabric of our society, impacting almost every aspect of it. Over the last decades, multiple competing OSNSs have emerged. As a result, users are trapped in the walled gardens of their OSNS, encountering restrictions about the people they can interact with. Our work aims at enabling users to meet and interact beyond the boundary of their OSNSs. We introduce USNB -Universal Social Network Bus which revisits the "service bus" paradigm to address the requirements of social interoperability.
We propose PTP-MF (Pairwise Trust Prediction through Matrix Factorization), an algorithm to predict the intensity of trust/distrust relations in Online Social Networks. The PTP-MF algorithm maps each user i onto two low-dimensional vectors, namely, the trustor profile (describing her/his inclination to trust others) and the trustee profile (modelling how others perceive i as trustworthy) and computes the trust j places in j as the dot product of trustor profile of i and the trustee profile of j. Experiments indicate that the PTP-MF algorithm is more accurate than the state-of-the-art approaches (up to 9.65%) and scales well on real life graphs
In this paper, we propose a Threat Management System (TMS) for Data-driven Internet-of-Things-based Collaborative Systems (DIoTCS). The novelty of the system is that it con nes the damage into partitions generated for the global dataset shared by the DIoTCS applications. We formulate the partitioning problem as a cost-driven optimization problem, which is proven to be NP-hard. Accordingly, two heuristics are proposed to solve this problem. For TMS, we propose response and recovery subsystems. We evaluate TMS experimentally and demonstrate that intelligent partitioning of global dataset improves the overall availability of the DIoTCS.
In Attribute-Based Access Control (ABAC), access to resources is given based on attributes of subjects, objects, and environment. There is an imminent need for the development of efficient algorithms that enable migration to ABAC. However, existing policy mining approaches do not consider possible adaptation to the policy of a similar organization. In this article, we address the problem of automatically determining an optimal assignment of attribute values to subjects for enabling the desired accesses to be granted while minimizing the number of ABAC rules used by each subject. We show the problem to be NP-Complete and propose a heuristic solution.
We propose an efficient anonymous, attribute based credential scheme capable of provisioning multi-level credential delegations. It is integrated with a mechanism to revoke the anonymity of credentials, for resolving access disputes and making users accountable for their actions. The proposed scheme has a lower end-user computational complexity in comparison to existing credential schemes with delegatability and has a comparable level of performance with the credential standards of U-Prove and Idemix. Furthermore, we demonstrate how the proposed scheme can be applied to a collaborative e-health environment to provide its users with the necessary anonymous access with delegation capabilities.
This study empirically investigates the impact of past experience on human trust in and reliance on agent teammates. We developed a repeated team coordination game, in which two players repeatedly cooperate to complete team tasks. The results show that positive (negative) past experience increases (decreases) human trust in and reliance agent teammates; lack of past experience lead to higher trust levels compared to positive past experience. These findings provide clear and significant evidence and enhance our understanding of the changes in human trust in peer-level agent teammates with respect to past experience
This paper studies synchronous online distributed software update, also known as rolling upgrade in DevOps, which in clouds upgrades software versions in virtual machine instances even when various failures may occur. The goal is to minimise completion time, availability degradation, and monetary cost for entire rolling upgrade by selecting proper parameters. For this goal we propose a stochastic model and a novel optimisation method. We validate our approach to minimise the objectives, through both experiments in Amazon Web Service (AWS) and simulations.
Trust is essential for collaboration. General reputation and ID mechanisms may support users trust assessment. However, they lack sensitivity to pairwise interactions and specific experience such as betrayal over time. Moreover, they place an interpretation burden that does not scale to dynamic, large-scale systems. While several pairwise trust mechanisms have been proposed, no empirical research examines trust scores influence on participant behavior. We study the influence of showing a partner trust score and/or ID on participants behavior in a trust game experiment. We show that the trust score availability has the same effect as an ID to improve cooperation.