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COVID-19 is a highly contagious disease that was first identified in 2019, and has since taken more than six million lives world wide till date, while also causing considerable economic, social, cultural and political turmoil. As a way to limit its spread, the World Health Organization and medical experts have advised properly wearing face masks, social distancing and hand sanitization, besides vaccination. However, people wear masks sometimes uncovering their mouths and/or noses consciously or unconsciously, thereby lessening the effectiveness of the protection they provide. A system capable of automatic recognition of face mask position could alert and ensure that an individual is wearing a mask properly before entering a crowded public area and putting themselves and others at risk. We first develop and publicly release a dataset of face mask images, which are collected from 391 individuals of different age groups and gender. Then, we study six different architectures of pre-trained deep learning models, and finally propose a model developed by fine tuning the pre-trained state of the art MobileNet model. We evaluate the performance (accuracy, F1-score, and Cohen's Kappa) of this model on the proposed dataset and MaskedFace-Net, a publicly available synthetic dataset created by image editing. Its performance is also compared to other existing methods. The proposed MobileNet is found as the best model providing an accuracy, F1-score, and Cohen's Kappa of 99.23%, 99.22%, and 99.19%, respectively for face mask position recognition. It outperforms the accuracy of the best existing model by about 2%. Finally, an automatic face mask position recognition system has been developed, which can recognize if an individual is wearing a mask correctly or incorrectly. The proposed model performs very well with no drop in recognition accuracy from real images captured by a camera., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 Published by Elsevier Inc.)

A principled approach to the analysis of eye movements for behavioural biometrics is laid down. The approach grounds in foraging theory, which provides a sound basis to capture the uniqueness of individual eye movement behaviour. We propose a composite Ornstein-Uhlenbeck process for quantifying the exploration/exploitation signature characterising the foraging eye behaviour. The relevant parameters of the composite model, inferred from eye-tracking data via Bayesian analysis, are shown to yield a suitable feature set for biometric identification; the latter is eventually accomplished via a classical classification technique. A proof of concept of the method is provided by measuring its identification performance on a publicly available dataset. Data and code for reproducing the analyses are made available. Overall, we argue that the approach offers a fresh view on either the analyses of eye-tracking data and prospective applications in this field.

Social media have become an integral part of our lives, expanding our interlinking capabilities to new levels. There is plenty to be said about their positive effects. On the other hand, however, some serious negative implications of social media have been repeatedly highlighted in recent years, pointing at various threats to society and its more vulnerable members, such as teenagers, in particular, ranging from much-discussed problems such as digital addiction and polarization to manipulative influences of algorithms and further to more teenager-specific issues (e.g., body stereotyping). The impact of social media-both at an individual and societal level-is characterized by the complex interplay between the users' interactions and the intelligent components of the platform. Thus, users' understanding of social media mechanisms plays a determinant role. We thus propose a theoretical framework based on an adaptive " Social Media Virtual Companion " for educating and supporting an entire community, teenage students, to interact in social media environments in order to achieve desirable conditions, defined in terms of a community-specific and participatory designed measure of Collective Well-Being (CWB). This Companion combines automatic processing with expert intervention and guidance. The virtual Companion will be powered by a Recommender System ( CWB-RS ) that will optimize a CWB metric instead of engagement or platform profit, which currently largely drives recommender systems thereby disregarding any societal collateral effect. CWB-RS will optimize CWB both in the short term by balancing the level of social media threats the users are exposed to, and in the long term by adopting an Intelligent Tutor System role and enabling adaptive and personalized sequencing of playful learning activities. We put an emphasis on experts and educators in the educationally managed social media community of the Companion. They play five key roles: (a) use the Companion in classroom-based educational activities; (b) guide the definition of the CWB; (c) provide a hierarchical structure of learning strategies, objectives and activities that will support and contain the adaptive sequencing algorithms of the CWB-RS based on hierarchical reinforcement learning; (d) act as moderators of direct conflicts between the members of the community; and, finally, (e) monitor and address ethical and educational issues that are beyond the intelligent agent's competence and control. This framework offers a possible approach to understanding how to design social media systems and embedded educational interventions that favor a more healthy and positive society. Preliminary results on the performance of the Companion's components and studies of the educational and psychological underlying principles are presented., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Ognibene, Wilkens, Taibi, Hernández-Leo, Kruschwitz, Donabauer, Theophilou, Lomonaco, Bursic, Lobo, Sánchez-Reina, Scifo, Schwarze, Börsting, Hoppe, Aprin, Malzahn and Eimler.)

The epidemiology of suicide in Serbia and Montenegro from 1989 to 2003, a period of civil war, is presented. Following the break-up of former Yugoslavia, Serbia and Montenegro underwent a period of war from 1991-1994 and another in 1999. During the war years, the number of suicides increased, reaching its peak in 1993. Male suicides outnumbered female suicides by a ratio of 2:1. Male suicides decreased slightly after the war of 1991-1994 only to rise in 1997 and continue at this higher level throughout the nineties. In Serbia alone, male suicide reached its peak in 2002 (nearly 29/100,000). The methods of suicide changed significantly, with the use of firearms doubling during and after the war years. Speculations are offered about the findings, many consistent with Durkheim's classical hypothesis concerning suicide and unpopular wars.

In order to examine the role depressive disorders play in the genesis of suicidal behavior of alcoholics, 330 alcoholic inpatients were examined. Within the total sample, 41% had distinct suicidal tendencies, and of those, 15% had made at least one serious suicide attempt. The MADRS and MMPI scales were used to obtain objective evaluation of the depressive disorders. The subjects were classified according to DSM-R criteria. The results show that suicidal alcoholics have statistically significantly more depressive characteristics.