Vol. 33 · No. 5

To increase the speed of information retrieval, one message may have multiple replicas in Delay Tolerant Networks (DTN). In this paper, we adopt a discrete time model and focus on the caching policy of stochastic updating information. In particular, the source creates new version in every time slot with certain probability. New version is usually more useful than the older one. We use a utility function to denote the availability of different versions. To constrain the number of replicas, we propose a probabilistic management policy and nodes to discard information with certain probability determined by the version of the information. Our objective is to find the best value of the probability to maximize the total utility value. Because new version is created with certain probability, nodes other than the source may not know whether the information stored in them is the latest version. Therefore, they can make decisions only according to the local state and decisions based on the local state can be seen as local-policy. We also explore the global-policy, that is, nodes understand the real state. We prove that the optimal policies in both cases conform to the threshold form. Simulations based on both synthetic and real motion traces show the accuracy of our theoretical model. Surprisingly, numerical results show that local-policy is better than the global-policy in some cases.

Stock market prediction is essential and of great interest because successful prediction of stock prices may promise smart benefits. These tasks are highly complicated and very difficult. Many researchers have made valiant attempts in data mining to devise an efficient system for stock market movement analysis. In this paper, we have developed an efficient approach to stock market prediction by employing fuzzy C-means clustering and artificial neural network. This research has been encouraged by the need of predicting the stock market to facilitate the investors about buy and hold strategy and to make profit. Firstly, the original stock market data are converted into interpreted historical (financial) data i.e. via technical indicators. Based on these technical indicators, datasets that are required for analysis are created. Subsequently, fuzzy-clustering technique is used to generate different training subsets. Subsequently, based on different training subsets, different ANN models are trained to formulate different base models. Finally, a meta-learner, fuzzy system module, is employed to predict the stock price. The results for the stock market prediction are validated through evaluation metrics, namely mean absolute deviation, mean square error, root mean square error, mean absolute percentage error used to estimate the forecasting accuracy in the stock market. Comparative analysis is carried out for single Neural Network (NN) and existing technique neural. The obtained results show that the proposed approach produces better results than the other techniques in terms of accuracy.

In the context of coverage planning and control, the power of the common pilot channel signal determines the coverage area of a network cell. It also impacts the network capacity and thus the quality of service. We consider the problem of minimizing the total amount of pilot power subject to a full coverage constraint. Our optimization approach, based on parallel autonomous agents, gives very good solutions within an acceptable amount of time. The parallel implementation takes full advantage of GPU hardware in order to achieve impressive speed-up. We report the results of our experiments for three UMTS networks of different sizes based on a real network currently deployed in Slovenia.

This paper presents an ant-inspired method for selecting the optimal or a near optimal solution in semantic Web service composition. The proposed method adapts and enhances the Ant Colony Optimization meta-heuristic and considers as selection criteria the QoS attributes of the services involved in the composition as well as the semantic similarity between them. To improve the performance of the proposed selection method a 1-OPT heuristic is defined which expands the search space in a controlled way so as to avoid the stagnation on local optimal solutions. The ant-inspired selection method has been evaluated on a set of scenarios having different complexities and comparatively analyzed with a cuckoo-inspired and a bee-inspired selection method.

In this paper, we propose a proficient method for knowledge retrieval in edaphology to assist the edaphologists and those who are related with agriculture in a big way. The proposed method mainly consists of two phases of which the first one is to build the knowledge base using XML and the latter part deals with information retrieval using fuzzy search. Initially, the relational database is converted to XML database. This paper discusses two algorithms, one is when the soil characteristics are given as input to have the plant list and in the other, plant names are given as input to have the soil characteristics suited for the plant. While retrieving the query result, the crisp numerical values are converted to fuzzy value using the triangular fuzzy membership function and matched to those in database. Those which satisfy are added to the result list and subsequently, the frequency is found out to rank the result list so as to obtain the final sorted list. Performances metrics are used in order to evaluate the method and compared to baseline paper to identify the number of plants retrieved, ranking efficiency, and computation time and memory usage. Results obtained proved the validity of the method and the method obtained the average computation time of 0.102 seconds and average memory usage of 2 486 Kb, which are all far better than our previous method results.

The widespread use of the Internet and evaluater-based technologies has transformed the way business is conducted. Traditional offline businesses have increasingly become online, and there are new kinds of businesses that solely exist online. Unlike offline business environments, interpersonal trust is generally lacking in online business settings. Trading partners might feel insecure about the exchange of products and services over the net as they have limited information about each other's reliability or about the product quality. Considering that enough trust needs to be created to get the online buyer and seller to take actions, trust is a precious asset in online transactions. In order to address the issue of evaluating trust and reputation in online transaction environments, this paper makes use of a social network that graphically represents interpersonal relationships. This paper proposes computational models that systematically evaluate the quantitative level of trust and reputation based on the social network. A method that combines the evaluated trust and reputation levels is also proposed to increase the reliability of online transactions.

In this article, an alternative approach to detecting the computation completion of combinatorial blocks in asynchronous digital systems is presented. The proposed methodology is based on well-known phenomenon that occurs in digital systems fabricated in CMOS technology. Such logic circuits exhibit significantly higher current consumption during the signal transitions than in the idle state. Duration of these current peaks correlates very well with the actual computation time of the combinatorial block. Hence, this fact can be exploited for separation of the computation activity from static state. The paper presents fundamental background of addressed alternative completion detection and its implementation in single-rail encoded asynchronous systems, the proposed current sensing circuitry, achieved simulation results as well as the comparison to the state-of-the-art methods of completion detection. The presented method promises the enhancement of the performance of an asynchronous circuit, and under certain circumstances it also reduces the silicon area requirements of the completion detection block.

In this paper we propose extensions to the existing utility-based reputation model for virtual organizations (VOs) in grids, and present a novel approach for integrating reputation into grid resource management system. The proposed extensions include: incorporation of statistical model of user behaviour (SMUB) to assess user reputation; a new approach for assigning initial reputation to a new entity in a VO; capturing alliance between consumer and resource; time decay and score functions. The addition of the SMUB model provides robustness and dynamics to the user reputation model comparing to the policy-based user reputation model in terms of adapting to user actions. We consider a problem of integrating reputation into grid scheduler as a multi-criteria optimization problem. A non-linear trade-off scheme is applied to form a composition of partial criteria to provide a single objective function. The advantage of using such a scheme is that it provides a Pareto-optimal solution partially satisfying criteria with corresponding weights. Experiments were run to evaluate performance of the model in terms of resource management using data collected within the EGEE Grid-Observatory project. Results of simulations showed that on average a 45

In this paper, the use of continuous mathematical models of an aircraft in an aircraft simulator is described. The models are of lower degree and less time-consuming for calculation. Computer implementation of the models capable to work faster and more accurately and efficiently is also described. The suggested approach allows to achieve the required precision at accelerated simulation speed using the continuous mathematical models of an aircraft. Frequency of the computation of continuous mathematical models of an aircraft is higher, reaching up to 200 times per second. The main focus of the paper is designing continuous mathematical models of an aircraft, their simulation and visualization in aircraft simulators. Current mathematical models of a control of objects motion are based on aircraft aerodynamics. In our approach, these models have impact on the quality and completeness of simulation process and are crucial for computer modeling and visualization of equations of the continuous mathematical models of an aircraft. In the paper, creating continuous mathematical models of an aircraft and the way of visualization of simulation results are described in detail. The main aim of computer simulation of continuous mathematical models of an aircraft is pilots training. Aircraft simulator plays a key role in the process of pilots training; it enables pilots to control the aircraft and its equipment. Standard computer with a graphic processing unit for the visualization results from continuous mathematical models of an aircraft can be utilized.

In this contribution we describe an efficient GPU implementation of the Monte-Carlo simulation of the Ginzburg-Landau model. We achieve the performance close to 50