Abstract

Abstract
Label Ranking problems are receiving increasing attention in machine learning. The goal is to predict not just a single value from a finite set of labels, but rather the permutation of that set that applies to a new example (e.g., the ranking of a set of financial analysts in terms of the quality of their recommendations). In this paper, we adapt a multilayer perceptron algorithm for label ranking. We focus on the adaptation of the Back-Propagation (BP) mechanism. Six approaches are proposed to estimate the error signal that is propagated by BP. The methods are discussed and empirically evaluated on a set of benchmark problems. © 2012 Springer-Verlag.

Abstract
Current state-of-The-Art speaker identification systems achieve high performances in reasonably well controlled conditions. However, some scenarios still elicit significant challenges, particularly in audio forensics when voice records are typically just a few seconds long and are severely affected by distortion, interferences, and abnormal speaking attitudes. In this paper we are inspired by the concept of minutiae in the context of fingerprinting, and try to extract localized, phase-related singularities from the speech signal denoting glottal source idiosyncratic information. First, we perform MFCC+GMM experiments in order to find the most effective phonetic segmentation of the speech signal for speaker modelling and discrimination. Secondly, we rely on effective phonetic segmentation and, in addition to MFCC features, we extract Normalized Relative Delays (NRDs) obtained from the phase of spectral harmonics. We use a Nearest Neighbour generalized classifier for speaker modelling and identification. Our results indicate that combining a careful phonetic segmentation and the inclusion of phase-related information, performance in speaker identification may increase significantly. Copyright © 2012 Audio Engineering Society, Inc.

Abstract
Modeling discrete event systems with sequential behavior can be a very hard and complex task. Some formalisms are used in this context, such as: Petri Nets, Statecharts, Finite automata, Grafcet and others. Among these, Grafcet seems to be a good choice because it is easy: to learn, to understand and to use. Teaching Grafcet is then relevant within engineering courses concerned with Industrial Automation. A virtual laboratory, e-GRAFCET, developed and first tested in UTAD University; it is a new, easy-to-use multimedia e-educational tool to support the self-learning process of Grafcet. This paper, reports a study of e-GRAFCET use by the students of University of Minho. A questionnaire was prepared and students asked to fulfill it in a volunteer basis. The results were statistically analyzed and the scores compared. The overall objective is to understand how the tool helps students in their study, and consequently improve their learning off Grafcet, independently of their engineering background.

Abstract
Dynamic partitioning is a promising technique where computations are transparently moved from a General Purpose Processor (GPP) to a coprocessor during application execution. To be effective, the mapping of computations to the coprocessor needs to consider aggressive optimizations. One of the mapping optimizations is loop pipelining, a technique extensively studied and known to allow substantial performance improvements. This paper describes a technique for pipelining Megablocks, a type of runtime loop developed for dynamic partitioning. The technique transforms the body of Megablocks into an acyclic dataflow graph which can be fully pipelined and is based on the atomic execution of loop iterations. For a set of 9 benchmarks without memory operations, we generated pipelined hardware versions of the loops and estimate that the presented loop pipelining technique increases the average speedup of non-pipelined coprocessor accelerated designs from 1.6x to 2.2x. For a larger set of 61 benchmarks which include memory operations, the technique achieves a speedup increase from 2.5x to 5.6x. ©2012 IEEE.

Abstract
This paper applies network analysis to study the evolution of aviation networks. It takes a different approach from previous research that usually only explores airline networks. The aviation network of the airports of Lisbon, Faro and Porto, is modelled using the supply of seats and the passenger demand between 2001 and 2010. This analysis is complemented with a study of the commercial evolution of the three airports. It is noticeable the impact of low-cost carriers in the evolution of the configuration of the network over time. A de-concentration effect is also shown to occur, due to the interaction between airport and airline decisions that favour network development. The results of the analysis highlight how critical it has become for airport managers to assess and satisfy the real requirements of the different types of airlines, in order to reduce uncertainty and increase traffic.