<i>MoMIT</i>: Porting a JavaScript Interpreter on a Quarter Coin
Why this work is in the frame
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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 propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MoMIT</i> , a multiobjective optimization approach to miniaturize JS apps to run on IoT devices. We implement <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MoMIT</i> using three different search algorithms. We miniaturize a JS interpreter and measure the characteristics of 23 apps before/after applying <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MoMIT</i> . We find reductions of code size, memory usage, and CPU time of 31, 56, and 36 percent, respectively (medians). We show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MoMIT</i> allows apps to run on up to two additional devices in comparison to the original JS interpreter.
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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