Federated Learning is a growing branch of Artificial Intelligence with the wide usage of
mobile computing and IoT technologies. Since this technology uses distributed computing …

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 …

Social media has become one of the main channels for people to access and consume
news, due to the rapidness and low cost of news dissemination on it. However, such …

Crowdsourcing has emerged as a novel problem-solving paradigm, which facilitates
addressing problems that are hard for computers, eg, entity resolution and sentiment …

Incentive plays an important role in knowledge discovery, as it impels users to provide high-
quality knowledge. To promise incentive schemes with transparency, blockchain technology …

Thanks to information explosion, data for the objects of interest can be collected from
increasingly more sources. However, for the same object, there usually exist conflicts among …

Collaborative (federated) learning enables multiple parties to train a model without sharing
their private data, but through repeated sharing of the parameters of their local models …

In many real world applications, the same item may be described by multiple sources. As a
consequence, conflicts among these sources are inevitable, which leads to an important …

The web is a huge source of valuable information. However, in recent times, there is an
increasing trend towards false claims in social media, other web-sources, and even in news …

Crowdsensing systems collect various types of data from sensors embedded on mobile
devices owned by individuals. These individuals are commonly referred to as workers that …