20 citations · 33 across the 7 of their papers we have counts for
9 papers
Networked Sensing for Radiation Detection, Localization, and Tracking
R. J. Cooper, N. Abgrall, G. Aversano +18
The detection, identification, and localization of illicit radiological and nuclear material continue to be key components of nuclear non-proliferation and nuclear security efforts…
Anticipating Technical Expertise and Capability Evolution in Research Communities using Dynamic Graph Transformers
Sameera Horawalavithana, Ellyn Ayton, Anastasiya Usenko +2
The ability to anticipate technical expertise and capability evolution trends globally is essential for national and global security, especially in safety-critical domains like nuc…
EXPERT: Public Benchmarks for Dynamic Heterogeneous Academic Graphs
Sameera Horawalavithana, Ellyn Ayton, Anastasiya Usenko +5
Machine learning models that learn from dynamic graphs face nontrivial challenges in learning and inference as both nodes and edges change over time. The existing large-scale graph…
Leveraging Community and Author Context to Explain the Performance and Bias of Text-Based Deception Detection Models
Galen Weld, Ellyn Ayton, Tim Althoff +1
Deceptive news posts shared in online communities can be detected with NLP models, and much recent research has focused on the development of such models. In this work, we use char…
Towards Trustworthy Deception Detection: Benchmarking Model Robustness across Domains, Modalities, and Languages
Maria Glenski, Ellyn Ayton, Robin Cosbey +2
Evaluating model robustness is critical when developing trustworthy models not only to gain deeper understanding of model behavior, strengths, and weaknesses, but also to develop f…
Evaluating Deception Detection Model Robustness To Linguistic Variation
Maria Glenski, Ellyn Ayton, Robin Cosbey +2
With the increasing use of machine-learning driven algorithmic judgements, it is critical to develop models that are robust to evolving or manipulated inputs. We propose an extensi…
CrossCheck: Rapid, Reproducible, and Interpretable Model Evaluation
Dustin Arendt, Zhuanyi Huang, Prasha Shrestha +3
Evaluation beyond aggregate performance metrics, e.g. F1-score, is crucial to both establish an appropriate level of trust in machine learning models and identify future model impr…
Multilingual Multimodal Digital Deception Detection and Disinformation Spread across Social Platforms
Maria Glenski, Ellyn Ayton, Josh Mendoza +1
Our main contribution in this work is novel results of multilingual models that go beyond typical applications of rumor or misinformation detection in English social news content t…
Fishing for Clickbaits in Social Images and Texts with Linguistically-Infused Neural Network Models
Maria Glenski, Ellyn Ayton, Dustin Arendt +1
This paper presents the results and conclusions of our participation in the Clickbait Challenge 2017 on automatic clickbait detection in social media. We first describe linguistica…