Recognizing human behavior through nonlinear dynamics and syntactic learning
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 work applies nonlinear dynamics to model the encoded time series describing human activities performed in the spatio-temporal domain. We augment the concepts of symbolic dynamics and formal language theory to pattern recognition in generating probabilistic feature extraction which are used to drive a Bayesian classifier for behavior recognition. Our motivation for using stochastic context-free grammar (SCGF) is to aggregate low-level events detected so that we can construct higher-level models of interaction. This is a novel attempt in coupling symbolic dynamics with a stochastic model to construct a spatio-temporal ordered SCFG. Extended statistical tests and comparative analysis with Bayesian classification and k-nearest neighbour (K-NN) classification of time series sequences demonstrate a superiority of the proposed method of gesture recognition using concepts from nonlinear dynamics.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it