Editorial Message - Special Track on Embedded Systems: Applications, Solutions, and Techniques
Bibliographic record
Abstract
Welcome to ACM SAC 2011, The 26th Symposium on Applied Computing, and welcome to TaiChung, one of the five major metropolitan cities in Taiwan.Following the tradition, SAC 2011 provides an international forum for applied computer scientists, computer engineers and application developers to interact and present their work.This year we have a very strong technical program.In addition to a keynote address, it also includes 237 rigorously refereed papers and 59 posters.We would like to thank the ACM Special Interest Group on Applied Computing (SIGAPP) for being a sponsor of this conference and Tunghai University as a co-sponsor for making all the local arrangements.We have received additional funding from Taiwan National Science Council, Ministry of Education, Bureau of Foreign Trade Ministry of Economic Affairs, and Tunghai University.Special thanks go to many individuals who have contributed to the success of this conference.We thank the authors for sharing their ideas with us, the reviewers for providing valuable feedback, all the track chairs, as well as steering committee and program committee members, especially the two Program co-Chairs: Professor Chi-Cheng Hung from Southern Polytechnic State, and Professor Mathew Palatal from IUPUI, for taking time from their busy schedules to help us organize this conference.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.036 | 0.024 |
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".