Extracting Java library subsets for deployment on embedded systems
Bibliographic record
Abstract
Embedded systems provide means for enhancing the functionality delivered by small-sized electronic devices such as hand-held computers and cellular phones. Java is a programming language which incorporates a number of features that are useful for developing such embedded systems. However the size and the complexity of the Java language and its libraries have slowed its adoption for embedded systems, due to the processing power and storage space limitations found in these systems. A common approach to address storage space limitations is for the vendor to offer special versions of the libraries with reduced functionality and size to meet the constraints of embedded systems. This paper presents a technique that is used for dynamically selecting, on an as needed basis, the subset of library entities that is exactly required for a given Java application to run. This subset can then be down-loaded to the device for execution. The advantage of this approach is that the developer can use arbitrary libraries, instead of being restricted to those which have been adapted for embedded systems by the vendors. A prototype system, that dynamically builds library subsets on an as needed per application basis, has been built and tested on several mid-size Java applications with positive results.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".