<SUP align="right">DEVS</SUP>Server: ambient intelligence and DEVS modelling-based simulation server for epidemic modelling
Bibliographic record
Abstract
To improve disease surveillance systems (DSS) with faster and accurate outbreak detection and epidemics propagation capabilities, the availability of fine-tuned models is required along with the design of server-based solutions that simulate the effects of public health authorities' measures and integrate ambient intelligence (AmI) capabilities to semantise epidemic models. Hosting discrete event system specifications (DEVS) models, these AmI servers and their communication protocols are different, miscellaneous and require interoperability. The triple-space computing (TSC) paradigm addresses interoperability by sharing information represented in a semantic format through a common virtual space. In this paper, we present DEVSServer, a fully distributed TSC simulation server solution (middleware) designed to meet the needs of parallel and distributed discrete event simulation. DEVSServer defines a service oriented architecture (SOA) interface for the TSC operations. This interface complies with DEVS formalism and focuses on simplicity, conviviality and modularity, so that a single or many simulations that support different models can still interact. To assess DEVSServer, we provide a tuberculosis epidemic model simulation in time-varying temporal network with genetic programming immunisation strategy approach.
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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 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.048 | 0.027 |
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".