Bibliographic record
Abstract
The 14th ACM Multimedia Systems Conference (with the associated workshops: NOSSDAV 2023, MMVE 2023, and the first edition of GMSys 2023) took place from 7th - 10th June 2023 in Vancouver, Canada. The MMSys conference brings together researchers in multimedia systems to showcase and exchange their cutting-edge research findings. Once again, there were technical talks spanning various multimedia domains and inspiring keynote presentations. Participants had also the opportunity to further interact with colleagues while enjoying the sunset with a 360° view of Vancouver on the Lookout tower or during a dinner in the core of the rainforest. Additionally, this year's event included a special session dedicated to the memory of Dr. Kuan-Ta Chen, to honor his invaluable contributions to the multimedia community and to inspire the future generation of researches. To encourage junior researchers to participate on-site, SIGMM has sponsored a group of students with Student Travel Grant Awards. For many of them, this was their first time presenting at an international conference, and it was a wonderful experience. In this article, the recipients of the travel grants share their experiences at MMSys 2023.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.385 | 0.384 |
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".