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Record W3042578773 · doi:10.1145/3405755.3406161

Let's Go There

2020· article· en· W3042578773 on OpenAlexaff
Jaisie Sin, Cosmin Munteanu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSpeech and dialogue systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceHuman–computer interactionIdentification (biology)Virtual realityTeamworkFeelingObject (grammar)Augmented realityNatural (archaeology)Tracking (education)Multimodal interactionVirtual machineMultimediaArtificial intelligencePsychology

Abstract

fetched live from OpenAlex

Hand-tracking has been advertised as a natural means to engage with a virtual environment that also enhances the feeling of presence in and lowers the barriers to entry to virtual reality. We seek to explore combining hand-tracking with voice input (which is then processed with automatic speech recognition) for a novel multimodal experience. Thus, we created Let's Go There, which explores this joint-input method for four functions in virtual reality environments: positioning, object identification, information mapping, and disambiguation. This combination may serve as a more intuitive means for users to communicate and navigate in virtual environments. We expect there to be multiple potential applications of this multimodal form of interaction across numerous domains including training, education, teamwork, and games.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.981
Threshold uncertainty score0.999

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)0.0000.002

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 head0.028
GPT teacher head0.217
Teacher spread0.189 · 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 designNot applicable
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

Citations4
Published2020
Admission routes1
Has abstractyes

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