Proceedings of the 1st symposium on Spatial user interaction
Bibliographic record
Abstract
It is our great pleasure to welcome you to the first ACM Symposium on Spatial User Interaction -- SUI'13. This new event focuses on the user interface challenges that appear when users interact in the space where the flat, twodimensional, digital world meets the volumetric, physical, three-dimensional (3D) space we live in. This considers both spatial input as well as output, with an emphasis on the issues around the interaction between humans and systems. The goal of the symposium is to provide an intensive exchange between academic and industrial researchers working in the area of SUI and to foster discussions among participants. The first SUI symposium was held July 20-21, 2013 in Los Angeles, USA. The call for papers attracted 31 submissions from Asia, Europe, Australia, and North and South America in all areas of Spatial User Interaction research. The international program committee consisting of 15 experts in the topic areas and the two program chairs handled the highly competitive and selective review process. Every submission received at least four detailed reviews, two from members of the international program committee and two or more from external reviewers. The reviewing process was double-blind, where only the program chairs as well as the program committee member, who was assigned to each paper to identify external reviewers, knew the identity of the authors. In the end, the program committee accepted overall 12 (8 full papers plus 4 short papers) out of 31 submissions, which corresponds to an acceptance rate of 26% for full papers (and 38% in total). Additionally 12 posters and demonstrations will complement the program and appear in the proceedings. The topics range from spatial interaction techniques, vision in 3D space, applications, to interaction with multi-touch technologies and in augmented reality. We hope that these proceedings will serve as a valuable reference for Spatial User Interaction researchers and developers. Putting together the content for SUI'13 was a team effort. We first thank the authors for providing the content of the program. Special thanks go to the members of the international program committee, who successfully dealt with the reviewing load. We also thank the external reviewers.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.139 | 0.067 |
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".