信息工程最新文献

{"title":"Backtesting framework for concentrated liquidity market makers on Uniswap V3 decentralized exchange","authors":"Andrey Urusov ,&nbsp;Rostislav Berezovskiy ,&nbsp;Yury Yanovich","doi":"10.1016/j.bcra.2024.100256","DOIUrl":"10.1016/j.bcra.2024.100256","url":null,"abstract":"<div><div>Decentralized Finance (DeFi) has revolutionized the financial landscape, with protocols like Uniswap offering innovative automated market-making mechanisms. This article explores the development of a backtesting framework specifically tailored for Concentrated Liquidity Market Makers (CLMMs). The focus is on leveraging the liquidity distribution approximated using a parametric model to estimate the rewards within liquidity pools. The article details the design, implementation, and insights derived from this novel approach to backtesting within the context of Uniswap V3. The developed backtester was successfully utilized to assess reward levels across several pools using historical data from 2023 (pools Uniswap V3 for pairs of altcoins, stablecoins, and USDC/ETH with different fee levels). Moreover, the error in modeling the level of rewards for the period under review for each pool was less than 1%. This demonstrated the effectiveness of the backtester in quantifying liquidity pool rewards and its potential in estimating revenues of Liquidity Provider (LP) as part of the pool rewards, which is the focus of our next research. The backtester serves as a tool to simulate trading strategies and liquidity provision scenarios, providing a quantitative assessment of potential returns for LPs. By incorporating statistical tools to mirror CLMM pool liquidity dynamics, this framework can be further leveraged for strategy enhancement and risk evaluation for LPs operating within decentralized exchanges.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"6 1","pages":"Article 100256"},"PeriodicalIF":6.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SSI-MedRx: A fraud-resilient healthcare system based on blockchain and SSI","authors":"Meriem Guerar ,&nbsp;Mauro Migliardi ,&nbsp;Enrico Russo ,&nbsp;Djamel Khadraoui ,&nbsp;Alessio Merlo","doi":"10.1016/j.bcra.2024.100242","DOIUrl":"10.1016/j.bcra.2024.100242","url":null,"abstract":"<div><div>Today, healthcare fraud poses a significant issue, encompassing everything from falsified billing claims and phantom services to the excessive prescription of opioid medications and medical identity theft. These deceptive activities cause substantial financial losses, erode patient trust, compromise healthcare quality, and threaten patient safety. In this paper, we introduce SSI-MedRx, a healthcare system based on blockchain technology and Self-Sovereign Identity (SSI). It is designed to ensure cross-border interoperability, preserve patient privacy, and prevent challenging healthcare frauds, including medical identity theft, phantom billing, kickbacks, and opioid overprescribing. By design, our system empowers patients by granting them complete control over their personal and health data. This shift toward patient-centric data management can potentially reduce the risk of data breaches, enhance care coordination, and improve overall healthcare outcomes.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"6 1","pages":"Article 100242"},"PeriodicalIF":6.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating blockchain platforms for IoT applications in Industry 5.0: A comprehensive review","authors":"Najmus Sakib Sizan ,&nbsp;Diganta Dey ,&nbsp;Md. Abu Layek ,&nbsp;Md Ashraf Uddin ,&nbsp;Eui-Nam Huh","doi":"10.1016/j.bcra.2025.100276","DOIUrl":"10.1016/j.bcra.2025.100276","url":null,"abstract":"<div><div>As Industry 5.0 emerges, the convergence of advanced technologies like the Internet of Things (IoT) and blockchain is vital in shaping the future of industrial automation. Industry 5.0 emphasizes the collaborative relationship between humans and machines, requiring robust, decentralized systems to ensure security, accountability, and trust in interconnected ecosystems. Currently, IoT data processing is cloud-centric, which introduces challenges like fragmented data silos, limiting the potential for seamless and secure real-time analytics. Blockchain technology offers a solution by providing a decentralized and transparent ledger that can enhance data integrity and security across IoT applications. This study investigates the integration of blockchain with the IoT in the context of Industry 5.0, highlighting the potential for improved data management, security, and human-machine collaboration. By conducting a comprehensive analysis of IoT application designs and blockchain platforms, we evaluate existing literature to uncover the challenges, benefits, and limitations of this integration. Our research contributes by proposing a framework for selecting optimal blockchain platforms for IoT applications in Industry 5.0, providing actionable recommendations for enhanced data trust and resilience. Future research directions are also outlined to address the evolving demands of this technological convergence, ensuring that IoT ecosystems are secure, scalable, and human-centered in the era of Industry 5.0.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"6 3","pages":"Article 100276"},"PeriodicalIF":5.6,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144772748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive control of bilateral teleoperation systems under denial-of-service attacks","authors":"Lanyan Wei,&nbsp;Yuling Li","doi":"10.1007/s43684-025-00093-1","DOIUrl":"10.1007/s43684-025-00093-1","url":null,"abstract":"<div><p>This paper investigates resilient consensus control for teleoperation systems under denial-of-service (DoS) attacks. We design resilient controllers with auxiliary systems based on sampled positions of both master and slave robots, enhancing robustness during DoS attacks. Additionally, we establish stability conditions on DoS attack duration and frequency by applying multivariate small-gain methods to ensure closed-loop stability without the need to solve linear matrix inequalities. Finally, the effectiveness of the controllers is validated through the simulation results, demonstrating that the master-slave synchronization is achieved.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-025-00093-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient and accurate road crack detection technology based on YOLOv8-ES","authors":"Kaili Zeng,&nbsp;Rui Fan,&nbsp;Xiaoyu Tang","doi":"10.1007/s43684-025-00091-3","DOIUrl":"10.1007/s43684-025-00091-3","url":null,"abstract":"<div><p>Road damage detection is an important aspect of road maintenance. Traditional manual inspections are laborious and imprecise. With the rise of deep learning technology, pavement detection methods employing deep neural networks give an efficient and accurate solution. However, due to background diversity, limited resolution, and fracture similarity, it is tough to detect road cracks with high accuracy. In this study, we offer a unique, efficient and accurate road crack damage detection, namely YOLOv8-ES. We present a novel dynamic convolutional layer(EDCM) that successfully increases the feature extraction capabilities for small fractures. At the same time, we also present a new attention mechanism (SGAM). It can effectively retain crucial information and increase the network feature extraction capacity. The Wise-IoU technique contains a dynamic, non-monotonic focusing mechanism designed to return to the goal-bounding box more precisely, especially for low-quality samples. We validate our method on both RDD2022 and VOC2007 datasets. The experimental results suggest that YOLOv8-ES performs well. This unique approach provides great support for the development of intelligent road maintenance systems and is projected to achieve further advances in future applications.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-025-00091-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143373272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A cooperative jamming decision-making method based on multi-agent reinforcement learning","authors":"Bingchen Cai,&nbsp;Haoran Li,&nbsp;Naimin Zhang,&nbsp;Mingyu Cao,&nbsp;Han Yu","doi":"10.1007/s43684-025-00090-4","DOIUrl":"10.1007/s43684-025-00090-4","url":null,"abstract":"<div><p>Electromagnetic jamming is a critical countermeasure in defense interception scenarios. This paper addresses the complex electromagnetic game involving multiple active jammers and radar systems by proposing a multi-agent reinforcement learning-based cooperative jamming decision-making method (MA-CJD). The proposed approach achieves high-quality and efficient target allocation, jamming mode selection, and power control. Mathematical models for radar systems and active jamming are developed to represent a multi-jammer and multi-radar electromagnetic confrontation scenario. The cooperative jamming decision-making process is then modeled as a Markov game, where the QMix multi-agent reinforcement learning algorithm is innovatively applied to handle inter-jammer cooperation. To tackle the challenges of a parameterized action space, the MP-DQN network structure is adopted, forming the basis of the MA-CJD algorithm. Simulation experiments validate the effectiveness of the proposed MA-CJD algorithm. Results show that MA-CJD significantly reduces the time defense units are detected while minimizing jamming resource consumption. Compared with existing algorithms, MA-CJD achieves better solutions, demonstrating its superiority in cooperative jamming scenarios.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-025-00090-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chasing in virtual environment:Dynamic alignment for multi-user collaborative redirected walking","authors":"Tianyang Dong,&nbsp;Shuqian Lv,&nbsp;Hubin Kong,&nbsp;Huanbo Zhang","doi":"10.1016/j.vrih.2024.07.002","DOIUrl":"10.1016/j.vrih.2024.07.002","url":null,"abstract":"<div><h3>Background</h3><div>The redirected walking (RDW) method for multi-user collaboration requires maintaining the relative position between users in a virtual environment (VE) and physical environment (PE). A chasing game in a VE is a typical virtual reality game that entails multi-user collaboration. When a user approaches and interacts with a target user in the VE, the user is expected to approach and interact with the target user in the corresponding PE as well. Existing methods of multi-user RDW mainly focus on obstacle avoidance, which does not account for the relative positional relationship between the users in both VE and PE.</div></div><div><h3>Methods</h3><div>To enhance the user experience and facilitate potential interaction, this paper presents a novel dynamic alignment algorithm for multi-user collaborative redirected walking (DA-RDW) in a shared PE where the target user and other users are moving. This algorithm adopts improved artificial potential fields, where the repulsive force is a function of the relative position and velocity of the user with respect to dynamic obstacles. For the best alignment, this algorithm sets the alignment-guidance force in several cases and then converts it into a constrained optimization problem to obtain the optimal direction. Moreover, this algorithm introduces a potential interaction object selection strategy for a dynamically uncertain environment to speed up the subsequent alignment. To balance obstacle avoidance and alignment, this algorithm uses the dynamic weightings of the virtual and physical distances between users and the target to determine the resultant force vector.</div></div><div><h3>Results</h3><div>The efficacy of the proposed method was evaluated using a series of simulations and live-user experiments. The experimental results demonstrate that our novel dynamic alignment method for multi-user collaborative redirected walking can reduce the distance error in both VE and PE to improve alignment with fewer collisions.</div></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"7 1","pages":"Pages 26-46"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing wireless sensor network topology with node load consideration","authors":"Ruizhi Chen","doi":"10.1016/j.vrih.2024.08.003","DOIUrl":"10.1016/j.vrih.2024.08.003","url":null,"abstract":"<div><h3>Background</h3><div>With the development of the Internet, the topology optimization of wireless sensor networks has received increasing attention. However, traditional optimization methods often overlook the energy imbalance caused by node loads, which affects network performance.</div></div><div><h3>Methods</h3><div>To improve the overall performance and efficiency of wireless sensor networks, a new method for optimizing the wireless sensor network topology based on K-means clustering and firefly algorithms is proposed. The K-means clustering algorithm partitions nodes by minimizing the within-cluster variance, while the firefly algorithm is an optimization algorithm based on swarm intelligence that simulates the flashing interaction between fireflies to guide the search process. The proposed method first introduces the K-means clustering algorithm to cluster nodes and then introduces a firefly algorithm to dynamically adjust the nodes.</div></div><div><h3>Results</h3><div>The results showed that the average clustering accuracies in the Wine and Iris data sets were 86.59% and 94.55%, respectively, demonstrating good clustering performance. When calculating the node mortality rate and network load balancing standard deviation, the proposed algorithm showed dead nodes at approximately 50 iterations, with an average load balancing standard deviation of 1.7×10⁴, proving its contribution to extending the network lifespan.</div></div><div><h3>Conclusions</h3><div>This demonstrates the superiority of the proposed algorithm in significantly improving the energy efficiency and load balancing of wireless sensor networks to extend the network lifespan. The research results indicate that wireless sensor networks have theoretical and practical significance in fields such as monitoring, healthcare, and agriculture.</div></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"7 1","pages":"Pages 47-61"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Finger tracking for wearable VR glove using flexible rack mechanism","authors":"Roshan Thilakarathna,&nbsp;Maroay Phlernjai","doi":"10.1016/j.vrih.2024.03.001","DOIUrl":"10.1016/j.vrih.2024.03.001","url":null,"abstract":"<div><h3>Background</h3><div>With the increasing prominence of hand and finger motion tracking in virtual reality (VR) applications and rehabilitation studies, data gloves have emerged as a prevalent solution. In this study, we developed an innovative, lightweight, and detachable data glove tailored for finger motion tracking in VR environments.</div></div><div><h3>Methods</h3><div>The glove design incorporates a potentiometer coupled with a flexible rack and pinion gear system, facilitating precise and natural hand gestures for interaction with VR applications. Initially, we calibrated the potentiometer to align with the actual finger bending angle, and verified the accuracy of angle measurements recorded by the data glove. To verify the precision and reliability of our data glove, we conducted repeatability testing for flexion (grip test) and extension (flat test), with 250 measurements each, across five users. We employed the Gage Repeatability and Reproducibility to analyze and interpret the repeatable data. Furthermore, we integrated the gloves into a SteamVR home environment using the OpenGlove auto-calibration tool.</div></div><div><h3>Conclusions</h3><div>The repeatability analysis revealed an aggregate error of 1.45 degrees in both the gripped and flat hand positions. This outcome was notably favorable when compared with the findings from assessments of nine alternative data gloves that employed similar protocols. In these experiments, users navigated and engaged with virtual objects, underlining the glove's exact tracking of finger motion. Furthermore, the proposed data glove exhibited a low response time of 17–34 ms and back-drive force of only 0.19 N. Additionally, according to a comfort evaluation using the Comfort Rating Scales, the proposed glove system is wearable, placing it at the WL1 level.</div></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"7 1","pages":"Pages 1-25"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FDCPNet:feature discrimination and context propagation network for 3D shape representation","authors":"Weimin SHI,&nbsp;Yuan XIONG,&nbsp;Qianwen WANG,&nbsp;Han JIANG,&nbsp;Zhong ZHOU","doi":"10.1016/j.vrih.2024.06.001","DOIUrl":"10.1016/j.vrih.2024.06.001","url":null,"abstract":"<div><h3>Background</h3><div>Three-dimensional (3D) shape representation using mesh data is essential in various applications, such as virtual reality and simulation technologies. Current methods for extracting features from mesh edges or faces struggle with complex 3D models because edge-based approaches miss global contexts and face-based methods overlook variations in adjacent areas, which affects the overall precision. To address these issues, we propose the Feature Discrimination and Context Propagation Network (FDCPNet), which is a novel approach that synergistically integrates local and global features in mesh datasets.</div></div><div><h3>Methods</h3><div>FDCPNet is composed of two modules: (1) the Feature Discrimination Module, which employs an attention mechanism to enhance the identification of key local features, and (2) the Context Propagation Module, which enriches key local features by integrating global contextual information, thereby facilitating a more detailed and comprehensive representation of crucial areas within the mesh model.</div></div><div><h3>Results</h3><div>Experiments on popular datasets validated the effectiveness of FDCPNet, showing an improvement in the classification accuracy over the baseline MeshNet. Furthermore, even with reduced mesh face numbers and limited training data, FDCPNet achieved promising results, demonstrating its robustness in scenarios of variable complexity.</div></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"7 1","pages":"Pages 83-94"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}