We address the problem of information fusion in uncertain environments. Imagine there are multiple experts building probabilistic models of the same situation and we wish to aggregate the information they provide. There are several problems we may run into by naively merging the information from each. For example, the experts may disagree on the probability of a certain event or they may disagr...

The widespread and ubiquitous nature of mobile devices makes them attractive as providers of information collected from their rich equipment of sensors (camera, microphone, GPS, etc.), and also from external sensors (placed on persons, or in the environment). Thus, we envision large-scale sensor networks that use mobile devices as raw data sources, but also aggregated information producers merg...

In modern production systems, customized mass production of complex products, such as automotive or white goods, is often realized at assembly lines with a high degree of manual labor. For firms that apply assembly systems the assembly line balancing problem (ALBP) arises, which is to assign optimally tasks to stations or workers with respect to some constraints and objectives. Although the lit...

We address the problem of information fusion in uncertain environments. Imagine there are multiple experts building probabilistic models of the same situation and we wish to aggregate the information they provide. There are several problems we may run into by naively merging the information from each. For example, the experts may disagree on the probability of a certain event or they may disagr...

The goal of this study is to (i) understand the characteristics of high-, averageand low-level performing students in a first year computer programming course, and (ii) investigate whether their performance can be predicted accurately and early enough in the semester for timely intervention. We triangulate data from three sources: submission steps and outcomes in an automatic marking system tha...

Collecting and processing data in order to detect and recognize emotions has become a research hot topic in educational scenarios. We have followed a multimodal approach to collect and process data from different sources to support emotion detection and recognition. To illustrate the approach, in this demo, participants will be shown what emotional data can be gathered while solving Math problems.

Most theoretical models of inductive inference make the idealized assumption that the data available to a learner is from a single and accurate source. The subject of inaccuracies in data emanating from a single source has been addressed by several authors. The present paper argues in favor of a more realistic learning model in which data emanates from multiple sources, some or all of which may...

Both multiple source data mining and streaming data mining problems have attracted much attention in the past decade. In contrast to traditional association-rule mining, to capture the quantitative association knowledge, a new paradigm called Ratio Rule (RR) was proposed recently. We extend this framework to mining ratio rules from multiple source data streams which is a novel and challenging p...