Proceedings of the 11th ACM symposium on Document engineering
Bibliographic record
Abstract
We're extremely pleased with the technical program again this year. The call for papers attracted 71 submissions, which were reviewed by 34 program committee members and 14 external reviewers. After careful review, we chose 38 papers, for an acceptance rate just over 53%. This year we're excited to present a choice of six half-day workshops before the symposium begins. The workshops span the spectrum from how-to to so-what and gee-whiz: Version Control presented by Neil Fraser (Google) Multimedia Document Processing in an HTML5 World presented by Dick Bulterman (CWI + VU Amsterdam + W3C), Rodrigo Laiola (CWI), Pablo Cesar (CWI), Ethan Munson (Univ. Wisconsin-Milwaukee) and Maria da Graca Pimentel (Univ. Sao Paulo) Secure Document Engineering presented by Helen Balinsky and Steven Simske (HP Labs) Making Accessible PDF Documents presented by Heather Devine, Andres Gonzales and Matthew Hardy (Adobe Systems) Documenting Social Networks presented by Maria da Graca Pimentel (Univ. Sao Paulo) Google Mystery Workshop presented by John Day-Richter (Google) We are happy to include the last of these as a late-breaking surprise. DocEng 2011 includes two keynote talks. The first is given by Dr. John E. Warnock, co-chairman of the Board of Directors of Adobe Systems, Inc. and one of its co-founders. Conference attendees have been looking forward to hearing a key person behind PostScript and PDF, as well as the inventor of Warnock's algorithm for rendering surfaces in computer graphics, describe challenges behind multimedia authoring. The second keynote is presented by Google's Dr. Mark Davis, co-founder of the Unicode project and president of the Unicode Consortium since its incorporation in 1991. We are anxious to learn about some of the difficulties behind internationalization from a leader in that field. The symposium also includes 19 full papers, eight short papers, and 11 posters and demos. In addition, seven of the 27 presentations are also supplemented by demos. The papers are organized into ten sessions, covering diverse problems in document engineering that span document manipulation and analysis, document representations, and document-centric applications. You are strongly encouraged to read about the exciting work as reported in these proceedings.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.114 | 0.092 |
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".