Short Training Techniques to Enhance Usability of System-Assigned PINs
Bibliographic record
Abstract
Personal Identification Numbers (PINs) are widely used for authentication on mobile devices such as smartphones, which act as gateways to many important accounts (e.g., financial, email, etc.). Unfortunately, people tend to choose easy-to-recall PINs involving birthdays, anniversaries, or keypad patterns that are vulnerable to guessing attacks. System-assigned PINs can improve PIN security in this regard; however, they have usability problems such as feeling the need to store the assigned PIN, longer login times, and difficulty remembering. In this paper, we propose, design, and evaluate a set of short training techniques (16-34 seconds) inspired by implicit learning techniques, to improve the usability of system-assigned PINs. We evaluated our designs in a two-session user study with 184 university students. Our results show that some designs offer significant improvements in the login success rate, login times, and user perceptions. These advantages are in addition to our design’s short single-session training, making it more compatible with typical registration workflows than previously proposed multisession training techniques.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".