Learning Bayesian networks from data is an N-P hard problem with important practical applications. Several researchers have designed algorithms to overcome the computational comple...
Optimistic concurrency algorithms provide good performance for parallel programs but they are extremely hard to reason about. Program logics such as concurrent separation logic and...
Ming Fu, Yong Li, Xinyu Feng, Zhong Shao, Yu Zhang
We present the design and development of a data stream system that captures data uncertainty from data collection to query processing to final result generation. Our system focuse...
Yanlei Diao, Boduo Li, Anna Liu, Liping Peng, Char...
Ubiquitous computing architectures enable interaction and collaboration in multi-user applications. We explore the challenges of integrating the disparate services required in such...
Romain Pellerin, Nicolas Bouillot, Tatiana Pietkie...
Probabilistic model building methods can render difficult problems feasible by identifying and exploiting dependencies. They build a probabilistic model from the statistical prope...