Guest Editorial Special Issue on Advanced Cognitive Computing for Data-Driven Computational Social Systems

Abstract

C OMPUTATIONAL social systems (CSSs) focus on topics such as modeling, simulation, analysis, and understanding of social systems from the quantitative and/or computational perspective. "Systems" can be man-man, manmachine, and machine-machine organizations and adversarial situations as well as social media structures and their dynamics [1], [2]. With the advance of the Internet of Things and communication technologies, various kinds of data from diverse areas can be acquired nowadays. As a result, CSSs are becoming ever more complex. Data-driven CSSs aim to conduct pre-competitive research on architectures and design, modeling, and analysis techniques for cyber-physical systems, with emphasis on making full use of big data and artificial intelligence. These applications include transportation systems, automation, security, smart buildings, smart cities, medical systems, energy generation and distribution, water distribution, agriculture, military systems, process control, asset management, and robotics [3], [4], [5]. However, due to the progressive transformation from host-centric networking to information-centric networking, CSSs pose fundamental challenges in multiple aspects, such as heterogeneous data generation, efficient data sensing and collection, real-time data processing, and greater request arrival rates. Thus, there is a great need for a powerful way that can deal with emerging issues in data-driven CSSs more efficiently and effectively in the age of big data.Recently, cognitive computing has emerged to provide new opportunities for the revolution of data-driven CSSs. It has been proven to be effective in a wide spectrum of fields, such as affective computing, social computing, graph-based machine learning, and so on. It is able to solve problems containing many entities linked together in a complex way with the model of perception, action, attention, learning and memory, decision making, language processing, communication, reasoning, problem-solving, and consciousness aspects of cognition. The biggest advantage of cognitive computing is its ability to "understand" unstructured data, including emotion, language, images, and video. With the help of advanced cognitive computing methods, we are able to discover new patterns and knowledge from largescale datasets, and to extract novel valuable information, which can promote product innovation, improves operation level, and production operation efficiency of manufacturing

AI-Assisted Chinese Language Teaching System in Distributed Sensor Networks

“…The main focus of gathering the data in the network with distributed sensors is to transmit the data that is being sensed from each of the current nodes to another base station. Data gathering is basically a process of utilizing the nodes to their maximum capacity before they stop working or die, and the selected network becomes out of operation or inoperable at all [19]. A full complete round indicates that a specific base station is able to collect all the data that are being sent from the sensor nodes stationed outside.…”