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A Threshold-Free Random Access Preamble Detection Method Based on Joint Clustering for 6G NTN

A Threshold-Free Random Access Preamble Detection Method Bas...

引用

2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Zhen, Li Wang, Yue Sun, Teng Wang, Wenjing Yu, Keping Kharel, Rupak Xi'An University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Xi'an China Global Information and Telecommunication Institute Waseda University Tokyo Japan Manchester Metropolitan University Department of Computing and Mathematics United Kingdom

ISBN: (纸本)9781665423908

Thanks to its irreplaceable superiority in providing seamless coverage, satellite communication is expected to play an important role in supporting ubiquitous and continuous connectivity services for 6G wireless networks. With the pre-compensation of timing and frequency offset, we propose a novel random access (RA) preamble detection method for the high-dynamic non-terrestrial networks (NTN). Unlike the conventional threshold-based methods, the proposed method leverages a joint clustering algorithm to accurately capture the timing position corresponding to the first arrival path without noise variance estimation. Simulation results validate the effectiveness of the proposed method in a typical satellite non line-of-sight (NLOS) scenario, and indicate that our method can achieve a notable performance enhancement in terms of the error detection probability (EDP) and the timing mean square error (MSE), in comparison to the exiting methods. © 2021 IEEE.

关键词： Mean square error

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Research on network Intrusion Detection Technology Based on DCGAN 5

Research on Network Intrusion Detection Technology Based on ...

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5th IEEE Advanced information Technology, Electronic and Automation Control Conference, IAEAC 2021

作者： Chao, Wang Wenhui, Wang Jiahan, Dong Guangxin, Guo State Grid Beijing Electric Power Company Beijing China Global Energy Interconnection Research Institute Beijing China State Grid Key Laboratory of Information and Network Security Beijing China

Traditional network intrusion detection algorithms tend to lack learning in a small number of classes due to data imbalance. In reality, intrusion detection systems pay more attention to the detection accuracy of a small number of classes, that is, attack samples. In order to improve the detection accuracy of intrusion detection system, a network intrusion detection method based on deep convolution generative adversarial networks (DCGAN) was proposed. Firstly, the sample data of network intrusion is preprocessed, and the character data set is replaced by image data. Then, DCGAN is used to train and test the sample data. Both the generator and the discriminator are constructed by CNN. The generator is used to construct attack samples, balance the number of training samples, and solve the over fitting problem caused by insufficient training samples. Finally, the trained discriminator is used to test the classification accuracy of samples. Experimental results show that, compared with the traditional algorithm, the proposed algorithm can not only balance the detection accuracy of various types of samples, but also has higher detection accuracy for attack samples. © 2021 IEEE.

关键词： Generative adversarial networks

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Trusted Auditing of Data Operation Behaviors in Cloud based on Blockchain and TEE

Trusted Auditing of Data Operation Behaviors in Cloud based ...

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Shujiang Xu Fansheng Wang Lianhai Wang Miodrag J. Mihaljević Shuhui Zhang Wei Shao Qihao Huang 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 Mathematical Institute the Serbian Academy of Sciences and Arts Belgrade Serbia Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

With the rapid development of the new generation of information technologies, including cloud computing, 5G, and the Internet of Things, data outsourcing storage in the cloud has brought great convenience to data storage or access anytime and anywhere. Due to the separation of ownership and management of outsourced stored data, it faces data security risks, including data loss and personal privacy disclosure. The third-party audit of data operational behavior by logs is often employed to strengthen the security of outsourced data. However, some privacy breach incidents in recent years show that third parties who may leak information about data and users’ identities are not always trustworthy. To achieve data security and privacy protection, an operation behavior auditing scheme for outsourced data is proposed with the blockchain rather than third parties. The data operation behavior audit is carried out from three dimensions: integrity of the operation behavior log, the credibility of the operation behavior information, and compliance of operation behavior. The security analysis and experiment results indicate that this scheme is secure and feasible.

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Intelligent Tracking Technology of Sensitive Data Based on Tag Distribution Learning 3

Intelligent Tracking Technology of Sensitive Data Based on T...

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2022 3rd International Conference on Computer information and Big Data Applications, CIBDA 2022

作者： Zhou, Xiaoming Yu, Hai Yu, Pengfei Ziyi, Teng State Grid Liaoning Electric Power Supply Co. Ltd State Grid of China Shenyang China State Grid Liaoning Information and Communication Company State Grid of China Shenyang China State Grid Key Laboratory of Information & Network Security Global Energy Interconnection Research Institute Co. Ltd Nanjing China

ISBN: (纸本)9783800758760

The frequent interaction and sharing of massive data in the big data environment brings increasingly serious data security problems, such as the theft of data by illegal users, the unauthorized use of data by legitimate users, and the illegal dis-closure and dissemination of data. In practical application, the phenomenon of data leakage is inevitable due to the influ-ence of environmental and human factors, but there are few studies on the traceability of data after data leakage. Data traceability means that when the data leaked to a third party is captured, the source head and relevant responsible person can be traced by analyzing the relevant characteristics of the illegal data. This paper studies the intelligent tracking tech-nology of sensitive data based on tag distribution learning, which consists of three stages: tag generation technology of sensitive data content, tag distribution generation method and tag distribution comparison technology of sensitive data. Firstly, aiming at the problem that the existing machine learning algorithm has low recognition accuracy for sensitive data with no significant features, the sensitive data content tag generation technology is studied, and the tags of structured data and unstructured data are extracted. Secondly, the generation method of label distribution is studied to realize data multi-label identification based on label distribution descriptor model. Finally, the label distribution comparison technol-ogy of sensitive data is studied to realize accurate identification and tracking of sensitive data based on label distribution learning. Aiming at the problem of frequent data flow and difficult monitoring of sensitive data flow, the generation of sensitive digital tags and the construction of association relationship in the process of data flow are studied, and the data tracking technology based on tag recognition is studied to realize the dynamic tracking and security monitoring of sensi-tive data. © VDE VERLAG GMBH -

关键词： Sensitive data

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Research on the Design of Government Data Governance Evaluation System under the Perspective of Digital Government

Research on the Design of Government Data Governance Evaluat...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Zirui Xu Jinyang Sun Zhe Lu Delong Han Information Research Institute of Shandong Academy of Sciences Qilu University of Technology(Shandong Academy of Sciences) Jinan China SHANDONG SCICOM Information and Economy Research Institute Co. Ltd Jinan China 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

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

In the context of the digital era, government data governance is a key factor in promoting the modernization of government governance, but there is a lack of a complete set of evaluation index system to standardize the content and scope of government data governance. This paper adopts the policy tool theory to code and analyze the 12 policies from the three policy tool dimensions of supply layer, demand layer, and environment layer, and proposes three first-level evaluation indicators of basic environment, actual effectiveness, and safeguard mechanism, on the basis of which the evaluation content is selected in a focused manner based on the integration of policy documents related to data governance. Meanwhile, a comprehensive, scientific and objective evaluation system of government data governance is constructed by adopting assessment means combining subjectivity and objectivity, and quantitative and qualitative. The system is designed to assess and guide the data governance practices of the domestic government, point out the direction for government departments at all levels to carry out data governance, and play an important role in promoting the transparency and efficiency of data management.

关键词： Codes Government Cyberspace Data science Data governance Indexes

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SCESP: An Edge Server Placement Method Based on Spectral Clustering in Mobile Edge computing 8th

SCESP: An Edge Server Placement Method Based on Spectral Cl...

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8th International Conference on Artificial Intelligence and security , ICAIS 2022

作者： Wang, Lijuan Guo, Yingya Yao, Jiangyuan Zhou, Siyu College of Computer and Data Science Fuzhou University Fuzhou350000 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350000 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350003 China School of Computer Science and Cyberspace Security Hainan University Haikou570228 China Tandon School of Engineering New York University New York10012 United States

ISBN: (纸本)9783031067600

With the rapid development of Internet of Things (IoT) and 5G, mobile edge computing is gaining popularity for its low computation latency, bandwidth costs and energy consumption. In mobile edge computing, the placement of edge servers is one of the most significant problems and attracts worldwide attention. However, two major problems of edge server placement: high access delay and unbalanced workload of edge servers, have not been completely solved yet. To better solve these two problems, this paper proposes a new Spectral-Clustering-based Edge Server Placement (SCESP) algorithm, which can effectively reduce the access delay and make the workload of each edge server more balanced. In the evaluation, we use the Shanghai Telecom’s base station dataset to test the performance of SCESP and extensive experiments demonstrate the superior performance of SCESP in reducing the access delay and balancing the edge server workload. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mobile edge computing

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NextSeg: Automatic COVID-19 Lung Infection Segmentation from CT Images Based on Next-ViT

NextSeg: Automatic COVID-19 Lung Infection Segmentation from...

引用

International Joint Conference on Neural networks (IJCNN)

作者： Aimei Dong Ruixin Wang Xuening Zhang Jian Liu 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

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The spread of COVID-19 has brought great disaster to the world, and automatic segmentation of the infected region can help doctors make a quick diagnosis and reduce workload. However, automatic segmentation of infected regions in computed tomography (CT) images still faces many challenges. The most central problem at the moment is that when capturing low-frequency signals and high-frequency signals, the use of Transformer blocks for capturing low-frequency signals may deteriorate the high-frequency signals (local texture information) to some extent. For this reason, in this paper, we propose a new network for automatic segmentation of COVID-19 lung infections from CT images, called NextSeg. NextSeg follows an encoder-decoder architecture, where the encoder part uses NCB and NTB convolutional attention modules to extract better features while reducing the deterioration of texture profile information. The decoder part uses an attention fusion module to learn the weight relationship between features and retain more important feature information. In addition to the difficulty of extracting the boundary details of the COVID-19 infected region as well as the loss of details the deeper the network is, we designed a new hybrid strategy. Traditional and state-of-the-art approaches to the COVID-19 segmentation domain are compared. Extensive experiments show that our proposed framework outperforms existing competitors in both qualitative and quantitative results.

关键词： COVID-19 Image segmentation Convolution Computed tomography Neural networks Lung Medical services

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Multi-Channel Hypergraph network for Sequential Diagnosis Prediction in Healthcare

Multi-Channel Hypergraph Network for Sequential Diagnosis Pr...

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International Conference on Computer Supported Cooperative Work in Design

作者： Xin Zhang Xueping Peng Weimin Chen Weiyu Zhang Xiaoqiang Ren Wenpeng Luv 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 Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia Shandong Mental Health Center Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Sequential diagnosis prediction (SDP) is a complex and challenging task, aming to predict future diagnoses of patients by analyzing their historical medical records. Although graph neural networks(GNNs) has been applied to successfully address the challenge of heterogeneous data integration in electronic health records, relatively limited work has been done on GNNs for sequential diagnosis prediction. Graph neural network-based methods, aimed at capturing structural and relational patterns of EHR data for sequential diagnosis prediction, explore code-code pairwise relationships, resulting in an inability to learn fine-grained, higher-order interaction relationships among different types medical codes. As a result, they are difficult to effectively model complex, multi-dimensional interactions among different types of medical codes necessary for accurate and nuanced diagnosis predictions. To address these challenges, this paper proposes a novel approach called Multi-Channel Hypergraph network (MCHN) predictive framework for sequential diagnosis prediction. The proposed method aims to explore the fine-grained higher-order interactions between different types of medical codes via multi-channel hypergraphs. Specifically, MCHN learns two levels of code embeddings from multi-channel hypergraph learning module and line graph learning module, respectively: (i) multi-channel hypergraph learning module, which is to learn multi-channel hypergraph level code embeddings by modeling the higher-order relationships between medical codes in different hypergraphs; and (ii) line graph learning module, which is to learn the line graph level code embeddings by modeling code-code pairwise relationships. In MCHN, we propose a novel channel-level attention mechanism to help our model attend to the informativeness of the different channel for forecasting future patient diagnoses. We also design a code-level attention mechanism, which can to pay more attention to the medical codes that are m

关键词： Codes Attention mechanisms Federated learning Medical services Predictive models Benchmark testing Sequential diagnosis

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A Survivable Routing Algorithm with Hybrid Protection in Elastic Optical networks

A Survivable Routing Algorithm with Hybrid Protection in Ela...

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Mobile Internet, Cloud computing and information security (MICCIS), International Conference on

作者： Hankun Zeng Xu Zhang Chuan Feng Xiaoxue Gong School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Institute of Intelligent Communication and Network Security Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350389906

ISBN: (纸本)9798350389913

Spectral efficiency is a crucial factor in the survivability of multi-class traffic in elastic optical networks (EONs). In this paper, we propose a spectrum-efficient hybrid protection scheme, namely dedicated path protection (DPP) and shared backup path protection (SBPP), to address the survivability issue in EONs. Meanwhile, we design a survivable routing, modulation level, and spectrum assignment (RMLSA) algorithm to achieve hybrid protection in EONs. The simulation results demonstrate that the proposed algorithm has a lower blocking rate and Spectrum occupation ratio.

关键词： Time-frequency analysis Spectral efficiency Simulation Modulation information security Optical fiber networks Routing

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Research on Classroom Behavior Target Detection Algorithm based on YOLOv7

Research on Classroom Behavior Target Detection Algorithm ba...

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IEEE Advanced information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Wenke Qiu Xiaoming Wu Xiangzhi Liu Computer Science and Qilu University of Technology (Shandong Academy of Sciences) Jinan ShanDong China 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

ISBN: (数字)9798350316537

ISBN: (纸本)9798350316544

The integration of video information for the purpose of detecting students’ behavior in the classroom, with the aim of enhancing teaching efficiency and maintaining a stable classroom environment, offers significant assistance. However, existing models usually struggle to achieve good accuracy due to the lack of datasets, dense targets, interclass occlusion, and other issues. To address these difficulties, the CD-YOLO classroom behavior detection algorithm is proposed in this paper. And the classroom behavior dataset (DSL-Dataset) is created. We propose a loss function named VAIoU, which can be combined with vector ratio to filter the prediction box. To further improve the detection accuracy of occluded targets, we propose an attention module CM module with command feature fusion. Moreover, in this paper, we improve the convolutional kernel to design a lightweight convolutional kernel FFconv. Experimental results on DSL-Dataset show that CD-YOLO outperforms existing one-stage detection models in terms of detection accuracy.

关键词： Accuracy Education Object detection Filtering algorithms Feature extraction Vectors information management

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