Proceedings of the 1st International Workshop on Emerging Multimedia Applications and Services for Smart Cities
Bibliographic record
Abstract
We are delighted to welcome you to the 2014 ACM MM Workshop on Emerging Multimedia Applications and Services for Smart Cities -- EMASC'14. This is the first workshop we organize on the emerging concept of smart cities. We aim this workshop to be a premier forum to report on the state-of-the-art techniques, methodologies, multimedia applications, and services relevant to smart city. The mission of the workshop is to address the many challenges that arise from the proliferation of multimedia, sensors, pervasive devices, and integrated infrastructure for realizing smart city. Addressing these challenges would contribute to improve the quality of life of smart city citizens in many aspects including public safety, healthcare, transportation, or energy. Our call for papers attracted many submissions from Taiwan, Finland, Italy, Qatar, United States, Canada, Iran, Malaysia, Saudi Arabia, and Australia. The program committee reviewed 17 full length technical papers and accepted 6 of them, having an acceptance ratio of 35%. We also invited two keynote speakers who are actively involved in IBM smart cities movement. We therefore encourage all attendees to attend the keynote presentations. These presentations will provide us valuable insights about the current status and future trends of smart cities movement in both the industry and the academia. The two keynote speeches are: Human Surrogates: Remote Presence for Collaboration and Education in Smart Cities by Charles Hughes, PhD (Professor, Computer Science, University of Central Florida, USA) Industrial and Business Systems for Smart Cities by Ben Amaba, PhD (PE, CPIM®, LEED® AP BD+C, IBM Corporation Worldwide Executive, Miami, Florida USA)
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.111 | 0.055 |
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".