Abstract
The main problem behind Automatic Transcription (Multiple Fundamental Frequency - F0 - Estimation) relies on its complexity. Harmonic collision and partial overlapping create a frequency lattice that is almost impossible to de-construct. Although traditional approaches to this problem of rely mainly in Digital Signal Processing (DSP) techniques, evolutionary algorithms have been applied recently to this problem and achieved competitive results. We describe all evolutionary approaches to the problem of automatic music transcription and how some were improved so they could achieve competitive results. Finally, we show how the best evolutionary approach performs on piano transcription, when compared with the state-of-the-art.

Abstract
A review of refractive index measurement based on different types of optical fiber sensor configurations and techniques is presented. It addresses the main developments in the area, with particular focus on results obtained at INESC Porto, Portugal. The optical fiber sensing structures studied include those based on Bragg and long period gratings, on micro-interferometers, on plasmonic effects in fibers and on multimode interference in a large spectrum of standard and microstructured optical fibers.

Abstract
This paper presents a new model for optimal trading of wind power in day-ahead (DA) electricity markets under uncertainty in wind power and prices. The model considers settlement mechanisms in markets with locational marginal prices (LMPs), where wind power is not necessarily penalized from deviations between DA schedule and real-time (RT) dispatch. We use kernel density estimation to produce a probabilistic wind power forecast, whereas uncertainties in DA and RT prices are assumed to be Gaussian. Utility theory and conditional value at risk (CVAR) are used to represent the risk preferences of the wind power producers. The model is tested on real-world data from a large-scale wind farm in the United States. Optimal DA bids are derived under different assumptions for risk preferences and deviation penalty schemes. The results show that in the absence of a deviation penalty, the optimal bidding strategy is largely driven by price expectations. A deviation penalty brings the bid closer to the expected wind power forecast. Furthermore, the results illustrate that the proposed model can effectively control the trade-off between risk and return for wind power producers operating in volatile electricity markets.

Abstract
This paper describes a performance assessment of Portuguese secondary schools using data envelopment analysis (DEA). The assessment adopts a perspective where schools are viewed as promoting students achievement given their characteristics in terms of academic abilities and socio-economic background. Our sample comprised all secondary schools in Portugal with both basic and secondary education levels. Two types of DEA analysis are performed: one using an output-oriented model that restricts output (exam scores) weights to be linked to the number of students that have done that exam in the school, and the other using a model that restricts factor weights to be equal for all schools. In this model the weight restrictions are linked to the total number of exams done nationally. The first model is well suited for identifying worst performing schools and to assess schools that may specialize in certain subjects, whereas the latter is best suited for improving discrimination between best performing schools when pursuing the identification of benchmarks, as well as to construct performance rankings. Journal of the Operational Research Society (2012) 63, 1098-1115. doi: 10.1057/jors.2011.114 Published online 16 November 2011

Abstract
Intelligence is increasingly emerging in our ambients. Evidences of this emergence are the existence of smart homes, smart vehicles, intelligent manufacturing systems and most importantly, the appearance of the concept of intelligent cities. Humans are presently surrounded by technology that is intended to increase their quality of life and simplify their daily activities. Multi-Agent Systems are an example of technology that can be used in these activities. The concept of ubiquitous computing is implicit in these technologies and can generate an invisible ambient of interactivity. This paper presents a survey and a comparative analysis of some of the research projects concerning Ambient Intelligence (AmI). The main objective of this work was to understand the current necessities, devices and the main results in the development of these projects. By analysing these projects using several evaluation criteria one of the main conclusions is that most projects do not explore the potential of human profiles in the context of ambient adaptation. Thus, this may be a very intersting research area for future work.

Abstract
Different meta-heuristics (MHs) may find the best solutions for different traveling salesman problem (TSP) instances. The a priori selection of the best MH for a given instance is a difficult task. We address this task by using a meta-learning based approach, which ranks different MHs according to their expected performance. Our approach uses Multilayer Perceptrons (MLPs) for label ranking. It is tested on two different TSP scenarios, namely: re-visiting customers and visiting prospects. The experimental results show that: 1) MLPs can accurately predict MH rankings for TSP, 2) better TSP solutions can be obtained from a label ranking compared to multilabel classification approach, and 3) it is important to consider different TSP application scenarios when using meta-learning for MH selection.