Special lssues

AI Powered Human-centric Computing with Cloud/Fog/Edge

Submission Deadline: 30 December 2023 (closed)

Guest Editors

Dr. Hongyang Yan, Guangzhou University, China.
Dr. Arif Ali Khan, University of Oulu, Finland.
Dr. Yanwei Xu, Tianjin University, China.
Dr. Maqbool Khan, Software Competence Center Hagenberg, Austria.
Dr. Wajid Rafique, University of Calgary, Canada.


In the past decade, human-centric computing (HCC) has emerged as a cross-disciplinary research domain that enables the effective integration of various human-related computational elements, benefiting the interactions and collaborations among the physical devices, cyber space and people significantly. Through intelligent HCC techniques, software and hardware engineers can develop various human-computer applications conveniently to satisfy the users’ sophisticated functional and non-functional requirements. However, HCC applications have been generating an unprecedented volume of industrial data and therefore, require the support of powerful computing and storage infrastructures. Fortunately, modern computing paradigms, e.g., cloud, fog and edge, provide a promising way to provision HCC applications the cloud/fog/edge resources in an economic and flexible manner. The adaption of cloud/fog/edge computing to HCC applications is a fundamental challenge and raise a variety of issues, e.g., time-efficient data transmission, energy-aware resource offloading, secure communications & collaborations, and so on.

Recently, Artificial Intelligence (AI) has emerged as one of the key technologies to realize intelligent cloud/fog/edge data processing. AI algorithms have the capability to process the streaming data generated at cloud/fog/edge networks, and provide powerful tools to deal with complex big data analytics. Therefore, the adaptation of AI-based methods is highly demanded to achieve their full potentials in cloud/fog/edge-based HCC applications. The security and privacy issues also call for efforts in order to guarantee service quality delivered by cloud/fog/edge-based HCC applications.


Human-centered semantic analyses in Cloud/Fog/Edge
Knowledge-driven human-computer interaction in Cloud/Fog/Edge
Collaborative systems and decisions in Cloud/Fog/Edge
Intelligent big data analyses in HCC with Cloud/Fog/Edge
Security, privacy and trust in Cloud/Fog/Edge
AI algorithms for multi-agent systems in HCC
People-cyber-physical interactions in Cloud/Fog/Edge
AI powered smart applications in HCC
Power, energy and cost of HCC with Cloud/Fog/Edge
Intelligent interfaces and user modeling
Smart service quality optimization in HCC with Cloud/Fog/Edge
Multimedia applications in HCC with Cloud/Fog/Edge

Published Papers

  • Open Access


    Attentive Neighborhood Feature Augmentation for Semi-supervised Learning

    Qi Liu, Jing Li, Xianmin Wang, Wenpeng Zhao
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1753-1771, 2023, DOI:10.32604/iasc.2023.039600
    (This article belongs to this Special Issue: AI Powered Human-centric Computing with Cloud/Fog/Edge)
    Abstract Recent state-of-the-art semi-supervised learning (SSL) methods usually use data augmentations as core components. Such methods, however, are limited to simple transformations such as the augmentations under the instance’s naive representations or the augmentations under the instance’s semantic representations. To tackle this problem, we offer a unique insight into data augmentations and propose a novel data-augmentation-based semi-supervised learning method, called Attentive Neighborhood Feature Augmentation (ANFA). The motivation of our method lies in the observation that the relationship between the given feature and its neighborhood may contribute to constructing more reliable transformations for the data, and further facilitating the classifier to distinguish… More >

  • Open Access


    Instance Reweighting Adversarial Training Based on Confused Label

    Zhicong Qiu, Xianmin Wang, Huawei Ma, Songcao Hou, Jing Li, Zuoyong Li
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1243-1256, 2023, DOI:10.32604/iasc.2023.038241
    (This article belongs to this Special Issue: AI Powered Human-centric Computing with Cloud/Fog/Edge)
    Abstract Reweighting adversarial examples during training plays an essential role in improving the robustness of neural networks, which lies in the fact that examples closer to the decision boundaries are much more vulnerable to being attacked and should be given larger weights. The probability margin (PM) method is a promising approach to continuously and path-independently measuring such closeness between the example and decision boundary. However, the performance of PM is limited due to the fact that PM fails to effectively distinguish the examples having only one misclassified category and the ones with multiple misclassified categories, where the latter is closer to… More >

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