Design, construction, and application of a generic visual language generation environment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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