检索结果-内蒙古大学图书馆

Postoperative Recovery Assessment for Parkinson's Patients via Light-weighted Topological Pose Estimation

IEEE Journal of Biomedical and Health Informatics 2025年

作者： Ma, Zeping Qin, Zhiyao Jiang, Botao Zhu, Guosong Qin, Zhen Geng, Ji Alenazi, Mohammed J.F. Kumari, Saru University of Electronic Science and Technology of China Network and Data Security Key Laboratory of Sichuan Province Chengdu611731 China Riyadh11451 Saudi Arabia Chaudhary Charan Singh University Department of Mathematics Uttar Pradesh Meerut India

The UPDRS III scale plays a critical role in diagnosing the progression of Parkinson's disease. Current methods often involve doctors guiding patients through specific actions on the scale, recording their performance, and assigning scores. However, this approach has several drawbacks, including the lengthy time required for doctorpatient communication, the high costs of patients traveling to hospitals for follow-up visits, and the reliance on subjective judgments from doctors, which lack standardized criteria. With advancements in artificial intelligence, many traditional processes have been partially automated. To help patients reduce diagnosis time, lower medical costs, and provide more accurate and objective evaluation results, this paper proposes a Transformer-based pose estimation model for assessing UPDRS III scale actions. By integrating skeleton-based evaluations from the network with a series of post-processing operations, the model enables patients to perform self-assessments of their post-treatment recovery at home, saving doctors significant time. This work introduces a cascaded graph self-attention module, SGAM (Spatial-Graphical Attention Module), to enhance the network's understanding of human topology. Additionally, it proposes a lightweight convolutional block, Chi-block, which employs a novel approach leveraging the attribute invariance of filters to interpret model performance and guide compression. This approach reduces computational costs and model parameters while preserving accuracy. The proposed method demonstrates robust performance on human pose estimation (HPE) datasets and showcases impressive lightweight performance on benchmark datasets such as ImageNet-1K and CIFAR-10. These results demonstrate the potential of artificial intelligence in enabling automated remote diagnosis and treatment for Parkinson's patients. © 2013 IEEE.

关键词： Neurodegenerative diseases

来源：评论

学校读者我要写书评

暂无评论

Multi-channel transmission meta-holograms enabled by a single-layer complex-amplitude metasurface

引用

Optics Letters 2025年第8期50卷 2542-2545页

作者： Zhu, Lei Wang, Yiya Dong, Liang Shang, Guanyu Burokur, Shah Nawaz Ding, Xumin School of Computer and Control Engineering Qiqihar University Qiqihar161006 China Communication and Electronics Engineering Institute Qiqihar University Qiqihar161006 China Key Laboratory of Big Data Network Security Detection and Analysis in Heilongjiang Qiqihar University Qiqihar161006 China Innovation Photonics and Imaging Center Instrumentation Science and Engineering Harbin Institute of Technology Harbin150001 China LEME UPL Univ. Paris Nanterre Ville d'AvrayF92410 France Advanced Microscopy and Instrumentation Research Center Harbin Institute of Technology Harbin150080 China

With an increasing demand for wireless communications, it is difficult for traditional communication technologies to achieve high-quality multi-channel wireless transmissions. Complex-amplitude metasurfaces provide an effective method to deal with this issue by jointly controlling the amplitude and phase of electromagnetic (EM) waves. However, the limitations of single-layer transmission efficiency and amplitude-phase coupling make it challenging to achieve multi-channel high-quality transmission and system integration. Here, a single-layer complex-amplitude metasurface design strategy for multi-channel high-quality transmission is proposed. The amplitude and phase of each transmission channel can be concurrently controlled by adjusting the structural parameters and rotation angles of the resonators. The metasurface successfully reconstructs different holograms in five transmission channels. All experimental results align well with the calculated and simulated results, demonstrating an innovative solution for multi-channel high-quality information transmission, with significant potential applications in the fields of wireless communications. © 2025 Optica Publishing Group.

关键词： Holograms

来源：评论

学校读者我要写书评

暂无评论

Using Depth-Enhanced Spatial Transformation for Student Gaze Target Estimation in Dual-View Classroom Images

Using Depth-Enhanced Spatial Transformation for Student Gaze...

引用

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

作者： Miao, Haonan Zhao, Peizheng Sun, Yuqi Nan, Fang Zhang, Xiaolong Wu, Yaqiang Tian, Feng School of Computer Science and Technology Xi'an Jiaotong University Xi'an710049 China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an710049 China School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou215123 China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)9798350368741

Dual-view gaze target estimation in classroom environments has not been thoroughly explored. Existing methods lack consideration of depth information, primarily focusing on 2D image information and neglecting the latent 3D spatial context, which could lead to suboptimal transformation and cause the gaze cone to intersect with an incorrect object. This paper introduces a novel dual-view gaze target estimation method tailored for classroom settings, leveraging depth-enhanced spatial transformations. By formulating a depth-enhanced 2D space, our method uses depth-enhanced spatial transformation to accurately project students' gaze cones to the teacher-oriented image. Additionally, we collected a dataset named DVSGE, specifically for student gaze target estimation in dual-view classroom images. Experimental results demonstrate significant performance improvements of 9.8% in AUC and 19.9% in L2-Distance for our method, surpassing existing methods. © 2025 IEEE.

关键词： classroom depth-enhanced 2D space dual-view gaze target estimation spatial transformation

来源：评论

学校读者我要写书评

暂无评论

CroPrompt: Cross-task Interactive Prompting for Zero-shot Spoken Language Understanding

CroPrompt: Cross-task Interactive Prompting for Zero-shot Sp...

引用

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

作者： Qin, Libo Wei, Fuxuan Chen, Qiguang Zhou, Jingxuan Huang, Shijue Si, Jiasheng Lu, Wenpeng Che, Wanxiang School of Computer Science and Engineering Central South University China Key Laboratory of Data Intelligence and Advanced Computing in Provincial Universities Soochow University China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology China Harbin Institute of Technology Shenzhen China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences China

ISBN: (纸本)9798350368741

Slot filling and intent detection are two highly correlated tasks in spoken language understanding (SLU). Recent SLU research attempts to explore zero-shot prompting techniques in large language models to alleviate the data scarcity problem. Nevertheless, the existing prompting work ignores the cross-task interaction information for SLU, which leads to sub-optimal performance. To solve this problem, we present the pioneering work of Cross-task Interactive Prompting (CroPrompt) for SLU, which enables the model to interactively leverage the information exchange across the correlated tasks in SLU. Additionally, we further introduce a multi-task self-consistency mechanism to mitigate the error propagation caused by the intent information injection. We conduct extensive experiments on the standard SLU benchmark and the results reveal that CroPrompt consistently outperforms the existing prompting approaches. In addition, the multi-task self-consistency mechanism can effectively ease the error propagation issue, thereby enhancing the performance. We hope this work can inspire more research on cross-task prompting for SLU. © 2025 IEEE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Blockchain and signcryption enabled asynchronous federated learning framework in fog computing

引用

Digital Communications and networks 2025年第02期 442-454页

作者： Zhou Zhou Youliang Tian Jinbo Xiong Changgen Peng Jing Li Nan Yang State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology

Federated learning combines with fog computing to transform data sharing into model sharing, which solves the issues of data isolation and privacy disclosure in fog computing. However, existing studies focus on centralized single-layer aggregation federated learning architecture, which lack the consideration of cross-domain and asynchronous robustness of federated learning, and rarely integrate verification mechanisms from the perspective of incentives. To address the above challenges, we propose a Blockchain and Signcryption enabled Asynchronous Federated Learning(BSAFL) framework based on dual aggregation for cross-domain scenarios. In particular, we first design two types of signcryption schemes to secure the interaction and access control of collaborative learning between domains. Second, we construct a differential privacy approach that adaptively adjusts privacy budgets to ensure data privacy and local models' availability of intra-domain user. Furthermore, we propose an asynchronous aggregation solution that incorporates consensus verification and elastic participation using blockchain. Finally, security analysis demonstrates the security and privacy effectiveness of BSAFL, and the evaluation on real datasets further validates the high model accuracy and performance of BSAFL.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A novel quantum multiparty summation protocol based on a cooperative random number mechanism

引用

EPJ Quantum Technology 2025年第1期12卷 1-19页

作者： Zhang, Kejia Zhang, Yu Zhang, Xue Liu, Hongyan Song, Tingting Du, Gang School of Computer Science and Big Data (School of Cybersecurity) Heilongjiang University Harbin China State Key Laboratory of Public Big Data Guizhou University Guiyang China School of Mathematical Science Heilongjiang University Harbin China Institute for Cryptology and Network Security Heilongjiang University Harbin China School of China Construction Bank University Northeastern Institute Harbin China College of Information Science and Technology Jinan University Guangzhou China

As a particular area of quantum security multiparty computation, quantum secure multiparty summation plays a critical role in modern cryptography. It is widely known that most of the existing quantum summation protocols are based on an honest or semi-honest third party (TP). However, the introduced TP makes the protocol difficult to implement in practice, as it may face a single-point-of-failure attack on TP. Although some TP-free protocols are proposed to mitigate this risk, the increased cost of communication reduces its efficiency. To address these issues, a novel quantum-secure multiparty summation protocol based on a cooperative random number distribution mechanism (QMS-CRM) is proposed in this paper for the first time. During it, this mechanism is designed using Shamir’s secret sharing scheme. Furthermore, this approach eliminates the requirement for random number exchange between participants without the help of TP, enhancing the efficiency of the protocol. The security analysis demonstrates that the proposed protocol can resist both external attacks and collusion attacks by up to $n - 2$ participants. Finally, we simulated the protocol on the IBM Quantum Cloud platform, confirming its feasibility.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Multimodal Dialogue Emotion Recognition Based on Label Optimization and Coarse-Grained Assisted Fine-Grained

Multimodal Dialogue Emotion Recognition Based on Label Optim...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhibo Zhang Zhenyu Yang Yan Huang Baojie Xu Wenyue Hu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multimodal dialogue emotion recognition integrates data from multiple modalities to accurately identify emotional states in conversations. However, differences in expression and information density across modalities complicate the fusion of features. Traditional methods may introduce redundant information from other utterances, reducing the accuracy of emotion recognition. Existing one-hot labels often fail to capture the full range of emotional expressions, leading to biased results. To address these issues, we propose a model that fuses different modalities within the same utterance to avoid redundancy. It employs a progressive classification process, refining emotion recognition from coarse to fine granularity. Additionally, we use emotion polarity probabilities as weights for fine-grained classification and introduce a multimodal information-rich label that considers both the data and their interactions. Experiments on IEMOCAP and MELD datasets demonstrate the model’s effectiveness, significantly improving dialog emotion recognition accuracy. Our code is available at https://***/r/LOCG-188E.

关键词： Emotion recognition Accuracy Fuses Refining Termination of employment Speech recognition Signal processing Speech processing Optimization Faces

来源：评论

学校读者我要写书评

暂无评论

Towards precision diagnosis: a novel hybrid DC-CAD model for lung disease detection leveraging multi-scale capsule networks and temporal dynamics

引用

Complex and Intelligent Systems 2025年第6期11卷 1-24页

作者： Aggrey, Esther Stacy E. B. Zhen, Qin Kodjiku, Seth Larweh Fiasam, Linda Delali Sey, Collins Ukwuoma, Chiagoziem C. Aidoo, Evans Osei-Mensah, Emmanuel School of Information and Software Engineering Chengdu 610054 China The Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu 610054 China School of Computer Science and Technology Hangzhou 310018 China College of Nuclear Technology and Automation Engineering Chengdu China School of Computer Science and Informatics Huddersfield United Kingdom

The early detection of lung diseases, including cancer, is essential for improving patient outcomes. However, traditional diagnostic approaches and standard deep learning models often face challenges in effectively analyzing the complex spatial and temporal variations in medical imaging data, particularly in CT scans. To address these challenges, we propose DC-CAD, a novel hybrid framework that integrates Dilated Capsule networks, Channel-wise Attention Mechanisms, and Distanced Long Short-Term Memory for precise and early diagnosis of lung diseases. DC-CAD is innovative in its ability to combine multi-scale feature extraction and temporal dynamic analysis, enabling the model to capture intricate spatial relationships and sequential changes in lung tissue. The model consists of three main contributions: (1) Dilated Capsule networks for improved multi-scale context aggregation, which captures subtle textural variations, (2) a Channel-wise Attention Mechanism to focus on the most relevant regions of interest, minimizing the impact of irrelevant features, and (3) Distanced LSTM layers to model temporal dependencies across sequential CT scans, providing insights into disease progression. Through comprehensive experiments on the LC25000 dataset, DC-CAD achieves 99.52% accuracy, significantly outperforming baseline models such as standard Capsule networks and Convolutional Neural networks. The model also reduces the error rate to 0.48%, demonstrating substantial improvements in diagnostic performance, including increased accuracy, sensitivity, and specificity. These results establish DC-CAD as a powerful and reliable tool for automated lung disease diagnosis, with significant potential to enhance clinical workflow by reducing radiologists’ workload through its interpretability and efficiency. Moving forward, we plan to extend the model to handle multi-modal data and investigate advanced attention mechanisms to further improve diagnostic accuracy and generalizability. © Th

关键词： Channel-wise attention mechanisms CT scans Dilated capsule networks Lung diseases Medical imaging

来源：评论

学校读者我要写书评

暂无评论

Efficient Batch Verification and Privacy-Preserving data Aggregation Scheme in V2G networks

引用

IEEE Transactions on Vehicular Technology 2025年

作者： Wang, Congcong Wang, Chen Shen, Jian Vasilakos, Athanasios V. Wang, Bin Wang, Weizheng Department of Software Engineering Nanjing University of Information Science and Technology Nanjing210044 China the Zhejiang Key Laboratory of Digital Fashion and Data Governance the Zhejiang Provincial International Cooperation Base for Science and Technology on Cloud Computing Security and Data Aggregation Zhejiang Sci-Tech University Hangzhou310018 China Zhejiang Provincial Innovation Center of Advanced Textile Technology Shaoxing312000 China College of Computer Science and Information Technology IAU Saudi Arabia Grimstad4879 Norway Network and Data Security Hangzhou310053 China Computer Science Department City University of Hong Kong Hong Kong the Division of Computer Science University of Aizu Fukushima965–8580 Japan

In recent years, against the backdrop of smart grids, vehicle-to-grid (V2G) interaction has become a hot topic. With the increasing number of new energy vehicles, handling massive amounts of electricity transaction data has become an urgent problem in need of resolution. Ensuring the security of the vast quantities of electricity transaction data has emerged as a significant challenge. Therefore, two data aggregation schemes are proposed in this paper, both supporting batch verification and privacy protection in centralized and distributed scenarios, respectively. Firstly, an improved Paillier cryptosystem is utilized to achieve lightweight encryption in the proposed schemes. In addition, a novel distributed data aggregation structure that combines a large number of geographically distributed fog nodes with centralized cloud servers is introduced. This structure not only meets the requirements of lightweight encryption and resistance against single point failures but also reduces the communication complexity of distributed data aggregation to O(n). According to the security proofs and experimental analysis, it can be demonstrated that both proposed schemes are effective and secure, meeting the lightweight requirements of modern smart grid systems. © 2025 IEEE. All rights reserved,

关键词： data aggregation

来源：评论

学校读者我要写书评

暂无评论

A Lightweight Collaborative Target Recognition Method for Unmanned Aerial Vehicle Cluster

引用

IEEE Internet of Things Journal 2025年

作者： Cao, Mingsheng Yin, Yiyang Zhang, Li Li, Weizhuang Liu, Ziqiang Zhu, Ruizheng Zhao, Yang University of Electronic Science and Technology of China Network and Data Security Key Laboratory of Sichuan Province School of Information and Software Engineering Chengdu611731 China University of Electronic Science and Technology of China ChengDu Research Institute Chengdu610054 China Nanjing University of Aeronautics and Astronautics School of Aerospace Engineering Nanjing210001 China

This paper aims to explore a reliable target recognition technique for unmanned aerial vehicle (UAV) clusters, and proposes a lightweight collaborative target recognition methods based on multiple viewpoints. In the proposed method, the UAVs are divided into the leaf node UAVs and the head node UAVs. Leaf node UAVs are used for multi-view image acquisition and image feature extraction by utilizing a lightweight feature extraction model. The head node UAVs realize efficient feature fusion from the collected image features by using graph convolutional network and graph coarsening techniques. Based on the above lightweight technologies, the proposed method can realize the efficient and accurate target recognition and reduce the demand for limited computing resources and communication resources in UAV clusters. Experimental results show that, compared with existing multi-view object recognition method, the proposed method has less computing cost and communication overhead while ensuring reliable recognition accuracy. © 2014 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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：