Recognition of Cheating Behaviors Based on Finetuning of Model Parameters
Bibliographic record
Abstract
There are many problems with the current recognition methods of test cheating behaviors, namely, low accuracy, poor efficiency, and imbalance between positive and negative samples. To solve the problems, this paper proposes a classification and recognition method for test cheating behaviors through the transfer learning of pretrained models. Firstly, cheating samples, which mainly cover three cheating behaviors (peeking, passing notes, and checking cellphone) were collected from surveillance videos of exam rooms. The samples were enhanced through size transform and image synthesis. Next, multiple strategies were adopted to freeze the feature weights of the convolutional layers in the Darknet, before retraining the cheating classifier. In this way, a classification and recognition model was obtained for cheating behaviors. The model was tested on a self-designed dataset of test cheating behaviors. The results show that our method recognized 95.57% of cheating behaviors accurately, which is much better than the accuracy of the other methods. The real-time performance and accuracy of our method meet the application requirements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".