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Record W3090901039 · doi:10.1145/3379503.3403566

Tent Mode Interactions: Exploring Collocated Multi-User Interaction on a Foldable Device

2020· article· en· W3090901039 on OpenAlexaff
Gazelle Saniee-Monfared, Kevin Fan, Qianq Xu, Sachi Mizobuchi, Lewis Zhou, Pourang Irani, Wei Li

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of ManitobaHuawei Technologies (Canada)
Fundersnot available
KeywordsComputer scienceLeverage (statistics)Human–computer interactionMobile interactionBridging (networking)Mode (computer interface)Mobile deviceSuiteSet (abstract data type)Space (punctuation)World Wide WebOperating systemArtificial intelligenceComputer network

Abstract

fetched live from OpenAlex

Foldable handheld displays have the potential to offer a rich interaction space, particularly as they fold into a convex form factor, for collocated multi-user interactions. In this paper, we explore Tent mode, a convex configuration of a foldable device partitioned into a primary and a secondary display, as well as a tertiary, Edge display that sits at the intersection of the two. We specifically explore the design space for a wide range of scenarios, such as co-browsing a gallery or co-planning a trip. Through a first collection of interviews, end-users identified a suite of apps that could leverage Tent mode for multi-user interactions. Based on these results we propose an interaction design space that builds on unique Tent mode properties, such as folding, flattening or tilting the device, and the interplay between the three sub-displays. We examine how end-users exploit this rich interaction space when presented with a set of collaborative tasks through a user study, and elicit potential interaction techniques. We implemented these interaction techniques and report on the preliminary user feedback we collected. Finally, we discuss the design implications for collocated interaction in Tent mode configurations.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.925
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.159
GPT teacher head0.332
Teacher spread0.173 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2020
Admission routes1
Has abstractyes

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