Analyzing video data is a complex problem for social science researchers. This study explored the use of an analytical visualization method to support the process of narrowing a video data corpus—focusing on meaningful moments for further qualitative analysis. We used the real-time observational coding tool to examine students’ activities and engineering design practices in a STEM curriculum. The findings suggest that using this tool supports time-efficient analyses of large corpuses of video data.
Projection of high-dimensional data is usually done by reducing dimensionality of the data and transforming the data to the latent space. We created synthetic data to simulate real gene-expression datasets and we tested methods on both synthetic and real data. With this work we address the visualization of our data through implementation of regularized singular value decomposition (SVD) for biclustering using L0-norm and L1-norm. Additional knowledge is introduced to the model through regularization with the two prior adjacency matrices. We show that L0-norm SVD and L1-norm SVD give better results than standard SVD.
In this paper we study radicals of a semigroup which is the union of a family of its subsets, indexed by a nonempty set, such that the intersection of two distinct subsets is contained in the set of left zeroes of that semigroup.
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