Bibliographic record
Abstract
As ubiquitous environments become increasingly commonplace with newer sensors and forms of computing devices (e.g. wearables, digital tabletops), researchers have continued to design and implement novel interaction possibilities. However, as the number of sensors and devices continues to rise, researchers still face numerous instrumentation, implementation and cost barriers before being able to take advantage of the additional capabilities. In this paper, we present the SoD-Toolkit -- a toolkit that facilitates the exploration and development of multi-device interactions, applications and ubiquitous environments by using combinations of low-cost sensors to provide spatial-awareness. The toolkit offers three main features. (1) A "plug and play" architecture for seamless multi-sensor integration, allowing for novel explorations and ad-hoc setups of ubiquitous environments. (2) Client libraries that integrate natively with several major device and UI platforms. (3) Unique tools that allow designers to prototype interactions and ubiquitous environments without a need for people, sensors, rooms or devices. We demonstrate and reflect on real-world case-studies from industry-based collaborations that influenced the design of our toolkit, as well as discuss advantages and limitations of our toolkit.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.068 | 0.054 |
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".