Improved SingleTapBraille: Developing a single tap text entry method based on Grade 1 and 2 braille encoding
Bibliographic record
Abstract
Touchscreen technology has brought about significant improvements for both normal sighted and visually impaired people. Visually impaired people tend to use touchscreen devices because these devices support a screen reader function, providing a cheaper, smaller alternative to screen reader machines. However, most of the available touchscreen keyboards are still largely inaccessible to blind and visually impaired people because they require the user to find an object location on a touchscreen in order to interact with an application. In this paper, we describe SingleTapBraille, a novel nonvisual text input approach for touchscreen devices. With SingleTapBraille, a user enters characters including text, numbers, and punctuations by tapping anywhere on the screen with one finger or a thumb several times based on braille patterns. This paper presents our initial keyboard design to enter Grade 1 and our explorative evaluation of SingleTapBraille conducted with braille instructors and visually impaired users. It also presents the implementation of Grade 2 and an initial evaluation of the improved SingleTapBraille keyboard with a blind user.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".