Abstract
This paper presents a novel dataset, composed of images of objects in two distinct environments and both controlled and uncontrolled capture conditions, aimed at serving as a benchmark for domain-adaptation image classification algorithms in an air versus underwater context. All images are fully annotated, extending the use of the dataset for detection as well as segmentation tasks. An exemplifying use-case is tested, where the performance of a Support Vector Machine applied to a Bag-of-Visual-Words and SIFT features is evaluated on both domains, with different training methodologies. Results demonstrate that the conventional classifier used has a lack of generalization ability, with a poor transfer of knowledge from the aerial to the aquatic domain.

Abstract
The territories promotion is an ongoing challenge for multiple knowledge disciplines. Tourism-related products have been the most suitable ambassador of territories and still have an untapped potential. In scenarios where destinations have territory museums - closed but also open air museums - exists an enormous opportunity to promote the territory through them. But, for accomplish this, ICT have a natural collaborative role, supporting the information and services needs of tourists/visitants either to make their experiences more immersive or/and to allow the visiting to know more about the surrounding environment - the territory. This paper, presenting a model, start to discusses the potential of territory museums as destinations promoters and the role of ICT to provide suitable models that allow in-situ immersive experiences and a full integration with the territories information and services. This approach, merging the physical and the virtual, allow the territories promotion and an integrated approach capable to meet the requirements of visitants that are growing entities for knowledge about he territories that they visit and that they desire to explore.

Abstract
One of the current trends in the luxury market is the development of offers targeted to the millennial generation. In fact, this generation is seen as a potentiator of market growth, considering that the future of the luxury sector will depend on the capacity to reach this generation. This topic is particularly relevant for the marketing area, namely for the tourism sector. Another of the major trends for the tourism sector is the sustainability issue, with an emphasis on ecotourism. In this work we discuss the main contributions of the literature that allow to interlink the domains of luxury marketing, millennial generation, and ecotourism, proposing a set of hypotheses for future research.

Abstract

Abstract
Since the entry into service of the so-called Primary Network in 1951 until the 1960s, when the first electrical power interconnection between Portugal and Spain was created, that the Portuguese National Transmission Network could be considered a closed system, however, since the year 1961, that was no longer true. Taking these facts into account, the need to think and draw up new standards and regulations, with the widest possible coverage, has arisen in order to oblige European operators to maintain a level of control, security and knowledge of their transmission networks, which ensure that these do not influence each other negatively and that there is a coordinated response to incidents that may occur. © 2019 IEEE.

Abstract
The first in-depth study on the use of electrocardiogram and electrooculogram for subject-dependent classification in driver sleepiness/fatigue under realistic driving conditions is presented in this work. Since acquisitions in simulated environments may be misleading for sleepiness assessment, performing studies on road are required. For that purpose, the authors present a database resulting from a field driving study performed in the SleepEye project. Based on previous research, supervised machine learning methods are implemented and applied to 16 heart- and 25 eye-based extracted features, mostly related to heart rate variability and blink events, respectively, in order to study the influence of subject dependency in sleepiness classification, using different classifiers and dealing with imbalanced class distributions. Results showed a significantly worse performance in subject-independent classification: a decrease of similar to 40 and 20% in the detection rate of the 'sleepy' class for two and three classes, respectively. Since physiological signals are the ones that present the most individual characteristics, a subject-independent classification can be even harder to perform. Transfer learning techniques and methods for imbalanced distributions are promising approaches and need further investigation.