检索结果-内蒙古大学图书馆

LOPE: A Low-Overhead Payment Verification Method for Blockchains

Chinese Journal of Electronics 2021年第2期30卷 349-358页

作者： ZHANG Ying HE Zhengyuan JIA Linpeng DING Donghui ZHANG Hanwen SUN Yi School of Software Engineering Tongji University Blockchain Lab Institute of Computing Technology Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences

With its characteristics of decentralization,security, data traceability, and tamper-resistance, the blockchain has been widely used in various *** the difference in the performance of the devices, the light client is proposed so that devices without the ability to store a full blockchain copy can also participate in the blockchain transactions. However,the light client has to communicate with full nodes and verify the authenticity of a transaction which brings in some extent of communication, computation, and storage overheads to the light client. These overheads cannot be ignored for some low-performance devices, such as embedded devices or Io T chips, and therefore the current light client scheme does not work in this situation. We propose LOPE(a Low-overhead payment v Erification method) for poor-capacity nodes in the blockchain *** LOPE, a grouping protocol is designed to partition full nodes into groups to serve the verification requests of the light client. In addition, Practical byzantine fault tolerance(PBFT) is used to ensure the light client to get a credible result in spite of a few dishonest nodes existing in the group. We conduct LOPE and evaluate it in a testbed. The experiment results show that LOPE reduces more than half of the communication overhead, degrades the computation overhead of the light client to a large extent, and avoids the storage overhead of the hash roots of block headers in the light client. We also conduct theoretical analysis to show the performance improvement and security issues of LOPE.

关键词： current light client scheme storage overhead blockchains Low-overhead payment vErification method peer-to-peer computing software fault tolerance electronic money data integrity LOPE low-performance devices cryptographic protocols fault tolerance blockchain transactions

来源：评论

学校读者我要写书评

暂无评论

Activation Space Selectable Kolmogorov-Arnold Networks

arXiv

引用

arXiv 2024年

作者： Yang, Zhuoqin Zhang, Jiansong Luo, Xiaoling Lu, Zheng Shen, Linlin School of Computer Science University of Nottingham Ningbo China Ningbo China School of Computer Science & Software Engineering Shenzhen University Shenzhen China

The multilayer perceptron (MLP), a fundamental paradigm in current artificial intelligence, is widely applied in fields such as computer vision and natural language processing. However, the recently proposed Kolmogorov-Arnold Network (KAN), based on nonlinear additive connections, has been proven to achieve performance comparable to MLPs with significantly fewer parameters. Despite this potential, the use of a single activation function space results in reduced performance of KAN and related works across different tasks. To address this issue, we propose an activation space Selectable KAN (S-KAN). S-KAN employs an adaptive strategy to choose the possible activation mode for data at each feedforward KAN node. Our approach outperforms baseline methods in seven representative function fitting tasks and significantly surpasses MLP methods with the same level of parameters. Furthermore, we extend the structure of S-KAN and propose an activation space selectable Convolutional KAN (S-ConvKAN), which achieves leading results on four general image classification datasets. Our method mitigates the performance variability of the original KAN across different tasks and demonstrates through extensive experiments that feedforward KANs with selectable activations can achieve or even exceed the performance of MLP-based methods. This work contributes to the understanding of the data-centric design of new AI paradigms and provides a foundational reference for innovations in KAN-based network architectures. Copyright © 2024, The Authors. All rights reserved.

关键词： Network architecture

来源：评论

学校读者我要写书评

暂无评论

Multi-hypergraph Neural Network with Fusion of Location Information for Session-based Recommendation

引用

IAENG International Journal of Applied Mathematics 2023年第4期53卷 1页

作者： Li, Ziang Wu, Jie Han, Guojing Ma, Chi Chen, Yuenai School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China Anshan Meteorological Service Anshan114001 China School of Computer Science and Engineering Huizhou University Huizhou516007 China School of Computer Science and Engineering Huizhou University Huizhou516007 China

Session recommendation is a technology that processes data referring to the user-anonymous session behavior analysis. This technique has a wide range of applications in e-commerce, videos, social networks, and other fields and it is a hot topic in current recommendation system research. Multi-hypergraph neural network with fusion of location information for session-based recommendation (MHGNN-LI) captures multi-dimensional interest features in the session through the multi-hypergraph representation and combines different contrastive learning methods to alleviate the problem of data sparsity. It also considers the dependence order of the recommended items to improve the recommendation effect. Concerning the methodology of work, the model first constructs a session hypergraph to obtain the interest features of multiple items in the session. Secondly, it decouples the interest features into global and local features within the session, and then it integrates the item features with location information into the local interest features. Finally, it introduces a line graph at the feature level between the different sessions and maximizes the mutual information between sessions and within them, through contrastive learning, to enhance the expression ability of session data. As for the validation, comparative experiments, with related models on two public datasets, Tmall and Diginetica, show that the MHGNN-LI model has improved on P@10 and MRR@10 by, at least, 15.47% and 20.51%, as well as 9.77% and 12.37% on the Tmall and Diginetica datasets, respectively. © (2023), (International Association of Engineers). All Rights Reserved.

关键词： Location

来源：评论

学校读者我要写书评

暂无评论

Identification of Switched Gated Recurrent Neural Networks Using the EM Algorithm 5

Identification of Switched Gated Recurrent Neural Networks U...

引用

5th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2023

作者： Bai, Wentao Gu, Suhang Yan, Chao Zhang, Haoyu Jiang, Chunli Guo, Fan School of Computer Science and Engineering Changshu Institute of Technology Changshu China School of Electrical Engineering and Automation Changshu Institute of Technology Changshu China School of Information Science and Technology Hangzhou Normal University Hangzhou China School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing China School of Automation Nanjing Institute of Technology Nanjing China

ISBN: (纸本)9798350393521

In the domain of nonlinear hybrid dynamic system modeling, the effectiveness of switched autoregressive exogenous (SARX) systems may face certain restrictions. To address this issue, this paper presents an enhanced switched system framework. In this framework, all SARX subsystems are replaced with gated recurrent neural networks, aiming to overcome these limitations. Importantly, the proposed switched system does not rely on any prior assumptions about the knowledge of operating modes. Finally, a new identification method is proposed based on the expectation-maximization (EM) algorithm, and its effectiveness is validated through a simulation example. © 2023 IEEE.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

VRNConnect: Towards More Intuitive Interaction of 3D Brain Connectivity Data in Virtual Environments

VRNConnect: Towards More Intuitive Interaction of 3D Brain C...

引用

Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Sepehr Jalayer Yiming Xiao Marta Kersten-Oertel Gina Cody School of Computer Science and Software Engineering Concordia University Montreal Canada

ISBN: (数字)9798350374490

ISBN: (纸本)9798350374506

With more and more availability of both functional Magnetic Resonance Imaging (fMRI) and Diffusion Tensor Imaging (DTI), there is increasing interest and evidence that brain network analysis can enable new possibilities to understand brain function and disease. We have developed an open-source virtual reality (VR) platform, VRNConnect, for exploring brain connectivity data to enable researchers, students, and clinicians to map the different regions of the brain and study their functions in a more interactive, intuitive and engaging way. VRNConnect allows users to interact with the data to view various graph network measurements such as node strength, degree, and clustering coefficient by using the Brain Connectivity Analysis tools during runtime. We performed a user study to de-termine the intuitiveness and usability of the platform as well as looked at differences in controller versus gesture interaction methods. Results showed that VRNConnect is easy to use, easy to learn and intuitive. In terms of the interaction methods, almost all participants performed significantly better, doing tasks with the controller as opposed to hand gestures in both task completion time and task errors. VRNConnect showed promising results in both usability scale (SUS) and cognitive load (NASA TLX), showing the potential to be used as an academic and analytical tool, with a customizable design for importing connectivity data from different modalities and flexible and intuitive interaction methods.

关键词： Three-dimensional displays Runtime Virtual environments Tutorials Network analyzers Functional magnetic resonance imaging Usability

来源：评论

学校读者我要写书评

暂无评论

Learning Order Forest for Qualitative-Attribute Data Clustering 27

Learning Order Forest for Qualitative-Attribute Data Cluster...

引用

27th European Conference on Artificial Intelligence, ECAI 2024

作者： Zhao, Mingjie Feng, Sen Zhang, Yiqun Li, Mengke Lu, Yang Cheung, Yiu-Ming School of Computer Science and Technology Guangdong University of Technology Guangzhou China Shenzhen China School of Computer Science and Software Engineering Shenzhen University Shenzhen China Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China Department of Computer Science Hong Kong Baptist University Hong Kong

ISBN: (纸本)9781643685489

Clustering is a fundamental approach to understanding data patterns, wherein the intuitive Euclidean distance space is commonly adopted. However, this is not the case for implicit cluster distributions reflected by qualitative attribute values, e.g., the nominal values of attributes like symptoms, marital status, etc. This paper, therefore, discovered a tree-like distance structure to flexibly represent the local order relationship among intra-attribute qualitative values. That is, treating a value as the vertex of the tree allows to capture rich order relationships among the vertex value and the others. To obtain the trees in a clustering-friendly form, a joint learning mechanism is proposed to iteratively obtain more appropriate tree structures and clusters. It turns out that the latent distance space of the whole dataset can be well-represented by a forest consisting of the learned trees. Extensive experiments demonstrate that the joint learning adapts the forest to the clustering task to yield accurate results. Comparisons of 10 counterparts on 12 real benchmark datasets with significance tests verify the superiority of the proposed method. Source code of the proposed method is available at [39]. © 2024 The Authors.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Applied Technology Research on the Construction of Bidirectional Knowledge Graph Curriculum System Based on SmartKG

Applied Technology Research on the Construction of Bidirecti...

引用

Artificial Intelligence and Education (WAIE), International Workshop on

作者： Su Jiang Wei Luo Hengji Cui School of Computer Science and Software Engineering Dalian Neusoft University of Information Dalian China

ISBN: (数字)9798350378894

ISBN: (纸本)9798350378900

Amidst the rapid advancements in artificial intelligence technology, it is imperative to apply these technological developments to the realm of education to enhance information-based teaching methodologies. This article introduces a knowledge management framework that aims to visualize curriculum knowledge systems using knowledge graphs in the educational sector. Taking courses like Fundamentals of computer Systems as exemplars, the article demonstrates the techniques for constructing knowledge graphs. Additionally, SmartKG's ease of deployment and application allows students to construct their own visual knowledge graphs, fostering a deeper comprehension of knowledge and fostering the profound integration of AI and teaching. Through the analysis of the curriculum system and students' self-constructed knowledge graphs, the efficacy of utilizing knowledge graphs in learning has been validated, propelling the application and practice of intelligent classroom teaching.

关键词： Visualization Conferences Knowledge based systems Knowledge graphs Educational technology Propulsion Big Data Market research Knowledge management Information technology

来源：评论

学校读者我要写书评

暂无评论

FPPL: An Efficient and Non-IID Robust Federated Continual Learning Framework

FPPL: An Efficient and Non-IID Robust Federated Continual Le...

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： He, Yuchen Shen, Chuyun Wang, Xiangfeng Jin, Bo Shanghai Formal-Tech Information Technology Co. Lt Shanghai200062 China East China Normal University School of Computer Science and Technology Shanghai200062 China TongJi University School of Software Engineering Shanghai200092 China

ISBN: (纸本)9798350362480

Federated continual learning (FCL) aims to learn from sequential data stream in the decentralized federated learning setting, while simultaneously mitigating the catastrophic forgetting issue in classical continual learning. Existing FCL methods usually employ typical rehearsal mechanisms, which could result in privacy violations or additional onerous storage and computational burdens. In this work, an efficient and non-IID robust federated continual learning framework, called Federated Prototype-Augmented Prompt Learning (FPPL), is proposed. The FPPL can collaboratively learn lightweight prompts augmented by prototypes without rehearsal. On the client side, a fusion function is employed to fully leverage the knowledge contained in task-specific prompts for alleviating catastrophic forgetting. Additionally, global prototypes aggregated from the server are used to obtain unified representation through contrastive learning, mitigating the impact of non-IID-derived data heterogeneity. On the server side, locally uploaded prototypes are utilized to perform debiasing on the classifier, further alleviating the performance degradation caused by both non-IID and catastrophic forgetting. Empirical evaluations demonstrate the effectiveness of FPPL, achieving notable performance with an efficient design while remaining robust to diverse non-IID degrees. Code is available at: https://***/ycheoo/FPPL. © 2024 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Using analytic hierarchy process to evaluate deep learning for infrared target recognition

引用

Multimedia Tools and Applications 2024年第38期83卷 86229-86245页

作者： Xu, Laixiang Cao, Bingxu Wei, Xinhong Li, Bei Su, Jingfeng Ning, Shiyuan Zhao, Junmin School of Computer and Data Science Henan University of Urban Construction Pingdingshan467036 China School of Information Engineering Luohe Vocational Technology College Henan University of Technology Luohe Institute of Technology Luohe462000 China School of Computer and Data Science Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Pingdingshan467036 China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Henan University of Urban Construction Pingdingshan467036 China Department of Software Information China Electronics Technology Group Corporation 36-th Research Institute Jiaxing314033 China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Research Center of Intelligent Campus Application Engineering of Henan Province Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Henan University of Urban Construction Pingdingshan467036 China

In order to promote the evaluation performance of deep learning infrared automatic target recognition (ATR) algorithms in the complex environment of air-to-air missile research, we proposed an analytic hierarchy process (AHP) evaluation system consisting of the target recognition ability, the generalization recognition ability, the anti-interference ability, the background suppression ability, and the cost metrics ability. According to detailed quantitative analysis and experimental results, we establish probability evaluation models and get their probability values. Compared with traditional evaluation systems, our proposed method implementation is showing high reliability. Similar superior performances are also achieved, such as quick, comprehensive, and scientific. Through the above research, the key technology of deep learning performance evaluation in automatic target recognition has been broken through, which provides some basis and guidance for the analysis of infrared imaging-guided weapon systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Infrared imaging

来源：评论

学校读者我要写书评

暂无评论

A ResNet-Based DVFS Regulator for Heterogeneous Multi-Core Mobile Processors

A ResNet-Based DVFS Regulator for Heterogeneous Multi-Core M...

引用

2024 IEEE Region 10 Conference, TENCON 2024

作者： Hu, Shibo Xu, Xinzi Chen, Yuze Qin, Muyun Lian, Yong Zhao, Yang School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Xiaomi Mobile Software Co. Ltd. Beijing China York University Department of Electrical Engineering and Computer Science Toronto Canada

ISBN: (纸本)9798350350821

Dynamic voltage and frequency scaling (DVFS) is commonly used for the balance of performance and power of mobile processors. Conventional DVFS regulators take the processor cores with the same power and performance weight showing their inability in the regulation of heterogeneous processors. This paper proposes a core-aware frequency-power evaluation scheme that utilizes a multilayer perceptron (MLP) to assess the efficacy of DVFS regulation actions. Additionally, a ResNet-based DVFS regulator, trained with the assistance of the MLP, is developed for heterogeneous multi-core processors. Evaluated on Xiaomi 13 Pro powered by a Qualcomm Snapdragon 8 Gen 2, the proposed DVFS approach achieves an up to ×1.47 improvement in FPS Performance Per Watt (FPPW) compared with the Qualcomm's default DVFS governor in video recording scenario. © 2024 IEEE.

关键词： Dynamic frequency scaling

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：