<i>MoMIT</i>: Porting a JavaScript Interpreter on a Quarter Coin
Bibliographic record
Abstract
The Internet of Things (IoT) is a network of physical, connected devices providing services through private networks and the Internet. The devices connect through the Internet to Web servers and other devices. One of the popular programming languages for communicating Web pages and Web apps is JavaScript (JS). Hence, the devices would benefit from JS apps. However, porting JS apps to the many IoT devices, e.g., System-on-a-Chip (SoCs) devices (e.g., Arduino Uno), is challenging because of their limited memory, storage, and CPU capabilities. Also, some devices may lack hardware/software capabilities for running JS apps “as is”. Thus, we proposeMoMIT, a multiobjective optimization approach to miniaturize JS apps to run on IoT devices. We implementMoMITusing three different search algorithms. We miniaturize a JS interpreter and measure the characteristics of 23 apps before/after applyingMoMIT. We find reductions of code size, memory usage, and CPU time of 31, 56, and 36 percent, respectively (medians). We show thatMoMITallows apps to run on up to two additional devices in comparison to the original JS interpreter.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".