English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The demand for safety-boosting systems is always increasing, especially to limit the rapid spread of COVID-19. Real-time social distance preservation is an essential application toward containing the pandemic outbreak. Few systems have been proposed which require infrastructure setup and high-end phones. Therefore, they have limited ubiquitous adoption. Cellular technology enjoys widespread availability and their support by commodity cellphones, which suggest leveraging it for social distance tracking. However, users sharing the same environment may be connected to different telecom providers of different network configurations. Traditional cellular-based localization systems usually build a separate model for each provider, leading to a drop in social distance performance. In this article, we propose <italic>CellTrace</italic>, a deep learning-based social distance preserving system. Specifically, <italic>CellTrace</italic> finds a cross-provider representation using a deep learning version of canonical correlation analysis. Different providers&#x2019; data are highly correlated in this representation and used to train a localization model for estimating the social distances. In addition, <italic>CellTrace</italic> incorporates different modules that improve the deep model&#x2019;s generalization against overtraining and noise. We have implemented and evaluated <italic>CellTrace</italic> in two different environments with a side-by-side comparison with the state-of-the-art cellular localization and contact tracing techniques. The results show that <italic>CellTrace</italic> can accurately localize users and estimate the contact occurrence, regardless of the connected providers, with a submeter median error and 97&#x0025; accuracy, respectively. In addition, we show that <italic>CellTrace</italic> has robust performance in various challenging scenarios.

Vaccinated, What Next? An Efficient Contact and Social Distance Tracing Based on Heterogeneous Telco Data