Using interface preferences as evidence of user identity: A feasibility study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Research on human-computer interaction currently focuses on enhancing system usability by establishing an appropriate user interface (UI) that depends on users’ features. Online users typically have different perceptions of their favored interface design depending on their preferences. Thus, those interface preferences could be utilized to recognize online users’ identities. User authentication is another critical issue that should be considered to improve online security mechanisms without compromising usability. This study investigates the feasibility of using UI preferences as evidence of user identity. The proposed method applies to the design preferences of users dealing with online systems (e.g., e-exam and e-banking). These preferences are closely associated with individual characteristics, whether physical, cognitive, psychological, psychomotor, demographic, or experience based. Many design characteristics could be used in online systems; for example, the e-exam interface design may use features such as the font (size, color, and face), the number of questions per page, background color, questions group, timer type, and sound alert. The feasibility evaluation of this study indicated that 96.8% of research participants have variations in their preferences, and each participant kept 94.5% of their design preferences throughout different sessions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.008 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it