WL++: a framework to build cross-platform mobile applications and RESTful back-ends
Bibliographic record
Abstract
About one in two adults and one in four teens own a smart phone in North America and use it to access online information and services. This, ever increasing, demand for mobile applications has given rise to the need for tools and methods to systematically support the design and construction of these applications. Responding to this need, we have developed WL++, a code-generation environment for mobile-application development. Using this tool, developers can create application-specific diagrams of the application's logical model and annotate them with information about the user-interface widgets appropriate for interacting with the model elements. WL++ then produces a relational back-end for storing the model data, a set of RESTful APIs for accessing and updating the back-end, and a multi-platform mobile application that relies on the IBM Worklight framework to render, interact with and store the relevant data, through the chosen widgets and APIs. In addition, a general service monitors and records the usage of the APIs and the data exchange between the application and the back-end. In this paper, we describe the WL++ cross-mobile application generation framework and we illustrate its functionality with an example.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".