Topic Editors

Dr. Jiahui Yu
Binjiang Institute, Zhejiang University, Hangzhou 310053, China
Bristol Robotics Laboratory, University of the West of England, Bristol, UK
Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310014, China
School of Computing, University of Portsmouth, Portsmouth, UK
School of Computing, University of Portsmouth, Portsmouth PO1 3HE, UK
Binjiang Institute, Zhejiang University, Hangzhou 310053, China

Theoretical and Applied Problems in Human-Computer Intelligent Systems

Abstract submission deadline
31 March 2025
Manuscript submission deadline
30 June 2025
Viewed by
8078

Topic Information

Dear Colleagues,

The advancement in human–computer interaction technology has been a driving force in developing robotics, intelligent systems, and medical applications. The ability to understand human–computer interaction through visual and text analyses, as well as ensuring security, is critical for the success of intelligent systems. To achieve this, it is necessary to extract valuable insights from visual and text information and to implement robust security measures to protect against potential attacks. Despite these advancements, many challenges remain to be addressed in this field. Recently, deep learning, AI defense and attack, real-time learning, robot control, and other cutting-edge technologies have been explored and applied to intelligent systems. The increasing demand and complex real-world problems have encouraged the growth of academic research in human–computer interaction. This Topic brings together experts, engineers, and researchers worldwide to present their latest findings and advancements in human–computer interaction. The focus is on innovative theories related to intelligent systems and machine learning, emphasizing applications involving human–machine interaction through visual and text information.

Dr. Jiahui Yu
Prof. Dr. Charlie Yang
Prof. Dr. Zhenyu Wen
Dr. Dalin Zhou
Dr. Dongxu Gao
Dr. Changting Lin
Topic Editors

Keywords

  • visual-based interaction
  • data analysis
  • robustness and security
  • machine learning
  • computer-aided medical analysis
  • robotic control and design
  • machine learning in signal transmission

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci
2.7 4.5 2011 16.9 Days CHF 2400 Submit
Electronics
electronics
2.9 4.7 2012 15.6 Days CHF 2400 Submit
Future Internet
futureinternet
3.4 6.7 2009 11.8 Days CHF 1600 Submit
Machines
machines
2.6 2.1 2013 15.6 Days CHF 2400 Submit
Systems
systems
1.9 3.3 2013 16.8 Days CHF 2400 Submit
Technologies
technologies
3.6 5.5 2013 19.7 Days CHF 1600 Submit
Biomimetics
biomimetics
4.5 4.5 2016 17.2 Days CHF 2200 Submit

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Published Papers (6 papers)

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19 pages, 16794 KiB  
Article
A Benchmark for UAV-View Natural Language-Guided Tracking
by Hengyou Li, Xinyan Liu and Guorong Li
Electronics 2024, 13(9), 1706; https://doi.org/10.3390/electronics13091706 - 28 Apr 2024
Viewed by 526
Abstract
We propose a new benchmark, UAVNLT (Unmanned Aerial Vehicle Natural Language Tracking), for the UAV-view natural language-guided tracking task. UAVNLT consists of videos taken from UAV cameras from four cities for vehicles on city roads. For each video, vehicles’ bounding boxes, trajectories, and [...] Read more.
We propose a new benchmark, UAVNLT (Unmanned Aerial Vehicle Natural Language Tracking), for the UAV-view natural language-guided tracking task. UAVNLT consists of videos taken from UAV cameras from four cities for vehicles on city roads. For each video, vehicles’ bounding boxes, trajectories, and natural language are carefully annotated. Compared to the existing data sets, which are only annotated with bounding boxes, the natural language sentences in our data set can be more suitable for many application fields where humans take part in the system for that language, being not only more friendly for human–computer interaction but also capable of overcoming the appearance features’ low uniqueness for tracking. We tested several existing methods on our new benchmarks and found that the performance of the existing methods was not satisfactory. To pave the way for future work, we propose a baseline method suitable for this task, achieving state-of-the-art performance. We believe our new data set and proposed baseline method will be helpful in many fields, such as smart city, smart transportation, vehicle management, etc. Full article
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20 pages, 1541 KiB  
Article
Developing a Performance Evaluation Framework Structural Model for Educational Metaverse
by Elena Tsappi, Ioannis Deliyannis and George Nathaniel Papageorgiou
Technologies 2024, 12(4), 53; https://doi.org/10.3390/technologies12040053 - 16 Apr 2024
Viewed by 975
Abstract
In response to the transformative impact of digital technology on education, this study introduces a novel performance management framework for virtual learning environments suitable for the metaverse era. Based on the Structural Equation Modeling (SEM) approach, this paper proposes a comprehensive evaluative model, [...] Read more.
In response to the transformative impact of digital technology on education, this study introduces a novel performance management framework for virtual learning environments suitable for the metaverse era. Based on the Structural Equation Modeling (SEM) approach, this paper proposes a comprehensive evaluative model, anchored on the integration of the Theory of Planned Behavior (TPB), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Community of Inquiry Framework (CoI). The model synthesizes five Key Performance Indicators (KPIs)—content delivery, student engagement, metaverse tool utilization, student performance, and adaptability—to intricately assess academic avatar performances in virtual educational settings. This theoretical approach marks a significant stride in understanding and enhancing avatar efficacy in the metaverse environment. It enriches the discourse on performance management in digital education and sets a foundation for future empirical studies. As virtual online environments gain prominence in education and training, this research study establishes the basic principles and highlights the key points for further empirical research in the new era of the metaverse educational environment. Full article
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32 pages, 24494 KiB  
Article
Interdisciplinary Dynamics in COVID-19 Research: Examining the Role of Computer Science and Collaboration Patterns
by Yunfan Li, Shiyong Liu, An Zeng, Jun Wu, Jiayu Zhang, Weiwei Zhang and Sheng Li
Systems 2024, 12(4), 113; https://doi.org/10.3390/systems12040113 - 28 Mar 2024
Viewed by 1389
Abstract
In academia, it is rare for an event or issue to foster the extensive participation of multiple disciplines. Research related to COVID-19 has undeniably yielded a wealth of valuable insights and impetus for the progress of interdisciplinary research, encompassing concepts, methodologies, intellectual approaches, [...] Read more.
In academia, it is rare for an event or issue to foster the extensive participation of multiple disciplines. Research related to COVID-19 has undeniably yielded a wealth of valuable insights and impetus for the progress of interdisciplinary research, encompassing concepts, methodologies, intellectual approaches, theories, frameworks, data integration and analysis, and pertinent considerations. In the academic community, there is a widespread expectation that as science and technology continue to progress, the convergence of medicine with various other fields will gain momentum. Fields like computer science are anticipated to see expanded applications in domains such as medicine, vaccine research, disease diagnosis, and more. This study aims to examine interdisciplinary approaches in health-related research, particularly in the context of COVID-19. The goal is to analyze and comprehend the involvement and collaboration patterns of various disciplines in pandemic research, with a specific emphasis on the role and integration level of computer science. This study analyzed 240,509 COVID-19 related articles published from December 2019 to September 2022 using methods such as chord diagrams, modularity analysis, and eigenvector centrality analysis in Social Networking Analysis (SNA). The findings revealed an emerging trend of integration trend between Humanities & Social Sciences and Natural Sciences. Expectations that computer science would prominently feature in pandemic research during this technology-driven era haven’t materialized. While it maintains links with engineering, it hasn’t formed strong connections with medicine. This indicates a gap between computer science and core medical research in large-scale health crises, where COVID-19 research remains centered on medicine with varying interdisciplinary collaboration, and high-tech disciplines like computer science have not achieved their expected influence in these studies. Full article
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20 pages, 4632 KiB  
Article
Predicting Maps Using In-Vehicle Cameras for Data-Driven Intelligent Transport
by Zhiguo Ma, Yutong Zhang and Meng Han
Electronics 2023, 12(24), 5017; https://doi.org/10.3390/electronics12245017 - 15 Dec 2023
Viewed by 773
Abstract
Bird’s eye view (BEV) semantic maps have evolved into a crucial element of urban intelligent traffic management and monitoring, offering invaluable visual and significant data representations for informed intelligent city decision making. Nevertheless, current methodologies continue underutilizing the temporal information embedded within dynamic [...] Read more.
Bird’s eye view (BEV) semantic maps have evolved into a crucial element of urban intelligent traffic management and monitoring, offering invaluable visual and significant data representations for informed intelligent city decision making. Nevertheless, current methodologies continue underutilizing the temporal information embedded within dynamic frames throughout the BEV feature transformation process. This limitation results in decreased accuracy when mapping high-speed moving objects, particularly in capturing their shape and dynamic trajectory. A framework is proposed for cross-view semantic segmentation to address this challenge, leveraging simulated environments as a starting point before applying it to real-life urban imaginative transportation scenarios. The view converter module is thoughtfully designed to collate information from multiple initial view observations captured from various angles and modes. This module outputs a top-down view semantic graph characterized by its object space layout to preserve beneficial temporal information in BEV transformation. The NuScenes dataset is used to evaluate model effectiveness. A novel application is also devised that harnesses transformer networks to map images and video sequences into top-down or comprehensive bird’s-eye views. By combining physics-based and constraint-based formulations and conducting ablation studies, the approach has been substantiated, highlighting the significance of context above and below a given point in generating these maps. This innovative method has been thoroughly validated on the NuScenes dataset. Notably, it has yielded state-of-the-art instantaneous mapping results, with particular benefits observed for smaller dynamic category displays. The experimental findings include comparing axial attention with the state-of-the-art (SOTA) model, demonstrating the performance enhancement associated with temporal awareness. Full article
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30 pages, 17684 KiB  
Article
Designing for Intergenerational Communication among Older Adults: A Systematic Inquiry in Old Residential Communities of China’s Yangtze River Delta
by Cun Li and Ming Cao
Systems 2023, 11(11), 528; https://doi.org/10.3390/systems11110528 - 29 Oct 2023
Viewed by 2213
Abstract
Presently, a substantial majority of older individuals in urban regions of China prefer to inhabit older residential communities over newer counterparts. Within these aging communities, the intricate matter of intergenerational communication among older adults presents a complex and multifaceted issue that warrants comprehensive [...] Read more.
Presently, a substantial majority of older individuals in urban regions of China prefer to inhabit older residential communities over newer counterparts. Within these aging communities, the intricate matter of intergenerational communication among older adults presents a complex and multifaceted issue that warrants comprehensive investigation from a systematic perspective. This paper first employs the observational method to study multiple old residential communities in a city in the Yangtze River Delta region of China. The POEMS framework and the AEIOU framework are applied, focusing on the analysis of individuals and the interaction between individuals and objects, respectively. Semistructured interviews are then conducted with three groups of people, emphasizing community participation by older adults, intergenerational interaction from the perspective of older adults, and intergenerational interaction from the perspective of young people. Finally, the paper categorizes the types and characteristics of individuals in the old communities, identifying the intersections between these groups. The current social situation of older adults and young people is summarized, including behavioral and psychological characteristics and social interaction challenges. Based on these findings, ten system design directions to enhance intergenerational interaction in old communities are proposed, and three of these system design directions are further developed. Full article
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20 pages, 1017 KiB  
Article
RAdam-DA-NLSTM: A Nested LSTM-Based Time Series Prediction Method for Human–Computer Intelligent Systems
by Banteng Liu, Wei Chen, Zhangquan Wang, Seyedamin Pouriyeh and Meng Han
Electronics 2023, 12(14), 3084; https://doi.org/10.3390/electronics12143084 - 16 Jul 2023
Cited by 2 | Viewed by 968
Abstract
At present, time series prediction methods are widely applied for Human–Computer Intelligent Systems in various fields such as Finance, Meteorology, and Medicine. To enhance the accuracy and stability of the prediction model, this paper proposes a time series prediction method called RAdam-Dual stage [...] Read more.
At present, time series prediction methods are widely applied for Human–Computer Intelligent Systems in various fields such as Finance, Meteorology, and Medicine. To enhance the accuracy and stability of the prediction model, this paper proposes a time series prediction method called RAdam-Dual stage Attention mechanism-Nested Long Short-Term Memory (RAdam-DA-NLSTM). First, we design a Nested LSTM (NLSTM), which adopts a new internal LSTM unit structure as the memory cell of LSTM to guide memory forgetting and memory selection. Then, we design a self-encoder network based on the Dual stage Attention mechanism (DA-NLSTM), which uses the NLSTM encoder based on the input attention mechanism, and uses the NLSTM decoder based on the time attention mechanism. Additionally, we adopt the RAdam optimizer to solve the objective function, which dynamically selects Adam and SGD optimizers according to the variance dispersion and constructs the rectifier term to fully express the adaptive momentum. Finally, we use multiple datasets, such as PM2.5 data set, stock data set, traffic data set, and biological signals, to analyze and test this method, and the experimental results show that RAdam-DA-NLSTM has higher prediction accuracy and stability compared with other traditional methods. Full article
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