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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

共 6946 条记录，以下是4651-4660 订阅

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BAV-DSS: Blockchain Assisted Verifiable data Sharing Scheme with Fast Encryption and Outsourced Decryption for IoT 24th

BAV-DSS: Blockchain Assisted Verifiable Data Sharing Scheme ...

引用

24th international conference on algorithms and Architectures for Parallel processing, ICA3PP 2024

作者： Huang, Yao Yu, Zhongyuan Wang, Guijuan Chai, Baobao Zhang, Hongliang Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250353 China School of Information Science and Engineering Lanzhou University Lanzhou730000 China School of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9789819615414

Many sophisticated cloud platforms are available to provide services such as storage and computation for Internet of Things (IoT) applications. However, with the concerns about the semi-trusted nature of cloud platforms, data security and privacy have been gaining broad attention. In this paper, we propose a blockchain-assisted verifiable data sharing scheme (BAV-DSS) for IoT. The BAV-DSS features fine-grained access control, fast encryption, and outsourced decryption with verifiability. Specifically, with the application of ciphertext-policy attribute-based encryption (CP-ABE), flexible access control is enabled. Blockchain is employed to store public keys, generate account addresses, and perform data verification. Furthermore, the selective replayable chosen ciphertext attacks (sRCCA) security of BAV-DSS is proved, and the simulation results indicate that BAV-DSS obtains high efficiency and practicality compared to other schemes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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Linear Regression Model for Predicting Virtual Machine Consolidation Within the Cloud data Centers (LrmP_VMC) 1

引用

4th international conference on Machine Intelligence and Signal processing, MISP 2022

作者： Tejaswini, M. Hari Sumanth, T. Jairam Naik, K. Department of Computer Science & Engineering National Institute of Technology Raipur Great Eastern Rd Chhattisgarh Raipur492010 India

ISBN: (数字)9789819900855

ISBN: (纸本)9789819900848

Cloud data centers have been growing rapidly in both capacity and number to meet the ever-increasing demands for the highly responsive computing and its massive storage space. The data centers of this kind utilize a large amount of electricity which in turn leads to high operating costs and CO2 emissions from these cloud data centers. This consolidation of virtual machines involves live migration of VMs and thus the ability of transferring a virtual machine (VM) between the physical servers with a close to zero down time. In our proposed model (LrmP_VMC), we are going to use Roulette–Roulette Wheel Method, in which the virtual machine selects a certain instance type and the physical machine using this Roulette Wheel Selection Mechanism. We put forward an approach where the allocation of virtual machines is done, but the genetic approach reduces energy consumption and consolidates the whole process of virtual machine placement. As per the results, the genetic approach gives off better results when there are a greater number of virtual machines involved in the allocation process, and it is said that LrmP_VMC approach is 15% more efficient with higher number of VMs compared to Random Forest Method on the basis of accuracy when a large number of inputs are taken. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Energy utilization

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Group Personalized Federated Learning

Group Personalized Federated Learning

引用

international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Zhe Liu Yue Hui Fuchun Peng Meta AI Menlo Park CA USA

Federated learning (FL) can help promote data privacy by training a shared model in a de-centralized manner on the physical devices of clients. In the presence of heterogeneous distributions of local data, personalized FL strategy is introduced to mitigate the potential client drift. In this paper, we present the group personalization approach for applications of FL in which there exist inherent partitions over clients that are significantly distinct. In our approach, the global FL model is fine-tuned through another FL training process over each homogeneous group of clients, after which each group-specific FL model is further adapted and personalized per client. The proposed method can be well interpreted from a Bayesian hierarchical modeling perspective. With experiments on two real-world datasets for language modeling task, we demonstrate this approach can achieve superior personalization performance than other FL counterparts.

关键词： Training Adaptation models data privacy Federated learning Signal processing algorithms Signal processing data models

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Distributed Attacks Classification Based on Radical Basis Function and Particle Swarm Optimization In Hypervisor Layer

Distributed Attacks Classification Based on Radical Basis Fu...

引用

international conference on Information Systems and Computer Networks (ISCON)

作者： Aditya Kumar Shukla Ashish Sharma Department of Computer Engineering & Applications GLA University Mathura (UP) India

The term ‘cloud computing’ refers to a concept for on-demand provisioning of computer resources (including hardware and software) through the Internet. Cloud computing’s primary benefits lie in the time and money savings it may provide to IT departments. While cloud computing offers many benefits It also raises many safety considerations. These include data privacy, data availability, data placement, data security credential, license, access management, network security, web data protection, virtual devices protection, etc. Recently, machine learning has shown to be an effective tool for safeguarding the cloud. In order to automate the process of identifying cloud assaults with better accuracy than any other technology, machine learning algorithms are trained on numerous legitimate datasets to develop models. To mitigate the effects of DDoS invasions, our research suggests implementing a detection method at the hypervisor layer. As a means of detecting and categorizing DDoS attacks in the communication between virtual computers, the suggested detection method combines the particle swarm optimization (PSO) with radial basis function (RBF). High classification accuracy against DDoS attacks is the focus of our investigation and recommended method.

关键词： Machine learning algorithms Virtual machine monitors Cloud computing security data protection Machine learning Denial-of-service attack Safety

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Big data Mining of Maritime Shipping Flows Based on Dependency Relationships

Big Data Mining of Maritime Shipping Flows Based on Dependen...

引用

6th international conference on Intelligent computing and Signal processing (ICSP)

作者： Yude Fu Xiang Li Xiongyi Lu Mengjun Yu Qizi Huangpeng Xiaojun Duan College of Science National University of Defense Technology Changsha China National Research Center of Parallel Computer Engineering and Technology Beijing China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

This research utilizes higher-order dependency relationships for the modeling of higher-order dependency networks from voluminous maritime flow data and introduces higher-order dependency mining to uncover critical port information in the China-Southeast Asia region as well as the coopetition relationships between ports. Analysis of maritime data from the years 2018 and 2023 indicates that global ports maintain stability and competitiveness through their evolution, with Singapore serving as the central hub connecting China and Southeast Asia. Subsequent analysis employing dependency paths indicates that in recent years, the dependency relationships among ports between China and Southeast Asia have achieved higher orders and greater complexity.

关键词： Asia Seaports Strategic planning Signal processing data models Stability analysis Trajectory data mining Optimization Logistics

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25th international conference on Medical Image computing and Computer-Assisted Intervention , MICCAI 2022

25th International Conference on Medical Image Computing and...

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25th international conference on Medical Image computing and Computer-Assisted Intervention , MICCAI 2022

ISBN: (纸本)9783031336577

The proceedings contain 25 papers. The special focus in this conference is on Medical Image computing and Computer-Assisted Intervention. The topics include: Semi-supervised Semantic Segmentation Methods for UW-OCTA Diabetic Retinopathy Grade Assessment;Image Quality Assessment Based on Multi-model Ensemble Class-Imbalance Repair Algorithm for Diabetic Retinopathy UW-OCTA Images;an Improved U-Net for Diabetic Retinopathy Segmentation;a Vision Transformer Based Deep Learning Architecture for Automatic Diagnosis of Diabetic Retinopathy in Optical Coherence Tomography Angiography;Segmentation, Classification, and Quality Assessment of UW-OCTA Images for the Diagnosis of Diabetic Retinopathy;data Augmentation by Fourier Transformation for Class-Imbalance: Application to Medical Image Quality Assessment;Automatic Image Quality Assessment and DR Grading Method Based on Convolutional Neural Network;a Transfer Learning Based Model Ensemble Method for Image Quality Assessment and Diabetic Retinopathy Grading;Automatic Diabetic Retinopathy Lesion Segmentation in UW-OCTA Images Using Transfer Learning;Reference algorithms for the Mitosis Domain Generalization (MIDOG) 2022 Challenge;automated Analysis of Diabetic Retinopathy Using Vessel Segmentation Maps as Inductive Bias;Radial Prediction Domain Adaption Classifier for the MIDOG 2022 Challenge;Detecting Mitoses with a Convolutional Neural Network for MIDOG 2022 Challenge;tackling Mitosis Domain Generalization in Histopathology Images with Color Normalization;a Deep Learning Based Ensemble Model for Generalized Mitosis Detection in H &E Stained Whole Slide Images;Fine-Grained Hard-Negative Mining: Generalizing Mitosis Detection with a Fifth of the MIDOG 2022 dataset;multi-task RetinaNet for Mitosis Detection;bag of Tricks for Diabetic Retinopathy Grading of Ultra-Wide Optical Coherence Tomography Angiography Images;deep Convolutional Neural Network for Image Quality Assessment and Diabetic Retinopathy Grading;diabetic Retinal

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Effective Surveillance using Computer Vision

Effective Surveillance using Computer Vision

引用

Sustainable computing and data Communication Systems (ICSCDS), international conference on

作者： Aditi Marwaha Abhijit Chirputkar P Ashok Symbiosis Institute of Digital and Telecom Management (SIDTM) Symbiosis International (Deemed University) (SIU) Lavale Pune Maharashtra India

Human society significantly relies on CCTV cameras to maintain a high standard of security. CCTV footage is usually only utilized a few hours or days following an incident. Although it frequently serves as important evidence in court, it is rarely used for crime prevention or real-time intervention. The fact that only few security officers are primarily in charge of watching the ample amounts of CCTV footage is what causes inefficiency. Human supervision cannot be completely relied due to the weariness and boredom among the workforce, and lag in observation. Developing an effective surveillance system is a significant challenge in computer vision, data processing, and decision-making process. Smart Surveillance Systems use image analysis and machine learning techniques to extract and analyze the data from CCTV footage far more quickly and effectively than a human observer. This would encourage the relevant authorities to create a platform using image processing and machine learning techniques so that the violence in public places might be prevented. The system must also involve cops when the situation worsens and make sure that it captures every human involved in the respected frame. The system must be trained thoroughly to ensure that it can recognize whether there is violence.

关键词： Computer vision Image analysis Surveillance Decision making Machine learning Observers data processing

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DDIOSim: A Microarchitecture Simulator for data Direct I/O Technology

DDIOSim: A Microarchitecture Simulator for Data Direct I/O T...

引用

international conference on High Performance computing

作者： Hari Sharan Mythili Vutukuru Biswabandan Panda Department of Computer Science and Engineering Indian Institute of Technology Bombay Mumbai India

This paper presents DDIOSim, a cycle-accurate microarchitecture simulator that simulates data Direct I/O-based network packet processing. Our open-source simulator consists of a front-end trace generator that generates traces of CPU instructions, memory accesses, and network I/O events across multiple networking applications, and a cycle-accurate backend simulator that processes these traces by simulating DDIO operations along with the entire CPU and cache/memory hierarchy. Our simulator can be used to explore various DDIO design and configuration options, and its interactions with other microarchitecture optimizations like cache hierarchy, hardware prefetchers, and DRAM schedulers.

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Aerial Image Stitching Using IMU data from a UAV

Aerial Image Stitching Using IMU Data from a UAV

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international conference on Image, Vision and computing (ICIVC)

作者： Selim Ahmet Iz Mustafa Unel Faculty of Engineering and Natural Sciences Sabanci University Istanbul Turkey

Unmanned Aerial Vehicles (UAVs) are widely used for aerial photography and remote sensing applications. One of the main challenges is to stitch together multiple images into a single high-resolution image that covers a large area. Feature-based image stitching algorithms are commonly used but can suffer from errors and ambiguities in feature detection and matching. To address this, several approaches have been proposed, including using bundle adjustment techniques or direct image alignment. In this paper, we present a novel method that uses a combination of IMU data and computer vision techniques for stitching images captured by a UAV. Our method involves several steps such as estimating the displacement and rotation of the UAV between consecutive images, correcting for perspective distortion, and computing a homography matrix. We then use a standard image stitching algorithm to align and blend the images together. Our proposed method leverages the additional information provided by the IMU data, corrects for various sources of distortion, and can be easily integrated into existing UAV workflows. Our experiments demonstrate the effectiveness and robustness of our method, outperforming some of the existing feature-based image stitching algorithms in terms of accuracy and reliability, particularly in challenging scenarios such as large displacements, rotations, and variations in camera pose.

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Unbiased Unsupervised Stimulus Reconstruction for EEG-Based Auditory Attention Decoding

Unbiased Unsupervised Stimulus Reconstruction for EEG-Based ...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Nicolas Heintz Simon Geirnaert Tom Francart Alexander Bertrand Department of Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Belgium Department of Neurosciences ExpORL KU Leuven Belgium

It is possible to decode auditory attention to speech from electrophysiological brain recordings such as electroencephalography (EEG). Such an auditory attention decoding (AAD) allows, e.g., to determine to which person a listener is attending in a multi-talker scenario. The vast majority of research has focused on developing supervised AAD algorithms in which the decoder is trained based on ground truth labels about the attention to each speaker. However, to work optimally, the trained decoders must be subject-specific and adapt over time to track sudden changes in signal statistics (e.g. electrode failures). Since it is often impractical to regularly retrain these decoders with a dedicated calibration session, an unsupervised algorithm has recently emerged as an *** this paper, we show that the state-of-the-art unsupervised AAD algorithm is biased by its initialisation, which leads to a suboptimal convergence. This bias has the largest effect when only a limited amount of data is available to train it, e.g. to train an unsupervised decoder that can quickly adapt to sudden changes. We show that this bias can be easily removed, leading to a better classification accuracy. However, the gain in accuracy reduces as the number of classified segments increases.

关键词： Electrodes Signal processing algorithms Electroencephalography Decoding Recording Classification algorithms Calibration

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