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2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2023

作者： Sozos, Kostas Deligiannidis, Stavros Mesaritakis, Charis Bogris, Adonis University of West Attica Dept. of Informatics and Computer Engineering. Aghiou Spiridonos Egaleo Athens12243 Greece University of the Aegean Dept. of Information and Communication Systems Engineering Palama 2 Karlovassi Samos83200 Greece

ISBN: (纸本)9798350345995

Nowadays, photonic analog computing in the form of neuromorphic photonics or photonic neural networks has resulted in powerful hardware platforms that accelerate difficult tasks with marginal power consumption. Although linear transformations are efficiently carried out in the optical domain, nonlinear transformations are also of paramount importance. Here, we propose four wave mixing (FWM) process as an elegant solution for the generation of diverse nonlinear higher order products of an original signal. On one hand, these products can act as activation functions in different types of neural networks, either recurrent neural networks (RNNs) and reservoir computers or in any other type of brain-inspired frameworks. On the other hand, the same products can form the basis for polynomial regression which is a very powerful technique used in machine learning in different tasks i.e. for the prediction of COVID-19 transmission [1]. © 2023 IEEE.

关键词： Recurrent neural networks

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Automated Knowledge Framework for IoT Cybersecurity Compliance

Automated Knowledge Framework for IoT Cybersecurity Complian...

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IEEE International Conference on Big Data

作者： Ikechukwu Oranekwu Lavanya Elluri Gunjan Batra Dept. of Computer Information Systems Texas A&M University - Central Texas Killeen USA Dept. of Information Systems and Security Kennesaw State University Kennesaw USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Rapid expansion in the manufacture and use of Internet of Things (IoT) devices has introduced significant challenges in ensuring compliance with cybersecurity standards. To protect user data and privacy, all organizations providing IoT devices must adhere to complex guidelines such as the National Institute of Standards and Technology Inter agency Report (NIST IR) 8259, which defines essential cybersecurity guidelines for IoT manufacturers. However, interpreting and applying these rules from these guidelines and the privacy policies remains a significant challenge for companies. Thus, this project presents a novel approach to extract knowledge from NIST 8259 for creating semantically rich ontology mappings. Our ontology captures key compliance rules, which are stored in a knowledge graph (KG) that allows organizations to crosscheck and update privacy policy documents with ease. The KG also enables real-time querying using SPARQL and offers a transparent view of regulatory adherence for IoT manufacturers and users. By automating the process of verifying cybersecurity compliance, the framework ensures that companies remain aligned with NIST standards, eliminating manual checks and reducing the risk of non-compliance. We also demonstrate that compared to the baseline Large Language Models (LLMs), our proposed framework has more compliance accuracy, and is more efficient and scalable.

关键词： Privacy Companies Manuals NIST Ontologies NIST Standards Real-time systems Internet of Things computer security Guidelines

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Optimizing Latency and Communication in Federated Edge Computing with LAFEO and Gradient Compression for Real-Time Edge Analytics 6

Optimizing Latency and Communication in Federated Edge Compu...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Namratha, P. Chaganti, Koushik Reddy Elicherla, Sivananda Lahari Reddy Guddati, Surekha Swarna, Akarapu Thiramal Reddy, P. Department of CSE GATES Institute of Technology and Science Anantapur India Dept. of Computer and Information Systems University of the Cumberlands KY United States Dept of CSE Dayananda Sagar University Bangalore India Dept of CSE Vidya Jyothi Institute of Technology Hyderabad India

ISBN: (纸本)9798331522667

The increasing need for real-time data analytics in edge computing is furthered by the latency pressures from computational and communication bottlenecks, thus demanding novel solutions. Fundamental approaches to federated learning and distributed machine learning do not accommodate changes in dynamic latency conditions very well, thereby being inefficient in terms of synchronization, communication overhead, and delay in task execution. This paper presents a holistic framework consisting of three innovative approaches to solve these problems: Latency-Aware Federated Edge-Aware Optimization (LAFEO), Latency-Driven Gradient Compression with Adaptive Sparsity (LAGCAS), and Predictive Latency Compensation with Dynamic Replication (PLCDR). The LAFEO framework consists of a double-layer optimization framework, combining latency-weighted scheduling with reinforcement learning to dynamically adapt the synchronization frequencies. This results in 25-30% end-to-end latency reduction with an accuracy deviation that is capped at 2% centralized models. LAGCAS reduces communication overhead by applying adaptive gradient scarification methods, bringing compressions of gradients under real-time accuracy thresholds: it reduces 40% communication volume and accelerates convergence by 20%. PLCDR uses predictive modelling to predict latency bottlenecks and dynamically replicates critical computations across the edge nodes. It hence offers 15-25% decrease in the completion time of tasks and up to 30% enhancement in the resilience of system sets. The existing methods will be addressed with the key limitations due to the introduction of latency-aware mechanisms that adapt to real-time edge conditions along with high accuracy and efficiency. Simulation results have shown marked scalability improvements, latency reductions, and resilience;in its positioning, this work's effort is a transformative advancement in the latency-sensitive edge computing process. © 2025 IEEE.

关键词： Federated learning

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miRcorrNetPro: Unraveling Algorithmic Insights through Cross-Validation in Multi-Omics Integration for Comprehensive Data Analysis

miRcorrNetPro: Unraveling Algorithmic Insights through Cross...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Yazici, Miray Unlu Yousef, Malik Marron, J.S. Bakir-Gungor, Burcu Abdullah Gül University Dept. of Bioengineering Kayseri Turkey Zefat Academic College Dept. of Information Systems Zefat Israel University of North Carolina Dept. of Statistics and Operations Research Chapel HillNC United States Abdullah Gul University Dept. of Computer Engineering Kayseri Turkey

ISBN: (纸本)9798350337488

High throughput -omics technologies facilitate the investigation of regulatory mechanisms of complex diseases. Along this line, scientists develop promising tools and methods to extend our understanding at the molecular and functional levels. To this end, miRcorrNet tool performs integrative analysis of microRNA (miRNA) and gene expression profiles via machine learning (ML) approach to identify significant miRNA groups and their associated target genes. In this study, we propose miRcorrNetPro tool, which extends miRcorrNet by tracking group scoring, ranking and other information through the cross-validation iterations. Heatmap visualizations enable deep novel insights into the collective behavior of clusters of groups in cellular signaling and hence facilitate detection of potential biomarkers for the disease under investigation. Although miRcorrNetPro is designed as a generic tool, here we present our findings and potential miRNA biomarkers for Breast Cancer (BRCA). The miRcorrNetPro tool and all other supplementary files are available at https://***/Miray-Unlu/miRcorrNetPro. © 2023 IEEE.

关键词： Breast Cancer Machine Learning microRNAs Multi-omics Integration

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Artificial Intelligence for Green Agriculture and Sustainable Resource Management: A Comprehensive Overview

Artificial Intelligence for Green Agriculture and Sustainabl...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Eman Alduweib Waseem Alromema Essa Hezzam Dept. of Computer Science University of Petra Amman Jordan Dept. of Computer Science and Information Applied College Taibah University Medina Saudi Arabia Dept. of Information Systems College of Computer Science and Engineering Taibah University Medina Saudi Arabia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Artificial Intelligence (AI) is a revolutionary technology disrupting the process of finding solutions for various sustainability challenges. Specifically, AI is having a significant influence on many sectors, especially agriculture, energy, waste management, and biodiversity conservation. This paper offers a comprehensive overview of AI's applications in sustainable resource management, with a special focus on green agriculture as a case study, in which the application of AI greatly enhances the efficiency of resource use, crop yield, and environmental conservation efforts. This overview describes the potential application of AI to address performance concerns (ethical value, energy use, and data quality deficiency), and proposes the future research directions such as the explainable AI, circular economies, and the integration of AI with the Internet of Things (IoT) to guide researchers and policymakers in exploiting AI to enhance sustainability in key domains.

关键词： Waste management Ethics Explainable AI Data integrity Transforms Resource management Internet of Things Biodiversity Artificial intelligence Sustainable development

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Integrated Push-and-Pull Update Model for Goal-Oriented Effective Communication

arXiv

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arXiv 2024年

作者： Agheli, Pouya Pappas, Nikolaos Popovski, Petar Kountouris, Marios Communication Systems Dept. EURECOM France Dept. of Computer Science and Artificial Intelligence University of Granada Spain Dept. of Computer and Information Science Linköping University Sweden Dept. of Electronic Systems Aalborg University Denmark

This paper studies decision-making for goal-oriented effective communication. We consider an end-to-end status update system where a sensing agent (SA) observes a source, generates and transmits updates to an actuation agent (AA), while the AA takes actions to accomplish a goal at the endpoint. We integrate the push- and pull-based update communication models to obtain a push-and-pull model, which allows the transmission controller at the SA to decide to push an update to the AA and the query controller at the AA to pull updates by raising queries at specific time instances. To gauge effectiveness, we utilize a grade of effectiveness (GoE) metric incorporating updates’ freshness, usefulness, and timeliness of actions as qualitative attributes. We then derive effect-aware policies to maximize the expected discounted sum of updates’ effectiveness subject to induced costs. The effect-aware policy at the SA considers the potential effectiveness of communicated updates at the endpoint, while at the AA, it accounts for the probabilistic evolution of the source and importance of generated updates. Our results show the proposed push-and-pull model outperforms models solely based on push- or pull-based updates both in terms of efficiency and effectiveness. Additionally, using effect-aware policies at both agents enhances effectiveness compared to periodic and/or probabilistic effect-agnostic policies at either or both agents. Copyright © 2024, The Authors. All rights reserved.

关键词： Decision making

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Pulling Figure of Dielectric Resonator for 3D Filament Defectoscopy 34

Pulling Figure of Dielectric Resonator for 3D Filament Defec...

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34th International Scientific Symposium Metrology and Metrology Assurance, MMA 2024

作者： Miroshnyk, Maryna Zaichenko, Olga Miroshnyk, Anatolii Hapon, Nataliia Pakhomov, Yurii Klymenko, Liubov V. N. Karazin Kharkiv National University Dept. of Theoretical and Applied System Engineering Kharkov Ukraine Kharkiv National University of Radioelectronics Dept. of Design and Operation of Electronic Devices Kharkiv Ukraine Kharkiv National University of Radioelectronics Dept. of Design Automation Kharkiv Ukraine O.M. Beketov National University of Urban Economy in Kharkiv Dept. of Computer Science and Information Technologies Kharkiv Ukraine Ukrainian State University of Railway Transport Dept. of Specialized Computer Systems Kharkiv Ukraine

ISBN: (纸本)9798350352832

The research aim is to derive connection between the parameters of dielectric resonators frequency shift and the warp defect of 3D printing filament. Object of investigation are the processes of interaction of electromagnetic wave of a dielectric resonator with filament for 3D printing. The proposed model describes the connection between the frequency shift and defect warp of filament frequency pulling depending on the load. © 2024 IEEE.

关键词： Q factor measurement

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Intelligent Anomaly Detection System Based on Ensemble and Deep Learning

Intelligent Anomaly Detection System Based on Ensemble and D...

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International Conference on Advanced Communication Technology (ICACT)

作者： Babu Kaji Baniya Thomas Rush Dept. of Computer Science & Information Systems Bradley University Peoria IL United States

The ubiquity of the Internet plays a pivotal role in connecting individuals and facilitating easy access to various essential services. As of 2022, the International Telecommunication Union (ITU) reports that approximately 5.3 billion people are connected to the internet, underscoring its widespread coverage and indispensability in our daily lives. This expansive coverage enables a myriad of services, including communication, e-banking, e-commerce, online social security access, medical reporting, education, entertainment, weather information, traffic monitoring, online surveys, and more. However, this open platform also exposes vulnerabilities to malicious users who actively seek to exploit weaknesses in the virtual domain, aiming to gain credentials, financial benefits, or reveal critical information through the use of malware. This constant threat poses a serious challenge in safeguarding sensitive information in cyberspace. To address this challenge, we propose the use of ensemble and deep neural network (DNN) based machine learning (ML) techniques to detect malicious intent packets before they can infiltrate or compromise systems and applications. Attackers employ various tactics to evade existing security systems, such as antivirus or intrusion detection systems, necessitating a robust defense mechanism. Our approach involves implementing an ensemble, a collection of diverse classifiers capable of capturing different attack patterns and better generalizing from highly relevant features, thus enhancing protection against a variety of attacks compared to a single classifier. Given the highly unbalanced dataset, the ensemble classifier effectively addresses this condition, and oversampling is also employed to minimize bias toward the majority class. To prevent overfitting, we utilize Random Forest (RF) and the dropout technique in the DNN. Furthermore, we introduce a DNN to assess its ability to recognize complex attack patterns and variations compared to the ens

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Enhancing Train Transportation in Sri Lanka: A Smart IOT Based Multi-Subsystem Approach Using MQTT

Enhancing Train Transportation in Sri Lanka: A Smart IOT Bas...

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IEEE Region 10 International Conference TENCON

作者： Dhanushka Balasingham Sadeesha Samarathunga Anuththara Bandara Gayantha Godakanda Arachchige Narmada Gamage Jaliya L. Wijayaraja Dept. of Computer Systems Engineering Sri Lanka Institute of Information Technology Sri Lanka

ISBN: (数字)9798350350821

ISBN: (纸本)9798350350838

This research proposes a system as a solution for the challenges faced by Sri Lanka's historic railway system, such as scheduling delays, overcrowding, manual ticketing, and management inefficiencies. It proposes a multi-subsystem approach, incorporating GPS tracking, RFID-based e-ticketing, seat reservation, and vision-based people counting. The GPS-based real-time train tracking system performs accurately within 24 meters, with the MQTT protocol showing twice the speed of the HTTP-based system. All subsystems use the MQTT protocol to enhance efficiency, reliability, and passenger experience. The study's data and methodology demonstrate the effectiveness of these innovations in improving scheduling, passenger flow, and overall system performance, offering promising solutions for modernizing Sri Lanka's railway infrastructure.

关键词： Meters Technological innovation System performance Machine vision Transportation Manuals Real-time systems Rail transportation Reliability Interoperability

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A Base Algorithm for Intelligent Traffic Management System for Urban Transportation using 6G Network

A Base Algorithm for Intelligent Traffic Management System f...

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International Symposium on Advanced Networks and Telecommunication systems (ANTS)

作者： Sameera M. Salam Abhinivesh Mitra Dept. of Computer Science and Information Systems BITS Pilani Hyderabad Campus Secunderabad India

ISBN: (数字)9798350391725

ISBN: (纸本)9798350391732

Efficient traffic management in urban environments is essential for minimizing congestion and ensuring smooth transportation flow. 6G network is expected to offer faster data speeds, lower latency, and enhanced connectivity. Traditional approaches often struggle to adapt to dynamic traffic patterns and overlook the influence of geographical factors on traffic flow. To address these challenges, this study proposes an innovative approach that integrates gravitational modeling and cluster detection techniques to optimize route planning and congestion management in transportation networks. The shorter response time of the algorithm makes it well-suited for traffic management in a 6G network. This capability allows for real-time data processing and immediate adjustments, enhancing overall traffic flow. The proposed approach utilizes gravitational modeling to simulate traffic interactions between road segments or intersections within the transportation network. By considering factors such as traffic density, user influence, and geographical proximity, the approach aims to identify clusters of interconnected road segments or intersections with similar traffic patterns. These clusters serve as strategic focal points for developing targeted route planning and congestion management strategies.

关键词： 6G mobile communication Roads Heuristic algorithms Urban areas Transportation Clustering algorithms Traffic control Real-time systems Planning Partitioning algorithms

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