ViDAQ: A Framework for Monitoring Human Machine Interfaces
Bibliographic record
Abstract
A novel case for visual data acquisition (ViDAQ) as an non-intrusive, scalable and reliable means of monitoring Human Machine Interfaces (HMIs) is envisioned. ViDAQ is a step towards achieving real-time cross-validation of human operator commands with respect to HMI states in large scale industrial control room environments. HMIs are integral in allowing human operators to safely command and monitor various critical processes, such as in nuclear power plants, commercial aviation, public transit vehicles, etc. However, HMI related perceptual dynamics presents a challenge to the designed safeguards against human-in-the-loop errors, which, ultimately is dependent on operator situational awareness. We envision, an expert supervisory framework for HMIs (EYE-on-HMI) utilizing ViDAQ, that is scalable and extensible to various industrial applications with prospective safety improvement to next generation commercial automation especially those with "driverless" operational modes. To this end, we present the design, implementation and evaluation of the ViDAQ to visually read rotary multi-dial meters herein.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".