Designers reportedly struggle with design optimization tasks where they are asked to find a combination of design parameters that maximizes a given set of objectives.
Deep learning approaches deliver state-of-the-art performance in recognition of spatiotemporal human motion data.
This paper presents the statistical model for Ticker , a novel probabilistic stereophonic single-switch text entry method for visually-impaired users with motor disabilities who rely on single-switch scanning systems to communicate.
Our models achieve a word level accuracy of $90\%$ and a character error rate CER of $2. 4\%$ over the Twitter typo dataset.