Earticle

This paper presents RE4Gaia, which is a requirements modeling approach for the development of multi-agent systems that extends the Gaia methodology. The approach focuses on dealing with the organizational structure as a means to adequately capture and understand required roles and associated functions in the context of an organization prior to the analysis and design of MAS using Gaia. In addition, a traceability framework is introduced to facilitate moving from the requirements models to the analysis and design models proposed in Gaia.

Radial basis function networks are known to have good performance compared to other artificial neural networks like multilayer perceptrons. Because the size of target data sets in data mining is very large and artificial neural networks including radial basis function networks require intensive computing, sampling is needed. So, because the sample size should be relatively small due to computational load to train radial basis function networks, simple random sampling for small size might not generate perfect and balanced samples. This paper suggests a better sampling technique based on branching information of decision tree for radial basis function networks when target data set is very large like census data. Experiments with census income data in UCI machine learning repository shows a promising result.

Among the various traditional approaches of pattern recognition the statistical approach has been most intensively studied and used in practice. More recently, the addition of artificial neural network techniques theory have been receiving significant attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system using ANN and identify research topics and applications which are at the forefront of this exciting and challenging field.

In this paper, we discuss an isomorphism between elliptic curves defined over binary fields (curves defined over F2n). We introduce a simple public-key encryption scheme for binary elliptic curves. Here we demonstrate that this encryption scheme is as secure as the EC El Gamal cryptosystem. The basis of the encryption scheme is this isomorphism between binary elliptic curves. We use this same isomorphism, as an implementation tool (to reduce the compu-tational complexity) and later we discuss a broadcast encryption scheme.