A supplying partner strategy for mobile networks-based 3D streaming - proof of concept
Bibliographic record
Abstract
With the advances of wireless communication and mobile computing, there is a growing interest among researchers about augmented reality and streaming 3D graphics on mobile devices for training first responders to be better prepared in a case of disaster scenarios. However, several challenges need to be resolved before this technology become a commodity. One of the major difficulties in 3D streaming over thin mobile devices is related to the supplying partner strategy as it is not easy to discover the peer that has the correct information and that posses enough bandwidth to send the required data quickly and efficiently to the peers in need. In this paper, we propose a new supplying partner strategy for mobile networks-based 3D streaming. The primary goal of the work presented in this paper is first to address the thin mobile devices low storage capabilities; and second to avoid the flooding problem that most wireless mobile networks suffer from. Our proposed protocol is based on the quick discovery of multiple supplying partners, by optimizing the time required by peers to acquire data, avoiding unnecessary messages propagation and network congestion, and decreasing the latency and the network bandwidth over utilization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".