Big data have the characteristics of enormous volume, high velocity, diversity, value-sparsity,
and uncertainty, which lead the knowledge learning from them full of challenges. With the …

Various correlations hidden in crowdsourcing annotation tasks bring opportunities to further
improve the accuracy of label aggregation. However, these relationships are usually …

Crowdsourcing plays a vital role in today's AI industry. However, existing crowdsourcing
research mainly focuses on those simple tasks that are often formulated as label …

Performance analysis is of great importance for management and optimization of space-
terrestrial integrated networks (STINs). Traditional approaches to network performance …

As a long-time wish, researchers always want to find a way to fuse human knowledge
directly into machine models that can fulfill intelligent tasks. Existing researches attempted to …

As the metaverse continues to expand, it becomes increasingly critical to have human-
centric networks that are both efficient and high-performing to optimize the user experience …

Crowdsourcing is a cost-effective method that gathers crowd wisdom to solve machine-hard
problems. In crowdsourcing systems, requesters post tasks for obtaining reliable solutions …

Crowdsourcing has become a popular paradigm for collecting large-scale labeled datasets
by leveraging numerous annotators. However, these annotators often provide noisy labels …

Crowdsourcing systems have been used to accumulate massive amounts of labeled data for
applications such as computer vision and natural language processing. However, because …

Crowdsourcing is a popular way of collecting crowd wisdom and has been deployed in
various senarios. Effective answer collection and answer aggregation are two important …