Bibliographic record
Abstract
Accessing the Web is crucially important in today’s society because of communication, education, and entertainment opportunities. Paralyzed or paretic individuals are unable to capitalize on these opportunities using traditional human-computer interaction methods. Thus, I develop a low-cost web browsing system for such individuals, integrating eye and brain control in a novel fashion to relay and interpret navigation commands. The system combines gaze position estimates obtained from a new image processing algorithm with brain concentration levels sensed and transmitted by an electroencephalogram headset. Since user intent may itself be uncertain, the system incorporates a novel fuzzy logic algorithm for combining brainwave and eye position inputs to determine the user’s targeted hyperlink. The algorithm adopts an approach based on exponential smoothing to efficiently keep a record of historical signals. Experimental evaluation established that the first attempt success rate of the system lies between 87% and 95% with 95% confidence. Error recovery accuracy is 98.4%, resulting in a second attempt success rate of 99.1%. This assistive technology, using off-the-shelf components, costs less than $100 excluding the tablet computer, and has the potential to affordably open up the Web to millions of individuals.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".