Welcome message from the chairs
Bibliographic record
Abstract
We are very pleased to welcome every one to the 12th ACM/IEEE International Conference on Methods and Models for System Design (MEMOCODE 2014). In 2003 we started this journey, and we are happy to see this conference sustain through these years, and maintain a great reputation among researchers who work in various aspects of system design - in particular the area of application of formal methods in system design. Incidentally, the 1st MEMOCODE in 2003 and this 12th MEMOCODE in 2014 have the same program committee chairs, bringing some of us full circle. We must also bring to your attention that there is a subtle change in the full form of the conference title this year. For the last eleven years, we have had the title “International Conference on Methods and Models for Co-Design”, even though, we always welcomed and accepted papers that are specifically in hardware design or in software design. Therefore, this year, to reflect the true nature of the conference, we have slightly changed the title, dropping Co-Design from the title, as co-design is but only a subset of design methodologies for electronic systems.
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 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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.012 | 0.023 |
| Insufficient payload (model declined to judge) | 0.127 | 0.125 |
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".