SmartHomeML: Towards a Domain-Specific Modeling Language for Creating Smart Home Applications
Bibliographic record
Abstract
There is an increasing demand for smart home connectivity from controlling the home temperature, to switching light bulbs, controlling the window shades and pet feeders. Smart home control systems such as Amazon Alexa and Google Home provide user interfaces to coordinate the operation of several home appliances. While this facilitate integrating the operations of several appliances, system integrators still need to specify and define the integration and communication logic. This logic depends on both the appliance and the control system providers. This paper introduces SmartHomeML, a domain specific modelling language for smart home applications, that allows users to define new skills (functionalities). SmartHomeML consists of a model designer that supports modelling smart home applications and a model generator that uses template-based transformation to automatically generate smart home device adapters and connectors that conform to the specification of a selected target home control system. We show through an example how to use SmartHomeML to model a smart home service independently from the target smart home provider and then generate Amazon Alexa Skill Adapters and SmartThings SmartApps automatically.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".