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/

Zendy Plus

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

Zendy Tools

Zendy Open

Smartphones have ubiquitously integrated into our home and work environments,however, users normally rely on explicit but inefficient identificationprocesses in a controlled environment. Therefore, when a device is stolen, athief can have access to the owner's personal information and services againstthe stored passwords. As a result of this potential scenario, this workproposes an automatic legitimate user identification system based on gaitbiometrics extracted from user walking patterns captured by a smartphone. A setof preprocessing schemes is applied to calibrate noisy and invalid samples andaugment the gait-induced time and frequency domain features, then furtheroptimized using a non-linear unsupervised feature selection method. Theselected features create an underlying gait biometric representation able todiscriminate among individuals and identify them uniquely. Differentclassifiers (i.e. Support Vector Machine (SVM), K-Nearest Neighbors (KNN),Bagging, and Extreme Learning Machine (ELM)) are adopted to achieve accuratelegitimate user identification. Extensive experiments on a group of $16$individuals in an indoor environment show the effectiveness of the proposedsolution: with $5$ to $70$ samples per window, KNN and bagging classifiersachieve $87-99\%$ accuracy, $82-98\%$ for ELM, and $81-94\%$ for SVM. Theproposed pipeline achieves a $100\%$ true positive and $0\%$ false-negativerate for almost all classifiers.

Multi Sensor-based Implicit User Identification