Enhancing Remote Walkthrough for E-learning Environments on Mobile Devices over Heterogeneous Networks
Bibliographic record
Abstract
With the growth of the Internet and availability of high bandwidth connections to domestic users, it became possible to deploy remote navigation within virtual environments over the Internet. For instance, virtual museum walkthrough, virtual mall, gaming, training, monitoring, and e-learning, just to name a few. We have also seen a new trend towards wireless networks and the use of mobile devices with wireless communication capabilities. Speci'cally, e-learning environments can bene't from remote exploration of virtual environments over wireless networks in order to provide users with rich 3D content such as virtual laboratories and remote visits where users can interact with the 3D e-learning system using mobile devices such as PDAs, cell phones, or laptops. However, the characteristics of wireless channels pose signi'cant problems to real-time interactive multimedia applications. Wireless bandwidth is always changing and the communication channel is highly susceptible to error. In this paper, we focus on the design of a remote walkthrough within realistic virtual environments over heterogeneous networks for mobile devices. We propose a real-time system that deals with the acquisition or remote geometry rendering, compression, packetization and transmission of images, which serve as input to an image-based rendering technique for fast creation of new views on the mobile client device. The objective is to contribute with a solution for remote walkthrough over wireless networks, while guaranteeing a good image quality and navigation at acceptable frame rates on thin client 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".