Quality Prediction-Based Dynamic Content Adaptation Framework Applied to Collaborative Mobile Presentations
Bibliographic record
Abstract
Today, professional documents, created in applications such as PowerPoint and Word, can be shared using ubiquitous mobile terminals connected to the Internet. GoogleDocs and EasyMeet are good examples of such collaborative web applications dedicated to professional documents. The static adaptation of professional documents has been studied extensively. Dynamic adaptation can be very useful and practical for interactive multimedia applications, because it allows the delivery of highly customized content to the end user without the need to generate and store multiple transcoded versions. In this paper, we propose a dynamic framework that enables us to estimate transcoding parameters on the fly to generate near-optimal adapted content for each user. The framework is compared to current dynamic methods as well as to static adaptation solutions. We show that the proposed framework provides a better tradeoff between quality and storage compared to other static and dynamic approaches. To quantify the quality of the adapted content, we introduce a measure of the quality of the experience based on the visual quality of the adapted content, as well as on the impact of its total delivery time. The framework has been tested on (but is not limited to) OpenOffice Impress presentations.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".