A novel solution for the development of collaborative virtual environment simulations in large scale
Bibliographic record
Abstract
There has been a growing interest in the application of collaborative virtual environments - CVEs in the WWW. However, most of the existing CVEs supporting systems are tuned to specific tasks and their architecture are, typically, tightly coupled to the applications. This makes any modification to the application dependent on programming, making CVEs construction and extension in large scale a challenging task. The ability to change an application without having to stop it is an important nonfunctional requirement for CVEs, especially if they are to be provided as Web services, available around the clock. This paper presents a novel solution for building and extending CVEs through the integration of interactive non-linear stories and virtual reality concepts. With this approach, CVEs applications can be composed as non-linear stories, which can be changed either completely or partly, making it easier for developers to build and/or extend CVEs applications. The innovative concept of this solution can make the development process of CVEs easier and faster, allowing their production in large scale. An art installation scenario is presented in this paper as a case study to illustrate how atomic simulations and VEML language can describe CVEs and extend them at runtime.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".