Design, construction, and application of a generic visual language generation environment
Bibliographic record
Abstract
The implementation of visual programming languages (VPLs) and their supporting environments is time-consuming and tedious. To ease the task, researchers have developed some high-level tools to reduce the development effort. None of these tools, however, can be easily used to create a complete visual language in a seamless way as the lex/yacc tools do for textual language constructions. This paper presents the design, construction and application of a generic visual language generation environment, called VisPro. The VisPro design model improves the conventional model-view-controller framework in that its functional modules are decoupled to allow independent development and integration. The VisPro environment consists of a set of visual programming tools. Using VisPro, the process of VPL construction can be divided into two steps: lexicon definition and grammar specification. The former step defines visual objects and a visual editor, and the latter step provides language grammars with graph rewriting rules. The compiler for the VPL is automatically created according to the grammar specification. A target VPL is generated as a programming environment which contains the compiler and the visual editor. The paper demonstrates how VisPro is used by building a simple visual language and a more complex visual modeling language for distributed programming.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".