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Zebra: A cluster-aware blockchain consensus algorithm

Journal of Network and computer Applications 2024年 232卷

作者： Wan, Ji Hu, Kai Li, Jie Guo, Yichen Su, Hao Li, Shenzhang Ye, Yafei School of Computer Science and Engineering Beihang University Beijing China Yunnan Power Grid Co Ltd China Southern Power Grid Co Ltd Kunming China Beihang Yunnan Innovation Institute Yunnan Key Laboratory of Blockchain Application Technology China

The Consensus algorithm is the core of the permissioned blockchain, it directly affects the performance and scalability of the system. Performance is limited by the computing power and network bandwidth of a single leader node. Most existing blockchain systems adopt mesh or star topology. blockchain performance decreases rapidly as the number of nodes increases. To solve this problem, we first design the n-k cluster tree and corresponding generation algorithm, which supports rapid reconfiguration of nodes. Then we propose the Zebra consensus algorithm, which is a cluster tree-based consensus algorithm. Compared to the PBFT, it has higher throughput and supports more nodes under the same hardware conditions. However, the tree network topology enhances scalability while also increasing latency among nodes. To reduce transaction latency, we designed the Pipeline-Zebra consensus algorithm that further improves the performance of blockchain systems in a tree network topology through parallel message propagation and block validation. The message complexity of the algorithm is O(n). Experimental results show that the performance of the algorithm proposed in this paper can reach 2.25 times that of the PBFT algorithm, and it supports four times the number of nodes under the same hardware. © 2024 Elsevier Ltd

关键词： Consensus algorithm

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Convolutional Neural Network Based Intelligent Handwritten Document Recognition

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computers, Materials & Continua 2022年第3期70卷 4563-4581页

作者： Sagheer Abbas Yousef Alhwaiti Areej Fatima Muhammad A.Khan Muhammad Adnan Khan Taher M.Ghazal Asma Kanwal Munir Ahmad Nouh Sabri Elmitwally School of Computer Science National College of Business administration and EconomicsLahore54000Pakistan College of Computer and Information Sciences Jouf UniversitySakaka72341Saudi Arabia Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)Bangi 43600SelangorMalaysia School of Information Technology Skyline University CollegeUniversity City Sharjah1797SharjahUAE Department of Computer Science GC UniversityLahorePakistan Department of Computer Science Faculty of Computers and Artificial IntelligenceCairo University12613Egypt

This paper presents a handwritten document recognition system based on the convolutional neural network *** today’s world,handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired *** technology is also helpful for the automatic data entry *** the proposed systemprepared a dataset of English language handwritten character *** proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten *** this research,multiple experiments get very worthy recognition *** proposed systemwill first performimage pre-processing stages to prepare data for training using a convolutional neural *** this processing,the input document is segmented using line,word and character *** proposed system get the accuracy during the character segmentation up to 86%.Then these segmented characters are sent to a convolutional neural network for their *** recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given *** proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%,and for validation that accuracy slightly decreases with 90.42%.

关键词： Convolutional neural network segmentation skew cursive characters recognition

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Instant Resonance: Dual Strategy Enhances the Data Consensus Success Rate of blockchain Threshold Signature Oracles

arXiv

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arXiv 2024年

作者： Xian, Youquan Zeng, Xueying Li, Chunpei Li, Dongcheng Wang, Peng Liu, Peng Li, Xianxian Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China School of Computer Science and Engineering Guangxi Normal University Guilin541004 China

With the rapid development of Decentralized Finance (DeFi) and Real-World Assets (RWA), the importance of blockchain oracles in real-time data acquisition has become increasingly prominent. Using cryptographic techniques, threshold signature oracles can achieve consensus on data from multiple nodes and provide corresponding proofs to ensure the credibility and security of the information. However, in real-time data acquisition, threshold signature methods face challenges such as data inconsistency and low success rates in heterogeneous environments, which limit their practical application potential. To address these issues, this paper proposes an innovative dual-strategy approach to enhance the success rate of data consensus in blockchain threshold signature oracles. Firstly, we introduce a Representative Enhanced Aggregation Strategy (REP-AG) that improves the representativeness of data submitted by nodes, ensuring consistency with data from other nodes, and thereby enhancing the usability of threshold signatures. Additionally, we present a Timing Optimization Strategy (TIM-OPT) that dynamically adjusts the timing of nodes' access to data sources to maximize consensus success rates. Experimental results indicate that REP-AG improves the aggregation success rate by approximately 56.6% compared to the optimal baseline, while the implementation of TIM-OPT leads to an average increase of approximately 32.9% in consensus success rates across all scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Decentralized finance

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Efficient Data Valuation Approximation in Federated Learning: A Sampling-based Approach

arXiv

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arXiv 2025年

作者： Wei, Shuyue Tong, Yongxin Zhou, Zimu He, Tianran Xu, Yi State Key Laboratory of Complex & Critical Software Environment Lab School of Computer Science and Engineering Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang University China City University of Hong Kong Hong Kong

Federated learning (FL) has emerged as a prominent distributed learning paradigm to utilize datasets across multiple data providers. In FL, cross-silo data providers often hesitate to share their high-quality dataset unless their data value can be fairly assessed. Shapley value (SV) has been advocated as the standard metric for data valuation in FL due to its desirable properties. However, the computational overhead of SV is prohibitive in practice, as it inherently requires training and evaluating an FL model across an exponential number of dataset combinations. Furthermore, existing solutions fail to achieve high accuracy and efficiency, making practical use of SV still out of reach, because they ignore choosing suitable computation scheme for approximation framework and overlook the property of utility function in FL. We first propose a unified stratified-sampling framework for two widely-used schemes. Then, we analyze and choose the more promising scheme under the FL linear regression assumption. After that, we identify a phenomenon termed key combinations, where only limited dataset combinations have a high-impact on final data value. Building on these insights, we propose a practical approximation algorithm, IPSS, which strategically selects high-impact dataset combinations rather than evaluating all possible combinations, thus substantially reducing time cost with minor approximation error. Furthermore, we conduct extensive evaluations on the FL benchmark datasets to demonstrate that our proposed algorithm outperforms a series of representative baselines in terms of efficiency and effectiveness. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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On the necessity of collaboration for online model selection with decentralized data 24

On the necessity of collaboration for online model selection...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Junfan Li Zheshun Wu Zenglin Xu Irwin King School of Computer Science and Technology Harbin Institute of Technology Shenzhen Pengcheng Lab and Artificial Intelligence Innovation and Incubation (AI 3 ) Institute Fudan University and Shanghai Academy of AI for Science Department of Computer Science and Engineering The Chinese University of Hong Kong

ISBN: (纸本)9798331314385

We consider online model selection with decentralized data over M clients, and study the necessity of collaboration among clients. Previous work proposed various federated algorithms without demonstrating their necessity, while we answer the question from a novel perspective of computational constraints. We prove lower bounds on the regret, and propose a federated algorithm and analyze the upper bound. Our results show (i) collaboration is unnecessary in the absence of computational constraints on clients; (ii) collaboration is necessary if the computational cost on each client is limited to o(K), where K is the number of candidate hypothesis spaces. We clarify the unnecessary nature of collaboration in previous federated algorithms for distributed online multi-kernel learning, and improve the regret bounds at a smaller computational and communication cost. Our algorithm relies on three new techniques including an improved Bernstein's inequality for martingale, a federated online mirror descent framework, and decoupling model selection and prediction, which might be of independent interest.

关键词：

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Fuzzy Concession Strategy for Emotional Human-computer Negotiation*

Fuzzy Concession Strategy for Emotional Human-Computer Negot...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Xudong Luo Ying Luo Kaili Sun Yanling Li Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Key Lab of Education Blockchain and Intelligent Technology of Ministry of Education School of Computer Science and Engineering Guangxi Normal University Guilin China

ISBN: (数字)9798331527235

ISBN: (纸本)9798331527242

This paper proposes an emotion-based human-computer negotiation system. Existing systems handle basic dialogues but struggle with complex negotiations involving human emotions. To address this, we design a fuzzy concession strategy that adjusts tactics through sentiment analysis, managing states like anger, happiness, and anxiety. Experiments show significant improvement in negotiation success and overall gains when considering emotional factors. Results demonstrate that this model enhances customer satisfaction and user experience by achieving agreements that satisfy users. This research highlights the potential of integrating fuzzy reasoning, sentiment analysis, and natural language processing into AI systems, laying the foundation for more intelligent, human-like dialogue agents and improving human-computer interaction and e-commerce engagement.

关键词： Human computer interaction Sentiment analysis Scalability Customer satisfaction Anxiety disorders Refining Machine learning User experience Electronic commerce Fuzzy reasoning

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Validity Matters: Uncertainty-Guided Testing of Deep Neural Networks

Validity Matters: Uncertainty-Guided Testing of Deep Neural ...

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作者： Jiang, Zhouxian Li, Honghui Wang, Rui Tian, Xuetao Liang, Ci Yan, Fei Zhang, Junwen Liu, Zhen School of Computer Science and Technology Beijing Jiaotong University Beijing China Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing China Faculty of Psychology Beijing Normal University Beijing China School of Transportation Science and Engineering Harbin Institute of Technology Harbin China School of Automation and Intelligence Beijing Jiaotong University Beijing China

Despite numerous applications of deep learning technologies on critical tasks in various domains, advanced deep neural networks (DNNs) face persistent safety and security challenges, such as the overconfidence in predicting out-of-distribution samples and susceptibility to adversarial examples. Thorough testing by exploring the input space serves as a key strategy to ensure their robustness and trustworthiness of these networks. However, existing testing methods focus on disclosing more erroneous model behaviours, overlooking the validity of the generated test inputs. To mitigate this issue, we investigate devising valid test input generation method for DNNs from a predictive uncertainty perspective. Through a large-scale empirical study across 11 predictive uncertainty metrics for DNNs, we explore the correlation between validity and uncertainty of test inputs. Our findings reveal that the predictive entropy-based and ensemble-based uncertainty metrics effectively characterize the input validity demonstration. Building on these insights, we introduce UCTest, an uncertainty-guided deep learning testing approach, to efficiently generate valid and authentic test inputs. We formulate a joint optimization objective: to uncover the model's misbehaviours by maximizing the loss function and concurrently generate valid test input by minimizing uncertainty. Extensive experiments demonstrate that our approach outperforms the current testing methods in generating valid test inputs. Furthermore, incorporating natural variation through data augmentation techniques into UCTest effectively boosts the diversity of generated test inputs. © 2024 John Wiley & Sons Ltd.

关键词： Adversarial machine learning

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B2IPTS: A blockchain-based intellectual property transaction framework

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7663-7678页

作者： Ai, Shan Chen, Chang Tang, Juan Huang, Pengfei Liu, Yanli Institute of Artificial Intelligence and Blockchain Guangzhou University Haisi Knowledge Center Jiulong Street Guangdong Guangzhou510700 China Pazhou Lab No.70 Yuean Road Haizhu District Guangdong Guangzhou510335 China School of Computer and Information Technology Beijing Jiaotong University No.3 Shangyuancun Haidian District Beijing100044 China School of Computer Science and Cyber Engineering Guangzhou University No.230 West Waihuan Street HEMC Guangdong Guangzhou510006 China Guangzhou Open University No.41West Lujing Road Guangdong Guangzhou510091 China

In the era of the digital economy, the protection of digital intellectual property is an important issue. At present, blockchain is widely used in the field of intellectual property protection. The market cap of NFT achieved tens of billions of dollars in a short period of time. Due to the lack of transaction openness, users are far less enthusiastic about the consortium blockchain-based intellectual property trading market. However, NFT is also plagued by a large number of pirated works. Existing methods are far from meeting the requirements of safe and efficient intellectual property protection tasks. To address this issue, we propose B2IPTS, a novel framework that integrates the advantages of consortium blockchain and cryptocurrency in the field of intellectual property protection. Our framework provides efficient ways to register intellectual property and execute transactions across blockchains. The property owners and property purchasers can exchange assets across heterogeneous blockchains without relying on a trusted intermediary. We use blockchain and cryptography to achieve three characteristics: (i) efficiency-fast on-chain certificate storage, (ii) versatility-compatibility with most blockchain structures, and (iii) atomicity of transactions-after a successful payment, the purchaser will definitely obtain the property. B2IPTS is designed as a decentralized system framework based on the consortium blockchain, and its primary functions rely on smart contracts. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： blockchain

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Find truth in the hands of the few:acquiring specific knowledge with crowdsourcing

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Frontiers of computer science 2021年第4期15卷 5-16页

作者： Tao HAN Hailong SUN Yangqiu SONG Yili FANG Xudong LIU SKLSDE Lab School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100191China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyClearwater BayHong Kong 999077China School of Computer and Information Engineering Zhejiang Gongshang UniversityHangzhou 310018China

Crowdsourcing has been a helpful mechanism to leverage human intelligence to acquire useful ***,when we aggregate the crowd knowledge based on the currently developed voting algorithms,it often results in common knowledge that may not be *** this paper,we consider the problem of collecting specific knowledge via *** the help of using external knowledge base such as WordNet,we incorporate the semantic relations between the alternative answers into a probabilisticmodel to determine which answer is more *** formulate the probabilistic model considering both worker’s ability and task’s difficulty from the basic assumption,and solve it by the expectation-maximization(EM)*** increase algorithm compatibility,we also refine our method into semi-supervised *** results show that our approach is robust with hyper-parameters and achieves better improvement thanmajority voting and other algorithms when more specific answers are expected,especially for sparse data.

关键词： crowdsourcing knowledge acquisition EM algorithm label aggregation

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SCPS:A Secure and Copyright-Preserving System for IIoT Based on Hyperledger Fabric 9

SCPS:A Secure and Copyright-Preserving System for IIoT Based...

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9th International Conference on Advanced Cloud and Big Data, CBD 2021

作者： Peng, Jiahui Li, Chunpei Yuan, Bo Meng, Hongwei Yu, Feng Li, Xianxian Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining Security GuiLin China Beihang University Development Environment School of Computer Science and Engineering State Key Laboratory of Software Beijing100191 China Xiong'An Intelligent City Innovation Federation Blockchain Lab Beijing China

ISBN: (纸本)9781665407458

Data, a key asset in Industrial Internet of Things(IIoT), has fueled the emergence of a new data trading in-dustry, which can be collected and traded for better development of IIoT. However, copyright protection is not considered in traditional transactions, resulting in the loss of copyright control of the data after the data was sold. A large number of piracy activities will reduce the enthusiasm of data owners to collect data. The solution we proposed was to use blockchain and smart contract to achieve a decentralized and secure copyright protection scheme. The Trusted Execution Environment(TEE) helped our system realized the piracy tracking. To be specific, our solution effectively achieves a secure and copyright protection system of IIoT data, we realize the real simulation on Hyperledger Fabric shows the good performance of our system. © 2022 IEEE.

关键词： blockchain

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