A Framework for Sensory-based P2P Collaborative Environment
Bibliographic record
Abstract
Operating multi-sensor mobile robots in hostile and hazardous environments, such as in rescue missions, while capturing and sending multimedia data-on-demand, in real-time to a collaborative group is a very challenging task. The task becomes even more complex when the sensory data to be disseminated in a peer-to-peer (P2P) multimedia collaborative environment. In the P2P environment, peers may not only request concurrent sensory data, like the panoramic view of the remote environment, temperature, distance to the nearest object, engage in audio/video conferencing, but also remotely dispatch control commands to guide the robot throughout the site. Many factors need to be carefully designed in order to achieve such a complex goal. In our current work, we design a sensory-based P2P multimedia collaborative environment where various types of sensory-data are sent to one of the peers in the collaborative session, which is then distributed among other peers within the group. As a proof of concept, we developed a prototype model of the system. Finally, we present our test results.
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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 | 0.002 | 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 | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".