Selected Papers: The 18th International Conference on Advanced Computer Theory and Engineering (ICACTE 2025)

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 728

Special Issue Editors


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Guest Editor
School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: metaverse; artificial intelligence; virtual reality

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Guest Editor
Department of Computer Science, Birla Institute of Technology and Science, Pilani, United Arab Emirates
Interests: edge intelligence; human-to-machine Interactions; low-latency wireless networks; Internet of Things
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Special Issue Information

Dear Colleagues,

The 18th International Conference on Advanced Computer Theory and Engineering (ICACTE 2025) is scheduled to take place in Nanjing, China, during September 26–28, 2025. Since its inception in 2008, ICACTE has been held in various locations across the globe, including Germany, Singapore, South Korea, and China. Each edition attracts participants from all over the world, providing them with an opportunity to collaborate and network.

Computer theory and engineering is an ever-evolving field that has witnessed significant advancements in recent years. With the explosive growth of technology, there has been a tremendous increase in the number of devices that rely on computers, such as smartphones, tablets, and wearable technology. In response to this growth, researchers in computer theory and engineering have been working tirelessly to develop new algorithms, hardware, and software to keep pace with the demands of these devices.

ICACTE 2025 will provide a platform for experts in different fields to interact with each other to deeply discuss the current status and possible future directions. Hopefully this will further the development of the featured research areas. The conference will cover eight topics: artificial intelligence and machine learning, computer systems and networks, data science and applications, human–computer interaction and multimedia, software engineering and applications, quantum information and quantum computing, blockchain technology and applications, and emerging technologies. We cordially invite you to submit full manuscripts and abstracts for oral presentations, and welcome your registration and participation in the ICACTE 2025.

This Special Issue will be closely aligned with the themes of the conference.

Symmetry is indexed within Scopus, SCIE (Web of Science), CAPlus/SciFinder, Inspec, Astrophysics Data System, and other databases, and has an Impact Factor of 2.2 and a 5-Year Impact Factor of 2.3.

Prof. Dr. Zhigeng Pan
Dr. Mithun Mukherjee
Guest Editors

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Keywords

  • artificial intelligence and machine learning
  • computer systems and networks
  • data science and applications
  • human–computer interaction and multimedia
  • software engineering and applications
  • quantum information and quantum computing
  • blockchain technology and applications

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Published Papers (1 paper)

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23 pages, 1202 KB  
Article
Short-Term Human Activity Recognition Based on Adaptive Variational Mode Decomposition and Information-Enhanced Hilbert Transform
by Min Sheng, Shanrong Wang, Zhixin Ge, Ping Qi, Qingfeng Tang and Benyue Su
Symmetry 2026, 18(5), 823; https://doi.org/10.3390/sym18050823 (registering DOI) - 10 May 2026
Viewed by 98
Abstract
Complex human activities consist of sequential, simple limb movements, acting as impulse responses from the motor system. In short-term human activity recognition (ST-HAR), the inherently brief observation window results in non-stationary signals and “information starvation,” breaking the time-translational symmetry of kinetic signals. Moreover, [...] Read more.
Complex human activities consist of sequential, simple limb movements, acting as impulse responses from the motor system. In short-term human activity recognition (ST-HAR), the inherently brief observation window results in non-stationary signals and “information starvation,” breaking the time-translational symmetry of kinetic signals. Moreover, traditional Variational Mode Decomposition (VMD) and Hilbert Transform (HT) suffer from suboptimal decomposition levels (K) and spectral asymmetry. This paper proposes an improved VMD-HT framework to enhance feature extraction from short-term Inertial Measurement Unit (IMU) signals. First, an instantaneous-frequency-driven adaptive VMD method is developed to mitigate mode mixing by automatically determining the optimal K. Second, an information-enhanced instantaneous energy density (IEIE) feature is introduced. By fusing kinetic energy from both positive and negative frequency domains, this feature restores the spectral symmetry of the energy representation, precisely quantifying fine motion variations and compensating for information loss caused by the limited temporal span. Experimental results on PAMAP2, WARD, and a self-collected dataset, NOITOM, demonstrate the method’s effectiveness. With a 0.5 s window, the proposed model achieves outstanding recognition accuracies of 93.60%, 96.41%, and 97.22%, respectively, outperforming state-of-the-art approaches in capturing transient short-term information. Full article
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