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Linguistic Knowledge Representation in DPoS Consensus Scheme for Blockchain

Computers, Materials & Continua 2023年第10期77卷 845-866页

作者： Yixia Chen Mingwei Lin College of Computer and Cyber Security Fujian Normal UniversityFuzhou350117China Fujian Provincial Engineering Research Center for Public Service Big Data Mining and Application Fujian Normal UniversityFuzhou350117China

The consensus scheme is an essential component in the real blockchain *** Delegated Proof of Stake(DPoS)is a competitive consensus scheme that can decrease energy costs,promote decentralization,and increase efficiency,***,how to study the knowledge representation of the collective voting information and then select delegates is a new open *** ensure the fairness and effectiveness of transactions in the blockchain,in this paper,we propose a novel fine-grained knowledge representation method,which improves the DPoS scheme based on the linguistic term set(LTS)and proportional hesitant fuzzy linguistic term set(PHFLTS).To this end,the symmetrical LTS is used in this study to express the fine-grained voting options that can be chosen to evaluate the blockchain *** is used to model the collective voting information on the voted blockchain nodes by aggregating the voting information from other blockchain *** rank the blockchain nodes and then choose the delegate,a novel delegate selection algorithm is proposed based on the cumulative possibility ***,the numerical examples are used to demonstrate the implementation process of the proposed DPoS consensus algorithm and also its ***,the superiority of the proposed DPoS consensus algorithm is *** results show that the proposed DPoS consensus algorithm shows better performance than the existing DPoS consensus algorithms.

关键词： Linguistic term set MCDM aggregation operator

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A secure access and associate deleting scheme for multi-replica in multi-cloud environment

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International Journal of High Performance Systems Architecture 2016年第3期6卷 143-152页

作者： Xiong, Jinbo Li, Suping Li, Xuan Zhang, Yuanyuan Jin, Biao Faculty of Software Fujian Normal University Fuzhou China Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal University Fuzhou China Fujian Engineering Research Center Public Service Big Data Mining and Application Fuzhou China

With the rapid development of cloud services in multi-cloud environment, it is convenient to generate multi-replica for user's data stored in multi-cloud servers. On the one hand, multi-replica is able to improve data availability and users' experience. But on the other hand, it becomes increasingly easy to disclose sensitive data and brings serious challenges to security and privacy for user's data and its multi-replica. In order to tackle the above issues, in this paper, we propose a secure access and associate deleting scheme for multi-replica (MADS) in multi-cloud environment, which is based on symmetric encryption algorithm, attribute-based encryption and replica location technology. We construct a novel multi-layered key structure, called multi-way search key tree (MSKT) that ensures no key material will be revealed, yet the data owner is able to control the master key and manage other keys by performing some tree operations. We also propose a data replica associated model, which is able to associate all the data replicas in multi-cloud environment. Comprehensive comparison and security analysis demonstrate that the proposed MADS scheme is effective and secure. Copyright © 2016 Inderscience Enterprises Ltd.

关键词： Sensitive data

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Differential Privacy Information Publishing Algorithm based on Cluster Anonymity

Differential Privacy Information Publishing Algorithm based ...

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2020 International Conference on big data, Artificial Intelligence and Internet of Things engineering, ICBAIE 2020

作者： Lin, Guobin Zhao, Huijun Zhao, Leyang Gan, Xinli Yao, Zhiqiang Spatial Data Mining and Application Fujian University Engineering Research Center Yango University Fu Zhou350015 China Fujian Engineering Research Center of Public Service Big Data Mining and Application Fuzhou350108 China

ISBN: (纸本)9781728164991

With the development of Internet technology, the attacker gets more and more complex background knowledge, which makes the anonymous model susceptible to background attack. Although the differential privacy model can resist the background attack, it reduces the versatility of the data. In this paper, this paper proposes a differential privacy information publishing algorithm based on clustering anonymity. The algorithm uses the cluster anonymous algorithm based on KD tree to cluster the original data sets and gets anonymous tables by anonymous operation. Finally, the algorithm adds noise to the anonymous table to satisfy the definition of differential privacy. The algorithm is compared with the DCMDP (Density-Based Clustering Mechanism with Differential Privacy, DCMDP) algorithm under different privacy budgets. The experiments show that as the privacy budget increases, the algorithm reduces the information loss by about 80% of the published data. © 2020 IEEE.

关键词： Budget control

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Holistic Comparison of Different Kernel Functions for Support Vector Regression Based on State-of-health Prediction of Lithium-ion Battery 11

Holistic Comparison of Different Kernel Functions for Suppor...

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11th International Conference on Prognostics and System Health Management, PHM-Jinan 2020

作者： Li, Yiting Wang, Shuai Chen, Wengxiang Han, Wei Fujian Normal University College of Mathematics and Informatics Fujian Prov. Eng. Technology Research Center for Public Service Big Data Mining and Application Fuzhou China

ISBN: (纸本)9781728151816

Battery health prediction is an important part of prognostic and health management. It is particularly important to accurately predict the state of health (SOH) in the management of lithium-ion batteries. Owing to its excellent regression ability, support vector regression (SVR) is the preferred data-driven method used to predict the remaining useful life (RUL) of batteries. Kernel functions, such as linear, polynomial, gaussian, and sigmoid kernels are commonly used to map samples from a lower to a higher dimensional space for accurate classification. Parameter estimation considerably affects the mapping ability of kernel functions. The size and distribution of the training set also affect the regression ability of SVR. In this study, lithium-ion battery data are divided into prophase, metaphase, and anaphase training data. Particle swarm optimization (PSO) and a genetic algorithm (GA) are applied to optimize the parameters of the four kernel functions. A comparison of the experimental results showed that GA-SVR with a radial basis function (RBF) and PSO-SVR with an RBF in the anaphase can accurately predict the RUL of lithium-ion batteries. © 2020 IEEE.

关键词： Lithium-ion batteries

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A Privacy Preserving Federated Learning Aggregation Algorithm for Noise Label 9

A Privacy Preserving Federated Learning Aggregation Algorith...

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9th International Conference on Computer and Communications, ICCC 2023

作者： Han, Weiguang Yan, Xishan Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China College of Computer and Cyber Secutiry Fujian Normal University Fuzhou China University of Chinese Academy of Sciences Beijing China Fujian Provincial Engineering Research Center for Public Service Big Data Mining and Application Fuzhou China

ISBN: (纸本)9798350317251

To solve the problem that a large number of noise labels lead to the reduction of the efficiency of existing aggregation Algorithmic efficiency and the difficulty of extending existing methods to the privacy protection framework, a noise label oriented privacy protection Federated learning aggregation algorithm PP-ASNL is proposed. This algorithm is mainly divided into two parts: a client-side noise label sample detection model and a server-side model rating aggregation mechanism. Simulation results show that the proposed algorithm has higher model accuracy and Rate of convergence than the existing methods. © 2023 IEEE.

关键词： Efficiency

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Holistic Comparison of Different Kernel Functions for Support Vector Regression Based on State-of-health Prediction of Lithium-ion Battery

Holistic Comparison of Different Kernel Functions for Suppor...

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Prognostics and System Health Management (PHM-Jinan), International Conference on

作者： Yiting Li Shuai Wang Wengxiang Chen Wei Han College of Mathematics and Informatics Fujian Provincial Engineering Technology Research Center for Public Service Big Data Mining and Application Fujian Normal University Fuzhou Fujian Digital Fujian Internet-of-Things Laboratory of Environmental Monitoring Fujian Provincial Engineering Technology Research Center for Public Service Big Data Mining and Application Fujian Normal University Fuzhou Fujian

ISBN: (数字)9781728151816

ISBN: (纸本)9781728151823

关键词： Kernel Lithium-ion batteries Genetic algorithms Prediction algorithms Optimization Support vector machines Training

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Novelty detection technology for Alzheimer's disease based on autoencoder feature extractor and MCD classification 7

Novelty detection technology for Alzheimer's disease based o...

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Real-time Photonic Measurements, data Management, and Processing VII 2023

作者： Zhang, Shuheng Zhang, Yan Pan, Jie Lin, Xintian Ding, Xuemei Yang, Hongqin Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education Provincial Key Laboratory for Photonics Technology Fujian Normal University Fuzhou350117 China Fujian Provincial Engineering Research Center for Public Service Big Data Mining and Application Fujian Normal University Fuzhou350117 China Xiamen Jingyi zhikang Technology Co. Ltd. Xiamen361000 China

ISBN: (纸本)9781510667938

Alzheimer's disease (AD) is a neurodegenerative disorder that affects the life quality of millions of people worldwide. To diagnose new cases in a timely manner, we propose a new novelty detection technique that combines Autoencoder and Minimum Covariance Determinant (MCD). The technique consists of two steps: first, we use an Autoencoder to extract low-dimensional and discriminative features from the publicly available ADNI dataset, where we only train the Autoencoder with normal data, making the abnormal data more distinguishable in the feature space;second, based on the features of normal data, we use MCD to construct a decision boundary, and judge the degree of abnormality by the distance of the test point to the boundary. Compared with traditional methods without using Autoencoder, our technique has significant advantages in terms of accuracy and sensitivity, and can effectively deal with data imbalance problem. Experimental results show that our method can efficiently detect novel AD cases, and has a wide range of application prospects. © 2023 SPIE.

关键词： Neurodegenerative diseases

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Self-Supervised Graph Representation with Proximity Matrix

SSRN

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SSRN 2023年

作者： Li, Huanhuan Luo, Haifeng Huang, Tianqiang Ding, Xuemei Huang, Liqing College of Computer and Cyber Security Fujian Normal University China Digital Fujian Big Data Security Technology Institute China Fujian Provincial Engineering Research Center for Public Service Big Data Mining and Application China

Graph representation learning(GRL) aims to extract valid graph information to generate informative representations for handling various tasks on graphs. Supervised graph representation learning (SGRL) has achieved remarkable success recently. However, when labels are missing, or task-free representations are expected, self-supervised graph representation learning (SSGRL) is a promising paradigm. Graph auto-encoders (GAEs) are one of the prevalent frameworks in SSGRL. There are two issues that hinder the effectiveness of existing GAEs: a) There is no appropriate strategy to fuse attributive and structural information in the reconstruction target;b) The decoder cannot fully retain valid information for classification tasks. In this work, we propose a self-supervised graph auto-encoders via proximity matrix reconstruction (PMGAE) to address the above issues. For the proper information fusion, a proximity matrix is designed as the reconstruction target. Then, we design a cosine decoder to enhance the decoder's ability to retain information. Moreover, to adapt PMGAE to the large-scale graph, a random sub-matrix reconstruction mechanism is introduced to reduce memory consumption and running time in the decoding stage. In the experiment, we used seven benchmarks to verify the effectiveness of PMGAE. The results show that the proposed PMGAE is significantly improved compared to the state-of-the-art methods. © 2023, The Authors. All rights reserved.

关键词： Decoding

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Differential Privacy Information Publishing Algorithm based on Cluster Anonymity

Differential Privacy Information Publishing Algorithm based ...

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big data, Artificial Intelligence and Internet of Things engineering (ICBAIE), International Conference on

作者： Guobin Lin Huijun Zhao Leyang Zhao Xinli Gan Zhiqiang Yao Spatial Data Mining and Application Fujian University Engineering Research Center Yango University Fu Zhou China Fujian University Engineering Research Center Fu Zhou China Fujian Engineering Research Center of Public Service Big Data Mining and Application Fuzhou China

ISBN: (数字)9781728164991

ISBN: (纸本)9781728165004

关键词： big data Artificial intelligence Internet of Things Clustering algorithms Publishing Complexity theory

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BioAR: Bio-Laboratory Automation Meets Augmented Reality 14th

BioAR: Bio-Laboratory Automation Meets Augmented Reality

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14th International Conference on Genetic and Evolutionary Computing, ICGEC 2021

作者： Zou, Zheng Liu, Shi-Jian College of Computer and Cyber Security Fujian Normal University No. 8 Xuefu South Road Fujian Shangjie Minhou Fuzhou China Fujian Provincial Engineering Technology Research Center for Public Service Big Data Mining and Application Fujian Normal University No. 8 Xuefu South Road Fujian Shangjie Minhou Fuzhou China Digital Fujian Institute of Big Data Security Technology Fujian Normal University No. 8 Xuefu South Road Fujian Shangjie Minhou Fuzhou China College of Computer Science and Mathematics Fujian University of Technology No. 3 Xueyuan Road University Town Fujian Minhou Fuzhou China

ISBN: (纸本)9789811684296

Modern laboratory is benefiting from science and engineering by adopting automated systems into labor-intensive lab routines. In this paper, a computer-aided system that involves augmented reality (AR) to promote the automatic levels of the bio-laboratory is introduced. The motivation of this work lies in the idea of offering visual guidance for users to pick a tube manually from the rack after the retrieval. Because it is difficult for people to tell the target tube from the others since they look almost the same. Design issues and implementation details are given after a careful analysis of the system requirements. The BioAR system is tested out and successfully applied to a bio-laboratory with tube related operations, whose performance is approved by the technicians. A video demo of the proposed system can be found at https://***/video/BV1FX4y157WB/. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Augmented reality

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