Speaker Identification by BYY Automatic Local Factor Analysis based Three-Level Voting Combination

Local Factor Analysis (LFA) is known as more general and powerful than Gaussian Mixture Model (GMM) in unsupervised learning with local subspace structure analysis. In the literature of text-independent speaker identification, GMM has been widely used and investigated, with some preprocessing or postprocessing approaches, while there still lacks efforts on LFA for this task. In pursuit of fast implementation for LFA modeling, this paper focuses on the Bayesian Ying-Yang automatic learning with data smoothing based regularization (BYY-A), which makes automatic model selection during parameter learning. Furthermore for sequence classification, based on trained LFA models, we design and analyze a three-level combination, namely sequence, classifier and committee, respectively. Different combination approaches are designed with variant sequential topologies and voting schemes. Experimental results on the KING speech corpus demonstrate the proposed approaches' effectiveness and potentials

Data based identification and prediction of nonlinear and complex dynamical systems

We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin

Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502

Using computer simulation in operating room management: impacts of information quality on process performance

High quality information has a significant impact on improving operation performance and patient satisfaction, as well as resolving patient disputes. Based on the analysis of the perioperative process, information quality is considered as an important contributory factor in improving patient throughput. In this paper, we propose a conceptual framework to use computer simulations in modeling information flow of hospital process for operating room management (ORM). Additionally, we conduct simulation studies in different levels of the information quality for ORM. The results of our studies provide evidence that information quality can drive process performance in several phases of the ORM