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Titlebook: Neural Information Processing; 23rd International C Akira Hirose,Seiichi Ozawa,Derong Liu Conference proceedings 2016 Springer Internationa

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Li Zhang,Mingna Cao,Bo Shiinitial project where a requirements specification document is prepared; and a follow-up project where the previously prepared requirements document is used as input to developing a software application. These follow-up projects can also be delegated to a third party, as occurs in numerous global so
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the quality of User Stories and its evolution over time. Firstly, we develop a method to automatically monitor the quality of User Stories. Secondly, we investigate the relationship between User Story quality and project performance measures such as the number of reported bugs and the occurrence of
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Emotion Prediction from User-Generated Videos by Emotion Wheel Guided Deep Learningstraction of human emotions. Evidenced by the recent success of deep learning (e.g. Convolutional Neural Networks, CNN) in several visual competitions, CNN is expected to be a possible solution to conquer certain challenges in human cognitive processing, such as emotion prediction. The emotion wheel
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Deep Q-Learning with Prioritized Samplingrich perception of high-dimensional sensory inputs and policy selection. A recent significant breakthrough in using deep neural networks as function approximators, termed Deep Q-Networks (DQN), proves to be very powerful for solving problems approaching real-world complexities such as Atari 2600 gam
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Deep Inverse Reinforcement Learning by Logistic Regressionat exploits the fact that the log of the ratio between an optimal state transition and a baseline one is given by a part of reward and the difference of the value functions under linearly solvable Markov decision processes and reward and value functions are estimated by logistic regression. However,
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Establishing Mechanism of Warning for River Dust Event Based on an Artificial Neural Networkt season. The Taan and Tachia river are this study area, and data on PM. concentration, PM. concentration and meteorological condition at air monitoring site are used to establish a model for predicting next PM. concentration (PM.(T + 1)) based on an artificial neural network (ANN) and to establish
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