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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".