MétaCan
Menu
Back to cohort
Record W2168015072 · doi:10.1109/ccece.2003.1226127

Formal description of an ATM system by RTPA

2004· article· en· W2168015072 on OpenAlexaff
Yingxu Wang, Yanan Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCognitive Computing and Networks
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer scienceCorrectnessDependabilityProcess (computing)Conceptual modelFinite-state machineAbstract state machinesSet (abstract data type)Formal specificationFormal verificationFormal methodsArchitectureModel checkingSoftware engineeringProgramming language

Abstract

fetched live from OpenAlex

An automated teller machine (ATM) is a safety-critical and real-time system. The modeling and description of an ATM is a classical real-world case because its conceptual model is well known as a working project in real-time system design. For ensuring correctness and dependability, real-time process algebra (RTPA) is adopted to specify a formal model of the ATM. By using RTPA, the architecture, static and dynamic behaviors of the ATM can be described formally, precisely, and consistently. This paper describes the conceptual and formal models of the ATM. The conceptual model of the ATM is described by a finite state machine (FSM), and the formal model is specified by RTPA. This paper demonstrates that the ATM can be formally described by a set of real-time processes in RTPA. It also shows the relationship between the RTPA model and the FSM model of the ATM system.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.002

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.

Opus teacher head0.015
GPT teacher head0.213
Teacher spread0.198 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2004
Admission routes1
Has abstractyes

Explore more

Same topicCognitive Computing and NetworksFrench-language works237,207