Bibliographic record
Abstract
In recent years, developing Modeling and Simulation (M&S) applications has become more and more complex.New technologies, like Web Services (WS) and Cloud computing, have been recently used in Modeling and Simulation (M&S).However, developing M&S applications using these technologies is still a complicated process.The reasons for this include: 1) it is hard to develop web services for varied M&S resources; 2) it is complicated to deploy M&S resources in the Cloud; 3) it is hard to integrate with varied M&S services; 4) it is complex to identify and select resources and services based on their meaning.In this research, we aim to simplify the development and integration of M&S applications using web technologies by solving the issues mentioned above.To do so, we propose the Mashup Architecture with Modeling and Simulation as a Service (termed MAMSaaS).MAMSaaS is a layered and lightweight M&S application development approach.It has five layers, which are Cloud, Box, Wiring, Mashup, and Tag Ontology Layers.It has a simplified life cycle to develop, deploy, identify, select, integrate and execute varied M&S resources as services in the Cloud.In the Cloud Layer, we developed CloudRISE middleware to expose RESTful Modeling and Simulation as a Service (MSaaS) for varied M&S resources; in addition, we propose new methods using Cloud computing and Experimental Framework concept to simplify the deployment of experiment environment.In the Box, Wiring and Mashup Layers, we present a new method based on mashup technologies to simplify the integration, execution and visualization of M&S applications.In the Tag Ontology Layer, we propose a new semantic selection approach using tag-mining and ontology-learning technologies, to identify and select M&S resources based on their meanings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".