Symmetry and Asymmetry in AI-Enabled Human-Centric Collaborative Computing

Dear Colleagues,

Over the past few decades, the trajectory of daily human activities has become closely intertwined with cyberspace, resulting in a vast amount of human-centric digital information on an unprecedented scale. Human-Centric Collaborative Computing (HCCC) has emerged as a cross-disciplinary cutting-edge research domain enabling the effective integration of these various human-related computational elements, thus significantly benefiting the interactions and collaborations among the physical devices, cyberspace and human activity. The unprecedented volume of human-centric data generated by HCCC requires the support of powerful computing, raising a serious challenge in this field.

Recently, Artificial Intelligence (AI), such as Deep Learning (DL), has emerged as a key technologies in realizing intelligent digital information processing. Through AI-based HCCC techniques, end users’ sophisticated functional and nonfunctional requirements can be satisfied. However, since the proportion of data with different labels is often uneven, some researchers have incorporated symmetries into deep learning models and architectures to solve this issue while reducing the model’s complexity. Symmetry and asymmetry, as key structural properties of human-centric data, are often ignored by state-of-the-art HCCC studies. Furthermore, studies have found that learning is most efficient when these symmetries are compatible with those of the data distribution.

Therefore, the symmetry and asymmetry issues in AI-based methods deserve more attention, calling for efforts aimed at guaranteeing computing quality and achieving their full potential in HCCC applications. We invite both original research and reviews presenting recent results in a unified and systematic way.

Prof. Dr. Lianyong Qi
Dr. Wajid Rafiq
Dr. Wenwen Gong
Dr. Maqbool Khan
Guest Editors

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