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Symmetry
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Symmetry and Asymmetry in Human-Computer Interaction
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Symmetry and Asymmetry in Human-Computer Interaction

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

Deadline for manuscript submissions: 31 December 2026 | Viewed by 2895

Special Issue Editors

Dr. Bin He

E-Mail Website
Guest Editor
Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China
Interests: artificial intelligence in education; intelligent educational technology; educational robot; human-computer interaction; video analysis
Prof. Dr. Xinguo Yu

E-Mail Website
Guest Editor
Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China
Interests: artificial intelligence in education; artificial intelligence for research; artificial intelligence for government; intelligent educational technology; video analysis; computer vision; human–computer interactions; multimedia technology
Special Issues, Collections and Topics in MDPI journals
Dr. Ting Zhang

E-Mail Website
Guest Editor
Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China
Interests: artificial intelligence in education; computer vision; human-computer interaction; text and graphics recognition

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to this Special Issue titled “Symmetry and Asymmetry in Human-Computer Interaction”. The rapid evolution of interactive technologies, driven by AI, adaptive systems, and immersive environments, has made Human-Computer Interaction (HCI) a cornerstone of modern interface design across various domains. The effectiveness of these technological interventions is deeply influenced by fundamental principles of perception and cognition, among which symmetry and asymmetry play a pivotal yet underexplored role. Symmetrical designs can improve usability, reduce cognitive load, and create a sense of balance and fairness, while deliberate asymmetries are essential for personalizing user experiences, directing attention, and accommodating diverse user needs. Understanding and harnessing these principles is therefore critical for developing next-generation interactive systems that are not only intelligent but also intuitive, engaging, and equitable.

This Special Issue aims to collate cutting-edge research that investigates the application, impact, and theoretical underpinnings of symmetry and asymmetry in HCI across various application domains. This topic is intrinsically related to the scope of Symmetry, which is devoted to the scientific concept of symmetry and its manifold applications. This Special Issue will explore symmetry as a structural, functional, and perceptual property within interactive interfaces, algorithms, and interaction paradigms. By focusing on the balance and intentional imbalance in HCI design, it will contribute to the journal's mission of advancing knowledge across the disciplinary boundaries of computer science, psychology, design, and engineering.

For this Special Issue, original research articles and reviews are welcome, with research areas including (but not limited to) the following:

  • Theoretical models and frameworks for symmetry/asymmetry in HCI design;
  • Symmetrical and asymmetrical design of user interfaces, dashboards, and information visualizations;
  • The role of symmetry in VR, AR, and MR applications for immersive experiences;
  • AI and symmetry: developing fair, unbiased, and symmetric interaction algorithms;
  • Agent-based human-computer interaction technologies and applications;
  • Balancing symmetry and asymmetry in adaptive and personalized systems;
  • Impact of visual symmetry/asymmetry on cognitive load, engagement, and information processing;
  • Human-robot interaction (HRI) for assistive technologies and interactive systems;
  • Brain-computer interfaces and brain-machine interaction: symmetric paradigms and applications;
  • Case studies on implementing the principles of symmetry in intelligent systems and interactive platforms;
  • Cross-cultural perspectives on perception of symmetry in interface design;
  • Neuroscientific approaches to understanding symmetry in human-computer interaction.

We look forward to receiving your contributions.

Dr. Bin He
Prof. Dr. Xinguo Yu
Dr. Ting Zhang
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 250 words) can be sent to the Editorial Office for assessment.

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

  • human-computer interaction
  • symmetric interface design
  • AI-enhanced interaction systems
  • adaptive user interfaces
  • embodied interaction
  • cognition-aware HCI
  • natural user interfaces
  • multimodal interaction
  • user-centered design
  • interaction personalization
  • brain-computer interfaces
  • robot-mediated interaction
  • intelligent interactive systems
  • interaction analytics
  • engagement-sensitive interfaces
  • perceptual symmetry
  • asymmetric design patterns
  • cross-domain HCI applications

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

Published Papers (4 papers)

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Research

32 pages, 1357 KB  
Article
Solving Geometry Problems: A Text–Formula–Image Multimodal Parsing and Fusion Model
by Pengpeng Jian, Zongxiang Song, Ting Song and Yanli Wang
Symmetry 2026, 18(5), 821; https://doi.org/10.3390/sym18050821 (registering DOI) - 10 May 2026
Viewed by 279
Abstract
Solving geometry problems is a critical challenge in education, for it demands the integration of textual semantic descriptions, mathematical formula logic and spatial graphical information, as well as rigorous geometric theorem application and stepwise logical deduction. These are core capabilities that underpin the [...] Read more.
Solving geometry problems is a critical challenge in education, for it demands the integration of textual semantic descriptions, mathematical formula logic and spatial graphical information, as well as rigorous geometric theorem application and stepwise logical deduction. These are core capabilities that underpin the realization of personalized intelligent tutoring and efficient educational resource allocation. Traditional geometry problem solving methods often suffer from deficiencies in accuracy and the fusion of text, formula and image features. Hence, this paper proposes a method of solving geometry problems based on a text–formula–image (TFI) multimodal parsing and fusion model. The TFI parser employs a self-attention multilayer Transformer to enhance the extraction of logical relations among geometric text expressions. Meanwhile, it parses formulas into tree structures to overcome the loss of formula structural features, which utilizes symbolic embedding and tree-structured encoding to preserve hierarchical logical information and yields unified formula representations via a multi-granularity fusion module. The TFI parser also leverages a Feature Pyramid Network (FPN) for the accurate detection of geometric and non-geometric instances, resolves the issues of blurred segmentation for slender geometric elements and the inaccurate localization of small-sized symbols through mask averaging and RoIAlign, and generates high-dimensional image features using DenseNet-121. The TFI multimodal fusion model integrates a contrastive learning mechanism and constructs fused feature representations by stacking self-attention and cross-attention layers. This design effectively narrows the semantic gap between text, formula, and image features, addressing the inadequacy of traditional fusion approaches in deep cross-modal feature alignment. An attention-augmented Gated Recurrent Unit (GRU) network processes the fused TFI features to produce target operation trees and geometry solutions, ensuring interpretable and precise reasoning performance. The proposed method is evaluated on the PGDP5K and GeoEval datasets, and it achieves an average accuracy of 59.63% in geometry problem solving, which validates its effectiveness. This paradigm offers a viable technical approach for uniformly modeling complex educational tasks, including geometry problem solving and timetable scheduling. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Human-Computer Interaction)
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31 pages, 2373 KB  
Article
Research on User Experience Evaluation of Intelligent Vehicles Oriented to Multi-Agent Collaboration
by Wang Zhang, Fuquan Zhao and Zongwei Liu
Symmetry 2026, 18(5), 722; https://doi.org/10.3390/sym18050722 - 24 Apr 2026
Viewed by 194
Abstract
Under the trend of AI-defined vehicles, multi-agent collaboration has become the core feature for intelligent vehicles to deliver superior user experience (UX). Traditional linear and independent evaluation methods can no longer adapt to the new technical characteristics and logic. Taking the agents of [...] Read more.
Under the trend of AI-defined vehicles, multi-agent collaboration has become the core feature for intelligent vehicles to deliver superior user experience (UX). Traditional linear and independent evaluation methods can no longer adapt to the new technical characteristics and logic. Taking the agents of four functional domains—intelligent driving, intelligent cockpit, intelligent vehicle control, and intelligent connectivity—and their cross-domain collaborative relationships as research objects, this study constructs a UX evaluation index system consisting of five primary indicators and 14 secondary indicators. Innovatively, the analytic network process is adopted for indicator weight allocation, which effectively characterizes the interdependencies among indicators caused by multi-agent collaboration. Meanwhile, the coupling coordination theory is introduced to construct a comprehensive UX index, enabling quantitative evaluation of the balanced development level across the five dimensions. The results show that in intelligent vehicle UX, excellence in a single dimension does not equal excellent overall UX. Only through the collaborative upgrading of multiple agents and balanced development of the five dimensions can the comprehensive UX be maximized. This study further reveals the UX mechanism of multi-agent collaboration in intelligent vehicles and determines the optimal collaborative evolution path based on the dynamic programming algorithm, providing theoretical support and practical guidance for automakers in rational product development planning. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Human-Computer Interaction)
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18 pages, 4834 KB  
Article
Syntax–Semantics–Numeracy Fusion for Improving Math Word Problem Representation and Solving
by Zihan Feng, Hao Ming and Xinguo Yu
Symmetry 2026, 18(3), 434; https://doi.org/10.3390/sym18030434 - 2 Mar 2026
Viewed by 445
Abstract
Most pre-trained language representation models are designed to encode contextualized semantic information for general language processing tasks. However, they are insufficient for math word problem (MWP) solving, which requires not only linguistic syntax and semantic understanding but also numerical reasoning. In this work, [...] Read more.
Most pre-trained language representation models are designed to encode contextualized semantic information for general language processing tasks. However, they are insufficient for math word problem (MWP) solving, which requires not only linguistic syntax and semantic understanding but also numerical reasoning. In this work, we introduce SSN4Solver, a deep neural solver that improves MWP-solving performance by symmetrically fusing syntax, semantics, and numeracy representations within its contextual encoder. Our approach jointly captures syntactic structures from dependency trees, semantic features from part-of-speech tags, and the attributes and relations of numerical entities. By treating these heterogeneous information sources in a balanced and aligned manner, SSN4Solver constructs a rich, multi-faceted representation for MWP solving without introducing substantial computational overhead, empowering human–computer interaction (HCI) applications such as adaptive educational interfaces and intelligent tutoring systems. Extensive experiments demonstrate that SSN4Solver outperforms existing baseline models. In addition, a visualization scheme is designed to elucidate how the three types of representations contribute to the solving process. SSN4Solver thus offers a scalable solution, contributing to the development of HCI systems that are both intelligent and mathematically effective. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Human-Computer Interaction)
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17 pages, 779 KB  
Article
Geometry Diagram Parsing and Reasoning Based on Deep Semantic Fusion
by Pengpeng Jian, Xuhui Zhang, Lei Wu, Bin Ma and Wangyang Hong
Symmetry 2026, 18(1), 92; https://doi.org/10.3390/sym18010092 - 4 Jan 2026
Viewed by 1013
Abstract
Effective Automated Geometric Problem Solving (AGP) requires a deep integration of visual perception and textual comprehension. To address this, we propose a dual-stream fusion model that injects deep semantic understanding from a Pre-trained Language Model (PLM) into the geometric diagram parsing pipeline. Our [...] Read more.
Effective Automated Geometric Problem Solving (AGP) requires a deep integration of visual perception and textual comprehension. To address this, we propose a dual-stream fusion model that injects deep semantic understanding from a Pre-trained Language Model (PLM) into the geometric diagram parsing pipeline. Our core innovation is a Semantic-Guided Cross-Attention (SGCA) mechanism, which uses the global semantic intent of the problem text to direct attention toward key visual primitives. This yields context-enriched visual representations that serve as inputs to a Graph Neural Network (GNN), enabling relational reasoning that is not only perception-driven but also context-aware. By explicitly bridging the semantic gap between text and diagrams, our approach delivers more robust and accurate predictions. To the best of our knowledge, this is the first study to introduce a semantic-guided cross-attention mechanism into geometric diagram parsing, establishing a new paradigm that effectively addresses the cross-modal semantic gap and achieves state-of-the-art performance. This is particularly effective for parsing problems involving geometric symmetries, where textual cues often clarify or define symmetrical relationships not obvious from the diagram alone. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Human-Computer Interaction)
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