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Self-Recovery in Fragile Image Watermarking Using Integer Wavelet Transform 8

Self-Recovery in Fragile Image Watermarking Using Integer Wa...

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8th IEEE international conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2022

作者： Ernawan, Ferda Aminuddin, Afrig Phon, Danakorn Nincarean Eh Alsheikh, Emad Addin Wibowo, Adi Universiti Malaysia Pahang Faculty of Computing Pekan Pahang Malaysia Universitas Amikom Yogyakarta Faculty of Computer Science Yogyakarta Indonesia Jazan University Faculty of Computer Science and Information Technology Jazan Saudi Arabia Diponegoro University Department of Informatics Semarang Indonesia

ISBN: (数字)9781665488006

ISBN: (纸本)9781665488006

the growth of internet technology makes digital images can be easily distributed and modified to change image information. this research proposes fragile image watermarking based on LSB Adjustment on integer wavelet transform. this study focuses on self-recovery and authenticating the integrity of image contents. the proposed scheme embeds a watermark into the two-LSB by using LSB adjustment. the authentication bits are randomly located in the two-LSB with a secret key. the recovery data is generated based on IWT for the recovery process. the proposed watermarking method uses multi-layer authentication to achieve high precision and accuracy. this research also uses image inpainting techniques to improve the recovery under tamper attacks. the experimental results produce high imperceptibility with PSNR of 45.5 dB and SSIM of 0.997. the proposed scheme produces better self-recovered image quality than the existing scheme with PSNR of 37.98 dB and SSIM of 0.9928 against a tampering rate of 10%. © 2022 IEEE.

关键词： Authentication

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distributed Uniform Partitioning of a Region using Opaque ASYNC Luminous Mobile Robots 24

Distributed Uniform Partitioning of a Region using Opaque AS...

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25th international conference on distributed computing and networking, ICDCN 2024

作者： Pramanick, Subhajit Jana, Saswata Bhattacharya, Adri Mandal, Partha Sarathi Indian Institute of Technology Guwahati Guwahati India

ISBN: (纸本)9798400716737

the uniform partitioning problem, driven by the need for an equitable distribution of workload, finds numerous practical applications in the real world. For that purpose, in this paper, we consider N autonomous mobile robots, deployed inside a bounded region. the robots are opaque which means that three collinear robots are unable to see each other as one of the robots acts as an obstruction for the other two. they operate in classical Look-Compute-Move (LCM) activation cycles. Moreover, the robots are oblivious except for a persistent light (which is why they are called Luminous robots) that can determine a color from a fixed color set. Obliviousness does not allow the robots to remember any information from past activation cycles. the Uniform Partitioning problem requires the robots to partition the whole region into sub-regions of equal area, each of which contains exactly one robot. Due to application-oriented motivation, we, in this paper consider the region to be well-known geometric shapes such as rectangle, square and circle. We investigate the problem in asynchronous setting where there is no notion of common time and any robot gets activated at any time with a fair assumption that every robot needs to get activated infinitely often. To the best of our knowledge, this is the first attempt to study the Uniform Partitioning problem using oblivious opaque robots working under asynchronous settings. We propose three algorithms considering three different regions: rectangle, square and circle. Robots partition the region in a distributed way and reach their respective positions in the partitions. the algorithms proposed for rectangular and square regions run in O(N) epochs whereas the algorithm for circular regions runs in O(N2) epochs, where an epoch is the smallest unit of time in which all robots are activated at least once and execute their LCM cycles. © 2024 ACM.

关键词： Multi agent systems

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One-shot distributed Algorithm for Generalized Eigenvalue Problem 22

One-shot Distributed Algorithm for Generalized Eigenvalue Pr...

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8th international conference on computing and Artificial Intelligence, ICCAI 2022

作者： Lv, Kexin Sun, Zheng He, Fan Huang, Xiaolin Yang, Jie Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China Quality and Business Operation Department Saic Motor Corporation Limited 489 Wei Hai Road Shanghai200041 China

ISBN: (纸本)9781450396110

Nowadays, more and more datasets are stored in a distributed way for the sake of memory storage or data privacy. the generalized eigenvalue problem (GEP) plays a vital role in a large family of high-dimensional statistical models. However, the existing distributed method for eigenvalue decomposition cannot be applied in GEP for the divergence of the empirical covariance matrix. Here we propose a general distributed GEP framework with one-shot communication for GEP. If the symmetric data covariance has repeated eigenvalues, e.g., in canonical component analysis, we further modify the method for better convergence. the theoretical analysis on approximation error is conducted and the relation to the divergence of the data covariance, the eigenvalues of the empirical data covariance, and the number of local servers is analyzed. Numerical experiments also show the effectiveness of the proposed algorithms both in the synthetic dataset and real-world datasets. © 2022 ACM.

关键词： Covariance matrix

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Parameters Compressed Mechanism in Federated Learning for Edge computing 8

Parameters Compressed Mechanism in Federated Learning for Ed...

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8th IEEE international conference on Cyber Security and Cloud computing and 2021 7th IEEE international conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2021

作者： Lu, Yongqiang Liu, Zhaobin Huang, Yiming Dalian Maritime University Dalian China

ISBN: (纸本)9781665443777

With the continuous improvement of the performance of the IoT and mobile devices, the IoT technology brings convenience to people's lives but leaks out personal privacy at the same time. Federated learning is a kind of machine learning technology that can protect data privacy. Different from current machine learning methods, Federated learning is usually facing non-Identically Independently Distributions (no-IID) data and keeps data in local places. therefore, the effectiveness of existing machine learning methods in federated learning non-IID issues decreases greatly. At the same time, federated learning needs to interact with multiple edge learning nodes. With the increasing number of edge nodes, the cost of communication becomes higher and higher, and the parameter weight calculation of more independent nodes is also a problem. In this paper, we propose to balance the federated learning model by using Earth Mover's Distance to calculate the weights of different node parameters. Our method can reduce the impact of data non-independent identically distributed problems on the model so that the federated learning model will not be biased to the distributed nodes in the data set. Besides, this paper also proposes a method to compress redundant communication between nodes and servers in the process of training. Experimental results show that this paper method improves the accuracy by about 4% compared with the traditional federal learning average algorithm, and the gradient communication is compressed to about 6% of the original communication times. © 2021 IEEE.

关键词： Internet of things

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RhythmEdge: Enabling Contactless Heart Rate Estimation on the Edge 8

RhythmEdge: Enabling Contactless Heart Rate Estimation on th...

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8th IEEE international conference on Smart computing (SMARTCOMP)

作者： Hasan, Zahid Dey, Emon Ramamurthy, Sreenivasan Ramasamy Roy, Nirmalya Misra, Archan Univ Maryland Baltimore Cty Ctr Real Time Distributed Sensing & Auton Cards Dept Informat Syst Mobile Pervas & Sensor Comp Lab Baltimore MD 21250 USA Singapore Management Univ Sch Comp & Informat Syst Singapore Singapore

ISBN: (纸本)9781665481526

the primary contribution of this paper is designing and prototyping a real-time edge computing system, RhythmEdge, that is capable of detecting changes in blood volume from facial videos (Remote Photoplethysmography;rPPG), enabling cardiovascular health assessment instantly. the benefits of RhythmEdge include non-invasive measurement of cardiovascular activity, real-time system operation, inexpensive sensing components, and computing. RhythmEdge captures a short video of the skin using a camera and extracts rPPG features to estimate the Photoplethysmography (PPG) signal using a multi-task learning framework while offloading the edge computation. In addition, we intelligently apply a transfer learning approach to the multi-task learning framework to mitigate sensor heterogeneities to scale the RhythmEdge prototype to work with a range of commercially available sensing and computing devices. Besides, to further adapt the software stack for resource-constrained devices, we postulate novel pruning and quantization techniques (Quantization: FP32, FP16;Pruned-Quantized: FP32, FP16) that efficiently optimize the deep feature learning while minimizing the runtime, latency, memory, and power usage. We benchmark RhythmEdge prototype for three different cameras and edge computing platforms while evaluating it on three publicly available datasets and an in-house dataset collected under challenging environmental circumstances. Our analysis indicates that RhythmEdge performs on par with the existing contactless heart rate monitoring systems while utilizing only half of its available resources. Furthermore, we perform an ablation study with and without pruning and quantization to report the model size (87%) vs. inference time (70%) reduction. We attested the efficacy of RhythmEdge prototype with a maximum power of 8W and a memory usage of 290MB, with a minimal latency of 0.0625 seconds and a runtime of 0.64 seconds per 30 frames.

关键词： rPPG Edge computing System Prototyping

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An Exploration of Machine and Deep Learning Models for Fake News Detection in Social Media 8

An Exploration of Machine and Deep Learning Models for Fake ...

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8th IEEE international conference on Behavioural and Social computing, BESC 2021

作者： Tian, Yi Gu, Jialing Jia, Yijun Sinnott, Richard O. School of Computing and Information Systems The University of Melbourne Melbourne Australia

ISBN: (纸本)9781665400237

Due to the popularity of online media and especially social media, accessing news online through social media platforms has become the preferred way for people to obtain their news. However, it is now widely recognized that there is an increasing amount of fake news on the Internet. Fake news can be very similar to real news, so it is often difficult for readers to distinguish the truth/veracity of the news they read. Machine learning and deep learning models can help in this regard. In this paper, we compare and analyse the performance of four machine learning models (Logistic regression, Random Forest, Support Vector Machines (SVM), Naïve Bayes) and three deep learning models (Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and BERT) on data sets comprising both true and fake news. We explore different methods for extracting language features from the news and evaluate the performance of the model using accuracy as an evaluation indicator. We identified that the BERT model reached an accuracy of 86.76 and was most effective. © 2021 IEEE

关键词： Social networking (online)

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

8th International Conference on Life System Modeling and Sim...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

ISBN: (纸本)9789819601875

the proceedings contain 158 papers. the special focus in this conference is on Life System Modeling and Simulation and Intelligent computing for Sustainable Energy and Environment. the topics include: A Method for Evaluating the Operating Health of Wind Turbines Under Wake Effect;toward Next-Generation Buildings: A Review of Renewable Energy Integration and Intelligent Building Energy Management Technologies;improving the Construction of a Multi-modal Underwater Image Recognition System for Anomaly Detection in Nuclear Power Plant Cooling Source Areas;A Novel Anti-noise Fault Diagnosis Method for Nuclear Energy Steam Turbine Based on VCformer;Short-Term Photovoltaic Power Forecasting Using Multi-timescale Information Based FFTformer Model;wind Power Forecasting Model Based on Feature Processing and Deep Hybrid Kernel Extreme Learning Machine;optimal User-Side Energy Arbitrage Strategy in Electricity Market with Accurate Battery Model Using Benders Decomposition;a Novel distributed Economic Dispatch Method for Smart Grid with Time-Varying Feedback Gain;benefits, Barriers, and Human Factors of V2X Implementation – A Brief Review;a General Pattern of Permanent Magnet Arrangement for Design Optimization of a Reluctance Magnetic Gear with Enhanced Torque Density for Electric Vehicles;wind Power Scheduling Deviation Compensation Based on Dual-Frequency Pulse Control;a Comprehensive Simulation and Validation of Single Particle Aging Mechanism Model for Lithium-Ion Batteries;semi-supervised State of Health Estimation for Lithium-Ion Batteries via Dynamical Self-encoding;a Reduce-Order Model to Understand the Multiscale Coupling of Battery Electrochemical-thermal Performance;RUL Prediction for Lithium-Ion Batteries Based on Sequence Decomposition and Transformer Networks;An Estimation Method of State of Health of Sodium-Ion Batteries Based on TCN.

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Fog computing based health care applications and frameworks: A review 8

Fog computing based health care applications and frameworks:...

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8th international conference on computing for Sustainable Global Development, INDIACom 2021

作者： Duggal, Shalu Kaur, Parminder Guru Nanak Dev University Department of Computer Science Amritsar India

ISBN: (纸本)9789380544434

Fog computing has emerged as a distributed computing structure that extends the cloud platform model. this paper presents a literature review of Fog computing-based health care applications and frameworks. the objective of the review is to assimilate intelligent fog computing for health care. It accentuates the features of fog computing and services it can provide in the healthcare system. the usefulness of artificial intelligent enabled fog computing for health care industry is perceived in this review. the review findings provide testimony for mining the power of artificial intelligent fog computing in healthcare system. © 2021 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to IEEE by Bharati Vidyapeeth.

关键词： Internet of things

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Brain-like Combination of Feedforward and Recurrent Network Components Achieves Prototype Extraction and Robust Pattern Recognition 8th

Brain-like Combination of Feedforward and Recurrent Network ...

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8th Annual international conference on Machine Learning, Optimization and Data science (LOD)

作者： Ravichandran, Naresh Balaji Lansner, Anders Herman, Pawel KTH Royal Inst Technol Sch Elect Engn & Comp Sci Div Computat Sci & Technol Stockholm Sweden Stockholm Univ Dept Math Stockholm Sweden KTH Royal Inst Technol Digital Futures Stockholm Sweden

ISBN: (纸本)9783031258909;9783031258916

Associative memory has been a prominent candidate for the computation performed by the massively recurrent neocortical networks. Attractor networks implementing associative memory have offered mechanistic explanation for many cognitive phenomena. However, attractor memory models are typically trained using orthogonal or random patterns to avoid interference between memories, which makes them unfeasible for naturally occurring complex correlated stimuli like images. We approach this problem by combining a recurrent attractor network with a feedforward network that learns distributed representations using an unsupervised Hebbian-Bayesian learning rule. the resulting network model incorporates many known biological properties: unsupervised learning, Hebbian plasticity, sparse distributed activations, sparse connectivity, columnar and laminar cortical architecture, etc. We evaluate the synergistic effects of the feedforward and recurrent network components in complex pattern recognition tasks on the MNIST handwritten digits dataset. We demonstrate that the recurrent attractor component implements associative memory when trained on the feedforward-driven internal (hidden) representations. the associative memory is also shown to perform prototype extraction from the training data and make the representations robust to severely distorted input. We argue that several aspects of the proposed integration of feedforward and recurrent computations are particularly attractive from a machine learning perspective.

关键词： Attractor Associative memory Unsupervised learning Hebbian learning Recurrent networks Feedforward networks Brain-like computing

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A New Method for AC Voltage Withstand Test of High-Voltage Power Cable Based on Gas-Insulated Line and Distribution Partial Discharge Measurement

A New Method for AC Voltage Withstand Test of High-Voltage P...

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IEEE international conference on Smart Grid and Smart Cities (ICSGSC)

作者： Wei Ding Lvxuan Huang Junrui Pan Xu Wang Zhenchang Wei Hao Zhang Guoming Wang Anhui Power Transmission and Transformation Co. Ltd. Hefei China Research and Development Center Hangzhou Guozhou Power Technology Co. Ltd. Hangzhou China

ISBN: (数字)9798350377408

ISBN: (纸本)9798350377415

the AC withstand voltage test is an effective method to identify the main insulation performance and insulation defects of high-voltage cable. In the paper, gas-insulated line is designed and fabricated to connect the series resonance system with the cable under test. It is filled with SF 6 gas with a pressure of 0.35MPa and is suitable for test in limited spaces such as indoor substation. the distributed partial discharge (PD) detection system is developed, which consists of high frequency current transformer, synchronous sensor, detection unit, and communication unit, elastic cloud server, and analysis software. By applying distributed networking, PD detection of terminal and joint along the whole cable length during the AC voltage withstand test can be accomplished simultaneously with high efficiency. In addition, the equivalent time and the equivalent frequency map is proposed for separating different type of detected signals, by which sensitivity and accuracy of PD detection can be improved.

关键词： Partial discharge measurement Cloud computing Voltage measurement Power cables Sulfur hexafluoride Power cable insulation High-voltage techniques Safety Servers Gas insulated transmission lines

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