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Record W2295393607 · doi:10.1145/1240624.1240834

Pressure marks

2007· article· en· W2295393607 on OpenAlexaff
Gonzalo Ramos, Ravin Balakrishnan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceSelection (genetic algorithm)Set (abstract data type)Action (physics)Sequence (biology)Lasso (programming language)Artificial intelligenceWorld Wide WebProgramming language

Abstract

fetched live from OpenAlex

Selections and actions in GUI's are often separated -- i.e. an action or command typically follows a selection. This sequence imposes a lower bound on the interaction time that is equal to or greater than the sum of its parts. In this paper, we introduce pressure marks -- pen strokes where the variations in pressure make it possible to indicate both a selection and an action simultaneously. We propose a series of design guidelines from which we develop a set of four basictypes of pressure marks. We first assess the viability of this set through an exploratory study that looks at the way users draw straight and lasso pressure marks of different sizes and orientations. We then present the results of a quantitative experiment that shows that users perform faster selection-action interactions with pressure marks than with a combination of lassos and pigtails. Based on these results, we present and discuss a number of interaction designs that incorporate pressure marks.

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)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.049
Threshold uncertainty score0.164

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0020.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0490.011

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 head0.250
Teacher spread0.244 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations60
Published2007
Admission routes1
Has abstractyes

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