The recent increase in computational capability has led to an unprecedented increase in the
range of new applications where machine learning can be used in real time …

Artificial intelligence (AI) generally and machine learning (ML) specifically demonstrate
impressive practical success in many different application domains, eg in autonomous …

The paper aims at establishing a common ground and understanding of collaborative
learning approaches between humans and computers to encourage Hybrid Intelligence …

With the growing number of applications of artificial intelligence such as autonomous cars or
smart industrial equipment, the inaccuracy of utilized machine learning algorithms could …

Cybersecurity named entity recognition is an important part of threat information extraction
from large-scale unstructured text collection in many cybersecurity applications. Most …

Active learning is a technique that helps to minimize the annotation budget required for the
creation of a labeled dataset while maximizing the performance of a model trained on this …

Recently, advanced text-mining techniques have been shown to speed up manual data
curation by providing human annotators with automated pre-annotations generated by rules …

Parkinson's disease (PD) results primarily from dying of dopaminergic neurons in the
Substantia Nigra, a part of the Mesencephalon (midbrain), which is not curable to date. PD …

Supervised Natural Language Processing (NLP) models can achieve high accuracy, but
they often require a significant amount of annotated data for training, which can be …

Hybrid intelligence systems (HIS) enable human users and Artificial Intelligence (AI) to
collaborate in activities complementing each other. They particularly allow the combination …