FPGA based Embedded Processing Architecture for the QRD-RLS Algorithm

Author(s):  
D. Boppana ◽  
K. Dhanoa ◽  
J. Kempa
Author(s):  
Ronnie W. Smith ◽  
D. Richard Hipp

As spoken natural language dialog systems technology continues to make great strides, numerous issues regarding dialog processing still need to be resolved. This book presents an exciting new dialog processing architecture that allows for a number of behaviors required for effective human-machine interactions, including: problem-solving to help the user carry out a task, coherent subdialog movement during the problem-solving process, user model usage, expectation usage for contextual interpretation and error correction, and variable initiative behavior for interacting with users of differing expertise. The book also details how different dialog problems in processing can be handled simultaneously, and provides instructions and in-depth result from pertinent experiments. Researchers and professionals in natural language systems will find this important new book an invaluable addition to their libraries.


Author(s):  
C.R. Rupp ◽  
M. Landguth ◽  
T. Garverick ◽  
E. Gomersall ◽  
H. Holt ◽  
...  

Author(s):  
Faxin Qi ◽  
Xiangrong Tong ◽  
Lei Yu ◽  
Yingjie Wang

AbstractWith the development of the Internet and the progress of human-centered computing (HCC), the mode of man-machine collaborative work has become more and more popular. Valuable information in the Internet, such as user behavior and social labels, is often provided by users. A recommendation based on trust is an important human-computer interaction recommendation application in a social network. However, previous studies generally assume that the trust value between users is static, unable to respond to the dynamic changes of user trust and preferences in a timely manner. In fact, after receiving the recommendation, there is a difference between actual evaluation and expected evaluation which is correlated with trust value. Based on the dynamics of trust and the changing process of trust between users, this paper proposes a trust boost method through reinforcement learning. Recursive least squares (RLS) algorithm is used to learn the dynamic impact of evaluation difference on user’s trust. In addition, a reinforcement learning method Deep Q-Learning (DQN) is studied to simulate the process of learning user’s preferences and boosting trust value. Experiments indicate that our method applied to recommendation systems could respond to the changes quickly on user’s preferences. Compared with other methods, our method has better accuracy on recommendation.


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