The 1st Workshop on 5G and Machine Learning for IoT and Unmanned Aerial Vehicles (UAV)
Bibliographic record
Abstract
The emergence of 5G and machine learning technologies leads to new opportunities and challenges to exploit new features for performance and security of Internet of Things (IoT) and robotic systems. The development of a new 5G-enabled and machine learning-aided communications & computing framework towards enhanced decision-making in IoT will be important in scenarios such as real-time surveillance for smart city, environmental sustainability, and defense applications. With advantages in mobility, higher line-of-sight and ease of use, unmanned aerial vehicles (UAV) has high potential for developments to enhance communication and processing. This workshop discussed the research and progress made in this direction, including some of the synergies and opportunities in 5G-VA V processing to enhance terrestrial IoT networks, machine learning for UAV optimization and prediction in 5G networks, as well as UAV's for situation awareness. The development of a new 5G-enabled and machine learning (ML)-aided communications & computing framework towards enhanced decision-making in IoT will be important in scenarios such as real-time surveillance for smart city, sustainability, and defence.
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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 | 0.003 | 0.002 |
| 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 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".