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Record W2112384146 · doi:10.1109/ccece.2003.1226136

Specification of abstract data types using real-time process algebra (RTFA)

2004· article· en· W2112384146 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
KeywordsComputer scienceProgramming languageProcess calculusAbstract data typeNotationFormal specificationQueueSet (abstract data type)Process (computing)Data typeTheoretical computer scienceArithmeticMathematics

Abstract

fetched live from OpenAlex

The real-time process algebra (RTFA) provides a new approach to the specification and refinement of real-time systems. This paper presents a study on the specification of a set of abstract data types (ADTs) by using RTPA. The objectives of this work are to demonstrate the expressiveness of the RTPA notations and specification method, and to build a fundamental ADT library for RTPA by recursively applying the RTPA notations. Eleven ADTs, such as stack, record, array, queue, sequence, list, etc., have been selected and specified in RTPA. An ADT, Queue, is adopted in this paper to shown the RTPA specification and refinement methods. The queue specification in RTPA is contrasted to a conventional logic-based specification, and the features and advantages of the RTPA notation system is demonstrated. This case study shows that with RTPA, ADTs can be described and specified not only as static data types, but also dynamic real-time components, which enables ADTs to be applied in the real-time environment as predefined or embedded special architectural components.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.811
Threshold uncertainty score0.279

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 science0.0010.000
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 head0.073
GPT teacher head0.317
Teacher spread0.244 · 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

Citations3
Published2004
Admission routes1
Has abstractyes

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