Complex event processing for content-based text, image, and video retrieval
Bibliographic record
Abstract
This report summarizes the findings of an exploratory team of the North Atlantic Treaty Organization (NATO) Information Systems Technology panel into Content-Based Analytics (CBA). The team carried out a technical review into the current status of theoretical and practical developments of methods, tools and techniques supporting joint exploitation of multimedia data sources. In particular, content-based information retrieval and analytics was considered as a means to allow military experts to exploit multiple data sources in a rapid fashion for sensemaking and knowledge generation. Elements included contextual understanding of complex events through computational/human processing techniques, event prediction through the automated extraction of network features, temporal trends, hidden clusters and resource flows, and the use of machine processing for automated translation, parsing, information extraction, and summarization of unstructured and semistructured data. The main conclusions of the study are that important research gaps exist in all the technical areas covered in this report. Though the research areas and developments are being advanced in the military sector and the civil sector, in particular, they remain at low levels of technical maturity for defense and security system applications. It is recommended that NATO collaborative research effort be expanded to advance those approaches that are most pertinent to our overall aim of enhancing the contextual understanding of complex events through CBA of heterogeneous multimedia streams.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".