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检索条件"任意字段=Proceedings of the 2015 ACM Conference on Innovation and Technology in Computer Science Education"

共 6353 条记录，以下是131-140 订阅

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Deformable Attention-Based Edge-Aware Network for Single Image Super-Resolution

Deformable Attention-Based Edge-Aware Network for Single Ima...

引用

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

作者： Zhang, Ju Zhong, Baojiang Ma, Kai-Kuang School of Computer Science and Technology Soochow University Suzhou China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9798350368741

Accurately reconstructing object edges is a key challenge in single image super-resolution (SISR), as it greatly influences our visual perception of image quality. To address this fundamental issue, we propose a novel SISR approach named the deformable attention-based edge-aware (DAE) network. The DAE network features a deformable attention block that dynamically adjusts attention weights to align with edge structures, thereby improving edge awareness and reconstruction quality. Moreover, our network incorporates a multi-patterns window block that captures fine-grained edge details and enhances information flow across network layers. This combination results in visually superior SISR outputs. Extensive experiments and comparisons with the state-of-the-art methods demonstrate that our DAE network excels in both quantitative metrics and visual quality. © 2025 IEEE.

关键词： deformable attention edge-aware single image super-resolution window attention

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Manna Methodology: A Novel Approach to education 5.0 Through Learning and Socio-Emotional Assessment in IoD Bootcamps

Manna Methodology: A Novel Approach to Education 5.0 Through...

引用

17th International conference on computer Supported education, CSEDU 2025

作者： Madrigar, T.T. Calvo, R. Ruiz Aylon, L.B. Manna Ecosystem State University of Maringá Brazil

ISBN: (纸本)9789897587467

This study explores the practical application of education 5.0 through a methodology that integrates exponential technologies, emphasizing the Internet of Drones (IoD) to enhance students' technical and socio-emotional competencies. Conducted within the Manna Ecosystem-Brazil's largest platform for teaching, research, and innovation in Exponential Technologies-the study evaluates the impact of personalized pedagogical approaches in public schools. The proposed methodology utilizes David Kolb's learning style model (Accommodator, Converger, Assimilator, and Diverger) and specific socio-emotional competencies, including emotional awareness, emotional regulation, and relationship skills. Based on these learning profiles and participants' socio-emotional competencies, teams were organized with complementary profiles, using Artificial Intelligence to create personalized groups that promoted greater engagement and synergy, enhancing performance in innovation activities. Four case studies were conducted, comparing experimental groups formed with the personalized methodology and control groups organized randomly, through diagnostic, formative, and summative assessments. Statistical analyses revealed that the experimental group showed significantly superior performance compared to the control group, highlighting the potential of the methodology in creating an interactive and collaborative learning environment. These findings reinforce the relevance of integrating exponential technologies, such as IoD, with active and adaptive teaching methodologies, contributing to the advancement of education 5.0 and preparing students for the challenges of the 21st century. Copyright © 2025 by SCITEPRESS - science and technology Publications, Lda.

关键词： Students

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Design and Implementation of real-time road surface detection System Based on YOLOv8-GD Algorithm 24

Design and Implementation of real-time road surface detectio...

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8th International conference on Electronic Information technology and computer Engineering, EITCE 2024

作者： Chen, Jie Ma, Qianli Fei, Yunxiang Yin, Jun Chen, Xinzhe College of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China Zhejiang Hangda Technology development Co. LTD Zhejiang Hangzhou China

ISBN: (纸本)9798400710094

The main content of this study is to improve the accuracy and speed of road potholes detection, and establish a system with real-time road surface detection and integrated data reporting. In response to some drawbacks of traditional methods, this paper proposes a lightweight road pothole detection model based on improved YOLOv8-GD. By replacing the neck feature fusion network in the YOLOv8 model with the aggregation and distribution mechanism (GD), the cross layer fusion ability of the model for feature information of potholes and the generalization ability of the model are improved. For the bumpy data-set collected in complex road environments, the author applied different exposure and noise reduction treatments to the images in the data-set. Based on the original data-set, different treatments were applied to each data-set. The trained model on this basis has higher recognition accuracy and makes the system’s model training more robust. evaluation results demonstrate promising performance across various IoU thresholds, indicating its effectiveness in accurately localizing fruits within bounding boxes. Additionally, YOLOv8-GD exhibits substantial improvements in detection performance, achieving high precision and recall rates. The system has improved accuracy and efficiency, making it more suitable for the deployment and application of road inspection and mobile detection equipment in practical scenarios. © 2024 Copyright held by the owner/author(s).

关键词： Noise abatement

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Fuzzy-AttnNet: A Deep Learning Framework for Image Retrieval Using Fuzzy Feature Fusion 16

Fuzzy-AttnNet: A Deep Learning Framework for Image Retrieval...

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2024 16th International conference on Graphics and Image Processing, ICGIP 2024

作者： Liu, Shengyu Pan, Jingshan Li, Na Hu, Tao Tian, Tangbin Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

Image retrieval is the process of searching for similar images based on their visual content, which has significant applications in the field of medical imaging. Quickly retrieving similar images from a medical image database can assist doctors in rapid browsing and diagnosis. Although feature fusion-based descriptors have achieved great success in the field of image retrieval, there are relatively few cases of using fusion feature descriptors for image retrieval in the medical imaging field. To address this gap, we propose a novel feature extraction model, Fuzzy-AttnNet, aimed at enhancing the performance of medical image retrieval using fusion descriptors. The model simulates dilated convolutions and self-attention mechanisms, comprehensively modeling both local and global features of medical images and performing feature fusion. The fused descriptors are then applied to image retrieval. Additionally, Fuzzy-AttnNet employs a parallel optimization strategy using Arcface loss and Softmax loss, which increases intra-class spacing and significantly improves the model's recognition performance across different categories. In subclass classification tasks, Fuzzy-AttnNet demonstrates significant advantages, with an average precision improvement of approximately 10% and a recall increase of about 30%. The experimental results indicate that our proposed feature extraction model significantly enhances the accuracy of medical image retrieval. © 2025 SPIE.

关键词： Medical imaging

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eXplainMR: Generating Real-time Textual and Visual eXplanations to Facilitate UltraSonography Learning in MR 25

eXplainMR: Generating Real-time Textual and Visual eXplanati...

引用

proceedings of the 2025 CHI conference on Human Factors in Computing Systems

作者： Jingying Wang Jingjing Zhang Juana Nicoll Capizzano Matthew Sigakis Xu Wang Vitaliy Popov Computer Science and Engineering University of Michigan Ann Arbor Michigan USA Medical School University of Michigan Ann Arbor Michigan USA

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Vehicle Information Monitoring and Identification on Highway Bridges Using an Array of Fiber Bragg Grating Sensors and Deep Learning Algorithms 4

Vehicle Information Monitoring and Identification on Highway...

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4th International Computational Imaging conference, CITA 2024

作者： Duan, Zhenhui Zhang, Faxiang Jiang, Shaodong Sun, Zhihui Liu, Zhaoying Shen, Yongxu Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781510688834

Traditional traffic management methods are increasingly inadequate in coping with the growing pressure on traffic systems. Consequently, there is a need for a more intelligent and efficient approach to monitoring and identifying vehicle information. Current vehicle monitoring methods often suffer from environmental interference and limited coverage.. To address these challenges, we propose a novel method for monitoring and identifying vehicle information on bridges using an array of Fiber Bragg Grating (FBG) strain sensors, combined with a hybrid deep learning algorithm based on Discrete Wavelet Transform (DWT) and Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM). Strain data were collected from a continuous hollow slab beam on a section of a highway bridge. Vehicles were classified and labeled using on-site video data to create a dataset, which was subsequently used to train and test the proposed CNN-LSTM model. The results demonstrate that the proposed method can accurately identify and classify the lane position, vehicle type, and vehicle speed category, achieving detection accuracies of 94.0%, 93.11%, and 90.67%, respectively. The DWT-CNN-LSTM method, utilizing the FBG strain sensor array, leverages existing strain monitoring platforms, supplements current traffic monitoring systems, and offers high efficiency and cost-effectiveness. This approach provides valuable data references for management and statistical analysis. © 2025 SPIE.

关键词： Discrete wavelet transforms

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SwinECA-Net: A Novel Dual-Scale Attention Network for Gastric Cancer Pathology Image Segmentation 24

SwinECA-Net: A Novel Dual-Scale Attention Network for Gastri...

引用

2024 International conference on Virtual Reality, Image and Signal Processing, ICVISP 2024

作者： Hu, Tao Pan, Jingshan Li, Na Tian, Tangbin Liu, Shengyu Han, Yujiao Zhang, Lu Xu, Jiwei Shandong Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan China

ISBN: (纸本)9798400710926

As a prevalent malignant tumor worldwide, accurate segmentation of gastric cancer pathology images is crucial for early diagnosis and treatment. The complexity of gastric cancer lesion morphology and the ambiguity of its boundaries pose challenges for deep learning image segmentation techniques. To address this, we propose a novel and efficient segmentation network called SwinECA-Net. This network utilizes Swin Transformer blocks for feature extraction and employs an Enhanced Channel Attention (ECA) module as a bottleneck to enhance local feature extraction capabilities. Additionally, we introduce the Dual-Scale Attention Fusion Module (DSAFM) between the encoder and decoder, which re-aligns feature maps in both channel and spatial dimensions to effectively extract and utilize global contextual information. Experimental results show that our method achieves an 89.62% Dice-Similarity coefficient (DSC) and a 95th percentile Hausdorff Distance (HD95) of 40.24 mm on gastric gland pathology slide images, significantly improving the segmentation accuracy of gastric cancer. © 2024 Copyright held by the owner/author(s).

关键词： Diseases

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Attention-Enhanced Feature Fusion Network for No-Reference Image Quality Assessment

Attention-Enhanced Feature Fusion Network for No-Reference I...

引用

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

作者： Ma, Jiliang Chen, Yihua Huang, Pengsheng Tang, Zhenjun Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China Guangxi Key Lab of MultiSource Information Mining & Security Guangxi Normal University Guilin541004 China Beijing MeiyiLab Co. Ltd. Beijing100080 China

ISBN: (纸本)9798350368741

No-Reference Image Quality Assessment (NR-IQA) is a fundamental computer vision task. In this paper, we propose a new Attention-Enhanced Feature Fusion Network for NR-IQA (AEFF-IQA) that integrates multi-scale local and non-local features. Firstly, multi-scale local and non-local features containing distortion information at semantic level are extracted by a feature extraction module consisting of two pre-trained deep neural networks. Secondly, a self-attention enhanced fusion module with four self-attention enhanced fusion components fuses the local and non-local features at the same scale to obtain multi-scale fusion features. Then, a cross-attention enhanced fusion module containing three cross-attention enhanced fusion components integrates these fusion features across multiple scales. Finally, the quality score is obtained by using a prediction module. Extensive experiments are conducted on five public datasets. The results show that our AEFF-IQA outperforms some state-of-the-art models and exhibits good generalization performance. © 2025 IEEE.

关键词： cross-attention multi-scale feature fusion No-reference image quality assessment self-attention

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DigiVillage Chronicles: A Serious Game Designed to Improve Digital Skills for Accessing Public Services

DigiVillage Chronicles: A Serious Game Designed to Improve D...

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17th International conference on computer Supported education, CSEDU 2025

作者： Fronza, Ilenia Parida, Shiva Saket Mehboob, Aziya Free University of Bozen Bolzano Italy

ISBN: (纸本)9789897587467

Smart villages have transformed rural areas by using innovative solutions to provide improved public services. However, citizens often struggle to access these services due to a lack of digital skills. To address this issue, we present DigiVillage Chronicles, a serious game designed for young adults aged 18 to 35 to enhance their digital skills to access public services in smart villages. The game is set in a fictional smart village where the player participates in digital activities related to public services and gains insight into navigating the platforms offering these services in real life. The activities include browsing and applying for jobs, transferring money online, and booking a doctor's appointment. In this paper, we present a proof of concept for the game and evaluate its quality using criteria related to both the serious and game components, as well as the balance between these elements. Copyright © 2025 by SCITEPRESS - science and technology Publications, Lda.

关键词： Engineering education

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CoGAP: A Personalized Federated Learning Method Using Collaborative Optimization for Medical Image Classification

CoGAP: A Personalized Federated Learning Method Using Collab...

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

作者： Zheng, Shenhai Li, Congyu Wen, Sian Gao, Xi Yu, Lei College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing China Emergency Department The Second Affiliated Hospital of Chongqing Medical University Chongqing China

ISBN: (纸本)9798350368741

Federated learning (FL) has been widely used in medical image processing to protect data privacy, but it has issues with data heterogeneity. Personalized federated learning have emerged to tackle these issues but often focuses too much on personalized models at the expense of global models. To address these problem, we propose a personalized federated learning method CoGAP, using collaborative optimization to enhance both personalized and global model performance. On the client side, it employs adaptive weight aggregation to initialize personalized models and uses a training strategy based on L2 regularization. On the server side, it implements a gradient accumulation-based aggregation method. These modules facilitate a collaborative optimization process where the global model guides the personalized models, and the personalized models provide positive feedback to the global model, ultimately leading to mutual improvement for both. We conducted extensive comparative experiments on the OCT2017 dataset, as well as the BloodMnist and PathMnist (subsets from MedMnist), to evaluate both global and personalized models. Results show that CoGAP own a unique strength which not only outperforms other personalized federated learning methods in personalization capability while also remarkably achieving satisfying global generalization. Code is available at https://***/lcyCQUPT/CoGAP. © 2025 IEEE.

关键词： collaborative optimization federated learning medical image classification

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