Bibliographic record
Abstract
Real-time process algebra (RTPA) is a set of mathematical notations for rigorous system specification. The RTPA notation has a structure comprising of operands, primitive types, abstract data types, control logic, and relationships. It is capable to effectively capture a system design in terms of its architecture, static behaviors, and dynamic behaviors. However, the preferred approach to codify system definition is through visualization in the form of UML. Although UML has expressive graphical constructs that is easily understood, it is generally viewed as being informal. This paper proposes an automatic transformation between UML and RTPA. A transformation template is in the form of an UML profile to be used in the design of a system, which is an extension mechanism that allows specialization of the UML for a particular domain. The approach is based upon understanding RTPA and UML constructs and proceeds to identify, characterize, and rank semantic relationships in order to construct an optimal translation. The semantic relationship refers to the distance between RTPA and UML constructs and uses a linguistic distance measure that is reliable and sufficient for determining correspondence. Ultimately, the transformation template becomes a schema to transform UML system models into RTPA notation
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.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".