Generative Adversarial Networks (GANs) have shown remarkable success in producing
realistic-looking images in the computer vision area. Recently, GAN-based techniques are …

Temporal data, notably time series and spatio-temporal data, are prevalent in real-world
applications. They capture dynamic system measurements and are produced in vast …

Self-Supervised Learning (SSL) is a new paradigm for learning discriminative
representations without labeled data, and has reached comparable or even state-of-the-art …

Change Point Detection (CPD) methods identify the times associated with changes in the
trends and properties of time series data in order to describe the underlying behaviour of the …

The study of natural and human-made processes often results in long sequences of
temporally-ordered values, aka time series (TS). Such processes often consist of multiple …

Due to its nonintrusive character, WiFi channel state information (CSI)-based activity
recognition has attracted tremendous attention in recent years. Since activity recognition …

Manufacturing process monitoring is crucial to ensure production quality. This paper
formulates the detection problem of abnormal changes in the manufacturing process as the …

We conducted a field study at a K-12 private school in the suburbs of Melbourne, Australia.
The data capture contained two elements: First, a 5-month longitudinal field study In-Gauge …

Boundaries are the timestamps at which a class in a time series changes. Recently,
representation-based boundary detection has gained popularity, but its emphasis on …

Time series data from monitoring applications reflect the physical or logical states of the
objects, which may produce time series of distinguishable characteristics in different states …