Bibliographic record
Abstract
Completely Automated Public Turing tests to tell Computers and Humans Apart (captcha) are challenge-response tests used on the web to distinguish human users from automated bots.Mobile devices such as smartphones and tablets have become a primary means of accessing online resources for many users, however most existing captchas do not properly fit mobile devices and may lead users to abandon tasks.Captchas have become sufficiently hard for users to solve that some web sites refrain from deploying them and others are actively looking at alternatives.For users of smartphones, the reduced screen size can lead to typing mistakes and loss of position.In addition, environmental context and device orientation also have an impact on the user experience.In this thesis, our research revolves around three primary, inter-related questions: How can we effectively assess usability issues of captchas accessed on smartphones?What are the most prevalent usability issues of captchas accessed on smartphones?How can we improve captchas for smartphone usage?We conducted lab and heuristic evaluations on existing and prototype captcha schemes, and identified areas for improvement.We developed, refined and tested a set of domain specific heuristics to evaluate captcha schemes on smartphones.We designed and tested four captcha prototypes to assess the viability of different input methods.From the empirical work, we identified design strategies for the development of new captcha schemes for smartphones.ii 4.4 MC Heuristics.Mean Likert scale responses for overall performance ratings. . . . . . . .
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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 | 0.005 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".