Media sharing based on colocation prediction in urban transport
Bibliographic record
Abstract
People living in urban areas spend a considerable amount of time on public transport, for example, commuting to/from work. During these periods, opportunities for inter-personal networking present themselves, as many members of the public now carry electronic devices equipped with Bluetooth or other wireless technology. Using these devices, individuals can share content (e.g., music, news and video clips) with fellow travellers that are on the same train or bus. Transferring media content takes time; in order to maximise the chances of successful downloads, users should identify neighbours that possess desirable content and who will travel with them for long-enough periods. In this paper, we propose a user-centric prediction scheme that collects historical colocation information to determine the best content sources. The scheme works on the assumption that people have a high degree of regularity in their movements. We first validate this assumption on a real dataset, that consists of traces of people moving in a large city's mass transit system. We then demonstrate experimentally on these traces that our prediction scheme significantly improves communication efficiency, when compared to a memory(history)-less source selection scheme.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".