High level information fusion through a fuzzy extension to Multi-Entity Bayesian Networks in Vehicular Ad-hoc Networks
Bibliographic record
Abstract
This paper presents a novel High-Level Information Fusion architecture based on a fuzzy extension to Multi-Entity Bayesian Networks (MEBN). Modeling both semantic and causal relationships between the existing entities in a specific context, MEBN are deemed a very well-studied and theoretically rich approach that takes advantage of the expressiveness power of First-order Logic, and uncertainty management of Bayesian Networks. However, MEBN lack the capability of modeling the ambiguity which is intrinsic to the knowledge gained through human language. In this paper, a fuzzy extension to MEBN is proposed based on the concept of Fuzzy Bayesian Networks, and a novel ambiguity propagation approach is introduced further. The applicability of the proposed architecture is investigated by implementing a Collision Warning System in Vehicular Ad-hoc Networks. It is shown that our system is capable of not only dealing with both semantic and causal relationships between the existing entities, but it also handles the inherent ambiguity which lies in the input information very efficiently.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".