Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As a consequence of the proliferation of multimedia contents, users are nowadays frustrated with the huge amount of available video information whose content is not targeted to their needs and preferences. Its challenging to analysis video content for video personalization due to the lack of semantic video summarization and retrieval techniques. In fact, most of current video personalization systems are using low-level features. However, users identify and select video content using high-level semantics. This creates a gap between user preferences and video content representation that must be bridged for video personalization systems.In this paper we present a new approach for video personalization based on domain knowledge. We first introduce an ontology based indexation approach to enhance retrieval performance. Then, we present a personalization strategy based on fine grained sequential pattern discovery. The proposed approach is based on both user and content personalization. The performance study and experiments show that the use of ontologies to index and represent video contents enhance running time and memory performances. This paper also describes VideoMiner, a system prototype that implement the proposed approach for video personalization.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it