Proceedings of the 4th ACM international workshop on Wireless mobile multimedia
Bibliographic record
Abstract
It is with great pleasure that we welcome you to the historical city of Rome, Italy and to the Fourth Annual International Workshop on Wireless Mobile Multimedia - WoWMoM '01We believe that this will be the best WoWMoM thus far. We base our assertion on this year's program, which we are very proud of. The program consists of 11 technical papers and presentations, broken up into three sessions. The papers contain research results on a variety of problems being faced by deployers and users of wireless technology world-wide.We are able to put together a strong program because of the excellent response we received to our call for papers. We received a total of 48 papers from some of the top research institutes from around the world. Statistically speaking, we received the largest number of papers inWoWMoM's history, 33% more papers than last year. 65% of the papers were from academia and 35% from industry. 47% of the papers were from the United States and Canada and 53% from Europe and Asia. These numbers reflect the fact that WoWMoM is truly an international event and that the area of wireless mobile multimedia is still growing. WoWMoM's success can be attributed to its International Program Committee (PC) whose members are some of the strongest active researchers in the field of wireless multimedia, and who represent our global community remarkably. This year's Program Committee had to work under severe time constraints but they came through with flying colors. All in all we generated a total of 144 reviews, with each committee member contributing over 17 reviews in less than 4 weeks. The process we employed for selecting the final set of papers was rigorous. Once all the reviews were in, we ranked order the papers using different criterions, (for example, overall recommendation, originality of the ideas, product of these two criterion etc.) and then looked at the resulting lists to identify the better papers. We then went through the reviews of each of the papers, reading reviewer comments while keeping in mind the confidence they had placed on their reviews, and looking at the relevance of the paper to WoWMoM. We marked papers with disparate reviews and asked the PC members to resolve their differences and come up with a unanimous recommendation. We discussed over 75% of the papers and following this came up with our list of recommendations. The PC reviewed these recommendations one more time and subsequently the final program was announced. Owing to the fact that WoWMoM is a one-day workshop and the quality of the papers was paramount to us, we selected less than 25% of the submissions. We feel that the result of all this effort is that we can bring to you a fairly high-quality program, which we hope you will enjoy and benefit from.
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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 | 0.002 | 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 | 0.005 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.091 | 0.053 |
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".