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检索条件"任意字段=International Conference on Parallel and Distributed Processing Techniques and Applications"

共 11287 条记录，以下是41-50 订阅

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A Comprehensive Scheme for Transaction and Fund Tracing in distributed Anonymous Transactions 24th

A Comprehensive Scheme for Transaction and Fund Tracing in...

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24th international conference on Algorithms and Architectures for parallel processing, ICA3PP 2024

作者： Liu, Hai Li, Fangqiong Peng, Hongye Key Laboratory of Blockchain and Fintech of Department of Education of Guizhou Province Guizhou University of Finance and Economics Guiyang China

ISBN: (纸本)9789819615247

Anonymous transaction technologies are crucial for safeguarding the privacy of users’ transactions, but they also pose challenges to traditional regulatory methods. However, existing supervision schemes for anonymous transactions often suffer from poor performance due to the use of complex cryptographic techniques. This paper proposes a transaction and fund tracing scheme for distributed anonymous transactions. To achieve traceability, the proposed scheme provides two models of supervision: transaction tracing to de-anonymize specific transactions and fund tracing to trace the flow of funds from illicit users. We have ingeniously designed a mechanism to distribute the critical information necessary for regulation among three parties, reducing the risk of misconduct by the supervisor and the platforms. Theoretical analysis shows that even in the face of external attacks and internal malicious activities, the proposed scheme can maintain the anonymity and traceability of transactions. Additionally, the performance of the proposal is evaluated through extensive experiments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Anonymity

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Empowering Health Insurance with Personalization: A Comprehensive Architecture Leveraging Federated Learning and BlockChain

Empowering Health Insurance with Personalization: A Comprehe...

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international conference on Advances and applications of Artificial Intelligence and Machine Learning, ICAAAIML 2023

作者： Jain, Anant Gupta, Gauranshi Johari, Rahul Vidyarthi, Deo Prakash Guru Gobind Singh Indraprastha University Delhi India School of Computer and Systems Sciences Parallel and Distributed System Lab JNU Delhi India

ISBN: (纸本)9789819795062

Historically, the health insurance domain has adhered to uniform premium pricing structures, often sidelining the distinct health nuances of each policyholder. Such broad-brush approaches can result in misaligned premiums, not truly reflective of individual health dynamics. Venturing into uncharted territories, this study proposes an innovative fusion of Federated Machine Learning (FML) and BlockChain methodologies to recalibrate health insurance pricing. FML, a cutting-edge evolution in the machine learning sphere, emphasizes decentralized data processing. By harnessing FML, timely insights can be gathered that mirror an individual’s health trajectory. This continuous insight generation facilitates agile adjustments to insurance premiums, ensuring they resonate with real-time health conditions. BlockChain’s decentralized ledger system acts as the bedrock of this model. Each premium modification, steered by FML-derived insights, is indelibly etched onto the BlockChain. This architecture not only masters transparency but also instills a heightened sense of trust, given the unalterable nature of BlockChain entries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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A Massive Open Online Course (MOOC) on High-Performance parallel Computing for *** Web-learning Platform

A Massive Open Online Course (MOOC) on High-Performance Para...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Giuliano Laccetti Marco Lapegna Ilaria Merciai dept. of Mathematics and Applications Univ. of Naples Federico II Naples Italy Federica Web Learning Univ. of Naples Fedrico II Naples Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Massive Open Online Courses (MOOCs) represent an accessible and user-friendly tool for disseminating innovative and cutting-edge topics to broad segments of civil society via online learning platforms, enabling users to learn at their own pace and on their own schedule. In this contribution, we describe the design and the implementation of a Massive Open Online Course on parallel Computing and High-Performance Computing, developed for Federica Web Learning: the University Center for innovation, experimentation, and dissemination of multimedia teaching at the University of Naples Federico II.

关键词： Training Computer aided instruction Technological innovation Schedules Electronic learning High performance computing Multimedia computing parallel processing Character recognition Videos

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An Efficient Private Information Retrieval Protocol Based on TFHE 19th

An Efficient Private Information Retrieval Protocol Based on...

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19th international conference on Security and Privacy in Communication Networks

作者： Tian, Haibo Lin, Yini Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Guangdong Prov Key Lab Informat Secur Technol Guangzhou 510006 Peoples R China

ISBN: (纸本)9783031649479;9783031649486

Private Information Retrieval (PIR) allows a user to query an entry from a database without revealing the index of the entry to the database owner. It is a building tool for many privacy enhancement applications, such as compromised credential checking and stock value query. However, the efficiency of PIR protocols has always been a bottleneck in their practical deployment. To address the challenge, we propose leveraging the fully homomorphic encryption over the torus (TFHE) scheme, which supports implementation on 32-bit unsigned integers and features a fast controlled selector (CMux) gate. The CMux gate can naturally construct a table lookup algorithm to serve PIR. We introduce compression and expansion algorithms for a PIR query, resulting in an encrypted query only a few hundred bytes in size. Further, we present a parallel PIR protocol and four simultaneous table lookup (STLU) algorithms based on TFHE, which are implemented on graphics processing units (GPUs) and make a distributed PIR service with a fast response time. On four servers with five GPUs, we obtain a PIR service with a response time of less than 2 seconds and a query size of less than 200 bytes on a database containing 2(22) entries, with each entry encoding a message of at least 256 bytes.

关键词： private information retrieval fully homomorphic encryption over the torus simultaneous table lookup graphics processing units

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Object Detection Using Efficient Partitioning and Frame Reduction

Object Detection Using Efficient Partitioning and Frame Redu...

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IEEE international conference on Consumer Electronics (ICCE)

作者： Omar Imran Sreeraman Rajan Shikharesh Majumdar Systems and Computer Engineering Carleton University Ottawa Canada

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Rapid object detection is crucial for safety-critical applications, such as post-event analysis of surveillance videos in crime investigations and the operation of autonomous vehicles. The objective of this paper is to improve the speed of object detection through efficient partitioning and frame reduction using parallel processing. techniques to improve Spark's default partitioning by using entropy-based algorithms to create more evenly distributed partitions based on the estimated workload of the frames are considered. Redundant frames are removed to efficiently reduce the workload. Algorithms for removing redundant frames are introduced and evaluated for their effectiveness in comparison to random and fixed frame removals. The results from this paper demonstrate that partitioning algorithms that estimate workload using entropy provide faster processing time when compared to those that use random partitioning. The results also indicate that frame removal algorithms that use frame-specific information, like entropy difference between frames, further improve performance without notably reducing detection accuracy when compared to those that do not utilize frame-specific information.

关键词： Accuracy System performance Surveillance Object detection parallel processing Transformers Entropy Partitioning algorithms Sparks Videos

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24th international conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, SAMOS 2024

24th International Conference on Embedded Computer Systems: ...

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24th international conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, SAMOS 2024

ISBN: (纸本)9783031783760

The proceedings contain 38 papers. The special focus in this conference is on Embedded Computer Systems: Architectures, Modeling, and Simulation. The topics include: QCEDA: Using Quantum Computers for EDA;Real-Time Linux on RISC-V: Long-Term Performance Analysis of PREEMPT_RT Patches;RV-VP2: Unlocking the Potential of RISC-V Packed-SIMD for Embedded processing;A Novel System Simulation Framework for HBM2 FPGA Platforms;ONNX-To-Hardware Design Flow for Adaptive Neural-Network Inference on FPGAs;efficient Post-training Augmentation for Adaptive Inference in Heterogeneous and distributed IoT Environments;pooling On-the-Go for NoC-Based Convolutional Neural Network Accelerator;Vitamin-V: Serverless Cloud Computing Porting on RISC-V;Design and Implementation of an Open Source OpenGL SC 2.0.1 Installable Client Driver and Offline Compiler;Plan Your Defense: A Comparative Analysis of Leakage Detection Methods on RISC-V Cores;iVault: Architectural Code Concealing techniques to Protect Cryptographic Keys;I2DS: FPGA-Based Deep Learning Industrial Intrusion Detection System;ACRA: A Cutting-Edge Analytics Platform for Advanced Real-Time Corruption Risk Assessment and Investigation Prioritization;post Quantum Cryptography Research Lines in the Italian Center for Security and Rights in Cyberspace;advancing Future 5G/B5G Systems: The Int5Gent Approach;RISC-V Accelerators, Enablement and applications for Automotive and Smart Home in the ISOLDE Project;PMDI: An AI-Enabled Ecosystem for Cooperative Urban Mobility;Open Source Software Randomisation Framework for Probabilistic WCET Prediction on Multicore CPUs, GPUs and Accelerators;a Hypervisor Based Platform for the Development and Verification of Reliable Software applications.

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24th international conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, SAMOS 2024

24th International Conference on Embedded Computer Systems: ...

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24th international conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, SAMOS 2024

ISBN: (纸本)9783031783791

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Two-Phase distributed Algorithm for Solving the Bi-Objective Minimum Spanning Tree Problem: A Preliminary Study 15th

Two-Phase Distributed Algorithm for Solving the Bi-Objecti...

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15th international conference on parallel processing and Applied Mathematics, PPAM 2024

作者： Amorosi, Lavinia Cairo, Mariagrazia Dell’Olmo, Paolo Di Rocco, Lorenzo Ferraro Petrillo, Umberto Department of Statistical Sciences Università di Roma "La Sapienza" P.le Aldo Moro 5 Rome00185 Italy

ISBN: (纸本)9783031856990

In this paper we present a novel distributed algorithm for solving the Bi-Objective Minimum Spanning Tree (BMST) problem using a two-phase method. The proposed approach leverages the MapReduce computing paradigm to define a distributed version of the most computational intensive part of this method, the second phase. In this phase, a recursive algorithm explores non-supported non-dominated points by evaluating spanning trees within specified regions in the objective space. The distributed nature of our approach allows parallel processing of multiple triangles in the objective space aiming at reducing the execution time and improving the scalability performance. Our preliminary experimental results, conducted using an HPC infrastructure, show that the improvements in the scalability performance when tasks show a balanced processing time are non-negligible but also highlight that a simple distribution of processes among the nodes of the distributed system is not enough to always achieve a good parallelization. Our analysis provided us with useful insights to achieve efficiency and effectiveness of this approach with respect to its sequential counterpart, pointing out its potential for practical applications in multi-objective optimization. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： MapReduce

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Contributions to Accelerating a Numerical Simulation of Free Flow parallel to a Porous Plane

Contributions to Accelerating a Numerical Simulation of Free...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Claudio Schepke Roberta A. Spigolon José Rufino Cesar F. Da C. Cristaldo Glener L. Pizzolato Laboratory of Advances Studies in Computation (LEA) Federal University of Pampa (UNIPAMPA) Alegrete Brazil Research Centre in Digitalization and Intelligent Robotics (CeDRI) Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC) Instituto Politécnico de Bragancá Bragança Portugal Laboratory of Computational Fluid Dynamics and Atmospheric Turbulence (LFCTA) Federal University of Pampa (UNIPAMPA) Alegrete Brazil

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Flow models over flat porous surfaces have applications in natural processes, such as material, food, chemical processing, or mountain mudflow simulations. The development of simplified analytical or numerical models can predict characteristics such as velocity, pressure, deviation length, and even temperature of such flows for geophysical and engineering purposes. In this context, there is considerable interest in theoretical and experimental models. Mathematical models to represent such phenomena for fluid mechanics have continuously been developed and implemented. Given this, we propose a mathematical and simulation model to describe a free-flowing flow parallel to a porous material and its transition zone. The objective of the application is to analyze the influence of the porous matrix on the flow under different matrix properties. We implement a Computational Fluid Dynamics scheme using the Finite Volume Method to simulate and calculate the numerical solutions for case studies. However, computational applications of this type demand high performance, requiring parallel execution techniques. Due to this, it is necessary to modify the sequential version of the code. So, we propose a methodology describing the steps required to adapt and improve the code. This approach decreases 5.3% the execution time of the sequential version of the code. Next, we adopt OpenMP for parallel versions and instantiate parallel code flows and executions on multi-core. We get a speedup of 10.4 by using 12 threads. The paper provides simulations that offer the correct understanding, modeling, and construction of abrupt transitions between free flow and porous media. The process presented here could expand to the simulations of other porous media problems. Furthermore, customized simulations require little processing time, thanks to parallel processing.

关键词： Codes Temperature Computational modeling Computational fluid dynamics Media parallel processing Numerical simulation Mathematical models Numerical models Surface treatment

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Privacy-Preserving in Medical Image Analysis: A Review of Methods and applications 25th

Privacy-Preserving in Medical Image Analysis: A Review of ...

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25th international conference on parallel and distributed Computing, applications and Technologies, PDCAT 2024

作者： Zhu, Yanming Yin, Xuefei Wee-Chung Liew, Alan Tian, Hui School of Information Communication and Technology Griffith University Gold Coast4215 Australia

ISBN: (纸本)9789819642069

With the rapid advancement of artificial intelligence and deep learning, medical image analysis has become a critical tool in modern healthcare, significantly improving diagnostic accuracy and efficiency. However, AI-based methods also raise serious privacy concerns, as medical images often contain highly sensitive patient information. This review offers a comprehensive overview of privacy-preserving techniques in medical image analysis, including encryption, differential privacy, homomorphic encryption, federated learning, and generative adversarial networks. We explore the application of these techniques across various medical image analysis tasks, such as diagnosis, pathology, and telemedicine. Notably, we organizes the review based on specific challenges and their corresponding solutions in different medical image analysis applications, so that technical applications are directly aligned with practical issues, addressing gaps in the current research landscape. Additionally, we discuss emerging trends, such as zero-knowledge proofs and secure multi-party computation, offering insights for future research. This review serves as a valuable resource for researchers and practitioners and can help advance privacy-preserving in medical image analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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