Pseudo-pressure detection and its use in predictive text entry on touchscreens
Why this work is in the frame
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Bibliographic record
Abstract
In this article we first present a new hybrid technique that combines existing time- and touch-point-based approaches to simulate pressure detection on standard touchscreens. Results of two user studies show that the new hybrid technique can distinguish (at least) two pressure levels, where the first requires on average 1.04 N and the second 3.24 N force on the surface. Then, we present a novel pressure-based predictive text entry technique that utilizes our hybrid pressure detection to enable users to bypass incorrect predictions by applying extra pressure on the next key. For inputting short English phrases with 10% non-dictionary words a comparison with conventional text entry in a study showed that the new technique increases entry speed by 9 % and decreases error rates by 25%. Also, most users (83%) favour the new technique. Author Keywords Mobile phone; touchscreen; mobile text entry; predictive
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it