NIDIT: Workshop on Novel Input Devices and Interaction Techniques
Bibliographic record
Abstract
Virtual reality has finally become a mainstream technology. Recent advances in commercial VR hardware have led to high-resolution, ergonomic, — and critically — low cost head-mounted displays. Advances in commercial input devices and interaction techniques have arguably not kept pace with advances in displays. For instance, most HMDs include a tracked input device: “wands” that are not dissimilar to the earliest examples of 3D controllers used in the VR systems of the 1980s. Interaction in commercial VR systems has similarly lagged; despite many advances in 3D interaction in the past three decades of VR research, interaction in commercial systems largely relies on classical techniques like the virtual hand, or raycasting. This full-day workshop will bring together researchers and industry practitioners to discuss and experience the future of input devices for VR, AR, and 3D User Interfaces, and help chart a course for the future of 3D interaction techniques. In addition to a presentation at the workshop, authors of all accepted submissions will be strongly encouraged to demonstrate their novel input device and interaction techniques in an interactive demo format following presentations.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.063 | 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".