847,440 research outputs found
Parameterized Algorithmics for Computational Social Choice: Nine Research Challenges
Computational Social Choice is an interdisciplinary research area involving
Economics, Political Science, and Social Science on the one side, and
Mathematics and Computer Science (including Artificial Intelligence and
Multiagent Systems) on the other side. Typical computational problems studied
in this field include the vulnerability of voting procedures against attacks,
or preference aggregation in multi-agent systems. Parameterized Algorithmics is
a subfield of Theoretical Computer Science seeking to exploit meaningful
problem-specific parameters in order to identify tractable special cases of in
general computationally hard problems. In this paper, we propose nine of our
favorite research challenges concerning the parameterized complexity of
problems appearing in this context
SAVOIAS: A Diverse, Multi-Category Visual Complexity Dataset
Visual complexity identifies the level of intricacy and details in an image
or the level of difficulty to describe the image. It is an important concept in
a variety of areas such as cognitive psychology, computer vision and
visualization, and advertisement. Yet, efforts to create large, downloadable
image datasets with diverse content and unbiased groundtruthing are lacking. In
this work, we introduce Savoias, a visual complexity dataset that compromises
of more than 1,400 images from seven image categories relevant to the above
research areas, namely Scenes, Advertisements, Visualization and infographics,
Objects, Interior design, Art, and Suprematism. The images in each category
portray diverse characteristics including various low-level and high-level
features, objects, backgrounds, textures and patterns, text, and graphics. The
ground truth for Savoias is obtained by crowdsourcing more than 37,000 pairwise
comparisons of images using the forced-choice methodology and with more than
1,600 contributors. The resulting relative scores are then converted to
absolute visual complexity scores using the Bradley-Terry method and matrix
completion. When applying five state-of-the-art algorithms to analyze the
visual complexity of the images in the Savoias dataset, we found that the
scores obtained from these baseline tools only correlate well with crowdsourced
labels for abstract patterns in the Suprematism category (Pearson correlation
r=0.84). For the other categories, in particular, the objects and advertisement
categories, low correlation coefficients were revealed (r=0.3 and 0.56,
respectively). These findings suggest that (1) state-of-the-art approaches are
mostly insufficient and (2) Savoias enables category-specific method
development, which is likely to improve the impact of visual complexity
analysis on specific application areas, including computer vision.Comment: 10 pages, 4 figures, 4 table
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