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A Secure Federated Learning Framework using Blockchain and Differential Privacy 9

A Secure Federated Learning Framework using Blockchain and D...

引用

9th IEEE international conference on Cyber Security and Cloud computing and 8th IEEE international conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2022

作者： Firdaus, Muhammad Larasati, Harashta Tatimma Rhee, Kyung-Hyune Pukyong National University Department of Artificial Intelligence Convergence Korea Republic of Pusan National University Department of Computer Engineering Korea Republic of Institut Teknologi Bandung School of Electrical Engineering and Informatics Indonesia

ISBN: (数字)9781665480666

ISBN: (纸本)9781665480666

Federated learning (FL) has considerably emerged as a promising solution to enhance user privacy and data security by enabling collaboratively multi-party model learning without exchanging confidential data. Nevertheless, most existing FL approaches still rely on a central server to obtain a global model by collecting all uploaded models from participants, which may lead to several threats from malicious participants and even expose participant privacy. Therefore, to tackle these problems, we proposed a secure FL framework by empowering blockchain to replace the centralized aggregator sever and utilize Differential Privacy (DP) to address various attacks, e.g., membership inference attacks, during the collaborative FL model training process. The proposed framework has been implemented through two scenarios, i.e., blockchain-based FL to form a decentralized system and DP-based FL to construct the randomized privacy protection using the IBM DP Library. © 2022 IEEE.

关键词： Blockchain

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An Improved Chemical Reaction Optimization Approach for Solving Multiple Sequence Alignment Problem 8

An Improved Chemical Reaction Optimization Approach for Solv...

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8th international conference on Advanced computing and Communication Systems, ICACCS 2022

作者： Paruchuri, Tirumala Kancharla, Gangadhara Rao Dara, Suresh College of Business Administration Jazan University Faculty of Mis Saudi Arabia Acharya Nagarjuna University Department of Computer Science and Engineering Andhra Pradesh Guntur India B V Raju Institute of Technology Department of Computer Science and Engineering Telangana Narsapur India

ISBN: (纸本)9781665408165

Multi-Sequence Alignment (MSA) is considered an NP problem in bioinformatics. Compared to traditional techniques, nature-inspired techniques produce accurate results. In this article, Improved Chemical Reaction Optimization (ICRO) addresses the problem of MSA. However, the CRO has a low population diversity and slow convergence, it is one of the problem with CRO. To overcome this problem, a pairwise alignment procedure is used to improve the initialization process to maintain diversity. Benchmark data set was assigned to the base version of Bali 2.0 in the performance analysis. Thus, the proposed method has made comparisons with three existing approaches such as GAPWA GSAMSA and IBBOMSA to show the betterment of the proposed ICRO. Thus, the proposed approach has achieved better results with the existing approaches. © 2022 IEEE.

关键词： Benchmarking

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Robust Sliding Mode Control with Reaching Laws for a Pediatric Lower-Limb Exoskeleton System 2

Robust Sliding Mode Control with Reaching Laws for a Pediatr...

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2nd international conference on Power, Control and computing Technologies, ICPC2T 2022

作者： Narayan, Jyotindra Bharti, Rahul Ranjan Dwivedy, Santosha K. Indian Institute of Technology Guwahati Department of Mechanical Engineering Guwahati701039 India

ISBN: (纸本)9781665458580

The precise control of an exoskeleton, especially for pediatric subjects, is a challenging task due to the presence of parametric perturbations and external disturbances (PPED). The sliding mode control is a well-known robust control scheme to address such dynamic uncertainties. Therefore, in this work, a robust sliding mode control (RSMC) with is proposed for a pediatric lower-limb exoskeleton system during passive-Assist therapy mode. The performance of different reaching laws (constant, power, exponential, and general) used in proposed control are compared with each other. Lyapunov stability analysis is carried out to ensure the asymptotic convergence of the gait tracking error. It is noted from the simulation runs that the proposed RSMC with exponential reaching law outperforms the different controllers for the less tracking error in passive-Assist gait rehabilitation. However, chattering is found to be less with constant reaching law. © 2022 IEEE.

关键词： Robust control Visualization Uncertainty Trajectory tracking Computational modeling Exoskeletons Power control

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Self-Homodyne Coherent Transmission for Datacenter Interconnections 27

Self-Homodyne Coherent Transmission for Datacenter Interconn...

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27th OptoElectronics and Communications conference/international conference on Photonics in Switching and computing, OECC/PSC 2022

作者： Zhou, Xian Gao, Yuyuan Fang, Qianwen Liu, Fei Yuan, Jinhui Long, Keping No.30 Xue Yuan Road Haidian Beijing100083 China Shunde Graduate School of University of Science and Technology Foshan Beijing528300 China

We review recent research progress in the self-homodyne optical coherent system to address two key challenges in length mismatch induced phase noise and LO power fading induced by polarization rotation. © 2022 IE... 详细信息

ISBN: (数字)9784885523366

ISBN: (纸本)9784885523366

关键词： Phase noise

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Ice hockey game using Deep Reinforcement Learning

Ice hockey game using Deep Reinforcement Learning

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international conference for convergence of technology (I2CT)

作者： Y. Mounika Y. Lakshmi Prasanna Y. Tarakaram J. Amudha K. Aiswariya Milan Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Modern deep Reinforcement Learning (RL) techniques are quite good at choosing the best possible rules to maximise rewards. By using rich visual information for policy selection, this method combined with Deep Learning methodologies shows promise for difficult tasks. In this work, we have used Double DQN and also three variants of Duelling DQN to implement the ice hockey environment. The Duelling DQN outperformed the Double DQN and gave better results.

关键词： Visualization Games Deep reinforcement learning Ice Task analysis Sports convergence

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Dynamic Optimization Framework for Multi-Hop Code Offloading in LEO Satellite Edge computing

Dynamic Optimization Framework for Multi-Hop Code Offloading...

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international conference on Information and Communication technology convergence (ICTC)

作者： Jeonghwan Kim Taewoo Lee Jeongho Kwak Electrical Engineering and Computer Science (EECS) Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu Republic of Korea School of Undergraduate Studies Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu Republic of Korea

In our swiftly digitizing world, the importance of low-earth-orbit satellite (LEO SAT) communication is escalating. Advancements in 5G, IoT, and AI underscore the need for a robust communication network. Research reveals that nearly 40% of the global population faces Internet service challenges due to limited high-performance access by 2022. This technology not only addresses coverage limitations but also mitigates latency issues. Multinational corporations like SpaceX and Amazon are already exploring its potential. As we approach the 6G era, users’ computational demands surge, prompting satellite edge computing with onboard processing. Our paper proposes a multi-hop code offloading model using LEO SAT, reducing energy consumption, and introduces a dynamic optimization algorithm for LEO SAT - mobile edge computing. The research aims to enhance satellite-based communication networks, with a focus on optimizing satellite edge computing efficiency.

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Design and Evaluation of Container-based Networking for Low-latency Edge Services 12

Design and Evaluation of Container-based Networking for Low-...

引用

12th international conference on ICT convergence (ICTC) - Beyond the Pandemic Era with ICT convergence Innovation

作者： Cha, Jae-Geun Kim, Sun Wook ETRI Artificial Intelligence Res Lab Daejeon South Korea

ISBN: (纸本)9781665423830

Edge computing processes and stores data on user devices located at the edge of the Internet to support latency-sensitive services such as autonomous vehicles, drones, and so on. This adopts container technology to quickly initialize and deploy applications. Containers interact with each other through Plugins implemented based on Container Network Interface (CNI). In particular, the network latency is different depending on the plugin. In this paper, we study plugins to provide low-latency edge services and evaluate the effect on data processing performance when multiple plugins are used at the same time. The SR-IOV plugins showed twice throughput than the kernel-based virtual network interface. Also, it had more than 50% bandwidth regardless of the addition of other virtual network interfaces on a single connection.

关键词： Edge computing Container network interface Low-latency edge service Kubernetes CNI plugin

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Secure Smart Parking Solution Using Image Processing and Machine Learning 7

Secure Smart Parking Solution Using Image Processing and Mac...

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7th IEEE international conference for convergence in technology, I2CT 2022

作者： Balasuriya, A.I.P. Dilitha, A.G.A.D. Perera, P.A.M.M. Jayaweera, D.K.S. Swarnakantha, N.H.P.R.S. Rajapaksha, U.U. Samantha Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9781665421683

With the IoT connecting the world, finding parking lots is much easier with smart parking solutions. Some of the parking areas in the world use at least some kind of smart parking solution these days. There are existing and broader solutions when it comes to parking a vehicle. So, in this project, we are mainly focusing on developing these existing solutions a step further. I hear we are mainly focusing on a solution that increases the effectiveness of the management processes in the currently existing solutions and developing a relevant and integrated security solution. The project will include a refined prediction system that can predict the outcomes of some of the main processes that are done inside a parking area so that smart parking management and be done much more smoothly and effectively. Prediction systems are designed on the idea of getting possible security issues, vehicle types on peak hours as an outcome. And even in the aspect of security rather than a separate system, there is an integrated one coming with this project that has improved functionality with regards to a parking area. The main possible security threats that happen in a parking area are being identified by us and implemented separate functionalities on detecting them. Furthermore, the project includes a decision-making system that can deduce and take the decision on slot management in a parking area so it can work effectively and with minimum human interaction. © 2022 IEEE.

关键词： Object detection

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Multilingual Conversational AI incorporated with Visual Questions Answering and Intelligent Disease Prediction for Healthcare Industry 7

Multilingual Conversational AI incorporated with Visual Ques...

引用

7th IEEE international conference for convergence in technology, I2CT 2022

作者： Sasmitha, N.U.A. Wathasha, H.K.G.V. Guruge, P.P.L. Silva, W.J.T. Rupasinghe, Lakmal Gunarathne, G.W.D.A. Sri Lanka Institute of Information Technology Faculty of Computing Colombo Sri Lanka

ISBN: (纸本)9781665421683

Artificial intelligence (AI) is becoming more active than ever in everyday life and steadily being incorporated to healthcare. AI, with its seemingly limitless power, affirms a promising future to a revolutionized healthcare system. This paper is proposing a conversational AI solution in two different languages, English and Sinhala, to predict diseases through a conversation, a visual question answering solution to generate answers are based on a given question and a medical image and a disease forecasting module. A robust, accurate prediction is a rather difficult task given the availability of data and absence of preprocessed, clean data. With the aid of outlier rejection, data imputation, vectorization, feature selection and data standardization, the proposed framework gets the advantage of latest machine learning advancements such as AI using DIET classifier and NLU pipelines, for the conversational disease diagnosis which uses support vector machine (SVM) achieved an accuracy of 0.93. Moreover, the visual questions answering module with VGG16 preprocessing, GoogleNews vectors, LSTM networks, scores an accuracy of 0.9721. In addition, time series analysis models such as ARIMA and adaptive models using PROPHET library for forecasting diseases, classification using random forest scoring an accuracy of 0.81, logistic regression scoring an accuracy of 0.84 for predicting diseases. The objective of this research is to compare and select the best fitting models to be used for a centralized framework for healthcare industry. © 2022 IEEE.

关键词： Time series analysis

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Modified Grey Wolf Optimization using Refraction Learning and Differential Evolution Mutation

Modified Grey Wolf Optimization using Refraction Learning an...

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Emerging Systems and Intelligent computing (ESIC), international conference on

作者： Pranshu Bajaj Siddharth Dhingra Karan Aggarwal Preeti Kaur Netaji Subhas University of Technology New Delhi India

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

An enhanced variant of the Grey Wolf Optimisation (GWO) technique is presented in this paper, addressing a number of issues, including reduced population diversity, slow convergence and vulnerability to local optima. The proposed variant incorporates innovative techniques, including Opposition Learning, Refraction Learning (RL), Differential Evolution Mutation and a non-linear time-varying hyperparameter tuning mechanism. Combining these strategies greatly enhances the standard GWO’s performance, leading to a more streamlined and effective search procedure. This enhancement leads to better performance in multi-modal landscapes, facilitates escaping local optima and allows the search space to be explored more extensively. Extensive testing across 23 benchmark functions demonstrates that the new variant outperforms the original GWO as well as other contemporary optimization methods. The results indicate substantial improvements in convergence speed, global search capability and solution accuracy, demonstrating how effective this method can be as an optimisation tool for solving challenging real-world issues.

关键词： Metaheuristics Diversity reception Optimization methods Benchmark testing Space exploration Particle swarm optimization Optimization Tuning Standards convergence

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