Data-Driven Monitoring for Distributed Sensor Networks: An End-to-End Strategy Based on Collaborative Learning
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Bibliographic record
Abstract
This article proposes an end-to-end event monitoring method for a distributed sensor network with non-concentrated label noises based on a collaborative soft-label network (CSLN). The proposed CSLN framework is composed of a fundamental learner (FL) and a collaborative label modification. FL is designed based on a bidirectional gated recurrent unit (BiGRU) with attention to tackle the indistinct boundary problem by modeling multiscale dependencies. BiGRU can learn latent representations through capturing local dependencies in a bidirectional time flow. The attention mechanism is capable of modeling long-range dependencies through assigning learnable weights to the latent representations. Then, the collaborative label modification is established to reduce label noises by combining a truncated loss function and a dual-space smoothing technique. The truncated loss function can prevent FL from overfitting to noisy labels by isolating them in optimization. The dual-space smoothing technique can generate soft labels based on the local similarities. Furthermore, the proposed CSLN method is optimized by a bi-loop recursive strategy to reduce label noises gradually through alternatively training FL and generating soft labels. The feasibility and effectiveness of the proposed method are validated through real-field experiments of perimeter security applications based on distributed optical fiber sensors (DOFSs).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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