Behavioral reactivity and real time programming in XML
Bibliographic record
Abstract
XML and its associated languages are emerging as powerful authoring tools for multimedia and hypermedia web content. Furthermore intelligent presentation generation engines have begun to appear as have models and platforms for adaptive presentations. However XML-based models are limited by their lack of expressiveness in presentation and animation. As a result authors of dynamic adaptive web content must often use considerable amounts of script or code. The use of such script or code has two serious drawbacks. First such code undermines the declarative description possible in the original presentation language and second the scripting/coding approach does not readily lend itself to authoring by non programmers. In this paper we describe a set of XML language extensions inspired by features from the functional programming world which are designed to widen the class of reactive systems which could be described in languages such as SMIL. The described features extend the power of declarative modeling for the web by allowing the introduction of web media items which may dynamically react to continuously varying inputs both in a continuous way and by triggering discrete user-defined events. The two extensions described herein are discussed in the context of SMIL Animation and SVG but could be applied to many XML-based languages.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".