Guidelines to efficient smart home design for rapid AI prototyping
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
Advances in ubiquitous technology have moved us towards the dream of creating intelligent houses that can help human in their everyday life. The next step in the completion of this vision is to make major breakthroughs in artificial intelligence. In fact, it is the key component for allowing sensors and effectors to give useful services when it is appropriate. In consequence, researchers need to conduct more experiments in realistic setting (e.g. smart home). In order to face this challenge, many research teams try to build new experimental infrastructures without any background experience, guidance or even a real idea of their research needs and issues. Our team is composed of specialists in AI for cognitive assistance and has worked with four major smart home infrastructures. From that experience, we propose, in this paper, a set of guidelines for designing and implementing an efficient smart home architecture on both hardware and software perspective. This paper aims to be a major step toward the AI development (rapid prototyping) and smart home research. Moreover, we share our recent experience with the construction of a new smart home and clinical trials conducted at our laboratory with real Alzheimer's subjects.
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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 | 0.002 | 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 | 0.001 |
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 it