2,047 research outputs found
Oxidation States of Graphene: Insights from Computational Spectroscopy
When it is oxidized, graphite can be easily exfoliated forming graphene oxide
(GO). GO is a critical intermediate for massive production of graphene, and it
is also an important material with various application potentials. With many
different oxidation species randomly distributed on the basal plane, GO has a
complicated nonstoichiometric atomic structure that is still not well
understood in spite of of intensive studies involving many experimental
techniques. Controversies often exist in experimental data interpretation. We
report here a first principles study on binding energy of carbon 1s orbital in
GO. The calculated results can be well used to interpret experimental X-ray
photoelectron spectroscopy (XPS) data and provide a unified spectral
assignment. Based on the first principles understanding of XPS, a GO structure
model containing new oxidation species epoxy pair and epoxy-hydroxy pair is
proposed. Our results demonstrate that first principles computational
spectroscopy provides a powerful means to investigate GO structure.Comment: accepted by J. Chem. Phy
KCD: Knowledge Walks and Textual Cues Enhanced Political Perspective Detection in News Media
Political perspective detection has become an increasingly important task
that can help combat echo chambers and political polarization. Previous
approaches generally focus on leveraging textual content to identify stances,
while they fail to reason with background knowledge or leverage the rich
semantic and syntactic textual labels in news articles. In light of these
limitations, we propose KCD, a political perspective detection approach to
enable multi-hop knowledge reasoning and incorporate textual cues as
paragraph-level labels. Specifically, we firstly generate random walks on
external knowledge graphs and infuse them with news text representations. We
then construct a heterogeneous information network to jointly model news
content as well as semantic, syntactic and entity cues in news articles.
Finally, we adopt relational graph neural networks for graph-level
representation learning and conduct political perspective detection. Extensive
experiments demonstrate that our approach outperforms state-of-the-art methods
on two benchmark datasets. We further examine the effect of knowledge walks and
textual cues and how they contribute to our approach's data efficiency.Comment: accepted at NAACL 2022 main conferenc
Coverage Goal Selector for Combining Multiple Criteria in Search-Based Unit Test Generation
Unit testing is critical to the software development process, ensuring the
correctness of basic programming units in a program (e.g., a method).
Search-based software testing (SBST) is an automated approach to generating
test cases. SBST generates test cases with genetic algorithms by specifying the
coverage criterion (e.g., branch coverage). However, a good test suite must
have different properties, which cannot be captured using an individual
coverage criterion. Therefore, the state-of-the-art approach combines multiple
criteria to generate test cases. Since combining multiple coverage criteria
brings multiple objectives for optimization, it hurts the test suites' coverage
for certain criteria compared with using the single criterion. To cope with
this problem, we propose a novel approach named \textbf{smart selection}. Based
on the coverage correlations among criteria and the subsumption relationships
among coverage goals, smart selection selects a subset of coverage goals to
reduce the number of optimization objectives and avoid missing any properties
of all criteria. We conduct experiments to evaluate smart selection on
Java classes with three state-of-the-art genetic algorithms under the
-minute budget. On average, smart selection outperforms combining all goals
on of the classes having significant differences between the two
approaches. Secondly, we conduct experiments to verify our assumptions about
coverage criteria relationships. Furthermore, we experiment with different
budgets of , , and minutes, confirming the advantage of smart
selection over combining all goals.Comment: arXiv admin note: substantial text overlap with arXiv:2208.0409
HOFA: Twitter Bot Detection with Homophily-Oriented Augmentation and Frequency Adaptive Attention
Twitter bot detection has become an increasingly important and challenging
task to combat online misinformation, facilitate social content moderation, and
safeguard the integrity of social platforms. Though existing graph-based
Twitter bot detection methods achieved state-of-the-art performance, they are
all based on the homophily assumption, which assumes users with the same label
are more likely to be connected, making it easy for Twitter bots to disguise
themselves by following a large number of genuine users. To address this issue,
we proposed HOFA, a novel graph-based Twitter bot detection framework that
combats the heterophilous disguise challenge with a homophily-oriented graph
augmentation module (Homo-Aug) and a frequency adaptive attention module
(FaAt). Specifically, the Homo-Aug extracts user representations and computes a
k-NN graph using an MLP and improves Twitter's homophily by injecting the k-NN
graph. For the FaAt, we propose an attention mechanism that adaptively serves
as a low-pass filter along a homophilic edge and a high-pass filter along a
heterophilic edge, preventing user features from being over-smoothed by their
neighborhood. We also introduce a weight guidance loss to guide the frequency
adaptive attention module. Our experiments demonstrate that HOFA achieves
state-of-the-art performance on three widely-acknowledged Twitter bot detection
benchmarks, which significantly outperforms vanilla graph-based bot detection
techniques and strong heterophilic baselines. Furthermore, extensive studies
confirm the effectiveness of our Homo-Aug and FaAt module, and HOFA's ability
to demystify the heterophilous disguise challenge.Comment: 11 pages, 7 figure
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