Zeinab Noorian

The state of the art in trust and reputation systems: a framework for comparison

We introduce a multidimensional framework for classifying and comparing trust and reputation (T&R) systems. The framework dimensions encompass both hard and soft features of such systems including different witness location approaches, various reputation calculation engines, variety of information sources and rating systems which are categorised as hard features, and also basic reputation measurement parameters, context diversity checking, reliability and honesty assessment and adaptability which are referred to as soft features. Specifically, the framework dimensions answer questions related to major characteristics of T&R systems including those parameters from the real world that should be imitated in a virtual environment. The proposed framework can serve as a basis to understand the current state of the art in the area of computational trust and reputation and also help in designing suitable control mechanisms for online communities. In addition, we have provided a critical analysis of some of the existing techniques in the literature compared within the context of the proposed framework dimensions.

Preference-oriented QoS-based service discovery with dynamic trust and reputation management

In the presence of a variety of service providers that offer web services with overlapping or identical functionality, service consumers need a mechanism to distinguish one service from another based on their own subjective quality of service (QoS) preferences. Typical approaches in this field rely on trusted third parties to monitor the behaviour of service providers and endorse their performance based on their delivered services to different users. However, the issue of evaluating the credibility of user reports is one of the essential problems yet to be solved in the e-Business application area. In this paper we propose a two-layered preference-oriented service selection framework that integrates trust and reputation management techniques with an advanced procurement auction model in order to choose the most pertinent service provider that meets a consumers QoS requirements. We will give a formal description of our approach and validate it with experiments demonstrating that our solution yields high-quality results under various realistic circumstances.

Performance enhancement of smith-waterman algorithm using hybrid model: Comparing the MPI and hybrid programming paradigm on SMP clusters

Nowadays, database pattern searching is the most heavily used operation in computational biology. Indeed, sequence alignment algorithm plays an important role to find the homologous groups of sequences which may help to determine the function of new sequences. Meanwhile Smith-Waterman algorithm is one of the most prominent pattern matching algorithms. However, it cost the large quantity of time and resource power. By the aid of parallel hardware and software architecture it becomes more feasible to get the fast and accurate result in efficient time. In this paper, Smith-Waterman algorithm is parallelized base on various types of parallel programming, pure MPI, pure OpenMP and Hybrid MPI-OpenMP model. In addition, based on the experiments it will be proved that hybrid programming which employ the coarse grain and fine grain parallelization, is more efficient compare with pure MPI and pure OpenMP in cluster of SMP machines.

Trust-oriented buyer strategies for seller reporting and selection in competitive electronic marketplaces

In competitive electronic marketplaces where some selling agents may be dishonest and quality products offered by good sellers are limited, selecting the most profitable sellers as transaction partners is challenging, especially when buying agents lack personal experience with sellers. Reputation systems help buyers to select sellers by aggregating seller information reported by other buyers (called advisers). However, in such competitive marketplaces, buyers may also be concerned about the possibility of losing business opportunities with good sellers if they report truthful seller information. In this paper, we propose a trust-oriented mechanism built on a game theoretic basis for buyers to: (1) determine an optimal seller reporting strategy, by modeling the trustworthiness (competency and willingness) of advisers in reporting seller information; (2) discover sellers who maximize their profit by modeling the trustworthiness of sellers and considering the buyers’ preferences on product quality. Experimental results confirm that competitive marketplaces operating with our mechanism lead to better profit for buyers and create incentives for seller honesty.

An autonomous agent-based framework for self-healing power grid

Reliable, secure and robust power grid network is a necessity for crucial financial, industrial and business networks. Since national electrical grid, telecommunication, information networks and transportation networks are interdependent critical infrastructures, having an agent-based self-healing framework to reduce cascading failures through the networks and finding reasonable solution for potential faults — would be an essential asset. In response to this need we propose a self-healing framework that employs advanced failure diagnosis techniques along with autonomous web services to provide temporary recovery solutions. Furthermore, it provides a cognitive planning cycle to find ultimate corrective solutions as well as evaluation service to verify the effectiveness and performance of the final solution.

Prob-Cog: An Adaptive Filtering Model for Trust Evaluation

Trust and reputation systems are central for resisting against threats from malicious agents in decentralized systems. In previous work we have introduced the Prob-Cog model of multi-layer filtering for consumer agents in e-marketplaces which provide mechanisms for identifying participants who disseminate unfair ratings by cognitively eliciting the behavioural characteristics of e-marketplace agents. We have argued that the notion of unfairness does not exclusively refer to deception but can also imply differences in dispositions. The proposed filtering approach goes beyond the inflexible judgements on the quality of participants and instead allows environmental circumstances and the human dispositions that we call optimism, pessimism and realism to be incorporated into our trustworthiness evaluation procedures. In this paper we briefly outline the two layers before providing a detailed exposition of our experimental results, comparing Prob-Cog to FIRE and the personalized approach under various attacks and normal situations.

SocialTrust: Adaptive Trust Oriented Incentive Mechanism for Social Commerce

In the absence of legal authorities and enforcement mechanisms in open e-marketplaces, it is extremely challenging for a user to validate the quality of opinions (i.e. Ratings and reviews) of products or services provided by other users (referred as advisers). Rationally, advisers tend to be reluctant to share their truthful experience with others. In this paper, we propose an adaptive incentive mechanism, where advisers are motivated to share their actual experiences with their trustworthy peers (friends/neighbors in the social network) in e-marketplaces (social commerce context), and malicious users will be eventually evacuated from the systems. Experimental results demonstrate the effectiveness of our mechanism in promoting the honesty of users in sharing their past experiences.

A context-aware reputation-based model of trust for open multi-agent environments

In this paper we have proposed a context-aware reputationbased trust model for multi-agent environments. Due to the lack of a general method for recognition and representation of context notion, we proposed a functional ontology of context for evaluating trust (FOCET) as the building block of our model. In addition, a computational reputation-based trust model based on this ontology is developed. Our model benefits from powerful reasoning facilities and the capability of adjusting the effect of context on trust assessment. Simulation results shows that an appropriate context weight results in the enhancement of the total profit in open systems.

Embedding unstructured side information in product recommendation

Abstract Various researchers have already engaged in using auxiliary side information within recommender applications to improve the quality and accuracy of recommendations. This side information has either been in the form of structured information such as product specifications and user demographic information or unstructured information such as product reviews. The abundance of unstructured information compared to structured information entices the use of such unstructured information in the recommendation process. Existing works that employ unstructured content have been confined to standard text modeling technique such as the use of frequency measures or topic modeling techniques. In this paper, we propose to model unstructured content about both products and users through the exploitation of word embedding techniques. More specifically, we propose to learn both user and product representations from any type of unstructured textual contents available in different external information sources using recurrent neural networks. We then apply our learnt product and user representations on two recommendation frameworks based on matrix factorization and link prediction to enhance the recommendation task. Experimental results on four datasets constructed from the Rotten Tomatoes website (movie review aggregator database) have shown the effectiveness of our proposed approach in different real-world situations compared to the state of the art.

Determining the Optimal Reporting Strategy in Competitive E-marketplaces

In a reputation system for multiagent based electronic marketplaces where the number of high quality products provided by good selling agents is unlimited, buying agents often share seller information without the need to consider possible utility loss. However, when those good sellers have limited inventory, buyers may have to be concerned about the possibility of losing the opportunity to do business with the good sellers if the buyers provide truthful information about sellers, due to the competition from other buyers. In this paper, we propose an adaptive mechanism built on a game theoretic basis for buyers to determine their optimal reputation reporting strategy, by modeling both the competency and willingness of other buyers in reporting seller reputation and strategically choosing reporting behaviours that maximize their utility according to the modeling results. The results of the experiments carried out in a simulated competitive e-marketplace confirm that our proposed mechanism leads to better utility for buyers in such an environment.