Autonomic computing investigates how systems can achieve (user) specified “control”
outcomes on their own, without the intervention of a human operator. Autonomic computing …

Conventional machine learning studies generally assume close-environment scenarios
where important factors of the learning process hold invariant. With the great success of …

Novel classes frequently arise in our dynamically changing world, eg, new users in the
authentication system, and a machine learning model should recognize new classes without …

The cementitious composites have different properties in the changing environment. Thus,
knowing their mechanical properties is very important for safety reasons. The most important …

Deep models, eg, CNNs and Vision Transformers, have achieved impressive achievements
in many vision tasks in the closed world. However, novel classes emerge from time to time in …

Abstract Internet of Things (IoT) is widely accepted technology in both industrial as well as
academic field. The objective of IoT is to combine the physical environment with the cyber …

Ensemble methods are considered the state‐of‐the art solution for many machine learning
challenges. Such methods improve the predictive performance of a single model by training …

Machine learning has evolved into an enabling technology for a wide range of highly
successful applications. The potential for this success to continue and accelerate has placed …

Incremental learning, online learning, and data stream learning are terms commonly
associated with learning algorithms that update their models given a continuous influx of …

Applications that generate huge amounts of data in the form of fast streams are becoming
increasingly prevalent, being therefore necessary to learn in an online manner. These …