Artificial intelligence for social good (AI4SG) is a research theme that aims to use and
advance artificial intelligence to address societal issues and improve the well-being of the …

Over the last decade, machine learning (ML) and deep learning (DL) algorithms have
significantly evolved and been employed in diverse applications, such as computer vision …

In this paper, we focus on the incentive mechanism design for a vehicle-based,
nondeterministic crowdsensing system. In this crowdsensing system, vehicles move along …

Recent years have witnessed the rising popularity of urban vehicular crowd sensing (UVCS)
systems that leverage drivers' mobile devices equipped with on-board sensors for various …

Mobile CrowdSensing (MCS) is a widely adopted approach that involves engaging mobile
users to collaboratively perform diverse sensing tasks. In Sparse CrowdSensing, the …

While Mobile CrowdSensing (MCS) has become a popular paradigm that recruits mobile
users to carry out various sensing tasks collaboratively, the performance of MCS is …

Mobile CrowdSensing (MCS) is a popular data collection paradigm which usually faces the
problem of sparse sensed data because of the limited sensing cost. In order to address the …

The predictive performance of a machine learning model highly depends on the
corresponding hyper-parameter setting. Hence, hyper-parameter tuning is often …

While spatial-temporal environment monitoring has become an indispensable way to collect
data for enabling smart cities and intelligent transportation applications, the cost to deploy …

Local spatial-aware community detection refers to detecting a spatial-aware community for a
given node using local information. A spatial-aware community means that nodes in the …