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Record W4366980597

Gluey: Developing a Head-Worn Display Interface to Unify the Interaction Experience in Distributed Display Environments

2015· preprint· en· W4366980597 on OpenAlex

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

VenueOpen Archive Toulouse Archive Ouverte (University of Toulouse) · 2015
Typepreprint
Languageen
FieldComputer Science
TopicUsability and User Interface Design
Canadian institutionsUniversity of CalgaryUniversity of Manitoba
Fundersnot available
KeywordsComputer scienceHuman–computer interactionHead (geology)Computer graphics (images)User interfaceInterface (matter)Head-up displayMultimediaOperating systemArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

Distributed display environments (DDEs) allow use of various specialized devices but challenge designers to provide a clean flow of data across multiple displays. Upcoming consumer-ready head-worn displays (HWDs) can play a central role in unifying the interaction experience in such ecosystems. In this paper, we report on the design and development of Gluey, a user interface that acts as a 'glue' to facilitate seamless input transitions and data movement across displays. Based on requirements we refine for such an interface, Gluey leverages inherent headworn display attributes such as field-of-view tracking and an always-available canvas to redirect input and migrate content across multiple displays, while minimizing device switching costs. We implemented a functional prototype integrating Gluey's numerous interaction possibilities. From our experience in this integration and from user evaluation results, we identify the open challenges in using HWDs to unify the interaction experience in DDEs.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science
Consensus categoriesOpen science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.679
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0110.022
Research integrity0.0000.002
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 head0.066
GPT teacher head0.308
Teacher spread0.242 · 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