Recent years have seen a substantial increase in the application of machine learning (ML)
for automated analysis of nondestructive examination (NDE) data. One of the applications of …

Artificial intelligence (AI) systems are trained to solve complex problems and learn to
perform specific tasks by using large volumes of data, such as prediction, classification …

Data-centric AI is at the center of a fundamental shift in software engineering where machine
learning becomes the new software, powered by big data and computing infrastructure …

Modern artificial intelligence (AI) applications require large quantities of training and test
data. This need creates critical challenges not only concerning the availability of such data …

With the increasing importance and complexity of data pipelines, data quality became one of
the key challenges in modern software applications. The importance of data quality has …

Current district heating networks are undergoing a sustainable transition towards the 4 th
and 5 th generation of district heating networks, characterized by the integration of different …

High data quality is fundamental for today's AI-based systems. However, although data
quality has been an object of research for decades, there is a clear lack of research on …

Data pipelines are an integral part of various modern data-driven systems. However, despite
their importance, they are often unreliable and deliver poor-quality data. A critical step …

Background and aim While artificial intelligence (AI) is being adapted for various life
domains and applications related to medicine and healthcare, the use of AI in nursing …

Machine learning (ML) models in production pipelines are frequently retrained on the latest
partitions of large, continually-growing datasets. Due to engineering bugs, partitions in such …