Un modèle pour l'ingénierie des systèmes interactifs dédiés à la manipulation de données
Bibliographic record
Abstract
Software engineering focuses, amongst others, on three development aspects of interactive systems (IS): the link between source data and their target presentations; the conception of the interactive facet; the execution of the same IS on different execution platforms. The evolutions of the web and data, the diversification of execution platforms, and the modern interaction techniques requires a review of the processing of these three aspects. Model-driven engineering (MDE) proposes a solution to the multi-platform execution aspect by grading the conception of IS using different abstraction levels. Our work is based on this principle. Firstly, we defined a mapping language called Malan, dedicated to the link between source data and their target presentations. Malan has the advantages of being: independent of data and HCI platforms ; able to carry out complex calculations for the layout of a target presentation; developed in the data-to-presentation context, contrary to classical transformation languages. Secondly, we developed a conceptual interaction model called Malai, gathering together principles from the major interaction models. Malai aims to: facilitate the conception of the interim feedback of instruments; reuse interactions already defined ; consider an action as a first-class object; describe modern and classical interactions ; generate code for a given execution platform.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".