most citedArtificial Intelligence can Recognize Whether a Job Applicant is Selling and/or Lying According to Facial Expressions and Head Movements Much More Correctly Than Human Interviewers

11 citations

6 papers

cs.NI2026

Predictive Lightweight MARL for Resilient Coverage in Sparse-Signaling Aerial Networks

Chuan-Chi Lai, Ang-Hsun Tsai

This letter proposes the Predictive Lightweight Multi-Agent Reinforcement Learning (PL-MARL) framework to ensure resilient coverage in bandwidth-constrained UAV swarms. To counter…

cs.DC2026

Totoro: An Adaptive and Scalable Edge Federated Learning System

Cheng-Wei Ching, Xin Chen, Taehwan Kim +3

Federated Learning (FL) is an emerging distributed machine learning (ML) technique that enables in-situ model training and inference on decentralized edge devices. We propose Totor…

cs.HC20264 cited

Teachers' Vocal Expressions and Student Engagement in Asynchronous Video Learning

Hung-Yue Suen, Yu-Sheng Su

Asynchronous video learning, including massive open online courses (MOOCs), offers flexibility but often lacks students' affective engagement. This study examines how teachers' ver…

cs.HC202611 cited

Artificial Intelligence can Recognize Whether a Job Applicant is Selling and/or Lying According to Facial Expressions and Head Movements Much More Correctly Than Human Interviewers

Hung-Yue Suen, Kuo-En Hung, Che-Wei Liu +2

Whether an interviewee's honest and deceptive responses can be detected by facial expression signals in videos has been debated and requires further research. We developed deep lea…

cs.CV2026

Heterogeneous Model Fusion for Privacy-Aware Multi-Camera Surveillance via Synthetic Domain Adaptation

Peggy Joy Lu, Wei-Yu Chen, Yao-Tsung Huang +1

We propose HeroCrystal, a novel privacy-preserving framework for multi-camera domain-adaptive object detection, addressing challenges such as data privacy, class imbalance, and het…

math.NA2026

Artifacts of Numerical Integration in Learning Dynamical Systems

Bing-Ze Lu, Richard Tsai

In many applications, one needs to learn a dynamical system from its solutions sampled at a finite number of time points. The learning problem is often formulated as an optimizatio…