We propose a nonparametric Bayesian probabilistic latent variable model for multi-view anomaly detection, which is the task of finding instances that have inconsistent views. With the proposed model, all views of a non-anomalous instance are assumed to be generated from a single latent vector. On the other hand, an anomalous instance is assumed to have multiple latent vectors, and its different...

Performing data assimilation (DA) at low cost is of prime concern in Earth system modeling, particularly the era Big Data, where huge quantities observations are available. Capitalizing on ability neural network techniques to approximate solution partial differential equations (PDEs), we incorporate deep learning (DL) methods into a DA framework. More precisely, exploit latent structure provide...

The purpose of this study is to examine different formats of comment data to predict student performance. Having students write comment data after every lesson can reflect students’ learning attitudes, tendencies and learning activities involved with the lesson. In this research, Latent Dirichlet Allocation (LDA) and Probabilistic Latent Semantic Analysis (pLSA) are employed to predict student ...

The large and catastrophic wildfires have been increasing across the globe in recent decade, highlighting importance of simulating forecasting fire dynamics near real-time. This is extremely challenging due to complexities physical models geographical features. Running physics-based simulations for wildfire events real-time are computationally expensive, if not infeasible. In this work, we deve...

We examine the Rasch model for latent structure para‎- ‎meters in ‎binary and multiple response questionnaires and develop‎ ‎methodologies and data-analytic tools for assessing‎ ‎collaboration/che‎- ‎ating in multiple choice tests