Jin-Hee Cho

DeepSAGE: Stage-Aware Reinforcement Learning for Structured CBT Counseling Dialogue

Qi Zhang, Heajun An, Prakriti Dumaru, Sang Won Lee et autres

Large Language Model (LLM)-based counseling agents can generate fluent and supportive responses, but they often lack the structured, goal-directed progression required to conduct a coherent therapeutic session. We present DeepSAGE (Strategic AI Guidance Engine), a hybrid LLM--Deep Reinforcement Learning (DRL) framework for …

The 5th Workshop on Uncertainty Reasoning and Quantification in Decision Making (UDM)

Xujiang Zhao, Chen Zhao, Feng Chen, Jin-Hee Cho et autres

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Sustainable Smart Farm Networks: A Decision Theory-Guided Deep Reinforcement Learning Approach

Dian Chen, Zelin Wan, Dong Sam Ha, Sook Shin et autres

Solar-powered sensor networks are reshaping agriculture by enabling continuous farm management and animal welfare monitoring through Internet-of-Things (IoT) devices, edge intelligence, and cloud analytics. Yet, their sustainability is threatened by two underexplored challenges: vulnerability to cyberattacks and instability under fluctuating energy supplies. …

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CR4T: Rewrite-Based Guardrails for Adolescent LLM Safety

Heajun An, Qi Zhang, Vedanth Achanta, Jin-Hee Cho

Large language models (LLMs) are increasingly embedded in adolescent digital environments, mediating information seeking, advice, and emotionally sensitive interactions. Yet existing safety mechanisms remain largely grounded in adult-centric norms and operationalize safety through refusal-oriented suppression. While such approaches may reduce immediate policy …

Grounded but Misleading: Evaluating Semantic Alignment in AI-Generated Security Explanations

Heajun An, Connor Ng, Sandesh Sharma Dulal, Junghwan Kim et autres

Online scams increasingly leverage fluent and context-aware social engineering strategies, creating growing demand for AI systems that explain why a message may be risky. However, explanations that cite detector-derived evidence may still semantically weaken or redirect the intended risk interpretation. We introduce …

StagePilot: Stage-Level Planning for Long-Horizon Dialogue Simulation in Cybergrooming

Heajun An, Qi Zhang, Minqian Liu, Xinyi Zhang et autres

Cybergrooming is an evolving threat to youth, requiring proactive educational interventions.We address this by modeling dialogue progression as a structured planning problem over stage-wise interactions.We propose StagePilot, a dialogue framework that separates stagelevel planning from response generation, in which the model selects …

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Evolutionary Tree Search for Turn-Based Strategy Games

Taylor Neller, Lifu Huang, Jin-Hee Cho

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Explainable AI for Cyberscam Prevention: A Survey of Multimodal Scam Detection, Evaluation, and Human-Centered Training

Heajun An, Hossein Salemi, Qi Zhang, Hemant Purohit et autres

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Dual-Use LLMs in Adolescent Online Safety: From Dangerous Persuaders to Cybergrooming Education

Prakriti Dumaru, Xinyi Zhang, Heajun An, Minqian Liu et autres

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DASH: Deception-Augmented Shared Mental Model for a Human-Machine Teaming System

Zelin Wan, Han Jun Yoon, Nithin Alluru, Terrence J. Moore et autres

We present DASH (Deception-Augmented Shared mental model for Human-machine teaming), a novel framework that enhances mission resilience by embedding proactive deception into Shared Mental Models (SMM). Designed for mission-critical applications such as surveillance and rescue, DASH introduces "bait tasks" to detect insider …

PRIVEE: Privacy-Preserving Vertical Federated Learning Against Feature Inference Attacks

Sindhuja Madabushi, Haider Ali, Ahmad Khan, Rui Ning et autres

Vertical Federated Learning (VFL) enables collaborative model training across organizations that share common user samples but hold disjoint feature spaces. Despite its potential, VFL is susceptible to feature inference attacks, in which adversarial parties exploit shared confidence scores (prediction probabilities) during inference …

Protecting federated learning from malicious attacks using consensus technique

Woocheol Kim, Jaehyoung Park, Jin-Hee Cho, Dong Seong Kim et autres

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