A semi-supervised deep residual network for mode detection in Wi-Fi\n signals
Bibliographic record
Abstract
Due to their ubiquitous and pervasive nature, Wi-Fi networks have the\npotential to collect large-scale, low-cost, and disaggregate data on multimodal\ntransportation. In this study, we develop a semi-supervised deep residual\nnetwork (ResNet) framework to utilize Wi-Fi communications obtained from\nsmartphones for the purpose of transportation mode detection. This framework is\nevaluated on data collected by Wi-Fi sensors located in a congested urban area\nin downtown Toronto. To tackle the intrinsic difficulties and costs associated\nwith labelled data collection, we utilize ample amount of easily collected\nlow-cost unlabelled data by implementing the semi-supervised part of the\nframework. By incorporating a ResNet architecture as the core of the framework,\nwe take advantage of the high-level features not considered in the traditional\nmachine learning frameworks. The proposed framework shows a promising\nperformance on the collected data, with a prediction accuracy of 81.8% for\nwalking, 82.5% for biking and 86.0% for the driving mode.\n
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".