High level information fusion through a fuzzy extension to Multi-Entity Bayesian Networks in Vehicular Ad-hoc Networks
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
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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it