Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: AdaptiFont: Increasing Individuals' Reading Speed with a Generative Font
  Model and Bayesian Optimization   Digital text has become one of the primary ways of exchanging knowledge, but
text needs to be rendered to a screen to be read. We present AdaptiFont, a
human-in-the-loop system that is aimed at interactively increasing readability
of text displayed on a monitor. To this end, we first learn a generative font
space with non-negative matrix factorization from a set of classic fonts. In
this space we generate new true-type-fonts through active learning, render
texts with the new font, and measure individual users' reading speed. Bayesian
optimization sequentially generates new fonts on the fly to progressively
increase individuals' reading speed. The results of a user study show that this
adaptive font generation system finds regions in the font space corresponding
to high reading speeds, that these fonts significantly increase participants'
reading speed, and that the found fonts are significantly different across
individual readers.

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The categories of this paper are: patt-sol
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The categories of this paper are: comp-gas
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The categories of this paper are: math-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-th
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The categories of this paper are: hep-lat
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The categories of this paper are: physics
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The categories of this paper are: bayes-an
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The categories of this paper are: q-fin
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: math
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The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
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The categories of this paper are: nucl-th
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The categories of this paper are: atom-ph