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Hierarchical Federated learning with Gaussian Differential Privacy 22

Hierarchical Federated Learning with Gaussian Differential P...

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proceedings of the 4th international conference on Advanced Information Science and System

作者： Tao Zhou China Telecom Cybersecurity Technology Co. Ltd China

ISBN: (纸本)9781450397933

Federated learning is a privacy preserving machine learning technology. Each participant can build the model without disclosing the underlying data, and only shares the weight update and gradient information of the model with the server. However, a lot of work shows that the attackers can easily obtain the client’s contributions and the relevant privacy training data from the public shared gradient, so the gradient exchange is no longer safe. In order to ensure the security of Federated learning, in the differential privacy method, noise is added to the model update to obscure the contribution of the client, thereby resisting member reasoning attacks, preventing malicious clients from knowing other client information, and ensuring private output. this paper proposes a new differential privacy aggregation scheme, which adopts a more fine-grained hierarchy update strategy. For the first time, the f-differential privacy (f-DP) method is used for the privacy analysis of federated aggregation. Adding Gaussian noise disturbance model update in order to protect the privacy of the client level. We prove that the f-DP differential privacy method improves the previous privacy analysis by experiments. It accurately captures the loss of privacy at every communication round in federal training, and overcome the problem of ensuring privacy at the cost of reducing model utility in most previous work. At the same time, it provides a federal model updating scheme with wider applicability and better utility. When enough users participate in federated learning, the client-level privacy guarantee is achieved while minimizing model loss.

关键词： Federated learning

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A machine learning approach for data protection in virtual reality therapy applications 17

A machine learning approach for data protection in virtual r...

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17th IEEE international conference on Intelligent computer Communication and Processing, ICCP 2021

作者： Mircea, Maria-Mǎdǎlina Boian, Rareş Czibula, Gabriela Babeş-Bolyai University Faculty of Mathematics and Computer Science Romania

ISBN: (纸本)9781665409766

Health information is a protected asset that should be kept private. the tradability of personal data brings risks to the health information shared by users online. Authentication is a crucial first step when working to keep personal information private. Virtual Reality applications usually bypass application-specific authentication in favor of provider-specific authentication (e.g. Steam, etc). this approach is not ideal for health applications. Virtual Reality secure authentication can be difficult because most methodologies are not user-friendly. Previously proposed Virtual Reality authentication systems use PINs, Patterns, or 3D object sequences. We propose an authentication method based on dynamic movements (i.e. dance moves). machine learning-based models are employed to determine if the received movement matches the user's previously chosen movement. the proposed method thus balances security with a better versatility than the one provided by traditional methods. © 2021 IEEE.

关键词： Virtual reality

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Node Localization in Range-Free 3D-WSNs Using New DV-Hop Algorithm Based machine learning Techniques 22

Node Localization in Range-Free 3D-WSNs Using New DV-Hop Alg...

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proceedings of the 2022 4th international conference on Software Engineering and Development

作者： Oumaima Liouane Smain Femmam Toufik Bakir Abdessalem Ben Abdelali University of Monastir National Engineering School of Monastir Tunisia Department of Networks and Communications Haute-Alsace University France Bourgogne University France University of Monastir ISIMM of Monastirr Tunisia

ISBN: (纸本)9781450397940

In many Wireless Sensor Network (WSN) applications, location is critical. Another intriguing aspect of the acquired data is the ability to obtain exact information about sensors' locations. In order to localize multi-hop WSNs, based connectivity algorithms use their benefits, such as simplicity and acceptable accuracy, to do so. However, the localization accuracy may be limited due to the two- or three-dimensional environment (2D or 3D). Range-Free 3D-WSNs can benefit from an analytic model for hop-size quantization and an Extreme learning machine (ELM) method for localization to reduce localization errors. the additional third dimension greatly affects the accuracy of localization. Since many applications require 3D localization, it is important to develop efficient self-localization algorithms for 3D WSNs. In this paper, for a uniform distribution of sensor nodes, a new probabilistic quantization of hop size in 3D WSNs is proposed. Moreover, the extreme learning machine (ELM) which represents a new approach to WSN localization is exploited combining a conventional method (probabilistic approach) with a non-conventional method (machine learning). For a variety of conditions, our algorithms have been tested through simulation in isotropic settings. the performance of the localization model was assessed using the average localization error (LE). When compared to previous 3D-DV-Hop heuristics, the suggested localization algorithm's performance in terms of accuracy is clearly demonstrated by simulation data. With the help of the predicted hop quantization for hop-size estimation and the ELM was used for position estimation, our localization method for 3D-WSNs lowers the average localization error of nodes and has a greater location accuracy compared to its rivals.

关键词： machine learning Localization hop-quantization 3D Wireless Sensors Network

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An Analysis of Computational Complexity and Accuracy of Two Supervised machine learning Algorithms—K-Nearest Neighbor and Support Vector machine 4th

An Analysis of Computational Complexity and Accuracy of Two ...

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4th international conference on Data Management, Analytics and Innovation, ICDMAI 2020

作者： Ray, Susmita Department of Computer Science and Technology Manav Rachna University Sector 43 Aravalli Hills Manav Rachna Campus Rd FaridabadHaryana121004 India

ISBN: (纸本)9789811556159

K-Nearest Neighbor (KNN) and Support Vector machine (SVM) are the two Supervised machine learning Algorithms which have been used extensively in solving classification and regression problems in various application domains—medical diagnosis, pattern recognition, and image classification being the popular ones. KNN has been attractive for its simplicity—SVM has been considered as complex. Computational complexity of KNN is less compared to SVM. However, accuracy of SVM in general has been higher compared to KNN. this paper carries out an analysis of complexity of KNN and SVM and then looks at the alternative strategies to reduce the complexity. Accuracies of both SVM and KNN are analyzed from literature survey of experimental results with different training datasets. Reduction of number of training instances and usage of kernel functions are found to be the solutions for reducing SVM complexity. Factors impacting KNN accuracy are analyzed and possible alternatives are assessed. Kernel functions and distance-weighted KNN are concluded as the solution to improve KNN accuracy to compete with SVM accuracy. To utilize the best potential of the two algorithms, the option of a hybrid algorithm combining both KNN and SVM is also proposed. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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Self-Supervised Superpixel Correspondences For Video Object Segmentation 22

Self-Supervised Superpixel Correspondences For Video Object ...

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proceedings of the 2022 4th international conference on Video, Signal and Image Processing

作者： Clement Tan Chai Kiat Yeo Cheston Tan Basura Fernando School of Computer Science and Engineering Nanyang Technological University Singapore and Agency for Science Technology and Research (A*STAR) Singapore School of Computer Science and Engineering Nanyang Technological University Singapore Centre for Frontier AI Research Agency for Science Technology and Research (A*STAR) Singapore Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) Singapore

ISBN: (纸本)9781450397810

Prior self-supervised video object segmentation models directly find pixel correspondences between pairs of frames. Instead, we propose a novel approach of employing superpixel features for learning visual correspondences between frames of a video sequence. With an attention mechanism, we train the model to reconstruct the next frame using superpixel features between adjacent frames of videos trained in a self-supervised manner. As superpixels are able to group pixels based on color and proximity, they reduce repetitive and noisy pixels that may confuse the model during tracking. In addition, the structural information provided by the superpixel features can help improve the segmentation of objects. We benchmark our method (S3CNet) against existing self-supervised pixel correspondence frameworks such as [12, 22, 24] and show that our self-supervised superpixel correspondence method performs better and is a viable alternative for visual tracking on the commonly-used DAVIS 2017 video segmentation benchmark.

关键词： Video Object Segmentation Self-Supervised learning Superpixels

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machine learning Methods for Vehicle Positioning in Vehicular Ad-Hoc Networks 4th

Machine Learning Methods for Vehicle Positioning in Vehicula...

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4th international conference on Advanced Computing and Intelligent Engineering, ICACIE 2019

作者： Nayak, Suryakanta Das, Partha Sarathi Panda, Satyasen Department of Computer Science and Application Utkal University BhubaneswarOdisha India Department of ECE GITA BhubaneswarOdisha India

ISBN: (纸本)9789811563522

Unambiguous vehicular sensing is one of the most important aspects in autonomous driving in vehicular ad-hoc networks. the conventional techniques such as communication-based technologies (e.g., GPS) or the reflection-based technologies (e.g., RADAR, LIDAR) have various limitations in detecting concealed vehicles in dense urban areas without line of sight which may trigger serious accidents for autonomous vehicles. To address this issue, this paper proposed a machine learning method based on stochastic Gaussian process regression (SGP) to position vehicles in a distributed vehicular system with received signal vector (RSV) information. To estimate the test vehicle position and respective position errors, the proposed SGP method records the RSV readings at neighboring locations with continuous approximation of the vehicle-to-vehicle (V2V) distance, angle of arrival (AoA), and path delay. then, the subsequent averaging of the training RSVs minimizes the effects of the shadowing noise and multipath fading. the prediction performance of the proposed learning approach is measured in terms of the root mean square prediction error (RMSE) in a realistic environment. Finally, the prediction performance of the proposed learning method is compared with other existing fingerprinting methods for error-free location estimation of the vehicular network. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Autonomous vehicles

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A comparative study on fetal head circumference measurement from ultrasound images using deep learning models 22

A comparative study on fetal head circumference measurement ...

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proceedings of the 2022 4th international conference on Robotics, Intelligent Control and Artificial Intelligence

作者： Yu Wang Yudong Yao College of Medicine and Biological Information Engineering Northeastern University China Department of Electrical and Computer Engineering Stevens Institute of Technology USA

ISBN: (纸本)9781450398343

Ultrasound imaging is the most commonly used imaging modality for the prenatal examination of pregnant women, with real-time imaging and no radiation characteristics. through a ultrasound image of the fetal head, doctors can measure the fetal head circumference (HC) to evaluate fetal growth and potential delivery mode. In practice, fetal HC is usually measured manually by doctors based on ultrasound images. Manual measurement of fetal HC is subjective and time-consuming, which has a negative impact on measurement accuracy and efficiency. At present, deep learning is widely investigated in the medical field. Many researchers apply deep learning to measuring fetal HC to assist doctors to accurately and quickly completing the measurement of fetal HC. In this paper, we compare the performance of eight deep learning models (U-Net, Attention U-Net, GINet, global reasoning unit (GloRe), SegFormer, Segmenter, BiSeNet V2, and short-term dense concatenate network (STDC)) on two fetal HC measurement datasets. SegFormer achieves the best results in Dice similarity coefficient (DSC), Hausdorff distance (HD), and absolute Difference (ADF). the performance of Attention U-Net is slightly worse than that of SegFormer.

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6th international conference on Advanced machine learning Technologies and applications, AMLTA 2021

6th International Conference on Advanced Machine Learning Te...

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6th international conference on Advanced machine learning Technologies and applications, AMLTA 2021

ISBN: (纸本)9783030697167

the proceedings contain 104 papers. the special focus in this conference is on Advanced machine learning Technologies and applications. the topics include: A Virtual Menu Using Gesture Recognition for 3D Object Manipulation in Mixed Reality;feedback Vocal Rehabilitation Software Applied to Mobile Devices for Maximum Phonation Time;Study of Data Repetitive Record Cleaning Technology of MES System in High-Tech Process;restraining Strategy of Photovoltaic Power Fluctuation Based on Hybrid Energy Storage System;Combining BRIEF and AD for Edge-Preserved Dense Stereo Matching;design of Turbo Codes for CubeSats;Design and Implementation of a Web-DNA-Based Security Provider Using Python;arabic Text Generation: Deep learning for Poetry Synthesis;deep learning Classification Methods for Brain-computer Interface: An Overview;Solar PV Power Forecasting Approach Based on Hybrid Deep Neural Network;panoramic Radiographic X-Ray Image Tooth Root Segmentation Based on LeNet-5 Networks;YOLO V3 and YOLO V4 for Masses Detection in Mammograms with ResNet and Inception for Masses Classification;classification of Date Fruits in a Controlled Environment Using Convolutional Neural Networks;spoken Arabic Digits Recognition System Using Convolutional Neural Network;a Holistic Neural Networks Classification for Wangiri Fraud Detection in Telecommunications Regulatory Authorities;LWCOV: LightWeight Deep Convolutional Neural Network for COVID-19 Detection;an Enhanced Image Fusion Model Based on Fuzzy Transform for 3D Imaging applications;genetic K-Means Adaption Algorithm for Clustering Stakeholders in System Requirements;study of Iterative learning Control Algorithm in Tourism Management IoT Platform application;the Importance of Discretization Methods in machine learning applications: A Case Study of Predicting ICU Mortality.

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Developing a Mobile application to Detect Improper Sitting Using Regression Analysis and an Accelerometer 2020

Developing a Mobile Application to Detect Improper Sitting U...

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4th international conference on machine learning and Soft Computing (ICMLSC)

作者： Phoophuangpairoj, Rong Charnkeitkong, Panida Rangsit Univ Dept Comp Engn Coll Engn Pathum Thani 12000 Thailand Rangsit Univ Dept Chem Engn Coll Engn Pathum Thani 12000 Thailand

ISBN: (纸本)9781450376310

Sitting improperly can impact on the amount of pressure put on the back of the body, causing the spine to degrade prematurely. therefore, this paper proposed a method of computing sitting angles and the creation of a mobile application to alert users when they are sitting improperly. First, a digital protractor and an accelerometer-equipped with a smartphone were employed to gather data. Next, regression equations derived from the collected data were applied to compute the angles between the upper and lower body as well as the angles on the left and right sides of the upper body while sitting. the computed angles were used to analyze the sitting posture. To expedite the testing process, an Android Sensor Fusion application was used to stream the accelerometer data over a local network through a router to a PC-based program. Finally, a mobile application was created. the results showed that the proposed method could be used to obtain sitting angles and alert users when they were sitting improperly.

关键词： accelerometer mobile application regression analysis sitting angle sitting posture

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QuACK: accelerating gradient-based quantum optimization with koopman operator learning 23

QuACK: accelerating gradient-based quantum optimization with...

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proceedings of the 37th international conference on Neural Information Processing Systems

作者： Di Luo Jiayu Shen Rumen Dangovski Marin Soljačić Center for Theoretical Physics Massachusetts Institute of Technology Cambridge MA and Department of Physics Harvard University Cambridge MA and The NSF AI Institute for Artificial Intelligence and Fundamental Interactions Department of Physics University of Illinois Urbana-Champaign Urbana IL and Illinois Quantum Information Science and Technology Center Illinois Center for Advanced Studies of the Universe Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA Department of Physics Massachusetts Institute of Technology Cambridge MA

Quantum optimization, a key application of quantum computing, has traditionally been stymied by the linearly increasing complexity of gradient calculations with an increasing number of parameters. this work bridges the gap between Koopman operator theory, which has found utility in applications because it allows for a linear representation of nonlinear dynamical systems, and natural gradient methods in quantum optimization, leading to a significant acceleration of gradient-based quantum optimization. We present Quantum-circuit Alternating Controlled Koopman learning (QuACK), a novel framework that leverages an alternating algorithm for efficient prediction of gradient dynamics on quantum computers. We demonstrate QuACK's remarkable ability to accelerate gradient-based optimization across a range of applications in quantum optimization and machine learning. In fact, our empirical studies, spanning quantum chemistry, quantum condensed matter, quantum machine learning, and noisy environments, have shown accelerations of more than 200x speedup in the overparameterized regime, 10x speedup in the smooth regime, and 3x speedup in the non-smooth regime. With QuACK, we offer a robust advancement that harnesses the advantage of gradient-based quantum optimization for practical benefits.

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