How can we recognize social roles of users, given a completely novel social network? We present a transfer learning approach based on feature transformation, in order to learn knowledge from networks that we know well, and apply the knowledge to predict user roles in other networks that are new to us. We implement our approach and evaluate it with real networks.
Interaction on the web or mobile platform is done by humans on devices designed by humans using interfaces that too has the human touch to it. And thats why this talk focus on understanding how the human signal responds to different interaction methods and how we can use these measurements to improve the existing framework or create interfaces or platforms for those who are challenged to use the conventional interaction methods.
PiGrid is a citizen science application based on the BOINC open-source network for volunteer computing from the Berkeley university. "Gridcoin" is a blockchain token that serves as a refund for giving computing power to research projects. PiGrid realizes an easy to use device based on a handy and smart microcontroller that realizes the services of a crypto-currency wallet. The device is also capable of directing its computing-power to work units in BOINC.
The business model of credit bureaus is to collect and provide relevant information for assessing creditors’ risks when interacting with consumers or companies. Central tasks in managing such information are to search and identify entities for which a credit report is requested and to predict and explain the future behaviour of these entities.
Data science which is nowadays strongly interconnected with Big Data analysis, machine learning and statistics is a key issue in Knowledge Discovery (KD) techniques from sources such as Databases (KDD) or Text (KDT). This presentation aims to discuss results of Evolutionary K-Means towards large scale bibliographic data analysis.
Crowdsourcing has become an extremely powerful and useful thing for many people.It will limit the improvement of crowdsourcing platforms if only focusing on the crowdsourcing platform itself. It is also important to listen to crowd workers and requesters in social media.
We present a new method for detecting interpretable subgroups with exceptional transition behavior in sequential data. To tackle this task we employ exceptional model mining.
The current Web of Data contains a large amount of interlinked data. However, there is still limited knowledge about the quality of the links connecting entities of different and distributed data sets. In this article, we present a framework for the intrinsic evaluation of RDF links, based on the core principles of Semantic Web data integration.
As the currently biggest online available encyclopedia, Wikipedia is a target of ontology extraction approaches. We assemble a suite of bad smells to perform a criteria based evaluation on an extracted ontology that aims at giving a classification taxonomy.
Saliency is a scene feature which sticks out of its surroundings most. Nowadays there exist a wide range of approaches intended to detect saliency from images. Comparison and evaluation of these models is a significant task in multimedia analysis.