Journal Menu► ▼ Journal Menu
Journal Browser► ▼ Journal Browser
Special Issue "Symmetry and Asymmetry in AI-Enabled Human-Centric Collaborative Computing"
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer Science and Symmetry/Asymmetry".
Deadline for manuscript submissions: 30 November 2023 | Viewed by 4847
Special Issue Editors
Interests: big data; AI; recommender systems
Special Issues, Collections and Topics in MDPI journals
Interests: internet of things; blockchain; wireless networks
Interests: big data; AI; recommender systems
Interests: big data analytics; cloud computing; predictive maintenance; explainable AI; knowledge graphs; data science and machine learning
Special Issue Information
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
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- symmetric learning frameworks
- deep learning for symmetry
- human-centric data management and balance/imbalance analysis
- information diffusion and modelling in HCCC
- deep learning for intelligent human computer interaction
- symmetry/asymmetry network structure and community evolution analysis
- human–cyber–physical interactions with symmetry/asymmetry
- knowledge-driven human–computer interaction in cloud/edge
- smart service quality optimization in HCCC
- AI-enabled multi-agent systems in HCCC
- AI-powered smart applications in HCCC