检索结果-内蒙古大学图书馆

2023 IEEE International Conference on Electrical, computer and Energy Technologies, ICECET 2023

作者： Alsamhi, Saeed Hamood Hawbani, Ammar Kumar, Santosh Porwol, Lukasz Curry, Edward Insight Centre for Data Analytics University of Galway Galway Ireland School of Computer Science Shenyang Aerospace University Shenyang China Computer Science and Engineering Iiit Naya Raipur Chhattisgarh India

ISBN: (纸本)9798350327816

The metaverse is a universal and immersive virtual world, which are components of cyber-physical-social systems (CPSS). The traditional centralized approach to building a metaverse poses risks to user privacy, security, and autonomy. A growing movement towards a decentralized metaverse, built on blockchain and distributed networks, has emerged as a response. This paper presents a conceptual framework for building a de-centralized metaverse emphasising security, user autonomy, and inclusivity within situation-aware CPSS. Drawing on literature from blockchain, virtual worlds, and decentralized systems, we explore the technical and regulatory challenges that must be addressed to create a more secure, autonomous, and inclusive virtual world. Our framework highlights the importance of community-driven governance and transparent data management for building a decentralized metaverse that empowers users and promotes collaboration. © 2023 IEEE.

关键词： Blockchain Cyber-Physical-Social Systems Decentralization Metaverse Situation Awareness

来源：评论

学校读者我要写书评

暂无评论

Blockchain-Based Cognitive Computing Model for data Security on a Cloud Platform

引用

computers, Materials & Continua 2023年第12期77卷 3305-3323页

作者： Xiangmin Guo Guangjun Liang Jiayin Liu Xianyi Chen Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjing210031China Jiangsu Electronic Data Forensics and Analysis Engineering Research Center Jiangsu Police InstituteNanjing210031China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China

Cloud storage is widely used by large companies to store vast amounts of data and files,offering flexibility,financial savings,and ***,information shoplifting poses significant threats,potentially leading to poor performance and privacy ***-based cognitive computing can help protect and maintain information security and privacy in cloud platforms,ensuring businesses can focus on business *** ensure data security in cloud platforms,this research proposed a blockchain-based Hybridized data Driven Cognitive Computing(HD2C)***,the proposed HD2C framework addresses breaches of the privacy information of mixed participants of the Internet of Things(IoT)in the ***2C is developed by combining Federated Learning(FL)with a Blockchain consensus algorithm to connect smart contracts with Proof of ***“data Island”problem can be solved by FL’s emphasis on privacy and lightning-fast processing,while Blockchain provides a decentralized incentive structure that is impervious to *** with Blockchain allows quick consensus through smart member selection and *** HD2C paradigm significantly improves the computational processing efficiency of intelligent *** analysis results derived from IIoT datasets confirm HD2C *** compared to other consensus algorithms,the Blockchain PoA’s foundational cost is *** accuracy and memory utilization evaluation results predict the total benefits of the *** comparison to the values 0.004 and 0.04,the value of 0.4 achieves good *** to the experiment results,the number of transactions per second has minimal impact on memory *** findings of this study resulted in the development of a brand-new IIoT framework based on blockchain technology.

关键词： Blockchain Internet of Things(IoT) blockchain based cognitive computing Hybridized data Driven Cognitive Computing(HD2C) Federated Learning(FL) Proof of Authority(PoA)

来源：评论

学校读者我要写书评

暂无评论

Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary relational Knowledge Graph Construction 38

Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary r...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Luo, Haoran Haihong, E. Yang, Yuhao Yao, Tianyu Guo, Yikai Tang, Zichen Zhang, Wentai Peng, Shiyao Wan, Kaiyang Song, Meina Lin, Wei Zhu, Yifan Tuan, Luu Anh School of Computer Science Beijing University of Posts and Telecommunications China School of Automation Science and Electrical Engineering Beihang University China Beijing Institute of Computer Technology and Application China Inspur Group Co. Ltd. China College of Computing and Data Science Nanyang Technological University Singapore

Beyond traditional binary relational facts, n-ary relational knowledge graphs (NKGs) are comprised of n-ary relational facts containing more than two entities, which are closer to real-world facts with broader applications. However, the construction of NKGs remains at a coarse-grained level, which is always in a single schema, ignoring the order and variable arity of entities. To address these restrictions, we propose Text2NKG, a novel fine-grained n-ary relation extraction framework for n-ary relational knowledge graph construction. We introduce a span-tuple classification approach with hetero-ordered merging and output merging to accomplish fine-grained n-ary relation extraction in different arity. Furthermore, Text2NKG supports four typical NKG schemas: hyper-relational schema, event-based schema, role-based schema, and hypergraph-based schema, with high flexibility and practicality. The experimental results demonstrate that Text2NKG achieves state-of-the-art performance in F1 scores on the fine-grained n-ary relation extraction benchmark. Our code and datasets are publicly available. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Deep Reinforcement Learning-Based Task Offloading Framework for Edge-Cloud Computing 2

A Deep Reinforcement Learning-Based Task Offloading Framewor...

引用

2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Kalpana, Ponugoti Almusawi, Muntather Chanti, Yerrolla Sunil Kumar, V. Varaprasad Rao, M. Avn Institute of Engineering & Technology Department of Computer Science and Engineering Telangana India The Islamic University Najaf Iraq Sr University School of Computer Science & Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India Cvr College of Engineering Department of Data Science Hyderabad India

ISBN: (纸本)9798350317558

By introducing the processing and resources storage to the network edge, edge computing is a new, promising computing paradigm that drastically decreases network traffic and service latency. Many edge computing applications consist of interdependent tasks, wherein the results of one task are the inputs of another. The important and difficult problem of where to position each running task to optimise Quality-of-Service (QoS) is how to offload these tasks to edge of network. In this work, implemented a novel Deep Reinforcement Learning based Task Offloading (DRLTO) method utilized as the intelligent task offloading that uses a Directed Acyclic Graph (DAG) to represent the dependent tasks and off-policy reinforcement learning powered by a Sequence-to-Sequence (S2S) neural network. This research outcomes show that the DRLTO achieved less cloud processing time, and number of single terminal tasks, and percentage of failed task when compared to Deep reinforcement learning-based cloud-edge collaborative mobile computation offloading (DRL-CCMCO) and DeepEdge. © 2024 IEEE.

关键词： and task offloading Deep reinforcement learning Multi-access edge computing sequence to sequence neural networks

来源：评论

学校读者我要写书评

暂无评论

A New Approach for Iris Segmentation Based on U-Net 6

A New Approach for Iris Segmentation Based on U-Net

引用

6th International Conference on computer Network, Electronic and Automation, ICCNEA 2023

作者： Yang, Yan Wang, Jianguo Xi'an Technological University School of Computer Science and Engineering Xi'an China Xi'an Technological University Research Institute of Artificial Intelligence and Data Science Xi'an China

ISBN: (纸本)9798350305388

The concept of iris segmentation was created to increase the accuracy of iris recognition. Past recognition methods used the entire eye image directly for recognition classification, which led to poor recognition results. For the sake of sovling this problem, this paper proposes the ResU-Net (RU-Net) model, which can guide the network to learn more features that distinguish between iris and non-iris pixels. First, based on the U-Net, the backbone network model is changed to ResNet50 in this paper. This has the advantage of reducing the number of parameters and network complexity, and improving the learning capability. For the sake of solving the problem of sample imbalance between iris region and background region, this paper introduces the Focal Loss loss function. focal loss can effectively deal with the case of sample category imbalance and make the network focus more on the pixels that are difficult to classify. In this paper, the proposed RU-Net model is experimentally evaluated on the CASIA-Iris-Thousand dataset. The experimental results demonstrate that RU-Net achieves significant improvements on NIR iris images, reaching 96.22% MIoU and 98.19% MPA. This indicates that the RU-Net method outperforms other representative iris segmentation methods and has better segmentation capability. © 2023 IEEE.

关键词： Pixels

来源：评论

学校读者我要写书评

暂无评论

Exploring Deep Convolutional Neural Networks: A Grad-CAM Enhanced Comparative Study for Automated COVID-19 Diagnosis from Chest X-Ray Images 3

Exploring Deep Convolutional Neural Networks: A Grad-CAM Enh...

引用

3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Reza, Md. Sazid Jahangir Kabir, Mir Md. Rakib Mollah, Md. Abdur Nahin, Nazmul Islam Data Science and Innovation Td School Department of Computer Science & Engineering India University of Technology Sydney Sydney Australia Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350319125

The emergence of COVID-19 in late 2019 sparked a global health crisis. Identifying infections quickly is key to providing proper care and limiting transmission. The Reverse Transcription-Polymerase Chain Reaction (RT-PCR) test, a widely recognized primary diagnostic method for detecting COVID-19, faces challenges associated with cost and compatibility in resource-constrained environments. This issue is resolved by chest X-ray imaging since it is non-invasive and inexpensive. In this domain, research often utilizes pre-trained models on relatively small datasets of chest X-ray images, limiting the potential for optimal generalization and accuracy achievable with larger training sets. Also, these research works limit the exploration of crucial information on salient regions within CXR images, constraining the depth of analysis and interpretation. This study addresses current research challenges through a detailed comparative analysis of convolutional neural network (CNN) architectures, including pre-trained models like Inception V3, Xception, DenseNet-121, VGG-19, and a Custom CNN developed from scratch. The evaluation focuses on their effectiveness in detecting COVID-19 using a relatively vast and comprehensive dataset. The evaluation focuses on assessing the efficacy of these models in detecting COVID-19 across binary-class and multi-class classifications within a large dataset, offering valuable insights into their overall effectiveness. Grad-CAM is employed in this study, which visualizes picture salient areas affecting deep neural network predictions and improves model interpretability. The heatmaps reveal the model's decision-making processes, making their behavior more understandable to medical practitioners. With resulting accuracies of 99.13% and 97.66% in binary-class and multi-class classification, Inception V3 demonstrated the most optimal performance. This proposed approach surpasses existing benchmark methods, underscoring its superiority. © 2023 IEEE.

关键词： chest X-ray CNN COVID-19 Deep Learning DenseNet-121 Grad-CAM Inception V3 ResNet-50 RT-PCR VGG-19 Xception

来源：评论

学校读者我要写书评

暂无评论

Variational Neural Inference Enhanced Text Semantic Communication System

引用

China Communications 2024年第7期21卷 50-64页

作者： Zhang Xi Zhang Yiqian Li Congduan Ma Xiao School of Electronics and Communication Engineering Sun Yat-Sen UniversityShenzhen 518107China Guizhou University State Key Laboratory of Public Big Data Guiyang 550025China Shenzhen Key Laboratory of Navigation and Communication Integration Shenzhen 518107China School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 510006China

Recently,deep learning-based semantic communication has garnered widespread attention,with numerous systems designed for transmitting diverse data sources,including text,image,and speech,*** efforts have been directed toward improving system performance,many studies have concentrated on enhancing the structure of the encoder and ***,this often overlooks the resulting increase in model complexity,imposing additional storage and computational burdens on smart ***,existing work tends to prioritize explicit semantics,neglecting the potential of implicit *** paper aims to easily and effectively enhance the receiver's decoding capability without modifying the encoder and decoder *** propose a novel semantic communication system with variational neural inference for text ***,we introduce a simple but effective variational neural inferer at the receiver to infer the latent semantic information within the received *** information is then utilized to assist in the decoding *** simulation results show a significant enhancement in system performance and improved robustness.

关键词： deep learning semantic communication variational neural inference

来源：评论

学校读者我要写书评

暂无评论

IPL data Analysis and Visualization for Team Selection and Profit Strategy 7

IPL Data Analysis and Visualization for Team Selection and P...

引用

7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Saranya, G. Swaminathan, Aravind Joel Benjamin, J. Surendran, R. Nelson, Leema Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai campus Chennai India Data Science Middlesex University Dubai United Arab Emirates Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Chitkara University Institute of Engineering & Technology Chitkara University Punjab India

ISBN: (纸本)9781665464086

Day by day, the role of data science and machine learning in cricket is increasing due to the large amount of data generated from a single player on a whole line. The field of data science is the intensive study of data to extract insights and knowledge from the data and apply the acquired knowledge and actionable insights. These available data and statistics are used to predict things like the team's first-innings score and the probability of winning the second team, etc. This study works on Indian Premier League (IPL) data Analysis 2022 and data Visualization for the duration (2008-2020) using Python. This application modules are followed by preprocessing, data analysis, and visualization, and finally create a model that predicts the team's overall score and the probability of winning. When building models, python algorithms such as Numpy is used for S cientific Computing, Pandas used to perform data Analysis, and finally Matplotlib and S eaborn to enable data Visualization. Finally, this paper helps the trainers and owners of the team during the auction to select an emerging player to win the matches in the entire season and to win the trophy and to get profit to the owners by the profit strategy done in this analysis report. © 2023 IEEE.

关键词： data visualization

来源：评论

学校读者我要写书评

暂无评论

An Improved Algorithm of Detecting Abnormal data in Geological Disaster System 6

An Improved Algorithm of Detecting Abnormal Data in Geologic...

引用

6th International Conference on computer Network, Electronic and Automation, ICCNEA 2023

作者： Chen, Yupeng Wang, Jianguo Xi'an Technological University School of Computer Science and Engineering Xi'an China Xi'an Technological University Research Institute of Artificial Intelligence and Data Science Xi'an China

ISBN: (纸本)9798350305388

A large amount of data is collected during geological hazard monitoring, which is extremely valuable for further data mining, hazard monitoring and decision analysis. However, in the process of data collection and transmission may be affected by interference and other factors, resulting in the generation of abnormal data. To extract higher value information from these basic data, it is necessary to improve the quality of source data first, so it is necessary to detect the abnormal data. The monitoring data of the geological disaster are time-series data, which have the characteristics of time-series correlation. The nearest neighbor difference jump anomaly detection algorithm is suitable for monitoring anomalous data of geological disaster system, but there are shortcomings in selecting floating values and correlation perception. To address these problems, the nearest neighbor difference jumping algorithm is improved and a algorithm is proposed to detect anomalous data of geological disasters, i.e., the data series are segmented by using sliding windows, the selection of floating values is improved, the calculation range of difference values is expanded to enhance the correlation of data, and the concept of change speed is incorporated to better perceive the trend of change before and after the data, and finally the anomaly is determined by using the anomaly probability and correlation. After the experimental comparison and analysis, the accuracy and recall rate of the proposed method on the detection of geological disaster data are improved and meet the expected results. © 2023 IEEE.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

Fixed-Time Consensus Tracking of Nonlinear Multi-Agent Systems Under DoS Attacks 63

Fixed-Time Consensus Tracking of Nonlinear Multi-Agent Syste...

引用

63rd IEEE Conference on Decision and Control, CDC 2024

作者： Wang, Xinhe Wen, Guanghui Zheng, Wei Xing Southeast University School of Cyber Science and Engineering Nanjing211189 China Southeast University School of Mathematics Department of Systems Science Nanjing211189 China Western Sydney University School of Computer Data and Mathematical Sciences SydneyNSW2751 Australia

ISBN: (纸本)9798350316339

This paper studies the fixed-time consensus tracking problem of nonlinear multi-agent systems, where communication links are subjected to denial-of-service (DoS) attacks. The DoS attacks make the communication networks switch arbitrarily and therefore the network connectivity may be broken, which poses a serious challenge to the consensus tracking problem within the fixed-time framework. To deal with this issue, a switching-based fixed-time control strategy is proposed. Critical conditions for fixed-time consensus tracking of nonlinear multi-agent systems under DoS attacks are developed. In particular, the adverse impact of DoS attacks on the settling time can be clearly reflected by the fixed-time strategy derived in this paper. Finally, numerical examples are provided to validate the theoretical analysis. © 2024 IEEE.

关键词： Denial-of-service attack

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：