Abstract
Robotic vehicles are already deployed around the globe as a preferred tool for ocean sampling, from surface coastal waters down to the deepest remote locations. They are mainly used to perform routine measurement tasks, with obvious benefits in terms of space and time density, as well as accuracy in the localization of measurements. As the underlying technology matures, new features are being introduced and validated in operational scenarios, enabling new paradigms in ocean observation. This chapter describes the latest developments in marine robotics, presented in different stages of maturity, and sheds light on upcoming features available to the scientific community.

Abstract
In the increasingly competitive fashion retail industry, companies are constantly adopting strategies focused on adjusting the products characteristics to closely satisfy customers' requirements and preferences. Although the lifecycles of fashion products are very short, the definition of inventory and purchasing strategies can be supported by the large amounts of historical data which are collected and stored in companies' databases. This study explores the use of a deep learning approach to forecast sales in fashion industry, predicting the sales of new individual products in future seasons. This study aims to support a fashion retail company in its purchasing operations and consequently the dataset under analysis is a real dataset provided by this company. The models were developed considering a wide and diverse set of variables, namely products' physical characteristics and the opinion of domain experts. Furthermore, this study compares the sales predictions obtained with the deep learning approach with those obtained with a set of shallow techniques, i.e. Decision Trees, Random Forest, Support Vector Regression, Artificial Neural Networks and Linear Regression. The model employing deep learning was found to have good performance to predict sales in fashion retail market, however for part of the evaluation metrics considered, it does not perform significantly better than some of the shallow techniques, namely Random Forest.

Abstract
The use of cutting-edge technologies such as wearable devices to control reactive audiovisual systems are rarely applied in more conventional stage performances, such as opera performances. This work reports a cross-disciplinary approach for the research and development of the WMT Sensor-Glove, a data-glove used in an opera performance to control audiovisual elements on stage through gestural movements. A system architecture of the interaction between the wireless wearable device and the different audiovisual systems is presented, taking advantage of the Open Sound Control (OSC) protocol. The developed wearable system was used as audiovisual controller in "As sete mulheres de Jeremias Epicentro", a portuguese opera by Quarteto Contratempus, which was premiered in September 2017.

Abstract
Magnitude-oriented approaches dominate the voice analysis front-ends of most current technologies addressing e.g. speaker identification, speech coding/compression, voice reconstruction and re-synthesis. A popular technique is all-pole vocal tract modeling. The phase response of all-pole models is known to be non-linear and highly dependent on the magnitude frequency response. In this paper, we use a shift-invariant phase-related feature that is estimated from signal harmonics in order to study the impact of all-pole models on the phase structure of voiced sounds. We relate that impact to the phase structure that is found in natural voiced sounds to conclude on the physiological validity of the group delay of all-pole vocal tract modeling. Our findings emphasize that harmonic phase models are idiosyncratic, and this is important in speaker identification, and in fostering the quality and naturalness of synthetic and reconstructed speech. © 2018 KASHYAP.

Abstract
This article presents original Probabilistic Price Forecasting Models, for day-ahead hourly price forecasts in electricity markets, based on a Nadaraya-Watson Kernel Density Estimator approach. A Gaussian Kernel Density Estimator function is used for each input variable, which allows to calculate the parameters of the probability density function (PDF) of a Beta distribution for the hourly price variable. Thus, valuable information is obtained from PDFs such as point forecasts, variance values, quantiles, probabilities of prices, and time series representations of forecast uncertainty. A Reliability Indicator is also introduced to give a measure of "reliability" of forecasts. The Probabilistic Price Forecasting Models were satisfactorily applied to the real-world case study of the Iberian Electricity Market. Input variables of these models include recent prices, power demands and power generations in the previous day, power demands in the previous week, forecasts of demand, wind power generation and weather for the day-ahead, and chronological data. The best model, corresponding to the best combination of input variables that achieves the lowest MAE, obtains one of the highest Reliability Indicator values. A systematic analysis of MAE values of the Probabilistic Price Forecasting Models for different combinations of input variables showed that as more types of input variables were considered in these models, MAE values improved and Reliability Indicator values usually increased.

Abstract