检索结果-内蒙古大学图书馆

DMHFR:Decoder with Multi-Head Feature Receptors for Tract Image Segmentation

computers, Materials & Continua 2025年第3期82卷 4841-4862页

作者： Jianuo Huang Bohan Lai Weiye Qiu Caixu Xu Jie He Department of Endoscopy Center Zhongshan Hospital(Xiamen)Fudan UniversityXiamen361015China School of Computing and Data Science Xiamen University MalaysiaSepang43900Malaysia School of Computer Science and Techonology Tongji UniversityShanghai200092China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou UniversityWuzhou543002China Xiamen Clinical Research Center for Cancer Therapy Xiamen361015China

The self-attention mechanism of Transformers,which captures long-range contextual information,has demonstrated significant potential in image ***,their ability to learn local,contextual relationships between pixels requires further *** methods face challenges in efficiently managing multi-scale fea-tures of different granularities from the encoder backbone,leaving room for improvement in their global representation and feature extraction *** address these challenges,we propose a novel Decoder with Multi-Head Feature Receptors(DMHFR),which receives multi-scale features from the encoder backbone and organizes them into three feature groups with different granularities:coarse,fine-grained,and full *** groups are subsequently processed by Multi-Head Feature Receptors(MHFRs)after feature capture and modeling *** include two Three-Head Feature Receptors(THFRs)and one Four-Head Feature Receptor(FHFR).Each group of features is passed through these MHFRs and then fed into axial transformers,which help the model capture long-range dependencies within the *** three MHFRs produce three distinct feature *** output from the FHFR serves as auxiliary auxiliary features in the prediction head,and the prediction output and their losses will eventually be *** results show that the Transformer using DMHFR outperforms 15 state of the arts(SOTA)methods on five public ***,it achieved significant improvements in mean DICE scores over the classic Parallel Reverse Attention Network(PraNet)method,with gains of 4.1%,2.2%,1.4%,8.9%,and 16.3%on the CVC-ClinicDB,Kvasir-SEG,CVC-T,CVC-ColonDB,and ETIS-LaribPolypDB datasets,respectively.

关键词： Medical image segmentation feature exploration feature aggregation deep learning multi-head feature receptor

来源：评论

学校读者我要写书评

暂无评论

AI-Driven Innovation Using Multimodal and Personalized Adaptive Education for Students With Special Needs

引用

IEEE Access 2025年 13卷 69790-69820页

作者： Farhah, Nesren Adnan, Muhammad Abdullah Alqarni, Ahmed Irfan Uddin, M. Aldhyani, Theyazn H. H. King Salman Center for Disability Research Riyadh11614 Saudi Arabia Saudi Electronic University College of Health Science Department of Health Informatics Riyadh11673 Saudi Arabia Institute of Computing Kohat University of Science and Technology Kohat26000 Pakistan Al-Baha University Department of Computer Sciences and Information Technology Al-Aqiq 65779 Saudi Arabia Al Hofuf31982 Saudi Arabia

This study provides an in-depth exploration of the use of multimodal techniques in developing adaptive learning systems designed for students with special needs using various neural network models: a Baseline Neural Network, Convolutional Neural Network, Attention Model, LSTM, GRU, and Transformer models. Adaptive learning systems play a major role in the customization of the educational process in such a way as to ensure the specific cognitive, behavioural and emotional needs of students with special educational needs are catered for. The models were trained using a sizeable amount of multimodal data that recorded factors such as academic achievement, behavioral interactions, and environmental factors. From our observations, it can be noted that transformer models and attention mechanisms allowed for higher performance in predicting and fulfilling the needs of students, and were more effective than simpler neural architectures. These findings suggest that more advanced and intelligent neural networks can improve how students with different learning abilities consume content and, consequently, provide better educational answers and solutions to the market. To identify the important features affecting student performance, various Explainable AI (XAI) techniques were utilized, including SHAP (Shapley Additive Explanations) values, logistic regression-based feature importance analysis, LIME (Local Interpretable Model-Agnostic Explanations), partial dependency plots, and whose global surrogate model was a Decision Tree Regressor. These XAI instruments allowed the model’s decision to be explained and visualized so that teachers could understand the main beneficiaries of the improved educational results. Such XAI methods enable transparency in how models make decisions, thus allowing educators to understand the reasons for certain recommended learning paths. © 2013 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Multi-modal classification of forest biodiversity potential from 2D orthophotos and 3D airborne laser scanning point clouds

arXiv

引用

arXiv 2025年

作者： Jensen, Simon B. Oehmcke, Stefan Møgelmose, Andreas Madadi, Meysam Igel, Christian Escalera, Sergio Moeslund, Thomas B. Visual Analysis and Perception Laboratory Aalborg University Denmark Pioneer Centre for Artificial Intelligence Denmark Department of Computer Science Copenhagen University Denmark Institute for Visual & Analytic Computing Rostock University Germany University of Barcelona and Computer Vision Center Spain

Accurate assessment of forest biodiversity is crucial for ecosystem management and conservation. While traditional field surveys provide high-quality assessments, they are labor-intensive and spatially limited. This study investigates whether deep learning-based fusion of close-range sensing data from 2D orthophotos (12.5 cm resolution) and 3D airborne laser scanning (ALS) point clouds (8 points/m2) can enhance biodiversity assessment. We introduce the BioVista dataset, comprising 44 378 paired samples of orthophotos and ALS point clouds from temperate forests in Denmark, designed to explore multi-modal fusion approaches for biodiversity potential classification. Using deep neural networks (ResNet for orthophotos and PointVector for ALS point clouds), we investigate each data modality's ability to assess forest biodiversity potential, achieving mean accuracies of 69.4% and 72.8%, respectively. We explore two fusion approaches: a confidence-based ensemble method and a feature-level concatenation strategy, with the latter achieving a mean accuracy of 75.5%. Our results demonstrate that spectral information from orthophotos and structural information from ALS point clouds effectively complement each other in forest biodiversity assessment. © 2025, CC BY.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Efficient and Distributed Computation of Electron Repulsion Integrals on AMD AI Engines

Efficient and Distributed Computation of Electron Repulsion ...

引用

Annual IEEE Symposium on Field-Programmable Custom computing Machines (FCCM)

作者： Johannes Menzel Christian Plessl Department of Computer Science Paderborn Center for Parallel Computing Paderborn University Paderborn Germany

ISBN: (数字)9798331502812

ISBN: (纸本)9798331502829

computing electron repulsion integrals (ERIs) is the major computational bottleneck of many quantum mechanical simulation methods, requiring trillions of ERI evaluations per time step. While the computation of independent ERIs is embarrassingly parallel, the efficient computation of individual ERIs on modern processor cores is difficult due to both an insufficient cache size for intermediates of the computation and irregular memory access patterns that are difficult to vectorize. In this paper, we present how our implementation on the AI Engine (AIE) architecture addresses both of these problems. First, we have defined a flexible graph structure, which we call an ERI-Engine, that can be implemented for all 231 canonical ERI quartets from {ss|ss} to {hh| hh} by distributing the computation over 2–14 AIEs. Second, for the larger quartets, we have devised a novel vectorization scheme that leverages the advanced floating-point unit of the AIEs, while also supporting vectorization of independent ERIs for the smaller quartets. Finally, ERI-Engines are horizontally and vertically stackable to fill the entire AIE array, and in particular, the vertically stacked ERI-Engines form a column that uses one or more time-shared channels to stream the results out of the AIE array, almost completely hiding the computational phases of individual ERI-Engines. In terms of absolute performance, we are competitive with recent high-performance implementations of ERI algorithms on FPGAs (SERI) and GPUs (LibintX), as well as well-established highly optimized CPU libraries (Libint, Libcint), while being the unequivocal leader in terms of energy efficiency.

关键词： Phased arrays Quantum computing Memory management Quantum mechanics Libraries Computational efficiency Artificial intelligence Engines Field programmable gate arrays Electrons

来源：评论

学校读者我要写书评

暂无评论

Questionable Fairness in Federated Learning 8

Questionable Fairness in Federated Learning

引用

8th International Conference on Data science and Machine Learning Applications, CDMA 2025

作者： Alharbi, Raed Nguyen, Truc Yousefzadeh, Nooshin Aljohani, Ahmed National Renewable Energy Laboratory Computatioual Science Center GoldenCO United States University of Florida Computer and Information Science and Engineering Department GainesvilleFL United States College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia University of North Texas Computer Science and Engineering Denton United States

ISBN: (纸本)9798331539696

Federated learning (FL) has emerged as a powerful framework for training deep learning models across numerous distributed clients, where a central server distributes and aggregates model updates without accessing clients' raw data, ensuring privacy. However, this decentralized approach introduces significant challenges regarding fairness. Existing research shows that non-i.i.d. data distributions among clients can lead to biased global models, particularly affecting decisions based on sensitive attributes. This paper aims to investigate these fairness issues rigorously by analyzing how disparities in client data distribution impact model fairness. We conduct experiments using various models and datasets to evaluate and mitigate biases. We also propose novel data partitioning strategies to reflect real-world client data distributions accurately. Our finding shows that FL is highly vulnerable to the fairness problem due to the Non-IID property. The experiment's code can be found here: https://***/raed19/Questionable-Fairness-in-Federated-Learning. © 2025 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

CLIMS++: Cross Language Image Matching with Automatic Context Discovery for Weakly Supervised Semantic Segmentation

引用

International Journal of computer Vision 2025年 1-20页

作者： Xie, Jinheng Deng, Songhe Hou, Xianxu Luo, Zhaochuan Shen, Linlin Huang, Yawen Zheng, Yefeng Shou, Mike Zheng Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Computer Science Wenzhou-Kean University Wenzhou325060 China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Show Lab National University of Singapore Singapore639798 Singapore School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou215400 China Jarvis Research Center Tencent YouTu Lab Shenzhen518703 China

While promising results have been achieved in weakly-supervised semantic segmentation (WSSS), limited supervision from image-level tags inevitably induces discriminative reliance and spurious relations between target classes and background regions. Thus, Class Activation Map (CAM) usually tends to activate discriminative object regions and falsely includes lots of class-related backgrounds. Without pixel-level supervisions, it could be very difficult to enlarge the foreground activation and suppress those false activation of background regions. In this paper, we propose a novel framework of Cross Language Image Matching with Automatic Context Discovery (CLIMS++), based on the recently introduced Contrastive Language-Image Pre-training (CLIP) model, for WSSS. The core idea of our framework is to introduce natural language supervision to activate more complete object regions and suppress class-related background regions in CAM. In particular, we design object, background region, and text label matching losses to guide the model to excite more reasonable object regions of each category. In addition, we propose to automatically find spurious relations between foreground categories and backgrounds, through which a background suppression loss is designed to suppress the activation of class-related backgrounds. The above designs enable the proposed CLIMS++ to generate a more complete and compact activation map for the target objects. Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 datasets show that our CLIMS++ significantly outperforms the previous state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

MatrikalinDiabetes: User-centered Design of a mHealth App for Gestational Diabetes Mellitus Management and Education Among Bangladeshi Women 26th

MatrikalinDiabetes: User-Centered Design of a mHealth App ...

引用

26th International Conference on Human-computer Interaction, HCII 2024

作者： Ratul, Mohammad Arshad Hossain Yanoor Bristy, Tunisha Sayeed, Noorjahan Islam, Ashraful Chaudhry, Beenish Moalla Center for Computational and Data Sciences Independent University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh School of Computing and Informatics University of Louisiana at Lafayette Louisiana United States

ISBN: (纸本)9783031768088

This study employs the User-centered Design (UCD) methodology to develop a mobile health (mHealth) application (app) specifically tailored for Bangladeshi women with Gestational Diabetes Mellitus (GDM). GDM affects approximately 10% of pregnant women globally and around 35% within Bangladesh, as reported by the World Health Organization (WHO). Despite its high prevalence, there is a significant lack of awareness and education about GDM in Bangladesh, compounded by language barriers that make existing digital solutions less accessible. The MatrikalinDiabetes mHealth app aims to overcome these barriers by providing comprehensive management and educational resources for GDM in the Bangla language, addressing both pre-pregnancy and post-pregnancy needs. To ensure the app meets the actual needs of potential users, personas were created based on a literature review, and a survey was conducted. This process informed the development of both a low-fidelity, paper-based prototype and a high-fidelity digital prototype. Key features of the MatrikalinDiabetes app include tracking of food and water intake, physical activity monitoring, reminders, GDM education, chat forums for community support, blood glucose level (BGL) monitoring, an SOS button for emergency contacts, and quizzes for user engagement. Preliminary feedback indicated that 53.8% of participants favored the integrated features and expressed willingness to use and recommend the app. The survey of the high-fidelity prototype yielded positive responses, with 57.9% of participants strongly agreeing and 42.1% agreeing that the features were beneficial. The user-friendly design of the high-fidelity prototype ensures intuitive interaction, aiding in health management, enhancing GDM understanding, and promoting a healthier lifestyle among pregnant women in Bangladesh. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Obstetrics

来源：评论

学校读者我要写书评

暂无评论

Memory Prefetching Evaluation of Scientific Applications on A Modern HPC Arm-based Processor

引用

IEEE Access 2025年 13卷 85898-85926页

作者： Ho, Nam Falquez, Carlos Portero, Antoni Suarez, Estela Pleiter, Dirk Forschungszentrum Jülich GmbH Jülich Supercomputing Centre Institute for Advanced Simulation Jülich Germany Computer Science Department Rhenish Friedrich Wilhelm University of Bonn Bonn Germany SiPEARL Maisons-Laffitte France KTH Royal Institute of Technology PDC Center for High Performance Computing Stockholm Sweden University of Groningen Bernoulli Institute FSE Department of Computer Science Groningen Netherlands

Memory prefetching is a well-known technique for mitigating the negative impact of memory access latencies on memory bandwidth. This problem has become more pressing as improvements in memory bandwidth have not kept pace with increases in computational power. While much existing work has been devoted to finding appropriate prefetching techniques for specific workloads, few provide insight into the behavior of scientific applications to better understand the impact of prefetchers. This paper investigates the impact of hardware prefetchers on the latest Arm-based high-end processor architectures. In this work, we investigate memory access patterns by analyzing locality properties and visualizing delta and repetitive address patterns. A deeper understanding of memory access patterns allows the use of the appropriate prefetcher and reaching a better correlation between access pattern properties and prefetcher performance. This can guide future co-design efforts. We evaluated traditional and innovative prefetchers using a gem5-based model of Arm Neoverse V1 cores. The model features a 16-core architecture, using Amazon's Graviton 3 processor as a hardware reference, but substituting DDR5 by high bandwidth memory (HBM2). We performed a detailed prefetching evaluation focusing on stencil, sparse matrix-vector multiplication, and Breadth-First Search kernels. These kernels represent a broad range of the applications running on today's High-Performance computing (HPC) systems, which are sensitive to memory performance. © 2013 IEEE.

关键词： computer simulation high performance computing Memory prefetcher

来源：评论

学校读者我要写书评

暂无评论

Location Privacy Preservation Crowdsensing With Federated Reinforcement Learning

引用

IEEE Transactions on Dependable and Secure computing 2025年第3期22卷 1877-1894页

作者： You, Zhichao Dong, Xuewen Liu, Ximeng Gao, Sheng Wang, Yongzhi Shen, Yulong Xidian University School of Computer Science and Technology The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education Xi'an710071 China Shaanxi Key Laboratory of Blockchain and Secure Computing Xi'an710071 China Fuzhou University College of Computer and Data Science Fuzhou350025 China Central University of Finance and Economics School of Information Beijing102206 China California State Polytechnic University Pomona Department of Computer Information Systems PomonaCA91768 United States Xidian University School of Computer Science and Technology Xi'an710071 China Shaanxi Key Laboratory of Network and System Security Xi'an710071 China

Crowdsensing has become a popular method of sensing data collection while facing the problem of protecting participants' location privacy. Existing location-privacy crowdsensing mechanisms focus on static tasks and participants without considering sensing tasks' time requirements and participants' mobility, which cannot achieve satisfactory collected data quality and task completion in crowdsensing with dynamic tasks and participants. Inspired by this, we proposed a location-preservation crowdsensing mechanism, FedSense, considering dynamic tasks and participants based on federated learning (FL) and reinforcement learning (RL). In FedSense, through RL's outstanding decision-making ability, participants select sensing tasks to perform by well-trained RL models without uploading location information to servers for task allocation. We propose an independent tasks selection environment that defines actions, states, and rewards of RL to enable FedSense to achieve satisfactory task completion and data quality while preserving location privacy. Besides, FedSense applies an asynchronous FL aggregation algorithm that reduces participants' network stabilization and device computing ability requirements. Analysis proves that participants' location information does not leave the local device during the model training and task selection process, effectively avoiding privacy leakage. Simulation shows that compared with existing location-preservation crowdsensing mechanisms, FedSense achieves the highest task completion and sensing accuracy for dynamic tasks and participants. © 2024 IEEE.

关键词： Data privacy

来源：评论

学校读者我要写书评

暂无评论

Exploring the Impact of Interactive Technologies on Student Engagement in Blended Learning Environments at Higher Education Institutions

International Journal of Interactive Mobile Technologies

引用

International Journal of Interactive Mobile Technologies 2025年第5期19卷 233-257页

作者： Ghai, Anupriya Sharma Altinay, Zehra Altinay, Fahriye Jasola, Sanjay Dagli, Gokmen Yikici, Betul School of Computing Graphic Era Hill University Uttarakhand Dehradun India Faculty of Education Societal Research and Development Center Near East University Mersin 10 Nicosia99138 Cyprus Department of Computer Science DBS Global University Uttarakhand Dehradun India Faculty of Education University of Kyrenia Mersin 10 Kyrenia99138 Cyprus

Adopting new technologies in higher education is essential for enhancing students’ skills and knowledge. Blended learning, an innovative approach integrating traditional classroom instruction with online components, has emerged as an effective educational solution. Incorporating online elements into blended learning models has shown great promise in enriching the learning experience. This study employs a structural equation model to investi-gate the key factors influencing students’ adoption of blended learning in higher educational institutions. The study identifies significant relationships among various factors, highlight-ing their roles in shaping student experiences and intentions. These findings provide prac-tical recommendations for educators and institutions aiming to optimize blended learning environments, ultimately fostering active student participation and satisfaction. This study contributes to the continuous improvement of educational practices and student outcomes within blended learning models, particularly with the integration of massive open online courses (MOOCs). © 2025 by the authors of this article. Published under CC-BY.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：