Machine learning is of rising importance in cybersecurity. The primary objective of applying
machine learning in cybersecurity is to make the process of malware detection more …

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 …

Deemed “one of the top ten data mining mistakes”, leakage is the introduction of information
about the data mining target that should not be legitimately available to mine from. In …

Abstract The Higgs Boson Machine Learning Challenge (HiggsML or the Challenge for
short) was organized to promote collaboration between high energy physicists and data …

Nowadays classification models have been widely adopted in healthcare, aiming at
supporting practitioners for disease diagnosis and human error reduction. The challenge is …

AIOps (Artificial Intelligence for IT Operations) leverages machine learning models to help
practitioners handle the massive data produced during the operations of large-scale …

Issue tracking data have been used extensively to aid in predicting or recommending
software development practices. Issue attributes typically change over time, but users may …

Statistical Constraints (SCs) play an important role in statistical modeling and analysis. This
paper brings the concept to data cleaning and studies how to leverage SCs for error …

Abstract Machine learning and other statistical pattern recognition techniques have the
potential to improve diagnosis in medicine and reduce medical error. But technology can be …

The Automatic Speaker Verification Spoofing and Countermeasures Challenges motivate
research in protecting speech biometric systems against a variety of different access attacks …