Agent-based models (ABMs) are increasingly used in the management sciences. Though
useful, ABMs are often critiqued: it is hard to discern why they produce the results they do …

Mobile data crowdsourcing has found a broad range of applications (eg, spectrum sensing,
environmental monitoring) by leveraging the" wisdom" of a potentially large crowd of" …

Incentive mechanism design and quality control are two key challenges in data
crowdsourcing, because of the need for recruitment of crowd users and their limited …

Data crowdsourcing is a promising paradigm that leverages the “wisdom” of a potentially
large crowd of “workers” in many application domains. Quality-aware crowdsourcing is …

Mobile crowdsensing has found a variety of applications (eg, spectrum sensing,
environmental monitoring) by leveraging the “wisdom” of a potentially large crowd of mobile …

Federated learning (FL) has recently emerged as a promising paradigm that trains machine
learning (ML) models on clients' devices in a distributed manner without the need of …

Linear regression is a fundamental building block of statistical data analysis. It amounts to
estimating the parameters of a linear model that maps input features to corresponding …

Mobile crowd sensing has found a variety of applications (eg, spectrum sensing,
environmental monitoring) by leveraging the" wisdom" of a potentially large crowd of mobile …

ABSTRACT A crowdsourcing system such as Amazon's Mechanical Turk allows a
crowdsource campaign initiator to recruit a large number of workers to accomplish a task …

Crowdsensing has found a variety of applications (eg, spectrum sensing, environmental
monitoring) by leveraging the" wisdom" of a potentially large crowd of mobile users as" …