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Record W2206818791 · doi:10.1109/have.2015.7359446

The clutch: two-handed mobile multi-touch 3D object translation and manipulation

2015· article· en· W2206818791 on OpenAlexaff
Ali Asghar Nazari Shirehjini, Mohammad Chegini, Shervin Shirmohammadi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of Ottawa
FundersSharif University of Technology
KeywordsMobile deviceComputer scienceTask (project management)Human–computer interactionObject (grammar)Virtual imageInput deviceVirtual realityComputer visionArtificial intelligenceComputer graphics (images)Computer hardwareEngineering

Abstract

fetched live from OpenAlex

Nowadays, handheld devices such as smartphones provide users with multi-touch input screens. Displaying interactive and touch-enabled 3D environments in such handheld devices has become popular in different applications like games or virtual reality. Technologies such as Web3D and WebGL have made the creation and display of 3D environments in mobile devices easier than ever. However, object manipulation techniques are not as well developed. For example, moving an object within the 3D environment or other similar object-specific manipulations are neither intuitive nor easy to perform. Current manipulation techniques like Gizmo that are successful in systems that use mouse and keyboard are not designed for and do not work well for multi-touch handheld devices. In this paper, we present a novel technique to perform object manipulation in 6DOF in multi-touch screens. Our performance evaluations show that our technique compared to existing techniques such as Gizmo improves task completion time by 63% while increasing task precision by 52%.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.956
Threshold uncertainty score0.209

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)0.0000.000

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 head0.070
GPT teacher head0.315
Teacher spread0.246 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2015
Admission routes1
Has abstractyes

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