T-OSN : a trust evaluation model in online social networks

Now days, the online social networks (OSN) have gained considerable popularity. More and more people use OSN to share their interests and make friends, also the OSN helps users overcome the geographical barriers. With the development of OSN, there is an important problem users have to face that is trust evaluation. Before user makes friends with a stranger, the user need to consider the following issues: Can a stranger be trusted? How much the stranger can be trusted? How to measure the trust of a stranger? In this paper, we take two factors, Degree and Contact Interval into consideration, which produce a new trust evaluation model (T-OSN). T-OSN is aimed to solve how to evaluate the trust value of an OSN user, also which is more efficient, more reliable and easy to implement. Base on our research, this model can be used in wide range, such as online social network (OSN) trust evaluation, mobile network message forwarding, ad hoc wireless networking, routing message on Internet and peer-to-peer file sharing network. The T-OSN model has following obvious advantages compare to other trust evaluate methods. First of all, it is not base on features of traditional social network, such as, distance and shortest path. We choose the special features of OSN to build up the model, that is including numbers of friends(Degree) and contact frequency(Contact Interval). These species features makes our model more suitable to evaluate OSN users trust value. Second, the formulations of our model are quite simple but effective. That means, to calculate the result by using our formulations will not cost too much resources. Last but not least, our model is easy to implement for an OSN website, because of the features that we used in our model, such as numbers of friends and contact frequency are easy to obtain. To sum up, our model is using a few resources to obtain a valuable trust value that can help OSN users to solve an important security problem, we believe that will be big step - or development of OSN.

Application of an Adaptive Neural Network to Medical Decision Support

Medical diagnostic and prognostic problems are prime examples of decision making in the face of uncertainty. In this paper, we investigate the applicability of the Fuzzy ARTMAP neural network as an intelligent decision support system in clinical medicine. In particular, Fuzzy ARTMAP is employed as a predictive model for prognosis of complications in patients admitted to the Coronary Care Units. A number of off-line and on-line experiments have been conducted with various network parameter settings, training methods, and learning rules. The results are compared with those from other systems including the logistic regression model. In addition, the outcomes of a set of on-line learning experiments revealed the potential of employing Fuzzy ARTMAP as an autono-mously learning system that is able to learn perpetually and, at the same time, to provide useful decision support.

A hybrid Kansei engineering system using the self-organizing map neural network

Kansei Engineering (KE), a technology founded in Japan initially for product design, translates human feelings into design parameters. Although various intelligent approaches to objectively model human functions and therelationships with the product design decisions have been introduced in KE systems, many or the approaches are not able to incorporate human subjective feelings and preferenees into the decision-making process. This paper proposes a new hybrid KE system that attempts to make the machine-based decision-making process closely resembles the real-world practice. The proposed approach assimilates human perceptive and associative abililities into the decision-making process of the computer. A number of techniques based on the Self-Organizing Map (SOM) neural network are employed in the backward KE system to reveal the underlying data structures that are involved in the decision-making process. A case study on interior design is presented to evaluate the efficacy of the proposed approach. The results obtained demonstrate tbe effectiveness of the proposed approach in developing an intelligent KE system which is able to combine huiiUUI feelings and preferences into its decision making process.

Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid FMM-CART model

In this paper, a novel approach to detect and classify comprehensive fault conditions of induction motors using a hybrid fuzzy min-max (FMM) neural network and classification and regression tree (CART) is proposed. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. A series of real experiments is conducted, whereby the motor current signature analysis method is applied to form a database comprising stator current signatures under different motor conditions. The signal harmonics from the power spectral density are extracted as discriminative input features for fault detection and classification with FMM-CART. A comprehensive list of induction motor fault conditions, viz., broken rotor bars, unbalanced voltages, stator winding faults, and eccentricity problems, has been successfully classified using FMM-CART with good accuracy rates. The results are comparable, if not better, than those reported in the literature. Useful explanatory rules in the form of a decision tree are also elicited from FMM-CART to analyze and understand different fault conditions of induction motors.

A network analysis of IT governance practices : a case study of an IT centralisation project

Information technology governance (ITG) practices rely on both human and nonhuman actors to support the creation of business value in organisations. The role of nonhuman actors in shaping the ITG practices has been given limited attention within the ITG literature. In proposing a model of ITG, this paper highlights the interplay of human and technological artefacts in shaping the ITG practices. Using a case study approach, this paper explores the relationship and interaction between ITG arrangements and IT infrastructure in an IT centralisation project conducted over a number of years in a university. The analysis from this study highlights how problems in aligning actors’ interests and lack of appropriate strategies lead to a failure in establishing a stable ITG network.

CodePipe : an opportunistic feeding and routing protocol for reliable multicast with pipelined network coding

Multicast is an important mechanism in modern wireless networks and has attracted significant efforts to improve its performance with different metrics including throughput, delay, energy efficiency, etc. Traditionally, an ideal loss-free channel model is widely used to facilitate routing protocol design. However, the quality of wireless links would be affected or even jeopardized by many factors like collisions, fading or the noise of environment. In this paper, we propose a reliable multicast protocol, called CodePipe, with advanced performance in terms of energy-efficiency, throughput and fairness in lossy wireless networks. Built upon opportunistic routing and random linear network coding, CodePipe not only simplifies transmission coordination between nodes, but also improves the multicast throughput significantly by exploiting both intra-batch and inter-batch coding opportunities. In particular, four key techniques, namely, LP-based opportunistic routing structure, opportunistic feeding, fast batch moving and inter-batch coding, are proposed to offer substantial improvement in throughput, energy-efficiency and fairness. We evaluate CodePipe on ns2 simulator by comparing with other two state-of-art multicast protocols, MORE and Pacifier. Simulation results show that CodePipe significantly outperforms both of them.

Power penalty assessment of OXADM device model - analytical analysis

This OXADM are located in the nodes, which have more than two switching directions in ring networks. The function of OXADM is to flexibility switch the wavelengths among the different input and output ports. Because of the OXADM's imperfect performance, the insertion loss and crosstalk are induced in the system. Analytical modeling method is using to analyze the OXADM structure in crosstalk or power leakage that lead to the power penalty. To overcome this problem, power penalty is needed to be supplied. The insertion of this power penalty depends on few parameters. The parameters that we going to investigate here will be in term of number of operating wavelengths and number of input/output ports as well as the Q factor. The variation of this parameters will affects the amount of the desired power penalty. Simulation results in higher crosstalk or higher power penalty needed as the number of OXADM increases. As the sum of the wavelength and the number of input/output for each OXADM increases, the power penalty will increased as well. Investigation on the maximum Q factors is 6 to get the minimum power penalty at the lowest BER for most of the combination of the sum of the wavelength and the number of input/output for each OXADM.

Investigation of process dynamics and control of polystyrene batch reactor using hybrid model

The effects of operating conditions such as initiator and monomer concentration as well as reactor temperature of polymerization reactors are studied in this work. A recently developed hybrid model for polystyrene batch reactor is utilized in simulation study. The simulation results reveal the sensitivity of polymer properties and monomer conversion to variation of process operating conditions. In the second phase of this study, the optimization problem involving minimum time optimal temperature policy is considered for control study. An advanced neural network-based model predictive controller (NN-MPC) is designed and tested online. The experimental studies reveal that the developed controller is able to track the optimal setpoint with a minor oscillation and overshoot.

A new hybrid method for Bayesian network learning With dependency constraints

A Bayes net has qualitative and quantitative aspects: The qualitative aspect is its graphical structure that corresponds to correlations among the variables in the Bayes net. The quantitative aspects are the net parameters. This paper develops a hybrid criterion for learning Bayes net structures that is based on both aspects. We combine model selection criteria measuring data fit with correlation information from statistical tests: Given a sample d, search for a structure G that maximizes score(G, d), over the set of structures G that satisfy the dependencies detected in d. We rely on the statistical test only to accept conditional dependencies, not conditional independencies. We show how to adapt local search algorithms to accommodate the observed dependencies. Simulation studies with GES search and the BDeu/BIC scores provide evidence that the additional dependency information leads to Bayes nets that better fit the target model in distribution and structure.

Experimental verification of theoretical prediction of fiber to fiber contacts in electrospun multilayer nano-microfibrous assemblies: effect of fiber diameter and network porosity

Average number of fiber-to-fiber contacts in a fibrous structure is a prerequisite to investigate the mechanical, optical and transport properties of stochastic nanomicrofibrous networks. In this research work, based on theoretical analysis presented for the estimation of the number of contacts between fibers in electrospun random multilayer nanofibrous assembles, experimental verification for theoretical dependence of fiber diameter and network porosity on the fiber to fiber contacts has been provided. The analytical model formulated is compared with the existing theories to predict the average number of fiber contacts of nanofiber structures. The effect of fiber diameters and network porosities on average number of fiber contacts of nano-microfiber mats has been investigated. A comparison is also made between the experimental and theoretical number of inter-fiber contacts of multilayer electrospun random nanomicrofibrous networks. It has been found that both the fiber diameter and the network porosity have significant effects on the properties of fiber-to-fiber contacts.

Analysing stakeholder-associated risks in green buildings : a social network analysis method

In the research of risk associated with developing energy and water efficient green buildings, previous studies had mainly focused on "what the risks are and how the risks may impact on project objectives", which were from an inward looking self-perspective and treated the risks in isolation from one another. While intensive research efforts have been dedicated to risk identification, assessment, classification, prioritisation and mitigation, a research gap exists, that is previous studies had ignored the fact that most risks are interrelated and associated with internal or external project stakeholders. To remedy the gap, this current research developed and presented a SNA (Social Network Analysis) based stakeholder-associated risk analysis method to assess risks in green buildings and the interactions between the risks. A case study has been conducted to demonstrate and validate this method. This research contributes to the development of a new theory to model the interdependent and interactive relationships of risks by using SNA as a methodology. This research should broaden project managers' awareness of the influential risks in green building and enhance their ability to perceive, understand, assess, and mitigate the risks in an effective and efficient way, thereby achieving higher performance in strategic risk management and stakeholder communication in green building development.