Automatic Data Quality Enhancement with Expert Knowledge for Mobile Crowdsensing
Bibliographic record
Abstract
Mobile crowdsensing (MCS) has recently found many applications in environmental monitoring and large-scale surveillance by recruiting crowd workers for data collection and labeling. The quality of labelled data from unknown crowd workers, however, is hard to guarantee. Therefore, it is critical to design a mechanism that can automatically make correct decisions from diverse and even conflicting labels from the crowd. To tackle the challenge, we propose a new algorithm, EFusion, which infuses knowledge from domain experts by asking them to check a small number of labels from the crowd. Taking advantage of cheaper but unreliable crowd workers as well as expensive but reliable experts, EFusion can greatly improve the accuracy in discovering the ground truth of classification-based mobile crowdsensing tasks. EFusion utilizes a probabilistic graphical model and the expectation maximization (EM) algorithm to infer the most likely expertise level for each crowd worker, the difficulty level of tasks, and the ground truth answers. EFusion has been evaluated using real-world case study as well as simulations. Evaluation results demonstrate that EFusion can return more accurate and stable classification results than the majority voting method and state-of-the-art methods.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".