Mobile crowdsensing engages a crowd of individuals to use their mobile devices to
cooperatively collect data about social events and phenomena for customers with common …

Digital citizen science platforms are prominent examples of modern volunteerism that
provide people with opportunities to observe natural phenomena and to engage in scientific …

Compared to image representation based on low-level local descriptors, deep neural
activations of Convolutional Neural Networks (CNNs) are richer in mid-level representation …

Social networks have attracted billions of users and supported a wide range of interests and
practices. Users of social networks can be connected with each other by different …

Background For many eServices, end-user trust is a crucial prerequisite for use. Within the
context of Telemedicine, the role of trust has hardly ever been studied. In this study, we …

Data collection is a fundamental yet challenging task of wireless sensor networks to support
a variety of applications, due to the inherent distinguish characteristics for sensor networks …

Many of the research approaches to smartphones actually regard them as more or less
transparent points of access to other kinds of communication experiences. That is, rather …

Data quality is key in the success of a citizen science project. Valid datasets serve as
evidence for scientific research. Numerous projects have highlighted the ways in which …

Mobile crowdsensing is a new paradigm which explores the mobility and intelligence of
mobile users to collect high-quality data from social events and phenomena for conducting …

In recent years, wireless sensor networks (WSNs) have been widely applied to sense the
physical environment, especially some difficult environment due to their ad-hoc nature with …