SMIL 2.0 Interactive Multimedia for Web and Mobile Devices by C. A. Dick Bulterman and Lloyd Rutledge, Springer X.media.publishing, 2004, 440pp, ISBN 3-540-20234-X
Bibliographic record
Abstract
This is the first textbook containing a complete, in depth description of SMIL.SMIL is an XML language, promulgated by the World Wide Web Consortium, for multimedia authoring.SMIL may be used to create sophisticated multi and hyper-media artifacts by means of the temporal and spatial integration of existing media items, and by defining certain forms of reactivity among such media items.SMIL is typically used for "rich media"/multimedia presentations which integrate streaming audio and video with images, text or any other media type.In common with all XML languages, SMIL is declarative in nature and permits the author to ascribe values to attribute names, such as duration, start, and so forth, which serve to define the behaviour of the items comprising the multimedia artifact.Despite considerable interest in SMIL, especially in the academic community, there has until now been a relative dearth of comprehensive, user-oriented material dealing with the language.This monograph fills the gap admirably.It is written by two of the foremost experts on SMIL, both of whom were intimately involved in the development of both SMIL 1.0 and SMIL 2.0.The authors, appropriately, claim that the book is designed for three audiences: existing SMIL users who need more information, new SMIL authors, and multimedia developers; the book more than adequately meets the needs of these three different audiences.The book is not intended to introduce the concepts of multimedia or, more specifically, multimedia timing concepts.The book certainly achieves its aims of meeting the needs of newcomers and more seasoned developers.In this regard, a particular strength of the volume is the diversity of examples used to describe features of the SMIL language.The newcomer is presented with six examples introduced in Chapter 1 of the book, where they are used to describe the power of SMIL and its diversity of applicability.A number of these examples reappear later at various stages of the book used later on when describing specific SMIL features.The book encourages a hands-on approach to learning SMIL.Part One of the book, comprising the three initial chapters, provides a complete overview enough features of SMIL that the reader will be in a position to modify existing SMIL presentations and create his or her own new ones once this material has been assimilated.It is noteworthy that in addition to the complete stand-alone presentations, the book contains numerous illustrative code fragments, many of which could be used as templates by the reader when creating her/his own scripts.In this regard, however, it is disappointing that the book does not include a CD ROM containing the examples and code fragments and a SMIL player.This is particularly so as the website to which the reader is referred for this purpose appears to lack the section containing the demos and code in the book!Some of these are on the website, but one has to dig deep to find them.Even so, the website will be of little help to the insomniac reader crossing the Atlantic, who wishes to pass the time by adapting one of the book's presentations.Parts Two and Three of the book provide a more structured, thorough treatment of basic and more advanced SMIL constructs.Once the multimedia author moves away from all but the simplest specifications, authoring becomes a far from simple task, and in all but its most
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.098 | 0.050 |
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".