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2025 Systems of Signals Generating and Processing in the Field of on Board Communications, SOSG 2025

作者： Filianin, Ivan Borisov, Nikita Bogatyrev, Vladimir Itmo University Faculty of Software Engineering and Computer Engineering St. Petersburg Russia Itmo University Faculty of Infocommunication Technologies St. Petersburg Russia Saint-Petersburg State University of Aerospace Instrumentation Department of Information Systems Security St. Petersburg Russia

ISBN: (纸本)9798331532635

This paper addresses the problem of deploying complex systems in Kubernetes clusters. It discusses using the OperatorSDK framework supported by RedHat as a basis for implementing the Kubernetes operator for Lightweight MultiAccess Edge Computing Platform Simulator (LWMECPS) deployment. Special attention is given to the operator Kubernetes architecture and how to use Operator Lifecycle Manager (OLM) to continuously deploy LWMECPS and machine learning models for it. © 2025 IEEE.

关键词： Edge computing

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Harnessing the Power of AI with Deep Learning: Innovations and Implications

Harnessing the Power of AI with Deep Learning: Innovations a...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Upender, Talluri Sathiyamoorthy, C. Udayakumar, K. Kirubakaran, D. Purushothaman, V. Raja, Kiruba Thangam CMR College of Engineering and Technology Department of CSE Medchal Telangana Hyderabad India Department of Commerce Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of CSE Tamil Nadu Chennai India R.M.K. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India VIT University Department of Software and Systems Engineering Tamil Nadu Vellore India

ISBN: (纸本)9798331512965

The rapid advancements in artificial intelligence (AI) and deep learning have revolutionized various sectors, enabling unprecedented levels of innovation and efficiency. This paper delves into the transformative impact of deep learning across multiple domains, highlighting key innovations and their broader implications. By examining cutting-edge applications in healthcare, finance, autonomous systems, and natural language processing, the study elucidates how deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are pushing the boundaries of what's possible. Moreover, the paper explores the integration of deep learning with emerging technologies such as the Internet of Things (IoT), edge computing, and quantum computing, underscoring the synergistic potential of these combinations. The role of deep learning in predictive analytics and personalized solutions is discussed, showcasing its ability to enhance decision-making processes and improve user experiences. Furthermore, the paper discusses the ethical considerations, challenges, and future prospects associated with the widespread adoption of deep learning technologies. The insights provided aim to foster a deeper understanding of how harnessing AI with deep learning can drive progress while addressing critical societal and technological issues. © 2025 IEEE.

关键词： Convolutional neural networks

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An Analysis of Anonymous MQTT for Publish-Subscribe-Based IoT Networks

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IEEE Transactions on Network Science and engineering 2025年

作者： Fukushima, Yudai Tsunamoto, Hotaru Sakai, Kazuya Sun, Min-Te Ku, Wei-Shinn Tokyo Metropolitan University Department of Electrical Engineering and Computer Science Tokyo191-0065 Japan National Central University Department of Computer Science and Information Engineering Taoyuan320 Taiwan Auburn University Department of Computer Science and Software Engineering AuburnAL36849 United States

Message queuing telemetry transport (MQTT) is widely used as a communication primitive in publish-subscribe-based IoT applications. However, the current MQTT standard does not support the privacy of IoT devices and users. Therefore, in this paper, we address the anonymous communications for MQTT-based IoT networks by tailored onion routing. To this end, we first introduce the concept of distributed IoT broker networks, in which a collection of IoT brokers are interconnected with each other. These brokers serve as onion routers. Then, we propose a generic anonymous messaging protocol (A-MQTT) for publish-subscribe-based IoT systems. Unlike typical anonymous communications, there are multiple destination IoT devices, called subscribers, in MQTT contexts. Hence, the privacy notions, such as traceable rate, device anonymity, and path anonymity, are redesigned. To illuminate the fundamental privacy issues, closed-form approximations for these privacy metrics are modeled. In addition, simulation results demonstrate that our A-MQTT significantly improves the security performance and that our proposed models closely approximate the various privacy performance. Furthermore, a proof-of-concept protocol is implemented on a Raspberry Pi 4 in order to show that the proposed A-MQTT can be deployed in an IoT environment. © 2013 IEEE.

关键词： Anonymity

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Adaptive Deep Reinforcement Learning-based Resource Management for Complex Decision Making in Industry Internet of Things Applications 4

Adaptive Deep Reinforcement Learning-based Resource Manageme...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Karne, Niharika Ramagundam, Chandra Shekar Patnala, Ranga Rao Ramagundam, Shashishekhar Tadiboina, Sai Nitisha Sr. Principal Systems Engineer in C&RS Platform Services University of South Alabama Department of Electrical Engineering Masters in Electrical Engineering Amtrak DT United States Software Engineer in Cloud Platform Services City of Philadelphia Pennsylvania United States Vice President Neuberger Berman United States Software Development & Engineering in Content Discovery & AI Troy University Department of Computer Science Masters in Computer Science AL Comcast Montgomery Campus United States Department of Information Technology GEICO - Chevy Chase Chevy ChaseMD20815 United States

An independent industrial system may transform into a connected network through the assistance of Industrial Internet of Things (IIoT). The deployed sensors in the IIoT maintain surveillance of the industrial machinery and equipment. As a result, safety and reliability emerge as the primary concerns in IIoT. This presents a variety of well-known and increasing issues related to the industrial system. The IIoT devices are exposed to a wide range of malware, threats, and assaults. To prevent the IIoT devices from malware effects, effective protection plans must be implemented. But adequate security mechanisms are not be incorporated in IIoT devices with limited resources. It is essential to ensure the accuracy and dependability of information gathered by IIoT devices. Decisions taken with incomplete or inaccurate data might be devastating. To overcome these difficulties deep learning with reinforcement learning for complex decision-making in industry applications is developed in this research work. In this developed model, an Adaptive Deep Reinforcement learning (ADRL)-based resource management is performed to reduce the operation cost associated with IIoT deployments. Energy efficiency is essential in IIoT ecosystem, particularly for the devices that run on batteries. Through dynamic resource allocation based on workload needs and energy limits, ADRL-based resource management optimizes the usage of energy. The reliability of the designed model is enhanced by fine-tuning the parameters from DRL using the Ship Rescue Optimization (SRO) algorithm. Thus, ADRL-based resource management systems make real-time decisions based on current environmental conditions and system requirements. This helps the IIoT systems to react quickly to change demands and optimize resource allocation. Finally, the experimental analysis is performed to find the success rate of the developed resource management system via various metrics. Throughout the validation, the statistical analysis of the

关键词： Deep reinforcement learning

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A New Flow-Based Approach for Enhancing Botnet Detection Efficiency Using Convolutional Neural Networks and Long Short-Term Memory

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Knowledge and Information Systems 2025年 1-32页

作者： Asadi, Mehdi Heidari, Arash Jafari Navimipour, Nima Department of Computer Engineering Islamic Azad University Khameneh Branch Khameneh Iran Department of Software Engineering Haliç University Istanbul Turkey Department of Computer Engineering Faculty of Engineering and Natural Science İstanbul Atlas University Istanbul Turkey Department of Computer Science and Engineering Qatar University Doha Qatar Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan Research Center of High Technologies and Innovative Engineering Western Caspian University Baku Azerbaijan

Despite the growing research and development of botnet detection tools, an ever-increasing spread of botnets and their victims is being witnessed. Due to the frequent adaptation of botnets to evolving responses offered by host-based and network-based detection mechanisms, traditional methods are found to lack adequate defense against botnet threats. In this regard, the suggestion is made to employ flow-based detection methods and conduct behavioral analysis of network traffic. To enhance the performance of these approaches, this paper proposes utilizing a hybrid deep learning method that combines convolutional neural network (CNN) and long short-term memory (LSTM) methods. CNN efficiently extracts spatial features from network traffic, such as patterns in flow characteristics, while LSTM captures temporal dependencies critical to detecting sequential patterns in botnet behaviors. Experimental results reveal the effectiveness of the proposed CNN-LSTM method in classifying botnet traffic. In comparison with the results obtained by the leading method on the identical dataset, the proposed approach showcased noteworthy enhancements, including a 0.61% increase in precision, a 0.03% augmentation in accuracy, a 0.42% enhancement in the recall, a 0.51% improvement in the F1-score, and a 0.10% reduction in the false-positive rate. Moreover, the utilization of the CNN-LSTM framework exhibited robust overall performance and notable expeditiousness in the realm of botnet traffic identification. Additionally, we conducted an evaluation concerning the impact of three widely recognized adversarial attacks on the Information Security Centre of Excellence dataset and the Information Security and Object Technology dataset. The findings underscored the proposed method’s propensity for delivering a promising performance in the face of these adversarial challenges.

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Enhancing Low-Resolution Person Attribute Recognition Through the Integration of Super Resolution and Attribute Learning

Enhancing Low-Resolution Person Attribute Recognition Throug...

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Women in Data Science at Prince Sultan University (WiDS PSU), International Conference of

作者： Ramin Abbaszadi Niayesh Gharaei Department of Software Engineering Faculty of Engineering OSTİM Technical University Ankara Turkey Department of Computer Engineering Faculty of Engineering OSTİM Technical University Ankara Turkey

ISBN: (数字)9798331520922

ISBN: (纸本)9798331520939

In the context of video monitoring or surveillance, particularly in safe city initiatives, visual attributes of individuals such as gender, backpacks, and types of clothing are essential for person search and re-identification. Effectively detecting and extracting these attributes usually necessitates high-quality videos and images, which are often lacking in standard surveillance footage. Beyond improving hardware technology, enhancing inference algorithms for low-resolution (LR) data is essential. This research presents two solutions: developing a unified neural network architecture derived from existing models and introducing a new method for re-identification in LR videos. The proposed architecture, named SRMAR, incorporates Super Resolution (SR) alongside Multi-Attribute Recognition (MAR) models into a single neural network, thereby improving recognition effectiveness. Tests conducted on two benchmark datasets show that the architecture is effective and suitable for recognizing multiple attributes in LR images.

关键词： Visualization Image recognition Surveillance Superresolution Neural networks Urban areas Benchmark testing Network architecture Inference algorithms Standards

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A Study on New EPC-MPSI for an Inputless External Party to Compute the Intersection of Multiple Private Datasets

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IET Communications 2025年第1期19卷

作者： Kim, Ki-Hwan Seo, Dae-Hee Lee, Im-Yeong Kim, Su-Hyun Department of Software Convergence Soonchunhyang University Asan Korea Republic of Faculty of Artificial Intelligence and Data Engineering Sangmyung University Seoul Korea Republic of Department of Computer Software Engineering Soonchunhyang University Asan Korea Republic of

Private set intersection (PSI) is a privacy-preserving scheme that computes the intersection of two datasets without leaking any other information. Additionally, there is multiparty private set intersection (MPSI) to extend the number of parties for computing the intersection of multiple private datasets. In the traditional PSI and MPSI studies, protocol parties input their private datasets, and one or all of them can compute the intersection. However, there are some scenarios where an inputless external party requires the intersection between private datasets of other parties. Thus, the external party PSI protocols have been recently studied for applications such as pandemic contact tracing, computing human genome information and evaluating policy effects. However, they are limited in applications because the external party can compute the intersection of two datasets. In this paper, we propose a new external party compute-MPSI (EPC-MPSI) protocols that allow an external party to compute the intersection of multiple datasets. We provide the extension of the number of parties and solve the limitation of prior external party PSI protocols. In addition, we analyze the correctness, security and the efficiency in terms of communication and computation costs compared to the prior traditional MPSI protocols. © 2025 The Author(s). IET Communications published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Sensitive data

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Generalization of Urban Wind Environment Using Fourier Neural Operator Across Different Wind Directions and Cities

arXiv

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arXiv 2025年

作者： Chen, Cheng Tian, Geng Qin, Shaoxiang Geng, Dingyang Zhan, Dongxue Yang, Jinqiu Vidal, David Wang, Liangzhu Concordia University Department of Building Civil and Environmental Engineering Department of Computer Science and Software Engineering Montreal Canada Polytechnique Montreal Department of Mechanical Engineering Montreal Canada

Simulation of urban wind environments is crucial for urban planning, pollution control, and renewable energy utilization. However, the computational requirements of high-fidelity computational fluid dynamics (CFD) methods make them impractical for real cities. To address these limitations, this study investigates the effectiveness of the Fourier Neural Operator (FNO) model in predicting flow fields under different wind directions and urban layouts. In this study, we investigate the effectiveness of the Fourier Neural Operator (FNO) model in predicting urban wind conditions under different wind directions and urban layouts. By training the model on velocity data from large eddy simulation data, we evaluate the performance of the model under different urban configurations and wind conditions. The results show that the FNO model can provide accurate predictions while significantly reducing the computational time by 99%. Our innovative approach of dividing the wind field into smaller spatial blocks for training improves the ability of the FNO model to capture wind frequency features effectively. The SDF data also provides important spatial building information, enhancing the model’s ability to recognize physical boundaries and generate more realistic predictions. The proposed FNO approach enhances the AI model’s generalizability for different wind directions and urban layouts. Copyright © 2025, The Authors. All rights reserved.

关键词： Computational fluid dynamics

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EvoChain: A Framework for Tracking and Visualizing Smart Contract Evolution

arXiv

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arXiv 2025年

作者： Qasse, Ilham Hamdaqa, Mohammad Jónsson, Björn Þór Department of Computer Science Reykjavik University Reykjavik Iceland Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada

Tracking the evolution of smart contracts is challenging due to their immutable nature and complex upgrade mechanisms. We introduce EvoChain, a comprehensive framework and dataset designed to track and visualize smart contract evolution. Building upon data from our previous empirical study, EvoChain models contract relationships using a Neo4j graph database and provides an interactive web interface for exploration. The framework consists of a data layer, an API layer, and a user interface layer. EvoChain allows stakeholders to analyze contract histories, upgrade paths, and associated vulnerabilities by leveraging these components. Our dataset encompasses approximately 1.3 million upgradeable proxies and nearly 15,000 historical versions, enhancing transparency and trust in blockchain ecosystems by providing an accessible platform for understanding smart contract evolution. © 2025, CC BY-NC-SA.

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A Neural Network Framework for Classifying Educational Content Aligned with Learning Outcomes 16

A Neural Network Framework for Classifying Educational Conte...

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16th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Alkhalil, Adel Department of Software Engineering College of Computer Science and Engineering University of ha'Il Ha'il81481 Saudi Arabia

Rapid growth of digital educational content necessitates efficient and accurate methods for organizing and mapping resources to ensure well-alignment with targeted learning outcomes, academic standards, and competency frameworks. Traditional text classification approaches, including rule-based and classical machine learning techniques, often fail to address the semantic diversity and scalability demands of modern educational systems. This study investigates the application of neural networks for text classification to automate the mapping of educational content into predefined categories. Leveraging state-of-the-art architectures such as Long Short-Term Memory (LSTM) networks, and transformers like BERT, we present an architecture of a systematic Classification of educational materials. We discuss the implications of this work for adaptive learning environments, emphasizing the potential of neural networks to enhance the efficiency and scalability of content mapping. This study contributes to the growing body of research in artificial intelligence for education and sets the stage for further exploration into multilingual and domain-specific content classification methods. © 2025 Elsevier B.V.. All rights reserved.

关键词： Mapping

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