Fuji Ren

TL;DR: This research highlights the existing technologies of listening, speaking, reading, writing, and other senses, which are widely used in human interaction, and introduces some intelligent robot systems and platforms.

TL;DR: A novel method by exploring the accumulated emotional information from people's daily writings, and examining these emotional traits that are predictive of suicidal behaviors, suggests that the emotion transition trait turns to be more discriminative of the suicide risk, and that the combination of three traits in linear regression would generate even more discrim inative predictions.

TL;DR: An electroencephalogram (EEG) feature extraction method that leverages empirical mode decomposition and Approximation Entropy is proposed that achieves an improved accuracy that is highly competitive to the state-of-the-art methods.

TL;DR: The proposed CGMVQA model, including classification and answer generation capabilities, is effective in medical visual question answering and can better assist doctors in clinical analysis and diagnosis.

TL;DR: Experimental results show that the performance of proposed DNN on sentiment classification is better than state-of-the-art surface learning models such as SVM or NB, which proves that the proposedDNN model is suitable for short-length document classification with the proposed feature dimensionality extension method.