Proceedings of the 13th ACM Symposium on QoS and Security for Wireless and Mobile Networks
Bibliographic record
Abstract
It is our great pleasure to welcome you to the 13th ACM International Symposium on QoS and Security for Wireless and Mobile Networks -- Q2SWinet'17. The Q2SWinet 2017 symposium aims at serving as a meeting point and a forum for exchanging ideas, discussing solutions, and sharing experiences among researchers, professionals, and application developers, both from industry and academia. As with the previous seven editions of the Q2SWinet symposium series, the scope of this year's symposium will remain on general issues related to QoS and security in wireless and mobile networking and computing. The attendees will enjoy the presentations and discussions on cuttingedge research achievements on the provisioning of QoS and Security in wireless and mobile networks. The symposium will also increase the synergy between academic and industry professionals working in this area. The call for papers of ACM Q2SWinet'17 attracted submissions from Asia, Canada, Europe, South America, and the United States. With a large number of submissions, the program committee dedicated efforts to review all papers, in which each paper received at least 3 reviews. The final acceptance ratio was about 32%. ACM Q2SWinet '17 will have Professor Stephan Olariu of Old Dominion University (USA) as the keynote speaker. Dr. Olariu is a leading authority in the areas of wireless and vehicular networks architecture, protocols, and computing systems. Besides the distinguished keynote, the symposium will have five technical sessions that span over security, privacy, modeling, QoS, performance, WSNs, and data analytics. Furthermore, the technical program will contain posters sessions on QoS, QoE, and mobility, shared with MSWiM conference and dedicated for fostering discussions on the presented works.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.066 | 0.031 |
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".