Getting Model of MVVM Pattern from UML Profile
Bibliographic record
Abstract
The rejuvenation of applications to harmonize with technological watch is the major challenge for all computer boxes, frameworks and languages are constantly proliferating by offering a range of improvements in terms of security and performance, which pushes all applications to invest in order to align oneself, to orient oneself towards another perspective of application implementation has become a primacy. MVW is considered the new concept of application models where the developer can choose according to his needs, which component, for example, it can be a controller, a directive or a unit test for applications where we use the AngularJS framework, modeling an application is one of the basic steps to reach it , the emergence of new patterns press IT companies to think to renew their application architecture for more security and performance, moving from an old to a new model meets this need. AngularJS is one of the widely used frameworks for modern single-page web application development which is designed to support dynamic views in the applications. We propose an UML profile for AngularJS for building a model of an AngularJS web application, and a set of transformations that transform the model into a code template.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".