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
3930

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 (3 papers)

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20 pages, 4632 KiB  
Article
Predicting Maps Using In-Vehicle Cameras for Data-Driven Intelligent Transport
Electronics 2023, 12(24), 5017; https://doi.org/10.3390/electronics12245017 - 15 Dec 2023
Viewed by 534
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 and
Systems 2023, 11(11), 528; https://doi.org/10.3390/systems11110528 - 29 Oct 2023
Viewed by 1721
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
Electronics 2023, 12(14), 3084; https://doi.org/10.3390/electronics12143084 - 16 Jul 2023
Cited by 1 | Viewed by 775
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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