17285338
A reputation-chain trust model for the semantic Web
Trust plays a central role for efficient and secure interactions on the semantic Web. Nowadays, more and more research has focused on this topic. In this paper, we introduce RCSW, a reputation-chain trust model for the semantic Web. We illustrate the basic definitions and mechanisms of RCSW and present the algorithm to calculate trust ratings through the chain of the trust network. RCSW incorporates pairwise trust values and reliable factors of acquaintances and constructs an edge-weighted graph to calculate trust ratings. We also define classes of trust functions to allow users to individualize their trust calculation. Our proposed model can deal with trust management effectively and can quickly adapt to the changing environment.
18767986
Applying Hypothetical Queries to E-Commerce Systems to Support Reservation and Personal Preferences
Online travel continues to be one of the most successful e-commerce categories on the Internet. As increasing number of people book online, existing e-ticket systems can not satisfy customers' growing demand. Customers complain about most of the current e-ticket systems for the lack of "reservation" functionality. They also complain that these systems are not "intelligent" enough as a traditional travel agent who can deal with more complicated personal preferences. In this paper, we propose a transaction model for e-commerce system to support reservation functionality while balancing the benefits and risks of both customers and airlines. We introduce the theory of hypothetical query to avoid unnecessary transactions and rollbacks on underlying database. Hypothetical queries can be generated automatically and transformed into relational algebra queries which can be optimized with conventional techniques. We extend the lazy approach for evaluating hypothetical queries with integrity constraints to deal with customers' personal preferences. We also provide the "watch" and "remind" mechanism to help customers to purchase their most desirable tickets. Although we have chosen ticket booking system as our target application, the framework presented in this paper is also applicable to other e-commerce systems.
46276460
ACO Based QoS Routing Algorithm for Wireless Sensor Networks
In this paper, we proposed an approach for Quality of Service (QoS) routing algorithm of Wireless Sensor Networks (WSNs) based on Ant Colony Optimization (ACO). The special characteristics of WSNs need to reduce the computational complexity and energy consumption of the QoS routing algorithm especially. We note that ACO algorithm using collective intelligence of artificial ants as intelligent agents is very appropriate to solve the combinatorial optimization problems in a fully distributed way, so in this paper we use modified ACO approach to solve Delay Constraint Maximum Energy Residual Ratio (DCMERR) QoS routing problem of WSNs. The QoS routing solution proposed in this manuscript, which is named as ACO based QoS routing algorithm (ACO-QoSR), searches for the best paths, which are satisfied with the QoS requirements with intelligent artificial ants. To overcome the problem of limited energy in WSNs, there are some modifications to enhance ACO’s convergence rate. ACO-QoSR algorithm is the tradeoff between a certain guaranteed QoS requirements and acceptable computational complexity. The simulation results verify that ACO-QoSR algorithm can reduce the selected paths’ delay and improve the selected paths’ normalized energy residual ratio at the similar levels of routing overhead.
38299104
A Computational Trust Model for Semantic Web Based on Bayesian Decision Theory
Enabling trust to ensure more effective and efficient agent interaction is at the heart of the Semantic Web vision. We propose a computational trust model based on Bayesian decision theory in this paper. Our trust model combines a variety of sources of information to assist users with making correct decision in choosing the appropriate providers according to their preferences that expressed by prior information and utility function, and takes three types of costs (operational, opportunity and service charges) into account during trust evaluating. Our approach gives trust a strict probabilistic interpretation and lays solid foundation for trust evaluating on the Semantic Web.
12630564
Develop a Computational Trust Prototype for the Semantic Web
Nowadays, more and more research has focused on the trust management of the Semantic Web. Some mature technologies from many fields such as multiagent systems, social network analysis and artificial intelligence have been introduced to this field. However, some unique characteristics of the Semantic Web are more or less ignored. In this paper, we try to explore the semantic aspects of the trust management. We present DartTrust, a trust prototype for the semantic web in the application context of Traditional Chinese Medicine (TCM). We introduce RCSW - a Reputation-Chain trust model for the Semantic Web, which incorporates pairwise trust factor and reliable factor and constructs an edge-weighted graph to calculate trust ratings. We define a small trust ontology to illustrate the trust relationships. The trust ontology is described in OWL that can be understood and processed by machines. Therefore, DartTrust can provide reasoning functions and facilitate trust management with little human effort. As a demonstration of the prototype, we present its implementation on Traditional Chinese Medicine (TCM) at the end of this paper.
14869838
A Social Network-Based Trust Model for the Semantic Web
Trust is very essential to secure and high quality interactions on the Semantic Web. In this paper, we introduce a trust model, which incorporates pairwise trust ratings and reliable factors of acquaintances and constructs an edge-weighted graph to calculate trust values. We interpret trust in two dimensions to better deal with it. We apply the theory in social network field to calculate trust and simulate the trust relationships between humans. We also take advantage of formulas in probability and statistics to analyze the similarities between agents. The algorithm of the trust model aims to be simple, efficient and flexible.
32502816
Fuzzy Chance-Constrained Goal Programming Model and Algorithm of Oilfield Measures
Measures programming is an important way for improving the effect of measures of oil fields. Based on the theory of fuzzy chance-constrained goal programming, a fuzzy chance-constrained goal programming model is put forward for measures programming of Oilfield, which uses the increasing production and cost as objectives. The process of its hybrid intelligent algorithm including fuzzy simulation, genetic algorithm and neural network is provided, and the sensitivity analysis on uncertain factors to determine the influence on the optimal result can also be carried on. Finally, an example is given to illustrate the validity of the model and approach.
17429523
A Novel Trust-based Cooperative Node Selection Scheme in Wireless Sensor Networks
For communication between the cluster head node (CHN) and the distant data fusion center (DFC) with the cooperation of intra-cluster nodes in wireless sensor networks (WSNs), reliable handling of packets by the cooperative node (CN) is difficult to ensure. In this paper, we propose a novel CN selection scheme based on trust for WSNs. This scheme can choose one of high trust nodes to assist in the transmission of CHN, which aims to improve the transmission performance over cooperative communication in the presence of potential malicious nodes. First, we establish a trust model to improve the reliability of transmission by CN in WSNs, which is based on both the direct trust and the indirect trust of nodes. Then the CHN selects a node based on the proposed trust model to aid the CHN in transmission. Simulation results show that the proposed scheme can mitigate the impact of the potential malicious nodes on transmission performance and the simulation results also demonstrate the efficiency of the proposed scheme.
17718771
Balancing Trust and Incentive in Peer-to-Peer Collaborative System
In order to maximize resource utilization as well as providing trust management in P2P computing environments, we propose a novel framework - Trust-Incentive Service Management (TIM). Having child model, club model, bid model and trust model, TIM is constructed based on role based price strategy. In this mechanism, providers set the price according to demand and supply, and consumers maximize the surplus upon budget and deadline. A weighted voting scheme is also proposed to secure the P2P system by declining the join request from malicious peers. TIM is scalable and efficient in that: (1) it is completely distributed without a central server; (2) it dynamically manages the price and service by integrating valuables of pricing, trust, and incentive. A TIM prototype has been successfully implemented in a real P2P system, CROWN. We evaluate the proposed approach through comprehensive experiments and achieve improved results in service allocation efficiency, system completion time, and aggregated resource utilization.
34552917
A new dynamic Bayesian network for integrating multiple data in estimating gene networks
We propose a novel method for estimating a gene regulatory network from both gene expression data and transcription factor binding location data. Based on dynamic Bayesian network (DBN) models, our method has two advantages. First, structural expectation maximization algorithm is embedded into a DBN framework, allowing it to learn the network with unknown structure and incomplete data, which cannot be tackled by expectation maximization algorithm. Second, since learning only from gene expression data is not enough to accurately estimate a gene network, we incorporate transcription factor binding location data through a structure prior. We demonstrate the effectiveness of the proposed method by the analysis of Saccharomyces Cerevisiae cell cycle data. The experimental results show that this new approach suits for dealing with missing values. Furthermore, combination of heterogeneous data from multiple sources ensures that our results are more accurate than those recovered from gene expression data alone.
14192001
Emergence of Social Norms in Complex Networks
This paper studies the problem that how social norms emerge even though agents are selfish and attempt to only maximize their own utility. We propose a new rule for social interactions. The rule is called Highest Rewarding Neighborhood (HRN). The HRN rule allows agents to remain selfish and be able to break relationships in order to maximize their utility. Our experiment shows that when agents are able to break unrewarding relationships that a Pareto-optimum strategy arises as the social normal. In addition we conclude the rate and amount of Pareto-optimum strategy that arises is dependent on the network structure when the networks are dynamic, and the rate is independent of the network structure when the networks are static.