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Record W2518480459 · doi:10.1145/2971763.2971786

Grabbing at an angle

2016· article· en· W2518480459 on OpenAlex
Nur Al-huda Hamdan, Jeffrey R. Blum, Florian Heller, Ravi Kanth Kosuru, Jan Borchers

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsMcGill University
FundersRWTH Aachen UniversityNatural Sciences and Engineering Research Council of CanadaCentre for Interdisciplinary Research in Music Media and TechnologyFaculty of Engineering, McGill University
KeywordsWorkloadComputer scienceTextileSelection (genetic algorithm)Computer visionArtificial intelligenceSimulationMaterials scienceComposite material

Abstract

fetched live from OpenAlex

This paper investigates the pinch angle as a menu selection technique for two-dimensional foldable textile controllers. Based on the principles of marking menus, the selection of a menu item is performed by grabbing a fold at a specific angle, while changing value is performed by rolling the fold between the fingers. In a first experiment we determined an upper bound for the number of different angles users can reliably grab into a piece of fabric on their forearm. Our results show that users can, without looking at it, reliably grab fabric on their forearm with an average accuracy between 30° and 45°, which would provide up to six different menu options selectable with the initial pinch. In a second experiment, we show that our textile sensor, Grabrics, can detect fold angles at 45° spacing with up to 85% accuracy. Our studies also found that user performance and workload are independent of the fabric types that were tested.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.822
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 communication0.0000.001
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 head0.017
GPT teacher head0.253
Teacher spread0.236 · 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

Quick stats

Citations27
Published2016
Admission routes2
Has abstractyes

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