Gaze-Controlled Arabic Virtual Keyboard: Design and Evaluation of a Novel Layout
Bibliographic record
Abstract
Artificial intelligence has become a vital component of contemporary technology.Despite advancements in computer technology, Arabic virtual keyboards face challenges in layout efficiency and gaze-based control due to the unique characteristics of the Arabic language.The virtual keyboard is an effective input mechanism for human-computer interaction systems.This paper suggests an Arabic virtual keyboard application system that utilizes artificial intelligence and can be controlled using eye movements.The user interacts with the system by utilizing the camera output displayed on the screen, while the webcam serves as an input device.The study proposed a novel method called Distinct Frequency-Alphabetical for virtual keyboard layout.Hence, it studied how to design a keyboard for typing Arabic text using an algorithm for gaze directions in real-world scenarios.The empirical results of the proposed system have produced better results than the previous systems.It achieves this with an average typing rate of 18 characters per minute and 4 words per minute.The testing of the proposed system received positive feedback from several users; it achieved a NASA-TLX score of 8% and 87.5% on the system usability scale.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".