Stochastic Resource Optimization for Metaverse Data Marketplace by Leveraging Quantum Neural Networks
Bibliographic record
Abstract
Metaverse can unleash the potentials of Internet of Sense (IoS) communication by intertwining objects and environment between physical world and parallel virtual world. In order to digitally experience smell or taste and navigate effortlessly in virtual reality, optimal resource allocation to strengthen sensing data based infrastructure system is a critical research challenge. The Metaverse Infrastructure Service Providers (MISPs) tap into data marketplace and subscribe to resources in advance for fulfilling the needs of data consumers and users. The demand of the data based services being uncertain, non-optimal subscription schemes may lead to unwanted resource wastage or shortage. Thus, we propose a Stochastic Integer Programming (SIP) model with two phase reservation and on-demand plans for optimal resource allocation in data marketplace. Further along this line, we strive to predict the demand by leveraging Quantum Neural Networks (QNN) that is able to learn with fewer historical data in comparison to classical machine/deep learning paradigms. Extensive simulation results justify that QNN as a supporting model can significantly reduce the computational complexities of SIP formulation. This research can contribute to reduce Metaverse resource fabrication costs, upgrade the profit margin for MISPs by increasing data based service sales revenue, provide real-time resource management decisions, and overall make real impacts in the virtual world.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".