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

Real-world datasets collected with sensor networks often contain incompleteand uncertain labels as well as artefacts arising from the system environment.Complete and reliable labeling is often infeasible for large-scale andlong-term sensor network deployments due to the labor and time overhead,limited availability of experts and missing ground truth. In addition, if themachine learning method used for analysis is sensitive to certain features of adeployment, labeling and learning needs to be repeated for every newdeployment. To address these challenges, we propose to make use of systemcontext information formalized in an information graph and embed it in thelearning process via contrastive learning. Based on real-world data we showthat this approach leads to an increased accuracy in case of weakly labeleddata and leads to an increased robustness and transferability of the classifierto new sensor locations.

Using system context information to complement weakly labeled data