Retargeting of virtual reality applications
Bibliographic record
Abstract
Current practices in development of Virtual Reality (VR) applications are very costly in general and difficult to adapt to various VR platforms. This thesis solves some of the issues related to the development of VR applications when a variety of hardware platforms is available, as in current VR labs and development sites. We make visible at a high level of abstraction the most important elements in the interface of a VR application. Current representation methods of VR are either too formal or too close to a programming language to be understood by users, which precludes the analysis, evaluation, and improvement of interface issues. We define a clean separation between different software components in a VR application and its associated semantics. This separation allows us to reuse VR components, without having to worry about unexpected side-effects. We also define a new way to transform an application from one hardware platform to another. We call this process retargeting, and it is based on our ability of component reuse and the high level of abstraction language we define. We separate two important roles in the development of VR applications. One is in charge of the overall architecture of the application. They pay attention to interface issues, requirements coverage, and component reuse. The second one is in charge of fine-detail development of components and its tuning to a particular deployment environment. We consider this separation an important way to handle complexity in the development process. It enables different people to concentrate on different issues and at the same time collaborate on the development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".