The kernel null-space technique is known to be an effective one-class classification (OCC)
technique. Nevertheless, the applicability of this method is limited due to its susceptibility to …

Failures in robotics can have disastrous consequences that worsen rapidly over time. This,
the ability to rely on robotic systems, depends on our ability to monitor them and intercede …

We propose a supervised anomaly detection method based on neural density estimators,
where the negative log likelihood is used for the anomaly score. Density estimators have …

Background: Information from historical infectious disease outbreaks provides real-world
data about outbreaks and their impacts on affected populations. These data can be used to …

Conformance checking is concerned with the task of assessing the quality of process
models describing actual behavior captured in an event log across different dimensions. In …

Supercomputers, high performance computers, and clusters are composed of very large
numbers of independent operating systems that are generating their own system logs …

This research project focusses on the development of representation learning-based
techniques for business processes. More specifically, both the architectural design as well …

The paper introduces a new efficient nonlinear one-class classifier formulated as the
Rayleigh quotient criterion optimisation. The method, operating in a reproducing kernel …

One-class classification is a specialized form of classification from the field of machine
learning. A traditional classifier always assigns a new element to one of the known classes …

Background: Information from historical infectious disease outbreaks provides real-world
data about outbreaks and its impacts on affected populations. These data can be used to …