Service computing provides the capability of binding each service invocation in a composition to a service selected among the same function attributes but different QoS attributes to achieve a QoS goal. Hence, service selection approach plays a very important role in service composition. However existing approaches little considered the computation time. In this study, we propose a effective QoS-aware web service selection approach. This approach adopts a particle swarm optimization algorithm select the most service with users’ QoS requirements Experimental results show that our approach can find best suitable services with low time cost in web service selection.

This paper presents hybrid of rationalized Haar (HRH) functions method for approximate the numerical solution of the fractional nonlinear Fredholm integro-differential equations (FNFIDEs). The fractional derivatives are considered in the sense of Caputo. The fractional operational matrix of hybrid of block-pulse and rationalized Haar functions are presented. This matrix together with the dual operational matrix are used to reduce the computation of FNFIDEs into a system of algebraic equations. Some numerical examples are given and the results of applying this method demonstrate time and computational are small.

One of social networks features is to recommend some items for users suitable and their profiles and needs. One of the used techniques of item recommendation is to calculate the average rating of other users' ratings of that item. The calculated average can be used as a rank for that item. New users' ratings are made within the lifetime of social networks. This dynamic sized set of ratings can lead to a performance problem. Ranking is not fixed since new users are constantly evaluating items in networks. However, updating ranking by traditional averaging can have a quadratic time growth. This paper aims to convert this aggregation method into linear growth instead of quadratic. The suggested algorithm needed extra storage capacity. Caching is suggested to speedup ranking with preserving storage resources. Another target is to have a high hit-ratio with better utilization of small cache size compared to the traditional Greedy- Dual- Size-Frequency algorithm. At the end of this paper an evaluation of the proposed technique performance is provided based on simulated experimental results. The experiment results show that the proposed technique has better performance rather than traditional averaging recommender systems without caching.

A mobile ad hoc network (MANET) is a dynamic wireless network that consists of mobile nodes communicating in the routing without any infrastructure. Routing is one of the interesting areas in MANETs. Routing in the MANET is a challenging task and selecting one routing protocol with all suitable features is difficult. A growing attention in MANETs is providing stable routes with long route lifetime, low control overhead, low end to end delay and high packet delivery ratio and etc. In this paper, we provide an overview and comparison of different reactive routing protocols for MANETs

Clustering is an important task that is used to find subsets of similar objects from a set of objects such that the objects in the same subsets are more similar than other subsets. Large number of algorithms has been developed to solve the clustering problem. K-Harmonic Mean (KHM) is one of the popular technique that has been applied in clustering as a substitute of K-Means algorithm because it is insensitive to initialization issues due to built in boosting function. But, this method is also trapped in local optima. On the other hand, Cat Swarm Optimization (CSO) is the latest population based optimization method used for global optimization. In this paper a hybrid data clustering method is proposed based on CSO and KHM which includes the advantage of both algorithms and named as CSOKHM. The hybrid CSOKHM not only improved the convergence speed of CSO but also escape the KHM method to run in local optima. The performance of the CSOKHM is evaluated using seven datasets and compared with KHM, PSO, PSOKHM, ACA, ACAKHM, GSAKHM, CSO methods. The experimental results show the applicability of CSOKHM method..

Most of the proposed self-similar traffic models could not address fractal onset time at which self-similar behavior actually begins. This parameter has considerable impact on network performance. Fractal point process (FPP) emulates self-similar traffic and involves fractal onset time (FOT). However, this process is asymptotic in nature and has less effective in queueing based performance. In this paper, we propose a model of variance based Markovian fitting. The proposed method is to match the variance of FPP and superposed Markov modulated Poisson Process (MMPP) while taking FOT into consideration. Superposition consists of several interrupted Poisson processes (IPPs) and Poisson process. We present how well resultant MMPP could approximate FPP which emulates self-similar traffic. We investigate queueing behavior of resultant queueing system in terms of a packet loss probability. We demonstrate how FOT affects the fitting model and queueing behavior. We conclude from the numerical example that network nodes with a self-similar input traffic can be well represented by a queueing system with MMPP input

This paper is involved with the complete synchronization problems for two identical delay financial hyperchaotic system with different initial conditions, and a simple complete synchronization scheme only with a single linear input is proposed. Based on the Lyapunov stability theory, both linear feedback control and adaptive control approaches is derived to complete synchronization between two nearly identical delay financial hyperchaotic systems with unknown parameters is also studied. Numerical simulation results are showing the effectiveness of the proposed hyperchaotic synchronization method.

In this paper, we propose a new approach to detect salient traffic signs, which is based on visual saliency and the auto segmentation method based on region merging. The proposed algorithm introduces the visual saliency model, and puts forward the top-down and bottom-up two-way fusion mechanism, tried to consciously take the initiative to find and simulate human visual unconscious passive visual process by attracting interact. Then the segmentation method based on region merging is used to extract the traffic sign. Experiments show that the two-way fusion mechanism in target detection has fast processing speed and high accuracy. Extensive experiments on public datasets show that our approach outperforms state-of-the-art methods remarkably in salient traffic sign detection. Moreover, the proposed detection method has higher accurate rate and robustness to different natural scenes.