Self-service spatiotemporal crowdsourcing (SSC), a booming variant of spatiotemporal
crowdsourcing (SC), emerges because of the vigorous development of the mobile Internet …

In this paper, we propose a Deep Reinforcement Learning (RL) framework for task
arrangement, which is a critical problem for the success of crowdsourcing platforms …

Data labelling is very important in many database and machine learning applications.
Traditional methods rely on humans (workers or experts) to acquire labels. However, the …

Spatial crowdsourcing (SC) allows requesters to crowdsource tasks to workers based on
location proximity. To preserve privacy, the location should not be disclosed to untrustworthy …

The use of a crowdsourcing platform is an option to get workers who will help complete the
work. Crowdsourcing is the process of gathering work, information, or opinions from a large …

Although crowdsourcing has emerged as a paradigm for leveraging human intelligence and
activity to solve a wide range of tasks, strategic workers will find enticement in their self …

Quasi group role assignment (QGRA) presents a novel social computing model designed to
address the burgeoning domain of self-service spatiotemporal crowdsourcing (SSC) …

Annotation through crowdsourcing draws incremental attention, which relies on an effective
selection scheme given a pool of workers. Existing methods propose to select workers …

Gathering and preparing high-quality data is one of the most significant and expensive steps
in data analytics. Crowdsourcing is an efficient way to create datasets for machine learning …

Abstract Machine learning-based approaches have greatly accelerated the progresses in
the field of micro-expression detection and recognition. However, many models, especially …