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Record W4402722170 · doi:10.1145/3670947.3670960

BendAide: A Deformable Interface to Augment Touchscreen Mobile Devices

2024· article· en· W4402722170 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGraphics Interface · 2024
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsSimon Fraser UniversityCarleton University
FundersUniversitas Brawijaya
KeywordsTouchscreenAugmentComputer scienceComputer graphics (images)Mobile deviceInterface (matter)Human–computer interactionOperating system

Abstract

fetched live from OpenAlex

The uses of handheld mobile devices are diverse, yet interaction is not; touchscreens are the singular primary interface on most mobiles. Touch interaction has usability issues (e.g., the “fat fingers problem”) which impair the fine control of small interface elements, such as when working with text. Beyond text entry, this includes tasks like placing the in-text cursor (caret), text selection, and copy/paste. Current solutions for touch usability issues do not address complex uses like working with text. We propose deformable interaction, specifically bend, added alongside touch to support working with text on mobile. We explore this through a study of BendAide, a novel deformable 3D printed case for mobiles that adds bend interaction to the device. We found that people perceive different advantages between bend and touch and that they will alternate between these inputs based on task demands and their personal abilities. Adding alternate input options to mobile could reduce the complexity of on-display interfaces and interactions and give people more choice in how they use their devices.

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 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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.913
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.016
GPT teacher head0.308
Teacher spread0.292 · 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