PPG-based Personalized Verification System
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
Convergence between online and off-line systems gives us a great chance to enrich our societies but it also requires a high secure system to verify true user from fraud. In this paper, we propose a novel deep learning-based verification model using Photoplethysmography (PPG) signals. The goal of this paper is to build a personalized data-driven network by employing convolution neural network (CNN) with long-short term memory (LSTM), to model the time-series sequence inherent within the PPG signal. After building each personalized network, each network can be applied to distinguish a true user from others. The proposed network was evaluated on the BioSec. Lab PPG dataset at University of Toronto, which achieved an average of 10-fold cross-validation accuracy of 96% (in single-session) and 72.7% (in two-sessions).
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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 | 0.000 | 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 | 0.001 |
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