Relations and Kleene Algebra in Computer Science - PHD Programme at RelMiCS10 / AKA5
Bibliographic record
Abstract
This volume contains the tutorial materials and the contributed extended abstracts of the PhD Programme at the 10th International Conference on Relational Methods in Computer Science (RelMiCS10) and the 5th International Conference on Applications of Kleene Algebra (AKA5). The programme has been organised for the second time in association with RelMiCS/AKA. It took place in Frauenwörth on an Island in Lake Chiem in Bavaria, from April 7 to April 11, 2008, and included invited tutorials, a student session and attendance at the conference. Eight extended abstracts by students were selected for the programme by the organisers due to the relevance and quality of their submissions. They nicely reflect the diverse applications of relations and Kleene algebras in computing. The student session allowed the participants to present and discuss their own work. In addition there were three invited tutorials: Basics of Relation Algebra by Jules Desharnais (Université Laval, Quebec, Canada), Basics of Modal Kleene Algebra by Georg Struth (University of Sheffield, UK) and Basics of Preference and Fuzzy Preference Modeling by Susanne Saminger (Universität Linz, Austria). The RelMiCS/AKA conference series is the main forum for the relational calculus as a conceptual and methodological tool and for topics related to Kleene algebras. This year's proceedings, published as volume 4988 of the Springer LNCS series, contain 28 contributions by researchers from all over the world. Next to 26 regular papers there are the invited papers Formal Methods and the Theory of Social Choice by Marc Pauly (Stanford University, USA) and Relations Making Their Way from Logics to Mathematics and Applied Sciences, by Gunther Schmidt (University of the Armed Forces Munich, Germany). The papers show that relational and Kleene Algebra methods have wide-ranging impact and applicability in theory and practice.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.064 | 0.035 |
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".