A design aid and real-time measurement framework for Virtual collaborative simulation Environment
Bibliographic record
Abstract
Distributed Virtual Environment (DVE) has attracted much attention in recent years due to the rapid advances in the areas of E-learning, Internet gaming, human-computer interfaces, and etc. However, the complexity of designing an efficient DVE greatly challenges today's researchers. Indeed, how to effectively measure a DVE or the design of DVE plays an important role in progressively guiding the DVE design. Meanwhile, the performance of protocols and schemes used in an existing DVE cannot be easily measured straightforwardly. Traditionally, simulation tools are involved to predict the performance of the DVE system or any used protocols; however, existing tools have limited capabilities in terms of accurately capturing the real-world performance. This is due to the statically configured simulations, hard-to-model hardware devices (such as haptic devices, mobile devices), single processor's execution of the simulations, and etc. Moreover, there exists no integrated simulating and measuring framework that can effectively support model reuse, dynamic reconfiguration of a simulation, real devices in the simulation loop (statically or run-time), and distributed simulation execution, just to name a few. In this paper, we propose and implement an integrative simulation and measuring framework; in particular, we design a generic real-time service oriented virtual simulation system which can effectively measure the real time performance of distributed virtual environments and virtual reality based applications. The main goal of our proposed framework lies in providing accurate and near-real-world measurements of a DVE and any related protocols, so that a highly cost-efficient DVE system design can be achieved. Moreover, our framework uses the Experimental Frame concept to separate simulation services from design models so that model reuses, model formalization and model validation can all be done within one layer. The idea of using experimental frame also makes possible a hardware-in-the-loop type of simulation, which is quite useful in DVE including haptic virtual environment, sensor network based virtual environment, and etc. As a case study, we investigate a QoS-aware adaptive load balance algorithm using our framework; and our real-time simulation results clearly indicate that the algorithm outperforms others in a near real-world scenario.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".