Sentic Computing & Big Data Analytics Sections
The following special sections of Cognitive Computation are now open for submissions.
Sentic Computing Section
Sentic computing is a rapidly growing multidisciplinary field that addresses typical issues of machine learning such as dependency and transparency in the context of natural language processing (NLP). It bridges the gap between statistical text analysis and many other disciplines that are necessary for understanding human language, such as linguistics, commonsense reasoning, semiotics, and affective computing. Sentic computing, whose term derives from the Latin sensus (as in commonsense) and sentire (root of words such as sentiment and sentience), enables the analysis of text not only at document, page or paragraph level, but also at sentence, clause, and concept level. This is enabled by sentic computing encapsulating both top-down and bottom-up analysis: top-down for the fact that sentic computing leverages symbolic models such as semantic networks and conceptual dependency representations to encode meaning; bottom-up as sub-symbolic methods such as deep neural networks and multiple kernel learning can be exploited to infer syntactic patterns from data.
This innovative and timely section of Cognitive Computation focuses on the introduction, presentation, and discussion of novel approaches that further develop and apply sentic computing models (e.g., the Hourglass of Emotions or Sentic Patterns), algorithms (e.g., Sentic LDA or Sentic LSTM), and resources (e.g., SenticNet or AffectiveSpace) for the design of next-generation emotion-sensitive applications.
Editor: Erik Cambria, Nanyang Technological University, Singapore
Big Data Analytics Section
This section solicits high-quality original research articles and critical reviews on current developments in the field, covering all aspects of cognitive systems in big data analytics, including, but not limited to the following topics:
Algorithmic, theoretical and computational approaches such as deep learning networks, nature-inspired and brain-inspired cognitive computation, statistical and mathematical analytics, visualization and informatics.Implementations and platforms such as neuromorphic, GPUs, clusters and clouds, and open-source software.Novel applications in diverse domains and fields including arts, business, engineering, humanities, life and physical science, social science, etc.
Prof Asim Roy, Ph.D (Arizona State University, USA)
Prof Kaizhu Huang , Ph.D (Xi'an Jiaotong-Liverpool University, China)
Dr Mufti Mahmud, M.S., Ph.D (Nottingham-Trent University, UK)