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13th International Conference on Educational and Information technology, ICEIT 2024

作者： Wu, Mei Wang, Xining Lu, Tao O'Hare, Gregory M.P School of Computer Science and Engineering School of Artificial Intelligence Wuhan Institute of Technology China School of Education School of Computer Science and Statistics Trinity College Dublin Dublin Ireland School of Computer Science and Statistics Trinity College Dublin Dublin Ireland

ISBN: (纸本)9798350372663

As English becomes the mainstream language in communication and education worldwide, more and more Chinese universities emphasize the importance of adding English content to the higher education curriculum. This paper explores the integration of English content into computer science education in Chinese universities, with a focus on bilingual programming courses. It proposes a blended learning strategy that combines face-to-face classroom interaction with platform-based self-directed learning. It designed the experiment and the number of participants is 84. The experimental results show that the adoption of this blended teaching strategy can enhance students' enthusiasm for bilingual programming courses and facilitate students in achieving the desired learning effect. © 2024 IEEE.

关键词： Students

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Hand-object pose estimation based on HOPE-net and attention mechanism 4

Hand-object pose estimation based on HOPE-net and attention ...

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2024 4th International Conference on artificial intelligence and Industrial technology Applications, AIITA 2024

作者： Li, Pangshan School of Artificial Intelligence and Computer Science Jiangnan University Jiangsu Wuxi China

Hand-object pose estimation is an important part of studying hand pose, focusing on the joint detection of hand and object poses. Although CNN has shown advantages in various hand pose estimation, due to various problems such as the complex structure of the hand pose and self-occlusion, there are limitations in the extraction of image features and the amount of calculation. Existing studies have shown that GCNs have a good performance in dealing with such problems. In this paper, we propose to combine the attention module with two adaptive GCNs, and perform feature enhancement operations on hand features. While ensuring the accuracy of feature extraction, it can effectively improve the network calculation speed and avoid some questions such as self-occlusion. Experimental results show that, through end-to-end training, the algorithm has a great performance on the problem of hand-object pose estimation. © Published under licence by IOP Publishing Ltd.

关键词： Palmprint recognition

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APTAnet: an atom-level peptide-TCR interaction affinity prediction model

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Biophysics Reports 2024年第1期10卷 1-14页

作者： Peng Xiong Anyi Liang Xunhui Cai Tian Xia School of Artificial Intelligence and Automation Huazhong University of Science and Technology Institute of Pathology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology

The prediction of affinity between TCRs and peptides is crucial for the further development of TIL(Tumor-Infiltrating Lymphocytes) immunotherapy. Inspired by the broader research of drug-protein interaction(DPI), we propose an atom-level peptide-TCR interaction(PTI) affinity prediction model APTAnet using natural language processing methods. APTAnet model achieved an average ROC-AUC and PR-AUC of 0.893 and 0.877, respectively, in ten-fold cross-validation on 25,675 pairs of PTI ***, experimental results on an independent test set from the McPAS database showed that APTAnet outperformed the current mainstream models. Finally, through the validation on 11 cases of real tumor patient data, we found that the APTAnet model can effectively identify tumor peptides and screen tumor-specific TCRs.

关键词： Immunotherapy Natural language processing Transfer learning TCR Antigen

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YOLO-TSD: A Recognition and Detection Method for Traffic Sign Based on YOLOv5 24

YOLO-TSD: A Recognition and Detection Method for Traffic Sig...

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24th IEEE International Conference on Software Quality, Reliability and Security Companion, QRS-C 2024

作者： Li, Zhe Li, Zuchun Lu, Jia Zhang, Yimiao Ye, Peng School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan China

ISBN: (纸本)9798350365658

In the realm of self-driving technology and vehicular security, the identification of traffic signs is of paramount importance. To address the challenges encountered in the detection process, such as insufficient effective features of traffic signs and complex background environment. Within this article, a novel optimization technique referred to as YOLO-TSD, which stands for a recognition and detection method for traffic signs employing YOLOv5, is presented. This approach incorporates an EVCBlock module as well as the EIOU loss function to augment the precision and constancy in bounding box regression. Initially, the integration of the EVCBlock into YOLOv5's fundamental network structure occurs to bolster pertinent features through the amalgamation of both comprehensive and detailed data. Then, we use EIOU loss function to accelerate the matching process between target boxes and anchor boxes. Ultimately, the developed algorithm is employed to identify the location of road signs within the photograph. The experimental results show that the MAP of YOLO-TSD on the TT100K training set has experienced a 4.91% enhancement when compared to the initial YOLOv5, and the model boasts robust stability that aids in the detection of traffic signals. © 2024 IEEE.

关键词： Traffic signs

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Enhanced Recognition of Youths' Abnormal Gait Using 3D ResNet and Time-Space Pyramid Pooling 5

Enhanced Recognition of Youths' Abnormal Gait Using 3D ResNe...

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5th International Seminar on artificial intelligence, Networking and Information technology, AINIT 2024

作者： Hou, Xuan School of Artificial Intelligence and Computer Science Jiangnan University Jiangsu Wuxi China

ISBN: (纸本)9798350385557

Recognition of abnormal gait from the front is particularly crucial in determining potential health problems for youths. However, there are almost no publicly available labeled datasets taken from the front and no corresponding identification methods. Additionally, existing gait recognition methods mainly use contour-based images, making it difficult to preserve temporal information from gait videos. To address these challenges, we introduce a novel model, ResNet-TSPP, which combines 3D ResNet with Time-Space Pyramid Pooling, specifically designed for frontal abnormal gait recognition. The choice of 3D ResNet is grounded in its exceptional ability to capture spatiotemporal information, crucial for analyzing dynamic gait patterns. Unlike other potential backbones for 3D neural networks, 3D ResNet offers a unique advantage due to its deep residual learning framework that alleviates the vanishing gradient problem, facilitating the learning of deep features across spatial and temporal dimensions. This capability is paramount for distinguishing subtle abnormalities in gait patterns from the front view. In this study, we also construct and exhaustively annotate a dataset named the Single Person Walking Video set to support the model training and evaluation. Experimental results indicate that the proposed ResNet-TSPP model possesses high accuracy in recognizing abnormal gaits from the front, providing significant support for understanding and analyzing various gait patterns. © 2024 IEEE.

关键词： Deep learning

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Provable data Possession with Outsourced Tag Generation for AI-Driven E-Commerce

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computers, Materials & Continua 2025年第5期83卷 2719-2734页

作者： Yi Li Wenying Zheng Yu-Sheng Su Meiqin Tang School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China School of Computer Science and Technology(School of Artificial Intelligence) Zhejiang Sci-Tech UniversityHangzhou310018China Department of Computer Science and Information Engineering National Chung Cheng UniversityChiayi621301TaiwanChina Advanced Institute of Manufacturing with High-tech Innovations National Chung Cheng UniversityChiayi621301TaiwanChina Department of Computer Science and Engineering Taiwan Ocean UniversityKeelung202301TaiwanChina School of Integrated Circuits Wuxi Vocational College of Science and TechnologyWuxi214028China

AI applications have become ubiquitous,bringing significant convenience to various *** e-commerce,AI can enhance product recommendations for individuals and provide businesses with more accurate predictions for market strategy ***,if the data used for AI applications is damaged or lost,it will inevitably affect the effectiveness of these AI ***,it is essential to verify the integrity of e-commerce *** existing Provable data Possession(PDP)protocols can verify the integrity of cloud data,they are not suitable for e-commerce scenarios due to the limited computational capabilities of edge servers,which cannot handle the high computational overhead of generating homomorphic verification tags in *** address this issue,we propose PDP with Outsourced Tag Generation for AI-driven e-commerce,which outsources the computation of homomorphic verification tags to cloud servers while introducing a lightweight verification method to ensure that the tags match the uploaded ***,the proposed scheme supports dynamic operations such as adding,deleting,and modifying data,enhancing its ***,experiments show that the additional computational overhead introduced by outsourcing homomorphic verification tags is acceptable compared to the original PDP.

关键词： Provable data possession data auditing cloud computing e-commerce bloom filter

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PRESS: Defending Privacy in Retrieval-Augmented Generation via Embedding Space Shifting

PRESS: Defending Privacy in Retrieval-Augmented Generation v...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： He, Jiaming Liu, Cheng Hou, Guanyu Jiang, Wenbo Li, Jiachen School of Computer Science and Engineering University of Electronic Science and Technology of China China College of Computer Science and Cyber Security Oxford Brookes College Chengdu University of Technology China School of Data Science The Chinese University of Hong Kong Shenzhen China School of Computer Science and Artificial Intelligence Wuhan University of Technology China

ISBN: (纸本)9798350368741

Retrieval-augmented generation (RAG) expands the capabilities of large language models (LLMs) in various applications by integrating relevant information retrieved from external data sources. However, the RAG systems are exposed to substantial privacy risks during the information retrieval process, leading to potential data leakage of private information. In this work, we present a Privacy-preserving Retrieval-augmented generation via Embedding Space Shifting (PRESS), systematically exploring how to protect privacy in RAG systems. Specifically, we first conduct proximal policy optimization (PPO) based training on pre-trained language models to generate target training samples. Then we employ a purposive shift fine-tuning on the text embedding model with the generated samples for guiding the RAG system to map potential privacy leaking queries to safe target in embedding space. Extensive experimental results on representative models and datasets demonstrate that our protection method achieves high defense performance with high efficiency while keeping the normal functionality of the RAG system. © 2025 IEEE.

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The further fully convolutional networks for medical image segmentation 3

The further fully convolutional networks for medical image s...

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3rd International Conference on Electronic Information Engineering and data Processing, EIEDP 2024

作者： Shen, Fengqiao Xu, Yixiang Jiangnan University School of Artificial Intelligence and Computer Science Jiangsu Wuxi China

ISBN: (纸本)9781510680531

Jonathan Long et al.'s Fully Convolutional Networks (FCN), introduced at CVPR 2015, have been widely adopted for semantic segmentation tasks. In traditional Convolutional Neural Networks, spatial resolution of the input image is gradually reduced through a series of convolution and pooling layers, culminating in a fixed-size feature map, followed by fully connected layers. However, FCN dispenses with the fully connected layers of CNNs and replaces them with fully convolutional layers, enabling pixel-wise mapping between input images and output feature maps. This is achieved by employing transpose convolution (also known as deconvolution) for upsampling the feature maps. Nonetheless, FCN still retains pooling layers. In this paper, we propose improvements to FCN by replacing pooling layers with convolutional layers, and we conduct a series of experiments based on the idea of whether convolutional layers can fully replace pooling layers. © 2024 SPIE.

关键词： Convolution

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EdgeReg: Edge-assisted Unsupervised Medical Image Registration

EdgeReg: Edge-assisted Unsupervised Medical Image Registrati...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Jun Wang, Wenyi Wang, Wei Luo, Gongning Li, Yacong Wang, Kuanquan Harbin Institute of Technology School of Computer Science and Technology Harbin China Harbin Institute of Technology Shenzhen School of Computer Science and Technology Shenzhen China Beijing Academy of Artificial Intelligence Beijing China

ISBN: (纸本)9798350386226

Medical image registration (MIR) is essential for various clinical diagnoses and treatments. Despite the rapid progress in deep learning-based MIR techniques, most methods focus on directly optimizing the raw image intensity information. In this paper, we explore the usage of edge information of anatomical structures associated with the spatial location of image intensities to assist in registration, termed EdgeReg. The intuition is that the edge information can provide additional rich boundary information to the raw images, enhancing the network's feature representation. Additionally, as the edge images are strictly spatially consistent with the raw images, additional supervised information can be added to network training. Specifically, we first extract the edge images from the raw moving and fixed images using the Sobel operator and feed these images into a lightweight feature extractor to merge the image intensity and edge information. The enriched features are subsequently input into established registration networks. Finally, similarity loss is applied to both the raw and edge images. Extensive experiments show that EdgeReg is compatible with various networks across diverse datasets and dimensions (2D and 3D), achieving superior registration performance. In particular, EdgeReg does not rely on segmentation labels and is trained in an unsupervised paradigm. Therefore, edge information is a beneficial assistance for unsupervised MIR. The code is available at https://***/PerceptionComputingLab/EdgeReg. © 2024 IEEE.

关键词： Image registration

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SUFT: Sparse and Uncertain Fusion Transformers for Multi-Atlas Brain Network Analysis

SUFT: Sparse and Uncertain Fusion Transformers for Multi-Atl...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Su, Zhan Huang, Jiashuang Jiang, Shu Wang, Mingliang Ding, Weiping School of Artificial Intelligence and Computer Science Nantong University Nantong China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9798350368741

The existing multi-atlas brain network analysis methods rely on some simple fusion methods (i.e., add and concatenation) and do not consider the information redundancy caused by increased brain regions. To improve upon these, we propose the Sparse and Uncertain Fusion Transformers (SUFT) for multi-atlas brain network analysis. First, multi-atlas brain network data is derived from original fMRI data using various atlases. An attention enhancement module then extracts and preserves essential features for minimal information loss during selection. These processed features are fed into a selection module that identifies disease-related brain regions based on their importance scores. The model utilizes only these sparse features for further processing. Finally, an uncertain fusion module assesses the confidence level of each atlas and implements a strategy to integrate results at the evidence level. Experimental results on 1138 subjects from the SRPBS dataset demonstrate that our SUFT outperforms several state-of-the-art methods in identifying brain disorders. © 2025 IEEE.

关键词： Brain Disorder Brain Regions Selection Multi-atlas Brain Networks Uncertain Fusion

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