A Novel Optimized Caching Technique for Mobile Gnutella Based Network to Support Large-Scale Collaborative Virtual Environment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Collaborative virtual environments (CVEs) such as massive multi-user 3D games and military training environments can place strict requirements on network when participating users are sharing the 3D virtual environment through mobile devices in an ad-hoc network. This paper presents an optimization mechanism for Gnutella network to better meet the mobility and the network requirements of collaborative virtual environments (CVEs). With the rapidly increasing use of mobile personal devices, we are facing new challenges and opportunities for making efficient mobile collaborative virtual environments (MCVEs) applications based on Gnutella network. The proposed work comprises an enhancement of the Gnutella network through an efficient overlay formation protocol, and a novel caching optimization technique implemented in specific nodes selected through a Gnutella Ultrapeer system (GUS) mechanism. The resulting approach should overcome several problems including flooding and the limited capacity of mobile devices. In order to evaluate our designed protocols, we run the system over several ad-hoc routing protocols, simulation results shows that each ad-hoc routing protocol performs well only under specific simulation scenarios.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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 it