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Coronavirus (COVID-19) in the United Kingdom: A personality-based perspective on concerns and intention to self-isolate
Objectives
Public behaviour change is necessary to contain the spread of coronavirus (COVIDâ19). Based on the reinforcement sensitivity theory (RST) framework, this study presents an examination of individual differences in some relevant psychological factors.
Design
Crossâsectional psychometric.
Methods
UK respondents (N = 202) completed a personality questionnaire (RSTâPQ), measures of illness attitudes, concerns about the impact of coronavirus on health services and socioâeconomic infrastructures, personal safety, and likelihood of voluntary selfâisolation.
Results
Respondents most concerned were older, had negative illness attitudes, and scored higher on reward reactivity (RR), indicating the motivation to take positive approach action despite prevailing worry/anxiety. Personal safety concerns were highest in those with negative illness attitudes and higher fightâflightâfreeze system (FFFS, reflecting fear/avoidance) scores. Results suggest people are experiencing psychological conflict: between the urge to stay safe (FFFFârelated) and the desire to maintain a normal, pleasurable (RRârelated) life. Ways of ameliorating conflict may include maladaptive behaviours (panic buying), reflecting rewardârelated displacement activity. Intended selfâisolation related to FFFS, but also low behavioural inhibition system (related to anxiety) scores. Older people reported themselves less likely to selfâisolate.
Conclusions
Interventions need to consider individual differences in psychological factors in behaviour change, and we discuss relevant literature to inform policy makers and communicators
Effect of climate and geography on worldwide fine resolution economic activity
Geography, including climatic factors, have long been considered potentially important elements in shaping socio-economic activities, alongside other determinants, such as institutions. Here we demonstrate that geography and climate variables satisfactorily explain the worldwide economic activity as measured by the per capita Gross Cell Product (GCP-PC) at a fine geographical resolution, typically much higher than country average. A 1° by 1° GCPPC dataset has been key for establishing and testing a direct relationship between 'local' geography/climate and GCP-PC. Not only have we tested the geography and climate hypothesis using many possible explanatory variables, importantly we have also predicted and reconstructed GCP-PC worldwide by retaining the most significant predictors. While this study confirms that latitude is the most important predictor for GCP-PC when taken in isolation, the accuracy of the GCP-PC prediction is greatly improved when other factors mainly related to variations in climatic variables, rather than average climatic conditions as typically used, are considered. However, latitude diminishes in importance when only the wealthier parts of the globe are considered. This work points to specific features of the climate system which explain economic activity, such as the variability in air pressure. Implications of these findings range from an improved understanding of why socio-economically better-off societies are geographically placed where they are in the present, past and future to informing where new economic activities could be established in order to yield favourable economic outcomes based on geography and climate conditions
Variance Loss in Variational Autoencoders
In this article, we highlight what appears to be major issue of Variational
Autoencoders, evinced from an extensive experimentation with different network
architectures and datasets: the variance of generated data is significantly
lower than that of training data. Since generative models are usually evaluated
with metrics such as the Frechet Inception Distance (FID) that compare the
distributions of (features of) real versus generated images, the variance loss
typically results in degraded scores. This problem is particularly relevant in
a two stage setting, where we use a second VAE to sample in the latent space of
the first VAE. The minor variance creates a mismatch between the actual
distribution of latent variables and those generated by the second VAE, that
hinders the beneficial effects of the second stage. Renormalizing the output of
the second VAE towards the expected normal spherical distribution, we obtain a
sudden burst in the quality of generated samples, as also testified in terms of
FID.Comment: Article accepted at the Sixth International Conference on Machine
Learning, Optimization, and Data Science. July 19-23, 2020 - Certosa di
Pontignano, Siena, Ital
Your Smart Home Can't Keep a Secret: Towards Automated Fingerprinting of IoT Traffic with Neural Networks
The IoT (Internet of Things) technology has been widely adopted in recent
years and has profoundly changed the people's daily lives. However, in the
meantime, such a fast-growing technology has also introduced new privacy
issues, which need to be better understood and measured. In this work, we look
into how private information can be leaked from network traffic generated in
the smart home network. Although researchers have proposed techniques to infer
IoT device types or user behaviors under clean experiment setup, the
effectiveness of such approaches become questionable in the complex but
realistic network environment, where common techniques like Network Address and
Port Translation (NAPT) and Virtual Private Network (VPN) are enabled. Traffic
analysis using traditional methods (e.g., through classical machine-learning
models) is much less effective under those settings, as the features picked
manually are not distinctive any more. In this work, we propose a traffic
analysis framework based on sequence-learning techniques like LSTM and
leveraged the temporal relations between packets for the attack of device
identification. We evaluated it under different environment settings (e.g.,
pure-IoT and noisy environment with multiple non-IoT devices). The results
showed our framework was able to differentiate device types with a high
accuracy. This result suggests IoT network communications pose prominent
challenges to users' privacy, even when they are protected by encryption and
morphed by the network gateway. As such, new privacy protection methods on IoT
traffic need to be developed towards mitigating this new issue
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