An efficient protocol for remote virtual environment exploration on wireless mobile devices
Bibliographic record
Abstract
The exploration of virtual environments in wireless mobile media devices has attracted the attention of researchers and developers mainly due to its potential applications in a variety of areas including entertainment, training, security, e-learning, etc. However, current technology of mobile devices lack the proper resources to handle complex and realistic 3D virtual environments. There has been a number of proposed ideas to solve this issue. The existing approaches use techniques that either employ limited user navigation modes or do not perform satisfactorily for interactive applications. In this paper, we propose a new protocol that offers the user a richer navigation by pre-streaming the necessary imagery data to generate new views as the user wanders within the 3D environment. We introduce the idea of key partial panoramas, i.e., panorama segments that cover movements in any direction by simply strafing from an appropriate key partial panorama and streaming the amount of lost pixels. We have implemented our ideas and evaluated it against two well-known approaches. Experimental results show that our solution outperforms the selected approaches by minimizing the delay between image updates and by allowing a more complex navigation scheme than previous works.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".