Abstract

Abstract
Context. Methods used to detect giant exoplanets can be broadly divided into two categories: indirect and direct. Indirect methods are more sensitive to planets with a small orbital period, whereas direct detection is more sensitive to planets orbiting at a large distance from their host star. This dichotomy makes it difficult to combine the two techniques on a single target at once.Aims. Simultaneous measurements made by direct and indirect techniques offer the possibility of determining the mass and luminosity of planets and a method of testing formation models. Here, we aim to show how long-baseline interferometric observations guided by radial-velocity can be used in such a way.Methods. We observed the recently-discovered giant planet beta Pictoris c with GRAVITY, mounted on the Very Large Telescope Interferometer.Results. This study constitutes the first direct confirmation of a planet discovered through radial velocity. We find that the planet has a temperature of T=125050 K and a dynamical mass of M=8.2 +/- 0.8 M-Jup. At 18.5 +/- 2.5 Myr, this puts beta Pic c close to a 'hot start' track, which is usually associated with formation via disk instability. Conversely, the planet orbits at a distance of 2.7 au, which is too close for disk instability to occur. The low apparent magnitude (M-K=14.3 +/- 0.1) favours a core accretion scenario.Conclusions. We suggest that this apparent contradiction is a sign of hot core accretion, for example, due to the mass of the planetary core or the existence of a high-temperature accretion shock during formation.

Abstract
Biometric authentication (i.e., verification of a given subject’s identity using biological characteristics) relying on gait characteristics obtained in a non-intrusive way can be very useful in the area of security, for smart surveillance and access control. In this contribution, we investigated the possibility of carrying out subject identification based on a predictive model built using machine learning techniques, and features extracted from 3-D body joint data provided by a single low-cost RGB-D camera (Microsoft Kinect v2). We obtained a dataset including 400 gait cycles from 20 healthy subjects, and 25 anthropometric measures and gait parameters per gait cycle. Different machine learning algorithms were explored: k-nearest neighbors, decision tree, random forest, support vector machines, multilayer perceptron, and multilayer perceptron ensemble. The algorithm that led to the model with best trade-off between the considered evaluation metrics was the random forest: overall accuracy of 99%, class accuracy of 100±Â0%, and F 1 score of 99±Â2%. These results show the potential of using a RGB-D camera for subject identification based on quantitative gait analysis. © 2020, Springer Nature Switzerland AG.

Abstract
The K-means algorithm is widely used to find correlations between data in different application domains. However, given the massive amount of data stored, known as Big Data, the need for high-speed processing to analyze data has become even more critical, especially for real-time applications. A solution that has been adopted to increase the processing speed is the use of parallel implementations on FPGA, which has proved to be more efficient than sequential systems. Hence, this paper proposes a fully parallel implementation of the K-means algorithm on FPGA to optimize the system & x2019;s processing time, thus enabling real-time applications. This proposal, unlike most implementations proposed in the literature, even parallel ones, do not have sequential steps, a limiting factor of processing speed. Results related to processing time (or throughput) and FPGA area occupancy (or hardware resources) were analyzed for different parameters, reaching performances higher than 53 millions of data points processed per second. Comparisons to the state of the art are also presented, showing speedups of more than over a partially serial implementation.

Abstract
Loss of motivation is one of the most prominent concerns in programming education as it negatively impacts time dedicated to practice, which is crucial for novice programmers. Of the distinct techniques introduced in the literature to engage students, gamification, is likely the most widely explored and fruitful. Game elements that intrinsically motivate students, such as graphical feedback and game-thinking, reveal more reliable long-term positive effects, but those involve significant development effort. This paper proposes a game-based assessment environment for programming challenges, built on top of a specialized framework, in which students develop a program to control the player, henceforth called Software Agent (SA). During the coding phase, students can resort to the graphical feedback demonstrating how the game unfolds to improve their programs and complete the proposed tasks. This environment also promotes competition through competitive evaluation and tournaments among SAs, optionally organized at the end by the teacher. Moreover, the validation of the effectiveness of Asura in increasing undergraduate students' motivation and, consequently, the practice of programming is reported.

Abstract
In the context of the digital society, educational systems should prepare the students to succeed in a really volatile environment. In order to do so they require to acquire some specific competences that use to be related to STEAM Education. However, integrating STEAM is hard and requires of new methodologies and tools. RoboSTEAM is an Erasmus+ project that aims to facilitate this by using Challenge Based Learning and applying Physical Devices and Robotics. In order to know if what RoboSTEAM proposes work properly it must be tested in different contexts with different educational systems. The results of these tests should be compared, which requires of a common knowledge background. In order to achieve it RoboSTEAM proposes students and teachers exchanges between similar and different sociocultural environments, so they can learn how other people work in the project challenges and if what they do can be addressed by them in a similar way. The present work describes these exchanges, how they were planned and carried out and the main results obtained. From the exchanges carried out until now it is possible to say that they facilitate sharing knowledge that later can lead to better results in the project challenges and that they are enriching experiences both for students and for teachers. © 2020, Springer Nature Switzerland AG.