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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Advanced computer vision technology can provide near real-time homemonitoring to support "aging in place" by detecting falls and symptoms relatedto seizures and stroke. Affordable webcams, together with cloud computingservices (to run machine learning algorithms), can potentially bringsignificant social benefits. However, it has not been deployed in practicebecause of privacy concerns. In this paper, we propose a strategy that useshomomorphic encryption to resolve this dilemma, which guarantees informationconfidentiality while retaining action detection. Our protocol for secureinference can distinguish falls from activities of daily living with 86.21%sensitivity and 99.14% specificity, with an average inference latency of 1.2seconds and 2.4 seconds on real-world test datasets using small and largeneural nets, respectively. We show that our method enables a 613x speedup overthe latency-optimized LoLa and achieves an average of 3.1x throughput increasein secure inference compared to the throughput-optimized nGraph-HE2.

Secure Human Action Recognition by Encrypted Neural Network Inference