Robust Interactive HMI for Occupancy Estimation in Smart Buildings (WIP)
Bibliographic record
Abstract
In this paper, we propose a Human-Machine Inter-face offering an efficient and privacy-conscious approach to train Occupancy Estimation Machine Learning models by interacting with users to request the occupancy level of a room. Although there are existing works that optimize the frequency of interactions, these techniques assume that the data is clean. Hence, anomalies in sensor measurements may lead to unnecessary interactions with users, resulting in their disengagement. To address this problem, we employ an Autoencoder neural network to detect anomalies in univariate time series sensor data using the reconstruction error. Furthermore, we tackle the autoencoder architecture selection challenge by utilizing a Reinforcement Learning-based Neural Architecture Search (RLNAS) approach, where an agent explores a predefined search space and identifies the optimal neural configuration by learning through trial and error. Experiments conducted on a custom anomaly detection dataset demonstrate competitive performance, and illustrate how this technique discovers effective architectures that may not be immediately apparent or intuitive.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".