Article
Chemistry, Analytical
Abdelatif Hafid, Abdelhakim Senhaji Hafid, Dimitrios Makrakis
Summary: Blockchain technology, although gaining interest in various sectors, has scalability issues. Sharding has emerged as a promising solution, divided into PoW and PoS blockchain protocols. This article focuses on the PoS category, discussing key components, PoS and pBFT consensus mechanisms, and security analysis. The findings indicate a failure timeframe of around 4000 years in a network of 4000 nodes, 10 shards, and 33% shard resiliency.
Review
Computer Science, Information Systems
Weiyu Zhong, Ce Yang, Wei Liang, Jiahong Cai, Lin Chen, Jing Liao, Naixue Xiong
Summary: This paper comprehensively investigates the research progress of Byzantine fault-tolerant consensus algorithms, ranging from classical algorithms to the latest ones, and categorizes them based on different methods used to improve algorithm performance. By analyzing the impact of these methods on the performance of the BFT consensus algorithm, future improvement directions are proposed.
Article
Computer Science, Information Systems
Hongwu Qin, Yuntao Cheng, Xiuqin Ma, Fei Li, Jemal Abawajy
Summary: This paper introduces a new consortium blockchain consensus algorithm, WBFT, which improves system throughput and consensus delay by employing a dynamic weighting mechanism for consensus nodes and reducing the influence of malicious nodes. Experimental results show that WBFT outperforms PBFT and RBFT in terms of system throughput, consensus delay, and security.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Jiamou Qi, Yepeng Guan
Summary: A comprehensive reputation-based Practical Byzantine Fault Tolerance consensus method (CRPBFT) has been proposed to address the challenges of consensus protocol. It evaluates the credibility of each node using a comprehensive reputation model and selects nodes with higher reputation to participate in the consensus process. The method also optimizes the consensus communication structure and proposes a rotation mechanism for replacing consensus nodes regularly to increase decentralization and enhance the robustness and dynamic of the consensus network. Experimental results demonstrate its excellent performance compared to state-of-the-art methods.
PEER-TO-PEER NETWORKING AND APPLICATIONS
(2023)
Article
Engineering, Multidisciplinary
Hossam Samy, Ashraf Tammam, Ahmed Fahmy, Bahaa Hasan
Summary: Blockchain technology with its consensus algorithm plays a crucial role in network security and integrity, but challenges like high energy consumption and low throughput exist. By modifying the Istanbul Byzantine Fault Tolerance algorithm, a higher throughput can be achieved, suitable for business use cases such as letters of credit in trade finance.
AIN SHAMS ENGINEERING JOURNAL
(2021)
Article
Automation & Control Systems
Mostefa Kara, Abdelkader Laouid, Mohammad Hammoudeh, Muath AlShaikh, Ahcene Bounceur
Summary: This article proposes a consensus algorithm, Proof of Chance (PoCh), for the industrial Internet of Things (IIoT). PoCh is designed to be scalable and extensible, with a controlled conformance delay and low hardware and computation requirements. It uses chance instead of computing power to achieve consensus, and has a fault tolerance of 5f/3+1.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Computer Science, Information Systems
Ji Wan, Kai Hu, Jie Li, Hao Su
Summary: This article introduces an AnonymousFox consensus algorithm suitable for consortium blockchains and private blockchains. It addresses the issues of leader nodes being targeted by attackers and the lack of scalability in blockchain systems by hiding the identity of the leader node and utilizing a consensus algorithm based on anonymous identity.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Automation & Control Systems
Xiao Chen
Summary: This article introduces a novel scalable multishard Byzantine fault tolerance consensus protocol combined with a blockchain sharding optimization scheme. The protocol enhances scalability and security, and employs a novel consensus voting mechanism and sharding optimization model to improve efficiency. Experimental results demonstrate significantly improved performance in a real-world environment.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Mathematics
Fredy Andres Aponte-Novoa, Ricardo Villanueva-Polanco
Summary: Consensus protocols are essential for any blockchain, but they have drawbacks, such as vulnerability to attacks. To address this, a proof-of-accuracy protocol has been proposed as an alternative solution to ensure the trustworthiness of the blockchain.
Article
Computer Science, Information Systems
Yu Zhan, Baocang Wang, Rongxing Lu, Yong Yu
Summary: Blockchain technology has been widely utilized in cryptocurrency and various industries. However, the efficiency, security, and reliability of consensus protocols in blockchain are becoming increasingly restricted by the complexity of the network environment and growing number of users. The proposed DRBFT consensus protocol aims to enhance the efficiency and reliability of the consensus procedure through randomness and impartiality, providing a promising solution for advancing blockchain technology.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Hardware & Architecture
Roberto Saltini
Summary: This paper presents BigFooT, a Byzantine fault-tolerant consensus protocol for permissioned blockchains, which ensures correct operation, dynamic validator set, low latency, resilience to Byzantine performance degradation, and resilience to lost messages. It is the first consensus protocol to combine all these properties. Additionally, it shows that a vanilla implementation of state machine reconfiguration protocols can fail to guarantee liveness.
Article
Computer Science, Information Systems
Zhen-Fei Wang, Yong-Wang Ren, Zhong-Ya Cao, Li-Ying Zhang
Summary: Blockchain technology has generated significant interest since the emergence of Bitcoin, with potential applications in various scenarios such as IoT, smart cities, and cloud computing. The consensus protocol is crucial for maintaining performance, stability, and security in blockchain networks. However, meeting these properties simultaneously becomes challenging with an increase in network nodes and complexity. In this paper, we propose an improved practical Byzantine consensus algorithm (LRBFT) that utilizes Lagrange interpolation for generating random seeds, optimizes the election process of the primary set, improves consensus efficiency through delegated nodes, and prevents malicious behavior by the primary through a supervisory mechanism. Experimental analysis demonstrates that LRBFT achieves 100 consensuses in only 0.83% of the time required by the PBFT protocol when there are 70 nodes and 7 delegated nodes.
PEER-TO-PEER NETWORKING AND APPLICATIONS
(2023)
Article
Mathematics
Fan-Qi Ma, Rui-Na Fan
Summary: This study proposes a consensus mechanism that combines DPoS and PBFT, and analyzes its performance using queuing models and theoretical methods. The research findings demonstrate the potential of this consensus mechanism to rapidly handle malicious nodes and improve system performance.
Article
Computer Science, Theory & Methods
Xin Wang, Sisi Duan, James Clavin, Haibin Zhang
Summary: A blockchain is a distributed system that provides strong security guarantees and builds a trustworthy decentralized system in an untrustworthy environment. This article focuses on the research of BFT protocols, categorizing them based on system models and workflow, and aims to answer questions regarding the evolution of BFT research in the past four decades, especially with the rise of blockchains, as well as the future needs for BFT research.
ACM COMPUTING SURVEYS
(2022)
Article
Computer Science, Hardware & Architecture
Christian Berger, Hans P. Reiser, Joao Sousa, Alysson Bessani
Summary: With the development of blockchain infrastructure, world-spanning Byzantine consensus becomes practical and necessary. However, in geographically distributed systems, the speed of achieving consensus is limited by the different latencies of connections. To address this issue and reduce consensus latency, this article proposes the Adaptive Wide-Area REplication (AWARE) mechanism, which dynamically adjusts voting weights and leader positions to support fast quorums in the system.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2022)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)