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Record W2088363628 · doi:10.1109/have.2010.5623973

Interpersonal haptic communication in second life

2010· article· en· W2088363628 on OpenAlexaff
SK Alamgir Hossain, A. Rahman, Abdulmotaleb El Saddik

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHaptic technologyVirtual realityComputer scienceVirtual worldInterpersonal communicationHuman–computer interactionMultimediaMetaverseNonverbal communicationVirtual machineSimulationPsychologyCommunication

Abstract

fetched live from OpenAlex

Potential of nonverbal communication as the communication medium between multiuser 3D virtual worlds and real environment are attracting the interest of many researchers around the world. Driven by the motivation, we explored the possibilities of integrating haptic interactions with Linden Lab's multiuser online virtual world, Second Life. We developed an add-on to the Second Life communication channel in order to facilitate emotional feedbacks such as human touch, encouraging pat and comforting hug to the participating users through real-world haptic simulation. These social touch that are fundamental to physical and emotional development in turn can enhance the users interactive and immersive experiences with the virtual social communities in the Second Life. In this paper, we describe the development of a prototype that realizes the aforementioned virtual-real communication through a haptic-jacket system. Some of the potential applications of the proposed approach include remote child caring, stress recovery, distant lover's communication etc.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.016
GPT teacher head0.266
Teacher spread0.250 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations13
Published2010
Admission routes1
Has abstractyes

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