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Joint Downlink-Uplink Beamforming for Wireless Multi-Antenna Federated Learning 21

Joint Downlink-Uplink Beamforming for Wireless Multi-Antenna...

引用

21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023

作者： Zhang, Chong Dong, Min Liang, Ben Afana, Ali Ahmed, Yahia Dept. of Electrical and Computer Engineering University of Toronto Canada Ericsson Canada Canada Dept. of Electrical Computer and Software Engineering Ontario Tech University Canada

ISBN: (纸本)9783903176553

We study joint downlink-uplink beamforming design for wireless federated learning (FL) with a multi-antenna base station. Considering analog transmission over noisy channels and uplink over-the-air aggregation, we derive the global model update expression over communication rounds. We then obtain an upper bound on the expected global loss function, capturing the downlink and uplink beamforming and receiver noise effect. We propose a low-complexity joint beamforming algorithm to minimize this upper bound, which employs alternating optimization to breakdown the problem into three subproblems, each solved via closed-form gradient updates. Simulation under practical wireless system setup shows that our proposed joint beamforming design solution substantially outperforms the conventional separate-link design approach and nearly attains the performance of ideal FL with error-free communication links. © 2023 IFIP.

关键词： Beamforming

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Integrating AI and Cybersecurity: Advancing Autonomous Vehicle Security and Response Mechanisms 4

Integrating AI and Cybersecurity: Advancing Autonomous Vehic...

引用

4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Namburi, Venkata Lakshmi Adapa, Srinivasa Rao Chamala, Shanti Swaroop Kumar Yerram, Manish Gupta, Pranjal Upreti, Kamal Software Systems Engineer Danlaw Inc MI United States Software Engineer OpenLogix LLC MI United States Software Developer Software Bots Inc MI United States Computer Science with IOT VNRVJIET Hyderabad India Computer Science Engineering ADGIPS Delhi India Dept. of Computer Science Christ University Delhi NCR Campus India

ISBN: (纸本)9798350353266

The rapid evolution of autonomous and connected vehicles has led to their integration with numerous technologies and software, rendering them vulnerable targets for cybersecurity attacks. While efforts have traditionally focused on preventing these attacks, the escalating risk underscores the importance of also vindicating their wallop. Nevertheless, this procedure is often onerous & facade scalability confronted, particularly due to connectivity issues in automobiles. This research advises a vehicle-based vibrant imposition response scheme, enabling swift responses to a variety of incidents and reducing reliance on external security centers. The classification encompasses an inclusive range of probable retorts, a procedure for evaluating retorts, & innumerable assortment approaches. Implemented on an embedded platform, the solution was evaluated using two distinct cyberattack use cases, highlighting its adaptability, responsiveness, volume for dynamic arrangement constraint alterations & nominal memory trail. Concurrently, this paper presents an innovative (AVSF) that synergistically integrates (AI) and cybersecurity techniques to fortify AV resilience against evolving threats. Additionally, the framework incorporates advanced cybersecurity measures such as encryption, authentication, and intrusion detection to mitigate vulnerabilities and safeguard critical AV systems. The fusion of AI and cybersecurity not only enhances AV security posture but also enables intelligent cyber threat monitoring and response capabilities. Extensive simulations and experimental evaluations demonstrate the efficacy of the AVSF in real-time scenarios, contributing to the development of robust security solutions for autonomous vehicle deployment and advancing safer transportation systems in the era of AI-driven mobility. © 2024 IEEE.

关键词： Autonomous vehicles

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Feature Selection-driven Bias Deduction in Histopathology Images: Tackling Site-Specific Influences 13

Feature Selection-driven Bias Deduction in Histopathology Im...

引用

13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Kheiri, Farnaz Bidgoli, Azam Asilian Makrehchi, Masoud Rahnamayan, Shahryar Ontario Tech University Dept. of Electrical Computer and Software Engineering Oshawa Canada Wilfrid Laurier University Dept. of Computer Science Waterloo Canada Brock University Dept. of Engineering St. Catharines Canada

ISBN: (纸本)9798350308365

The emergence of bias in deep neural models represents a significant reliability concern, which may lead to overoptimistic results on seen data while compromising the model's ability to generalize effectively on unseen datasets. Recent studies conducted on The Cancer Genome Atlas (TCGA), which is a publicly available repository of histopathology images, reveal that the TCGA cancerous features extracted by deep neural networks surprisingly are able to discriminate slides based on their origin sites. This finding undoubtedly indicates the existence of site-specific patterns embedded in the extracted features learned by deep networks rather than focusing on histomorphologic patterns. Consequently, this biased behavior raises concerns about the reliability of these networks. This observation motivates us to conduct a series of experiments in which we present two distinct evolutionary feature selection strategies, each differentiated by its objective function evaluation. The primary goal is to select the features with a minimized foot-print of data source signatures, thereby ensuring a more accurate and site-independent cancer classification. We have conducted nine comprehensive independent experiments across nine cancer types, employing each evolutionary strategy. The comparison between results obtained through evolutionary strategies and the original feature sets highlights the substantial impact of feature selection methods on bias reduction while maintaining accuracy in cancer-type discrimination. Furthermore, the comparison of the two strategies with each other demonstrates the intricate nature of bias and its integration with cancerous features during the training process. © 2024 IEEE.

关键词： Lung cancer

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Systematic Literature Review: Evaluating Effects of Adversarial Attacks and Attack Generation Methods

Systematic Literature Review: Evaluating Effects of Adversar...

引用

2023 International Conference on Energy, Power, Environment, Control, and Computing, ICEPECC 2023

作者： Akram, Saira Bazai, Sibghat Ullah Ghafoor, Muhammad Imran Marjan, Shah Hamza, Muhammad Shah, Syed Ali Asghar Dept. of Computer Engineering Quetta Pakistan Pakistan Television Corporation Dept. of Engineering Lahore Pakistan Dept. of Software Engineering Quetta Pakistan

ISBN: (纸本)9781665476010

Advancement in Artificial Intelligence (AI) aims to train the Machine Learning (ML) Models in such a way that they would be able to take decisions spontaneously, however on the other side attackers attempt to manipulate the results generated by these models which makes the application of these models difficult in security-critical areas including classification of medical images, autonomous system installed in vehicles, street lights, malware detection and identification of a person as criminal or innocent. Advancement in research proved the vulnerability of these classifiers due to adversarial attacks that can alter their result in training as well as in testing phase of the model. Causative attacks are the training phase attacks whereas the attacks done at testing phase are the exploratory attacks. This systematic literature Review (SLR) is conducted to gain in-depth knowledge of adversarial attacks, which is the most effective type of exploratory attack, parameters on which these attacks are based, along with the most conventional methods of generating adversarial attacks. © 2023 IEEE.

关键词： Well testing

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Advancements in Dental Diagnostics: YOLOv3-Powered Machine Learning for Automated Teeth Problem Detection 24

Advancements in Dental Diagnostics: YOLOv3-Powered Machine L...

引用

24th International Arab Conference on Information Technology, ACIT 2023

作者： Odeh, Suhail Samara, Anas Zreineh, Nicola Obeid, Seif Ibrieghieth, Maen Obaid, Mahmoud Bethlehem University Software Engineering Dept. Palestine Arab American University Computer System Engineering Jenin Palestine

ISBN: (纸本)9798350384307

This paper presents a rigorous investigation into the application of state-of-the-art machine learning techniques for the automated detection of dental issues, utilizing the YOLOv3 Algorithm, a cutting-edge one-stage object detection method. The study encompasses the meticulous annotation and utilization of a carefully curated dataset featuring 126 panoramic X-ray images, illustrating a diverse range of dental conditions. Among these conditions, the primary emphasis is placed on the precise detection of six specific dental problems. Employing advanced computer vision methodologies, the model demonstrates exceptional accuracy in the identification and precise localization of these targeted dental issues. The implications of these findings are profound for the field of dental healthcare, as the automated detection of dental problems holds the potential to significantly enhance the diagnostic and treatment planning capabilities of dental professionals. The results presented in this research represent a notable stride in the ongoing evolution of machine learning and underscore its capacity to revolutionize the landscape of dental diagnostics, ultimately contributing to the advancement of oral health management and patient care. © 2023 IEEE.

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A Tool for Security Risk Assessment for APT Attacks: using Scenarios, Security Requirements, and Evidence 31

A Tool for Security Risk Assessment for APT Attacks: using S...

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31st IEEE International Requirements engineering Conference, RE 2023

作者： Park, Sihn-Hye Kim, Dongyoon Lee, Seok-Won Ajou University Dept. of Artificial Intelligence Dept. Software and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798350326895

Advanced Persistent Threat (APT) attacks are complex, employing diverse attack elements and increasingly intelligent techniques. This paper introduces a tool for security risk assessment specifically designed for these attacks. This tool assists security teams in systematically analyzing APT attacks to derive adaptive security requirements for mission-critical target systems. Additionally, the tool facilitates the assessment of security risks, providing a comprehensive understanding of their impact on target systems. By leveraging this tool, security teams can enhance defense strategies, mitigating potential threats and ensuring the security of target systems. © 2023 IEEE.

关键词： Risk assessment

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Deep Learning-Based Energy Efficiency Maximization in Massive MIMO-NOMA Networks with Multiple RISs 6

Deep Learning-Based Energy Efficiency Maximization in Massiv...

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

作者： Perdana, Ridho Hendra Yoga Nguyen, Toan-Van Pramitarini, Yushintia An, Beongku Hongik University Dept. of Software and Communications Engineering in Graduate School Korea Republic of San Diego State University Department of Electrical and Computer Engineering San DiegoCA United States Hongik University Dept. of Software and Communications Engineering Korea Republic of

ISBN: (纸本)9798350344349

In this paper, we study a deep learning (DL)-based energy efficiency maximization (EEM) problem in massive multiple-input multiple-output (MIMO)-non-orthogonal multiple access (NOMA) networks with multiple reconfigurable intelligent surfaces (RISs). These RISs are deployed randomly at the edge of the near radius to assist the base station (BS) in communicating with near and far users. We formulate the problem of jointly optimizing the precoding matrix and phase shift of the RISs to maximize the overall energy efficiency subject to the quality-of-service of each user, phase shift of RISs, and power budget of the BS constraints. To address this challenging non-convex problem with mixed-integer constraints, the original problem is decoupled into phase shift and beamforming sub-problems, then addressed them separately. We introduce the bisection search algorithm to address the challenge of the phase shifts optimization problem. For the beamforming optimization, it is transformed into an equivalent non-convex problem but with a more tractable form. Then, we propose an iterative algorithm based on the inner approximation method for its solution. To support real-Time optimization, we design a deep learning framework to predict optimal solutions of phase shifts at RISs and precoding matrix under different parameter settings. Simulation results show that the proposed DL-based approach can predict the optimal solution with high accuracy in a short time compared to the conventional approach. Additionally, the effect of the maximum power budget at the base station, the number of RISs, and BS's antennas are evaluated thoroughly. © 2024 IEEE.

关键词： Energy efficiency

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CoML: Machine Learning based approach for COVID-19 related Suicidal Ideation detection 6

CoML: Machine Learning based approach for COVID-19 related S...

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6th International Conference on Intelligent Systems and computer Vision, ISCV 2024

作者： Bouktif, Salah Ud Din Khanday, Akib Mohi Ouni, Ali CIT United Arab Emirates University Dept. of Computer Science and Software Engineering Al Ain United Arab Emirates École de Technologie Supérieure University of Quebec Dept. of Software Engineering and IT MontrealQC Canada

ISBN: (纸本)9798350350180

The pervasive use of online social media platforms has changed the communication habits and subsequently the content and the frequency of the human exchanges. People share their thoughts on social media more frequently and freely. In particular and due to the societal stigma, people find less obstacle to discuss mental health problems on social media compared to face-o-face discussion. Mining social media for the sake of analysing suicidal intention is an attractive and at the same time challenging task especially during COVID-19 era. The main objective is to leverage machine learning to classify the social media user suicidal behavior while considering the impact of COVID-19 circumstances. We propose a rigorous feature engineering technique based on TF/IDF and Bag of Words. Machine learning classifiers and ensemble models are compared to find that the Neural Network(NN) classifier outperforms the benchmarks with a precision of 94%, a Recall of 94%, an F1 Score of 94%, and an overall accuracy of 94%. In the future, more feature selection techniques and deep learning can be used. © 2024 IEEE.

关键词： Adversarial machine learning

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RT-Swap: Addressing GPU Memory Bottlenecks for Real-Time Multi-DNN Inference 30

RT-Swap: Addressing GPU Memory Bottlenecks for Real-Time Mul...

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30th IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS 2024

作者： Kang, Woosung Lee, Jinkyu Lee, Youngmoon Oh, Sangeun Lee, Kilho Chwa, Hoon Sung DGIST Dept. of Electrical Engineering and Computer Science Korea Republic of Dept. of Computer Science and Engineering Korea Republic of Hanyang University Dept. of Robotics Korea Republic of Ajou University Department of Software and Computer Engineering Korea Republic of School of AI Convergence Soongsil University Korea Republic of

ISBN: (纸本)9798350358414

The increasing complexity and memory demands of Deep Neural Networks (DNNs) for real-Time systems pose new significant challenges, one of which is the GPU memory capacity bottleneck, where the limited physical memory inside GPUs impedes the deployment of sophisticated DNN models. This paper presents, to the best of our knowledge, the first study of addressing the GPU memory bottleneck issues, while simultaneously ensuring the timely inference of multiple DNN tasks. We propose RT-Swap, a real-Time memory management framework, that enables transparent and efficient swap scheduling of memory objects, employing the relatively larger CPU memory to extend the available GPU memory capacity, without compromising timing guarantees. We have implemented RT-Swap on top of representative machine-learning frameworks, demonstrating its effectiveness in making significantly more DNN task sets schedulable at least 72% over existing approaches even when the task sets demand up to 96.2% more memory than the GPU's physical capacity. © 2024 IEEE.

关键词： Deep neural networks

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Vote Bank Forecast Based on Social Media Sentiment Analysis 17

Vote Bank Forecast Based on Social Media Sentiment Analysis

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17th International Conference on Open Source Systems and Technologies, ICOSST 2023

作者： Zaland, Zubair Bazai, Sibghat Ullah Ghafoor, Muhammad Imran Akram, Saira Waseem, Muhammad Qaseem, Muhammad Dept. of Software Engineering Quetta Pakistan Dept.of Computer Engineering Quetta Pakistan Pakistan Television Corporation Lahore Pakistan Dept. of Engineering Pakistan

ISBN: (纸本)9798350381320

Sentiment Analysis of human behavior from social media revolutionized many fields, such as health, business, and election prediction. Sentiment analysis is the process of analyzing an individual's opinions, attitudes, views, sentiments, and emotions. In order to predict sentiments, predicting the polarity of words and categorizing them as positive or negative are the major goals. A quick and effective way to determine what the public thinks about one's own political party, the opposition party, and political figures is through Twitter. prior sentiment analysis techniques have primarily struggled with classification accuracy because biases in the training data cause a sizable portion of tweets to be incorrectly classified. As a remedy, we set out to collect real-Time data in order to forecast Pakistan's next election results. Users can quickly share real-Time updates on a variety of activities and events on Twitter, promoting the rapid diffusion of information and news. To do this, we accessed the Twitter API through the 'Tweepy'' Python package and retrieved real-Time data from the service. Through keyword searches, we were able to find more than 5,000 tweets. We attributed feelings to particular tweets using Python's 'TextBlob'' package. We are able to generate prediction graphics by determining the frequency of positive attitudes with respect to a particular candidate. © 2023 IEEE.

关键词： Forecasting

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