9 citations · 15 across the 7 of their papers we have counts for
8 papers
Selecting Open-Weight Language Models for Zero-Shot Intent Classification: A Systematic Evaluation of 41 Models
Parishruthi Ganesh, Gerry Dozier, Cheryl Seals
Intent classification is a core component of task-oriented dialogue systems, yet practitioners have limited systematic guidance for selecting deployable open-weight language models…
Towards Effective Authorship Attribution: Integrating Class-Incremental Learning
Mostafa Rahgouy, Hamed Babaei Giglou, Mehnaz Tabassum +5
AA is the process of attributing an unidentified document to its true author from a predefined group of known candidates, each possessing multiple samples. The nature of AA necessi…
Investigating Annotator Bias in Large Language Models for Hate Speech Detection
Amit Das, Zheng Zhang, Najib Hasan +12
Data annotation, the practice of assigning descriptive labels to raw data, is pivotal in optimizing the performance of machine learning models. However, it is a resource-intensive…
OffensiveLang: A Community Based Implicit Offensive Language Dataset
Amit Das, Mostafa Rahgouy, Dongji Feng +10
The widespread presence of hateful languages on social media has resulted in adverse effects on societal well-being. As a result, addressing this issue with high priority has becom…
Automating Defense Against Adversarial Attacks: Discovery of Vulnerabilities and Application of Multi-INT Imagery to Protect Deployed Models
Josh Kalin, David Noever, Matthew Ciolino +2
Image classification is a common step in image recognition for machine learning in overhead applications. When applying popular model architectures like MobileNetV2, known vulnerab…
Local Translation Services for Neglected Languages
David Noever, Josh Kalin, Matt Ciolino +2
Taking advantage of computationally lightweight, but high-quality translators prompt consideration of new applications that address neglected languages. Locally run translators for…
Black Box to White Box: Discover Model Characteristics Based on Strategic Probing
Josh Kalin, Matthew Ciolino, David Noever +1
In Machine Learning, White Box Adversarial Attacks rely on knowing underlying knowledge about the model attributes. This works focuses on discovering to distrinct pieces of model i…
Systematic Attack Surface Reduction For Deployed Sentiment Analysis Models
Josh Kalin, David Noever, Gerry Dozier
This work proposes a structured approach to baselining a model, identifying attack vectors, and securing the machine learning models after deployment. This method for securing each…