Formal specification of CORBA-based distributed objects and behaviors
Bibliographic record
Abstract
A distributed object system consists of a set of objects that interact by invoking services to one another. For successful cooperation between these objects, they must have capabilities that enable them to represent, use and share information. Existing middleware technologies define the remote object classes in terms of their interfaces only and do not give any semantic or behavioral specifications of the remote objects resident in clients and servers. This paper formalizes the behavior of distributed objects based on the Common Object Request Broker Architecture (CORBA). The capturing of information by each distributed object can be compared to the way information is captured using the Object-Attribute-Relation (OAR) model in which information about an object is represented by a 3-tuple (0, A, R), where O represents the object ID used to identify an object, A the set of object attributes used to denote detailed characteristics of an object and R a set of defined relationships used to make connections to other objects. Real-Time Process Algebra (RTPA) is used to model the architecture of a distributed system. Based on this architecture, a formal specification of the behavior of the distributed objects is presented through a case study.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".