The crowd-powered systems have been shown to be highly successful in the current decade
to manage collective contribution of online workers for solving different complex tasks. It can …

This paper investigates how the heterogenous relationships around us affect the spread of
diverse opinions in the population. We apply the Potts model, derived from condensed …

Benefiting from the fast development of human-carried mobile devices, crowd sensing has
become an emerging paradigm to sense and collect data. However, reliability of sensory …

Biclustering is a statistical learning methodology that simultaneously partitions rows and
columns of a rectangular data array into homogeneous subsets. Biclustering is known to be …

The amount of labeled data (referred to as absolute information) obtained from experts has
an obvious effect on the performance of an ordinal classifier. In order to make up for a lack of …

This paper reports initial efforts towards building BDBComp, a digital library for the Brazilian
computer science community BDBComp is based on a number of standards (eg, OAI, Dublin …

Crowdsourcing has already been shown to be a promising tool in solving many real-life
problems in time and cost-effective way. For example, in city planning, to install some …

Using proper social media content in decision making is an ongoing challenge for modern
information management. Since such content could be highly subjective, diverse and, thus …

Multimodal clustering is an unsupervised technique for mining interesting patterns in n-adic
binary relations or n-mode networks. Among different types of such generalised patterns one …

Collecting opinions from multiple crowd workers has been proved to be very effective to
reach into a prompt and robust decision. There are many real-life applications (like smart city …