Collaborative content synchronization through an event-based framework
Bibliographic record
Abstract
Web-based user-driven multimedia applications such as Facebook, Flickr, YouTube, and MySpace have gained enormous popularity in recent years and have enabled the sharing of billions of multimedia objects. Multimedia files, especially images and videos, have a natural chronological ordering based on capture date and time. However, in most cases capture information is no longer available once images have been uploaded, emailed, or edited. In this paper, we focus on the problem of adding groups of images with missing temporal information (but ordered temporally) into a primary, organized image collection. We formulate the problem of adding images to an existing collection as a discrete optimization problem in which the objective function incorporates intuitive notions of temporal ordering and similarity with the existing images. We use the event and sub-event structure of the collection to identify potential matches. Specifically, we maximize the sum of similarity scores of the added images while maintaining their temporal order in an event-based framework. We also propose a greedy algorithm for adding images with the objective of minimizing the temporal spread of the images in the merged collection. We evaluate our algorithms using consumer image collections.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".