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Record W2767516884 · doi:10.21307/ijssis-2017-308

Department of Mechanical and Industrial Engineering, University of Toronto,5 King's College Road, Toronto, ON, M5S 3G8, Canada

2008· article· en· W2767516884 on OpenAlexaboutno aff
Sangho Kim, Kosuke Sekiyama, Toshio Fukuda

Bibliographic record

VenueRePEc: Research Papers in Economics · 2008
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsKeypadInterface (matter)Computer scienceComputer visionArtificial intelligenceSequence (biology)Computer hardwareEngineering

Abstract

fetched live from OpenAlex

In this paper we propose a pattern adaptive keypad interface for in-vehicle information system. The keypad interface recommends the estimated input sequence to fit the user's preference based on individual model of operation pattern. Pattern (shape) of button switches corresponding to the estimated input sequence is actively reformed. Button switches are displayed tactilely and visually. Finger image is shown on the monitor in real-time in order to guide input operation on the tactile input device. To confirm the effect of the keypad interface, experiments are performed comparing with a touch screen on which pattern (shape) of buttons switch is unchanged.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.578
Threshold uncertainty score0.739

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)0.0000.000

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 head0.041
GPT teacher head0.262
Teacher spread0.221 · 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 designBench or experimental
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

Citations23
Published2008
Admission routes1
Has abstractyes

Explore more

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