Bibliographic record
Abstract
Touchscreens that permit multi-touch and gestures interaction are now commonplace.This thesis explores whether these new capabilities might support novel password schemes that could be a viable alternative to traditional text passwords.We conducted a preliminary study with a multi-touch graphically oriented password scheme that we designed called Passgrid and a main study with a multi-touch password scheme called GesturePass, which we designed to focus more on gestures.Our study compared Passgrid to a text password scheme in terms of login time, effect of screen size, and the overall user experience.Our findings showed that users made little use of the multi-touch capability and so Passgrid had longer login times.We also found that users preferred using smaller touchscreen devices, and users responded favourably to the use of gestures.We then designed GesturePass specifically to focus on gestures and smaller touchscreen devices.Our study compared the usability of GesturePass to a PIN password scheme.GesturePass required more initial practices, and had somewhat longer login times, but required a similar number of login attempts.We determined that the longer login times stemmed from certain complex gestures that could potentially be simplified, and that GesturePass could potentially be a viable authentication approach.Chris Joslin for their time and feedback.Finally, I would also like to thank my parents
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".