Cognitive Computation - Cognitive and Social Decision Making (CSDM), Sentic Computing, & Big Data Analytics Sections

The following special sections of Cognitive Computation are now open for submissions. 

Cognitive and Social Decision Making (CSDM)

Information seeking, knowledge learning, and decision-making play fundamental roles in cognitive computation. This special section focuses on multidisciplinary scientific studies of cognitive aspects and complex social environments of decision-making including their interactions with other cognitive tasks. The main aim is to introduce and promote an innovative and timely new subfield of Cognitive Computing titled: Cognitive and Social Decision Making (CSDM).

The CSDM Section solicits high-quality original research articles, including position papers, critical reviews and novel theories and applications that cover but are not limited to the following topics: decision-making with granular computing and rough sets; cognitive group decision-making; neuroscience of decision-making; three-way decision making; social cognition and computing; explainable social artificial-intelligence; human-machine co-intelligence; social and complex network analysis; game-theoretic and information-theoretic models.

Section Editors:
Professor JingTao Yao (University of Regina, Canada)
Professor Yiyu Yao (University of Regina, Canada)

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.

Senior Editor
Prof Asim Roy, Ph.D (Arizona State University, USA)

Co-Editors
Prof Kaizhu Huang , Ph.D (Xi'an Jiaotong-Liverpool University, China)
Dr Mufti Mahmud, M.S., Ph.D (Nottingham-Trent University, UK)