Titlebook: Cognitive Computing – ICCC 2019; Third International Ruifeng Xu,Jianzong Wang,Liang-Jie Zhang Conference proceedings 2019 Springer Nature

窒息

Small Traffic Sign Detection and Recognition in High-Resolution Images,nt role in driver-assistance systems and driverless operation. Detecting signs, especially small ones, remains challenging under a variety of road traffic conditions. In this manuscript, we propose an end-to-end deep learning model for detecting and recognizing traffic signs in high-resolution image

Instantaneous

漸變

Structural-Based Graph Publishing Under Differential Privacy,by large-scale graphs are often sensitive and private. It is a necessity of developing algorithms to publish large-scale graph while not revealing sensitive information. As a standard for data privacy preservation, differential privacy based algorithm are also widely used in publishing graph-based d

騷動

Face Detection Using Bionic Cascaded Framework,ing due to restrictions on computing capabilities and power, the character of real-time and requirements of the interaction pattern. Recent studies show that deep learning approaches can achieve impressive performance on these kinds of tasks. Therefore, a bionic cascaded framework adopted a cascaded

巧思

sebaceous-gland

Using GAN to Generate Sport News from Live Game Stats,a-to-text solutions have been proposed and successfully used in real applied domains. Our work focuses on a new domain, Automatic Sport News Generating, which aims to produce sport news immediately after each match is over so that both time and labor can be saved on writing the news articles. We pro

GROSS

Speech Emotion Recognition Using Multi-granularity Feature Fusion Through Auditory Cognitive Mechans in discrete speech emotion recognition. Firstly, preliminary cognitive mechanism of auditory emotion is explored through cognitive experiments, and then a multi-granularity fusion feature extraction method inspired by the mechanism for discrete emotional speech signals is proposed. The method can

Anemia

Learning Contextual Features with Multi-head Self-attention for Fake News Detection,y written to mislead readers, lexical features based methods have great limitations. Previous work has proven the effectiveness of contextual information for fake news detection. However, they ignore the influence of sequence order when extract features from contextual information. Inspired by trans

Licentious

Epithelium