Abstract

Abstract
Accurate predictions of time series are increasingly required to support judgments in a variety of decisions. Several predictive models are available to support these predictions, depending on how each field offers a data variety with varied behavior. The use of artificial neural networks (ANN) at the beginning of the COVID-19 pandemic was significant since the tool may offer forecasting data for various conditions and hence assist in governing critical choices. In this context, this paper describes a system for predicting the daily number of cases, fatalities, and Intensive Care Unit (ICU) patients for the next 28 days in five European countries: Portugal, the United Kingdom, France, Italy, and Germany. The database selection is based on comparable mitigation processes to analyze the impact of safety procedure flexibilization with the most recent numbers of COVID-19. Additionally, it is intended to check the algorithm's adaptability to different variants throughout time. The network's input data has been normalized to account for the size of the countries in the study and smoothed by seven days. The mean absolute error (MAE) was employed as a comparing criterion of two datasets, one with data from the beginning of the pandemic and another with data from the last year, since all variables (cases, deaths, and ICU patients) may be tendentious in percentage analysis. The best architecture produced a general MAE prediction for the 28 days ahead of 256,53 daily cases, 0,59 daily deaths, and 1,63 ICU patients, all numbers normalized by million people.

Abstract

Abstract

Abstract
Managing a team of heterogeneous robots in a dynamic environment poses a challenging job. In this paper a model for a multi-purpose, real-time, adaptable, strategical coordination layer is presented. Based on previous work developed for the RoboCup Soccer simulation, small-size, middle-size and legged leagues, a generic coordination model was built. As both centralized and distributed environment are handled by the layer, communication was an important factor to consider only introducing a minor overhead. A multi-level hierarchical approach was followed with hybrid methods used to switch between concepts. The model was tested with two strategy instances, RoboCup Rescue Simulation and RoboCup Soccer. Strategies are designed with the help of a graphical tool. Results achieved by the team in RoboCup Rescue and Soccer Simulation competitions demonstrate the usefulness of this approach. Copyright © 2011-2012 ICST

Abstract
BioStories is the outcome of a four-year research project focused in uniting affective and ubiquitous computing with context aware multimedia environments real-time generation. Its initial premise was based in the possibility of performing real-time automatic emotion assessment trough online biometric channels monitoring and use this information to design on-the-fly dynamic multimedia storylines emotionally adapted, so that end users would unconsciously be determining the story graph. The emotion assessment process was based on biometric channels dynamic fusion such as EEG, GSR, respiration rate and volume, skin temperature and heart rate on top of Russell's circumplex model of affect. BioStories' broad scope also allowed for some spin-off projects namely mouse control through EMG that resulted in a tested technology for alternative/inclusive interfaces. Exhaustive experiments showed 86% of success rate for emotion assessment, IC(95%)(p)approximate to(0.81, 0.90), in a dynamic tridimensional virtual environment with an immersiveness user score of 4.3 out of 5. The success of the proposed approach allows the vision of its appliance in several domains such as virtual entertainment, videogames and cinema as well as direct marketing, digital TV and domotic appliances.