Bibliographic record
Abstract
We present Shuriken, a method for user grouping and data transfer based on relative position estimates of smart devices that are in close proximity. The relative positions are then used for identifying the recipient of transferred data by performing a swipe on the screen of the sending device towards the physical direction of the recipient. Shuriken is built upon the Bluetooth Low Energy (BLE) framework and only uses built-in sensors of typical smart devices. Users link their devices by pointing them towards each other to form a group and to create a BLE connection. The received radio signal strength and the digital compass readings are obtained and then distributed to estimate the relative positions of the devices. Additional devices can be included in an existing group by performing the same action with any device in the group. Devices in the group can perform data transfer and the data is passed through linked devices in a multi-hop approach. We envision practical uses of Shuriken in collaborative shopping in a café, data transfer in business meetings and localisation of multiple smart devices that are in close proximity. To the best of our knowledge, Shuriken is the first approach that performs user grouping and data transfer based on the inter-device relative positions calculated from sensor readings available in off-the-shelf smart devices.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.013 |
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".