检索结果-内蒙古大学图书馆

4th International Conference on Blockchain Technology and information Security, ICBCTIS 2024

作者： Li, Yan Zhang, Guofeng Feng, Bin Yang, Shihao Zhao, Chuan Wang, Peng Shen, Cong Zhang, Yanan School of Information Science and Engineering University of Jinan Shandong Jinan250022 China School of Information Science and Technology Taishan University Shandong Taian271000 China Shandong Provincial Key Laboratory of Network-based Intelligent Computing Shandong Jinan250022 China College of Electronic Information Shandong Xiandai University Shandong Jinan250104 China

ISBN: (纸本)9798331528324

Medical data sharing can provide secure, trust-worthy and high-quality health care services to a wide range of patients and healthcare providers, but the data sharing process faces the risk of identity and information security leakage. This study begins by examining access control technologies for health data sharing, emphasizing the analysis of blockchain-based solutions for securing various types of medical data. It reviews and assesses the current state of these technologies across four dimensions-attributes, signatures, roles, and multi-smart contracts-and further develops a blockchain-based system for controlling access to medical data. Finally, it summarizes and analyzes the challenges faced by medical data sharing, and provides an outlook on the future development of the technology. This study is of great sig- nificance in guiding the deep integration of blockchain technology and medical digitization, and guaranteeing the secu-rity of medical data throughout its life cycle. © 2024 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

A New Method for Predefined-Time Synchronization Control of Memristive Cohen-Grossberg Neural networks

A New Method for Predefined-Time Synchronization Control of ...

引用

2023 IEEE International Conference on Memristive computing and Applications, ICMCA 2023

作者： Liu, Aidi Yu, Wenyue Huang, Lili Guo, Jian Lei, Tengfei Zhao, Hui School of Computer and Information Engineering Qilu Institute of Technology Jinan China School of Computer and Information Engineering Qilu Institute of Technology China Collaborative Innovation Center of Memristive Computing Application School of Computer and Information Engineering Qilu Institute of Technology China School of Information Science and Engineering University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China

ISBN: (纸本)9798350316230

This article mainly conducts a series of research on the synchronization control of Cohen-Grossberg neural networks. Firstly, a memristive Cohen-Grossberg neural network model with time-varying delay was constructed, and an equivalent system was obtained by combining inverse function theory, differential inclusion, and set-valued mapping. Secondly, a new predefined-time stability theorem was proposed, and sufficient conditions were obtained to ensure that the drive-response system achieves predefined-time synchronization by designing appropriate control strategy. The simulation results show that the stability time of the error system can be adjusted by the user, verifying the correctness and of the theory. © 2023 IEEE.

关键词： Neural network models

来源：评论

学校读者我要写书评

暂无评论

Understanding the Influence of Extremely High-Degree Nodes on Graph Anomaly Detection 27th

Understanding the Influence of Extremely High-Degree Nodes...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Sun, Xun Xiao, Xi Wang, Zhaoguo Hu, Guangwu Jiang, Xuhui Zhang, Bin Li, Hao Shenzhen International Graduate School Tsinghua University Shenzhen China Ministry of Education Chengdu China Shenzhen China Shenzhen Institute of Information Technology Shenzhen China Institute of Computing Technology CAS Beijing China Peng Cheng Laboratory Shenzhen China National Key Laboratory of Advanced Communication Network Shijiazhuang China

ISBN: (纸本)9783031781827

Graph Anomaly Detection (GAD) has attracted considerable attention for its potential in detecting anomalies. However, an overlooked issue in prior research is the presence of extremely high-degree node, which can introduce noise into GAD, escalate computational costs, and intensify the problem of over-smoothing. To tackle this issue, this paper first presents a novel graph anomaly dataset, NFTGraph, characterized by a notable presence of extremely high-degree nodes. A series of experiments on this dataset sheds light on the influence of such nodes on GAD. Moreover, we introduce a novel model, the Super Node-Aware Graph Neural network (SNGNN), designed to mitigate the noise emanating from extremely high-degree nodes. Experimental results demonstrate that SNGNN outperforms extant models, achieving an average improvement of over 2% in the Area Under the ROC Curve (AUROC), and effectively reducing noise. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Cross-chain Architecture of Blockchain Integrating Notary Mechanism and Relay-chain Technology 4

Cross-chain Architecture of Blockchain Integrating Notary Me...

引用

4th International Conference on Blockchain Technology and information Security, ICBCTIS 2024

作者： Yang, ShiHao Zhang, GuoFeng Feng, Bin Li, Yan Zhao, Chuan Wang, Peng Shen, Cong Zhang, YaNan School of Information Science and Engineering University of Jinan Shandong Jinan250022 China School of Information Science and Technology Taishan University Shandong Taian271000 China Shandong Provincial Key Laboratory of Network-based Intelligent Computing Shandong Jinan250022 China College of Electronic Information Shandong Xiandai University Shandong Jinan250104 China

ISBN: (纸本)9798331528324

Due to the independent nature of Blockchain systems in different organizations, data sharing and asset transfer between these Blockchain systems face extreme challenges. The phenomenon of data silos is gradually emerging, and Blockchain cross chain technology provides important technical support for bridging data silos, achieving interoperability and data sharing between Blockchains. The focus of this article is on the systematic study and comparison of four widely used cross-chain technologies and platforms, and designs a Blockchain cross-chain architecture that integrates notary and relay chain technologies based on the analysis results. Compared with using a single notary mechanism or a single relay cross-chain technology, it has better scalability, stronger customization, and higher security. This work has a certain promoting effect on the development of cross chain technology. © 2024 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Machine Learning-Based Prediction of MPI Communication Time in High-Performance computing Applications

Machine Learning-Based Prediction of MPI Communication Time ...

引用

Frontiers Technology of information and Computer (ICFTIC), IEEE International Conference on

作者： Zhuang Wang Yuan Zhuang Yunhui Zeng 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 Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

The rapid advancements in high-performance computing (HPC) have made large-scale parallel computing feasible. As a commonly used parallel programming model, Message Passing Interface (MPI) plays a crucial role in HPC systems and directly affects the performance and efficiency of these applications. Therefore, modeling and predicting the performance of MPI communication is critically important. This paper utilizes a machine learning-based approach to predict MPI communication time. It uses performance analysis tools to collect real-time communication data and select critical features related to execution time. The data is categorized, then, for each type of MPI communication, a dedicated regression model is trained. Firstly, XGBoost and Artificial Neural networks (ANN) are employed for function level modeling. Then, fusion techniques are used to combine XGBoost and ANN models to improve prediction accuracy. Finally, these function level models are integrated into an application level model to predict the overall communication time. This model is evaluated using CG, FT, and CPMD. Experimental results show that the average prediction errors of the model are 12.6%, 13.2%, and 13.5%, with a minimum error of 7%.

关键词： Accuracy Computational modeling High performance computing Message passing Artificial neural networks Machine learning Predictive models Data models Supercomputers Tuning

来源：评论

学校读者我要写书评

暂无评论

A Self-adaptive Single-Objective Multitasking Optimization Algorithm 1

引用

17th International Conference on Bio-Inspired computing: Theories and Applications, BIC-TA 2022

作者： Li, Xiaoyu Wang, Lei Jiang, Qiaoyong Li, Wei Wang, Bin The Key Laboratory of Network Computing and Security Technology of Shaanxi Province Xi’an University of Technology Xi’an710048 China The Key Laboratory of Industrial Automation of Shaanxi Province Shaanxi University of Technology Hanzhong723001 China School of Electronic and Information Engineering Ankang University Ankang725000 China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

Evolutionary multitasking optimization algorithms have been presented for dealing with multiple tasks simultaneously. Many studies have proved that EMTOs often perform better than conventional single-task evolutionary. Transferring knowledge plays a very important role in multitask optimization algorithms. Many existing methods transfer elite solutions between tasks to improve algorithm performance, however, these methods may or produce negative transfer if inter-task similarity is low or irrelevant. This paper presents a self-adaptive multitasking optimization algorithm, SAMTOA, to find more valuable transferred solutions between tasks. In SAMTOA, the solutions for the next generation transfer are adaptively determined based on the successful transfer solutions of the previous generation. The method can effectively reduce the probability of transferring useless solutions between tasks and effectively utilize valuable solutions in tasks to improve the efficiency of knowledge transfer between tasks. Experimental results on single-objective multitasking optimization benchmark problems indicate that SAMTOA outperforms the other the state-of-the-art EMTO algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Optimization

来源：评论

学校读者我要写书评

暂无评论

A gesture recognition algorithm for smart gloves 2

A gesture recognition algorithm for smart gloves

引用

2nd International Conference on Computer Graphics, Image and Virtualization, ICCGIV 2022

作者： Cui, Hong University of Jinan School of Information Science and Engineering Jinan China Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan250022 China

ISBN: (纸本)9781665492508

This paper proposes a gesture recognition algorithm for smart gloves. The main innovations of the method are: (a) When the slope method is used to fit the curve, the two curve equations are assumed, and then the error size of the two curves is judged, and the curve where the smaller error is located is the fitted curve. (b) Jaccard coefficient is introduced into the gesture recognition algorithm, and by comparing the trend of two gestures in the same position segmentation interval, it judges whether they are the same gesture. Compared with the previous trend determination condition[1] the determination condition is simpler and the output result is more intuitive. The experiment was conducted by this algorithm, and better experimental results were achieved. © 2022 IEEE.

关键词： Gesture recognition

来源：评论

学校读者我要写书评

暂无评论

STAD: Joint Spatial-Temporal Dimension and Channel Correlation for Time Series Anomaly Detection

STAD: Joint Spatial-Temporal Dimension and Channel Correlati...

引用

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

作者： Mingle Zhou Xingli Wang Delong Han Jin Wan Jiachen Li 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 Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China SHANDONG SCICOM Information and Economy Research Institute Co. Ltd

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Accurately identifying real anomalies and pseudo-anomalies in complex multi-dimensional time series data has been a difficult problem in time series anomaly detection. To solve this problem, this paper proposes a new framework, STAD, that joint temporal and spatial dimensions. This framework guides the model to capture the correlation information between channels It also aims to learn the deep feature representation of sequences by mining potential information in the spatialtemporal dimension. It can effectively distinguish between true and false anomalies by comparing information from spatial and temporal dimensions. STAD identifies and integrates correlated channels by using a correlation aggregation mechanism to join multiple channels and detect *** addition, the KAN mixer designed in this paper can effectively extract features from different spatial locations in the spatial-temporal dimension. Through extensive experiments on several public datasets, STAD demonstrates its superiority in terms of accuracy and robustness.

关键词： Correlation Accuracy Time series analysis Signal processing Feature extraction Robustness Acoustics Speech processing Mixers Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

DAC-UNet: Dual Attention CNN-Enhanced CswinUnet for Gastric Cancer Pathological Image Segmentation

DAC-UNet: Dual Attention CNN-Enhanced CswinUnet for Gastric ...

引用

Medical Artificial Intelligence (MedAI), IEEE International Conference on

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

ISBN: (数字)9798350377613

ISBN: (纸本)9798350377620

Gastric cancer is one of the most lethal cancers. Currently, clinical diagnosis primarily relies on gastroscopic biopsies and the manual experience of doctors to assess the development of lesions in tissue sections. However, pathological screening requires a significant amount of time from specialized pathologists. Due to variations in clinical experience and limitations in medical resources, the detection of gastric cancer still faces challenges. Traditional methods for segmenting gastric cancer images often overlook the hierarchical structure of features and the spatial details, both local and global, in the lesion images. To address the above issues, this paper proposes a hybrid and efficient pathological image segmentation network named DAC-UNet, designed for precise pathological image segmentation tasks. DAC-UNet uses Cswin Transformer blocks to build a hierarchical structure, allowing it to capture global information with less computational complexity. The network also includes multi-scale dual attention skip connections between the encoder and decoder to preserve local spatial details and improve global interactions across different scales. We conducted a thorough theoretical analysis of this method and validated it on a gastric slice dataset. The experimental results demonstrate that our proposed method achieves outstanding performance on both our gastric slice dataset and the MoNuSeg dataset. Specifically, on our gastric slice dataset, we obtained a Dice similarity coefficient (DSC) of 89.42% and an HD95 of 47.24, while on the MoNuSeg dataset, we achieved a DSC of 79.28% and a 95th percentile Hausdorff distance (HD95) of 19.38.

关键词： Image segmentation Pathology Semantics Manuals Transformers Decoding Lesions Medical diagnostic imaging Faces Cancer

来源：评论

学校读者我要写书评

暂无评论

ZnO-ITO/WO_(3-x)heterojunction structured memristor for optoelectronic co-modulation neuromorphic computation

引用

Science China Materials 2024年第9期67卷 2838-2847页

作者： Jianyong Pan Tong Wu Wenhao Yang Yang Li Jiaqi Zhang Hao Kan Shandong Provincial Key Laboratory of Network Based Intelligent Computing School of Information Science and EngineeringUniversity of JinanJinan250022China School of Integrated Circuits Shandong UniversityJinan250101China Key Laboratory of Automobile Materials Ministry of Education College of Materials Science and EngineeringJilin UniversityChangchun130012China

Traditional transistors confront severe challenges of insufficient computing capability and excessive power consumption in large-scale neuromorphic *** address these critical bottlenecks,we propose an optoelectronic memristor based on zinc oxide-indium tin oxide/tungsten oxide(ZnO-ITO/WO_(3-x))heterojunctions as a promising *** applying different types of electrical and optical signals,the device successfully emulates diverse synaptic functions including short-term/long-term synaptic plasticity,alongside short-term and long-term *** the ZnO-ITO functional layer enhances the photoresponse of the WO_(3-x)-based memristor and demonstrates“learning-forgetting-relearning”behavior under optical ***,based on the photoelectric cooperative memristor array,a convolutional neural network for vehicle type recognition is constructed,which solves the problem of zero weight and negative weight *** regard to energy efficiency,the neural network built with this device operates at a power level of only 10^(-3)W,representing a reduction of more than 4 orders of magnitude compared with a standard central ***,the photoelectric memristor proposed in this work provides a new idea for neuromorphic computing and is expected to promote the development of energy-efficient brain-like computing.

关键词： ZnO-ITO/WO_(3-x)heterojunction optoelectronic memristor synaptic plasticity neuromorphic computing convolutional neural network

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：