Virtual Reality Intelligent Hardware Pub Date : 2024-10-01 DOI: 10.1016/j.vrih.2024.06.005

Philippe Latour, Marc Van Droogenbroeck

{"title":"CURDIS: A template for incremental curve discretization algorithms and its application to conics","authors":"Philippe Latour, Marc Van Droogenbroeck","doi":"10.1016/j.vrih.2024.06.005","DOIUrl":"10.1016/j.vrih.2024.06.005","url":null,"abstract":"<div><div>We introduce CURDIS, a template for algorithms to discretize arcs of regular curves by incrementally producing a list of support pixels covering the arc. In this template, algorithms proceed by finding the tangent quadrant at each point of the arc and determining which side the curve exits the pixel according to a tailored criterion. These two elements can be adapted for any type of curve, leading to algorithms dedicated to the shape of specific curves. While the calculation of the tangent quadrant for various curves, such as lines, conics, or cubics, is simple, it is more complex to analyze how pixels are traversed by the curve. In the case of conic arcs, we found a criterion for determining the pixel exit side. This leads us to present a new algorithm, called CURDIS-C, specific to the discretization of conics, for which we provide all the details. Surprisingly, the criterion for conics requires between one and three sign tests and four additions per pixel, making the algorithm efficient for resource-constrained systems and feasible for fixed-point or integer arithmetic implementations. Our algorithm also perfectly handles the pathological cases in which the conic intersects a pixel twice or changes quadrants multiple times within this pixel, achieving this generality at the cost of potentially computing up to two square roots per arc. We illustrate the use of CURDIS for the discretization of different curves, such as ellipses, hyperbolas, and parabolas, even when they degenerate into lines or corners.</div></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"6 5","pages":"Pages 358-382"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586957","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}

引用次数: 0

Leveraging multi-output modelling for CIELAB using colour difference formula towards sustainable textile dyeing 利用色差公式为 CIELAB 建立多输出模型，实现可持续纺织品染色

自主智能系统(英文) Pub Date : 2024-09-26 DOI: 10.1007/s43684-024-00076-8

Zheyuan Chen, Jian Liu, Jian Li, Mukun Yuan, Guangping Yu

{"title":"Leveraging multi-output modelling for CIELAB using colour difference formula towards sustainable textile dyeing","authors":"Zheyuan Chen, Jian Liu, Jian Li, Mukun Yuan, Guangping Yu","doi":"10.1007/s43684-024-00076-8","DOIUrl":"10.1007/s43684-024-00076-8","url":null,"abstract":"<div><p>Textile dyeing requires optimizing combinations of ingredients and process parameters to achieve target colour properties. Modelling the complex relationships between these factors and the resulting colour is challenging. In this case, a physics-informed approach for multi-output regression to model CIELAB colour values from dyeing ingredient and process inputs is proposed. Leveraging attention mechanisms and multi-task learning, the model outperforms baseline methods at predicting multiple colour outputs jointly. Specifically, the Transformer model’s attention mechanism captures the complex interactions between dyeing ingredients and process parameters, while the multi-task learning framework exploits the intrinsic correlations among the L*, a*, and b* dimensions of the CIELAB colour space. In addition, the incorporation of physical knowledge through a physics-informed loss function integrates the CMC colour difference formula. This loss function, along with the attention mechanisms, enables the model to learn the nuanced relationships between the dyeing process variables and the final colour output, thereby improving the overall prediction accuracy. This reduces trial-and-error costs and resource waste, contributing to environmental sustainability by minimizing water and energy consumption and chemical emissions.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00076-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142413919","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}

引用次数: 0

Improved vision-only localization method for mobile robots in indoor environments 改进的室内环境移动机器人纯视觉定位方法

自主智能系统(英文) Pub Date : 2024-09-18 DOI: 10.1007/s43684-024-00075-9

Gang Huang, Liangzhu Lu, Yifan Zhang, Gangfu Cao, Zhe Zhou

引用次数: 0

Numerical simulations of a two-strain dengue model to investigate the efficacy of the deployment of Wolbachia-carrying mosquitoes and vaccination for reducing the incidence of dengue infections 对双菌株登革热模型进行数值模拟，研究部署携带沃尔巴克氏体的蚊子和接种疫苗对降低登革热感染率的功效

Journal of Biosafety and Biosecurity Pub Date : 2024-09-05 DOI: 10.1016/j.jobb.2024.08.003

Meksianis Z. Ndii , Nursanti Anggriani , Bertha S. Djahi , Sanubari Tansah Tresna , Fatuh Inayaturohmat

引用次数: 0

A stochastic epidemic model with time delays and unreported cases based on Markovian switching 基于马尔可夫转换的具有时间延迟和未报告病例的随机流行病模型

Journal of Biosafety and Biosecurity Pub Date : 2024-09-05 DOI: 10.1016/j.jobb.2024.08.002

H.J. Alsakaji , Y.A. El-Khatib , F.A. Rihan (PhD; DSc) , A. Hashish

引用次数: 0

Detecting anomalies in blockchain transactions using machine learning classifiers and explainability analysis 利用机器学习分类器和可解释性分析检测区块链交易中的异常情况

IF 6.9 3区计算机科学

Blockchain-Research and Applications Pub Date : 2024-09-01 DOI: 10.1016/j.bcra.2024.100207

Mohammad Hasan , Mohammad Shahriar Rahman , Helge Janicke , Iqbal H. Sarker

{"title":"Detecting anomalies in blockchain transactions using machine learning classifiers and explainability analysis","authors":"Mohammad Hasan , Mohammad Shahriar Rahman , Helge Janicke , Iqbal H. Sarker","doi":"10.1016/j.bcra.2024.100207","DOIUrl":"10.1016/j.bcra.2024.100207","url":null,"abstract":"<div><div>As the use of blockchain for digital payments continues to rise, it becomes susceptible to various malicious attacks. Successfully detecting anomalies within blockchain transactions is essential for bolstering trust in digital payments. However, the task of anomaly detection in blockchain transaction data is challenging due to the infrequent occurrence of illicit transactions. Although several studies have been conducted in the field, a limitation persists: the lack of explanations for the model's predictions. This study seeks to overcome this limitation by integrating explainable artificial intelligence (XAI) techniques and anomaly rules into tree-based ensemble classifiers for detecting anomalous Bitcoin transactions. The shapley additive explanation (SHAP) method is employed to measure the contribution of each feature, and it is compatible with ensemble models. Moreover, we present rules for interpreting whether a Bitcoin transaction is anomalous or not. Additionally, we introduce an under-sampling algorithm named XGBCLUS, designed to balance anomalous and non-anomalous transaction data. This algorithm is compared against other commonly used under-sampling and over-sampling techniques. Finally, the outcomes of various tree-based single classifiers are compared with those of stacking and voting ensemble classifiers. Our experimental results demonstrate that: (i) XGBCLUS enhances true positive rate (TPR) and receiver operating characteristic-area under curve (ROC-AUC) scores compared to state-of-the-art under-sampling and over-sampling techniques, and (ii) our proposed ensemble classifiers outperform traditional single tree-based machine learning classifiers in terms of accuracy, TPR, and false positive rate (FPR) scores.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"5 3","pages":"Article 100207"},"PeriodicalIF":6.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320259","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}

引用次数: 0

Dual-blockchain based multi-layer grouping federated learning scheme for heterogeneous data in industrial IoT 基于双区块链的工业物联网异构数据多层分组联合学习方案

IF 6.9 3区计算机科学

Blockchain-Research and Applications Pub Date : 2024-09-01 DOI: 10.1016/j.bcra.2024.100195

{"title":"Dual-blockchain based multi-layer grouping federated learning scheme for heterogeneous data in industrial IoT","authors":"","doi":"10.1016/j.bcra.2024.100195","DOIUrl":"10.1016/j.bcra.2024.100195","url":null,"abstract":"<div><p>Federated learning (FL) allows data owners to train neural networks together without sharing local data, allowing the industrial Internet of Things (IIoT) to share a variety of data. However, traditional FL frameworks suffer from data heterogeneity and outdated models. To address these issues, this paper proposes a dual-blockchain based multi-layer grouping federated learning (BMFL) architecture. BMFL divides the participant groups based on the training tasks, then realizes the model training by combining synchronous and asynchronous FL through the multi-layer grouping structure, and uses the model blockchain to record the characteristic tags of the global model, allowing group-manners to extract the model based on the feature requirements and solving the problem of data heterogeneity. In addition, to protect the privacy of the model gradient parameters and manage the key, the global model is stored in ciphertext, and the chameleon hash algorithm is used to perform the modification and management of the encrypted key on the key blockchain while keeping the block header hash unchanged. Finally, we evaluate the performance of BMFL on different public datasets and verify the practicality of the scheme with real fault datasets. The experimental results show that the proposed BMFL exhibits more stable and accurate convergence behavior than the classic FL algorithm, and the key revocation overhead time is reasonable.</p></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"5 3","pages":"Article 100195"},"PeriodicalIF":6.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096720924000083/pdfft?md5=17c58876f9db0cb915d1b0b20cbe64c3&pid=1-s2.0-S2096720924000083-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140468200","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}

引用次数: 0

An interpretable model for large-scale smart contract vulnerability detection 大规模智能合约漏洞检测的可解释模型

IF 6.9 3区计算机科学

Blockchain-Research and Applications Pub Date : 2024-09-01 DOI: 10.1016/j.bcra.2024.100209

{"title":"An interpretable model for large-scale smart contract vulnerability detection","authors":"","doi":"10.1016/j.bcra.2024.100209","DOIUrl":"10.1016/j.bcra.2024.100209","url":null,"abstract":"<div><div>Smart contracts hold billions of dollars in digital currency, and their security vulnerabilities have drawn a lot of attention in recent years. Traditional methods for detecting smart contract vulnerabilities rely primarily on symbol execution, which makes them time-consuming with high false positive rates. Recently, deep learning approaches have alleviated these issues but still face several major limitations, such as lack of interpretability and susceptibility to evasion techniques. In this paper, we propose a feature selection method for uplifting modeling. The fundamental concept of this method is a feature selection algorithm, utilizing interpretation outcomes to select critical features, thereby reducing the scales of features. The learning process could be accelerated significantly because of the reduction of the feature size. The experiment shows that our proposed model performs well in six types of vulnerability detection. The accuracy of each type is higher than 93% and the average detection time of each smart contract is less than 1 ms. Notably, through our proposed feature selection algorithm, the training time of each type of vulnerability is reduced by nearly 80% compared with that of its original.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"5 3","pages":"Article 100209"},"PeriodicalIF":6.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141390225","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}

引用次数: 0

How can the holder trust the verifier? A CP-ABPRE-based solution to control the access to claims in a Self-Sovereign-Identity scenario 持有人如何信任验证者？一种基于 CP-ABPRE 的解决方案，用于控制自我主权身份情况下对权利主张的访问

IF 6.9 3区计算机科学

Blockchain-Research and Applications Pub Date : 2024-09-01 DOI: 10.1016/j.bcra.2024.100196

{"title":"How can the holder trust the verifier? A CP-ABPRE-based solution to control the access to claims in a Self-Sovereign-Identity scenario","authors":"","doi":"10.1016/j.bcra.2024.100196","DOIUrl":"10.1016/j.bcra.2024.100196","url":null,"abstract":"<div><p>The interest in Self-Sovereign Identity (SSI) in research, industry, and governments is rapidly increasing. SSI is a paradigm where users hold their identity and credentials issued by authorized entities. SSI is revolutionizing the concept of digital identity and enabling the definition of a trust framework wherein a service provider (verifier) validates the claims presented by a user (holder) for accessing services. However, current SSI solutions primarily focus on the presentation and verification of claims, overlooking a dual aspect: ensuring that the verifier is authorized to access the holder's claims. Addressing this gap, this paper introduces an innovative SSI-based solution that integrates decentralized wallets with Ciphertext-Policy Attribute-Based Proxy Re-Encryption (CP-ABPRE). This combination effectively addresses the challenge of verifier authorization. Our solution, implemented on the Ethereum platform, enhances accountability by notarizing key operations through a smart contract. This paper also offers a prototype demonstrating the practicality of the proposed approach. Furthermore, it provides an extensive evaluation of the solution's performance, emphasizing its feasibility and efficiency in real-world applications.</p></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"5 3","pages":"Article 100196"},"PeriodicalIF":6.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096720924000095/pdfft?md5=670501a0e4d21da648399fb2a95be292&pid=1-s2.0-S2096720924000095-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140763959","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}

引用次数: 0

Energy-aware proof-of-authority: Blockchain consensus for clustered wireless sensor network 能量感知的授权证明：集群无线传感器网络的区块链共识

IF 6.9 3区计算机科学

Blockchain-Research and Applications Pub Date : 2024-09-01 DOI: 10.1016/j.bcra.2024.100211

{"title":"Energy-aware proof-of-authority: Blockchain consensus for clustered wireless sensor network","authors":"","doi":"10.1016/j.bcra.2024.100211","DOIUrl":"10.1016/j.bcra.2024.100211","url":null,"abstract":"<div><div>This study addresses integrating blockchain technology into lightweight devices, specifically clustered Wireless Sensor Networks (WSNs). Integrating blockchain into the WSNs solves the problems of heterogeneity, data integrity, and data confidentiality. However, no blockchain integration considers network lifetime in WSNs. This research focuses on developing a permissioned blockchain system that incorporates a consensus mechanism known as Proof-of-Authority (PoA) within clustered WSNs with two main features. The first feature is to enhance the network lifetime by introducing a rotational selection of block proposers using an Energy-Aware PoA (EA-PoA) weighting mechanism. Known as the Multi-Level Blockchain Model (MLBM), the subsequent feature is to create a hierarchical network model within a blockchain network. The MLBM network comprises both local and master blockchains. Each cluster inside a WSN possesses its local blockchain network. In the MLBM, the local blockchain creates a block on the main blockchain by proposing the headers of every 10 blocks to improve data integrity. Each local blockchain has its leader, which can increase block production. The results show that the proposed solution can overcome traditional PoA performance and is suitable for clustered WSNs. In terms of lifetime, the EA-PoA selection method can extend the network lifetime by up to 10%. In addition, the MLBM can increase block production by up to twice each additional cluster compared to a single blockchain network used in traditional PoA.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"5 3","pages":"Article 100211"},"PeriodicalIF":6.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141397258","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}

引用次数: 0

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