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A blockchain-based data transaction method with privacy protection and fairness

Peer-to-Peer Networking and Applications 2025年第4期18卷 1-13页

作者： Yuan, Ye Yang, Mingxing Xiong, Ruoting Duan, Jia Liu, Xi Zhang, Lianchong Ren, Wei Zhang, Xin Hubei Land Resources Vocational College Wuhan China Gemological Institute China University of Geosciences Wuhan China School of Computing Science University of East Anglia Norwich UK The Third Surveying and Mapping Institute of Hunan Province Hunan China Hunan Engineering Research Center of Geographic Information Security and Application Hunan China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Jiaxing University Jiaxing China School of Computer Science China University of Geosciences Wuhan China

In online trading, buyers may not be willing to reveal their identity, thus anonymous payment systems such as Central Bank Digital Currencies (CBDC) or blockchain systems may be preferred, instead of centralized payment systems. However, when relying on those payment systems, cheating or unfair behaviors from anonymous accounts should be defended against. In this paper, we propose a scheme that not only guarantees stronger customer privacy protection in terms of payment anonymity and transaction information anonymity (e.g., shopping user name, address), but also maintains the fairness in transactions. Our proposed system consists of three web systems: B2C web, M2B web, and delivery web, as well as an anonymous payment system (or blockchain, distributed ledger). The detailed models and protocols are proposed for trading, pre-payment, payment, arbitration between customers, merchants, manufacturers, third evaluators, and arbitrators. Experimental results show the low gas costs and execution time of each function on smart contract. The fairness in transactions is yet maintained because locked payment can be invoked by smart contract from oracle from the delivery web upon confirming the recipient of purchased merchandise by the customer.

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TEVA: Training-Efficient and Verifiable Aggregation for Federated Learning for Consumer Electronics in Industry 5.0

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IEEE Transactions on Consumer Electronics 2024年

作者： Xia, Yuanjun Liu, Yining Chen, Jingxue Liang, Yangfan Khan, Fazlullah Alturki, Ryan Wang, Xiaopei Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software School of Computer and Information Security Guilin541004 China Wenzhou University of Technology School of Data Science and Artificial Intelligence Wenzhou325027 China University of Technology School of Mathematical and Physical Sciences SydneyNSW2007 Australia Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems The Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province The Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing314001 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Zhejiang Ningbo315104 China Umm AI-Qura University Makkah Department of Software Engineering College of Computing Saudi Arabia Riverside Department of Computer Science and Engineering University of California CA92521 United States

Federated learning (FL) has been widely used for privacy-preserving model updates in Industry 5.0, facilitated by 6G networks. Despite FL's privacy-preserving advantages, it remains vulnerable to attacks where adversaries can infer private data from local models or manipulate the central server (CS) to deliver falsified global models. Current privacy-preserving approaches, primarily based on the FedAvg algorithm, fail to optimize training efficiency for non-independent and identically distributed (non-IID) data. This article proposes training-efficient and verifiable aggregation (TEVA) for FL to resolve these issues. This scheme combines threshold Paillier homomorphic encryption (TPHE), verifiable aggregation, and an optimized double momentum update mechanism (OdMum). TEVA not only leverages TPHE to protect the privacy of local models but also ensures the integrity of the global model through a verifiable aggregation mechanism. Additionally, TEVA integrates the OdMum algorithm to effectively address the challenges posed by non-IID data, promoting rapid model convergence and significantly enhancing overall training efficiency. Security analysis indicates that TEVA meets the requirements for privacy protection. Extensive experimental results demonstrate that TEVA can accelerate model convergence while incurring lower computational and communication overheads. © 1975-2011 IEEE.

关键词： Differential privacy

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Decoherence on staggered quantum walks

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Physical Review A 2022年第3期105卷 032452-032452页

作者： Raqueline A. M. Santos Franklin de L. Marquezino Center for Quantum Computer Science Faculty of Computing University of Latvia Raina bulvaris 19 Riga LV-1586 Latvia Systems Engineering and Computer Science Alberto Luiz Coimbra Institute for Graduate Studies and Research in Engineering Federal University of Rio de Janeiro Avenida Horacio Macedo 2030 H319 Rio de Janeiro 21941-972 Brazil Multidisciplinary Laboratory for Research in Computer Science Federal University of Rio de Janeiro Rodovia Washington Luiz 19593 Duque de Caxias 25240-005 Brazil

The decoherence phenomenon has been widely studied in different types of quantum walks. In this work we show how to model decoherence inspired by percolation on staggered quantum walks. Two models of unitary noise are described: breaking polygons and breaking vertices. The evolution operators subject to these noises are obtained, and the equivalence to the coined quantum walk model is presented. Further, we numerically analyze the effect of these decoherence models on the two-dimensional grid of 4-cliques. We examine how these perturbations affect the quantum walk based search algorithm in this graph and how expanding the tessellation intersection can make it more robust against decoherence.

关键词： Network searches Quantum algorithms Quantum computation Quantum information processing Quantum walks

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DMNER: Biomedical Named Entity Recognition by Detection and Matching

DMNER: Biomedical Named Entity Recognition by Detection and ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Junyi Bian Rongze Jiang Weiqi Zhai Tianyang Huang Xiaodi Huang Hong Zhou Shanfeng Zhu School of Computer Science Fudan University Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China School of Computing Mathematics and Engineering Charles Sturt University New South Wales Australia Atypon Systems LLC UK Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science Shanghai Key Lab of Intelligent Information Processing Zhangjiang Fudan International Innovation Center Fudan University Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Biomedical Named Entity Recognition (NER) is a crucial task in extracting information from biomedical texts. However, the diversity of professional terminology, semantic complexity, and the widespread presence of synonyms pose significant challenges. Traditional methods that rely on sequence labeling training datasets often struggle to handle these complexities. To address this, we introduce a novel framework for BioNER, termed DMNER, which leverages external knowledge and operates in two steps: entity boundary detection and entity category identification through matching. The core of DMNER is its second step, which determines the entity category by retrieving similar entities and their categories from a knowledge dictionary using semantic similarity matching. Our experiments on 10 biomedical datasets demonstrate that DMNER outperforms baselines across these tasks, proving its effectiveness and adaptability. DMNER is versatile and can be applied to various NER tasks, including supervised NER, distantly supervised NER, and NER on multiple datasets with disjoint label sets. The DMNER code is publicly available 1 .

关键词： Training Dictionaries Codes Semantics Named entity recognition Complexity theory Labeling Data mining Bioinformatics

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GSLB: The Graph Structure Learning Benchmark

arXiv

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arXiv 2023年

作者： Li, Zhixun Wang, Liang Sun, Xin Luo, Yifan Zhu, Yanqiao Chen, Dingshuo Luo, Yingtao Zhou, Xiangxin Liu, Qiang Wu, Shu Yu, Jeffrey Xu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Center for Research on Intelligent Perception Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Department of Automation University of Science and Technology of China China School of Cyberspace Security Beijing University of Posts and Telecommunications China Department of Computer Science University of California Los Angeles United States Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University United States

Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB. © 2023, CC BY.

关键词： Benchmarking

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Implementing two-qubit gates at the quantum speed limit

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Physical Review Research 2023年第4期5卷 043194-043194页

作者： Joel Howard Alexander Lidiak Casey Jameson Bora Basyildiz Kyle Clark Tongyu Zhao Mustafa Bal Junling Long David P. Pappas Meenakshi Singh Zhexuan Gong Department of Physics Colorado School of Mines Golden Colorado 80401 USA Rigetti Computing 775 Heinz Avenue Berkeley California 94701 USA Department of Computer Science Colorado School of Mines Golden Colorado 80401 USA Department of Physics University of Colorado Boulder Colorado 80309 USA Superconducting Quantum Materials and Systems Center Fermi National Accelerator Laboratory Batavia Illinois 60510 USA National Institute of Standards and Technology Boulder Colorado 80305 USA

The speed of elementary quantum gates, particularly two-qubit gates, ultimately sets the limit on the speed at which quantum circuits can operate. In this work, we experimentally demonstrate commonly used two-qubit gates at nearly the fastest possible speed allowed by the physical interaction strength between two superconducting transmon qubits. We achieve this quantum speed limit by implementing experimental gates designed using a machine-learning-inspired optimal control method. Importantly, our method only requires the single-qubit drive strength to be moderately larger than the interaction strength to achieve an arbitrary two-qubit gate close to its analytical speed limit with high fidelity. Thus the method is applicable to a variety of platforms, including those with comparable single-qubit and two-qubit gate speeds, or those with always-on interactions. We expect our method to offer significant speedups for non-native two-qubit gates that are typically achieved with a long sequence of single-qubit and native two-qubit gates.

关键词： Quantum circuits Quantum control Quantum gates Quantum information processing Superconducting qubits

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Graph Confidence Intercalibration Network for Intracranial Aneurysm Lesion Instance Segmentation in Dsa

SSRN

引用

SSRN 2024年

作者： Ye, Haili Mo, Yancheng Tang, Chen Liao, Mingqian Zhang, Xiaoqing Dai, Limeng Li, Baihua Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Computer Science Loughborough University LoughboroughLE11 3TU United Kingdom School of Ophthalmology and Optometry Eye Hospital Wenzhou Medical University Wenzhou325027 China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen People’s Hospital Neurosurgery Department Shenzhen518020 China China

Intracranial aneurysm (IA) lesion segmentation is significant for its treatment and prognosis. Although exiting deep network-based instance methods have good IA lesion segmentation results based on digital subtraction angiography (DSA) images, they still face great challenges with instance confidence bias and imprecise boundary segmentation, which may negatively affect IA diagnosis. To tackle these problems, this paper proposes a novel graph confidence intercalibration network (GCINet) to automatically segment IA lesions from DSA images. To be specific, we design a graph confidence intercalibration (GCI) module to mitigate instance confidence bias by dynamically adjusting their confidence distributions. At the same time, we propose an edge space perception (ESP) module to correct ambiguous segmentation boundaries. Extensive experiments on a clinical IA-DSA and a publicly available LiTS dataset demonstrate that our GCINet outperforms state-of-the-art methods. Additionally, visual analysis and ablation studies are provided to verify the effectiveness of each module in GCINet. © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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Research on robot path planning based on multi-strategy genetic ant colony optimization algorithm

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Information Sciences 2025年 718卷

作者： Li, Zheng Du, Mingyuan Qin, Jinlei Chen, Xuechao Department of Computer North China Electric Power University Baoding071003 China Hebei Key Laboratory of Knowledge Computing for Energy & Power Baoding071003 China Engineering Research Center of Intelligent Computing for Complex Energy Systems Ministry of Education Baoding071003 China

The ant colony optimization (ACO) algorithm is commonly used for path planning but faces issues like slow convergence, a tendency to get trapped in local optima, and instability in performance. This paper presents the multi-strategy genetic ant colony optimization (MSGACO) algorithm, which enhances ACO through several innovations: adaptive pheromone distribution initialization, a three-level node selection mechanism, a direction heuristic function, a selective crossover mechanism, and an elitist selection strategy. The adaptive pheromone distribution initialization, combined with the three-level node selection mechanism and direction heuristic function, increases the selection probability for nodes nearer to the destination. Additionally, the selective crossover mechanism and elitist selection strategy optimize the searched paths, resulting in more high-quality solutions and allocating pheromones to them. The main parameters of these strategies are optimized, and their effectiveness is verified. To showcase the benefits of MSGACO, experiments are performed utilizing five exemplary environmental models, with comparisons made to existing methodologies. The results showed a reduction of approximately 60.7% in path length and 52% in convergence iterations compared to the traditional ant colony optimization algorithm. © 2025 Elsevier Inc.

关键词： Ant colony optimization

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Adaptive Wavelet Filters as Practical Texture Feature Amplifiers for Parkinson’s Disease Screening in OCT

arXiv

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arXiv 2025年

作者： Zhang, Xiaoqing Shi, Hanfeng Li, Xiangyu Ye, Haili Xu, Tao Li, Na Hu, Yan Lv, Fan Chen, Jiangfan Liu, Jiang Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Ophthalmology Optometry and Vision Science Wenzhou Medical University Wenzhou China Centre for Computational Science and Mathematical Modelling Coventry University Coventry United Kingdom School of Computer Science University of Nottingham Ningbo China Ningbo315100 China School of Ophthalmology and Optometry Wenzhou Medical University Wenzhou325035 China Department of Electronic and Information Engineering Changchun University Changchun130022 China

Parkinson’s disease (PD) is a prevalent neurodegenerative disorder globally. The eye’s retina is an extension of the brain and has great potential in PD screening. Recent studies have suggested that texture features extracted from retinal layers can be adopted as biomarkers for PD diagnosis under optical coherence tomography (OCT) images. Frequency domain learning techniques can enhance the feature representations of deep neural networks (DNNs) by decomposing frequency components involving rich texture features. Additionally, previous works have not exploited texture features for automated PD screening in OCT. Motivated by the above analysis, we propose a novel Adaptive Wavelet Filter (AWF) that serves as the Practical Texture Feature Amplifier to fully leverage the merits of texture features to boost the PD screening performance of DNNs with the aid of frequency domain learning. Specifically, AWF first enhances texture feature representation diversities via channel mixer, then emphasizes informative texture feature representations with the well-designed adaptive wavelet filtering token mixer. By combining the AWFs with the DNN stem, AWFNet is constructed for automated PD screening. Additionally, we introduce a novel Balanced Confidence (BC) Loss by mining the potential of sample-wise predicted probabilities of all classes and class frequency prior, to further boost the PD screening performance and trustworthiness of AWFNet. The extensive experiments manifest the superiority of our AWFNet and BC over state-of-the-art methods in terms of PD screening performance and trustworthiness. © 2025, CC BY-NC-SA.

关键词： Neurodegenerative diseases

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When Evolutionary Computation Meets Privacy

arXiv

引用

arXiv 2023年

作者： Zhao, Bowen Chen, Wei-Neng Li, Xiaoguo Liu, Ximeng Pei, Qingqi Zhang, Jun Guangzhou Institute of Technology Xidian University Guangzhou China The School of Computer Science and Technology South China University of Technology Guangzhou China The School of Computing and Information Systems Singapore Management University Singapore Singapore The College of Computer and Data Science Fuzhou University Fujian China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen China School of Telecommunications Engineering Xidian University Xian China Zhejiang Normal University Jinhua China Hanyang University Ansan Korea Republic of

Recently, evolutionary computation (EC) has been promoted by machine learning, distributed computing, and big data technologies, resulting in new research directions of EC like distributed EC and surrogate-assisted EC. These advances have significantly improved the performance and the application scope of EC, but also trigger privacy leakages, such as the leakage of optimal results and surrogate model. Accordingly, evolutionary computation combined with privacy protection is becoming an emerging topic. However, privacy concerns in evolutionary computation lack a systematic exploration, especially for the object, motivation, position, and method of privacy protection. To this end, in this paper, we discuss three typical optimization paradigms (i.e., centralized optimization, distributed optimization, and data-driven optimization) to characterize optimization modes of evolutionary computation and propose BOOM (i.e., oBject, mOtivation, pOsition, and Method) to sort out privacy concerns in evolutionary computation. Specifically, the centralized optimization paradigm allows clients to outsource optimization problems to the centralized server and obtain optimization solutions from the server. While the distributed optimization paradigm exploits the storage and computational power of distributed devices to solve optimization problems. Also, the data-driven optimization paradigm utilizes data collected in history to tackle optimization problems lacking explicit objective functions. Particularly, this paper adopts BOOM to characterize the object and motivation of privacy protection in three typical optimization paradigms and discusses potential privacy-preserving technologies balancing optimization performance and privacy guarantees in three typical optimization paradigms. Furthermore, this paper attempts to foresee some new research directions of privacy-preserving evolutionary computation. © 2023, CC BY.

关键词： Optimization

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