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Record W2906445267

Bubble radar : efficient pen-based interaction

2006· article· en· W2906445267 on OpenAlexaff
Dzmitry Aliakseyeu, Miguel A. Nacenta, Sriram Subramanian, Carl Gutwin

Bibliographic record

VenueTU/e Research Portal (Eindhoven University of Technology) · 2006
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsComputer scienceRadarCursor (databases)Interaction techniqueBubbleComputer visionComputer graphics (images)Artificial intelligenceHuman–computer interaction
DOInot available

Abstract

fetched live from OpenAlex

The rapid increase in display sizes and resolutions has led to the re-emergence of many pen-based interaction systems like tabletop and wall display environments. Pointing in these environments is an important task, but techniques have not exploited the manipulation of control and display parameters to the extent seen in desktop environments. We have overcome these in the design of a new pen-based interaction technique – Bubble Radar. Bubble Radar allows users to reach both specific targets and empty space, and supports dynamic switching between selecting and placing. The technique is based on combining the benefits of a successful pen-based pointing technique, the Radar View, with a successful desktop object pointing technique – the Bubble Cursor. We tested the new technique in a user study and found that it was significantly faster than existing techniques, both for overall pointing and for targeting specific objects. Categories and Subject Descriptors H5.2 [Information interfaces and presentation]: User Interfaces.- Graphical user interfaces

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.294
Threshold uncertainty score0.643

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.019
GPT teacher head0.283
Teacher spread0.264 · 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 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

Citations2
Published2006
Admission routes1
Has abstractyes

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