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Hybrid Deep Relation Matrix Bidirectional Approach for Relational Triple Extraction

Hybrid Deep Relation Matrix Bidirectional Approach for Relat...

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International Conference on Computer Supported Cooperative Work in Design

作者： Yushuai Hu Jing Zhao Ming Li Lin Yuan Qingzhi Zou Ronghuan Zhang 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 Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology 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 School of Intelligence and Information Engineering Shandong University of Traditional Chinese Medicine Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Relation extraction is a crucial task within information extraction, and numerous models have demonstrated impressive results. However, most of the tagging-based relation triple extraction methods employ unidirectional approaches to extract subjects, objects, and relations, which may overlook crucial information. In this paper, we introduce a novel deep matrix-based bidirectional relation extraction model. Firstly, we extract forward and backward entity pairs. During the bidirectional extraction process,there may be some redundant relationships,so we use a shared encoder to connect and enhance the extraction process. Secondly, we design a low-complexity relation extraction matrix to allocate all possible relations. We assess our model using diverse benchmark datasets, and comprehensive experiments show that our approach effectively addresses subsequent triple extraction issues stemming from entity extraction failures.

关键词： Learning systems Federated learning Computational modeling Benchmark testing information retrieval Data mining Task analysis

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FedLRDP: Federated Learning Framework with Local Random Differential Privacy

FedLRDP: Federated Learning Framework with Local Random Diff...

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International Joint Conference on Neural networks (IJCNN)

作者： Runtian Zhou Anming Dong Jiguo Yu Qingyan Ding 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 School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Big Data Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Federated learning (FL) is a distributed machine learning framework enabling multiple clients to collaboratively train a shared ML model without sharing raw data. Despite its aim to safeguard data security and privacy, FL faces risks from advanced adversarial attacks like membership inference attack (MIA), potentially leaking sensitive information. To counter these threats, differential privacy (DP) methods add noise to shared model parameters. However, traditional DP methods struggle with balancing model accuracy, training efficiency, and privacy protection, often compromising one for the other. Additionally, uniform DP mechanisms may not cater to varying client privacy and performance needs. To tackle these challenges, we propose a federated learning framework based on localized random differential privacy (FedLRDP). This approach empowers each client to control noise levels based on their privacy requirements, enhancing model performance while preserving data privacy. We further optimize client-side loss functions to enhance model performance. Theoretical analysis establishes the convergence bounds for FedLRDP, demonstrating its superior convergence performance. Experimental results on MNIST reveal that FedLRDP improves model accuracy by 3.28% compared to traditional DP methods while mitigating MIA with a 72.48% lower attack success rate than FedAvg. Moreover, on EMNIST, FedLRDP boosts model accuracy by 7.31% compared to traditional DP methods, maintaining a 59.4% lower attack success rate than FedAvg. These findings underscore FedLRDP’s efficacy in meeting client privacy needs while enhancing model performance.

关键词： Training Differential privacy Privacy Accuracy Federated learning Noise Data models

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Data-Driven Active power Dispatch for Wind Farms

Data-Driven Active Power Dispatch for Wind Farms

引用

Data Driven Control and Learning Systems (DDCLS)

作者： Xuguo Jiao Daoyuan Zhang Xin Wang Zhaoxing Ma Zhenyong Zhang Wenfeng Liu School of Information and Control Engineering Qingdao University of Technology Qingdao China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China 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 State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Civil Engineering Qingdao University of Technology Qingdao China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the standard deviation of wind turbines' output power series to reduce their fluctuations. First, to build the optimization problem, the historical active power output data of each wind turbine is collected from the Supervisory Control And Data Acquisition (SCADA) system. Then, the objective function is developed based on the idea of minimizing the standard deviation of the output power time series. Meanwhile, considering the constraints of wind turbines' output power and active power balance of the grid, the feasible domain of the cost function is constructed. Further, to solve the proposed nonlinear optimization problem with equality and inequality constraints, the trust region interior point method (TRIPM) is employed to explore optimal solution within the feasible domain. Finally, actual data collected from wind farms is used to test effectiveness of the designed approach and experimental results show that our method can achieve better output power smoothing performance than conventional Proportional Dispatch (PD) method.

关键词： Fluctuations Time series analysis SCADA systems power smoothing Wind farms Linear programming power grids

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Medical Image Segmentation Use Convolutional Attention Augmentation TransUNet with Skip Connection Enhancement

Medical Image Segmentation Use Convolutional Attention Augme...

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International Conference on Computer Supported Cooperative Work in Design

作者： Qingzhi Zou Jing Zhao Ming Li Ling Chen Lin Yuan Ronghuan Zhang Yushuai Hu 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 Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology 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 School of Intelligence and Information Engineering Shandong University of Traditional Chinese Medicine Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The primary objective of medical image segmentation is the isolation of pathological tissues and distinct organs from medical images, thereby assisting in medical diagnosis. The methods employed for medical image segmentation encompass convolutional neural networks and transformer-based methods. Although the self-attention mechanism in Transformers improves its capability to capture long-range dependencies, it have limitations in learning local (contextual) relationships between pixels. Some previous research has tried to solve this problem by incorporating convolutional layers into the encoder or decoder of transformers, but sometimes feature inconsistencies arise. To better extract local features from images using convolutional neural networks and to fuse low-resolution and high-resolution features from higher-level and lower features, we propose the Convolutional Attention Augmentation TransUNet (CAA-TransUNet) model. In our model, firstly, we propose a convolutional attention augmentation module that enhances both local and global features by suppressing irrelevant background information. Secondly, we have integrated attention gates into the skip connections to aggregate feature information from various stages of the encoder during the upsampling process. Finally, we employ the technique of aggregating the loss of multi-stage features to expedite convergence speed and enhance performance. The experimental results on three public datasets demonstrate that our proposed model significantly outperforms the baseline methods.

关键词： Image segmentation Pathology Computational modeling Aggregates Logic gates Feature extraction Transformers

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Intrusion Detection Model For power information network Based On Multi-layer Attention Mechanism 10

Intrusion Detection Model For Power Information Network Base...

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10th IEEE Joint International information Technology and Artificial Intelligence Conference, ITAIC 2022

作者： Yang, Huiting Bai, Yunxiao Chen, Tao Shi, Yuanyuan Yang, Ruxia Ma, Haibo State Grid Xinjiang Electric Power Research Institute Urumqi China State Grid Xinjiang Electric Power Co. Ltd. Urumqi China State Grid Kuitun Electric Power Supply Company Kuitun China State Grid Key Laboratory of Information & Network Security Nanjing210003 China Global Energy Interconnection Research Institute Co. Ltd. Jiangsu Nanjing210003 China State Grid Tachen Electric Power Supply Company Tachen China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

As the construction work of energy Internet and new power system continues to advance, the scale of power information network continues to expand, and network attacks against power systems are becoming more and more frequent. Although the existing intrusion detection technologies can defend against some network intrusions and attacks, the detection efficiency and accuracy of related algorithms need to be improved. In this paper, we propose a multi-layer attention mechanism-based intrusion detection model for power information network, which divides feature extraction into two different attention layers at feature level and slice level, and combines recurrent neural networks to fully exploit the value of historical information and current input. Experiments prove that the intrusion detection model proposed in this paper improves the detection accuracy at a lower cost, which in turn improves the security of power information network. © 2022 IEEE.

关键词： Internet of Energy

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LGDB-Net: Dual-Branch Path for Building Extraction from Remote Sensing Image

LGDB-Net: Dual-Branch Path for Building Extraction from Remo...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Ronghuan Zhang Jing Zhao Ming Li Qingzhi Zou 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 Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology 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 School of Intelligence and Information Engineering Shandong University of Traditional Chinese Medicine Jinan China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Extracting buildings from remote sensing images using deep learning techniques is a widely applied and crucial task. Convolutional Neural networks (CNNs) adopt hierarchical feature representation, showcasing powerful capabilities in extracting local information but facing challenges in capturing global features. Transformers can address this limitation, but they perform poorly in extracting local features and significantly increase memory requirements and computational complexity. To overcome these challenges, we propose a method for building extraction from remote sensing images called LGDB-Net (Local-Global Dual-Branch network), employing a dual-branch approach. Firstly, inspired by Swin Transformer, we designed GB-Former(Global Branch-Former) as the backbone network to model global information. We use a linear multi-head self-attention mechanism to reduce time and memory complexity while maintaining a large global receptive field. Additionally, we replace the traditional multi-layer perceptron with a convolution-enhanced multi-layer perceptron to improve channel feature representation, reduce model parameters, and enhance segmentation performance. Secondly, we use multiple Depth-wise Conv3×3 + LN (Layer Normalization) + GeLU (Gaussian Error Linear Unit) modules as the auxiliary branch for local detailed feature extraction. Finally, we adopt a multi-scale feature fusion strategy to integrate feature information from both branches. We conduct a series of experiments on three datasets: WHU, Massachusetts, and Inria. The experimental results demonstrate that the proposed method not only effectively improves segmentation accuracy with lower building omission and commission rates but also significantly reduces model parameters and computational complexity.

关键词： Accuracy Computational modeling Memory management Neural networks Feature extraction Transformers Sensors Data mining Computational complexity Remote sensing

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Research on Nondestructive Vulnerability Detection Technology of power Industrial Control System 6

Research on Nondestructive Vulnerability Detection Technolog...

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6th IEEE information Technology and Mechatronics Engineering Conference, ITOEC 2022

作者： Zou, Zhenwan Yin, Jun Yang, Ling Luo, Cheng Fei, Jiaxuan Electric Power Research Institute of State Grid Xinjiang Electric Power Co. Ltd. Urumqi China Bazhou Power Supply Company of State Grid Xinjiang Electric Power Co. Ltd. Bazhou China Kashi Power Supply Company of State Grid Xinjiang Electric Power Co. Ltd. Kashi China Global Energy Interconnection Research Institute Co. Ltd. Jiangsu Nanjing210003 China State Grid Key Laboratory of Information and Network Security Jiangsu Nanjing210003 China

ISBN: (纸本)9781665431859

The power industrial control system is an important part of the national critical information infrastructure. Its security is related to the national strategic security and has become an important target of cyber attacks. In order to solve the problem that the vulnerability detection technology of power industrial control system cannot meet the requirement of non-destructive, this paper proposes an industrial control vulnerability analysis technology combined with dynamic and static analysis technology. On this basis, an industrial control non-destructive vulnerability detection system is designed, and a simulation verification platform is built to verify the effectiveness of the industrial control non-destructive vulnerability detection system. These provide technical support for the safety protection research of the power industrial control system. © 2022 IEEE.

关键词： Static analysis

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Research and Application of Smart Water Meter Monitoring System Based on NB-IoT

Research and Application of Smart Water Meter Monitoring Sys...

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IEEE information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Yuanyuan Zhao Tixiang Zhang Huijuan Hao Xiaojie Zhang Shandong Computer Science Center Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Qilu University of Technology (Shandong Academy of Sciences) Jinan P. R. China Shandong Yonate Environmental Technology Co. Ltd. Jinan P. R. China

With the increasing shortage of water resources and the improvement of environmental awareness, intelligent water meter monitoring systems have become a hot research topic in the field of water conservation. This paper studies and designs a smart water meter system based on NB-IoT, and discusses its effect in practical applications. This smart water meter monitoring system adopts STM8 microcontroller as the main control chip of the water meter, and adopts the low-power NB-IoT module BC28 module to upload the monitored voltage data, network signal quantity, current water consumption and collection time to the TLink cloud platform in real time, which solves the problems of unstable communication in the working environment, high power consumption of long-term sensor collection and transmission data. At the same time, the water meter can achieve the function of remote control of the water valve switch by driving the stepper motor, which perfectly solves the problems and shortcomings of the current traditional water meter and smart water meter, and greatly meets the development needs of the water industry.

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EMO: An Ontology for Supporting the Management of Epidemic

EMO: An Ontology for Supporting the Management of Epidemic

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Applied Physics and computing (ICAPC), International Conference on

作者： Yongchao Gao Qiuyue Wang Mark S. Fox Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Qilu University of Technology (Shandong Academy of Sciences) Jinan China Standard Institute of Emerging Technologies and Innovations Jinan China Urban Data Centre University of Toronto Toronto Canada

In order to improve the coverage of concepts in epidemic management, this paper constructs an epidemic management ontology EMO to help manage and analyse epidemic data. First, based on the ISO/lEC 5087 city data model series of standards and TOVE method, EMO is constructed using Protégé. Secondly, the data types and their relationships required in the epidemic management use cases are defined. Finally, the instance data help to verify the ability of EMO to represent the city epidemic management data. Results show that EMO provides a clear, accurate and unified definition of epidemic management data, improves the efficiency of data sharing and integration, and reduces the difficulty of developing epidemic management tools.

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Mix-Net: Automatic Segmentation of Covid-19 ct Images Based on Parallel Design

Mix-Net: Automatic Segmentation of Covid-19 ct Images Based ...

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IEEE International Conference on Image Processing

作者： Aimei Dong Ruixin Wang Guohua Lv Guixin Zhao Yi Zhai 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 Faculty of Computer Science and Technology 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

Since the discovery of COVID-19 in late 2019, the viral pneumonia crisis has begun to spread rapidly around the world. Lesion segmentation can remove unnecessary background areas and help doctors diagnose the condition. However, the infected areas showed differences at different stages, and the border between the infected areas and the surrounding tissue was blurred. To solve this problem, a novel COVID-19 lung infection segmentation network (Mix-Net) is designed for the automatic identification of infected areas from chest CT slices. Specifically, first, the local and global features of the infected areas are extracted and interacted with using the mixing block. Then, the features extracted from multiple layers of the encoder are fused and connected to the decoder. Experiments show that Mix-Net outperforms most cutting-edge segmentation models and achieves good segmentation results.

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