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Record W2096003374 · doi:10.1145/1753326.1753695

LensMouse

2010· article· en· W2096003374 on OpenAlex
Xing-Dong Yang, Edward Mak, David McCallum, Pourang Irani, Xiang Cao, Shahram Izadi

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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of ManitobaUniversity of Alberta
Fundersnot available
KeywordsCursor (databases)Computer scienceMicrosoft WindowsWindow (computing)Computer graphics (images)Human–computer interactionInput deviceComputer hardwareOperating systemComputer visionSoftware

Abstract

fetched live from OpenAlex

We introduce LensMouse, a novel device that embeds a touch-screen display -- or tangible 'lens' -- onto a mouse. Users interact with the display of the mouse using direct touch, whilst also performing regular cursor-based mouse interactions. We demonstrate some of the unique capabili-ties of such a device, in particular for interacting with auxil-iary windows, such as toolbars, palettes, pop-ups and dia-log-boxes. By migrating these windows onto LensMouse, challenges such as screen real-estate use and window man-agement can be alleviated. In a controlled experiment, we evaluate the effectiveness of LensMouse in reducing cursor movements for interacting with auxiliary windows. We also consider the concerns involving the view separation that results from introducing such a display-based device. Our results reveal that overall users are more effective with LenseMouse than with auxiliary application windows that are managed either in single or dual-monitor setups. We conclude by presenting other application scenarios that LensMouse could support.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.849
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 communication0.0000.000
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 head0.005
GPT teacher head0.228
Teacher spread0.223 · 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

Citations46
Published2010
Admission routes1
Has abstractyes

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