Combining Sketching and Traditional Diagram Editing Tools

Plimmer, Beryl; Delaney, Aidan; Rodgers, Peter

doi:10.1145/2631925

Cited by 8 publications

(2 citation statements)

References 40 publications

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“…Applying linear classifiers to these gesture sets, Rubine reports recognition rates of over 96%, even for relatively small training set sizes of 15 samples per class. Rubine's feature set has been successfully applied in pen-based intelligent user interfaces (Stapleton et al, 2015 ; Williford et al, 2020 ), multi-touch gesture recognition (Cirelli and Nakamura, 2014 ; Rekik et al, 2014 ) and even eye-tracking analysis (Çağla Çğ and Metin Sezgin, 2015 ; Alamudun et al, 2017 ).…”

Section: Related Workmentioning

confidence: 99%

Modeling Users' Cognitive Performance Using Digital Pen Features

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Sonntag

2022

Front. Artif. Intell.

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Digital pen features model characteristics of sketches and user behavior, and can be used for various supervised machine learning (ML) applications, such as multi-stroke sketch recognition and user modeling. In this work, we use a state-of-the-art set of more than 170 digital pen features, which we implement and make publicly available. The feature set is evaluated in the use case of analyzing paper-pencil-based neurocognitive assessments in the medical domain. Most cognitive assessments, for dementia screening for example, are conducted with a pen on normal paper. We record these tests with a digital pen as part of a new interactive cognitive assessment tool with automatic analysis of pen input. The physician can, first, observe the sketching process in real-time on a mobile tablet, e.g., in telemedicine settings or to follow Covid-19 distancing regulations. Second, the results of an automatic test analysis are presented to the physician in real-time, thereby reducing manual scoring effort and producing objective reports. As part of our evaluation we examine how accurately different feature-based, supervised ML models can automatically score cognitive tests, with and without semantic content analysis. A series of ML-based sketch recognition experiments is conducted, evaluating 10 modern off-the-shelf ML classifiers (i.e., SVMs, Deep Learning, etc.) on a sketch data set which we recorded with 40 subjects from a geriatrics daycare clinic. In addition, an automated ML approach (AutoML) is explored for fine-tuning and optimizing classification performance on the data set, achieving superior recognition accuracies. Using standard ML techniques our feature set outperforms all previous approaches on the cognitive tests considered, i.e., the Clock Drawing Test, the Rey-Osterrieth Complex Figure Test, and the Trail Making Test, by automatically scoring cognitive tests with up to 87.5% accuracy in a binary classification task.

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Section: Related Workmentioning

confidence: 99%

Modeling Users' Cognitive Performance Using Digital Pen Features

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2022

Front. Artif. Intell.

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“…Numerous sketch tools have been proposed for visual languages including concept maps [14], graphs [18], UML class diagrams [4,10] and Euler diagrams [35]. Of particular relevance to Speedith is SketchSet which provides sketch recognition and conversion of some components of unitary spider diagrams [26]; SketchSet extends SketchNode which was developed for graphs in isolation [19]. Similarly to SketchSet, Speedith can recognise closed cures, their labels, and spiders.…”

Section: Diagrammatic Logicsmentioning

confidence: 99%

Speedith: A Reasoner for Spider Diagrams

Urbas

Jamnik

2015

J of Log Lang and Inf

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In this paper, we introduce Speedith which is an interactive diagrammatic theorem prover for the well-known language of spider diagrams. Speedith provides a way to input spider diagrams, transform them via the diagrammatic inference rules, and prove diagrammatic theorems. Speedith's inference rules are sound and complete, extending previous research by including all the classical logic connectives. In addition to being a stand-alone proof system, Speedith is also designed as a program that plugs into existing general purpose theorem provers. This allows for other systems to access diagrammatic reasoning via Speedith, as well as a formal verification of diagrammatic proof steps within standard sentential proof assistants. We describe the general structure of Speedith, the diagrammatic language, the automatic mechanism that draws the diagrams when inference rules are applied on them, and how formal diagrammatic proofs are constructed.

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Exploration of artistic creation of Chinese ink style painting based on deep learning framework and convolutional neural network model

Chen

2019

Soft Comput

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Combining Sketching and Traditional Diagram Editing Tools

Cited by 8 publications

References 40 publications

Modeling Users' Cognitive Performance Using Digital Pen Features

Modeling Users' Cognitive Performance Using Digital Pen Features

Speedith: A Reasoner for Spider Diagrams

Exploration of artistic creation of Chinese ink style painting based on deep learning framework and convolutional neural network model

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