Abstract
During a pandemic, public discussion and decision-making may be required in face of limited evidence. Data-grounded analysis can support decision-makers in such contexts, contributing to inform public policies. We present an empirical analysis method based on regression modelling and hypotheses testing to assess events for the possibility of occurrence of superspreading contagion with geographically heterogeneous impacts. We demonstrate the method by evaluating the case of the May 1st, 2020 Demonstration in Lisbon, Portugal, on regional growth patterns of COVID-19 cases. The methodology enabled concluding that the counties associated with the change in the growth pattern were those where likely means of travel to the demonstration were chartered buses or private cars, rather than subway or trains. Consequently, superspreading was likely due to travelling to/from the event, not from participating in it. The method is straightforward, prescribing systematic steps. Its application to events subject to media controversy enables extracting well founded conclusions, contributing to informed public discussion and decision-making, within a short time frame of the event occurring.

Abstract

Abstract
The Reinforcement Learning (RL) paradigm is showing promising results as a generic purpose framework for solving decision-making problems (e.g., robotics, games, finance). The aim of this work is to reduce the learning barriers and inspire young students, researchers and educators to use RL as an obvious tool to solve robotics problems. This paper provides an intelligible step-by-step RL problem formulation and the availability of an easy-to-use interactive simulator for students at various levels (e.g., undergraduate, bachelor, master, doctorate), researchers and educators. The interactive tool facilitates the familiarization with the key concepts of RL, its problem formulation and implementation. In this work, RL is used for solving a robotics 2D navigational problem where the robot needs to avoid collisions with obstacles while aiming to reach a goal point. A navigational problem is simple and convenient for educational purposes, since the outcome is unambiguous (e.g., the goal is reached or not, a collision happened or not). Due to a lack of open-source graphical interactive simulators concerning the field of RL, this paper combines theoretical exposition with an accessible practical tool to facilitate the apprehension. The results demonstrated are produced by a Python script that is released as open-source to reduce the learning barriers in such innovative research topic in robotics.

Abstract
As the digital world grows, data is being collected at high speed on a continuous and real-time scale. Hence, the imposed imbalanced and evolving scenario that introduces learning from streaming data remains a challenge. As the research field is still open to consistent strategies that assess continuous and evolving data properties, this paper proposes an unsupervised, online, and incremental anomaly detection ensemble of influence trees that implement adaptive mechanisms to deal with inactive or saturated leaves. This proposal features the fourth standardized moment, also known as kurtosis, as the splitting criteria and the isolation score, Shannon's information content, and the influence function of an instance as the anomaly score. In addition to improving interpretability, this proposal is also evaluated on publicly available datasets, providing a detailed discussion of the results.

Abstract
Machine learning models are widely used in time series forecasting. One way to reduce its computational cost and increase its efficiency is to select only the relevant exogenous features to be fed into the model. With this intention, a study on the feature selection methods: Pearson correlation coefficient, Boruta, Boruta-Shap, IMV-LSTM, and LIME is performed. A new method focused on interpretability, SHAP-LSTM, is proposed, using a deep learning model training process as part of a feature selection algorithm. The methods were compared in 2 different datasets showing comparable results with lesser computational cost when compared with the use of all features. In all datasets, SHAP-LSTM showed competitive results, having comparatively better results on the data with a higher presence of scarce occurring categorical features.

Abstract