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2024 International conference Optoelectronic Information and optical Engineering, OIOE 2024

作者： Luo, Xin Zhou, Linrong School of Management Zunyi Normal University Zunyi563006 China

ISBN: (数字)9781510688209

ISBN: (纸本)9781510688193

In the country to vigorously support the development of rural e-commerce industry, how to use cloud computing and big data technology construction planning rural e-commerce logistics, efficient integration of local logistics resources, reduce logistics transportation costs, and promote the steady development of rural e-commerce industry, is the current national and social comprehensive research of the main topic. Considering the development of rural economy in Guizhou in recent years, under the background of "Internet + efficient logistics", it is an effective way to optimize the efficiency of rural express delivery to build and optimize rural fast trunk transportation and improve rural express joint delivery capacity. Therefore, on the basis of understanding the development status of "Internet + efficient logistics", this paper mainly discusses the rural e-commerce logistics information architecture based on cloud computing and big data according to the current organization mode and transport capacity optimization of rural express joint distribution, defines the mathematical model affecting express transportation, and then regards the minimization of total transportation and distribution cost as the development goal. Scientific coordination of all distribution centers and related services, and comprehensive optimization of Guizhou rural express trunk transportation level. © 2025 SPIE.

关键词： Transportation Internet Industry data processing data storage

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Forensic Examinations of Alexa for Smart Home Privacy and Cybercrime Investigation

Forensic Examinations of Alexa for Smart Home Privacy and Cy...

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20th International conference on Cyber Warfare and Security, ICCWS 2025

作者： Aljneibi, Bashaer Alameri, Mahra Alhashmi, Noora Ikuesan, Richard Iqbal, Farkhund College of Technological Innovation Zayed University Abu Dhabi United Arab Emirates

ISBN: (纸本)9781917204316

Integrating Internet of Things (IoT) devices into smart homes has necessitated the development of novel strategies to address the difficulties and complexities of cyber-attacks and privacy concerns in the current digital threat landscape. One unaddressed challenge is the lack of clarity of information collected and stored by these IoT devices in smart homes. The data storage process and privacy compliance of smart home appliances, such as security cameras, thermostats, and smart speakers, are examined in this study. More specifically, this study focuses on sensitive data storage and potential breach exposure, including user commands, timestamps, and network traffic logs of these devices. To achieve this, forensic tools were deployed to collect and examine data from gadgets like Google Nest and Amazon Alexa/Echo following an experimental setup. These technologies were used in a hypothetical investigation that intentionally breached a restricted smart home network and replicated criminal activities. The gathered data was examined to determine the proof of the breach and ensure the chain of events. The findings provided a thorough forensic investigation into the potential digital artifacts within the device and exposed prevalent vulnerabilities of IoT ecosystems regarding usage privacy. This study advances the fields of digital forensics and smart home security by offering useful insights and suggestions for improving the security of IoT devices. © 2025 Proceedings of the International conference on Information Warfare and Security (ICCWS). All rights reserved.

关键词： Sensitive data

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Detecting reading orders with block-based models for OCR for classical Chinese documents: concepts and demonstrations

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Multimedia Tools and Applications 2025年 1-23页

作者： Ma, Hsing-Yuan Liu, Chao-Lin Huang, Hen-Hsen National Chengchi University Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan

Texts, printed or written, are the most important form of cultural preservation. This is true for Chinese studies and many cultures. For this reason, optical character recognition (OCR) is one of the most important tools for heritage preservation. With OCR and reading order detection (ROD), we convert the information in their hard-copy forms to digitized documents, allowing both lasting storage and further analysis. Given a page image, the step of ROD helps us determine the order of the texts on the page. An effective ROD reduces the cost of post-OCR human labor for correction. Rule-based and n-gram models are conceivable and popular techniques for implementing ROD, considering the coordinates and the alternative orders of the individually detected characters. In this paper, we report a block-based approach that does not rely on the n-gram techniques for ROD. Instead, we identified the text blocks, and ordered the blocks based on the ideas of learning-to-rank, and concatenated the texts within the blocks by their orders. We evaluated our approach on the MTHv2 dataset. Experimental results show that our approach achieved impressive results, reducing the position error rate (PER) to 5.4%, compared to 61.7% for heuristic methods and 56.5% for MLP, representing a significant improvement of 91.3% and 90.4%, respectively. Additional experiments show that we could have used less data than one might expect to achieve a satisfactory quality of ROD. We implemented and demonstrated a fully functioning prototype at a recent AAAI conference. ∗ The actual performance of the prototype provides solid evidence of the effectiveness and extensibility of our design to more general contexts, e.g., Japanese texts. © The Author(s) 2025.

关键词： optical character recognition

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Enhancing Adaptive data Management Middleware: Techniques for Federated Cloud Environments 8th

Enhancing Adaptive Data Management Middleware: Techniques fo...

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8th IFIP TC 12 International conference on Computer, Communication and Signal Processing, ICCCSP 2024

作者： Kolekar, Vikas K. Sakhare, Sachin R. Department of Computer Engineering SMT. Kashibai Navale College of Engineering Pune411041 India Department of Computer Engineering Vishwakarma Institute of Information Technology Pune411048 India

ISBN: (纸本)9783031736162

The adaptive data management middleware is crucial for facilitating efficient and dynamic data management in distributed computing environments. Nevertheless, the widespread adoption of federated cloud environments brings about new complexities and difficulties in data administration, requiring inventive methods to improve the functionalities of current middleware systems. This research paper introduces innovative strategies for enhancing adaptive data management middleware that is especially designed for federated cloud environments. The proposed methodology addresses several fundamental components of adaptive data management middleware. Initially, proposed the methods for dynamically provisioning and allocating resources to enhance the efficiency of data processing and storage across federated cloud environments. In addition, provided the strategies for implementing data governance and enforcing security measures to meet regulatory standards and protect confidential information in federated cloud environments. Furthermore, investigated the techniques for duplicating and synchronization data to improve the accessibility and uniformity of data in federated environments. To evaluate the results of the proposed techniques, we conducted comprehensive experimental evaluations using simulated federated cloud infrastructures. The results demonstrate the substantial enhancements in the performance of data management, utilisation of resources, and capacity to scale when compared to existing methods. Lastly discussed the implications of our findings and emphasised potential areas for further investigation in this rapidly evolving domain. © IFIP International Federation for Information Processing 2025.

关键词： Cloud platforms

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Automated layout planning of walkway plates in climbing scaffolding platform using contour data

Automated layout planning of walkway plates in climbing scaf...

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2024 International conference on Mechatronic Engineering and Artificial Intelligence, MEAI 2024

作者： An, Di Zhong, Hui Fu, Yangyi Li, Jiajia Qiao, Feng School of Intelligence and Engineering Shenyang City University Shenyang110112 China School of Computer Science and Engineering Shenyang Jianzhu University Shenyang110168 China

ISBN: (纸本)9781510689176

The rapid advancement of information technology has made computer-aided design a primary focus in the construction industry. This paper addresses the challenge of automating the layout of walkway plates in climbing scaffolding platforms in high-rise buildings. This paper presents a comprehensive approach that uses building contour data for one-click layout planning of perimeter walkway plates. The approach processes building contour data to fit the dimensions and storage forms of walkway plates. It generates a partition diagram based on point relationships and extracts individual walkway plate data needed for modeling. This data is then integrated with Revit to expedite the planning process. © 2025 SPIE.

关键词： Tall buildings

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Utilizing Big data Algorithms and Cloud Computing to Establish Centralized Electronic Health Records 5th

Utilizing Big Data Algorithms and Cloud Computing to Establi...

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5th International conference on data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Sharma, Khushboo Saxena, Kumud Bargavi, Manju Chaudhary, Chetan Department of Electrical Engineering Vivekananda Global University Jaipur India Department of Computer Science Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

This paper aims to leverage massive statistics algorithms and cloud computing to create centralized electronic fitness statistics (EHRs) that are quickly and easily handy for all authorized healthcare professionals. The paper will evaluate the critical principles and technological advances that might be essential to setting up a possible EHR gadget. It will start with a dialogue about what an EHR is and how it has grown to be an increasing number of critical these days. The discussion will also include an overview of the different styles of EHRs and their benefits and disadvantages. The paper will then study the benefits of big statistics algorithms and cloud computing for EHRs. It will discuss how cloud computing permits efficient records retrieval, storage, and analysis competencies, in addition to how big facts analytics may be used for meaningful evaluation of large datasets. Eventually, the paper will consider the demanding situations related to using significant facts algorithms and cloud computing and recommend a few possible solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Design and Implementation of a Big data Platform for Production and Business Management: A Case Study from Vietnam National Coal and Mineral Industries Group 3rd

Design and Implementation of a Big Data Platform for Product...

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3rd International conference on advances in Information and Communication technology, ICTA 2024

作者： Do, Thi-Thu-Trang Tran, Van-Dung Ngo, Manh-Hung Dang, Dinh-Dao Dam, Quang-Thinh Mai-Hoang, Thai-Bao Nguyen, Van-Quyet Huynh, Quyet-Thang Faculty of Information Technology Hung Yen University of Technology and Education Hungyen160000 Viet Nam Vinacomin-Mining Chemical Industry Holding Corporation Limited Hanoi100000 Viet Nam AI Academy Viet Nam Hanoi100000 Viet Nam School of Information and Communication Technology Hanoi University of Science and Technology Hanoi100000 Viet Nam

ISBN: (纸本)9783031809422

This paper introduces a modern Big data platform for production and business management, utilizing a hybrid architecture that integrates distributed and cloud-based technologies to enhance processing efficiency. The system employs advanced big data processing technologies, including Hadoop, Spark, Kafka, and AI techniques such as YOLO and CNN, to effectively manage unstructured data. This platform improves system performance, facilitates detailed business queries, and enhances storage and query capabilities. We apply this system to the Vietnam National Coal and Mineral Industry Group (VINACOMIN), resolving inefficiencies in their existing software and achieving a tenfold improvement in speed and report generation efficiency. Experiments show that the system can analyze video data under various conditions using advanced AI techniques, particularly the DeepSearch algorithm. The results demonstrate that the platform optimizes report generation and provides a scalable framework for future Big data applications in similar industrial settings. This research underscores the transformative potential of innovative Big data solutions in improving data processing, enabling timely decision-making, and maintaining a competitive advantage in the current business environment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Competition

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Efficient and precious surface subsidence monitor using continuous InSAR data based on Kalman filter 6

Efficient and precious surface subsidence monitor using cont...

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6th International conference on Geoscience and Remote Sensing Mapping, GRSM 2024

作者： Xiao, Yixin Tao, Qiuxiang College of Geodesy and Geomatics Shandong University of Science and Technology Qingdao266000 China

ISBN: (纸本)9781510688018

Satellites such as Sentinel-1A/B, the newly planned NASA-ISRO SAR (NISAR), and the German Tandem-L with reentry intervals as short as 6-12 days have caused numerous SAR data. These data has provided a rich database for InSAR time-series analyses but posed a challenge to efficiently and consistently invert surface deformation. The traditional Small Baseline Subset Interferometric Synthetic Aperture Radar technique (SBAS InSAR) can be applied for surface deformation inversion but with limitation of processing efficiency. SBAS InSAR is challenging to meet the demand for dynamic, efficient and continuous surface deformation monitoring. In this study, we propose a high-efficient deformation monitoring method based on the Kalman filter (KF) and InSAR data. First, we use SBAS technology to obtain the surface deformation information and construct the state equations according to the time series characteristics of deformation. Second, the new interferograms are generated with the new and original images, and the observation equations are constructed with the unwrapped phase of the interferometric phase as the observation value. Finally, the surface deformation at the new moment is quickly estimated based on our KF dynamic processing. Compared with the traditional SBAS method, our method effectively reduces redundant calculations and improves computational efficiency. Actual experimental results from a mine in Xinjiang show that our method not only has high storage performance, but also demonstrates obvious advantages in dynamic estimation computational efficiency. Our method improves the timeliness and accuracy of deformation monitoring and adapts to the real-time monitoring needs brought by the growth of SAR data. © 2025 SPIE.

关键词： NASA

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Learning Structured Compressed Sensing with Automatic Resource Allocation

Learning Structured Compressed Sensing with Automatic Resour...

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2025 IEEE International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Han Pérez, Eduardo Huijben, Iris A.M. van Gorp, Hans van Sloun, Ruud Römer, Florian Applied AI Signal Processing and Data Analysis Fraunhofer Institute for Nondestructive Testing IZFP Germany Department of Electrical Engineering Eindhoven University of Technology Netherlands Electronic Measurements and Signal Processing Ilmenau University of Technology Germany

ISBN: (纸本)9798350368741

Multidimensional data acquisition often requires extensive time and poses significant challenges for hardware and software regarding data storage and processing. Rather than designing a single compression matrix as in conventional compressed sensing, structured compressed sensing yields dimension-specific compression matrices, reducing the number of optimizable parameters. Recent advances in machine learning (ML) have enabled task-based supervised learning of subsampling matrices, albeit at the expense of complex downstream models. Additionally, the sampling resource allocation across dimensions is often determined in advance through heuristics. To address these challenges, we introduce Structured COmpressed Sensing with Automatic Resource Allocation (SCOSARA) with an information theory-based unsupervised learning strategy. SCOSARA adaptively distributes samples across sampling dimensions while maximizing Fisher information content. Using ultrasound localization as a case study, we compare SCOSARA to state-of-the-art ML-based and greedy search algorithms. Simulation results demonstrate that SCOSARA can produce high-quality subsampling matrices that achieve lower Cramér Rao Bound values than the baselines. In addition, SCOSARA outperforms other ML-based algorithms in terms of the number of trainable parameters, computational complexity, and memory requirements while automatically choosing the number of samples per axis. © 2025 IEEE.

关键词： Compressed Sensing Information Theory Subsampling Unsupervised Learning

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The role of optical coherence tomography angiography in assessing diabetic choroidopathy: a systematic review

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International Journal of Retina and Vitreous 2025年第1期11卷 1-15页

作者： Nowroozzadeh, M. Hossein Bagheri, Mansoureh Poostchi Ophthalmology Research Center Department of Ophthalmology School of Medicine Shiraz University of Medical Sciences Shiraz Iran Department of Surgical Subspecialties Service of Ophthalmology at Health Sciences North Northern Ontario School of Medicine University Sudbury P3E 5J1 ON Canada

Background: Diabetic retinopathy (DR) is a leading cause of vision impairment worldwide, affecting both retinal and choroidal vasculature. While advances in imaging technology, particularly optical coherence tomography angiography (OCTA), provide new opportunities to assess choroidal changes in diabetic patients, the role of OCTA in early diagnosis and monitoring of diabetic choroidopathy remains unclear. Objective: This review aims to evaluate the potential role of OCTA in diagnosing and monitoring diabetic choroidopathy. Methods: A systematic review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. databases including PubMed, Embase, Cochrane Library, Google Scholar, ISI, and Scopus were searched for studies on diabetic choroidopathy assessed by OCTA. Studies included were peer-reviewed, published in English, and excluded case reports, conference proceedings, and studies on treated DR patients. Two independent reviewers screened articles for eligibility based on predefined criteria. Results: OCTA allows for non-invasive, high-resolution visualization of retinal and choroidal microvasculature, providing both qualitative and quantitative data. The majority of studies indicate a significant decrease in choroidal perfusion parameters in diabetic patients without DR compared to healthy controls. Conflicting evidence exists regarding the correlation between choriocapillaris flow reduction and DR severity. OCTA may also predict changes in visual function related to choroidal perfusion, though it cannot fully replace clinical examinations. Conclusions: OCTA is a valuable tool for early detection and monitoring of diabetic choroidopathy. However, its role is limited by variability in findings and its inability to detect certain features of diabetic microangiopathy. Further studies are needed to clarify its clinical utility and standardize assessment methods. © The Author(s) 2024.

关键词： Choriocapillaris Choroidal vasculature Diabetic choroidopathy Diabetic retinopathy optical coherence tomography angiography

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