System Description
System descriptions encompass the design, implementation, and evaluation of computational systems addressing diverse challenges. Current research focuses on improving system efficiency and accuracy through techniques like hybrid neural networks for optimal control, fine-tuned BERT models for question answering, and various large language model (LLM) applications for tasks ranging from automatic scoring to creative idea generation. These advancements are significant for improving automation in various fields, from energy management and disaster response to healthcare and education, and for advancing our understanding of AI capabilities and limitations.
Papers
Shake-VLA: Vision-Language-Action Model-Based System for Bimanual Robotic Manipulations and Liquid Mixing
Muhamamd Haris Khan, Selamawit Asfaw, Dmitrii Iarchuk, Miguel Altamirano Cabrera, Luis Moreno, Issatay Tokmurziyev, Dzmitry Tsetserukou
A novel multi-agent dynamic portfolio optimization learning system based on hierarchical deep reinforcement learning
Ruoyu Sun, Yue Xi, Angelos Stefanidis, Zhengyong Jiang, Jionglong Su
Plant Leaf Disease Detection and Classification Using Deep Learning: A Review and A Proposed System on Bangladesh's Perspective
Md. Jalal Uddin Chowdhury, Zumana Islam Mou, Rezwana Afrin, Shafkat Kibria
Are GNNs Effective for Multimodal Fault Diagnosis in Microservice Systems?
Fei Gao, Ruyue Xin, Yaqiang Zhang