Improvement Strategies for Device Interoperability Middleware using Formal Reliability Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ensuring the correctness of middleware that ensures interoperability of various medical devices is one of the biggest challenges in the e-health domain. Traditionally, these Device Interoperability Middleware (DIM) are analyzed using software testing. However, given the inherent incompleteness of testing and the randomness of the user behaviours, the analysis results are not guaranteed to be accurate. Some of these inaccuracies in analysis results could even put human life at risk. In order to overcome these limitations, we propose to use a probabilistic model checker PRISM for analyzing DIM. The proposed approach allows us to rigorously verify reliability properties of the given DIM and thus allows the designers to make appropriate measures to design more reliable systems. For illustration, we formally analyze a middleware that uses the HL7 FHIR and ontology-based description of the devices and a communication protocol to bridge the gap in heterogeneity for dealing with different vendors and incompatible data formats.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it