MétaCan
Menu
Back to cohort
Record W2000734728 · doi:10.1109/haptics.2014.6775541

[D12] mHIVE: A WYFIWIF design tool

2014· article· en· W2000734728 on OpenAlexaff
Oliver Schneider, Karon E. MacLean

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Summary form only given, as follows. Current haptic design tools are inadequate for the increasing expectations of haptic feedback. Designers are disconnected from both their designs and their end-users, relying on programming languages or GUI tools that limit hands-on exploration and the elicitation of feedback. Haptic instruments are a new type of tool for haptic designers aimed at bridging these gaps. Inspired by the musical kind, they provide real-time feedback over a parameterized sensation, and use multiple output displays to create a “what-you-feel-is-what-I-feel” interface to improve communication. mHIVE is a first instance that lets designers dynamically control vibrotactile actuators with a touch-screen tablet and, through dual outputs, facilitates communication and rapid feedback and assessment. In this demo, you can try mHIVE and explore this new class of tool. Let us know what you think, and inspire us with future designs for and possible uses of haptic instruments!

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.915
Threshold uncertainty score0.689

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 head0.006
GPT teacher head0.173
Teacher spread0.167 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2014
Admission routes1
Has abstractyes

Explore more

Same topicCCD and CMOS Imaging SensorsFrench-language works237,207