Abstract
Purpose - This paper aims to investigate if the inclusion of Bitcoin among the assets available to retail investors in the Brazilian market has an impact on the efficient frontier and, therefore, on the optimal choices of investors. Design/methodology/approach - This study calculates the efficient frontier with and without the inclusion of Bitcoin and estimates optimal portfolios for different criteria and time intervals. The sample period runs from 07/01/2013 to 06/30/2018 and the daily closing values of the selected assets/indices were used. Findings - This study finds evidence that the inclusion of Bitcoin among the investment alternatives would cause a statistically significant positive displacement and an expansion of the efficient frontier of the Brazilian retail market. This would result in a significant increase in the return on the tangency portfolio. In addition to improving the indicators of optimization of the risk-return binomial, the cryptoasset would be included in many optimal portfolios in the 2013Q3-2018Q2 period. Originality/value - The results obtained show that, as reported for more developed markets, Bitcoin has caused an expansion of the efficient frontier of the Brazilian retail market.

Abstract
The ever-growing capabilities of computers have enabled pursuing Computer Vision through Machine Learning (i.e., MLCV). ML tools require large amounts of information to learn from (ML datasets). These are costly to produce but have received reduced attention regarding standardization. This prevents the cooperative production and exploitation of these resources, impedes countless synergies, and hinders ML research. No global view exists of the MLCV dataset tissue. Acquiring it is fundamental to enable standardization. We provide an extensive survey of the evolution and current state of MLCV datasets (1994 to 2019) for a set of specific CV areas as well as a quantitative and qualitative analysis of the results. Data were gathered from online scientific databases (e.g., Google Scholar, CiteSeerX). We reveal the heterogeneous plethora that comprises the MLCV dataset tissue; their continuous growth in volume and complexity; the specificities of the evolution of their media and metadata components regarding a range of aspects; and that MLCV progress requires the construction of a global standardized (structuring, manipulating, and sharing) MLCV "library". Accordingly, we formulate a novel interpretation of this dataset collective as a global tissue of synthetic cognitive visual memories and define the immediately necessary steps to advance its standardization and integration.

Abstract
Convergence in productivity examines if entities in an industry get closer to the best practices or if the gap between the frontiers of the best and worst performers decreases over time. In a multi-input multioutput setting, the assessment of sigma- and beta-convergence can be measured with the use of non-parametric frontier techniques, such as data envelopment analysis. We propose an innovative approach to estimate convergence in the context of performance assessments resting on composite indicators, accounting for desirable and undesirable indicators. This methodology rests on 'Benefit-of-the-Doubt' models, specified with a directional distance function. It is applied to the Member States of the World Health Organization (WHO) in order to study their convergence in terms of the United Nations' Sustainable Development Goal (SDG) 'Good health and well-being'. We collected data for all years since the proposal of the SDGs, covering the period between 2016 and 2020. The results show that all WHO regions are (beta) over cap -divergent, especially because of the generalised decline of the Worst Practice Frontier (WPF), alongside an improvement at a lower rate of the Best Practice Frontier (BPF). The regional analysis also revealed (sigma) over cap -convergence in the Region of the Americas and the Eastern Mediterranean Region; the South-East Asia and African Regions exhibited (sigma) over cap -divergence; the Western Pacific and European Regions remained stable in terms of the performance spread regarding the BPF. At the worldwide level, we also observed an increase of the gap between the BPF and the WPF, although the performance spread around the worldwide BPF remained relatively stable.

Abstract
Nowadays, educational robotics is part of the learning activities in many K-12 schools. With the increasing interest in Computer Thinking education and acknowledging the importance of using tangible devices, many different educational robots for primary education have become available. With them, new research activities bring about new results concerning the use of robots in classes and how they can improve learning in STEAM areas. In this paper, a prototype of a new robot for primary school is presented. It has similar features to many other robots used in early school years (e.g. easy robot’s interface and one or two sensors, motor actuators), but with the advantage of having a low cost, being a do-it-yourself (DIY) kit and including a participation strategy, clarifying some of the learning targets, addressing the concept of alignment in learning activities.

Abstract
About 160 years ago, the concept of entropy was introduced in thermodynamics by Rudolf Clausius. Since then, it has been continually extended, interpreted, and applied by researchers in many scientific fields, such as general physics, information theory, chaos theory, data mining, and mathematical linguistics. This paper presents The Entropy Universe, which aims to review the many variants of entropies applied to time-series. The purpose is to answer research questions such as: How did each entropy emerge? What is the mathematical definition of each variant of entropy? How are entropies related to each other? What are the most applied scientific fields for each entropy? We describe in-depth the relationship between the most applied entropies in time-series for different scientific fields, establishing bases for researchers to properly choose the variant of entropy most suitable for their data. The number of citations over the past sixteen years of each paper proposing a new entropy was also accessed. The Shannon/differential, the Tsallis, the sample, the permutation, and the approximate entropies were the most cited ones. Based on the ten research areas with the most significant number of records obtained in the Web of Science and Scopus, the areas in which the entropies are more applied are computer science, physics, mathematics, and engineering. The universe of entropies is growing each day, either due to the introducing new variants either due to novel applications. Knowing each entropy's strengths and of limitations is essential to ensure the proper improvement of this research field.

Abstract
The ability to operate virtually anywhere and carry payload makes Unmanned Aerial Vehicles (UAVs) perfect platforms to carry communications nodes, including Wi-Fi Access Points (APs) and cellular Base Stations (BSs). This is paving the way to the deployment of flying networks that enable communications to ground users on demand. Still, flying networks impose significant challenges in order to meet the Quality of Experience expectations. State of the art works addressed these challenges, but have been focused on routing and the placement of the UAVs as APs and BSs serving the ground users, overlooking the backhaul network design. The main contribution of this paper is a centralized traffic-aware Gateway UAV Placement (GWP) algorithm for flying networks with controlled topology. GWP takes advantage of the knowledge of the offered traffic and the future topologies of the flying network to enable backhaul communications paths with high enough capacity. The performance achieved using the GWP algorithm is evaluated using ns-3 simulations. The obtained results demonstrate significant gains regarding aggregate throughput and delay.