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Found 12 papers, 3 papers with code
Patent Value Characterization -- An Empirical Analysis of Elevator Industry Patents
The global patent application count has steadily increased, achieving eight consecutive years of growth. The global patent industry has shown a general trend of expansion.
Federated Learning Hyper-Parameter Tuning from a System Perspective
In this paper, we propose FedTune, an automatic FL hyper-parameter tuning algorithm tailored to applications' diverse system requirements in FL training.
Client Selection in Federated Learning: Principles, Challenges, and Opportunities
As a privacy-preserving paradigm for training Machine Learning (ML) models, Federated Learning (FL) has received tremendous attention from both industry and academia.
MASTAF: A Model-Agnostic Spatio-Temporal Attention Fusion Network for Few-shot Video Classification
We propose MASTAF, a Model-Agnostic Spatio-Temporal Attention Fusion network for few-shot video classification.
Spectroscopy Approaches for Food Safety Applications: Improving Data Efficiency Using Active Learning and Semi-Supervised Learning
The past decade witnesses a rapid development in the measurement and monitoring technologies for food science.
FedTune: Automatic Tuning of Federated Learning Hyper-Parameters from System Perspective
Federated learning (FL) hyper-parameters significantly affect the training overheads in terms of computation time, transmission time, computation load, and transmission load.
Automatic Tuning of Federated Learning Hyper-Parameters from System Perspective
Federated Learning (FL) is a distributed model training paradigm that preserves clients' data privacy.
Early Mobility Recognition for Intensive Care Unit Patients Using Accelerometers
In this paper, we target a new healthcare application of human activity recognition, early mobility recognition for Intensive Care Unit(ICU) patients.
Towards a Federated Learning Framework for Heterogeneous Devices of Internet of Things
Federated Learning (FL) has received a significant amount of attention in the industry and research community due to its capability of keeping data on local devices.
Gait Characterization in Duchenne Muscular Dystrophy (DMD) Using a Single-Sensor Accelerometer: Classical Machine Learning and Deep Learning Approaches
We extracted temporospatial gait clinical features (CFs) and applied multiple machine learning (ML) approaches to differentiate between DMD and TD children using extracted temporospatial gait CFs and raw data.
An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence
With the rapid development of the internet of things (IoT) and artificial intelligence (AI) technologies, human activity recognition (HAR) has been applied in a variety of domains such as security and surveillance, human-robot interaction, and entertainment.
Towards Learning-automation IoT Attack Detection through Reinforcement Learning
These low-rate attacks are challenging to detect and can persist in the networks.
