Search Results for author:
Found 10 papers, 0 papers with code
Deep Generative Domain Adaptation with Temporal Relation Knowledge for Cross-User Activity Recognition
To bridge this gap, our study introduces a Conditional Variational Autoencoder with Universal Sequence Mapping (CVAE-USM) approach, which addresses the unique challenges of time-series domain adaptation in HAR by relaxing the i. i. d.
Cross-user activity recognition using deep domain adaptation with temporal relation information
To address this challenge, we introduce the Deep Temporal State Domain Adaptation (DTSDA) model, an innovative approach tailored for time series domain adaptation in cross-user HAR.
Cross-user activity recognition via temporal relation optimal transport
$ and do not consider the knowledge of temporal relation hidden in time series data for aligning data distribution.
Machine Learning Techniques for Sensor-based Human Activity Recognition with Data Heterogeneity -- A Review
Sensor-based Human Activity Recognition (HAR) is crucial in ubiquitous computing, analysing behaviours through multi-dimensional observations.
Deep Generative Domain Adaptation with Temporal Attention for Cross-User Activity Recognition
Addressing this oversight, our research presents the Deep Generative Domain Adaptation with Temporal Attention (DGDATA) method.
AIGC Empowering Telecom Sector White Paper_chinese
Through the study of GPT, a typical representative of AIGC, the authors have analyzed how GPT empowers the telecom sector in the form of scenarios, discussed the gap between the current GPT general model and telecom services, proposed for the first time a Telco Augmented Cognition capability system, provided answers to how to construct a telecom service GPT in the telecom sector, and carried out various practices.
AdaptiveNet: Post-deployment Neural Architecture Adaptation for Diverse Edge Environments
Model elastification generates a high-quality search space of model architectures with the guidance of a developer-specified oracle model.
Vertical Federated Learning: Concepts, Advances and Challenges
Motivated by the rapid growth in VFL research and real-world applications, we provide a comprehensive review of the concept and algorithms of VFL, as well as current advances and challenges in various aspects, including effectiveness, efficiency, and privacy.
Reinforcement Learning in Computing and Network Convergence Orchestration
This paper enlightens the RL application on CNC orchestration.
4G 5G Cell-level Multi-indicator Forecasting based on Dense-MLP
With the development of 4G/5G, the rapid growth of traffic has caused a large number of cell indicators to exceed the warning threshold, and network quality has deteriorated.
