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Temporal networks are essential for modeling and understanding time-dependent systems, from social interactions to biological systems. However, real-world data to construct meaningful temporal networks are expensive to collect or unshareable due to privacy concerns. Generating arbitrarily large and anonymized synthetic graphs with the properties of real-world networks, namely surrogate networks, is a potential way to bypass the problem. However, it is not easy to build surrogate temporal networks which do not lack information on the temporal and/or topological properties of the input network and their correlations. Here, we propose a simple and efficient method that decomposes the input network into star-like structures evolving in time, used in turn to generate a surrogate temporal network. The model is compared with state-of-the-art models in terms of similarity of the generated networks with the original ones, showing its effectiveness and its efficiency in terms of execution time. The simplicity of the algorithm makes it interpretable, extendable and scalable.

Contains fulltext : 234102.pdf (Publisher’s version ) (Open Access), Human faces convey a range of emotions and psychobiological signals that support social interactions. Multiple factors potentially mediate the facial expressions of emotions across cultures. To further determine the mechanisms underlying human emotion recognition in a complex and ecological environment, we hypothesized that both behavioral and neurophysiological measures would be influenced by stimuli ethnicity (Japanese, Caucasian) in the context of ambiguous emotional expressions (mid-happy, angry). We assessed the neurophysiological and behavioral responses of neurotypical Japanese adults (N = 27, 13 males) involved in a facial expression recognition task. Results uncover an interaction between universal and culturally-driven mechanisms. No differences in behavioral responses are found between male and female participants, male and female faces, and neutral Japanese versus Caucasian faces. However, Caucasian ambiguous emotional expressions which require more energy-consuming processing, as highlighted by neurophysiological results of the Arousal Index, were judged more accurately than Japanese ones. Additionally, a differential Frontal Asymmetry Index in neuronal activation, the signature of an approach versus avoidance response, is found in male participants according to the gender and emotional valence of the stimuli.

Contains fulltext : 234102.pdf (Publisher’s version ) (Open Access), Human faces convey a range of emotions and psychobiological signals that support social interactions. Multiple factors potentially mediate the facial expressions of emotions across cultures. To further determine the mechanisms underlying human emotion recognition in a complex and ecological environment, we hypothesized that both behavioral and neurophysiological measures would be influenced by stimuli ethnicity (Japanese, Caucasian) in the context of ambiguous emotional expressions (mid-happy, angry). We assessed the neurophysiological and behavioral responses of neurotypical Japanese adults (N = 27, 13 males) involved in a facial expression recognition task. Results uncover an interaction between universal and culturally-driven mechanisms. No differences in behavioral responses are found between male and female participants, male and female faces, and neutral Japanese versus Caucasian faces. However, Caucasian ambiguous emotional expressions which require more energy-consuming processing, as highlighted by neurophysiological results of the Arousal Index, were judged more accurately than Japanese ones. Additionally, a differential Frontal Asymmetry Index in neuronal activation, the signature of an approach versus avoidance response, is found in male participants according to the gender and emotional valence of the stimuli.

Policy makers have implemented multiple non-pharmaceutical strategies to mitigate the COVID-19 worldwide crisis. Interventions had the aim of reducing close proximity interactions, which drive the spread of the disease. A deeper knowledge of human physical interactions has revealed necessary, especially in all settings involving children, whose education and gathering activities should be preserved. Despite their relevance, almost no data are available on close proximity contacts among children in schools or other educational settings during the pandemic. Contact data are usually gathered via Bluetooth, which nonetheless offers a low temporal and spatial resolution. Recently, ultra-wideband (UWB) radios emerged as a more accurate alternative that nonetheless exhibits a significantly higher energy consumption, limiting in-field studies. In this paper, we leverage a novel approach, embodied by the Janus system that combines these radios by exploiting their complementary benefits. The very accurate proximity data gathered in-field by Janus, once augmented with several metadata, unlocks unprecedented levels of information, enabling the development of novel multi-level risk analyses. By means of this technology, we have collected real contact data of children and educators in three summer camps during summer 2020 in the province of Trento, Italy. The wide variety of performed daily activities induced multiple individual behaviors, allowing a rich investigation of social environments from the contagion risk perspective. We consider risk based on duration and proximity of contacts and classify interactions according to different risk levels. We can then evaluate the summer camps' organization, observe the effect of partition in small groups, or social bubbles, and identify the organized activities that mitigate the riskier behaviors. [...]

The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the “phase 2” of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens’ privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens’ “personal data stores”, to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates—if an

© 2020, University of Skovde. All rights reserved. The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the “phase 2” of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens’ privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens’ “personal data stores”, to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: It allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow

People spend considerable effort managing the impressions they give others. Social psychologists have shown that people manage these impressions differently depending upon their personality. Facebook and other social media provide a new forum for this fundamental process; hence, understanding people's behaviour on social media could provide interesting insights on their personality. In this paper we investigate automatic personality recognition from Facebook profile pictures. We analyze the effectiveness of four families of visual features and we discuss some human interpretable patterns that explain the personality traits of the individuals. For example, extroverts and agreeable individuals tend to have warm colored pictures and to exhibit many faces in their portraits, mirroring their inclination to socialize; while neurotic ones have a prevalence of pictures of indoor places. Then, we propose a classification approach to automatically recognize personality traits from these visual features. Finally, we compare the performance of our classification approach to the one obtained by human raters and we show that computer-based classifications are significantly more accurate than averaged human-based classifications for Extraversion and Neuroticism.