检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Li, Xiang Song, Jian Zhao, Zhigang Wang, Chunxiao Song, Dawei Hu, Bin 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 Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Institute of Engineering Medicine Beijing Institute of Technology Beijing China

This study introduces a novel Supervised Info-enhanced Contrastive Learning framework for EEG based Emotion Recognition (SI-CLEER). SI-CLEER employs multi-granularity contrastive learning to create robust EEG contextual representations, potentially improving emotion recognition effectiveness. Unlike existing methods solely guided by classification loss, we propose a joint learning model combining self-supervised contrastive learning loss and supervised classification loss. This model optimizes both loss functions, capturing subtle EEG signal differences specific to emotion detection. Extensive experiments demonstrate SI-CLEER's robustness and superior accuracy on the SEED dataset compared to state-of-the-art methods. Furthermore, we analyze electrode performance, highlighting the significance of central frontal and temporal brain region EEGs in emotion detection. This study offers an universally applicable approach with potential benefits for diverse EEG classification tasks. © 2024, CC0.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Parallel Pattern Matching over Brotli Compressed Network Traffic

Parallel Pattern Matching over Brotli Compressed Network Tra...

引用

IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Xiuwen Sun Guangzheng Zhang Di Wu Qingying Yu Jie Cui Hong Zhong School of Computer Science and Technology Anhui University Anhui Provincial Key Laboratory of Network and Information Security Anhui Normal Unviersity

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Pattern matching is a crucial technique for network traffic detection applications. As a fundamental computation model used by pattern matching, the finite state automata execute sequential matching due to the state dependence among transitions. Meanwhile, most services tend to compress their data to improve transmission or storage efficiency. The increased compressed data challenges the straightforward method of matching the whole decompressed data and incurs data dependence among the compression encodings. The related approaches either leverage techniques to break the state dependence of matching uncompressed data or accelerate matching compressed data in a single-threaded manner without considering the state and data dependence. None of them can perform parallel matching over compressed data. This paper provides PETALS, a parallel pattern matching method over Brotli compressed network traffic. PETALS partitions the original compressed traffic into fixed- length blocks for parallel matching and patches the broken compression encodings crossing blocks to break the data dependence. Then, it merges the compressed traffic matching method into path fusion, an enumerative parallelization of finite state automata, to present parallel matching over compressed traffic. Evaluation using real-world network traffic and regular expressions shows that PETALS can raise the speedup from 1.53x to 3.53x of the state-of-the-art parallelization schemes on a 56-cores machine.

关键词： Privacy Instruction sets Computational modeling Automata Telecommunication traffic Programming Parallel processing

来源：评论

学校读者我要写书评

暂无评论

Non-symmetrical Sibling-Stream Network with Adaptive Positional Encoding for Automatic Medical Report Generation

Non-symmetrical Sibling-Stream Network with Adaptive Positio...

引用

Intelligent Medicine and Image Processing (IMIP), International Conference on

作者： Jiayu Si Haifeng Zhao Lili Huang Zhengzheng Tu Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei Anhui China

Automatic medical report generation technology aims to alleviate the workload of radiologists in writing reports and avoid possible misdiagnosis and missed diagnosis. Recent solutions in medical report generation are mainly based on two mainstream architectures: convolutional neural networks (CNNs) and transformer. However, these two architectures are insufficient for the medical report generation task due to the following three reasons. First, CNN lacks the global context information due to the constrained receptive field of convolution. Second, the complete image is cut into small patches before being fed to the transformer, which leads to the damage to the local details. Third, position encoding is critical to compensate the lost position information caused by serialized input, but the conventional position encoding (e.g., absolute positional encoding) inadequately encodes position information. Therefore, we propose a novel Non-Symmetrical Sibling-Stream Network with Adaptive Positional Encoding which includes three modules: sibling-stream encoder (SSE) module, feature interaction (FI) module, and memory-driven decoder (MDD) module. In detail, in order to cope with respective drawbacks of both architectures, we first propose sibling-stream encoder (SSE) module which combines the global context by transformer and local details (e.g., texture and edge) by CNN. Concurrently, we design a new encoding method named adaptive positional encoding (APE), which can effectively encode the absolute position information within the patch and the relative position information between patches, so as to improve the accuracy of medical report. Then, the feature interaction (FI) module is designed to enhance the image feature representation by interacting the information of sibling-stream. Finally, we introduce a memory-driven decoder to implicitly model and memorize similar patterns in different medical reports during the generation process. Extensive experiments on two public data

关键词：

来源：评论

学校读者我要写书评

暂无评论

Adaptive Loose Optimization for Robust Question Answering

arXiv

引用

arXiv 2023年

作者： Ma, Jie Wang, Pinghui Wang, Zewei Kong, Dechen Hu, Min Han, Ting Liu, Jun The Ministry of Education of Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China The Ministry of Education of Key Laboratory for Intelligent Networks and Network Security School of Automation Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China The China Mobile Research Institute China The Shannxi Provincial Key Laboratory of Big Data Knowledge Engineering School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China

Question answering methods are well-known for leveraging data bias, such as the language prior in visual question answering and the position bias in machine reading comprehension (extractive question answering). Current debiasing methods often come at the cost of significant in-distribution performance to achieve favorable out-of-distribution generalization ability, while non-debiasing methods sacrifice a considerable amount of out-of-distribution performance in order to obtain high in-distribution performance. Therefore, it is challenging for them to deal with the complicated changing real-world situations. In this paper, we propose a simple yet effective novel loss function with adaptive loose optimization, which seeks to make the best of both worlds for question answering. Our main technical contribution is to reduce the loss adaptively according to the ratio between the previous and the current optimization state on mini-batch training data. This loose optimization can be used to prevent non-debiasing methods from overlearning data bias while enabling debiasing methods to maintain slight bias learning. Experiments on the visual question answering datasets, including VQA v2, VQA-CP v1, VQA-CP v2, GQA-OOD, and the extractive question answering dataset SQuAD demonstrate that our approach enables QA methods to obtain state-of-the-art in- and out-of-distribution performance in most cases. The source code has been released publicly in https://***/reml-group/ALO. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

BEW-YOLO: An Improved Method for PCB Defect Detection Based on YOLOv7

BEW-YOLO: An Improved Method for PCB Defect Detection Based ...

引用

International Conference on Parallel and Distributed Systems (ICPADS)

作者： Zhiyao Li Aimin Li Yuechen Zhang Xiaotong Kong Wenqiang Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

The PCB defect object size is small and the detection process is susceptible to background interference, usually have the problem of missed and false detection. In order to solve the above problems, an improved method based on YOLOv7 is proposed in this paper. Firstly, the bi-level routing attention (BRA) has been added to the header of the original YOLOv7 model to capture global dependencies and ensure the accuracy of small object detection and localization. Secondly, the explicit visual center (EVC) block is introduced before the fusion of mid-level features and high-level features to capture the global remote dependencies of top-level features, extract the features in local corner regions, achieve a comprehensive feature representation. Finally, the loss function is improved by changing the loss function of the original model to Wise-IoU, which allows our model to focus more on ordinary-quality anchor boxes and improve the performance of the detector while decreasing the competitiveness of anchor boxes for high-quality samples and reducing the influence of low-quality samples on the detection results. The experimental results show that the improved model improves 5% in AP and 1.9% in both AR and mAP over the original YOLOv7 model. Meanwhile, comparison experiments are conducted on the data-enhanced PCB dataset, which proves the superiority of our model over other state-of-the-art models.

关键词：

来源：评论

学校读者我要写书评

暂无评论

VFGCN: A Vertical Federated Learning Framework With Privacy Preserving for Graph Convolutional Network

引用

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Wang, Gang Li, Qingming Liu, Ximeng Yan, Xiaoran Dong, Qingkuan Wu, Huiwen Kong, Xiangjie Zhou, Li Zhejiang Lab HangZhou310000 China Zhejiang University College of Computer Science and Technology Hangzhou310058 China Fuzhou University Key Laboratory of Information Security of Network Systems College of Computer and Big Data Fuzhou350108 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China Zhejiang University of Technology College of Computer Science and Technology Hangzhou310023 China Dalian University of Technology School of Software Dalian116620 China

Due to the robust representational capabilities of graph data, employing graph neural networks for its processing has demonstrated superior performance over conventional deep learning algorithms. Graph data encompasses abundant features and structural information;however, its large-scale collection is often challenging in practice. This difficulty arises because data predominantly exists in isolated compartments, making it arduous to harmonize information across various organizations or to enable multiple organizations to collaborate effectively while safeguarding local data privacy. In light of an extreme data distribution scenario, where each client possesses distinct nodes with partially overlapping segments yet divergent data features, we introduce a dual-cloud server architecture. This framework encompasses the design of four secure subprotocols: ReEnc (secure re-encryption), SecPSI (secure outsourcing of PSI), SecWeight (secure weight calculation), and SecAgg (secure aggregation). Together, these components facilitate a vertical federated learning framework for graph convolutional networks, ensuring privacy preservation. We provide a security proof for the entire system and extensive evaluation on three benchmark datasets (Cora, Citeseer, and Pubmed) illustrates that our Vertical Federated Graph Convolutional Network (VFGCN) surpasses existing privacy-preserving methodologies. © 2004-2012 IEEE.

关键词： Privacy by design

来源：评论

学校读者我要写书评

暂无评论

Few-shot Face Image Translation via GAN Prior Distillation

arXiv

引用

arXiv 2023年

作者： Zhao, Ruoyu Zhu, Mingrui Wang, Xiaoyu Wang, Nannan State Key Laboratory of Integrated Services Networks Xidian University Xi’an China School of Computer Science and Technology University of Science and Technology of China China

Face image translation has made notable progress in recent years. However, when training on limited data, the performance of existing approaches significantly declines. Although some studies have attempted to tackle this problem, they either failed to achieve the few-shot setting (less than 10) or can only get suboptimal results. In this paper, we propose GAN Prior Distillation (GPD) to enable effective few-shot face image translation. GPD contains two models: a teacher network with GAN Prior and a student network that fulfills end-to-end translation. Specifically, we adapt the teacher network trained on large-scale data in the source domain to the target domain with only a few samples, where it can learn the target domain’s knowledge. Then, we can achieve few-shot augmentation by generating source domain and target domain images simultaneously with the same latent codes. We propose an anchor-based knowledge distillation module that can fully use the difference between the training and the augmented data to distill the knowledge of the teacher network into the student network. The trained student network achieves excellent generalization performance with the absorption of additional knowledge. Qualitative and quantitative experiments demonstrate that our method achieves superior results than state-of-the-art approaches in a few-shot setting. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

来源：评论

学校读者我要写书评

暂无评论

Dual-Domain Attention Based Adaptive Graph Convolutional Network for EEG Emotion Recognition

Dual-Domain Attention Based Adaptive Graph Convolutional Net...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Xu, Tie Zhang, Tong Chen, Bianna Chen, C.L. Philip All authors are with the Guangdong Provincial Key Laboratory of AI Large Model and Intelligent Cognition the School of Computer Science and Engineering South China University of Technology Guangzhou 510006 China and are with the Pazhou Lab Guangzhou 510335 China and are with Engineering Research Center of the Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China.

ISBN: (纸本)9781665410205

The asymmetry of emotional responses is observed in electroencephalogram (EEG) of different frequency bands across various spatial brain regions in neuroscience research. Many prior works have primarily emphasized the dependencies among channels in the spatial domain, neglecting the dynamic interaction of EEG in both spatial and frequency domains, which may limit the performance of EEG emotion recognition. To address these issues, we propose the dual-domain attention based adaptive graph convolutional network (DDA-AGCN) for EEG emotion recognition. Specifically, we propose the lightweight dual-domain attention mechanism (DDA) based on random vector similarity measurement and the squeezeexcitation technique to capture important characteristics in the channel and frequency domain respectively. Furthermore, the adaptive graph convolutional network (AGCN) is utilized to adaptively filter and refine low signal-to-noise ratio EEG data, while also learning the dynamic connectivity patterns among important EEG channels and extracting higher-level abstract features for emotion recognition tasks. To validate the effectiveness of the proposed method, experimental comparisons were conducted on SEED, SEED-IV, and MPED. The experimental results show that our method achieves highly competitive classification performance compared to existing methods. Moreover, under fair comparison, the DDA demonstrates better performance and computational efficiency than self-attention. © 2024 IEEE.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Self‑Generated Buried Submicrocavities for High‑Performance Near‑Infrared Perovskite Light‑Emitting Diode

引用

Nano-Micro Letters 2023年第8期15卷 355-367页

作者： Jiong Li Chenghao Duan Qianpeng Zhang Chang Chen Qiaoyun Wen Minchao Qin Christopher C.S.Chan Shibing Zou Jianwu Wei Zuo Xiao Chuantian Zuo Xinhui Lu Kam Sing Wong Zhiyong Fan keyou Yan School of Environment and Energy State Key Lab of Luminescent Materials and DevicesGuangdong Provincial Key Laboratory of Solid Wastes Pollution Control and RecyclingSouth China University of TechnologyGuangzhou 510000People’s Republic of China Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongPeople’s Republic of China Department of Physics The Chinese University of Hong KongShatinHong KongPeople’s Republic of China Department of Physics The Hong Kong University of Science and TechnologyClear Water BayKowloon 999077Hong KongPeople’s Republic of China School of Chemistry and Chemical Engineering Guangxi UniversityNanning 530004People’s Republic of China Center for Excellence in Nanoscience(CAS) Key Laboratory of Nanosystem and Hierarchical Fabrication(CAS)National Center for Nanoscience and TechnologyBeijing 100190People’s Republic of China

ABSTRACT Embedding submicrocavities is an effective approach to improve the light out-coupling efficiency(LOCE)for planar perovskite light-emitting diodes(PeLEDs).In this work,we employ phenethylammonium iodide(PEAI)to trigger the Ostwald ripening for the downward recrystallization of perovskite,resulting in spontaneous formation of buried submicrocavities as light output *** simulation suggests the buried submicrocavities can improve the LOCE from 26.8 to 36.2%for near-infrared ***,PeLED yields peak external quantum efficiency(EQE)increasing from 17.3%at current density of 114 mA cm^(−2)to 25.5%at current density of 109 mA cm^(−2)and a radiance increasing from 109 to 487 W sr^(−1)m^(−2)with low *** turn-on voltage decreased from 1.25 to 1.15 V at 0.1 W sr^(−1)m^(−2).Besides,downward recrystallization process slightly reduces the trap density from 8.90×10^(15)to 7.27×10^(15)cm^(−3).This work provides a self-assembly method to integrate buried output coupler for boosting the performance of PeLEDs.

关键词： Perovskite light-emitting diodes Downward recrystallization Buried submicrocavities Light out-coupling efficiency Radiative recombination

来源：评论

学校读者我要写书评

暂无评论

Symbiosis Rather Than Aggregation: Towards Generalized Federated Learning via Model Symbiosis

引用

IEEE Internet of Things Journal 2025年

作者： Liu, Yuange Liu, Yuru Zhang, Weishan Zheng, Chaoqun Jiang, Wei Luo, Daobin Qiao, Qiao Meng, Lingzhao Zhao, Hongwei Yang, Su College of Computer Science and Technology Qingdao266580 China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan250014 China Information Science Academy of China Electronics Technology Group Corporation Beijing100036 China Fudan University School of Computer Science Shanghai200433 China

Federated Learning (FL) faces significant challenges in scenarios with non-IID (non-independent and identically distributed) data distributions across participating clients. Traditional aggregation-based approaches often struggle with the inherent misalignment between local and global optimization objectives, leadingwhich leads to gradient divergence and suboptimal generalization performance. This paper proposes a novel FL framework that replaces conventional aggregation with a biologically inspired model symbiosis approach called FedSym, which employs a dual-level symbiotic mechanism. Ectosymbiosis performs coarse-grained hierarchical parameter recombinations through random layer-wise model combination, while endosymbiosis enables fine-grained intra-layer parameter fusion through weighted averaging, collectively steering model updates towards flatter loss landscapes. Our theoretical analysis demonstrates that FedSym's convergence rate is O(1T) under non-IID conditions, which matches the convergence properties of FedAvg. Extensive experimentsevaluations across multiple datasets and model architectures show that FedSym achieves substantial improvements over state-of-the-art FL methods, particularly in challenging scenarios with high data heterogeneity, and demonstrates robust performance across varying numbers of participating clients and federation scales. © 2014 IEEE.

关键词： Federated 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：