Nowadays users of the Web are encouraged to generate content on the Web by themselves. Weblogs are the most tools to do this. In fact Weblogs are one kind of social networks and they are one of the most important components in Web 2.0. The increasing growth of blogsphere implies that users need to know more popular blogs. This paper introduces a new algorithm for rankingWeblogs according to a link structure specific to the blogsphere. Our CRE-Rank algorithm tries to identify popular focal blogs. Popular blogs are those blogs which are more linked and commented, Focal blogs are those which users can access popular Weblogs from them.

This paper poses a magnetic field problem in cylindrical coordinate for two regions with different permeabilities for each one. The linear partial differential equation governing this problem is in the form of Helm-Holtz equation. This problem is then solved using classical Gauss-Seidel Algorithm for the Finite difference (FD) solution of linear partial differential equation. In order to obtain adequate solution for a reasonable number of grid points for the regions under consideration a considerable amount of time will take for the program to converge. The paper presents a different technique known as Multi-Grid which will speed up the convergence process. In this method, the solution to the differential equation between two grid points for obtaining the initial condition is considered to be linear at first and then of the second order in nature. The main contribution is made by regarding the effect of the initial values of the variable vector in the convergence time of the Gauss- Seidel algorithm.

Several methodologies with their own characteristics have been proposed in the area of agent-oriented software engineering. Consequently, deciding which methodology to select in a specific case is an important issue and it can lead to decrease software development cost and effort. Thus, importance of evaluation of methodologies will be highlighted in choosing the appropriate methodology in the development process of an application. It can also help in developing new methodologies and improving existing ones. In this paper, we are going to provide an evaluation framework of agent oriented methodologies. To demonstrate the usage of the suggested framework, it is applied to evaluate two methodologies (MESSAGE and Prometheus) using a proper example. Results show that, using our method, methodologies can be truly compared and evaluated.

paper, we have used Tilt intonation theory to represent pitch contour as symbolic elements. Also, classification and regression trees are considered to estimate Tilt parameters in our text-to-speech system. To train regression trees, about 70 minutes of speech are used as corpus and more than 100 input features are extracted from this corpus. Further, some experiments such as incremental adding tilt parameters as input features during training and test models or purposing stress syllables as accent events are examined to achieve optimal regression trees. Moreover, vector quantization method and building a codebook of tilt parameters are investigated to predict pitch contours. Finally 61.1% and 25/386 hertz are obtained for correlation coefficient and RMSE values between predicted and real pitch contours of test set using optimal regression trees.

We present a new method to implement QDI asynchronous pipeline stages. Previous implementations of these pipelines were based on the pre-charge logic implementation which imposes some limitations on the size of the logic cell due to the stacking problem. To overcome this limitation, we propose a novel method that uses standard single-rail computational circuits in a dual rail handshake environment. Our method does not have any stacking problem and is not limited in size. The results show that a 4-input lookup table implemented by this method outperforms a previous 3- input lookup table by 16% in speed and 29% in power with a negligible area overhead.

Effective scheduling is of great importance to parallel programming environments. The aim is to minimize the completion time of task graphs. The completion time of a task graph is directly affected by the length of its critical path. Hence, the trend of an evolutionary approach for task graph scheduling can be biased towards reduction of the critical path. Task graph scheduling is a NP-hard problem. Deterministic approaches are not applicable in this context. Thereof, application of evolutionary processing and especially genetic algorithms are effective for solving scheduling problems. In this paper, a new genetic scheduling algorithm is presented. The algorithm, in the first priority, minimizes the critical path length of the parallel program task graph and in the second priority minimizes the inter-processor communication time. Thereby, it achieves a better scheduling in comparison with the existing approaches such as BCGA, CGL, MCP.

A new method for power estimation in behavioral specification of asynchronous circuits is presented. The estimation is performed by counting the number of read and write accesses on ports of the circuit in behavioral simulation. A linear relationship between the number of reads/writes and the energy consumption of the system has been established. The method is applied to the Reed Solomon error detector as the benchmark. In comparison with the earlier gate level estimation methods based on transition counting we have shown that accurate results within %18 imprecision bound can be achieved.

In this paper a new genetic algorithm is designed to solve a class of maze problems, more efficiently than the state of the arts. Here the solution is a set of paths directing every agent from any position in the maze towards a single goal point. The novel points of the proposed algorithm are the innovative crossover and mutation operators that improved the results. To evaluate the algorithm and compare it with other algorithms, different experiments were done. Here each algorithm for any problem was run five times and the average of the results was considered as the evaluation measure. All experiments showed that the proposed algorithm is the most efficient one.

In this work, Scanner was used for predicting textile fabrics color by relationship between devicedependent color space and the device independent CIE color spaces. The scanner was characterized by polynomial regression. The method adopted in the paper through several test experiments consists of a non-linear filtering applied to the scanner RGB values and a polynomial regression function directly applied to the CIELAB space.

In this work, The scanner is calibrated by neural network techniques. for calibration, the RGB values converts to the device independent CIE color space. The method adopted in the paper through several test experiments consists of several structure and topology for neural network and non-linear filtering applied to the scanner RGB values. we devised. The experimental results are very considerable. The accuracy of this works is as good as other works.

The unit selection speech synthesis method is accounted of concatenative speech synthesis methods that it produces more natural quality speech than other approaches. So developing and improvement of this approach attracts great attraction to produce more natural output speech. Synthesis unit is one of important factors that have great effect on output quality of synthesizer systems. Using phones as synthesis unit can cause more natural output, however the discontinuity at the boundaries of concatenated phonemes can be potentially increased in output speech. In this paper, we proposed using unseen unit generation idea to eliminate discontinuities at phoneme boundaries when phone instances are used as synthesis units. In proposed method, existence discontinuities at phoneme boundaries in output speech are firstly found and then if it possible, one of adjacent phonemes of these boundaries are replaced by a suitable unseen unit. To synthesizing every unseen unit, left half phone of one existence instance of corpus are merged by right half phone of other existence instance. Perceptual Evaluations show quality output improvement due to decreasing discontinuities at boundaries.

A wireless network consisting of a large number of small sensors with low-power transceivers can be an effective tool for gathering data in a variety of environments. The data collected by each sensor is communicated through the network to a single processing center that uses all reported data to determine characteristics of the environment or detect an event. The communication or message passing process must be designed to conserve the limited energy resources of the sensors. This paper derives an algorithm for minimizing energy consumption by a hierarchical wireless sensor network in transmitting data from source to sink. We assume a single-hop multi-level clustered network. The stochastic geometry is used to analytically determine the optimum number of clusters at each level, to show that a multi-level approach can significantly reduce the required network energy.

In recent years, optimization problems are considered as complex problems which require accurate and fast search methods. Traditional search methods such as iterative search and evolutionary algorithms are not efficient since they are not complete and their convergence rate is slow. A lot of efforts have been carried out to improve the performance of genetic algorithms as a special class of evolutionary algorithms. The most considerable ones are related to using the idea of cellular automata due to its nature of local operation. However, a genetic cellular automaton considers the relationship between chromosomes, but sometimes is not efficient enough due to the early convergence problem. Also, the tradeoff between fast convergence and optimum exploration is unavoidable. In this paper, we propose a new genetic-based search method using cellular automata. In this method, in contrast to the traditional genetic cellular automata, the transition rule of cellular automata is utilized to promote the individuals before genetic operations have been accomplished globally. The experimental results have shown better convergence rate and also exploration accuracy compared to the traditional search methods.

This paper presents a survey as well as a new sweep-circle algorithm, on plane sweep algorithms for computing the Delaunay triangulation. The algorithms examined are: Fortune’s sweep-line algorithm, Zalik’s sweep-line algorithm, and a sweep-circle algorithm proposed by Adam, Kauffmann, Schmitt, and Spehner. We test implementations of these algorithms on a number of uniform and none-uniform distributed sites. We also analyze the major high-level primitives that algorithms use and do an experimental analysis of how often implementations of these algorithms perform each operation.

In this paper a conceptual design for development of an e-research environment is proposed. In this environment services are proposed by several components such as virtual library ,data mining tool , frame based causal model , Hyper value & reference and report generator and a computer aided model analysis component. Prototype of this environment has been used to implement two different applied researches.

In this article an intelligent ETO(Electronic Trade Opportunity) help desk is conceptually designed to be implemented as an e-service on IRAN Trade Point. Users including exporters and importers may access to information services , distributed ETO , latest export and import workflow and updated regulations .These services will be accessible for stakeholders and partners of trade system over Internet. Feasibility study of implementation alternatives also has been carried out by a score model.

In a two player game, the choices of our player can be modeled by angelic nondeterminism and those of our opponent by demonic nondeterminism. In this paper, we introduce some notations and semantics to Martin- Löf's theory of types which facilitate the use of angelic and demonic interpretations of nondeterminism in type theoretical specifications. Using the proposed, nondeterministic constructs, we can formally specify two player games and derive them from correctness proofs of their formal specifications.

Language identification from text has received less attention than identification from other forms of input. This is due to the fact that it is considered an easy problem. Several techniques exist and it is possible to gain perfect accuracy in identifying the language of the text in some methods. Nevertheless, there has been very few works on the accuracy and performance of different techniques with limited input, i.e., accurate detection of the language with less input length and identification of the language of a short sentence or a single word. In this paper, we present a method based on Hidden Markov Models (HMMs) for language identification from text. We use the power of HMMs for detecting language of character strings and show the benefits of using this model over a simple model. We will show how an extremely simple realization of this model outperforms simple models in accurately identifying languages of short input strings.

This paper provides technical solutions for two problems selected from Soccer Simulation 3D domain. The agent movement equation problem and the localization problem are selected from this domain and investigated using several machine learning methods, including neural networks, evolutionary learning and statistical learning. Results show the remarkable advantage of reinforcing ordinary multi-layer perceptron neural networks with evolutionary algorithms. Also they confirm the superior performance of support vector machines for regression tasks when the underlying system is neither dynamic nor chaotic.

The single traffic signal control agent improves its control ability with the NNQ-learning method. This paper proposes a Neural_Network_Q_learning (NNQL) approach with fuzzy reward designed for online learning of traffic lights behaviors .The Q-function table becomes too large for the required state/action resolution. In these cases, tabular Q-learning needs a very long learning time and memory requirements which makes the implementation of the algorithm in real-time control architecture impractical. To solve the problem of coordination between three TSCAs (Traffic Signal Control Agents) we used game theory. To test the efficiency of the coordination mechanism, a prototype traffic simulator was programmed in visual C++. Results using cooperative traffic agents are compared to results of control simulations where noncooperative agents were deployed. The result indicates that the new coordination method proposed in this paper is effective.