信息工程最新文献

{"title":"PureQuantum: Towards a scalable blockchain channel security in IoT networks","authors":"Collins Izuchukwu Okafor,&nbsp;Love Allen Chijioke Ahakonye,&nbsp;Jae Min Lee,&nbsp;Dong-Seong Kim","doi":"10.1016/j.bcra.2025.100372","DOIUrl":"10.1016/j.bcra.2025.100372","url":null,"abstract":"<div><div>Integrating Internet of Things (IoT) networks with blockchain technology has introduced transformative potential for secure, decentralized applications. However, with expanding IoT networks and the rapid advancements in quantum computing, which can break the classical encryption mechanism employed in these networks, securing validator communications in blockchain consensus mechanisms becomes increasingly challenging. This study presents PureQuantum, a novel quantum-enhanced framework that integrates quantum key distribution (QKD) into a proof-of-authority and association consensus mechanism. Our experimental evaluation demonstrates that PureQuantum records an approximate key generation time of 2.315<em>s</em> (BB84), maintaining a minimal error rate of 0.2493, thereby ensuring robust resistance to classical and quantum computational threats. Exploring the architecture, design, and performance of PureQuantum, this concept highlights its ability to balance scalability, energy efficiency, and quantum-resilient security. By integrating QKD into validator communication protocols, PureQuantum provides a future-proof framework capable of supporting the demands of IoT-enabled decentralized systems.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"7 4","pages":"Article 100372"},"PeriodicalIF":6.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148738728","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":"Lightweight honeypot contract detection method based on tree pruning and graph auto-encoder","authors":"Youwei Wang,&nbsp;Yudong Hou,&nbsp;Lizhou Feng","doi":"10.1016/j.bcra.2025.100352","DOIUrl":"10.1016/j.bcra.2025.100352","url":null,"abstract":"<div><div>To address the high complexity of current honeypot contract detection methods, a lightweight honeypot contract detection method based on pruning and graph auto-encoder (called CSSDetector) is proposed. First, the contract source code is converted into an XML parse tree, and the tree is pruned using term frequency-inverse document frequency (TF-IDF) to reduce the number of nodes. Then, to improve training efficiency while guaranteeing detection accuracy, the pruned XML parse tree is converted into a graph, and the Solidity source code is learned using the Word2vec model and the graph auto-encoder to reduce the node dimension. Finally, to reduce the impact of the dataset imbalance problem, three data augmentation methods that can improve model robustness and classification accuracy are used to increase the diversity of the training samples. The experimental results show that the CSSDetector significantly reduces the model training time and storage space while ensuring honeypot contract detection performance. Compared with the CSGDetector, the CSSDetector reduces the temporal cost by more than 70% and the spatial cost by more than 90%, verifying the execution efficiency and classification accuracy of the proposed method.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"7 4","pages":"Article 100352"},"PeriodicalIF":6.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148738729","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":"Analytical solution for real-time simulation of membrane deformation","authors":"Ivan F. COSTA,&nbsp;Armando M. MAROJA,&nbsp;Edivaldo J. da SILVA,&nbsp;Geraldo B. OLIVEIRA FILHO,&nbsp;Rafael S. COSTA,&nbsp;Eduardo LEONARDECZ","doi":"10.1016/j.vrih.2026.07.003","DOIUrl":"10.1016/j.vrih.2026.07.003","url":null,"abstract":"<div><div>Despite considerable efforts in simulating the behavior of soft tissues in the last decades, real-time modeling of soft tissue deformation remains a challenge. Current deformable models face several challenges that limit their use in interactive applications. In particular, the primary limitations with regard to effciency and stability should be addressed to achieve graphic and haptic interactivity. Herein, a new analytical approach for real-time simulation of object deformation is proposed. This approach utilizes physically motivated analytical equations to achieve deformations. Moreover, we developed an analytical solution for elastic two-dimensional (2D) membranes and three-dimensional (3D) objects enclosed by a fluid-filled membrane. We solved Poisson’s equation with a Dirichlet boundary condition using Green’s functions to obtain the analytical solutions. We obtained the equilibrium configurations of a deformed 2D membrane in contact with a probe and compared the results to the experimental data through statistical validation. Subsequently, the proposed approach was extended to simulate the surface deformation of a 3D object with volume conservation. Furthermore, the methodology was successfully implemented in a virtual-reality environment with haptic force feedback to enhance the user’s immersion. Our results demonstrated that the proposed analytical approach enables real-time modeling of membrane deformations, and the statistical analysis provides a deep understanding of the model’s strengths and limitations.</div></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"8 4","pages":"Pages 457-480"},"PeriodicalIF":6.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149028328","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":"Parametric plane detection from 3D point cloud via probabilistic clustering","authors":"Pengxu LI,&nbsp;Yinchang ZHOU,&nbsp;Xiaoliang LUO,&nbsp;Weiyang MA,&nbsp;Lüchun WANG,&nbsp;Junpeng HUANG,&nbsp;Xiangyang LI,&nbsp;Xiaopeng ZHANG,&nbsp;Jianwei GUO","doi":"10.1016/j.vrih.2026.07.002","DOIUrl":"10.1016/j.vrih.2026.07.002","url":null,"abstract":"<div><div>A plane is the most fundamental isotropic geometric shape commonly found in human-made environments. Plane detection serves as a bridge between low-level 3D raw data and high-level structural representations of underlying 3D shapes. Existing methods often require extensive parameter tuning, rely on normal information, or depend on large training datasets, limiting their generalization across diverse shapes. In this paper, we introduce a novel algorithm for extracting high-quality planar information from 3D point clouds, inspired by unsupervised probabilistic clustering. Our approach reformulates plane detection as a Bayesian probabilistic modeling problem within a machine learning framework. By theoretically deriving a maximum likelihood function for plane parameters, we leverage the classic yet powerful expectation-maximization (EM) algorithm to iteratively solve for maximum likelihood estimates. Our framework is highly flexible, enabling the accurate extraction of parametric planes from various scenes and sensor types. Extensive experiments and comparisons with both analytical and learning-based methods demonstrate the superiority of our approach. Moreover, our algorithm is simple, efficient, and easy to implement. The code is released to facilitate future research: <span><span>https://github.com/dengdaigithub/PPD</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"8 4","pages":"Pages 383-401"},"PeriodicalIF":6.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149028329","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":"Towards decentralized cybersecurity: A novel privacy-preserving federated learning approach for botnet attack detection","authors":"Md. Alamgir Hossain,&nbsp;Md. Samiul Islam","doi":"10.1016/j.bcra.2025.100355","DOIUrl":"10.1016/j.bcra.2025.100355","url":null,"abstract":"<div><div>The exponential proliferation of IoT devices has led to an alarming surge in botnet-based cyberattacks, necessitating advanced, privacy-preserving, and scalable detection mechanisms. Traditional centralized machine learning approaches for botnet detection face critical challenges, including data privacy risks, communication overhead, and model generalization issues. To address these concerns, this research presents a privacy-preserving Federated Learning (FL) approach for decentralized cybersecurity, using SHapley Additive exPlanations (SHAP)-based feature selection to enhance interpretability and model efficiency. The proposed method enables local models to be trained on edge devices without sharing raw data, preserving user privacy while maintaining robust detection capabilities. Using the MedBiot dataset, we evaluate the effectiveness of FL under both non-Independent and Identically Distributed (non-IID) and IID settings, where botnet attack types (e.g., Bashlite, Mirai, and Torii) are distributed across multiple federated clients. The experimental results indicate that the non-IID federated models achieved exceptional detection recall (99.98%), with high precision, accuracy, and F1-score across all clients, demonstrating resilience even in highly heterogeneous environments. The IID setting, where data were randomly distributed among clients, also maintained high classification accuracy (≈99.98%), highlighting the significance of localized attack patterns in federated cybersecurity. Additionally, SHAP-based feature selection optimizes performance by identifying the most critical features, reducing computational overhead while preserving explainability. The findings underscore the efficacy of FL for real-world IoT botnet detection, offering a scalable, secure, and interpretable solution to modern cybersecurity challenges.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"7 4","pages":"Article 100355"},"PeriodicalIF":6.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148843957","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":"SmartML: Enhancing security and reliability in smart contract development","authors":"Adele Veschetti,&nbsp;Richard Bubel,&nbsp;Reiner Hähnle","doi":"10.1016/j.bcra.2025.100362","DOIUrl":"10.1016/j.bcra.2025.100362","url":null,"abstract":"<div><div>Smart contracts, as integral components of blockchain technology, play a pivotal role in automating and executing agreements in a trust-free manner. However, ensuring the correctness and reliability of smart contracts remains a significant challenge due to their complex nature and potential vulnerabilities. To address this challenge, we propose <span>SmartML</span>, a novel modeling language tailored specifically to smart contracts. This paper presents an in-depth exploration of the features, advantages, and applications of the proposed modeling language. By providing a precise and intuitive means of describing smart contract behaviors, <span>SmartML</span> aims to support the development, verification, and validation of smart contracts, ultimately reinforcing trust and confidence in them. Furthermore, we discuss the relation of existing blockchain technologies to our modeling language and highlight its potential to streamline integration efforts. We posit <span>SmartML</span> as a versatile tool to advance interoperability, reliability, and security of smart contracts in blockchain ecosystems.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"7 4","pages":"Article 100362"},"PeriodicalIF":6.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148843958","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":"EVMLiSA: Sound static control-flow graph construction for EVM bytecode","authors":"Vincenzo Arceri,&nbsp;Saverio Mattia Merenda,&nbsp;Luca Negrini,&nbsp;Luca Olivieri,&nbsp;Enea Zaffanella","doi":"10.1016/j.bcra.2025.100384","DOIUrl":"10.1016/j.bcra.2025.100384","url":null,"abstract":"<div><div>Ethereum enables the creation and execution of decentralized applications through smart contracts that are compiled into the Ethereum Virtual Machine (EVM) bytecode. Once deployed on the blockchain, the bytecode is immutable; therefore, ensuring that smart contracts are bug-free before their deployment is of utmost importance. A crucial preliminary step for any effective static analysis of EVM bytecode is the extraction of the control flow graph (CFG): This presents significant challenges due to potentially statically unknown jump destinations. In this paper, we present a novel approach, based on abstract interpretation, aiming to build a sound CFG from EVM bytecode smart contracts. Our analysis, which is implemented in our static analyzer Ethereum Virtual Machine Library for Static Analysis (EVMLiSA), is based on a parametric abstract domain that approximates concrete execution stacks at each program point as an <em>l</em>-sized set of abstract stacks of maximal height <em>h</em>; the results of the analysis are then used to resolve the jump destinations at jump nodes. Furthermore, EVMLiSA includes a checker for reentrancy detection, working on the constructed CFG. Our experiments show that, by fine-tuning the analysis parameters, EVMLiSA is able to build sound CFGs for all real-world smart contracts in the considered benchmark suite. Moreover, EVMLiSA successfully detects all reentrancy vulnerabilities in EVM bytecode smart contracts, while producing a small number of false positives.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"7 4","pages":"Article 100384"},"PeriodicalIF":6.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148738726","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":"Augmented reality-guided precision in mandibular reconstruction: Non-contact laser point cloud registration for simultaneous osteotomy and implantation","authors":"Long SHAO,&nbsp;Zhao ZHENG,&nbsp;Tianyu FU,&nbsp;Deqiang XIAO,&nbsp;Danni AI,&nbsp;Jingfan FAN,&nbsp;Hong SONG,&nbsp;Ruiqi ZHAO,&nbsp;Jixin LIU,&nbsp;Tao ZHANG,&nbsp;Jian YANG","doi":"10.1016/j.vrih.2026.06.001","DOIUrl":"10.1016/j.vrih.2026.06.001","url":null,"abstract":"<div><div>Maxillofacial reconstruction often results in significant mandibular defects, necessitating precise surgical interventions. Traditional approaches suffer from lengthy preoperative preparations and substantial radiation exposure. This study introduces a novel augmented reality (AR) framework for simultaneous fibula flap mandibular reconstruction and implant placement, leveraging non-contact laser point cloud registration. Our method employs adaptive line laser scanning (ALLS) for surface reconstruction, enabling markerless registration to integrate preoperative virtual surgical plans with intraoperative reality. Virtual-real view calibration, enhanced by particle swarm optimization (PSO), ensures high alignment accuracy. Cadaver experiments demonstrated superior performance, with mandibular osteotomy achieving a 2.38 mm control point position error and fibular osteotomy an accuracy of 1.29 mm. AR-guided simultaneous implantation showed a position error of 1.28 mm, and overall mandibular reconstruction errors were approximately 1.77 mm, ensuring stable temporomandibular joint functionality. This framework offers a new technological paradigm for minimally invasive and intelligent precision maxillofacial reconstruction.</div></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"8 4","pages":"Pages 417-439"},"PeriodicalIF":6.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149028331","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":"Learning from Terra-Luna: A simulation-based study on stabilizing algorithmic stablecoins","authors":"Federico Calandra,&nbsp;Francesco P. Rossi,&nbsp;Francesco Fabris,&nbsp;Marco Bernardo","doi":"10.1016/j.bcra.2025.100364","DOIUrl":"10.1016/j.bcra.2025.100364","url":null,"abstract":"<div><div>This paper investigates the dynamics behind the catastrophic collapse of the Terra-Luna ecosystem in May 2022, where the TerraUSD stablecoin de-pegged and lost nearly all its value. Using a custom-built simulation environment, we reproduce the free-market interactions and protocol mechanisms that triggered the crash, offering a new perspective on the vulnerabilities of algorithmic stablecoins based on the dual-token seigniorage model. Building on this analysis, we propose four stabilization strategies: Two purely algorithmic mechanisms, designed to prevent de-pegging and mitigate token hyperinflation, and two hybrid approaches, introducing partial collateralization through <em>Tether USD</em> (USDT) and <em>bitcoin</em> (BTC) reserves. Our simulations show that these strategies significantly reduce collapse events under extreme market conditions, with the best improvements ranging from 60.6% to 95.8%. Additionally, we gained better control over LUNA's circulating supply. Our simulation environment showcases how each proposal effectively mitigates systemic risks and enhances stability, thus providing valuable insights for designing future decentralized algorithmic stablecoins that can withstand market crises.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"7 4","pages":"Article 100364"},"PeriodicalIF":6.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148636829","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":"MeDeA: Decomposable attention for intrinsically interpretable ECG diagnosis","authors":"Yulong GE,&nbsp;Yingying WANG,&nbsp;Qing MING,&nbsp;Liang SHAO,&nbsp;Xiangyun LIAO,&nbsp;Weixin SI","doi":"10.1016/j.vrih.2026.05.001","DOIUrl":"10.1016/j.vrih.2026.05.001","url":null,"abstract":"<div><div>Deep learning has achieved strong performance on electrocardiogram (ECG) classification, yet many high-performing models still provide limited class-specific interpretability. We present MeDeA (multi-disease decompositional attention), a decomposable attention framework for intrinsically interpretable ECG diagnosis. The central idea is to combine a shared 1D backbone with disease-specific attention heads so that each predicted condition is associated with a dedicated explanatory pathway instead of relying solely on post-hoc attribution. We further describe a multi-query formulation, termed multi-query attention head (MQAH), in which each disease head contains a small set of global learnable queries to capture complementary morphological patterns. Experiments on the PTB-XL dataset show that MeDeA achieves a macro-F1 score of 0.775, matching strong black-box baselines while improving attribution faithfulness. We also report efficiency metrics including parameter count, weight footprint, and forward latency. Finally, we discuss limitations related to retrospective public datasets, shifts across hospitals and devices, sampling-rate mismatch, and annotation quality.</div></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"8 4","pages":"Pages 402-416"},"PeriodicalIF":6.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149028330","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}