COVID-19 Detection from Cough Recording by means of Explainable Deep Learning

The new coronavirus disease (COVID-19), declared a pandemic on 11 March 2020 by the World Health Organization, has caused over 6 million victims worldwide. Because of the rapid spread of the virus, with the aim to perform screening we exploit deep learning model to quickly diagnose altered respiratory conditions. In this paper, we propose a method to recognize and classify cough audio files into three classes to distinguish patients with COVID-19 disease, symptomatic ones and healthy subjects, with the use of a convolutional neural network (CNN). Cough audios were recorded by using a smartphone and its built-in microphone. From cough recordings, we generate spectrogram images and we obtain an accuracy equal to 0.82 with a deep learning network developed by authors. Our method also provides heatmaps, which show the relevant input areas used by the model for the final forecast, and this aspect ensures the explainability of the method. © 2022 IEEE.

Rethinking spatial policy in an era of multiple crises

After more than thirty years of post-war relative regional convergence, since the 1980s geographical inequalities in economic prosperity and social conditions have widened again in most capitalist countries. In this paper we argue that this resumption of spatial inequality is in part explained by the significant changes observed in the role of the state and in public intervention in the shift from the post-war 'Keynesian' regime of state regulation to the 'Neoliberal' regime that has held sway over the past four decades. We argue that most public policies enacted in this latter period have actually exacerbated socioeconomic - and spatial - polarization, favouring a few metropolitan areas and regions at the expense of a substantial number of what are now commonly referred to as 'left behind places'. We contend that we are now at a new juncture in the evolution of capitalism: in the space of little more than decade the global system has been destabilized by a major financial crisis (2008) and the COVID-19 pandemic (2020), both with enduring socio-economic aftershocks, while the climate emergency is reaching existential proportions. In this Editorial Introduction we call for a bold 'rethinking' of public action - and especially spatial policy - to face these recurring crises, and we outline some pointers for more effective and inclusive policies. © 2022 The Author(s). Published by Oxford University Press on behalf of the Cambridge Political Economy Society. All rights reserved.

Rethinking spatial policy in an era of multiple crises

After more than thirty years of post-war relative regional convergence, since the 1980s geographical inequalities in economic prosperity and social conditions have widened again in most capitalist countries. In this paper we argue that this resumption of spatial inequality is in part explained by the significant changes observed in the role of the state and in public intervention in the shift from the post-war 'Keynesian' regime of state regulation to the 'Neoliberal' regime that has held sway over the past four decades. We argue that most public policies enacted in this latter period have actually exacerbated socioeconomic - and spatial - polarization, favouring a few metropolitan areas and regions at the expense of a substantial number of what are now commonly referred to as 'left behind places'. We contend that we are now at a new juncture in the evolution of capitalism: in the space of little more than decade the global system has been destabilized by a major financial crisis (2008) and the COVID-19 pandemic (2020), both with enduring socio-economic aftershocks, while the climate emergency is reaching existential proportions. In this Editorial Introduction we call for a bold 'rethinking' of public action - and especially spatial policy - to face these recurring crises, and we outline some pointers for more effective and inclusive policies.

A Proposal to Ensure Social Distancing with Deep Learning-based Object Detection

Social distancing is becoming really important in last month as a vehicle to limit the COVID-19 Coronavirus pandemic. Generally speaking, it is effective to control the spread of contagious diseases. In this context there is the need to monitor social distancing violations: for this reason in this paper we propose a social distancing detector able to count the violations by analysing video streams. Preliminary results show that the proposed method can be employed to guarantee social distancing. Moreover we discuss several suggestions aimed to improve the following proposal.