1,756 research outputs found
The regularity of solutions to Navier-Stokes equations at t=0 for bounded space domain
This paper studies the regularity in space-time of local solutions to 3D
Navier-Stokes equations at . The solutions will belong to
instead of
, where is a bounded set
of
An Investigation of Three Subjective Rating Scales of Mental Workload in Third Level Education
Mental Workload assessment in educational settings is still recognized as an open research problem. Although its application is useful for instructional design, it is still unclear how it can be formally shaped and which factors compose it. This paper is aimed at investigating a set of features believed to shape the construct of mental workload and aggregating them together in models trained with supervised machine learning techniques. In detail, multiple linear regression and decision trees have been chosen for training models with features extracted respectively from the NASA Task Load Index and the Workload Profile, well-known self-reporting instruments for assessing mental workload. Additionally, a third feature set was formed as a combination of the two aforementioned feature sets and a number of other features believed to contribute to mental workload modeling in education. Models were trained with cross-validation due to the limited sample size. On the one hand, results show how the features of the NASA Task Load index are more expressive for a regression problem than the other two feature sets. On the other hand, results show how the newly formed feature set can lead to the development of models of the mental workload with a lower error when compared to models built with the other two feature sets and when employed for a classification task
Volumes of Blurred-Invariant Gaussians for Dynamic Texture Classification
International audienceAn effective model, which jointly captures shape and motion cues, for dynamic texture (DT) description is introduced by taking into account advantages of volumes of blurred-invariant features in three main following stages. First, a 3-dimensional Gaussian kernel is used to form smoothed sequences that allow to deal with well-known limitations of local encoding such as near uniform regions and sensitivity to noise. Second , a receptive volume of the Difference of Gaussians (DoG) is figured out to mitigate the negative impacts of environmental and illumination changes which are major challenges in DT understanding. Finally, a local encoding operator is addressed to construct a discriminative descriptor of enhancing patterns extracted from the filtered volumes. Evaluations on benchmark datasets (i.e., UCLA, DynTex, and DynTex++) for issue of DT classification have positively validated our crucial contributions
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