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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Tang, Ming Wong, Vincent W. S. Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798350334142

In federated learning, clients cooperatively train a global model by training local models over their datasets under the coordination of a central server. However, clients may sometimes be unavailable for training due to their network connections and energy levels. Considering the highly non-independent and identically distributed (non-IID) degree of the clients' datasets, the local models of the available clients being sampled for training may not represent those of all other clients. This is referred as system induced bias. In this work, we quantify the system induced bias due to time-varying client availability. The theoretical result shows that this bias occurs independently of the number of available clients and the number of clients being sampled in each training round. To address system induced bias, we propose a FedSS algorithm by incorporating stratified sampling and prove that the proposed algorithm is unbiased. We quantify the impact of system parameters on the algorithm performance and derive the performance guarantee of our proposed FedSS algorithm. Theoretical and experimental results on CIFAR-10 and MNIST datasets show that our proposed FedSS algorithm outperforms several benchmark algorithms by up to 5.1 times in terms of the algorithm convergence rate. © 2023 IEEE.

关键词： Learning systems

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Integrated Global-Local Gaussian Attention for Image Compression

Integrated Global-Local Gaussian Attention for Image Compres...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Khoshkhahtinat, Atefeh Mehta, Piyush M. Department of Computer Science and Electrical Engineering West Virginia University WV United States

ISBN: (纸本)9798350368741

Transformers have demonstrated outstanding performance in learned image compression (LIC) due to their high capacity for modeling complex dependencies. However, existing methods employ window-based attention mechanisms to reduce overhead, which limits the long-range dependency modeling capabilities of transformers. In this study, we present a novel transformer block designed to efficiently leverage global spatial information while keeping computational costs low. Additionally, our proposed transformer incorporates a local attention module that utilizes Gaussian positional encoding to enhance spatial awareness. These improvements contribute to achieving less redundant latent representations and improving compression efficiency. In addition to enhance the decorrelating capability of the transformation component, we propose a novel channel-wise entropy model that effectively leverages channel dependencies, thereby providing a more accurate estimation of the latent space distribution. Experimental results indicate that our framework surpasses both conventional and neural codecs. © 2025 IEEE.

关键词： Entropy model Guassian positional encoding Learned Image Compression Transformer

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Performance-based Data-driven Assessment of Trust 4

Performance-based Data-driven Assessment of Trust

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4th IEEE International Conference on Human-Machine Systems, ICHMS 2024

作者： Kio, Onoise G. Yuan, Mingfeng Allison, Robert S. Shan, Jinjun York University Department Of Electrical Engineering And Computer Science Toronto Canada York University Department Of Earth And Space Science And Engineering Toronto Canada

ISBN: (纸本)9798350315790

The likelihood of an agent's success in achieving its goals and subgoals under specific operational constraints is an important factor in performance-based trust. Trust management systems are being proposed to monitor performance and other trust-related system variables to improve operational efficiency. We present in this paper an exploratory study on the feasibility of assessing the performance of drone operators in a target-finding task through subjective and objective evaluations of the drone's flight data. Thirty-nine participants were trained in using a teleoperated drone to find specific alphanumeric targets arranged on the laboratory floor and thereafter performed the task three times in separate three-minute runs. Following this, they made subjective assessments of other operators' performance based on viewing the flight paths flown. Linear mixed effects modelling of subjective performance assessments showed either non-significant or weak relationships between subjective assessments and actual operator performance. Binomial logistic regression modelling showed significant relationships between features of the drone's flight pattern and operator performance. These results could inform the development of data-driven, performance-based trust management systems for use in collaborative human and machine environments. © 2024 IEEE.

关键词： Drones

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A Lightweight and Robust User Authentication Protocol with User Anonymity for IoT-Based Healthcare

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computer Modeling in engineering & sciences 2022年第4期131卷 307-329页

作者： Chien-Ming Chen Shuangshuang Liu Shehzad Ashraf Chaudhry Yeh-Cheng Chen Muhammad Asghar khan College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Computer Engineering Istanbul Gelisim UniversityIstanbul34310Turkey Department of Computer Science University of CaliforniaDavisCA95616USA Department of Electrical Engineering Hamdard UniversityIslamabad44000Pakistan

With the rise of the Internet of Things(IoT),the word“intelligent medical care”has increasingly become a major *** medicine adopts the most advanced IoT technology to realize the interaction between patients and people,medical institutions,andmedical ***,with the openness of network transmission,the security and privacy of information transmission have become a major ***,Masud et *** a lightweight anonymous user authentication protocol for IoT medical treatment,claiming that their method can resist various ***,through analysis of the protocol,we observed that their protocol cannot effectively resist privileged internal attacks,sensor node capture attacks,and stolen authentication attacks,and their protocol does not have perfect forward ***,we propose a new protocol to resolve the security vulnerabilities in Masud’s protocol and remove some redundant parameters,so as tomake the protocolmore compact and *** addition,we evaluate the security and performance of the new protocol and prove that the overall performance of the new protocol is better than that of other related protocols.

关键词： IoT intelligent medical user authentication

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Analyze Shor Algorithm Optimization Trends and Suggest Optimization Directions 10

Analyze Shor Algorithm Optimization Trends and Suggest Optim...

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10th International Conference on Platform Technology and Service, PlatCon 2024

作者： Cho, Jaehan Shin, Dawit Hwang, Yeonjeong Kim, Howon School of Computer Science and Engineering Pusan National University Busan Korea Republic of Department of Electrical and Computer Engineering Pusan National University Busan Korea Republic of

ISBN: (纸本)9798350367874

Quantum computers leverage the principles of quantum mechanics to solve complex problems beyond the capabilities of classical computers, such as prime factorization, which poses a significant threat to cryptographic algorithms like RSA and ECC. Although current quantum resources are insufficient for effective decryption, ongoing research focuses on optimizing Shor's algorithm to overcome these limitations. Shor's algorithm, a quantum algorithm designed for efficient integer factorization, exploits quantum parallelism and entanglement to perform prime factorization exponentially faster than classical algorithms. This paper reviews recent trends in Shor algorithm optimization, particularly in enhancing the efficiency of ripple-carry operators and the Karatsuba algorithm in multiplication and proposes a multiplication method that applies the Strassen algorithm to constant adders. © 2024 IEEE.

关键词： Quantum computers

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Enhancing Educational Technology in Lectures for School Students with Learning Disabilities: A Comprehensive Analysis

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SN computer science 2024年第6期5卷 1-17页

作者： Adjiovski, Benjamin Bogatinoska, Dijana Capeska Ismajloska, Mersiha Malekian, Reza University of Information Science and Technology “St. Paul the Apostle” Department of Electrical Electronic and Computer Engineering University of Pretoria

Learning disabilities present formidable obstacles for children in their educational journeys, often overlooked or misunderstood. This research aims to address two critical issues: the differentiation between learning and intellectual disabilities, and the resistance of teachers to integrating technology into education. In response, a comprehensive approach was adopted. Firstly, a mobile application was developed to screen for potential learning disabilities, offering teachers insights into individual student challenges and personalized lecture adaptations. Secondly, a quasi-experimental pretest–posttest study was conducted, comparing student performance following traditional and technologically supported lectures. Results from the study revealed significant score improvements among students identified as potentially having learning disabilities when exposed to technologically supported lectures. Conversely, students not identified as potential cases remained within the normal range of performance. These findings underscore the potential of technology to enhance educational outcomes for children with learning disabilities while highlighting the importance of recognizing and addressing these challenges in educational settings. By bridging the gap between identification and intervention, this research contributes to a more inclusive and effective educational environment. © The Author(s) 2024.

关键词： Assistive technology Inclusive education Learning disabilities Mobile application screening Technology in education

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Enhanced Intrusion Detection in IIoT Networks: A Lightweight Approach with Autoencoder-Based Feature Learning 10

Enhanced Intrusion Detection in IIoT Networks: A Lightweight...

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10th International Conference on Internet of Things, Big Data and Security, IoTBDS 2025

作者： Hasan, Tasnimul Hossain, Abrar Ansari, Mufakir Qamar Syed, Talha Hussain Department of Electrical Engineering and Computer Science University of Toledo ToledoOH United States

ISBN: (纸本)9789897587504

The rapid expansion of the Industrial Internet of Things (IIoT) has significantly advanced digital technologies and interconnected industrial sys- tems, creating substantial opportunities for growth. However, this growth has also heightened the risk of cyberattacks, necessitating robust security measures to protect IIoT networks. Intrusion Detection Systems (IDS) are essential for identifying and preventing abnormal network behaviors and malicious activities. Despite the potential of Machine Learning (ML)-based IDS solutions, existing models often face challenges with class imbalance and multiclass IIoT datasets, resulting in reduced detection accuracy. This research directly addresses these challenges by implementing six innovative approaches to enhance IDS perfor- mance, including leveraging an autoencoder for di- mensional reduction, which improves feature learning and overall detection accuracy. Our proposed Decision Tree model achieved an exceptional F1 score and accuracy of 99.94% on the Edge-IIoTset dataset. Furthermore, we prioritized lightweight model design, ensuring deployability on resource-constrained edge devices. Notably, we are the first to deploy our model on a Jetson Nano, achieving inference times of 0.185 ms for binary classification and 0.187 ms for multiclass classification. These results highlight the novelty and robustness of our approach, offering a practical and efficient solution to the challenges posed by imbalanced and multiclass IIoT datasets, thereby enhancing the detection and prevention of network intrusions. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Industrial internet of things (IIoT)

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An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems

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Big Data Mining and Analytics 2024年第2期7卷 399-418页

作者： Sankaramoorthy Muthubalaji Naresh Kumar Muniyaraj Sarvade Pedda Venkata Subba Rao Kavitha Thandapani Pasupuleti Rama Mohan Thangam Somasundaram Yousef Farhaoui Department of Electrical and Electronics Engineering CMR College of Engineering&TechnologyHyderabad 501401India Department of Electronics and Communication Engineering Vardhaman College of Engineering KacharamShamshabad 501218India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and TechnologyHyderabad 501301India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India Department of Electrical and Electronics Engineering Bharat Institute of Engineering and TechnologyHyderabad 501510India Department of Computer Science and Engineering Amrita School of ComputingAmrita Vishwa VidhyapeethamBengaluru 560035India T-IDMS Department of Computer ScienceFaculty of Sciences and TechniquesMoulay Ismail UniversityErrachidia 52000Morocco

Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial *** is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced *** main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information *** original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI ***,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting ***,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more *** Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis ***,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security *** results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.

关键词： smart grid Machine Learning(ML) big data analytics AdaBelief Exponential Feature Selection(AEFS) Polar Bear Optimization(PBO) Kernel Extreme Neural Network(KENN)

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Scalable Polynomial RegEM(a)O for Multi-lMany-objective Platform-based Design Optimization Problems 13

Scalable Polynomial RegEM(a)O for Multi-lMany-objective Plat...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Guha, Ritam Deb, Kalyanmoy Michigan State University Computer Science and Engineering Department East Lansing United States Michigan State University Electrical and Computer Engineering Department East Lansing United States

ISBN: (纸本)9798350308365

The goal of a generic evolutionary multi- or many-objective algorithm is to explore a search space and find the trade-off optimal solutions for two or more conflicting objectives. In platform-based practical design optimization problems, it is not sufficient to just find a set of trade-off optimal solutions, certain regularity properties are expected in the entire fleet of trade-off solutions. For this purpose, we propose a scalable regularity-based optimization framework - RegEM(a)O - which automatically extracts polynomial regularity principles from the resulting Pareto-optimal front of multi- or many-objective problems. Thereafter, it attempts to search for a regular front of trade-off solutions following a similar form of polynomial regularity principles. Despite being slightly worse than the true Pareto-optimal solutions, regular solutions possess simple properties among them, making them easily interpretable, their inventory easily maintainable, and easily scalable. In this paper, we apply RegEM(a)O to a number of small and large-scale real-world engineering design problems to demonstrate its practical advantage. © 2024 IEEE.

关键词： Optimization algorithms

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Optimizing QoE in IoT-Based Video Streaming through Deep Learning Algorithms 6

Optimizing QoE in IoT-Based Video Streaming through Deep Lea...

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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Darwich, Mahmoud Khalil, Kasem Bayoumi, Magdy University of Mount Union Department of Mathematics and Computer Science AllianceOH44601 United States University of Mississippi Department of Electrical and Computer Engineering MS38677 United States University of Louisiana Department of Electrical and Computer Engineering LafayetteLA70504 United States

ISBN: (纸本)9798350344349

The demand for video streaming services over Internet of Things (IoT) networks has surged, yet maintaining a high Quality of Experience (QoE) remains challenging due to network heterogeneity and resource constraints. This paper presents an innovative deep learning-based approach to optimize QoE in IoT-based video streaming. Leveraging convolutional neural networks (CNN) and long short-Term memory (LSTM) networks, the proposed framework dynamically adapts video streaming parameters in real-Time, such as bit rate, frame rate, and resolution, based on the existing network conditions and device capabilities. Through extensive simulations and real-world deployments, our approach exhibited a 24.5% reduction in re-buffering ratio, a 12.5% increase in average bitrate, and a 15% improvement in video quality measured by SSIM, compared to conventional methods. Furthermore, it reduced network bandwidth consumption by up to 150% without compromising on video quality. These results underscore the effectiveness of applying deep learning algorithms to optimize video streaming in complex IoT environments. © 2024 IEEE.

关键词： Video streaming

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