MEC-Based Evacuation Planning Using Variance Fractal Dimension Trajectory for Speech Classification
Bibliographic record
Abstract
Evacuation models are used in crisis scenarios to optimize the path for occupants to escape a dangerous situation. The behavior of occupants in crisis scenarios has been demonstrated to differ between genders. This paper proposes an end-to-end system that can automatically model an evacuation plan based on the distribution of genders in a given space. Using MEC connected edge devices that can detect speech signals, and interfacing with the cloud through edge servers equipped with learning capabilities, an evacuation model can be generated in real-time according to current gender distribution in an occupied area. The key to making this system successful is an accurate gender speech classifier computed at the edge level. The classification model used was an SVM classifier, and the features used were MFCCs and VFDTs. The MFCCs displayed a gender classification accuracy of 91.47%, and when adding VFDT features, the gender learning accuracy was increased to 92.19%. This shows the benefit of adding VFDT features to speech classification accuracy. Indeed, running the classifier on the edge level is enabling the system to meet the maximum delay deadline (10 sec), as required by the NFPA code standards.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".