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high Precision Method of Federated Learning Based on Cosine Similarity and Differential Privacy 15

High Precision Method of Federated Learning Based on Cosine ...

引用

2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of things, ithings 2022, 18th IEEE International Conferences on Green computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social computing, CPSCom 2022 and 8th IEEE International Conference on Smart Data, SmartData 2022

作者： Wang, Jia Li, Yazheng Ye, Ronghang Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering Shenzhen China Zhengzhou University of Industrial Technology College of Computer Science and Software Engineering Zhengzhou China

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

Federated learning has emerged as an efficient way to exploit distributed data in recent years. It allows multiple client nodes to collaboratively train an optimized machine learning model without revealing the participants' data. However, there are some shortcomings in the existing federated learning algorithms: the existence of poor quality nodes leads to undesirable effects on the overall gradient descent direction of the model. And the typical iterative training and noising way also make the privacy loss of the model reaches the privacy budget quickly. Hence, the model training may stop before convergence. In this paper, we propose a federated learning gradient adaptive aggregation method based on cosine similarity and a central node privacy protection method based on random differential privacy to solve the above-mentioned problems respectively. the proposed scheme was implemented on two public general datasets (MNIST and SVHN) and a medical dataset (UCI diabetes). Experimental results show that the federated learning adaptive method based on cosine similarity could effectively eliminate the negative influence of nodes with poor data quality on the overall model and maintain stable performance. the central node privacy protection method based on random differential privacy could also improve the performance of the training model under the same privacy budget and privacy loss threshold. © 2022 IEEE.

关键词： Learning algorithms

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Energy Efficiency in Edge Environments: a Serverless computing Approach 18th

Energy Efficiency in Edge Environments: a Serverless Computi...

引用

18th International Conference on the Economics of Grids, Clouds, Systems, and Services (GECON )

作者： Djemame, Karim Univ Leeds Sch Comp Leeds W Yorkshire England

ISBN: (纸本)9783030929169;9783030929152

the paper revisits the Internet architecture by leveraging Software Defined Networks (SDN) with Network Function Virtualisation (NFV) technologies to allow efficient and on-demand placement of Virtual Network Functions (VNF) on a serverless platform for energy-aware function provisioning in edge environments. Edge computing is seen as critical for supporting the next generation of services and applications that demand high speeds and low-latencies though energy consumption is a matter of concern. Serverless computing as a paradigm in virtualisation is considered as a low-latency and a rapidly deployable alternative to traditional virtualisation approaches. Event-triggered serverless functions incentivise energy efficient resource usage and provide granular reporting on a function level. the research will develop a new building block that satisfies the services performance while reducing the energy consumption in edge environments.

关键词： Serverless computing Energy efficiency Software Defined Networks Network Function Virtualisation

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Polyjuice: high-performance Transactions via Learned Concurrency Control 15

Polyjuice: High-Performance Transactions via Learned Concurr...

引用

USENIX Annual Technical Conference / 15th USENIX symposium on Operating Systems Design and Implementation (OSDI)

作者： Wang, Jiachen Ding, Ding Wang, Huan Christensen, Conrad Wang, Zhaoguo Chen, Haibo Li, Jinyang Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China Minist Educ Engn Res Ctr Domain Specif Operating Syst Shanghai Peoples R China NYU Dept Comp Sci New York NY 10003 USA

ISBN: (纸本)9781939133229

Concurrency control algorithms are key determinants of the performance of in-memory databases. Existing algorithms are designed to work well for certain workloads. For example, optimistic concurrency control (OCC) is better than two-phase-locking (2PL) under low contention, while the converse is true under high contention. To adapt to different workloads, prior works mix or switch between a few known algorithms using manual insights or simple heuristics. We propose a learning-based framework that instead explicitly optimizes concurrency control via offline training to maximize performance. Instead of choosing among a small number of known algorithms, our approach searches in a "policy space" of fine-grained actions, resulting in novel algorithms that can outperform existing algorithms by specializing to a given workload. We build Polyjuice based on our learning framework and evaluate it against several existing algorithms. Under different configurations of TPC-C and TPC-E, Polyjuice can achieve throughput numbers higher than the best of existing algorithms by 15% to 56%.

关键词： Concurrency control

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Enhancing Medical Imaging Accessibility: An AI-Powered Automated Tactile and 3D-Printable X-Ray System

Enhancing Medical Imaging Accessibility: An AI-Powered Autom...

引用

IEEE Annual computing and Communication Workshop and Conference (CCWC)

作者： Priti Yadav Anujkumarsinh Donvir Independent Researcher Surrey BC Canada Application Development Wayne NJ USA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

this research addresses the critical need for accessible medical imaging for visually impaired doctors and patients. Current methods for converting medical images into tactile formats are time-consuming, expensive, and often lack sufficient detail for accurate diagnosis. this study presents a novel AI-powered system for automated generation of tactile and 3D-printable representations of medical X-ray images. the system employs image processing techniques, including depth estimation and image enhancement, to create high-resolution depth maps suitable for tactile rendering. the system's performance is evaluated using objective metrics (accuracy, processing time) and will be further assessed through user feedback from visually impaired healthcare professionals. this research demonstrates the feasibility of an automated solution for improving access to medical imaging, potentially enhancing diagnostic accuracy and promoting inclusivity in healthcare. Future work will focus on expanding the system to other imaging modalities and optimizing for real-time clinical use.

关键词： Accuracy Pneumonia System performance Visual impairment Rendering (computer graphics) Real-time systems Medical diagnosis Usability Medical diagnostic imaging X-ray imaging

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Improved GAN for Efficient Cyber Forensics

Improved GAN for Efficient Cyber Forensics

引用

International Conference on computing and Networking Technology (ICCNT)

作者： Vimal Kumar Udyan Sharma Alan Marc Louis SCSET Bennett University Greater Noida India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

the society is now deeply connected with digital exchanges running on cyber-physical systems for their daily operations. this gives rise to cyber crime and creates a need for cyber forensics. this paper presents an innovative approach based on improved Generative Adversarial Networks(GAN) for efficient cyber forensics. the proposed system uses the existing GAN networks of SRGAN and DCGAN to generate high quality images from low quality images. We experiment with different activation functions, number of layers, nodes and network architectures to improve the accuracy achieved in super resolution of images. the system has been tested for use in cyber forensics to super resolve the images of accused in a video frame and for better detection of car number plate images taken from cameras on traffic lights and CCTV cameras. the experimental results show that our proposed system outperforms existing GAN networks in terms of PSNR value by ~2.24% and the SSIM method by ~3.59%.

关键词： Accuracy Digital forensics Superresolution Network architecture Cyber-physical systems Generative adversarial networks Cameras Automobiles computer crime License plate recognition

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Finer-LRU: A Scalable Page Management Scheme for HPC Manycore architectures 35

Finer-LRU: A Scalable Page Management Scheme for HPC Manycor...

引用

35th IEEE International Parallel and Distributed Processing symposium (IPDPS)

作者： Bang, Jiwoo Kim, Chungyong Kim, Sunggon Chen, Qichen Lee, Cheongjun Byun, Eun-Kyu Lee, Jaehwan Eom, Hyeonsang Seoul Natl Univ Dept Comp Sci & Engn Seoul South Korea Korea Aerosp Univ Dept Elect & Informat Engn Goyang South Korea Korea Inst Sci & Technol Informat Div Natl Supercomp Daejeon South Korea

ISBN: (纸本)9781665440660

In HPC systems, the increasing need for a higher level of concurrency has led to packing inure cores within a single chip. However, since multiple processes share memory space, the frequent access to resources in critical sections where only atomic operation has to be executed can result in poor performance. In this paper, we focus on reducing lock contention on the memory management system of an HPC manycore architecture. One of the critical sections causing severe lock contention in the I/O path is in the page management system, which uses multiple Least Recently Used (LRU) lists with a single lock instance. To solve this problem, we propose a Finer-LRU scheme, which optimizes the page reclamation process by splitting LRU lists into multiple sub-lists, each having its own lock instance. Our evaluation result shows that the Finer-LRU scheme can improve sequential write throughput by 57.93% and reduce latency by 98.94% compared to the baseline Linux kernel version 5.2.8 in the Intel Knights Landing (KNL) architecture.

关键词： high performance computing Manycore architecture Page reclamation process Fine-grained lock

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Enhanced Fish Species Identification Using Transfer Learning on Balanced Datasets

Enhanced Fish Species Identification Using Transfer Learning...

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International Conference on computing and Networking Technology (ICCNT)

作者： Shubham Berlia Vishal Kumar Singh Manish Kumar Ramkaran Mahato Monalisa Mishra Department of Computer Science and Engineering C.V. Raman Global University Bhubaneswar India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

the study aimed to enhance the accuracy and reliability of automatic fish detection and species classification from aquatic images and videos. Traditional manual methods are time-consuming, and existing automatic systems struggle with challenges such as environmental lighting fluctuations, fish camouflage, dynamic backgrounds, murky water, low resolution, shape distortions, and subtle differences between species. To address these issues, the proposed model leveraged the MobileNet V2 architecture for transfer learning and fine-tuned it by incorporating edge detection, texture detection, and shape detection techniques. the integration of these transfer learning methods was critical in achieving the study’s objective. through meticulous preprocessing, balancing the dataset, and effectively applying transfer learning, the model achieved an impressive accuracy of 99.83%. this high accuracy underscores the model’s effectiveness and reduced reliance on high-quality images typically required by other detection models. the results indicate significant progress in automated fish detection and species classification, offering a promising tool for marine scientists and conservationists to monitor fish populations more efficiently.

关键词： Training Accuracy Systematics Shape Transfer learning Lighting Manuals Fish Monitoring Videos

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Efficient Data Management through ML-Based Feature Extraction

Efficient Data Management through ML-Based Feature Extractio...

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International Conference on computing and Networking Technology (ICCNT)

作者： E R Adwaith Krishna M. Varundev Sidharth Surendran Lekha S Nair Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Multimedia data production, such as audio, video, and acoustic measurements in large volumes and speed, has approached the scale of big data in many sensor networks. Multimedia Sensor Networks (MSN) are popular because of their capability to manage and integrate the different domains of sensor outputs, their representations, and the encoding of these measurements. Studies on generic and specific models for detection and tracing events in a sensor network have been established by multiple models. At the same time, there is still the requirement to handle these measurements from the Complex Dataset perspective. In the current world scenario, sensor networks are not limited to producing high-quality and diverse sensor readings but also large volumes at high speed. the most intuitive methods of handling them focus only on the interested range of measurement or store-for-later use. the possibility of data analysis in such an information-rich big data environment can be improved. In this study, we propose a model, “Multilink” that helps to align the multimedia data generated by a sensor network to a standard easy-to-process feature space with the incorporation of Machine Learning centric architecture.

关键词： Current measurement Scalability Machine learning Streaming media Big Data Feature extraction Acoustic measurements Data models Time measurement Security

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Ontology for Structuring a Digital Databases for Decision Making in Grain Production 15

Ontology for Structuring a Digital Databases for Decision Ma...

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15th IEEE International Conference on Semantic computing (ICSC)

作者： Neves, Ricardo A. Cruvinel, Paulo E. Embrapa Instrumentat Sao Carlos SP Brazil Univ Fed Sao Carlos Postgrad Program Comp Sci Sao Carlos SP Brazil Fed Inst Sao Paulo Sao Joao Da Boa Vista SP Brazil

ISBN: (纸本)9781728188997

this paper presents an ontology for the structuring of digital databases with the objective of acting in a cloud environment and meeting big data sources in the agricultural context of grain production. Its conception is structured in three stages: the first stage presents an ontological architecture aimed at public and private cloud environments, the second stage deals with a semantic model at process level, and a pseudocode for ontological application is elaborated in the third stage, considering the technologies applied to the cloud. this work combines advanced features to support decision making from Data Lake storage solutions, semantic treatment of big data, as well as the presentation of strategies based on machine learning and data quality analysis to obtain data and metadata organized for application in a decision model. the configuration of the ontology presented meets the diversity of big data projects in the grain production context, the characteristics of which are based on interoperability in the use of heterogeneous data and its integration, elasticity of computational resources, and high availability of cloud access.

关键词： Ontology Agriculture Digital Database Cloud computing Big Data Decision Making

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Diabetes Prediction Using Hybrid 1D CNN and PeeHole Long Short-Term Memory Algorithm 5

Diabetes Prediction Using Hybrid 1D CNN and PeeHole Long Sho...

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5th International Conference on Emerging Systems and Intelligent computing, ESIC 2025

作者： Behera, Mandakini Mishra, Debahuti Sarangi, Archana Sarangi, Shubhendu Deemed to be University School of Computer Engineering Bhubaneswar India University Department of Computer Science & Engineering Bhubaneswar India University Department of Electronics and Telecommunications Bhubaneswar India University Faculty of Engineering Bhubaneswar India

ISBN: (纸本)9798331522100

According to the survey of International Diabetes Federation (IDF) the number of persons with diabetes is continuously increasing. there are currently 3820 million diabetics worldwide, and during the next 15 years, this figure might double. Diabetes can create several life-threatening diseases, including heart attack, renal failure, and stroke. To sustain a healthy life, early diabetes identification is crucial. machine learning (ML) is a approach which automatically learns from experience to improve performance and produce more accurate predictions, that can be used to forecast diabetes. Deep learning is a branch of machine learning, and the human brain inspires both its architecture and the way it *** manuscript presents a hybrid method that combines peephole LSTM and 1D CNN to improve the accuracy of diabetes prediction. performance is evaluated based on F1 score, accuracy, specificity, sensitivity, and epoch-wise accuracy and *** Pima Indian diabetes dataset is utilized for experimental validation. Experimental results demonstrate high prediction accuracy of 96.74% on training and 75.32% on testing for diabetes prediction. © 2025 IEEE.

关键词： Deep learning

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