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Record W2227417233

Processing desktop work on a large high-resolution display: studies and designs

2011· dissertation· en· W2227417233 on OpenAlexaff
Ravin Balakrishnan, Xiaojun Bi

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDisplay sizeComputer scienceDisplay resolutionDisplay deviceInformation displayContext (archaeology)Human–computer interactionVisualizationProcess (computing)PixelComputer graphics (images)MultimediaArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

With the ever increasing amount of digital information, information workers desire more screen real estate to process their daily desktop work. Thanks to the quick advance in display technology, big screens are increasingly affordable and have been gradually adopted in desktop computing environments. A large wall-size high resolution display, a recent emerging class of display which possesses a huge visualization surface, could potentially benefit information processing work. In this dissertation we investigate such a large display as the primary working space for information processing work. We firstly conducted a longitudinal diary study and three control experiments investigating effects of a large display on information processing work. The longitudinal diary study investigates large display use in a personal desktop computing context by comparing it with single- and dual-monitor. The three controlled experiments further investigate the effects of two factors determining resolution of a display—physical size and pixel-density on users' performance and behaviors. The diary study reveals the distinct behavior patterns of large display users in partitioning screen space and managing windows, while the control experiments deeply reveal the effects of the physical size and pixel density of a display on different information processing tasks. Aside from studying a continuous large display, we also articulate how interior bezels within a tiled-monitor large display affect users' performance and behaviors in basic visual search and action tasks via a series of controlled experiments. Based on the understanding of large display effects and users' behavior patterns, we then design new interaction techniques to address a big challenge of working on a large display: managing overflowing windows. We design and implement a large display oriented window management system prototype: WallTop. It includes a set of interaction techniques that provide greater flexibility for managing windows. Usability tests show that users can quickly and easily learn the new techniques and apply them to realistic window management tasks with increased efficiency on a large display.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.703
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)0.0000.000

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 head0.050
GPT teacher head0.316
Teacher spread0.266 · 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.

Study designTheoretical or conceptual
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

Citations0
Published2011
Admission routes1
Has abstractyes

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