Bibliographic record
Abstract
Upcoming mobile devices will have flexible displays, allowing us to explore new forms of user authentication.On flexible displays, users interact with the device by deforming the surface of the display through bending.In this thesis, we present a new type of user authentication that uses bend gestures as its input modality.We ran three user studies to evaluate the usability and security of our new authentication scheme and compared it to PINs on a mobile phone.Our first two studies evaluated the creation and memorability of bend passwords and PINs.The third study looked at the security problem of shoulder-surfing passwords on mobile devices.Our results show that bend passwords are a promising authentication mechanism for flexible display devices.We also provide eight design recommendations for implementing bend passwords on flexible display devices, based on our results.First, I would like to thank my supervisor Sonia Chiasson for her advice, feedback and support throughout this thesis.Sonia, I admire your dedication to your students.You are not only a great supervisor but also a great mentor.Thank you for the informal chats and advice about graduate school and academia
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".