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Symmetry
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Applications Based on Symmetry and Asymmetry in Deep Learning and...
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Applications Based on Symmetry and Asymmetry in Deep Learning and Artificial Intelligence Methods

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 875

Special Issue Editors

Dr. Changshuo Wang

E-Mail Website
Guest Editor
Cyber Security Research Centre, Nanyang Technological University, Singapore, Singapore
Interests: scene understanding and generation; multimodal representation; human-centered visual understanding
Special Issues, Collections and Topics in MDPI journals
Dr. Zhijian Hu

E-Mail Website
Guest Editor
LAAS-CNRS, Toulouse, France
Interests: model predictive control; robust control and filtering; fuzzy control; distributed control; resilient control; intrusion detection and security control; reinforcement learning; neural network; the applications in electric vehicle and power systems
Special Issues, Collections and Topics in MDPI journals
Dr. Meiqing Wu

E-Mail
Guest Editor
School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
Interests: stereo vision; motion analysis; object detection; tracking for urban traffic scene understanding

Special Issue Information

Dear Colleagues,

This Special Issue explores the fundamental role of symmetry and asymmetry principles in deep learning and artificial intelligence, with a special focus on 3D point cloud processing and computer vision applications. We investigate how symmetrical and asymmetrical patterns influence neural network architectures and learning algorithms, particularly in processing unordered point clouds and visual data. Key areas include symmetry-aware point cloud analysis, geometric deep learning, symmetry detection in 3D shapes, and invariant feature learning in computer vision tasks. This collection bridges theoretical symmetry concepts with practical implementations, advancing both algorithmic designs and real-world applications.

Dr. Changshuo Wang
Dr. Zhijian Hu
Dr. Meiqing Wu
Guest Editors

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 2400 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.

Keywords

  • point cloud symmetry
  • neural network architecture
  • symmetry-aware feature learning
  • 3D shape analysis
  • computer vision symmetry
  • deep learning invariance
  • geometric deep learning
  • symmetry detection
  • vision transformers
  • symmetrical pattern recognition

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

20 pages, 1336 KiB  
Article
Complex Question Decomposition Based on Causal Reinforcement Learning
by Dezhi Li, Yunjun Lu, Jianping Wu, Wenlu Zhou and Guangjun Zeng
Symmetry 2025, 17(7), 1022; https://doi.org/10.3390/sym17071022 - 29 Jun 2025
Viewed by 167
Abstract
Complex question decomposition is an important research topic in the field of natural language processing (NLP). It refers to the decomposition of a compound question containing multiple ontologies and classes into a simple question containing only a single attribute or entity. Most previous [...] Read more.
Complex question decomposition is an important research topic in the field of natural language processing (NLP). It refers to the decomposition of a compound question containing multiple ontologies and classes into a simple question containing only a single attribute or entity. Most previous studies focus on how to generate simple questions using a single attribute or entity but pay little attention to the generation order of simple questions, which may lead to an inaccurate decomposition or longer execution time. In this study, we propose a new method based on causal reinforcement learning, which combines the advantages of the current optimal performance reinforcement learning method and the causal inference method. Compared with previous methods, causal reinforcement learning can find the generation order of sub-questions more accurately, so as to better decompose complex questions. In particular, the prior knowledge is extracted using the counterfactual method in causal reasoning and is integrated into the policy network of the reinforcement learning model, and the reward rules of reinforcement learning are designed from the perspective of symmetry (positive reward and negative punishment), thus the intelligent body is guided to choose the sub-question with a greater benefit and less risk of decomposing. We compare the proposed method with the baseline method on three datasets. The experimental results show that the performance of our method is improved by 5–10% compared with the baseline method on Hits@n (n = 1, 3, 10), which proves the effectiveness of our proposed method. Full article
(This article belongs to the Special Issue Applications Based on Symmetry and Asymmetry in Deep Learning and Artificial Intelligence Methods)
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31 pages, 9659 KiB  
Article
Full-Element Analysis of Side-Channel Leakage Dataset on Symmetric Cryptographic Advanced Encryption Standard
by Weifeng Liu, Wenchang Li, Xiaodong Cao, Yihao Fu, Juping Wu, Jian Liu, Aidong Chen, Yanlong Zhang, Shuo Wang and Jing Zhou
Symmetry 2025, 17(5), 769; https://doi.org/10.3390/sym17050769 - 15 May 2025
Viewed by 425
Abstract
The application of deep learning in side-channel analysis faces critical challenges arising from dispersed public datasets—i.e., datasets collected from heterogeneous sources and platforms with varying formats, labeling schemes, and sampling settings—and insufficient sample distribution uniformity, characterized by imbalanced class distributions and long-tailed label [...] Read more.
The application of deep learning in side-channel analysis faces critical challenges arising from dispersed public datasets—i.e., datasets collected from heterogeneous sources and platforms with varying formats, labeling schemes, and sampling settings—and insufficient sample distribution uniformity, characterized by imbalanced class distributions and long-tailed label samples. This paper presents a systematic analysis of symmetric cryptographic AES side-channel leakage datasets, examining how these issues impact the performance of deep learning-based side-channel analysis (DL-SCA) models. We analyze over 10 widely used datasets, including DPA Contest and ASCAD, and highlight key inconsistencies via visualization, statistical metrics, and model performance evaluations. For instance, the DPA_v4 dataset exhibits extreme label imbalance with a long-tailed distribution, while the ASCAD datasets demonstrate missing leakage features. Experiments conducted using CNN and Transformer models show that such imbalances lead to high accuracy for a few labels (e.g., label 14 in DPA_v4) but also extremely poor accuracy (<0.5%) for others, severely degrading generalization. We propose targeted improvements through enhanced data collection protocols, training strategies, and feature alignment techniques. Our findings emphasize that constructing balanced datasets covering the full key space is vital to achieving robust and generalizable DL-SCA performance. This work contributes both empirical insights and methodological guidance for standardizing the design of side-channel datasets. Full article
(This article belongs to the Special Issue Applications Based on Symmetry and Asymmetry in Deep Learning and Artificial Intelligence Methods)
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