Bibliographic record
Abstract
Security proponents heavily emphasize the importance of choosing a strong password (one with high entropy). Unfortunately, by design, most humans are apparently incapable of generating such passwords, or memorizing a random-looking, machine-generated one for longterm use. Infrequently used passwords pose even bigger security and usability problems. We exploit the fact that many users now own or have access to a large quantity of digitized personal or personally meaningful content in designing an object-based password scheme called ObPwd. ObPwd enables users to select a password generating object from their local collection or from the web, and then converts the password object (e.g. an image, a particular piece of music, excerpt from a book) to a (potentially) high-entropy text password that can be used for regular or secondary web authentication, or in local applications (e.g. encryption). Instead of requiring users to memorize an exact password, ObPwd only requires one to remember a hint or pointer to the password object used. We believe that choosing digital objects as passwords is an interesting alternative to explore, and may enable users to create and maintain high quality passwords. We have implemented a prototype, and solicit feedback from the research community in regard to using digital objects as passwords. 1
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".