Concept of Using Eye Tracking Technology to Assess and Ensure Cybersecurity, Functional Safety and Usability
Bibliographic record
Abstract
Eye tracking technology is based on tracking the trajectory of human eye movement. As a rule, it is implemented in the form of an additional device attached under the monitor or in the form of glasses. On the basis of a mathematical model, the focus of a person's attention is calculated and, accordingly, the user's visual route is built. Eye tracking technology is used to solve various problems, e.g. for marketing research, assessing the quality of user interfaces, developing simulators for operators, etc. The article discusses the concept of using eye tracking technology to assess and ensure cyber security, functional safety and usability. The possibility of using eye tracking technology (ETT) to solve the problem of identifying a person's personality is considered separately. The solution is achieved by reproducing a certain trajectory by a person's vision. This technique can be used as a basic or additional technique for identifying a person's personality. It also analyzes the results of using eye tracking to study the interface of an automated information system for operator support based on algorithms for symptom-oriented emergency instructions (ASOEI), which is used at nuclear power plants (NPP).
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".