H
Huansheng Ning
Researcher at University of Science and Technology Beijing
Publications - 17
Citations - 320
Huansheng Ning is an academic researcher from University of Science and Technology Beijing. The author has contributed to research in topics: The Internet & Edge computing. The author has an hindex of 6, co-authored 17 publications receiving 102 citations. Previous affiliations of Huansheng Ning include Linyi University.
Papers
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Distributed Ledger Technology for eHealth Identity Privacy: State of The Art and Future Perspective.
TL;DR: The current decentralized identity projects are discussed and new challenges based on the existing solutions and the limitations when applying it to healthcare as a particular use case are identified.
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IoT-Enabled Social Relationships Meet Artificial Social Intelligence
TL;DR: In this article, the authors discuss the role of IoT in social relationships management, the problem of social relationships explosion in IoT, and review the proposed solutions using ASI, including social-oriented machine-learning and deep-learning techniques.
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A Lightweight Blockchain-Based IoT Identity Management Approach
TL;DR: In this paper, a lightweight architecture and protocols for consortium blockchain-based identity management are proposed to address privacy, security, and scalability issues in a centralized system for IoT, and a proof-of-concept prototype is implemented and evaluated.
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General Cyberspace: Cyberspace and Cyber-Enabled Spaces
TL;DR: This paper proposes the definition of GC and investigates it from its three main aspects: 1) existence; 2) interactions; and 3) applications/services, respectively, in terms of philosophy, science, and technology outlook.
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A survey on personality-aware recommendation systems
Sahraoui Dhelim,Sahraoui Dhelim,Nyothiri Aung,Nyothiri Aung,Mohammed Amine Bouras,Huansheng Ning,Huansheng Ning,Erik Cambria +7 more
TL;DR: In this paper, a survey of personality-aware recommendation systems is presented, by comparing their personality modeling methods, as well as their recommendation techniques, and presenting the commonly used datasets and point out some of the challenges of personality aware recommendation systems.