Passwords for both Mobile and Desktop Computers: ObPwd for Firefox and Android
Bibliographic record
Abstract
Many users now access password-protected accounts and websites alternately from desktop machines, and mobile devices (e.g., smartphones, tablets). The input mechanisms of the mobile devices are often miniature physical or virtual on-screen keyboards, posing challenges for users trying to type passwords with mixed-case and special-characters expected by websites and more easily entered on desktop keyboards. We begin with a review of these challenges and existing proposals addressing cross-device password entry, including some password managers. We then bring the issues into focus with detailed discussion of the interoperation challenges, and implementation details, and interface details of the object-based password “ObPwd” mechanism, as implemented for the Android platform, plus compatible browser-based and stand-alone implementations for desktop environments. ObPwd generates a password from a user-selected digital object (e.g., image), does not require changes to server-side software, and avoids the text-input challenges of mobile devices. We also briefly evaluate ObPwd using a recently proposed evaluation framework for password authentication schemes. A major goal is to increase attention to the cross-device password authentication problem.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".