ETS System for AV+EC 2015 Challenge
Bibliographic record
Abstract
This paper presents the system that we have developed for the AV+EC 2015 challenge which is mainly based on deep neural networks (DNNs). We have investigated different options using the audio feature set as a base system. The improvements that were achieved on this specific modality have been applied to other modalities. One of our main findings is that the frame stacking technique improves the quality of the predictions made by our model, and the improvements were also observed in all other modalities. Besides that, we also present a new feature set derived from the cardiac rhythm that were extracted from electrocardiogram readings. Such a new feature set helped us to improve the concordance correlation coefficient from 0.088 to 0.124 (on the development set) for the valence, an improvement of 25%. Finally, the fusion of all modalities has been studied using fusion at feature level using a DNN and at prediction level by training linear and random forest regressors. Both fusion schemes provided promising results.
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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".