Machine learning (ML) is increasingly being adopted in a wide variety of application
domains. Usually, a well-performing ML model relies on a large volume of training data and …

The recent progression of Large Language Models (LLMs) has witnessed great success in
the fields of data-centric applications. LLMs trained on massive textual datasets showed …

The advancement and introduction of computing technologies has proven to be highly
effective and has resulted in the production of large amount of data that is to be analyzed …

Ensuring the usefulness of electronic data sources while providing necessary privacy
guarantees is an important unsolved problem. This problem drives the need for an analytical …

Abstract The growth of Big Data, especially personal data dispersed in multiple data
sources, presents enormous opportunities and insights for businesses to explore and …

Business reliance on algorithms is becoming ubiquitous, and companies are increasingly
concerned about their algorithms causing major financial or reputational damage. High …

Building energy data has been used for decades to understand energy flows in buildings
and plan for future energy demand. Recent market, technology and policy drivers have …

With the growing number of competing Web services that provide similar functionality,
Quality-of-Service (QoS) prediction is becoming increasingly important for various QoS …

Recently, there has been a growing debate over approaches for handling and analyzing
private data. Research has identified issues with syntactic anonymity models. Differential …

State-of-art preprocessing methods for Particle Image Velocimetry (PIV) are severely
challenged by time-dependent light reflections and strongly non-uniform background. In this …