Abstract
Machine learning users need methods that can help them identify algorithms or even workflows (combination of algorithms with preprocessing tasks, using or not hyperparameter configurations that are different from the defaults), that achieve the potentially best performance. Our study was oriented towards average ranking (AR), an algorithm selection method that exploits meta-data obtained on prior datasets. We focused on extending the use of a variant of AR* that takes A3R as the relevant metric (combining accuracy and run time). The extension is made at the level of diversity of the portfolio of workflows that is made available to AR. Our aim was to establish whether feature selection and different hyperparameter configurations improve the process of identifying a good solution. To evaluate our proposal we have carried out extensive experiments in a leave-one-out mode. The results show that AR* was able to select workflows that are likely to lead to good results, especially when the portfolio is diverse. We additionally performed a comparison of AR* with Auto-WEKA, running with different time budgets. Our proposed method shows some advantage over Auto-WEKA, particularly when the time budgets are small.

Abstract
Compositional data are considered as data where relative contributions of parts on a whole, conveyed by (log-)ratios between them, are essential for the analysis. In Symbolic Data Analysis (SDA), we are in the framework of interval data when elements are characterized by variables whose values are intervals on representing inherent variability. In this paper, we address the special problem of the analysis of interval compositions, i.e., when the interval data are obtained by the aggregation of compositions. It is assumed that the interval information is represented by the respective midpoints and ranges, and both sources of information are considered as compositions. In this context, we introduce the representation of interval data as three-way data. In the framework of the log-ratio approach from compositional data analysis, it is outlined how interval compositions can be treated in an exploratory context. The goal of the analysis is to represent the compositions by coordinates which are interpretable in terms of the original compositional parts. This is achieved by summarizing all relative information (logratios) about each part into one coordinate from the coordinate system. Based on an example from the European Union Statistics on Income and Living Conditions (EU-SILC), several possibilities for an exploratory data analysis approach for interval compositions are outlined and investigated.

Abstract

Abstract
The number of software-defined controllers available in the market has increased greatly in the last few years. Nowadays, it is possible to find proprietary controllers as well as open source controllers. Examples are companies as HPE and Cisco that offer those two types of controllers to their clients simultaneously. It is also important to note that in both, the open source version is a commercial distribution of OpenDaylight. In the current market it is possible to find controllers for different areas of deployment, with a different programming language and supporting different southbound protocols. Therefore, we considered worth providing a description and a comparison of the main existing controllers.

Abstract
Autonomous Underwater Vehicles are remarkable machines that revolutionized the collection of data at sea. There are many examples of highly operational man-portable vehicles for shallow waters, but there was no similar solution for deep water operations. This paper describes the development of a portable, modular, hovering AUV for deep water operations. The vehicle has little over 50kg, 2.4m of length, and a depth rating of 4000m. The first version of the vehicle has been assembled, it has gone through the initial tests in water tanks, and it is being prepared for the first operations at sea. © 2017 Marine Technology Society.

Abstract
In continental Portugal, the publicly financed supply of gastroenterology exams was limited since the end of the last century, restricted to a fixed set of private providers that was hired by the Portuguese state. This way of contracting created market entry barriers and is inefficient, since prices are administratively set. Besides, it produced access inequalities, because of the way that the supply was geographically distributed. This paper applies the Extended Kernel Density 4-Step Floating Catchment Area (EKD4SFCA) method to identify priority districts for the promotion of new supply by the state, in order to choose the appropriate way of contracting new private supply, as determined by current law, and to reduce access inequalities. The applied method enables the identification of the Portuguese regions with strong competition between health care providers and where patients’ access to publicly financed gastroenterology exams is relatively low. In these regions, the state should promote public bids to stimulate new supply, exploring thereby the potential for setting lower prices and reducing access inequalities.