An integrated approach for Fuzzy Multi-entity Bayesian Networks and semantic analysis for soft and hard data fusion
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, a soft+hard data fusion model is proposed that is capable of combining the data generated from human-based sources with those generated by physical sensors. The basis of this model is our previously introduced Fuzzy extension to the Mutli-Entity Bayesian Network (MEBN) language, which is a High-Level Information Fusion (HLIF) framework capable of expressing the semantic and causal relationships between the entities constituting a world model, as well as managing their ambiguity and uncertainty. In our proposed model, the unstructured soft data is presented by undergoing a novel soft-data-association process, through which the data is semantically analyzed, and accurately structured in a fuzzy random variable. Moreover, the clique tree inference algorithm for Bayesian Networks is modified to handle fuzzy evidence in Fuzzy-MEBN. The simulation results, in transportation domain, show that our improved HLIF model is capable of handling both soft and hard data, and consequently, provide the user with more precise situation assessment.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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