26,752 research outputs found
Empirical stationary correlations for semi-supervised learning on graphs
In semi-supervised learning on graphs, response variables observed at one
node are used to estimate missing values at other nodes. The methods exploit
correlations between nearby nodes in the graph. In this paper we prove that
many such proposals are equivalent to kriging predictors based on a fixed
covariance matrix driven by the link structure of the graph. We then propose a
data-driven estimator of the correlation structure that exploits patterns among
the observed response values. By incorporating even a small fraction of
observed covariation into the predictions, we are able to obtain much improved
prediction on two graph data sets.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS293 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
DNA-inspired online behavioral modeling and its application to spambot detection
We propose a strikingly novel, simple, and effective approach to model online
user behavior: we extract and analyze digital DNA sequences from user online
actions and we use Twitter as a benchmark to test our proposal. We obtain an
incisive and compact DNA-inspired characterization of user actions. Then, we
apply standard DNA analysis techniques to discriminate between genuine and
spambot accounts on Twitter. An experimental campaign supports our proposal,
showing its effectiveness and viability. To the best of our knowledge, we are
the first ones to identify and adapt DNA-inspired techniques to online user
behavioral modeling. While Twitter spambot detection is a specific use case on
a specific social media, our proposed methodology is platform and technology
agnostic, hence paving the way for diverse behavioral characterization tasks
An Online Tutor for Astronomy: The GEAS Self-Review Library
We introduce an interactive online resource for use by students and college
instructors in introductory astronomy courses. The General Education Astronomy
Source (GEAS) online tutor guides students developing mastery of core
astronomical concepts and mathematical applications of general astronomy
material. It contains over 12,000 questions, with linked hints and solutions.
Students who master the material quickly can advance through the topics, while
under-prepared or hesitant students can focus on questions on a certain topic
for as long as needed, with minimal repetition. Students receive individual
accounts for study and course instructors are provided with overview tracking
information, by time and by topic, for entire cohorts of students. Diagnostic
tools support self-evaluation and close collaboration between instructor and
student, even for distance learners. An initial usage study shows clear trends
in performance which increase with study time, and indicates that distance
learners using these materials perform as well as or better than a comparison
cohort of on-campus astronomy students. We are actively seeking new
collaborators to use this resource in astronomy courses and other educational
venues.Comment: 15 pages, 9 figures; Vogt, N. P., and A. S. Muise. 2015. An online
tutor for general astronomy: The GEAS self-review library. Cogent Education,
2 (1
Creativity Skills Applied to Earth Science Education: Examples from K-12 Teachers in a Graduate Creativity Class
NOTE: This is a large file, 10.7 mb in size! This article briefly explores different aspects of creativity, and then examines K-12 teachers' reactions to exercises applied to earth science concepts in a graduate creativity class. Different types of puzzle activities centering on geoscience content include a quiz game based on Odyssey of the Mind spontaneous problems, and other exercises related to embedded words, transformed cliches, remotely associated word sets, and wordsmithing. Teachers used visualization for an imaginary interview with a geoscientist, along with personal analogy of an earth science feature. As a culminating activity, teachers fashioned a geoscience curriculum material with a given set of items. Ideas for applying the activities to geoscience classes at various grade levels are included. Educational levels: Graduate or professional, Graduate or professional
Faculty Excellence
Each year, the University of New Hampshire selects a small number of its outstanding faculty for special recognition of their achievements in teaching, scholarship and service. Awards for Excellence in Teaching are given in each college and school, and university-wide awards recognize public service, research, teaching and engagement. This booklet details the year\u27s award winners\u27 accomplishments in short profiles with photographs and text
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