Trust estimation of the semantic web using semantic web clustering

Abstract

Development of semantic web and social network is undeniable in the Internet world these days. Widespread nature of semantic web has been very challenging to assess the trust in this field. In recent years, extensive researches have been done to estimate the trust of semantic web. Since trust of semantic web is a multidimensional problem, in this paper, we used parameters of social network authority, the value of pages links authority and semantic authority to assess the trust. Due to the large space of semantic network, we considered the problem scope to the clusters of semantic subnetworks and obtained the trust of each cluster elements as local and calculated the trust of outside resources according to their local trusts and trust of clusters to each other. According to the experimental result, the proposed method shows more than 79% Fscore that is about 11.9% in average more than Eigen, Tidal and centralised trust methods. Mean of error in this proposed method is 12.936, that is 9.75% in average less than Eigen and Tidal trust methods.     

A multi-objective facility location model with batch arrivals: two parameter-tuned meta-heuristic algorithms

Many research works in mathematical modeling of the facility location problem have been carried out in discrete and continuous optimization area to obtain the optimum number of required facilities along with the relevant allocation processes. This paper proposes a new multi-objective facility-location problem within the batch arrival queuing framework. Three objective functions are considered: (I) minimizing the weighted sum of the waiting and the traveling times, (II) minimizing the maximum idle time pertinent to each facility, and (III) minimizing the total cost associated with the opened facilities. In this way, the best combination of the facilities is determined in the sense of economical, equilibrium, and enhancing service quality viewpoints. As the model is shown strongly NP-hard, two meta-heuristic algorithms, namely genetic algorithm (GA) and simulated annealing (SA) are proposed to solve the model. Not only new coding is developed in these solution algorithms, but also a random search algorithm is proposed to justify the efficiency of both algorithms. Since the solution-quality of all meta-heuristic algorithms severely depends on their parameters, design of experiments and response surface methodologies have been utilized to calibrate the parameters of both algorithms. Finally, computational results obtained by implementing both algorithms on several problems of different sizes demonstrate the performances of the proposed methodology.     

Time-interval model reduction of bilinear systems

In this paper, a new method for model reduction of bilinear systems is presented. The proposed technique is from the family of gramian-based model reduction methods. The method uses time-interval generalised gramians in the reduction procedure rather than the ordinary generalised gramians and in such a way that it improves the accuracy of the approximation within the time-interval for which the method is applied. The time-interval generalised gramians are the solutions to the generalised time-interval Lyapunov equations. The conditions for these equations to be solvable are derived and an algorithm is proposed to solve these equations iteratively. The method is further illustrated with the help of two illustrative examples. The numerical results show that the method is more accurate than its previous counterpart which is based on the ordinary gramians.     

Chemical composition, in vitro cytotoxic and antioxidant activities of the essential oil and major constituents of Cymbopogon jawarancusa (Kashmir)

The chemical composition of the hydrodistillate of aerial parts of Cymbopogon jawarancusa, a natural grass considered as major forage for animal nutrition, used in food because of the presence of sufficient concentration of minerals like calcium and potassium was analysed by capillary GC–FID, GC–MS and ¹³C NMR. Seventeen constituents representing 97.8% of the total oil with piperitone (58.6%) and elemol (18.6%) as major constituents were identified. In vitro cytotoxicity of the oil and its constituents on human cancer cell lines THP-1 (leukemia), A-549 (lung), HEP-2 (liver) and IGR-OV-1 (ovary) was evaluated by Sulphorhodamine-B assay. The oil was found to be more potent than its components against cancer cell lines tested with IC₅₀ of 6.5 μg/ml (THP-1), 6.3 μg/ml (A-549), 7.2 μg/ml (HEP-2) and 34.4 μg/ml (IGR-OV-1). Antioxidant activity of oil and its constituents was evaluated by DPPH assay. In conclusion, the results demonstrate potent cytotoxic and antioxidant effects of oil, and its components like piperitone, α-pinene, β-caryophyllene and β-elemene.     

Preparation of a novel potassium ion imprinted polymeric nanoparticles based on dicyclohexyl 18C6 for selective determination of K+ ion in different water samples

This work reports the first application of the ion imprinting technology for determination of potassium ion by precipitation polymerization method. Ion imprinted polymeric (IIP) nanoparticles were prepared by using dicyclohexyl 18C6 (DC18C6) as a K⁺ ion selective crown ether, in the acetonitrile–dimethylsulfoxide (3:1; v/v) mixture as porogen. The imprint potassium ion was removed from the polymeric matrix using 0.5 M HNO₃. The scanning electron microscopy (SEM) micrographs showed colloidal nanoparticles of 60–90 nm in diameter and slightly irregular in shape. The obtained ion-imprinted particles for K⁺ showed selective recognition with rapid adsorption and desorption processes. It was found that imprinting results in increased affinity of the material toward K⁺ ion over other competitor metal ions with the same charge and/or close ionic radius. The synthesized IIP nanobeads were shown to be promising for solid-phase extraction coupled with flame photometry for determination of trace K⁺ ion in different water samples.     

A high-performance switch fabric for integrated circuit and packet switching

This paper describes an architecture for a high-performance switching fabric that can accommodate circuit-switched and packet-switched traffic in a unified manner. The switch fabric is self-routeing and uses fixed-length minipackets within the switching fabric for all types of connections. Its kernel architecture is based on a routeing topology with individual connection paths from all inputs to all outputs and with FIFO queuing at each output. Owing to the disjoint connection paths, there is no internal blocking, and because of output queueing, output port blocking is prevented to a great extent. The uniformity in architecture allows construction of any size fabric from a single basic module which could be realized on a single chip. Larger-size configurations can be realized either as single-stage or multistage configuration. The second part of this paper discusses performance aspects and gives results and dimensioning guidelines for both circuit-switched and packet-switched traffic.     

Capacity Optimization of MIMO Wireless Communication Systems Using a Hybrid Genetic-Taguchi Algorithm

The purpose of this paper is to use a hybrid Taguchi-genetic algorithm to optimize the capacity of MIMO systems with different linear array geometrical configurations at both communication ends (Transmitter and Receiver). The method combines the traditional genetic algorithm, known to have a powerful global exploration capability, and the Taguchi method, which can exploit the optimum offspring and thus, enhance the genetic algorithm. The idea is to use the spatial channel model where the effect of mutual coupling is considered. Each array geometrical configuration has its own contribution in the total channel matrix depending on its physical parameters. The end result is to find out which combination(s) produce the highest capacity value.     

Coping with information technology challenges to identity: A theoretical framework

Drawing on ideas from identity control theory and coping theory and on a diverse range of social psychology literature, we propose an integrative theoretical framework that unpacks and traces the processes by which information technology comes to affect users’ identity. We define four types of strategies (acting on the situation, adjusting the self, cathartic practices and distancing) through which people cope with technological challenges to the self. We suggest that these strategies may lead to four individual-level outcomes, namely reinforced identity, redefined identity, ambivalent identity and anti-identity. The model is provided with a preliminary support through reference to real life situations, carefully selected from extant empirical IS enquiries.     

Multiobjective shape optimization of speed humps

This paper presents a new approach to the shape optimization of road speed humps. The proposed approach is based on multiobjective genetic optimization of the hump profile while taking into account the separation phenomenon, which occurs when the front tires of the vehicle momentarily lose contact with the road surface. The optimization is carried out for speeds up to twice the authorized speed (throughout this article, the term authorized speed refers to speed limits enforced in speed reduction [bump] areas of the roads) rather than for illegally high speeds as adopted by many of the previous works. A 6-degree of freedom non-linear dynamic model is used to identify the speeds at which separation occurs, and hump profiles associated with these speeds are discarded as infeasible solutions. Three independent objective functions are selected for optimization. They include the maximum vertical acceleration experienced by the driver when crossing the hump below the authorized speed limit (to be minimized), the same vertical acceleration at speeds above the authorized speed (to be maximized), and the ascending ratio of the “speed—vertical acceleration” curve (to be maximized). These objective functions are evaluated for more than 10,000 humps of two popular profile types (sinusoidal and flat top with straight ramps) and optimum profiles for three speed limits of 20, 25, and 30km/h are determined using the multiobjective nondominated sorting genetic algorithm II. As a result, a Pareto front of at least ten optimal points is achieved for each of the two hump profile types. Furthermore, to incorporate the economical aspects of the real-world problem, Pareto optimal points for the two profile types were compared based on their lateral section areas (an indication of the manufacturing cost). The comparison shows that sinusoidal humps more often than not outdo their flat top rivals economically.