检索结果-内蒙古大学图书馆

SSRN 2025年

作者： Fan, Lin Ding, Xiaojia Wang, Zhongmin Wang, Hai Zhang, Rong School of Computer Science and Technology Xi’an University of Posts and Telecommunications Shaanxi Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi’an710121 China Xi’an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi’an710121 China The School of Information Science and Technology Northwest University Shaanxi Xi’an710121 China

3D medical image segmentation is vital for disease diagnosis and effective treatment strategies. Despite the advancements in Convolutional Neural networks (CNN), their fixed receptive fields constrain global context modeling, leading to suboptimal performance, particularly with complex shapes and multi-scale variations. Transformer enhances the global modeling capability through self-attention mechanism. However, challenges persist in capturing fine-grained local features and extracting edge features. To overcome these issues, In this study, a dual-path encoder is proposed, which consists of a 3D PDC and a Swin Transformer and is combined with a CNN for the decoder. The 3D PDC module extracts local features by calculating the differences between neighboring pixels. To improve the ability of capturing edge structures, three 3D PDC variants (3D CPDC, 3D APDC, and 3D RPDC) are proposed to optimize the processing ability of complex edges, multi-directional edges, and multi-scale structures, respectively. After performance evaluation, the selected CARV combinations (3D CPDC, 3D APDC, 3D RPDC, and 3D ordinary convolution) achieve a compromise between high segmentation accuracy and low computational cost. The Swin Transformer encoder captures global context using a hierarchical shift-window mechanism, while the CNN decoder fuses features progressively to produce precise pixel-level segmentation. The proposed method exceeds the performance of existing state-of-the-art techniques, as shown by experiments conducted on the BTCV, FLARE21, and AMOS22 datasets. © 2025, The Authors. All rights reserved.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

THC-DL: Three-Party Homomorphic Computing Delegation Learning for Secure Optimization

引用

IEEE Internet of Things Journal 2025年

作者： Yang, Ze Tian, Youliang Xiong, Jinbo Niu, Kun Li, Mengqian Zhang, Xinyu Ma, Jianfeng Guizhou University Guizhou Provincial Key Laboratory of Cryptography and Blockchain Technology College of Computer Science and Technology Guiyang550025 China Guizhou University College of Big Data and Information Engineering Guiyang550025 China Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fuzhou350117 China Xidian University State Key Laboratory of Integrated Services Network Xi’an710071 China Xidian University State Key Laboratory of Integrated Services Network School of Cyber Engineering Xi’an710071 China

Delegation Learning(DL) flourishes data sharing, enabling agents to delegate data to the cloud for model training. To preserve privacy, homomorphic encryption (HE) offers an effective solution for privacy-preserving machine learning (PPML) in delegation learning, yet faces critical challenges in functionality (non-linear activation support), practicality (ciphertext blow-up from iterative computations), and security (data leakage risks caused by public knowledge of the mathematical principles applied in model training). To tackle these challenges, we propose THC-DL, a three-party HE framework addressing these challenges holistically for the first time. We elaborately design the ciphertext secure comparison (DL-CSC) protocol to satisfy secure comparison with private inputs, enabling efficient non-linear operations with O(1) communication complexity that reduces runtime to 12.5% of the DGK (Dolev-Greensmith-Kent protocol, the well-known comparison protocol). Second, we construct a Truncation-Mapping (Tru-Map) scheme, a mechanism that transforms input data by truncating and mapping it into a domain that facilitates more efficient processing, and the addition of truncated mapped data to resolve ciphertext blow-up by adaptively scaling ciphertexts during iterative training, ensuring correctness. Third, we formalize data leakage risks in HE-based quadratic convex optimization (standard in ML) and apply THC-DL to construct a secure optimization scheme. Theoretical analysis confirms THC-DL’s resilience against input recovery attacks, even when adversaries exploit public model parameters. Experiments on a real-world platform validate DL-CSC’s efficiency and scalability while reducing the computational complexity and communication complexity from O(n) to O(1) where n denotes the length of the input bits. © 2014 IEEE.

关键词： Convex optimization

来源：评论

学校读者我要写书评

暂无评论

Enhancing Speech Emotion Recognition With Conditional Emotion Feature Diffusion and Progressive Interleaved Learning Strategy

IEEE Transactions on Audio, Speech and Language Processing

引用

IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 1787-1800页

作者： Yang Liu Xin Chen Zhichao Peng Yongwei Li Xingfeng Li Peng Song Masashi Unoki Zhen Zhao School of Information Science and Technology Qingdao University of Science and Technology Qingdao China School of Information Hunan University of Humanities Science and Technology Loudi China CAS Key Laboratory of Behavioral Science Institute of Psychology Chinese Academy of Sciences Beijing China Faculty of Data Science City University of Macau Macau China School of Computer and Control Engineering Yantai University Yantai China School of Information Science Japan Advanced Institute of Science and Technology Nomi Japan

Speech emotion recognition (SER) aims to identify the speaker's emotional states in specific utterances accurately. However, existing methods still face feature confusion when attempting to recognize certain emotions because traditional acoustic feature extraction methods fail to capture dynamic emotional changes, blurring emotional boundaries. Additionally, existing classification networks (CNs) are constrained by fixed learning strategies, hindering their ability to capture subtle emotional nuances and resulting in label confusion. To address these two issues, we introduce 3D multiresolution modulation filtered cochleogram (MMCG) features by computing the deltas and delta-deltas of MMCG features to enhance the dynamic emotional changes and produce distinct emotional boundaries. We then customize a conditional emotion feature diffusion (CEFD) module, which progressively diffuses features based on emotional context to retain emotional nuances effectively and reduce reliance on conditioned information. In addition, a confidence filtering module is used to filter diffused features based on confidence-based posterior probabilities to ensure enhanced feature discrimination. We design a flexible training strategy named the progressive interleaved learning strategy (PILS) to learn further complex emotional nuances, which consists of two alternating stages: fine-tuning the CN parameters and supervising the CEFD output. Testing on the IEMOCAP, casIA, and EMODB corpora demonstrates significant performance improvements in SER.

关键词： Feature extraction Emotion recognition Training Three-dimensional displays Diffusion models Speech enhancement Electronic mail Speech recognition Semantics Face recognition

来源：评论

学校读者我要写书评

暂无评论

Real-Time Proportional-Integral-Derivative(PID)Tuning Based on Back Propagation(BP)Neural network for Intelligent Vehicle Motion Control

引用

Computers, Materials & Continua 2025年第5期83卷 2375-2401页

作者： Liang Zhou Qiyao Hu Xianlin Peng Qianlong Liu School of Information Science and Technology Northwest UniversityXi’an710127China State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services Northwest UniversityXi’an710127China Generative Artificial Intelligence and Mixed Reality Key Laboratory of Higher Education Institutions in Shaanxi Province Xi’an710127China Shaanxi Silk Road Cultural Heritage Digital Protection and Inheritance Collaborative Innovation Center Xi’an710127China School of Art Northwest UniversityXi’an710127China Network and Data Center Northwest UniversityXi’an710127China

Over 1.3 million people die annually in traffic accidents,and this tragic fact highlights the urgent need to enhance the intelligence of traffic safety and control *** modern industrial and technological applications and collaborative edge intelligence,control systems are crucial for ensuring efficiency and ***,deficiencies in these systems can lead to significant operational *** paper uses edge intelligence to address the challenges of achieving target speeds and improving efficiency in vehicle control,particularly the limitations of traditional Proportional-Integral-Derivative(PID)controllers inmanaging nonlinear and time-varying dynamics,such as varying road conditions and vehicle behavior,which often result in substantial discrepancies between desired and actual speeds,as well as inefficiencies due to manual parameter *** paper uses edge intelligence to propose a novel PID control algorithm that integrates Backpropagation(BP)neural networks to enhance robustness and *** BP neural network is first trained to capture the nonlinear dynamic characteristics of the *** network is then combined with the PID controller to forma hybrid control *** output layer of the neural network directly adjusts the PIDparameters(k_(p),k_(i),k_(d)),optimizing performance for specific driving scenarios through self-learning and weight *** experiments demonstrate that our BP neural network-based PID design significantly outperforms traditional methods,with the response time for acceleration from 0 to 1 m/s improved from 0.25 s to just 0.065 ***,real-world tests on an intelligent vehicle show its ability to make timely adjustments in response to complex road conditions,ensuring consistent speed maintenance and enhancing overall system performance.

关键词： PID control backpropagation neural network hybrid control nonlinear dynamic processes edge intelligence

来源：评论

学校读者我要写书评

暂无评论

Provably Secure Authenticated key-Management Mechanism for e-Healthcare Environment

引用

IEEE Internet of Things Journal 2025年

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Ghaffar, Zahid Xie, Yong Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Douliu64002 Taiwan Qinghai Institute of Technology School of Computer and Information Science China Qinghai Provincial Laboratory for Intelligent Computing and Application China Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan250353 China

The Internet of Things (IoT) is rapidly permeating all aspects of human life, involving a network of devices that share sensitive data. A notable application is the e-healthcare systems, which employ connected sensors, medical servers, and wearable devices. However, the public nature of communication in e-healthcare systems poses challenges such as security, privacy, and authentication of participating entities. Recently, many authentication protocols have been introduced to address these challenges. However, most of these protocols remain vulnerable to various security attacks, including device or medical server impersonation, denial of service, physical or cloning, and de-synchronization attacks. Therefore, we introduce an authenticated key-management protocol utilizing hash functions and Cipher-Block Chaining-Advanced Encryption Standard encryption (CBC-AES) encryption. The proposed protocol also employs the Physical Unclonable Function (PUF), which makes it more robust and efficient in resisting physical or cloning attacks. Additionally, the proposed scheme resists various security threats, including impersonation, session key leakage, ephemeral secret leakage, and de-synchronization attacks. We analyze the scheme’s security and reliability through formal and informal analysis. The informal analysis demonstrates that the scheme encompasses crucial security features, while the formal analysis substantiates. Moreover, performance analysis of the proposed protocol with various competing results indicates that our protocol achieves an average reduction in communication and computation overheads by 36.03.% and 41.79%, respectively. © 2014 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

CPSNet: Comprehensive Enhancement Representation for Polyp Segmentation Task

CPSNet: Comprehensive Enhancement Representation for Polyp S...

引用

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

作者： Jiati Cai Xiaogang Liu Hongjie Yang Yi Ding Ting Zhong Zhen Qin Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu China Department of Gastroenterology and Hepatology University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Accurately segmenting polyp regions in colonoscopy images is crucial for the diagnosis and intervention of colorectal cancer. However, the task of polyp segmentation remains challenging due to the diverse size and shape variations among polyps, their extreme similarity to the background, and frequent rotation of the lens, which further increases the diversity in polyp presentation. To address these challenges effectively, we propose a comprehensive polyp segmentation network (CPSNet). Specifically, we introduce a Comprehensive Spatial Feature Extraction Module (CFEM) that progressively and densely integrates features while forming receptive windows with various shapes. This enables enhanced perception of polyps at manifold sizes and shapes. Additionally, we propose a Fine-grained Region Strengthen Module (FGSM) to supplement uncertain areas around polyps by mitigating background noise interference. In terms of training strategy, we further introduce a Rotation-augmented Constrained Loss (RC Loss), which reinforces consistency constraints on polyp images under multiple rotation angles. Qualitative and quantitative experiments conducted on five public datasets demonstrate both the plug-and-play capability of CFEM as well as the effectiveness and excellence achieved by CPSNet.

关键词： Training Manifolds Image segmentation Shape Video sequences Morphology Speech enhancement Signal processing Feature extraction Lenses

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

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

Graph Representation Learning for Microarchitecture Design Space Exploration 23

Graph Representation Learning for Microarchitecture Design S...

引用

Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Xiaoling Yi Jialin Lu Xiankui Xiong Dong Xu Li Shang Fan Yang State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China and School of Microelectronics Fudan University Shanghai China ZTE Corporation China and State Key Laboratory of Mobile Network and Mobile Multimedia Technology China State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China and China and Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9798350323481

Design optimization of modern microprocessors is a complex task due to the exponential growth of the design space. This work presents GRL-DSE, an automatic microarchitecture search framework based on graph embeddings. GRL-DSE uses graph representation learning to build a compact and continuous embedding space. Multi-objective Bayesian optimization using an ensemble surrogate model conducts microarchitecture design space exploration in the graph embedding space to efficiently and holistically optimize performance-power-area (PPA) objectives. Experimental studies on RISC-V BOOM show that GRL-DSE outperforms previous techniques by 74.59% on Pareto front quality and outperforms manual designs in terms of PPA.

关键词：

来源：评论

学校读者我要写书评

暂无评论

DAPO-QAOA: An algorithm for solving combinatorial optimization problems by dynamically constructing phase operators

arXiv

引用

arXiv 2025年

作者： Wang, Yukun Li, ZeYang Wan, Linchun Beijing Key Laboratory of Petroleum Data Mining China University of Petroleum Beijing102249 China State Key Lab of Processors Institute of Computing Technology CAS Beijing100190 China School of Computer and Information Science Southwest University Chongqing400715 China

The Quantum Approximate Optimization Algorithm (QAOA) is a well-known hybrid quantum-classical algorithm for combinatorial optimization problems. Improving QAOA involves enhancing its approximation ratio while addressing practical constraints of Noisy Intermediate Scale Quantum (NISQ) devices, such as minimizing the number of two-qubit gates and reducing circuit depth. Although existing research has optimized designs for phase and mixer operators to improve performance, challenges remain, particularly concerning the excessive use of two-qubit gates in the construction of phase operators. To address these issues, we introduce a Dynamic Adaptive Phase Operator (DAPO) algorithm, which dynamically constructs phase operators based on the output of previous layers and neighborhood search approach, optimizing the problem Hamiltonian more efficiently. By using solutions generated by QAOA itself to simplify the problem Hamiltonian at each layer, the algorithm captures the problem’s structural properties more effectively, progressively steering the solution closer to the optimal target. Experimental results on MaxCut and NAE3SAT problems show that DAPO achieves higher approximation ratios and significantly reduces two-qubit RZZ gates, especially in dense graphs. Compared to vanilla QAOA, DAPO uses only 66% of RZZ gates at the same depth while delivering better results, demonstrating its potential for efficient combinatorial optimization in the NISQ era. © 2025, CC BY.

关键词： Hamiltonians

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：