Mapping the Dynamics of Unemployment: Impacts of the Covid-19 Pandemic in Portugal

The Great Confinement affected the labour market, particularly the dynamics of unemployment. Based on Instituto do Emprego e For-macao Profissional (IEFP) data on registered unemployment, this article analyses the impact of the pandemic crisis in unemployment situations in mainland Portugal. The categorical and territorial distribution of the unemployed is highlighted, as well as the temporal dimen-sion of the problem. The quantitative/extensive analysis carried out, as well as the cluster analysis, indicates that the incidence of unemployment is not identical for all social groups or for the entire territory, affecting some more than others, with emphasis on some spatial concentrations, particularly in Algarve;and the variations are directly related to temporali-ties resulting from periods marked by confinement or deconfinement.

MAPEAMENTO DAS DINÂMICAS DO DESEMPREGO: IMPACTOS DA PANDEMIA DA COVID-19 EM PORTUGAL CONTINENTAL

MAPPING THE DYNAMICS OF UNEMPLOYMENT: IMPACTS OF THE COVID-19 PANDEMIC IN PORTUGAL. The Great Confinement affected the labour market, particularly the dynamics of unemployment. Based on Instituto do Emprego e Formação Profissional (IEFP) data on registered unemployment, this article analyses the impact of the pandemic crisis in unemployment situations in mainland Portugal. The categorical and territorial distribution of the unemployed is highlighted, as well as the temporal dimension of the problem. The quantitative/extensive analysis carried out, as well as the cluster analysis, indicates that the incidence of unemployment is not identical for all social groups or for the entire territory, affecting some more than others, with emphasis on some spatial concentrations, particularly in Algarve;and the variations are directly related to temporalities resulting from periods marked by confinement or deconfinement. © Published under the terms and conditions of an Attribution-NonCommercial-NoDerivatives 4.0 International license.

Transfer learning and data augmentation techniques to the COVID-19 identification tasks in ComParE 2021

In this work, we propose several techniques to address data scarceness in ComParE 2021 COVID-19 identification tasks for the application of deep models such as Convolutional Neural Networks. Data is initially preprocessed into spectrogram or MFCC-gram formats. After preprocessing, we combine three different data augmentation techniques to be applied in model training. Then we employ transfer learning techniques from pretrained audio neural networks. Those techniques are applied to several distinct neural architectures. For COVID-19 identification in speech segments, we obtained competitive results. On the other hand, in the identification task based on cough data, we succeeded in producing a noticeable improvement on existing baselines, reaching 75.9% unweighted average recall (UAR). Copyright © 2021 ISCA.