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

How can we find patterns and anomalies in a tensor, or multi-dimensionalarray, in an efficient and directly interpretable way? How can we do this in anonline environment, where a new tensor arrives each time step? Finding patternsand anomalies in a tensor is a crucial problem with many applications,including building safety monitoring, patient health monitoring, cybersecurity, terrorist detection, and fake user detection in social networks.Standard PARAFAC and Tucker decomposition results are not directlyinterpretable. Although a few sampling-based methods have previously beenproposed towards better interpretability, they need to be made faster, morememory efficient, and more accurate.  In this paper, we propose CTD, a fast, accurate, and directly interpretabletensor decomposition method based on sampling. CTD-S, the static version ofCTD, provably guarantees a high accuracy that is 17 ~ 83x more accurate thanthat of the state-of-the-art method. Also, CTD-S is made 5 ~ 86x faster, and 7~ 12x more memory-efficient than the state-of-the-art method by removingredundancy. CTD-D, the dynamic version of CTD, is the first interpretabledynamic tensor decomposition method ever proposed. Also, it is made 2 ~ 3xfaster than already fast CTD-S by exploiting factors at previous time step andby reordering operations. With CTD, we demonstrate how the results can beeffectively interpreted in the online distributed denial of service (DDoS)attack detection.

CTD: Fast, Accurate, and Interpretable Method for Static and Dynamic Tensor Decompositions