Abstract
This research pretends to understand the profile of the young Portuguese army in levels of Aggressiveness, Perfectionism and Self-concept, according to comparative analyze by gender and military rank. The sample consisted of 183 soldiers, aged between 18 and 35 years, which are 18 female and 165 male. The study variables were evaluated by applying three instruments, respectively, Multidimensional Scale of Perfectionism, Aggression Questionnaire and Self-Concept Clinical Inventory. The main results show that elevated levels of general Perfectionism and general Self-concept, as moderate levels of general Aggressiveness, characterize the overall profile of the young military. The comparative gender analysis showed that there are significant differences in the results regarding levels of aggression only. On the other hand the comparative analysis of military ranks indicates the existence of significant differences in levels of self-concept and aggression. Finally, the correlation analysis shows that aggressiveness correlates negatively with self-concept and positively with perfectionism.

Abstract
This paper develops a decision support tool that can help fishery authorities to forecast bivalve landings for the dredge fleet accounting for several contextual conditions. These include weather conditions, phytotoxins episodes, stock-biomass indicators per species and tourism levels. Vessel characteristics and fishing effort are also taken into account for the estimation of landings. The relationship between these factors and monthly quantities landed per vessel is explored using multiple linear regression models and data mining techniques (random forests, support vector machines and neural networks). The models are specified for different regions in the Portugal mainland (Northwest, Southwest and South) using six years of data 2010-2015). Results showed that the impact of the contextual factors varies between regions and also depends on the vessels target species. The data mining techniques, namely the random forests, proved to be a robust decision support tool in this context, outperforming the predictive performance of the most popular technique used in this context, i.e. linear regression.

Abstract
This paper describes a set of models and algorithms used under a Tourism Recommendation System based in Users and Points-of-Interest (POI) profiles. This work aims to propose a recommendation system that considers user's functionality levels regarding physical and psychological issues. This proposal considers also in a different way to classify (POI) including their accessibility levels mapped with similar physical and psychological issues.

Abstract
Scheduling real-time applications on general purpose multicore platforms is a challenging problem from a timing analysis perspective. Such platforms expose uncontrolled sources of interference whenever concurrent accesses to memory are performed. The non-deterministic bus and memory access behavior complicates the estimations of applications' worst-case execution times (WCET). The 3-phase task model seems a good candidate to circumvent the uncontrolled sources of interference by isolating concurrent memory accesses. A task is divided in three successive phases; first, the task loads its instruction and data in a local memory, then it executes non-preemptively using those pre-loaded instructions and data, and finally, the modified data are pushed back to main memory. Following this execution model, tasks never access the bus during their execution phase. Instead, all the bus accesses are performed during the first and third phases. In this paper, we focus on the global fixed-priority scheduling of the 3-phase task model. A new schedulability test is derived by modelling the interference happening on the bus rather than the interference on the cores as in the state-ot-the-art techniques. The effectiveness of the test is evaluated by comparing it against the state-of-the-art.

Abstract

Abstract
In the area machine learning / data mining many diverse algorithms are available nowadays and hence the selection of the most suitable algorithm may be a challenge. Tbhis is aggravated by the fact that many algorithms require that certain parameters be set. If a wrong algorithm and/or parameter configuration is selected, substandard results may be obtained. The topic of metalearning aims to facilitate this task. Metalearning typically proceeds in two phases. First, a given set of algorithms A (e.g. classification algorithms) and datasets D is identified and different pairs < ai,dj > from these two sets are chosen for testing. The dataset di is described by certain meta-features which together with the performance result of algorithm ai constitute a part of the metadata. In the second phase the metadata is used to construct a model, usually again with recourse to machine learning methods. The model represents a generalization of various base-level experiments. The model can then be applied to the new dataset to recommend the most suitable algorithm or a ranking ordered by relative performance. This article provides more details about this area. Besides, it discusses also how the method can be combined with hyperparameter optimization and extended to sequences of operations (workflows).