Study of image sensors for enhanced face recognition at a distance in the Smart City context.

Smart monitoring and surveillance systems have become one of the fundamental areas in the context of security applications in Smart Cities. In particular, video surveillance for Human Activity Recognition (HAR) applied to the recognition of potential offenders and to the detection and prevention of violent acts is a challenging task that is still undergoing. This paper presents a method based on deep learning for face recognition at a distance for security applications. Due to the absence of available datasets on face recognition at a distance, a methodology to generate a reliable dataset that relates the distance of the individuals from the camera, the focal length of the image sensors and the size in pixels of the target face is introduced. To generate the extended dataset, the Georgia Tech Face and Quality Dataset for Distance Faces databases were chosen. Our method is then tested and applied to a set of commercial image sensors for surveillance cameras using this dataset. The system achieves an average accuracy above 99% for several sensors and allows to calculate the maximum distance for a sensor to get the required accuracy in the recognition, which could be crucial in security applications in smart cities.

Surviving COVID-19: Biopsychosocial Impacts, Death Anxiety, and Coping Strategies.

As the COVID-19 pandemic erupted, attempts to contain the spread of the virus took two concurrent forms, including mobility restrictions (aka lockdowns) and the race to produce a vaccine. However, it is quite striking that, amidst both the lockdown and the race to produce a vaccine, the question of how COVID-19 survivors/patients coped with the disease has not received the degree of attention it deserved. To navigate this issue, we employed a sample consisting of 100 COVID-19 survivors; this paper explores the relationship between the biopsychosocial (BPS) impacts of COVID-19, death anxiety, and coping strategies. In this context, the mediating role of death anxiety is placed in the spotlight. The analysis reveals a significant positive association between the BPS impact of COVID-19 and death anxiety and a significant negative association between death anxiety and coping strategies among COVID-19 survivors. Thus, death anxiety mediates the relationship between the BPS impact and the coping strategies that COVID-19 survivors adopt. Given the general recognition of the validity of the BPS model in contemporary medical science and practice, a thorough examination of COVID-19 survivors and their experiences related to surviving is necessary to match the challenges of today, including the increased probability of pandemics.

Social media mining for smart cities and smart villages research.

The imperative of well-being and improved quality of life in smart cities context can only be attained if the smart services, so central to the concept of smart cities, correspond with the needs, expectations and skills of cities' inhabitants. Considering that social media generate and/or open real-time entry points to vast amounts of data pertinent to well-being and quality of life, such as citizens' expectations, opinions, as well as to recent developments related to regulatory frameworks, debates, political decisions and policymaking, the big question is how to exploit the potential inherent in social media and use it to enhance the value added smart cities generate. Social mining is traditionally understood as the process of representing, analyzing, and extracting actionable patterns and trends from raw social media data. In the context of smart cities, this special issue focuses on how social media data, also potentially combined with other data, can be used to optimize the efficiency of city operations and services, and thereby contribute more efficiently to citizens' well-being and quality of life.