Abstract
The Traveling Salesman Problem (TSP) is one of the most studied optimization problems. Various meta heuristics (MHs) have been proposed and investigated on many instances of this problem. It is widely accepted that the best MH varies for different instances. Ideally, one should be able to recommend the best MHs for a new TSP instance without having to execute them. However, this is a very difficult task. We address this task by using a meta-learning approach based on label ranking algorithms. These algorithms build a mapping that relates the characteristics of those instances (i.e., the meta-features) with the relative performance (i.e., the ranking) of MHs, based on (meta-)data extracted from TSP instances that have been already solved by those MHs. The success of this approach depends on the quality of the meta-features that describe the instances. In this work, we investigate four different sets of meta-features based on different measurements of the properties of TSP instances: edge and vertex measures, complex network measures, properties from the MHs, and subsampling landmarkers properties. The models are investigated in four different TSP scenarios presenting symmetry and connection strength variations. The experimental results indicate that meta-learning models can accurately predict rankings of MHs for different TSP scenarios. Good solutions for the investigated TSP instances can be obtained from the prediction of rankings of MHs, regardless of the learning algorithm used at the meta level. The experimental results also show that the definition of the set of meta-features has an important impact on the quality of the solutions obtained.

Abstract
Entrepreneurship has become a dynamic field of research in the last two decades. However, rural entrepreneurship' has been largely overlooked. It seems therefore timely to present a quantitative survey of the literature in this particular area. Based on 181 articles on rural entrepreneurship published in journals indexed in Scopus, we found that rural entrepreneurship is an essentially European concern, whose most prolific authors are affiliated with institutions in the UK and Spain. Organisational characteristics, policy measures and institutional frameworks and governance have attracted considerable attention in recent years, being considered emergent topics of research. In contrast, theory building has not attracted much research over the period in analysis, which suggests that the theoretical body of rural entrepreneurship is still incipient, hindering the establishment of its boundaries and of a suitable research agenda. Empirical literature on rural entrepreneurship has focused mainly on developed countries, most notably, the UK, the USA, Spain, Finland and Greece. Given the potential rural entrepreneurship represents for less developed and underdeveloped countries, more research on the topic targeting these countries is an imperative.

Abstract
Apresentamos o resultado de uma exploração prática da tecnologia de mundos virtuais imersivos multiutilizador High Fidelity, baseada em tecnologia Web. Esta tecnologia permite criar mundos virtuais cujas formas de interligação, controlo e interação tiram partido de uma Interface de Programação de Aplicações em JavaScript. Através do caso prático de desenvolvimento de um protótipo de jogo educativo simples, descrevemos a tecnologia High Fidelity, incluindo o tipo de scripts, a arquitetura inerente e as suas características de desenvolvimento e utilização. Destacamos as dificuldades inerentes ao estado atual da plataforma, em constante reformulação e algumas peculiaridades.

Abstract

Abstract
Interest in robotics field as a teaching tool to promote the STEM areas - Science, Technology, Engineering and Mathematics has grown in the past years. The search for costless solutions to promote robotics is a major challenge and the use of real robots always increases associated costs. An alternative to this is the use of a simulator. The construction of a simulator related with the Portuguese Autonomous Driving Competition using Gazebo as 3D simulator and Robotics Operating System (ROS) as a middleware connection to promote, attract, and enthusiasm university students to the mobile robotics challenges is presented. The proposed simulator focus on the autonomous driving competition task, such as semaphore recognition, localization, and motion control. An evaluation of the simulator is also performed, leading to an absolute error of 5.11% and a relative error of 2.76% on best case scenarios relating to the odometry tests and an accuracy of 99.37% regarding to the semaphore recognition tests performed.

Abstract
In different areas of knowledge, phenomena are represented by directional-angular or periodic-data; from wind direction and geographical coordinates to time references like days of the week or months of the calendar. These values are usually represented in a linear scale, and restricted to a given range (e.g. [0,2 pi)), hiding the real nature of this information. Therefore, dealing with directional data requires special methods. So far, the design of classifiers for periodic variables adopts a generative approach based on the usage of the von Mises distribution or variants. Since for non-periodic variables state of the art approaches are based on non-generative methods, it is pertinent to investigate the suitability of other approaches for periodic variables. We propose a discriminative Directional Logistic Regression model able to deal with angular data, which does not make any assumption on the data distribution. Also, we study the expressiveness of this model for any number of features. Finally, we validate our model against the previously proposed directional naive Bayes approach and against a Support Vector Machine with a directional Radial Basis Function kernel with synthetic and real data obtaining competitive results.