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Enhancing Performance in Hybrid Satellite-Terrestrial Networks: A Novel Joint NOMA-NC Approach

Enhancing Performance in Hybrid Satellite-Terrestrial Networ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Michail Karavolos Nektarios Moraitis Lefteris Tsipi Demosthenes Vouyioukas Dept. of Information and Communication Systems Engineering University of the Aegean Samos Greece School of Electrical and Computer Engineering National Technical University of Athens Zografou Greece Dept. of Digital Systems University of Piraeus Piraeus Greece

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

An appealing alternative towards dealing with mobility and coverage challenges in future wireless networks, are hybrid satellite-terrestrial networks (HSTNs). The combination of network coding (NC) and non-orthogonal multiple access (NOMA) techniques, have recently gained popularity due to their performance gains in improving the quality of a wireless transmission and exploiting the accessible spectrum. This paper introduces a joint NOMA-NC approach, integrated to an HSTN comprising a satellite, a terrestrial base station (BS), and multiple ground-based mobile terminals (MTs). The suggested method facilitates a cluster of end-users to be concurrently served through NOMA from a satellite using optimal power allocation, whereas the BS utilizes random linear NC to augment the MTs’ reception in the event of errors. The simulation results confirm the effectiveness of the proposed methodology in improving system sum throughput, demonstrating substantial enhancements compared with both OMA with NC, and standalone NOMA and OMA approaches. The resilience of the suggested method is proved, improving the sum throughput even at low power transmissions and in the worst case channel conditions.

关键词： NOMA Satellites Wireless networks Simulation Network coding Space-air-ground integrated networks Throughput Hybrid power systems Resource management Resilience

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Machine-Learning-Based Path Loss Prediction for In-Cabin Wireless Networks

Machine-Learning-Based Path Loss Prediction for In-Cabin Wir...

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Machine Learning for Communication and Networking (ICMLCN), IEEE International Conference on

作者： Nektarios Moraitis Lefteris Tsipi Demosthenes Vouyioukas School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Dept. of Information and Communication Systems Engineering University of the Aegean Samos Greece Dept. of Digital Systems University of Piraeus Piraeus Greece

ISBN: (数字)9798350343199

ISBN: (纸本)9798350343205

This article examines the performance of machine learning (ML) techniques for accurate path loss predictions inside aircrafts. An artificial neural network (ANN) and support vector regression (SVR) methods are introduced and assessed. Both algorithms are trained, validated and tested with measurements collected inside a long-range Airbus aircraft at three different frequencies. According to the statistical analysis both methods adapt very well to the measured data, delivering low root-mean-square errors, being 1.5 and 1.9 dB for ANN and SVR models, respectively. In any case, the ANN method exhibits better performance in predicting accurately path loss. Finally, the results verified that the appropriateness of the applied ML techniques is much better than that of conventional empirical models, thus forecasting path loss with remarkable efficacy.

关键词： Support vector machines Accuracy Statistical analysis Atmospheric modeling Wireless networks Artificial neural networks Machine learning

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Misogynistic Content Detection in Roman Urdu Tweets Based on Transformer Models

Misogynistic Content Detection in Roman Urdu Tweets Based on...

引用

IEEE International Conference on Multi Topic

作者： Waseem Ullah Khan Salman Ahmed Safdar Nawaz Khan Marwat Yousaf Khan Aftab Khan Shafi Ullah Kamran Dept. of Computer Systems Engg University of Engineering & Technology Peshawar Pakistan Dept. of Electrical Engg University of Engineering & Technology Peshawar Pakistan Dept. of Computer Science Allama Iqbal Open University Islamabad Pakistan Zong CMPak Islamabad Pakistan

ISBN: (数字)9798331507213

ISBN: (纸本)9798331507220

Facebook, Instagram and Twitter serve as influential social media platforms for individuals where they express and share their thoughts, skills, knowledge and talents with a broad audience. However, these platforms are also used to disseminate offensive content including trolling and content that targets a person’s gender, religion, or race. When women become the targets of such content, it is often manifests as misogyny. In recent years, the growing prevalence of racial and verbal abuse directed at women on social media has drawn considerable attention to the issue of online misogyny and women based offending, by making the automatic detection of such offensive content an urgent priority. Moreover, many researchers have addressed misogyny detection in high resource languages i.e., English, Italian, Arabic, Hindi, and more, tackling this issue in low resource language like Roman Urdu presents a significant challenge. In this paper, a framework is proposed for detection of misogynistic content from Roman Urdu tweets. This paper leverages well known transformer models such as BERT, RoBERTa, GPT-2, and XLNet to train and evaluate misogyny detection performance. The research findings reveal that RoBERTa surpasses the other models in misogyny detection, by achieving the highest accuracy, precision, F1-score and recall i.e., 90.87%, 90%, 89%, and 91%, respectively.

关键词： Accuracy Social networking (online) Blogs Linguistics Transformers Web sites Reliability Multimedia communication

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Faster Response Chemical Reaction Using Machine Learning

Faster Response Chemical Reaction Using Machine Learning

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2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Srivastava, Bharvee Sivakumar, C. Kumar, Prashant Maguluri, Lakshmana Phaneendra Naini Prayagraj India Dept. of Computer Science and Systems Engineering Andhra Pradesh Tirupati India Lovely Professional University Department of Chemical Engineering Punjab India Koneru Lakshmaiah Education Foundation Department of Cse Guntur AP India

ISBN: (纸本)9798350323887

The majority of advancements are still generally attributed to trial-and-error experimentation, which takes a long time and a lot of money and can be a significant portion of research in physics, chemistry, and materials science. This type of experimentation can be a significant portion of the process. Despite the fact that a large percentage of research is devoted to the identification and characterization of novel chemicals that have the potential to be of use to society. In this paper, the chemical reactions are quickened using machine learning modelling. The paper uses supervised machine learning approach to fasten the process of chemical reaction by learning it in a supervised manner. The research provides details of how the process of chemical reactions taking place in conventional and using machine learning models. The simulation is conducted in python to test if the chemical reactions are produced with faster convergence time than the existing methods. © 2023 IEEE.

关键词： Chemical reactions

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Resource Allocation as a Market: A Case Study on Multi-Server Multi-Model Federated Learning

Resource Allocation as a Market: A Case Study on Multi-Serve...

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Annual Conference on Wireless On Demand Network systems and Services (WONS)

作者： Maria Diamanti Aisha B Rahman Panagiotis Charatsaris Eirini Eleni Tsiropoulou Symeon Papavassiliou Institute of Communication and Computer Systems School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Dept. of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA

ISBN: (数字)9783903176713

ISBN: (纸本)9798331522025

6G networks envision a seamless integration of data and AI into their core operations, marking a new era where distributed client devices actively engage in intelligent processes, supported by a diverse ecosystem of AI service providers. This shift necessitates approaching traditional resource allocation problems from a market perspective, transparently reflecting the gains and losses of clients and providers within the ecosystem. In this paper, we use a multi-server, multi-model Federated Learning (FL) network as a case study to propose a market-based resource allocation framework. The framework models the joint problem of client resource allocation and pricing as a Fisher market, where servers compete to attract clients with private data and local computing resources for distributed model training. The joint problem is formulated as a convex program, aiming to maximize the total server utility related to their model’s accuracy, while incorporating clients’ total computing resource constraints and servers’ budget constraints. An analytical solution to the convex program is derived concluding the so-called Market Equilibrium (ME) point, where market clearance is achieved. The effectiveness of the proposed market-based resource allocation framework is validated through comparisons with established resource allocation and sharing schemes from the literature.

关键词： Federated learning Computational modeling Biological system modeling Ecosystems Distributed databases Pricing Data models Resource management Servers Artificial intelligence

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A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning

A Tensor Low-Rank Approximation for Value Functions in Multi...

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Asilomar Conference on Signals, systems & computers

作者： Sergio Rozada Santiago Paternain Juan Andrés Bazerque Antonio G. Marques Dept. of Signal Theory and Communications King Juan Carlos University Madrid Spain Dept. of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY USA Dept. of Electrical and Computer Engineering University of Pittsburgh Pittsburgh PA USA

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

In pursuit of reinforcement learning systems that could train in physical environments, we investigate multi-task approaches as a means to alleviate the need for massive data acquisition. In a tabular scenario where the Q-functions are collected across tasks, we model our learning problem as optimizing a higher order tensor structure. Recognizing that close-related tasks may require similar actions, our proposed method imposes a low-rank condition on this aggregated Q-tensor. The rationale behind this approach to multi-task learning is that the low-rank structure enforces the notion of similarity, without the need to explicitly prescribe which tasks are similar, but inferring this information from a reduced amount of data simultaneously with the stochastic optimization of the Q-tensor. The efficiency of our low-rank tensor approach to multi-task learning is demonstrated in two numerical experiments, first in a benchmark environment formed by a collection of inverted pendulums, and then into a practical scenario involving multiple wireless communication devices.

关键词： Wireless communication Training Performance evaluation Tensors Stochastic processes Reinforcement learning Multitasking Throughput Parametric statistics Optimization

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Simulation and information-theoretic analysis of a hybrid CT+XRD imaging system 8

Simulation and information-theoretic analysis of a hybrid CT...

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Anomaly Detection and Imaging with X-Rays (ADIX) VIII 2023

作者： Ruiz, Sarah D. Hurlock, Ava X. Yang, Shu Martinez, Mauricio Thamvichai, Ratchaneekorn Greenberg, Joel A. Ashok, Amit Gehm, Michael E. Duke University Dept. of Electrical and Computer Engineering DurhamNC United States Duke University Medical Physics Program DurhamNC United States University of Arizona TusconAZ United States

ISBN: (纸本)9781510661769

Simulation is a valuable tool for designing and evaluating the performance of x-ray imaging systems. In previous work,1, 2 a hybrid computed tomography(CT) and x-ray diffraction(XRD) imaging system was developed for improved identification of threat objects in checked baggage. Through large-scale simulations of this hybrid CT+XRD system, we can investigate the impact of various parameters on system performance. These parameters include multi-energy acquisitions, varying energy resolution, and additional system views. We will report on our findings and evaluate the system performance resulting from these variations of the simulated system as well as discuss how these findings may influence future system design. © 2023 SPIE.

关键词： computerized tomography

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Efficient Hardware Implementation of Seizure Detector Using Machine Learning

Efficient Hardware Implementation of Seizure Detector Using ...

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International Conference on Microelectronics, ICM

作者： Mariam K. El-Tanbadawy Mohammed A. Megeed Salem Mohamed A. Abd ElGhany Electronics Engineering Dept. German University in Cairo Egypt Computer Engineering Dept. German University in Cairo Egypt Integrated Electronic Systems Lab TU Darmstadt Germany

ISBN: (数字)9798350379396

ISBN: (纸本)9798350379402

In the past few decades, Seizure Detection has been a vital cornerstone in biomedical research. Both hardware and software implementations have been explored to improve algorithms, detection performance and resources. In this paper, three handcrafted features composed of Coastline Function, Hurst Exponent and Fractal Dimension are used as training data for supervised learning models to build an efficient and sensitive seizure detector. Random Forest classifier exhibits the best accuracy at 98%. Hardware efficiency, too, is exploited through a modified Feature Extractor that combines the aforementioned features. The features are adjusted to reduce their hardware resources on Virtex-7 FPGA. The area utilization is improved by 10%, power utilization is improved by 20% and latency decreased by 32%.

关键词： Training Coastlines Accuracy Training data Detectors Feature extraction Hardware Software Fractals Random forests

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Blockchain Based Digital Forensic Data for Unbreakable Trust and Security

Blockchain Based Digital Forensic Data for Unbreakable Trust...

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： M. Imrankhan M. Viji R. Sathishkumar T. Gnanaprakash R. Karthik Roshan G. Saravanan Dept. of CSE Manakula Vinayagar Institute of Technology Puducherry Dept. of Computer Science and Business Systems Sri Manakula Vinayagar Engineering College Puducherry

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

This can be one of the most magnificent advantages realized from applying blockchain technology in a forensic investigation context, namely, a transformative approach to address challenges embedded within legal and criminal justice systems. For example, smart contracts, being self-executing agreements with the automated inclusion of predefined rules, may even automate and secure the process of tracking evidence, managing chain of custody, and access control. This smart contract will ensure that forensic operations are transparent and of sound integrity. Every transaction and action performed on the blockchain is recorded there, instantaneously bringing down tampering to the lowest possibility and any chance for human mistakes. Regarding decentralized nature and cryptography-based security combined with immutability, blockchain is a technology known to have safeguards over sensitive data. Once recorded on the blockchain, the information thereupon becomes completely un-changeable, assuring the reliability and authenticity of evidence. This will be especially important in the management of an unforgeable chain of custody and in ensuring that forensic evidence remains unchanged and can be relied on with integrity throughout the court process.[6] Critical data can then be maintained tamper-proof for management and storage by developing Ethereum-based smart contracts on Remix IDE using Solidity.[15]Each transaction will be time-stamped and safely logged on the Ethereum blockchain, thus developing an unchallenged ledger that makes any investigation that much more trustworthy. In a nutshell, the application of blockchain in forensic activities builds greater control and efficiencies, reduces possible errors and conflicts, and provides transparency, integrity, and an unchangeable, permanent record for forensic processes. The invention thereby significantly upgrades the general integrity and confidence in legal and criminal justice inquiries.

关键词： Technological innovation Forensics Smart contracts Digital forensics Process control Market research Blockchains Reliability Protection Standards

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Enhancing Cybersecurity: A Multilayered Approach to Phishing Website Detection Using Machine Learning

Enhancing Cybersecurity: A Multilayered Approach to Phishing...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： Lakshmi Narayanaa T Skandarsini R R S. Jhansi Ida Dept. of Computer Science and Business Systems RMK Engineering College Thiruvallur India

This paper employs machine learning techniques to combat the escalating threat of phishing attacks in the digital realm. The research builds a predictive model capable of differentiating between phishing and legitimate websites by examining a broad range of information retrieved from website URLs, including address bar-based properties, domain characteristics, and webpage content. To ascertain their effectiveness in this task, six well-known machine learning algorithms—Decision Tree, Random Forest, XGBoost, Deep Learning, Autoencoder Neural Network, and Support Vector Machines—are rigorously investigated. Notably, the Multilayer Perceptrons algorithm emerges as the standout performer, achieving an 86.4% accuracy in identifying phishing websites. This endeavor not only advances the field of cybersecurity but also empowers individuals to proactively safeguard themselves against the pervasive threat of phishing attacks in an increasingly interconnected digital landscape.

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