信息工程最新文献_第7页

Segmentation of CAD models using hybrid representation 基于混合表示的CAD模型分割

Virtual Reality Intelligent Hardware Pub Date : 2025-04-01 DOI: 10.1016/j.vrih.2025.01.001

Claude Uwimana , Shengdi Zhou , Limei Yang , Zhuqing Li , Norbelt Mutagisha , Edouard Niyongabo , Bin Zhou

{"title":"Segmentation of CAD models using hybrid representation","authors":"Claude Uwimana , Shengdi Zhou , Limei Yang , Zhuqing Li , Norbelt Mutagisha , Edouard Niyongabo , Bin Zhou","doi":"10.1016/j.vrih.2025.01.001","DOIUrl":"10.1016/j.vrih.2025.01.001","url":null,"abstract":"<div><div>In this paper, we introduce an innovative method for computer-aided design (CAD) segmentation by concatenating meshes and CAD models. Many previous CAD segmentation methods have achieved impressive performance using single representations, such as meshes, CAD, and point clouds. However, existing methods cannot effectively combine different three-dimensional model types for the direct conversion, alignment, and integrity maintenance of geometric and topological information. Hence, we propose an integration approach that combines the geometric accuracy of CAD data with the flexibility of mesh representations, as well as introduce a unique hybrid representation that combines CAD and mesh models to enhance segmentation accuracy. To combine these two model types, our hybrid system utilizes advanced-neural-network techniques to convert CAD models into mesh models. For complex CAD models, model segmentation is crucial for model retrieval and reuse. In partial retrieval, it aims to segment a complex CAD model into several simple components. The first component of our hybrid system involves advanced mesh-labeling algorithms that harness the digitization of CAD properties to mesh models. The second component integrates labelled face features for CAD segmentation by leveraging the abundant multisemantic information embedded in CAD models. This combination of mesh and CAD not only refines the accuracy of boundary delineation but also provides a comprehensive understanding of the underlying object semantics. This study uses the Fusion 360 Gallery dataset. Experimental results indicate that our hybrid method can segment these models with higher accuracy than other methods that use single representations.</div></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"7 2","pages":"Pages 188-202"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient and lightweight 3D building reconstruction from drone imagery using sparse line and point clouds 使用稀疏的线和点云从无人机图像中高效和轻量级的3D建筑重建

Virtual Reality Intelligent Hardware Pub Date : 2025-04-01 DOI: 10.1016/j.vrih.2025.02.001

Xiongjie Yin , Jinquan He , Zhanglin Cheng

引用次数: 0

Deconfounded fashion image captioning with transformer and multimodal retrieval 用变压器和多模态检索解构时尚图像字幕

Virtual Reality Intelligent Hardware Pub Date : 2025-04-01 DOI: 10.1016/j.vrih.2024.08.002

Tao Peng, Weiqiao Yin, Junping Liu, Li Li, Xinrong Hu

{"title":"Deconfounded fashion image captioning with transformer and multimodal retrieval","authors":"Tao Peng, Weiqiao Yin, Junping Liu, Li Li, Xinrong Hu","doi":"10.1016/j.vrih.2024.08.002","DOIUrl":"10.1016/j.vrih.2024.08.002","url":null,"abstract":"<div><h3>Background</h3><div>The annotation of fashion images is a significantly important task in the fashion industry as well as social media and e-commerce. However, owing to the complexity and diversity of fashion images, this task entails multiple challenges, including the lack of fine-grained captions and confounders caused by dataset bias. Specifically, confounders often cause models to learn spurious correlations, thereby reducing their generalization capabilities.</div></div><div><h3>Method</h3><div>In this work, we propose the Deconfounded Fashion Image Captioning (DFIC) framework, which first uses multimodal retrieval to enrich the predicted captions of clothing, and then constructs a detailed causal graph using causal inference in the decoder to perform deconfounding. Multimodal retrieval is used to obtain semantic words related to image features, which are input into the decoder as prompt words to enrich sentence descriptions. In the decoder, causal inference is applied to disentangle visual and semantic features while concurrently eliminating visual and language confounding.</div></div><div><h3>Results</h3><div>Overall, our method can not only effectively enrich the captions of target images, but also greatly reduce confounders caused by the dataset. To verify the effectiveness of the proposed framework, the model was experimentally verified using the FACAD dataset.</div></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"7 2","pages":"Pages 127-138"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DeepSafe:Two-level deep learning approach for disaster victims detection DeepSafe：用于灾难受害者检测的两级深度学习方法

Virtual Reality Intelligent Hardware Pub Date : 2025-04-01 DOI: 10.1016/j.vrih.2024.08.005

Amir Azizi , Panayiotis Charalambous , Yiorgos Chrysanthou

{"title":"DeepSafe:Two-level deep learning approach for disaster victims detection","authors":"Amir Azizi , Panayiotis Charalambous , Yiorgos Chrysanthou","doi":"10.1016/j.vrih.2024.08.005","DOIUrl":"10.1016/j.vrih.2024.08.005","url":null,"abstract":"<div><h3>Background</h3><div>Efficient disaster victim detection (DVD) in urban areas after natural disasters is crucial for minimizing losses. However, conventional search and rescue (SAR) methods often experience delays, which can hinder the timely detection of victims. SAR teams face various challenges, including limited access to debris and collapsed structures, safety risks due to unstable conditions, and disrupted communication networks.</div></div><div><h3>Methods</h3><div>In this paper, we present DeepSafe, a novel two-level deep learning approach for multilevel classification and object detection using a simulated disaster victim dataset. DeepSafe first employs YOLOv8 to classify images into victim and non-victim categories. Subsequently, Detectron2 is used to precisely locate and outline the victims.</div></div><div><h3>Results</h3><div>Experimental results demonstrate the promising performance of DeepSafe in both victim classification and detection. The model effectively identified and located victims under the challenging conditions presented in the dataset.</div></div><div><h3>Conclusion</h3><div>DeepSafe offers a practical tool for real-time disaster management and SAR operations, significantly improving conventional methods by reducing delays and enhancing victim detection accuracy in disaster-stricken urban areas.</div></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"7 2","pages":"Pages 139-154"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automated mechanism to support trade transactions in smart contracts with upgrade and repair 支持智能合约中贸易交易的自动机制，包括升级和修复

IF 6.9 3区计算机科学

Blockchain-Research and Applications Pub Date : 2025-03-28 DOI: 10.1016/j.bcra.2025.100285

Christian Gang Liu , Peter Bodorik , Dawn Jutla

{"title":"Automated mechanism to support trade transactions in smart contracts with upgrade and repair","authors":"Christian Gang Liu , Peter Bodorik , Dawn Jutla","doi":"10.1016/j.bcra.2025.100285","DOIUrl":"10.1016/j.bcra.2025.100285","url":null,"abstract":"<div><div>In our previous research, we addressed the problem of automated transformation of models, represented using the business process model and notation (BPMN) standard, into the methods of a smart contract. The transformation supports BPMN models that contain complex multi-step activities that are supported using our concept of multi-step nested trade transactions, wherein the transactional properties are enforced by a mechanism generated automatically by the transformation process from a BPMN model to a smart contract. In this paper, we present a methodology for repairing a smart contract that cannot be completed due to events that were not anticipated by the developer and thus prevent the completion of the smart contract. The repair process starts with the original BPMN model fragment causing the issue, providing the modeler with the innermost transaction fragment containing the failed activity. The modeler amends the BPMN pattern on the basis of the successful completion of previous activities. If repairs exceed the inner transaction’s scope, they are addressed using the parent transaction’s BPMN model. The amended BPMN model is then transformed into a new smart contract, ensuring consistent data and logic transitions. We previously developed a tool, called TABS+, as a proof of concept (PoC) to transform BPMN models into smart contracts for nested transactions. This paper describes the tool TABS+<em>R</em>, developed by extending the TABS+ tool, to allow the repair of smart contracts.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"6 3","pages":"Article 100285"},"PeriodicalIF":6.9,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144611703","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}

引用次数: 0

ACOFuzz: An ant colony algorithm-based fuzzer for smart contracts ACOFuzz：基于蚁群算法的智能合约模糊器

IF 5.6 3区计算机科学

Blockchain-Research and Applications Pub Date : 2025-03-25 DOI: 10.1016/j.bcra.2025.100279

Peixuan Feng , Wenrui Cao , Siqi Lu , Yongjuan Wang , Haoyuan Xue , Runnan Yang

{"title":"ACOFuzz: An ant colony algorithm-based fuzzer for smart contracts","authors":"Peixuan Feng , Wenrui Cao , Siqi Lu , Yongjuan Wang , Haoyuan Xue , Runnan Yang","doi":"10.1016/j.bcra.2025.100279","DOIUrl":"10.1016/j.bcra.2025.100279","url":null,"abstract":"<div><div>In today's blockchain landscape, smart contracts are assuming a pivotal role, albeit accompanied by a heightened risk of exploitation by attackers. As smart contracts grow in complexity, vulnerabilities lurking within deeper layers of code become more prevalent. Existing analysis tools primarily focus on data flow and a priori knowledge based on symbolic execution as a test case generation strategy, often falling short in uncovering vulnerabilities nested within intricate conditional statements. To address this challenge, we present ACOFuzz, an advanced fuzzer for Ethereum smart contracts. ACOFuzz employs the ant colony optimization (ACO) algorithm to traverse the control flow graph (CFG) of smart contracts, systematically exploring execution paths and generating test cases. Subsequently, it strategically directs the search towards paths that are more susceptible to vulnerabilities within the CFG, leveraging block coverage data obtained from executing the test cases. In a comprehensive evaluation, we demonstrate that ACOFuzz excels in covering a wider array of paths within a contract while exhibiting enhanced accuracy in pinpointing specific vulnerabilities compared to contemporary fuzzers.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"6 3","pages":"Article 100279"},"PeriodicalIF":5.6,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861166","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}

引用次数: 0

A blockchain-based platform for ensuring provenance and traceability of donations for cultural heritage 一个基于区块链的平台，用于确保文化遗产捐赠的来源和可追溯性

IF 5.6 3区计算机科学

Blockchain-Research and Applications Pub Date : 2025-03-21 DOI: 10.1016/j.bcra.2025.100278

Sara Migliorini, Mauro Gambini, Alberto Belussi

{"title":"A blockchain-based platform for ensuring provenance and traceability of donations for cultural heritage","authors":"Sara Migliorini, Mauro Gambini, Alberto Belussi","doi":"10.1016/j.bcra.2025.100278","DOIUrl":"10.1016/j.bcra.2025.100278","url":null,"abstract":"<div><div>The preservation and restoration of cultural heritage has acquired increasing attention in recent years since it has priceless value from both historical and touristic points of view. However, this activity requires considerable funds to be carried out, and frequently, such costs cannot rely entirely on public sources. At the same time, crowdfunding platforms are becoming a widely recognized way to collect funds and finance projects. Indeed, in the literature, some attempts have been made to use crowdfunding platforms to support renovation and restoration projects for cultural heritage items. Even if the benefits of their use in general, particularly for cultural heritage, are widely recognized, skepticism remains regarding transparency, reliability, and trustworthiness. In this regard, the emerging blockchain technology could represent an innovative solution for promoting and guaranteeing such properties through the entire crowdfunding process. However, existing solutions based on the direct use of cryptocurrencies for collecting funds have encountered users' fear and reluctance due to their novelty and the absence of clear and complete regulation by governments. For this reason, in this paper, we propose a solution that is not based on using cryptocurrencies but concentrates on the immutability, traceability, and trustworthiness properties that blockchain offers. To do so, an integrated solution is proposed that combines traditional platforms with a set of smart contracts and a Decentralized Application (dApp), allowing the immutable storage of information inside the blockchain and their subsequent validation by the donors.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"6 3","pages":"Article 100278"},"PeriodicalIF":5.6,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861165","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}

引用次数: 0

Federated Large Domain Model System 联邦大型领域模型系统

IF 5.6 3区计算机科学

Blockchain-Research and Applications Pub Date : 2025-03-21 DOI: 10.1016/j.bcra.2025.100277

Chunming Rong , Jungwon Seo , Zihan Zhao , Ferhat Ozgur Catak , Jiahui Geng , Martin Gilje Jaatun

{"title":"Federated Large Domain Model System","authors":"Chunming Rong , Jungwon Seo , Zihan Zhao , Ferhat Ozgur Catak , Jiahui Geng , Martin Gilje Jaatun","doi":"10.1016/j.bcra.2025.100277","DOIUrl":"10.1016/j.bcra.2025.100277","url":null,"abstract":"<div><div>As organizations increasingly seek to build Foundation Models (FMs) using their own proprietary data, many are adopting private and in-house cloud infrastructures (often in addition to public clouds) to address concerns over cost, data privacy, and data sovereignty. However, these isolated private clouds frequently lack interoperability, creating barriers to cross-institutional collaboration, which is vital for training robust Domain-Specific Foundation Models (DSFMs) that rely on large and diverse datasets. Additionally, underutilized resources in private clouds lead to significant global energy inefficiencies. In this paper, we propose the Federated Large Domain Model System (FLDMS), a conceptual framework designed to facilitate collaborative foundation model development across multiple private cloud environments. We review the necessary enabling technologies, including decentralized protocols for data privacy and Large Language Models (LLMs) for automated orchestration, and present a high-level system design demonstrating how these components can be integrated. By enabling secure and efficient cross-organization cooperation, FLDMS provides a blueprint for building DSFMs while addressing the inefficiencies inherent in siloed private cloud systems.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"6 3","pages":"Article 100277"},"PeriodicalIF":5.6,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780043","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}

引用次数: 0

Intelligent hierarchical federated learning system based on semi-asynchronous and scheduled synchronous control strategies in satellite network 卫星网络中基于半异步和定时同步控制策略的智能分层联邦学习系统

自主智能系统(英文) Pub Date : 2025-03-20 DOI: 10.1007/s43684-025-00095-z

Qiang Mei, Rui Huang, Duo Li, Jingyi Li, Nan Shi, Mei Du, Yingkang Zhong, Chunqi Tian

{"title":"Intelligent hierarchical federated learning system based on semi-asynchronous and scheduled synchronous control strategies in satellite network","authors":"Qiang Mei, Rui Huang, Duo Li, Jingyi Li, Nan Shi, Mei Du, Yingkang Zhong, Chunqi Tian","doi":"10.1007/s43684-025-00095-z","DOIUrl":"10.1007/s43684-025-00095-z","url":null,"abstract":"<div><p>Federated learning (FL) is a technology that allows multiple devices to collaboratively train a global model without sharing original data, which is a hot topic in distributed intelligent systems. Combined with satellite network, FL can overcome the geographical limitation and achieve broader applications. However, it also faces the issues such as straggler effect, unreliable network environments and non-independent and identically distributed (Non-IID) samples. To address these problems, we propose an intelligent hierarchical FL system based on semi-asynchronous and scheduled synchronous control strategies in cloud-edge-client structure for satellite network. Our intelligent system effectively handles multiple client requests by distributing the communication load of the central cloud to various edge clouds. Additionally, the cloud server selection algorithm and the edge-client semi-asynchronous control strategy minimize clients’ waiting time, improving the overall efficiency of the FL process. Furthermore, the center-edge scheduled synchronous control strategy ensures the timeliness of partial models. Based on the experiment results, our proposed intelligent hierarchical FL system demonstrates a distinct advantage in global accuracy over traditional FedAvg, achieving 2% higher global accuracy within the same time frame and reducing 52% training time to achieve the target accuracy.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-025-00095-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143655326","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

A glance over the past decade: road scene parsing towards safe and comfortable autonomous driving 回顾过去十年：路况解析走向安全舒适的自动驾驶

自主智能系统(英文) Pub Date : 2025-03-13 DOI: 10.1007/s43684-025-00096-y

Rui Fan, Jiahang Li, Jiaqi Li, Jiale Wang, Ziwei Long, Ning Jia, Yanan Liu, Wenshuo Wang, Mohammud J. Bocus, Sergey Vityazev, Xieyuanli Chen, Junhao Xiao, Stepan Andreev, Huimin Lu, Alexander Dvorkovich

引用次数: 0