Predicting human mobility via graph convolutional dual-attentive networks
Human mobility prediction is of great importance for various applications such as smart
transportation and personalized recommender systems. Although many traditional pattern …
transportation and personalized recommender systems. Although many traditional pattern …
Learning holistic interactions in LBSNs with high-order, dynamic, and multi-role contexts
Location-based social networks (LBSNs) have emerged over the past few years. Their
exponential network effects depend on the fact that each user can share her daily digital …
exponential network effects depend on the fact that each user can share her daily digital …
HealthWalks: Sensing fine-grained individual health condition via mobility data
Can health conditions be inferred from an individual's mobility pattern? Existing research
has discussed the relationship between individual physical activity/mobility and well-being …
has discussed the relationship between individual physical activity/mobility and well-being …
Semantic-aware spatio-temporal app usage representation via graph convolutional network
Recent years have witnessed a rapid proliferation of personalized mobile Apps, which
poses a pressing need for user experience improvement. A promising solution is to model …
poses a pressing need for user experience improvement. A promising solution is to model …
Neural architecture search for time series classification
Neural architecture search (NAS) has achieved great success in different computer vision
tasks such as object detection and image recognition. Moreover, deep learning models have …
tasks such as object detection and image recognition. Moreover, deep learning models have …
Will you come back/check-in again? understanding characteristics leading to urban revisitation and re-check-in
Recent years have witnessed much work unraveling human mobility patterns through urban
visitation and location check-in data. Traditionally, user visitation and check-in have been …
visitation and location check-in data. Traditionally, user visitation and check-in have been …
Understanding metropolitan crowd mobility via mobile cellular accessing data
Understanding crowd mobility in a metropolitan area is extremely valuable for city planners
and decision makers. However, crowd mobility is a relatively new area of research and has …
and decision makers. However, crowd mobility is a relatively new area of research and has …
Rlmob: Deep reinforcement learning for successive mobility prediction
Human mobility prediction is an important task in the field of spatiotemporal sequential data
mining and urban computing. Despite the extensive work on mining human mobility …
mining and urban computing. Despite the extensive work on mining human mobility …
Human intention-aware motion planning and adaptive fuzzy control for a collaborative robot with flexible joints
This paper presents a framework to enable a human and a robot to perform collaborative
tasks safely and efficiently. It consists of three functions. First, human motion is predicted by …
tasks safely and efficiently. It consists of three functions. First, human motion is predicted by …
Hopping time estimation of frequency-hopping signals based on HMM-enhanced Bayesian compressive sensing with missing observations
H Wang, B Zhang, H Wang, B Wu… - IEEE Communications …, 2022 - ieeexplore.ieee.org
The hopping time reflects the time-varying characteristics of frequency-hopping (FH) signals,
which are essential parameters for the spectrum estimation of FH signals. In this study, we …
which are essential parameters for the spectrum estimation of FH signals. In this study, we …