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IEEE Southeastcon

作者： Deborsi Basu Ritu Chaudhary Uttam Ghosh Raja Datta G. S. Sanyal School of Telecommunications Indian Institute of Technology Kharagpur India Dept. of Electronics and Electrical Communication Engineering Indian Institute of Technology Kharagpur India Dept. of Computer Science and Data Science School of Applied Computational Sciences MMC TN USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

The advancement in distributed Federated Learning (FL) techniques is transforming next-generation edge computing, especially in e-Helathcare systems where data integrity and security are critical concerns. Collecting, processing and analyzing such substantial medical data is highly challenging due to their non-identical and versatile nature. Conventional FL approaches often show unsatisfactory and inaccurate results while operating on those data. Traditional FL methods also lack optimal dynamic management mechanisms for edge devices in ehealthcare, which is crucial to ensure that only authorized devices participate in handling any system failures and new registrations. To tackle such important aspects, in this work, we propose a personalized FL technique to handle abrupt label shifts among clients' data. The proposed approach further targets to achieve personalized models for local edge clients. A statistical batch normalization is proposed on customized edge servers, keeping the data specificity intact. To make the proposed system more secure and robust, a Software Defined Network-based (SDN-based) centralized server is used that allows only authorized and efficient edge clients to participate. The proposed comprehensive experiments on two healthcare datasets demonstrate better accuracy than traditional FL methods with enhanced security and privacy.

关键词： Data privacy Federated learning Data integrity Medical services Servers Security Software defined networking Next generation networking Batch normalization Edge computing

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Simulating Online Social Response: A Stimulus/Response Perspective

Simulating Online Social Response: A Stimulus/Response Persp...

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2021 Winter Simulation Conference, WSC 2021

作者： Shao, Huajie Abdelzaher, Tarek Han, Jiawei Jiang, Minhao Mao, Yuning Meng, Yu Qiu, Wenda Sun, Dachun Wang, Ruijie Yang, Chaoqi Yang, Zhenzhou Zhang, Xinyang Zhang, Yu Cohen, Sam Flamino, James Korniss, Gyorgy Malik, Omar Mandviwalla, Aamir Szymanski, Boleslaw Yin, Lake University of Illinois at Urbana Champaign Department of Computer Science UrbanaIL61801 United States Rensselaer Polytechnic Institute Dept. of Physics Applied Physics Astronomy Dept. of Computer Science TroyNY12180 United States

ISBN: (纸本)9781665433112

The paper describes a methodology for simulating online social media activities that occur in response to external events. A large number of social media simulators model information diffusion on online social networks. However, information cascades do not originate in vacuum. Rather, they often originate as a reaction to events external to the online medium. Thus, to predict activity on the social medium, one must investigate the relation between external stimuli and online social responses. The paper presents a simulation pipeline that features stimulus/response models describing how social systems react to external events of relevance to them. Two case studies are presented to test the fidelity of different models. One investigates online responses to events in the Venezuela election crisis. The other investigates online responses to developments of the China Pakistan Economic Corridor (CPEC). These case studies indicate that simple macroscopic stimulus/response models can accurately predict aggregate online trends. © 2021 IEEE.

关键词： Social networking (online)

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Joint Sensing and Communication with Graphene FETs Targeting Terahertz Band

Joint Sensing and Communication with Graphene FETs Targeting...

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International Symposium on Communication Systems, Networks and Digital Signal Processing, CNSDSP

作者： Monica La Mura Patrizia Lamberti Alessandro Stuart Savoia Roberto Alesii Dajana Cassioli Vincenzo Tucci Dept. of Industrial Electronic and Mechanical Engineering Roma Tre University Rome Italy Dept. of Information and Electrical Engineering and Applied Mathematics University of Salerno Fisciano Italy Dept. of Information Engineering Computer Science and Mathematics University of L'Aquila L'Aquila Italy

ISBN: (数字)9798350348743

ISBN: (纸本)9798350348750

The upcoming 6G paradigm for wireless communication networks promises enhanced data rates, reduced latency, and pervasive connectivity. In this scenario, intelligent and adaptive communication protocols and innovative smart sensing devices can be efficiently integrated to offer innovative systems for different applications. In the future, graphene technology might greatly contribute to the evolving landscape of sensing combined with high-frequency communication. This paper explores possible architectures for radiofrequency (RF) electronics for joint sensing and modulation based on graphene field-effect transistors (GFETs). Current challenges and limitations of graphene technology are also discussed.

关键词： Radio frequency Wireless communication 6G mobile communication Technological innovation Protocols Graphene Sensor phenomena and characterization

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Modeling Hot Rolling Process Forces Using Bio-Inspired Metaheuristic Search Algorithm

Modeling Hot Rolling Process Forces Using Bio-Inspired Metah...

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Intelligent Methods, Systems, and Applications (IMSA)

作者： Alaa Sheta Malik Braik Ertan Öznergiz Walaa H. Elashmawi Peter Rausch Emad S. Othman Department of Computer Science Southern Connecticut State University New Haven CT USA Computer Science Department Al-Balqa Applied University Salt Jordan Mechanical Engineering Faculty Istanbul Technical University Istanbul Turkey Dept of Computer Science Misr International University Cairo Egypt Computer Science Department George Simon Ohm Univ. of Applied Sciences Nuremberg Germany Management Information System Dept. AL-Shorouk Academy Cairo Egypt

ISBN: (数字)9798350362633

ISBN: (纸本)9798350362640

This research introduces the application of an innovative bio-inspired metaheuristic technique, termed the Crow Search Algorithm (CSA), to model a crucial industrial process - hot rolling manufacturing. Inspired by the foraging patterns of crows, the CSA algorithm has demonstrated its prowess in solving diverse optimization challenges. In the context of this study, the CSA algorithm is harnessed to fine-tune the parameters of a simulation model focused on predicting the force exerted during a hot rolling procedure. The proposed model takes into consideration a range of influential factors, including the initial temperature (Ti), width (W s ), carbon equivalent (C e ), gauge (h i ), draft (i), and roll diameter (R). The findings underscore the CSA's capability to deliver an exceptional modeling performance characterized by swift convergence and high solution quality. By getting along very well with the proposed model with the CSA algorithm, a robust and efficient avenue to optimize the hot rolling process emerges, with the potential for expansion into other manufacturing domains. The computational and simulation results demonstrated that the proposed approach-based CSA outperformed different meta-heuristic search algorithms, such as the Salp Swarm Algorithm (SSA), Dandelion Optimizer (DO), Particle Swarm Optimization (PSO), Gray Wolf Optimizer (GWO), and Moth-Flame Optimization (MFO), in all test cases. The CSA has achieved the highest coefficient of determination (R 2 ), equal to 0.97244, and the lowest mean squared error (MSE), equal to 1904.97, compared to its opponent algorithms.

关键词： Cutting tools Temperature distribution Computational modeling Simulation Biological system modeling Metaheuristics Force

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A Birds Species Detection Utilizing an Effective Hybrid Model

A Birds Species Detection Utilizing an Effective Hybrid Mode...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Sara Abbas Rafa Zohaa Mubarak Al-qfail Ahmed Adil Nafea Samia F. Abd-hood Mohammed M Al-Ani Saeed Amer Alameri dept. Law College of law and political science University of Anbar Ramadi Iraq dept. Artificail inteligence College of Computer Science and Information Technology University of Anbar Ramadi Iraq dept. Center for Artificial Intelligence Technology (CAIT) Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Bangi Selangor Malaysia dept. Computer Science Faculty of Applied Sciences Seiyun University Hadhramout Yemen Faculty of Computers and Information Hadhramout University Almukalla Hadhramout Yemen dept. Department of Information Technology Seiyun University Hadhramout Yemen

ISBN: (数字)9798350374131

ISBN: (纸本)9798350374148

The aim of this study is to improve the performance of detection Birds Species by proposed a hybrid model using a combination of MobileNetV2 for feature extraction, an Autoencoder for dimensionality reduction, and Support Vector Machines (SVM) for classification. The methodology includes preprocessing, augmentation, feature extraction, and classification phases. The proposed model is evaluated using the Bird Species dataset, consisting of 525 bird species the dataset is carefully curated to ensure cleanliness and absence of leakage between sets. The evaluation metrics include precision, recall, accuracy, and F-measure. The results have shown the efficiency of the proposed model in bird species classification.

关键词： Support vector machines Dimensionality reduction Measurement Accuracy Biological system modeling Birds Feature extraction

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An Infrastructure Software Perspective Toward Computation Offloading between Executable Specifications and Foundation Models

arXiv

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arXiv 2025年

作者： Ran, Dezhi Wu, Mengzhou Cao, Yuan Marron, Assaf Harel, David Xie, Tao MOE SCS Peking University Beijing100871 China School of EECS Peking University Beijing100871 China Dept. of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot6 - 7632706 Israel

Foundation Models (FMs) have become essential components in modern softwaresystems, excelling in tasks such as pattern recognition and unstructured dataprocessing. However, their capabilities are complemented by the precision,verifiability, and deterministic nature of executable specifications, such assymbolic programs. This paper explores a new perspective on computationoffloading, proposing a framework that strategically distributes computationaltasks between FMs and executable specifications based on their respectivestrengths. We discuss the potential design of an infrastructure softwareframework to enable this offloading, focusing on key mechanisms such as taskdecomposition, resource allocation, and adaptive optimization. Furthermore, weidentify critical technical challenges, including semantic-gap resolution,reliability, and scalability, that must be addressed to realize this *** leveraging the complementary strengths of FMs and symbolic programs, thisperspective lays the groundwork for advancing hybrid software systems that areboth efficient and reliable. Copyright © 2025, The Authors. All rights reserved.

关键词： Resource allocation

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Visual Exploratory Data Analysis of the Covid-19 Pandemic in Nigeria: Two Years after the Outbreak

arXiv

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arXiv 2023年

作者： Orji, Ugochukwu E. Ukwandu, Elochukwu Obianuju, Ezugwu A. Ezema, Modesta E. Ugwuishiwu, Chikaodili H. Egbugha, Malachi C. Dept. of Computer Science University of Nigeria Enugu Nsukka Nigeria Dept. of Applied Computing & Engineering Cardiff School of Technologies Cardiff Metropolitan University United Kingdom Dept of Computer Engineering Ahmadu Bello University Zaria Nigeria

The outbreak of the coronavirus disease in Nigeria and all over the world in 2019/2020 caused havoc on the world's economy and put a strain on global healthcare facilities and personnel. It also threw up many opportunities to improve processes using artificial intelligence techniques like big data analytics and business intelligence. The need to speedily make decisions that could have far-reaching effects is prompting the boom in data analytics which is achieved via exploratory data analysis (EDA) to see trends, patterns, and relationships in the data. Today, big data analytics is revolutionizing processes and helping improve productivity and decision-making capabilities in all aspects of life. The large amount of heterogeneous and, in most cases, opaque data now available has made it possible for researchers and businesses of all sizes to effectively deploy data analytics to gain action-oriented insights into various problems in real time. In this paper, we deployed Microsoft Excel and Python to perform EDA of the covid-19 pandemic data in Nigeria and presented our results via visualizations and a dashboard using Tableau. The dataset is from the Nigeria Centre for Disease Control (NCDC) recorded between February 28th, 2020, and July 19th, 2022. This paper aims to follow the data and visually show the trends over the past 2 years and also show the powerful capabilities of these data analytics tools and techniques. Furthermore, our findings contribute to the current literature on Covid-19 research by showcasing how the virus has progressed in Nigeria over time and the insights thus far. © 2023, CC BY-NC-SA.

关键词： Data Analytics

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Ontology population using CNN model: Application to COVID-19 diagnosis

Ontology population using CNN model: Application to COVID-19...

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International Conference on Pattern Analysis and Intelligent Systems (PAIS)

作者： Naçima Mellal Asma Saighi Dept. Networks and Telecommunication Research Laboratory of Artificial Intelligence and Autonomous Objects Institute of Applied Science & Technology University of Oum El Bouaghi Algeria Dept. Computer Science Research Laboratory of Artificial Intelligence and Autonomous Objects University of Oum El Bouaghi Algeria

ISBN: (数字)9798350350265

ISBN: (纸本)9798350350272

AI models and especially deep learning models have found applications across various medical domains. While many studies focus on using electronic health records (EHRs) data to train AI models, implementing the reverse process poses several challenges. Also, the heterogeneous nature of the used data in EHRs, such as diagnoses, clinical reports, test results, prescriptions, and medical images, makes their interpretation difficult for the practitioners which require a unified understanding. Medical ontologies are useful to improve data interpretability and semantics. In this paper, we aim to create a synergy between ontologies and deep learning techniques to enhance efficiency, data understanding, and provide healthcare quality. We propose a system in which our developed ontology is populated automatically using the predictions of an AI model. We have developed and trained a CNN model to aid in the COVID-19 diagnosis. The initial obtained results are promising with an accuracy of 98%.

关键词： Deep learning COVID-19 Sociology Semantics Medical services Ontologies Predictive models

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P3GNN: A Privacy-Preserving Provenance Graph-Based Model for APT Detection in Software Defined Networking

arXiv

引用

arXiv 2024年

作者： Nazari, Hedyeh Yazdinejad, Abbas Dehghantanha, Ali Zarrinkalam, Fattane Srivastava, Gautam Cyber Science Lab Canada Cyber Foundry University of Guelph Canada Dept. of Engineering University of Guelph Canada Dept. of Math and Computer Science Brandon University Manitoba Canada Research Centre for Interneural Computing China Medical University Taichung Taiwan

Software Defined Networking (SDN) has brought significant advancements in network management and programmability. However, this evolution has also heightened vulnerability to Advanced Persistent Threats (APTs), sophisticated and stealthy cyberattacks that traditional detection methods often fail to counter, especially in the face of zero-day exploits. A prevalent issue is the inadequacy of existing strategies to detect novel threats while addressing data privacy concerns in collaborative learning scenarios. This paper presents P3GNN (privacy-preserving provenance graph-based graph neural network (GNN) model), a novel model that synergizes Federated Learning (FL) with Graph Convolutional Networks (GCN) for effective APT detection in SDN environments. P3GNN utilizes unsupervised learning to analyze operational patterns within provenance graphs, identifying deviations indicative of security breaches. Its core feature is the integration of FL with homomorphic encryption, which fortifies data confidentiality and gradient integrity during collaborative learning. This approach addresses the critical challenge of data privacy in shared learning contexts. Key innovations of P3GNN include its ability to detect anomalies at the node level within provenance graphs, offering a detailed view of attack trajectories and enhancing security analysis. Furthermore, the model's unsupervised learning capability enables it to identify zero-day attacks by learning standard operational patterns. Empirical evaluation using the DARPA TCE3 dataset demonstrates P3GNN's exceptional performance, achieving an accuracy of 0.93 and a low false positive rate of 0.06. © 2024, CC BY.

关键词： Federated learning

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A technical Concept for enhancing the Student Experience in Hybrid Lecture Scenarios

A technical Concept for enhancing the Student Experience in ...

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IEEE International Symposium on Multimedia

作者： Florian Schimanke Robert Mertens Felix Prankel Dept. of Computer Science and Engineering HSW University of Applied Sciences Hameln Germany Vocational Training KG Sennheiser electronic SE & Co. KG Wedemark Germany

ISBN: (数字)9798331511111

ISBN: (纸本)9798331511128

During the COVID-19 pandemic, learning had to be shifted from in-person settings to entirely online or hybrid teaching scenarios. Schools and universities in Germany were forced to quickly deploy solutions which often provided lackluster quality. This shift was also implemented indiscriminately across subjects so that screen intensive courses (such as programming) were impacted in the same way as practical courses like hardware engineering. In many cases, this left a negative impression of such hybrid scenarios on participants. However, with steady improvements in technology, hybrid meetings have established themselves in corporate environments and steadily gained popularity in parts of higher education programs. Especially in the case of dual programs, this is also applicable to lectures and seminars. This paper explores using products from Sennheiser electronic SE & Co. KG with microphone arrays and audio-over-IP technology in combination with network cameras as an example, this concept aims to create an environment for seamlessly integrated hybrid lectures with about 30 participants in presence.

关键词： Seminars COVID-19 Electronic learning Pandemics Education Cameras Microphone arrays Hardware Programming profession

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