Opportunities and challenges for cross-device interactions in the wild
Bibliographic record
Abstract
People are increasingly using multiple computing devices in their daily lives as portals into a shared online information space.We can select devices based on their form factor and affordances to match our task and context of use.Moreover, we are often using multiple devices at once, for example when sharing content, playing games, controlling other devices such as a smart TV, or collaboratively editing and presenting content in a meeting.The seamless use of multiple devices to work toward achieving the same goal is known as cross-device interaction.Cross-device interaction opens up new opportunities for how we interact with digital technology, but it also presents several fundamental challenges.To uncover some of these challenges, problems, and opportunities with cross-device interaction, we organized the well-attended Cross-Surface workshop series (http://www.cross-surface.com/).We discussed how such technologies could be used in the wild, supporting new domains and use cases at ACM Interactive Tabletops and Surfaces (ITS) 2015 [1]; how we could move away from Weiser's vision of ubiquitous devices to a "bring your own device" approach at CHI 2016 [2]; and finally, how space and spatial relations between people and devices could be used to support better device awareness at ACM Interactive Spaces and Surfaces (ISS) 2016 [3].In 1991, Mark Weiser outlined his vision for ubiquitous computing [4], in which people interact with multiple computing devices in different form factors.In his vision, people have ready access to a plethora of devices to pick up and use seamlessly, allowing them to interact with content through cross-device interactions.This work accelerated academic research into providing new technologies, conceptual models, and interaction techniques that support interactions across device ecologies [5,6].However, despite the availability of such a range of devices, technologies, and techniques, has this vision of cross-device ecologies really materialized?Weiser introduced three types of devices: inch-scale pads (similar to today's smartphones), footscale pads (similar to today's tablets), and yard-scale boards (similar to today's large displays).Two decades later, this aspect of Weiser's vision concerning interaction with multiple devices in Nicolai Marquardt is a senior lecturer (associate professor) in physical computing at the University College London.At the UCL Interaction Centre, he works on projects in the research areas of ubiquitous computing, interactive surfaces, sensor-based systems, prototyping toolkits, and physical user interfaces.n.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.014 | 0.029 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".