Proceedings of the 2001 international workshop on Multimedia middleware
Bibliographic record
Abstract
Welcome to the 9th ACM Multimedia Conference, held Sept. 30-Oct.5, 2001 in Ottawa, Ontario, the capital city of Canada. This beautiful city is often called Silicon Valley North because of the high concentration of major telecommunications and software companies, such as NORTEL, ALCATEL, CISCO, MITEL, COREL, COGNOS, JDS Uniphase, Entrust and many others. The conference complements this setting by presenting and exploring technological and artistic advancements in multimedia. Technical issues, theory and practice, artistic and consumer innovations will bring together researchers, artists, developers, educators, performers, and practitioners of multimedia. This conference is sponsored by NTT, ALCATEL, HP, Fuji-Xerox Palo Alto Laboratory, the Association for Computing Machinery (ACM) and its special interest groups SIGMM, SIGGRAPH, and SIGCOMM.Brigitte Kerherve did a tremendous job of selecting three outstanding workshops on emerging topics. Tamer Ozsu organized an exciting plenary panel and Ketan Mayer-Patel did again a wonderful job of organizing this year's doctoral symposium. Vincent Ofia did a splendid job in chasing the authors and organizing the papers. Roger Price has been working on the electronic proceedings since 1997.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.066 | 0.028 |
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".