Bibliographic record
Abstract
Despite Bluetooth's popularity, low cost, and low power requirements, Bluetooth applications remain remarkably unsophisticated. Although the research community and industry have designed games, cell-phone backup, and contextual advertising systems with Bluetooth, few such applications have been prototyped or evaluated on a large scale. Evaluating Bluetooth applications requires recruiting devices in the wild and developing robust software that can adapt to the heterogeneity of these devices. These requirements have limited both the number and the magnitude of the experiments with Bluetooth applications.This paper proposes BlueMonarch, a systemfor evaluating Bluetooth applications in the wild. BlueMonarch emulates a Bluetooth transfer to any device responding to Bluetooth Service Discovery requests; because many cell-phones, laptops, and PDAs in the wild respond to such probes, BlueMonarch enables quick prototyping of Bluetooth applications in the wild, to hundreds of unmodified Bluetooth devices. After we present the feasibility and accuracy of BlueMonarch, we use BlueMonarch to evaluate a content delivery system for Bluetooth. With BlueMonarch, we evaluated our system inside a mall and a subway system; we were able to send tens of megabytes of data to hundreds of Bluetooth devices in just a little over an hour.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.096 | 0.036 |
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".