The discovery of conserved (repeated) patterns in time series is arguably the most important
primitive in time series data mining. Called time series motifs, these primitive patterns are …

In recent years, time series motif discovery has emerged as perhaps the most important
primitive for many analytical tasks, including clustering, classification, rule discovery …

We present a method to explain the decisions of black box models for time series
classification. The explanation consists of factual and counterfactual shapelet-based rules …

Abstract Representation learning for time series has gained increasing attention in
healthcare domain. The recent advancement in semantic learning allows researcher to learn …

Background Sensor-based remote health monitoring can be used for the timely detection of
health deterioration in people living with dementia with minimal impact on their day-to-day …

Recently, the spread of IoT technologies has led to an unprecedented acquisition of energy-
related data providing accessible knowledge on the actual performance of buildings during …

Learning-based stereo matching techniques have made significant progress. However
existing methods inevitably lose geometrical structure information during the feature channel …

Over the last decade, time series motif discovery has emerged as a useful primitive for many
downstream analytical tasks, including clustering, classification, rule discovery …

The Industrial Internet of Things drastically increases connectivity of devices in industrial
applications. In addition to the benefits in efficiency, scalability and ease of use, this creates …

Poultry farms are an important contributor to the human food chain. Worldwide, humankind
keeps an enormous number of domesticated birds (eg chickens) for their eggs and their …