SHARK: sparse human action recovery with knowledge of appliances and load curve data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Occupancy detection can greatly facilitate heating, ventilation and cooling and lightning control for building energy saving. Sensor-based occupancy detection is usually costly and may suffer from high false positive rates. As such, occupancy detection using load curve data has been proposed. Such methods, however, normally (i) rely on tedious and nontrivial model training process and (ii) do not consider the influence of corrupted data in load curve. To overcome these pitfalls, we develop a practical, robust non-intrusive occupancy detection approach that does not require model training and data cleansing. Only using load curve data and readily available appliance knowledge, the method achieves occupancy detection by three main steps: (i) the appliances’ mode states are firstly decoded via a carefully designed robust sparse switching event recovering model; (ii) the human actions are recovered with a priori knowledge of human-activated switching events; (iii) the occupancy states are then inferred based on the recovered human actions along with empirical strategies and association rules. We evaluate our approach and compare it with existing methods with real-world data. The results show that our approach can achieve similar performance to those using supervised machine learning.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it