Jin-Hee Cho

Quantifying Classification Uncertainty using Regularized Evidential Neural Networks

2019 · arXiv (Cornell University)

Traditional deep neural nets (NNs) have shown the state-of-the-art performance in the task of classification in various applications. However, NNs have not considered any types of uncertainty associated with the class probabilities to minimize risk …

ScanFed: Scalable Behavior-Based Backdoor Detection in Federated Learning

2023

Federated Learning (FL) has been adopted in practical network applications and plays a critical role. As FL allows participants to contribute to the global model by training locally with private data, it is known particularly …

Privacy-Preserving and Diversity-Aware Trust-based Team Formation in Online Social Networks

2024 · ACM Transactions on Intelligent Systems and Technology

As online social networks (OSNs) become more prevalent, a new paradigm for problem-solving through crowd-sourcing has emerged. By leveraging the OSN platforms, users can post a problem to be solved and then form a team …

RESONANT: Reinforcement Learning-based Moving Target Defense for Credit Card Fraud Detection

2024

According to security.org, as of 2023, 65% of credit card (CC) users in the US have been subjected to fraud at some point in their lives, which equates to about 151 million Americans. The proliferation …

Advancing Human-Machine Teaming: Concepts, Challenges, and Applications

2025 · arXiv (Cornell University)

Human-Machine Teaming (HMT) is revolutionizing collaboration across domains such as defense, healthcare, and autonomous systems by integrating AI-driven decision-making, trust calibration, and adaptive teaming. This survey presents a comprehensive taxonomy of HMT, analyzing theoretical models, …