Bibliographic record
Abstract
The Ubiquitous Cloud Computing (UCC) refers to the usage of multiple devices (e.g., smartphones, tablets, etc.) to consume services (e.g., data and application) from multi-cloud sources. To facilitate the deployment of UCC systems, there is the need to build a brokerage platform that aggregates the multi-cloud services that are mostly siloed and divergent. In this work, we discuss our proposed Cloud Services Brokerage for Ubiquitous Cloud Computing (CSB-UCC). The aim of the CSB-UCC is to present a single dimensional view of the services to the consumer who wants to access services from multi-cloud sources. We achieve this by integrating the APIs from different cloud layers such as the IaaS, PaaS, and SaaS. Further, based on the user's security preferences and settings on the brokerage, updates that are published on the cloud sources are automatically pushed to the n-mobile devices of the user without the user explicitly issuing a request. The qualities of the framework are: 1) high scalability in terms of serving higher number of consumer devices, 2) agility to accommodate API-oriented IaaS, SaaS, and PaaS cloud layers, 3) services transparency, and 4) low-latency services synchronization which aims at ensuring application and data consistency across the user's 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.243 | 0.166 |
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".