Proceedings of the 14th ACM International Symposium on Mobility Management and Wireless Access
Bibliographic record
Abstract
On behalf of the organizing committees, it is our great pleasure to welcome you to the 14th ACM International Symposium on Mobility Management and Wireless Access -- MobiWac 2016. Previous editions of this symposium took place in Dallas/Fort Worth (TX, USA), Philadelphia (PA), Maui (Hawaii), Torremolinos (Spain), Chania (Greece), Vancouver (Canada), Tenerife (Spain), Bodrum (Turkey), Miami (FL, USA), Paphos (Cyprus), Barcelona (Spain) Montreal (Canada), Cancun (Mexico). This year MobiWac takes place in Malta, and it continues its successful track record of being a forum where researchers from academy and industry gather to discuss novel advances in mobility, wireless access and related topics, aiming at advancing knowledge and identifying new directions for future research and development. The call for papers attracted a large number of submissions from Africa, America, Asia and Europe. From these works, the program committee has reviewed all papers and selected 28% of the best papers and put together the program you have in front of you. Accepted papers cover a wide variety of topics, including mobility management and medium access, MANET networking, tracking, quality of service, security and applications. The accepted papers come from 11 countries (Brazil, Italy, UK, Turkey, USA, Spain, Germany, Canada, Germany, Norway, Greece), which reflects the international nature of the symposium.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.096 | 0.070 |
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".