Clustering spatio-temporal bi-partite graphs for finding crowdsourcing communities in IoMT networks
Why this work is in the frame
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Bibliographic record
Abstract
The Internet of Moving Things is rapidly becoming a reality where intelligent devices and infrastructures are fostering real-time data sustainability in smart cities and advancing crowdsourced tasks to improve energy consumption, waste management, and traffic operations. These intelligent devices create a complex network scenario in which they often move together or in conjunction with one another to complete crowdsourced tasks. Our research premise is that mobility relationships matter when performing these tasks, and therefore, a graph model based on representing the changes in mobility relationships is needed to help identify the neighbour devices that are moving close to one another in our physical world but also seamlessly connected in their virtual world. We propose a bi-partite community mobility graph model for linking intelligent devices in both virtual and physical worlds, as well as reaching a trade-off between crowdsourced tasks designed with explicit and implicit citizen participation. This paper aims to explore a bi-partite graph as a promising spatio-temporal representation of IoMT networks since changes in mobility relationships over time can indicate volunteer organisation at the device and community levels. The Louvain community detection method is proposed to find communities of intelligent devices to reveal a value conscious participation of citizens. The proposed bi-partite graph model is evaluated using a real-world scenario in transportation, confirming the main role of evolving communities in developing crowdsourcing IoMT networks.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it