Multi-Agent Architecture for the Design of WSN Applications

Complex and distributed systems are more and more associated with the application of WSN (Wireless Sensor Network) technology. The design of such applications presents important challenges and requires the assistance of several methodologies and tools. Multi-Agent systems (MAS) have been identified as one of the most suitable technologies to contribute to this domain due to their appropriateness for modeling distributed and autonomous complex systems. This work aims to contribute in the help of the design of WSN applications. The proposed architecture exploits the advantages of MAS for modeling WSN services, network topologies and sensor device architectures.

A Novel Active Learning Method Using SVM for Text Classification

Support vector machines（SVMs） are a popular class of supervised learning algorithms, and are particularly applicable to large and high-dimensional classification problems. Like most machine learning methods for data classification and information retrieval, they require manually labeled data samples in the training stage. However, manual labeling is a time consuming and errorprone task. One possible solution to this issue is to exploit the large number of unlabeled samples that are easily accessible via the internet. This paper presents a novel active learning method for text categorization. The main objective of active learning is to reduce the labeling effort, without compromising the accuracy of classification, by intelligently selecting which samples should be labeled.The proposed method selects a batch of informative samples using the posterior probabilities provided by a set of multi-class SVM classifiers, and these samples are then manually labeled by an expert. Experimental results indicate that the proposed active learning method significantly reduces the labeling effort, while simultaneously enhancing the classification accuracy.