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检索条件"任意字段=IEEE International Conference on Data Science and Data Intensive Systems"

共 86302 条记录，以下是241-250 订阅

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Enhancing RF Fingerprinting for Indoor Positioning systems Using data Augmentation

Enhancing RF Fingerprinting for Indoor Positioning Systems U...

引用

2024 ieee international conference on Consumer Electronics, ICCE 2024

作者： Junoh, Suhardi Azliy Jamil, Shawana Pyun, Jae-Young Chosun University Department of Information and Communication Engineering Gwangju Korea Republic of

ISBN: (纸本)9798350324136

Indoor Positioning systems (IPS) have recently emerged as a crucial technology in the Internet of Things (IoT), with widespread applications in smart cities and homes. Radio frequency-based fingerprinting, enabling location estimation through signal observations, requires manual surveys for constructing location maps. This process involves annotating radio signatures with corresponding locations, rendering it time-consuming and labor-intensive. To address this challenge, our paper proposes a data augmentation method that leverages a conditional generative adversarial network with LSTM and CNN. This approach effectively captures patterns in the training data, generating synthetic data that aligns with the distribution. Experiments in a real scenario demonstrate an average localization error of 1.966 and 1.218 m for Wi-Fi and Bluetooth low energy (BLE), surpassing traditional fingerprinting and comparable to the baseline data augmentation methods. © 2024 ieee.

关键词： Bluetooth low energy (BLE) data augmentation fingerprinting localization Generative adversarial network (GAN) Internet of Things Wi-Fi

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The Implementation of Blockchain Technology to Manage data in a Secure and Transparent Manner for Biomedical Applications 9

The Implementation of Blockchain Technology to Manage Data i...

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9th international conference on Communication and Electronics systems, ICCES 2024

作者： Velmurugan, M. Nirmala, D. Pandi, V. Samuthira Jeyanthi, P. Monisha, T. Bhagyalakshmi, T. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi-Vel Tech Road Vel Nagar Avadi Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biosciences Chennai India Jeppiaar Engineering College Department of Cse Semmencherry Chennai India Jeppiaar Engineering College Department of It Semmencherry Chennai India

ISBN: (纸本)9798350377972

This research investigates the application of Blockchain technology to enhance the security, transparency, and efficiency of biomedical data management. By analyzing existing data management practices in the biomedical industry and identifying their limitations, the study explores the potential of blockchain to address these challenges. The research focuses on developing a blockchain-based framework for secure data sharing, leveraging the inherent features of blockchain technology, such as decentralization, immutability, and cryptographic security. Through simulations and case studies, the study evaluates the feasibility and effectiveness of the proposed framework in addressing critical issues such as data security, privacy, and interoperability. The findings of this research demonstrate the potential of blockchain technology to revolutionize biomedical data management, enabling secure and efficient data sharing while ensuring patient confidentiality and regulatory compliance. This research has broader implications for other data-intensive sectors, highlighting the potential of blockchain technology to enhance data security and integrity across various domains. © 2024 ieee.

关键词： data integrity

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A Dual-Model Anomaly Detection Algorithm for non-linear stream data in Smart City Environments 20

A Dual-Model Anomaly Detection Algorithm for non-linear stre...

引用

20th Annual international conference on Distributed Computing in Smart Sensor systems and the Internet of Things (DCOSS-IoT)

作者： Bustamante, Anthony J. Asad, Sarah Nicklas, Daniela Lagesse, Brent Univ Washington Bothell Bothell WA USA Otto Friedrich Univ Bamberg Bamberg Germany

ISBN: (纸本)9798350369458;9798350369441

In this paper, we introduce a complementary and straightforward mechanism for anomaly detection tailored for smart city infrastructures, utilizing a combination of regression algorithms. Our methodology employs two distinct regression models to generate future predictions from a given dataset. The primary model is crafted to yield high-fidelity predictions, while the secondary model is purposefully designed to introduce a degree of noise. Both models work together as a defense against Flooding attacks through the detection of abnormal levels of data inflow (detection of outliers). We calculate the alignment cost, or Euclidean distance, between the predictions from these two models, establishing a threshold against which real future traffic can be evaluated. The alignment cost or euclidean distance of the actual traffic is computed in relation to the high-quality predictions and then compared with the established threshold to pinpoint anomalies. Through experimentation with various regression algorithms, including linear regression, support vector regression, decision trees, etc., we identified an optimal combination for peak performance. Our assessments, grounded in comprehensive smart city datasets, center on the process of transforming complex non-linear data into a more appropriate form to detect anomalous data points. Conclusively, the dualmodel anomaly detection framework we propose stands out as an invaluable tool in defending smart city infrastructures from data irregularities and potential threats, highlighting the criticality of bespoke solutions in contemporary urban digital environments.

关键词： Smart City Security Flooding attacks Denial of Service Attacks(DoS) Anomaly Detection Pattern Recognition Outlier Detection

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Optimizing Scientific data Transfer on Globus with Error-bounded Lossy Compression 43

Optimizing Scientific Data Transfer on Globus with Error-bou...

引用

43rd ieee international conference on Distributed Computing systems (ICDCS)

作者： Liu, Yuanjian Di, Sheng Chard, Kyle Foster, Ian Cappello, Franck Univ Chicago Chicago IL 60637 USA Argonne Natl Lab Lemont IL USA

ISBN: (纸本)9798350339864

The increasing volume and velocity of science data necessitate the frequent movement of enormous data volumes as part of routine research activities. As a result, limited wide-area bandwidth often leads to bottlenecks in research progress. However, in many cases, consuming applications (e.g., for analysis, visualization, and machine learning) can achieve acceptable performance on reduced-precision data, and thus researchers may wish to compromise on data precision to reduce transfer and storage costs. Error-bounded lossy compression presents a promising approach as it can significantly reduce data volumes while preserving data integrity based on user-specified error bounds. In this paper, we propose a novel data transfer framework called Ocelot that integrates error-bounded lossy compression into the Globus data transfer infrastructure. We note four key contributions: (1) Ocelot is the first integration of lossy compression in Globus to significantly improve scientific data transfer performance over wide area network (WAN). (2) We propose an effective machine-learning based lossy compression quality estimation model that can predict the quality of error-bounded lossy compressors, which is fundamental to ensure that transferred data are acceptable to users. (3) We develop optimized strategies to reduce the compression time overhead, counter the compute-node waiting time, and improve transfer speed for compressed files. (4) We perform evaluations using many real-world scientific applications across different domains and distributed Globus endpoints. Our experiments show that Ocelot can improve dataset transfer performance substantially, and the quality of lossy compression (time, ratio and data distortion) can be predicted accurately for the purpose of quality assurance.

关键词： Lossy Compression Performance data Transfer Globus WAN

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Research on Point Cloud data Processing in Multiple Scenarios 5

Research on Point Cloud Data Processing in Multiple Scenario...

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5th international conference on Information science, Parallel and Distributed systems, ISPDS 2024

作者： Huang, Jinkui Ma, Xiangyao Wang, Jingyuan Li, Jianhua Army Engineering University of Pla College of Communication Engineering Nanjing China

ISBN: (纸本)9798350352719

With the development of 3D laser scanning technology, point cloud data has been widely used in various fields. As the most critical link after the point cloud data collection, the denoising process of point cloud data directly affects the subsequent point cloud data splicing, 3D reconstruction and other links. At present, there are still gaps in the research of point cloud data processing methods under different application scenarios. Therefore, this paper studies the statistical filtering and radius filtering methods of point cloud data in different scenarios and environments, and combines the characteristics of the two filtering methods to obtain a point cloud data filtering method suitable for different scenarios. © 2024 ieee.

关键词： filtering scene point cloud filtering

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An Exploratory Study of Vulnerabilities in Big data systems

An Exploratory Study of Vulnerabilities in Big Data Systems

引用

2023 ieee international conference on Big data, Bigdata 2023

作者： Tyllis, Nikolas Ullah, Faheem Uzair, Muhammad The University of Adelaide AdelaideSA Australia

ISBN: (纸本)9798350324457

The use of big data systems has become prevalent across sensitive domains, including health, defense and finance, among others. These big data systems are often complex and with complexity often comes vulnerabilities. Most big data systems also have Application Programming Interfaces (API) where these vulnerabilities can be present. This results in introducing security risks into the projects using these big data tools. To address this issue, this paper presents and uses a research process that can be used to assess the state of big data security in open source projects. The study examines tool versioning in various open source projects to uncover the vulnerabilities present and then assess the significant factors of these vulnerabilities. Furthermore, we provide insights into the vulnerabilities of different big data system APIs and how each of the vulnerabilities can manifest in a project using the specific tool. This study serves as a guideline for big data system developers to develop highly secure data-intensive systems. CCS CONCEPTS • Security and privacy → database and storage security;Software and application security;© 2023 ieee.

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Performance Analysis of mdx II: A Next-Generation Cloud Platform for Cross-Disciplinary data science Research 15

Performance Analysis of mdx II: A Next-Generation Cloud Plat...

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15th international conference on Cloud Computing and Services science, CLOSER 2025

作者： Takahashi, Keichi Hayami, Tomonori Mukaizono, Yu Teramae, Yuki Date, Susumu D3 Center University of Osaka Osaka Japan

ISBN: (纸本)9789897587474

mdx II is an Infrastructure-as-a-Service (IaaS) cloud platform designed to accelerate data science research and foster cross-disciplinary collaborations among universities and research institutions in Japan. Unlike traditional high-performance computing systems, mdx II leverages OpenStack to provide customizable and isolated computing environments consisting of virtual machines, virtual networks, and advanced storage. This paper presents a comprehensive performance evaluation of mdx II, including a comparison to Amazon Web Services (AWS). We evaluated the performance of a 16-vCPU VM from multiple aspects including floating-point computing performance, memory throughput, network throughput, file system and object storage performance, and real-world application performance. Compared to an AWS 16-vCPU instance, the results indicated that mdx II outperforms AWS in many aspects and demonstrated that mdx II holds significant promise for high-performance data analytics (HPDA) workloads. We also evaluated the virtualization overhead using a 224-vCPU VM occupying an entire host. The results suggested that the virtualization overhead is minimal for compute-intensive benchmarks, while memory-intensive benchmarks experienced larger overheads. These findings are expected to help users of mdx II to obtain high performance for their data science workloads and offer insights to the designers of future data-centric cloud platforms. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： data science

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Point Cloud Completion Method Assisted by Projected Image

Point Cloud Completion Method Assisted by Projected Image

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2024 ieee international conference on systems, Man, and Cybernetics, SMC 2024

作者： Lin, Shujin Li, Zhaowen Wu, Runxun Zhou, Fan U-niversity of Sun Yat-sen Faculty of Journalism and Communication Guangdong Provincial Science and Technology Collaborative Innovation Center for Culture and Tourism Guangzhou China School of Computer Science and Engineering University of Sun Yat-sen Guangdong Provincial Science and Technology Collaborative Innovation Center for Culture and Tourism Guangzhou China

ISBN: (纸本)9781665410205

Image-guided point cloud completion task aims to utilize image information to address the uncertainties in point cloud completion inference. Although acquiring 2D image data is relatively simpler than 3D data, it is still ineffective in scenarios with occlusions where image data cannot be reliably obtained as a reference. Therefore, we propose a point cloud completion model assisted by projected image data, which addresses the limitations of acquiring 2D images by constructing projected images of the point cloud. Extensive experiments demonstrate that our proposed method enhances the quality of point cloud completion and outperforms other advanced methods. © 2024 ieee.

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AutoEDA: Iterative data Focusing and Exploratory Analysis Based on Attribute Frequency

AutoEDA: Iterative Data Focusing and Exploratory Analysis Ba...

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2024 ieee international conference on systems, Man, and Cybernetics, SMC 2024

作者： Wu, Tong Wang, Song Peng, Xin School of Computer Science and Technology Southwest University of Science and Technology Department of Computer Science Mianyang China

ISBN: (纸本)9781665410205

The paper proposes an automated data exploration and analysis method based on Attribute Frequency Statistical Feature Ratio (AFSFR). It integrates AutoVis and data Preprocessing Methods to design and develop AutoEDA-Segment. Addressing the Concentrate on Field Sequences (CFS) problem in data exploration and analysis, this study employs various classification models and combines AFSFR with field type and the Elbow Inflection Point (EIP) of index features to design a field type identification and field value assessment method. For evaluating the effectiveness of focus analysis, the approach provides clustering visualization effects and an analysis scheme based on Field Type Search Tree (FST) and cluster comparison profiles, using a custom CFS approach. Additionally, to enhance the value of focused subset analysis data, the approach introduces a Parallel Coordinates-Based data Filter (PCF), forming an EDA feedback loop to achieve Iterative Exploratory data Analysis for User-inferred Cognition (IEDA-UC). Finally, we engaged graduate students with varying levels of experience in visualization research for collaboration and discussion, validating the effectiveness and feasibility of the approach using structured data from Kaggle. © 2024 ieee.

关键词： Visualization

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Research on Multi-modal data Management System for Intelligent Connected Vehicle 4

Research on Multi-modal Data Management System for Intellige...

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4th ieee international conference on Information Technology, Big data and Artificial Intelligence, ICIBA 2024

作者： Zhang, Lu Guangxi Vocational Normal University School of Computer and Information Engineering Guangxi China

ISBN: (纸本)9798350363586

The development of intelligent networked vehicle technology and the testing of related algorithms require a large number of datasets as the foundation. The existing datasets are mainly collected from foreign traffic scenarios, which are significantly different from the actual road traffic environment of China. Therefore, it is particularly important to construct local datasets and data management systems. This paper describes in detail the design of multi-modal data management system for Intelligent Connected Vehicle, covering the overall architecture of systems, hardware composition, the design of software function modules, database design, and key steps for data processing. Experiments and tests verified the reliability and effectiveness of the data management system. © 2024 ieee.

关键词： data assimilation

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