Abstract
The requirements of Machine Learning applications are changing rapidly. Machine Learning models need to deal with increasing volumes of data, and need to do so quicker as responses are expected more than ever in real-time. Plus, sources of data are becoming more and more dynamic, with patterns that change more frequently. This calls for new approaches and algorithms, that are able to efficiently deal with these challenges. In this paper we propose the use of a Genetic Algorithm to Optimize a Stacking Ensemble specifically developed for streaming scenarios. A pool of solutions is maintained in which each solution represents a distribution of weights in the ensemble. The Genetic Algorithm continuously optimizes these weights to minimize the cost function. Moreover, new models are added at regular intervals, trained on more recent data. These models eventually replace older and less accurate ones, making the ensemble adapt continuously do changes in the distribution of the data.

Abstract
The heart-rate dynamics are one of the most analyzed physiological interactions. Many mathematical methods were proposed to evaluate heart-rate variability. These methods have been successfully applied in research to expand knowledge concerning the cardiovascular dynamics in healthy as well as in pathological conditions. Notwithstanding, they are still far from clinical practice. In this paper, we aim to review the nonlinear methods most used to assess heart-rate dynamics. We focused on methods based on concepts of chaos, fractality, and complexity: Poincare plot, recurrence plot analysis, fractal dimension (and the correlation dimension), detrended fluctuation analysis, Hurst exponent, Lyapunov exponent entropies (Shannon, conditional, approximate, sample entropy, and multiscale entropy), and symbolic dynamics. We present the description of the methods along with their most notable applications.

Abstract
Tensor decompositions are multi-way analysis tools which have been successfully applied in a wide range of different fields. However, there are still challenges that remain few explored, namely the following: when applying tensor decomposition techniques, what should we expect from the result? How can we evaluate its quality? It is expected that, when the number of components is suitable, then few redundancy is observed in the decomposition result. Based on this assumption, we propose a new method, NORMO, which aims at estimating the number of components in CANDECOMP/PARAFAC (CP) decomposition so that no redundancy is observed in the result. To the best of our knowledge, this work encompasses the first attempt to tackle such problem. According to our experiments, the number of non-redundant components estimated by NORMO is among the most accurate estimates of the true CP number of components in both synthetic and real-world tensor datasets (thus validating the rationale guiding our method). Moreover, NORMO is more efficient than most of its competitors. Additionally, our method can be used to discover multi-levels of granularity in the patterns discovered.

Abstract
Breast cancer image fusion consists of registering and visualizing different sets of a patient synchronized torso and radiological images into a 3D model. Breast spatial interpretation and visualization by the treating physician can be augmented with a patient-specific digital breast model that integrates radiological images. But the absence of a ground truth for a good correlation between surface and radiological information has impaired the development of potential clinical applications. A new image acquisition protocol was designed to acquire breast Magnetic Resonance Imaging (MRI) and 3D surface scan data with surface markers on the patient's breasts and torso. A patient-specific digital breast model integrating the real breast torso and the tumor location was created and validated with a MRI/3D surface scan fusion algorithm in 16 breast cancer patients. This protocol was used to quantify breast shape differences between different modalities, and to measure the target registration error of several variants of the MRI/3D scan fusion algorithm. The fusion of single breasts without the biomechanical model of pose transformation had acceptable registration errors and accurate tumor locations. The performance of the fusion algorithm was not affected by breast volume. Further research and virtual clinical interfaces could lead to fast integration of this fusion technology into clinical practice. (C) 2020 The Authors. Published by Elsevier Ltd.

Abstract

Abstract
After the computational thinking sessions in the previous 2016-2019 editions of TEEM Conference, the fifth edition of this track has been organized in the current 2020 edition. Computational thinking is still a very significant topic, especially, but not only, in pre-university education. In this edition, the robotic has a special role in the track, with a strength relationship with the STEM and STEAM education of children at the pre-university levels, seeding the future of our society. © 2020 ACM.