Analyzing time series from eye tracking using Symbolic Aggregate Approximation
This thesis explores the viability of transforming the data produced when tracking the eyes into a discrete symbolic representation. For this transformation, we utilize Symbolic Aggregate Approximation to investigate a new possibility for effectively categorizing data collected via eye tracking technologies.
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Influence of rationality levels on dynamics of heterogeneous Cournot duopolists with quadratic costs. [PDF]
Li X, Jiang Y.
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Lipschitz-based robustness estimation for hyperdimensional learning. [PDF]
Yeung C +5 more
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Decision Dynamics in Early Numerical Estimation: Evidence from the Dual-NLET and Drift Diffusion Modeling. [PDF]
Morell-Ruiz M +5 more
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Source camera attribution using a rule-based explainable convolutional neural network. [PDF]
Nayerifard T +2 more
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Prediction of Modulus of Elasticity of Concrete Using Different Homogenization Methods. [PDF]
Zhou J, Lin H, Qiu K, Ou K, Nie F.
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Quantifying Knowledge Production Efficiency with Thermodynamics: A Data-Driven Study of Scientific Concepts. [PDF]
Chumachenko A, Buttliere B.
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EffiShapeFormer: Shapelet-Based Sensor Time Series Classification with Dual Filtering and Convolutional-Inverted Attention. [PDF]
Bao J +12 more
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