James She, Department of Electronic and Computer Engineering
Hong Kong University of Science and Technology
Besat Kassaie, PhD candidate
David R. Cheriton School of Computer Science
Alireza Heidari, PhD candidate
David R. Cheriton School of Computer Science
We introduce a few-shot learning framework for error detection. We show that data augmentation (a form of weak supervision) is key to training high-quality, ML-based error detection models that require minimal human involvement.
Siddhartha Sahu, PhD candidate
David R. Cheriton School of Computer Science
Joan Bartlett, School of Information Studies
McGill University
Millennials have been found to rely heavily on information obtained from the web and social networks; but it is also seen that they may not be able to judge the authenticity, validity and reliability of the digital information, and may share misinformation among themselves.
Michael Abebe, PhD candidate
David R. Cheriton School of Computer Science
Workload access patterns can limit the performance achievable with static data placement in distributed database systems. Dynamic physical database designs in which data item master locations, partitioning schemes and replica locations can vary with the workload help improve system performance.
Siddhartha Sahu, PhD candidate
David R. Cheriton School of Computer Science
Bradley Glasbergen, PhD candidate
David R. Cheriton School of Computer Science
Chang Ge, PhD candidate
David R. Cheriton School of Computer Science
Khaled Ammar, PhD candidate
David R. Cheriton School of Computer Science
Differential Computation (DC) has shown strong performance for maintaining the answer of different data flow queries as data change over time.