The rapid development of Machine Learning (ML) has demonstrated superior performance
in many areas, such as computer vision and video and speech recognition. It has now been …

Machine learning (ML) has grown in popularity in the software industry due to its ability to
solve complex problems. Developing ML systems involves more uncertainty and risk …

Abstract Machine Learning (ML) is integrated into a growing number of systems for various
applications. Because the performance of an ML model is highly dependent on the quality of …

Background: As Machine Learning (ML) advances rapidly in many fields, it is being adopted
by academics and businesses alike. However, ML has a number of different challenges in …

The metaverse is a nascent concept that envisions a virtual universe, a collaborative space
where individuals can interact, create, and participate in a wide range of activities. Privacy in …

Quantization is one of the most applied Deep Neural Network (DNN) compression
strategies, when deploying a trained DNN model on an embedded system or a cell phone …

At present, we are witnessing an increasing effort to improve the performance and
trustworthiness of Deep Neural Networks (DNNs), with the aim to enable their adoption in …

Autonomous driving systems (ADS) have achieved spectacular development and have been
utilized in numerous safety-critical tasks. Nonetheless, in spite of their considerable …

Agile4MLS - Leveraging Agile Practices for Developing ML-enabled systems: An Industrial
Experience Page 1 Agile4MLS - Leveraging Agile Practices for Developing ML-enabled systems …

Abstract Machine learning (ML) techniques and algorithms have been successfully and
widely used in various areas including software engineering tasks. Like other software …