Finger-based gesture control of a collaborative online workspace
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A gesture-based human computer interface can make computers and devices easier to use, such as by allowing people to share photos by moving their hands through the air. Existing solutions have relied on exotic hardware, often involving elaborate setups limited to the research lab. Gesture recognition algorithms used so far are not practical or responsive enough for real-world use, partially due to the inadequate data on which the image processing is applied. Most importantly, existing solutions have lacked a workspace that allows users to perform common collaborative tasks by using their hands and fingers. In this paper, a new paradigm for next-generation computer interfaces is introduced. The method presented is based on a custom 3D camera that is easy to set up and has a flexible detection range. This method accurately detects hand gestures from depth data, allowing them to be used to control any application or device. The paper proposes the control of application windows and their content in collaborative online workspaces on which many teams cooperate to complete useful tasks, as shown with examples.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it