Deep learning techniques have become one of the main propellers for solving engineering
problems effectively and efficiently. For instance, Predictive Maintenance methods have …

Federated Learning (FL) under distributed concept drift is a largely unexplored area.
Although concept drift is itself a well-studied phenomenon, it poses particular challenges for …

Continuous learning has recently shown promising results for video analytics by adapting a
lightweight" expert" DNN model for each specific video scene to cope with the data drift in …

Decreasing costs of deploying space vehicles to low-Earth orbit have fostered an
emergence of large constellations of satellites. However, high satellite velocities, large …

Video analytics systems perform automatic events, movements, and actions recognition in a
video and make it possible to execute queries on the video. As a result of a large number of …

With the rapid adoption of machine learning (ML), a number of domains now use the
approach of fine tuning models which were pre-trained on a large corpus of data. However …

Video analytics pipelines have steadily shifted to edge deployments to reduce bandwidth
overheads and privacy violations, but in doing so, face an ever-growing resource tension …

We present a new video storage system (VSS) designed to decouple high-level video
operations from the low-level details required to store and efficiently retrieve video data. VSS …

Aalborg Universitet Seasons in Drift: A Long-Term Thermal Imaging Dataset for Studying Concept
Drift Nikolov, Ivan Adriyanov; Ph Page 1 Aalborg Universitet Seasons in Drift: A Long-Term …

Process mining provides techniques to learn models from event data. These models can be
descriptive (eg, Petri nets) or predictive (eg, neural networks). The learned models offer …