Monitoring the behavior of automated real-time stream processing systems has become one of the most relevant problems in real world applications. Such systems have grown in complexity relying heavily on high dimensional input data, and data hungry Machine Learning (ML) algorithms. We propose a flexible system, Feature Monitoring (FM), that detects data drifts in such data sets, with a small and constant memory footprint and a small computational cost in streaming applications. The method is based on a multi-variate statistical test and is data driven by design (full reference distributions are estimated from the data). It monitors all features that are used by the system, while providing an interpretable features ranking whenever an alarm occurs (to aid in root cause analysis). The computational and memory lightness of the system results from the use of Exponential Moving Histograms. In our experimental study, we analyze the system's behavior with its parameters and, more importantly, show examples where it detects problems that are not directly related to a single feature. This illustrates how FM eliminates the need to add custom signals to detect specific types of problems and that monitoring the available space of features is often enough.
Lightweight Hopfield Neural Networks for Bioacoustic Detection and Call Monitoring of Captive Primates
School of Engineering, Computing and Mathematical Sciences（工程、计算与数学科学学院）；School of Pharmacy and Life Sciences（药学与生命科学学院）；University of Wolverhampton（沃里克大学）
LDGCN: An Edge-End Lightweight Dual GCN Based on Single-Channel EEG for Driver Drowsiness Monitoring
College of Computer and Big Data, Fuzhou University（福州大学计算机与大数据学院）；Polytechnic University of Catalonia（加泰罗尼亚理工大学）；New Engineering Industry College, Putian University（莆田大学新工程产业学院）；School of Computer and Artificial Intelligence, Zhengzhou University（郑州大学计算机与人工智能学院）；College of Computer and Control Engineering, Minjiang University（闽江学院计算机与控制工程学院）；Sichuan Conservatory of Music（四川音乐学院）；Fuzhou Technology Innovation Center of Intelligent Manufacturing information System, Minjiang University（闽江学院福州智能制造信息系统技术创新中心）；Fujian Polytechnic Normal University（福建技术师范学院）
SCOPE: A Lightweight-training LLM Framework for Air Traffic Control Readback Monitoring
Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology（香港科学与技术大学机械与航空航天工程系）；School of Electronic and Information Engineering, Beihang University（北航电子与信息工程学院）；State Key Laboratory of CNS/ATM（国家空管自动化系统实验室）
RAFM-SER++: A Lightweight Multimodal Emotion Recognition Framework for Real-Time Behavioral Monitoring in Surveillance Systems
Industrial University of HCM City（胡志明市工业大学）；Le Hong Phong High School for the Gifted（黎鸿峰天才高中）；FPT University（菲普特大学）；University of Science, VNU-HCM（胡志明市国家大学科学大学）
Towards Scalable Insect Monitoring: Ultra-Lightweight CNNs as On-Device Triggers for Insect Camera Traps
University of Exeter（埃克塞特大学）；Institute for Data Science and AI, University of Exeter（埃克塞特大学数据科学与人工智能学院）；Centre for Ecology and Conservation, University of Exeter（埃克塞特大学生态与保护中心）
针对 PIR 传感器难以触发昆虫相机陷阱的问题，本文提出在低功耗微控制器上运行超轻量级 CNN 检测连续图像中的昆虫，实现零延迟、高 AUC、低功耗与更长部署。
Online Tool Condition Monitoring Based on Parsimonious Ensemble+
Nanyang Technological University（南洋理工大学）；University Teknologi Brunei（文莱理工大学）；Singapore Centre for 3D Printing（新加坡3D打印中心）；School of Mechanical and Aerospace Engineering（机械与航天工程学院）；University of Alberta（阿尔伯塔大学）；Johanes Kepler University（约翰内斯开普勒大学）
