Abstract
Through the years, several CAD systems have been developed to help radiologists in the hard task of detecting signs of cancer in the numerous screening mammograms. A more recent trend includes the development of pre-CAD systems aiming at identifying normal mammograms instead of detecting suspicious ones. Normal breasts are screened-out from the process, leaving radiologists more time to focus on more difficult cases. In this work, a new approach for the identification of normal breasts is presented. Considering that even breasts with malignant findings are mostly constituted by normal tissue, the breast area is divided into blocks which are then compared pairwise. If all blocks are very similar, the breast is labelled as normal, and as suspicious otherwise. Features characterizing the pairwise block similarity and characterizing the intra-block pixel distribution are used to design a predictive method based on machine learning techniques. The proposed solution was applied on a real world screening setting composed by nearly 18000 mammograms. Results are similar to the more complex state of the art approaches by correctly identifying more than 20% of the normal mammograms. These results suggest the usefulness of the relative comparison instead of the absolute classification. When properly used, simple statistics can suffice to distinguish the clearly normal breasts.

Abstract
This paper presents an analytical modelthat evaluates the performance of the Maximum Packing channel allocation technique on linear andplanar cellular systems. The main innovations introduced by this model are the deterministic identification of the system space-state SMP and its application to a multi-dimensional Markov chain. The model was thereafter applied to severalcellular systems with different characteristics: number of cells, number of channels, interferenceconstraints and offered traffic values. Simulation results have validated the model and shown that Maximum Packing technique provides the best performance among all the available algorithms. © Springer Science+Business Media Dordrecht 2014.

Abstract
In previous work we proposed a performance analysis model for automatically identifying potential root causes of performance problems in personal software development. In this paper we present an approach for automatically ranking those potential root causes based on a cost-benefit estimate that takes into account historical data. The approach was applied for the Personal Software Process, taking advantage of a large data set referring to more than 30,000 projects, but can be replicated in other contexts.

Abstract
Over time, biological societies such ashumans, ants or bees have shown us the advantages inherent to the collective work. It is based on such results that many researchers have been trying to successfully develop new approaches in Multi-Robot Systems. Nevertheless, several assumptions need to be assured for collective work toemerge. In this paper, it is presented the significance and the advantages of cooperation in thedifferent societies bridging the gap to the concept of robot society. In order to compare th advantages of cooperative robots, it is considered essential the development of computational simulation based on the robotic cooperation in unstructured environments. Hence, a Multi-Robot Simultaneous Localization and Mapping (SLAM) using Rao-Blackwellized particle filter is implemented ina simulation environment developed in the Player/Stage platform for robot and sensor applications. © Springer Science+Business Media Dordrecht 2014.

Abstract

Abstract
In this paper we introduce a new conceptualization for the reduction of the number of support vectors (SVs) for an efficient design of support vector machines. The techniques here presented provide a good balance between SVs reduction and generalization capability. Our proposal explores concepts from classification with reject option. These methods output a third class (the rejected instances) for a binary problem when a prediction cannot be given with sufficient confidence. Rejected instances along with misclassified ones are discarded from the original data to give rise to a classification problem that can be linearly solved. Our experimental study on two benchmark datasets show significant gains in terms of SVs reduction with competitive performances.