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Record W2163965488 · doi:10.1109/ccece.2005.1557391

Denotational semantics for RTPA

2006· article· en· W2163965488 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCognitive Computing and Networks
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsConcurrencyDenotational semanticsComputer scienceProcess calculusInterruptSemantics (computer science)Operational semanticsProgramming languageProcess (computing)Event (particle physics)Denotational semantics of the Actor modelInterval (graph theory)Set (abstract data type)Communicating sequential processesTheoretical computer scienceMathematicsEmbedded system

Abstract

fetched live from OpenAlex

Real-time process algebra (RTPA) is designed to deal with a rich set of fundamental real-time processes such as timing, interrupt, concurrency, and event/time-driven. Some of the RTPA processes cannot be described adequately in conventional denotational semantics paradigms. This paper develops a new framework for modeling time and processes in order to represent RTPA in denotational semantics. Within this framework, time is modeled by elapse of process execution. The process environment encompasses states of all variables, represented as mathematical maps, which project variables to their corresponding values. Duration is introduced as a pair of time interval and the environment to represent the process environment change during a time interval. Temporal ordered durations and operations on them are used to denote process executions. With all these means, the semantics of RTPA processes of timing, interrupt, concurrency, event/time-driven, and traditional sequential processes can be formally expressed.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.885
Threshold uncertainty score0.155

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.225
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations1
Published2006
Admission routes1
Has abstractyes

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