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Selected Papers from CCF 39th China Computer Application Conference (CCF NCCA 2024)

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 December 2024) | Viewed by 8486

Special Issue Editor

Prof. Dr. Guangjie Han

E-Mail Website
Guest Editor
Information Department, Hohai University, Changzhou 213022, China
Interests: Internet of Things; machine learning and artificial intelligence; smart ocean; industrial internet; security and privacy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue comprises selected papers presented at the 39th China Computer Application Conference (CCF NCCA 2024), organized by the China Computer Federation (CCF). The conference provided a platform for researchers, practitioners, and industry experts to exchange insights and innovations in the field of computer applications. The selected papers cover a wide range of topics, including artificial intelligence, data mining, computer vision, cybersecurity, and human–computer interactions. These contributions reflect the latest advancements and challenges in computer application research and highlight the diverse perspectives shaping the future of technology.

Prof. Dr. Guangjie Han
Guest Editor

Manuscript Submission Information

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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. Applied Sciences is an international peer-reviewed open access semimonthly 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

  • artificial intelligence
  • data mining
  • computer vision
  • cybersecurity
  • human–computer interactions
  • natural language processing (NLP)

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Published Papers (4 papers)

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Research

16 pages, 1439 KiB  
Article
Human Action Recognition Based on 3D Convolution and Multi-Attention Transformer
by Minghua Liu, Wenjing Li, Bo He, Chuanxu Wang and Lianen Qu
Appl. Sci. 2025, 15(5), 2695; https://doi.org/10.3390/app15052695 - 3 Mar 2025
Viewed by 733
Abstract
To address the limitations of traditional two-stream networks, such as inadequate spatiotemporal information fusion, limited feature diversity, and insufficient accuracy, we propose an improved two-stream network for human action recognition based on multi-scale attention Transformer and 3D convolutional (C3D) fusion. In the temporal [...] Read more.
To address the limitations of traditional two-stream networks, such as inadequate spatiotemporal information fusion, limited feature diversity, and insufficient accuracy, we propose an improved two-stream network for human action recognition based on multi-scale attention Transformer and 3D convolutional (C3D) fusion. In the temporal stream, the traditional 2D convolutional is replaced with a C3D network to effectively capture temporal dynamics and spatial features. In the spatial stream, a multi-scale convolutional Transformer encoder is introduced to extract features. Leveraging the multi-scale attention mechanism, the model captures and enhances features at various scales, which are then adaptively fused using a weighted strategy to improve feature representation. Furthermore, through extensive experiments on feature fusion methods, the optimal fusion strategy for the two-stream network is identified. Experimental results on benchmark datasets such as UCF101 and HMDB51 demonstrate that the proposed model achieves superior performance in action recognition tasks. Full article
(This article belongs to the Special Issue Selected Papers from CCF 39th China Computer Application Conference (CCF NCCA 2024))
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14 pages, 769 KiB  
Article
Speech Emotion Recognition Using Multi-Scale Global–Local Representation Learning with Feature Pyramid Network
by Yuhua Wang, Jianxing Huang, Zhengdao Zhao, Haiyan Lan and Xinjia Zhang
Appl. Sci. 2024, 14(24), 11494; https://doi.org/10.3390/app142411494 - 10 Dec 2024
Viewed by 967
Abstract
Speech emotion recognition (SER) is important in facilitating natural human–computer interactions. In speech sequence modeling, a vital challenge is to learn context-aware sentence expression and temporal dynamics of paralinguistic features to achieve unambiguous emotional semantic understanding. In previous studies, the SER method based [...] Read more.
Speech emotion recognition (SER) is important in facilitating natural human–computer interactions. In speech sequence modeling, a vital challenge is to learn context-aware sentence expression and temporal dynamics of paralinguistic features to achieve unambiguous emotional semantic understanding. In previous studies, the SER method based on the single-scale cascade feature extraction module could not effectively preserve the temporal structure of speech signals in the deep layer, downgrading the sequence modeling performance. To address these challenges, this paper proposes a novel multi-scale feature pyramid network. The enhanced multi-scale convolutional neural networks (MSCNNs) significantly improve the ability to extract multi-granular emotional features. Experimental results on the IEMOCAP corpus demonstrate the effectiveness of the proposed approach, achieving a weighted accuracy (WA) of 71.79% and an unweighted accuracy (UA) of 73.39%. Furthermore, on the RAVDESS dataset, the model achieves an unweighted accuracy (UA) of 86.5%. These results validate the system’s performance and highlight its competitive advantage. Full article
(This article belongs to the Special Issue Selected Papers from CCF 39th China Computer Application Conference (CCF NCCA 2024))
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18 pages, 4262 KiB  
Article
Cyclic Consistent Image Style Transformation: From Model to System
by Jun Peng, Kaiyi Chen, Yuqing Gong, Tianxiang Zhang and Baohua Su
Appl. Sci. 2024, 14(17), 7637; https://doi.org/10.3390/app14177637 - 29 Aug 2024
Cited by 1 | Viewed by 1433
Abstract
Generative Adversarial Networks (GANs) have achieved remarkable success in various tasks, including image generation, editing, and reconstruction, as well as in unsupervised and representation learning. Despite their impressive capabilities, GANs are often plagued by challenges such as unstable training dynamics and limitations in [...] Read more.
Generative Adversarial Networks (GANs) have achieved remarkable success in various tasks, including image generation, editing, and reconstruction, as well as in unsupervised and representation learning. Despite their impressive capabilities, GANs are often plagued by challenges such as unstable training dynamics and limitations in generating complex patterns. To address these challenges, we propose a novel image style transfer method, named C3GAN, which leverages CycleGAN architecture to achieve consistent and stable transformation of image style. In this context, “image style” refers to the distinct visual characteristics or artistic elements, such as the color schemes, textures, and brushstrokes that define the overall appearance of an image. Our method incorporates cyclic consistency, ensuring that the style transformation remains coherent and visually appealing, thus enhancing the training stability and overcoming the generative limitations of traditional GAN models. Additionally, we have developed a robust and efficient image style transfer system by integrating Flask for web development and MySQL for database management. Our system demonstrates superior performance in transferring complex styles compared to existing model-based approaches. This paper presents the development of a comprehensive image style transfer system based on our advanced C3GAN model, effectively addressing the challenges of GANs and expanding application potential in domains such as artistic creation and cinematic special effects. Full article
(This article belongs to the Special Issue Selected Papers from CCF 39th China Computer Application Conference (CCF NCCA 2024))
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21 pages, 3662 KiB  
Article
Empirical Research on AI Technology-Supported Precision Teaching in High School Science Subjects
by Miaomiao Hao, Yi Wang and Jun Peng
Appl. Sci. 2024, 14(17), 7544; https://doi.org/10.3390/app14177544 - 26 Aug 2024
Viewed by 4096
Abstract
The empowerment of educational reform and innovation through AI technology has become a topic of increasing interest in the field of education. The advent of AI technology has made comprehensive and in-depth teaching evaluation possible, serving as a significant driving force for efficient [...] Read more.
The empowerment of educational reform and innovation through AI technology has become a topic of increasing interest in the field of education. The advent of AI technology has made comprehensive and in-depth teaching evaluation possible, serving as a significant driving force for efficient and precise teaching. There were few empirical studies on the application of high-quality precision teaching models in the field of compulsory education, and the learning difficulty of technology and the teaching burden on teachers have become significant factors hindering the use of technology to support education. This study analyzed teaching models from the perspectives of teachers’ teaching burdens and students’ learning obstacles, and was committed to relying on intelligent technology to construct a new precision teaching model, an educational diagnosis–feedback–intervention path that covered the entire teaching process, from the dimensions of teacher behavior, student behavior, and parent behavior, aiming to assist teachers in efficient teaching and students in personalized learning. This study was conducted with nine science classes, including about 540 people in the second year of high school at a Middle School in China; six classes were the intervention groups while the last three classes were control groups, and a survey of 19 teachers from the intervention classes was carried out. The results showed that this model can significantly improve students’ academic performance in science subjects, especially in mathematics and chemistry. It has increased the proportion of high-achieving students, reduced the proportion of low-achieving students, stimulated students’ self-directed learning ability, cultivated a positive attitude towards science learning, and explained the key points of using a precision teaching model in different disciplines. It has achieved a deep integration of education and technology, helping to increase the efficiency and reduce the burden of teaching. Full article
(This article belongs to the Special Issue Selected Papers from CCF 39th China Computer Application Conference (CCF NCCA 2024))
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