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Record W1574311809 · doi:10.1007/978-0-387-35599-3

Design and analysis of distributed embedded systems : IFIP 17th World Computer Congress - TC10 stream on distributed and parallel embedded systems (DIPES 2002), August 25-29, 2002, Montréal, Québec, Canada

2002· book· en· W1574311809 on OpenAlexaboutno aff
Bernd Kleinjohann

Bibliographic record

Venuenot available
Typebook
Languageen
FieldComputer Science
TopicPetri Nets in System Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceMiddleware (distributed applications)DependabilitySession (web analytics)Embedded systemFault toleranceOperating systemSoftware engineering

Abstract

fetched live from OpenAlex

Preface. Workshop Organization. Session 1: Modelling and Specification. Can UML be a System-Level Language for Embedded Software? J.M. Fernandes, R.J. Machado. PEARL for Distributed Embedded Systems R. Gumzej, W.A. Halang. Universal Plug and Play Machine Models U. Glasser, M. Veanes. Session 2: Specification and Analysis. Analysis of Event-Driven Real-Time Systems with Time Petri Nets: A Translation-Based Approach Zonghua Gu, Kang G. Shin. Petri Net Based Design of Reconfigurable Embedded Real-Time Systems C. Rust, F. Stappert, R. Bernhardi-Grisson. Model Checking Robustness to Desynchronization J.-P. Talpin. Session 3: Verification and Validation. A Semi-Formal Method to Verify Correctness of Functional Requirements Specifications of Complex Systems N. Kececi, W.A. Halang, A. Abran. Towards Design Verification and Validation at Multiple Levels of Abstraction H. Giese, M. Kardos, U. Nickel. Modeling and Verification of Pipelined Embedded Processors in the Presence of Hazards and Exceptions P. Mishra, N. Dutt. Session 4: Fault Tolerance and Detection. Statistical Analysis of a Hybrid Replication Model E.R. de Oliveira Jr., I. Jansch Port. Building Embedded Fault-Tolerant Systems for Critical Applications: An Experimental Stud P. Townend, Jie Xu, M. Munro. Fault Detection in Safety-Critical Embedded Systems D. Verber, M. Colnaric, W.A. Halang. Session 5: Middleware and Reuse. Dependability Characterization of Middleware Services E. Marsden, N. Perrot, J.-C. Fabre, J. Arlat. Adaptive Middleware for Ubiquitous Computing Environments S.S. Yau, F. Karim. Finegrained Application Specific Customization of Embedded Software D. Beuche, O. Spinczyk, W.Schroder-Preikschat. Session 6: Timing and Performance Analysis. Checking the Temporal Behaviour of Distributed and Parallel Embedded Systems W.A. Halang, N. Kececi, G. Tsai. Transforming Execution-Time Boundable Code into Temporally Predictable Code P. Puschner. Bottom-Up Performance Analysis of HW/SW Platforms K. Richter, D. Ziegenbein, M. Jersak, R. Ernst. Session 7: Partitioning and Scheduling. Temporal Partitioning and Sequencing of Dataflow Graphs on Reconfigurable Systems C. Bobda. Integration of Low Power Analysis into High-Level Synthesis A. Rettberg, B. Kleinjohann, F.J. Rammig. Going Beyond Deadline-Driven Low-Level Scheduling in Distributed Real-Time Computing Systems K.H. (Kane) Kim, Juqiang Liu. Session 8: Communication and Application. IEEE-1394 A Standard to Interconnect Distributed Systems R. Santamaria. Deterministic and High-Performance Communication System for the Distributed Control of Mechatronic Systems Using the IEEE1394a M. Zanella, T. Lehmann, T. Hestermeyer, A. Pottharst. A Consistent Design Methodology for Configurable HW/SW-Interfaces in Embedded Systems S. Ihmor, M. Visarius, W. Hardt. Low Latency Color Segmentation on Embedded Real-Time Systems D. Stichling, B. Kleinjohann. Session 9: Design Methods and Frameworks. Soft IP Design Framework Using Metaprogramming Techniques V. Stuikys, R. Damasevicius, G. Ziberkas, G. Majauskas. How to integrate Webservices in Embedded System Design? A. Rettberg, W. Thronick. Design and Realization of Distributed Real-Time Controllers for Mechatronic Systems M. Deppe, M. Zanella.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.005

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 head0.020
GPT teacher head0.216
Teacher spread0.196 · 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 designNot applicable
Domainnot available
GenreOther

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".

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Citations1
Published2002
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