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15th International Conference on Performance Evaluation Methodologies and Tools, VALUETOOLS 2022

作者： Nazarov, Anatoly Phung-Duc, Tuan Shulgina, Ksenia Lizyura, Olga Paul, Svetlana Shashev, Dmitriy Institute of Applied Mathematics and Computer Science National Research Tomsk State University 36 Lenina ave. Tomsk634050 Russia Faculty of Engineering Information and Systems University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8573 Japan Faculty of Innovative Technologies National Research Tomsk State University 36/3 Lenina ave. Tomsk634050 Russia

ISBN: (纸本)9783031312335

This paper considers a mathematical model of a cyclic multiple access communication network. The model can be used to build specialized "flying" FANET data transmission networks. We consider a single server retrial queue for modeling one node in such network. The input consists of multiple Poisson processes with different arrival rates. Service and retrial rates depend on the origin flow. Thus, each flow has its own orbit for redial. Under the condition when the retrial rate is low, we obtain an asymptotic probability distribution of the number of customers in the orbits. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Asymptotic analysis

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A power-efficient security device leveraging deep learning (dl)-inspired facial recognition 3rd

A power-efficient security device leveraging deep learning (...

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3rd International Conference on Big-Data and Cloud Computing, ICBDCC 2019

作者： Saundharya Thejaswini, R.S. Rajaraajeswari, S. Raj, Pethuru Department of Electronics and Communication Engineering Ramaiah University of Applied Sciences Peenya Industry Bengaluru India Department of Computer Science Vidhya Arts and Science College Tiruvannamalai India AVANA Building Iblur Village Sarjapur Road Bengaluru India

ISBN: (纸本)9789811552847

For establishing and sustaining scores of smarter environments such as smart buildings, retail stores, and manufacturing floors, highly unbreakable and impenetrable security solutions and services are very vital for ensuring tighter safety and security for the adorable success of those technologically advanced environments. It is indispensable for guaranteeing the security for the owners, occupants, and assets of those environments. With the arrival and acceptance of cutting-edge technologies, integrated and insightful platforms, and state-of-the-art infrastructures, premium and pioneering services, systems and environments are being easily and quickly realized. At the same time, on the other side, the security vulnerabilities and threats are definitely looming large. Thus, there is a clarion call for breakthrough and foolproof security mechanisms mandated for boosting the users’ confidence on new-generation technologies and tools. This paper describes an original security device for intrusion recognition and detection. © Springer Nature Singapore Pte Ltd 2021.

关键词： Retail stores

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Contrastive learning for multi-layer network community detection via learnable network augmentation

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IEEE Transactions on Network science and engineering 2025年

作者： Teng, Min Yin, Ze Huang, Jiajin Gao, Chao Li, Xianghua Nekorkin, Vladimir Wang, Zhen Northwestern Polytechnical University School of Cybersecurity Shaanxi Xi'an710072 China Hunan University College of Computer Science and Electronic Engineering Hunan Changsha410082 China Beijing University of Technology School of Information Science and Technology Beijing100124 China Shaanxi Xi'an710072 China Russian Academy of Sciences A.V. Gaponov-Grekhov Institute of Applied Physics Nizhny 603950 Russia

Community detection in multi-layer networks is crucial for revealing the functions of entities and understanding their connections across dimensions. However, existing semi-supervised methods often rely on manual labels, leading to the significant computational overhead in networks with complex structures. Moreover, unsupervised and self-supervised methods usually struggle to integrate the intra-layer and inter-layer features, as well as the local and global features of networks, resulting in the limited accuracy. To address these challenges, this paper proposes a self-supervised Network Augmentation Contrastive Constraint (NACC) method for multi-layer network community detection. Leveraging the ideas of network augmentation and contrastive learning, NACC detects the community structure based on the rich features contained in datasets. Specifically, NACC first integrates the intra-layer and inter-layer features of the multi-layer network to generate a learnable feature-augmented network. Then, it encodes the node and topology features, capturing both the local and global features, and generating the low-dimensional node representations for multi-layer and augmented networks. Moreover, the contrastive learning among different layers is proposed to train the above node representations, further enhancing the fusion of features. Finally, consense communities are detected based on the trained node representation. Extensive experiments demonstrate the performance of NACC in handling networks with numerous layers and complex structures, showcasing its reliability in real-world applications. © 2013 IEEE.

关键词： Contrastive Learning

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A Survey on Decentralized Identifiers and Verifiable Credentials

arXiv

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arXiv 2024年

作者： Mazzocca, Carlo Acar, Abbas Uluagac, Selcuk Montanari, Rebecca Bellavista, Paolo Conti, Mauro Department of Information and Electrical Engineering and Applied Mathematics University of Salerno Fisciano84084 Italy Department of Computer Science and Engineering University of Bologna Bologna40136 Italy Cyber-Physical Systems Security Lab School of Computing and Information Science Florida International University MiamiFL33174 United States Department of Mathematics University of Padua Padua35131 Italy

Digital identity has always been one of the keystones for implementing secure and trustworthy communications among parties. The ever-evolving digital landscape has undergone numerous technological transformations that have profoundly reshaped digital identity management, leading to a major shift from centralized to decentralized identity models. The latest stage of this evolution is represented by the emerging paradigm of Self-Sovereign Identity (SSI), which gives identity owners full control over their data. SSI leverages Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs), which have been recently standardized by the World Wide Web Consortium (W3C). These technologies have the potential to build more secure and decentralized digital identity systems, significantly strengthening communication security in scenarios involving many distributed participants. It is worth noting that use of DIDs and VCs is not limited to individuals but extends to a wide range of entities including cloud, edge, and Internet of Things (IoT) resources. However, due to their novelty, existing literature lacks a comprehensive survey on DIDs and VCs beyond the scope of SSI. This paper fills this gap by providing a comprehensive overview of DIDs and VCs from multiple perspectives. It identifies key security threats and mitigation strategies, analyzes available implementations to guide practitioners in making informed decisions, and reviews the adoption of these technologies across various application domains. Moreover, it also examines related regulations, projects, and consortiums emerging worldwide. Finally, it discusses the primary challenges hindering their real-world adoption and outlines future research directions. © 2024, CC BY.

关键词： Internet of things

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Cross-Modal Few-Shot Learning with Second-Order Neural Ordinary Differential Equations

arXiv

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arXiv 2024年

作者： Zhang, Yi Cheng, Chun-Wun He, Junyi He, Zhihai Schönlieb, Carola-Bibiane Chen, Yuyan Aviles-Rivero, Angelica I. Harbin Institute of Technology China Department of Electrical and Electronic Engineering Southern University of Science and Technology China Department of Applied Mathematics and Theoretical Physics University of Cambridge United Kingdom Pengcheng Laboratory China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Yau Mathematical Sciences Center Tsinghua University China

We introduce SONO, a novel method leveraging Second-Order Neural Ordinary Differential Equations (Second-Order NODEs) to enhance cross-modal few-shot learning. By employing a simple yet effective architecture consisting of a Second-Order NODEs model paired with a cross-modal classifier, SONO addresses the significant challenge of overfitting, which is common in few-shot scenarios due to limited training examples. Our second-order approach can approximate a broader class of functions, enhancing the model’s expressive power and feature generalization capabilities. We initialize our cross-modal classifier with text embeddings derived from class-relevant prompts, streamlining training efficiency by avoiding the need for frequent text encoder processing. Additionally, we utilize text-based image augmentation, exploiting CLIP’s robust image-text correlation to enrich training data significantly. Extensive experiments across multiple datasets demonstrate that SONO outperforms existing state-of-the-art methods in few-shot learning performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

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Modeling and numerical analysis of nanoliquid (titanium oxide, graphene oxide) flow viscous fluid with second order velocity slip and entropy generation

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Chinese Journal of Chemical engineering 2021年第3期34卷 17-25页

作者： M.Ijaz Khan Seifedine Kadry Yuming Chu M.Waqas Department of Mathematics Riphah International UniversityFaisalabad CampusFaisalabad 38000Pakistan Department of Mathematics and Computer Science Beirut Arab UniversityBeirutLebanon Department of Mathematics Huzhou UniversityHuzhou 313000China Hunan Provincial Key Laboratory of Mathematical Modeling and Analysis in Engineering Changsha University of Science&TechnologyChangsha 410114China NUTECH School of Applied Sciences and Humanities National University of TechnologyIslamabad 44000Pakistan

The prime objective of the present communication is to examine the entropy-optimized second order velocity slip Darcy–Forchheimer hybrid nanofluid flow of viscous material between two rotating *** conducting flow is considered and saturated through Darcy–Forchheimer *** the disks are rotating with different angular frequencies and stretches with different *** graphene oxide and titanium dioxide are considered for hybrid nanoparticles and water as a continuous phase *** heating,heat generation/absorption and viscous dissipation effects are incorporated in the mathematical modeling of energy ***,binary chemical reaction with activation energy is *** total entropy rate is calculated in the presence of heat transfer irreversibility,fluid friction irreversibility,Joule heating irreversibility,porosity irreversibility and chemical reaction irreversibility through thermodynamics second *** nonlinear governing equations are first converted into ordinary differential equations through implementation of appropriate similarity transformations and then numerical solutions are calculated through Built-in-Shooting *** of sundry flow variables on the entropy generation rate,velocity,concentration,Bejan number,temperature are discussed graphically for both graphene oxide and titanium dioxide hybrid *** engineering interest like skin friction coefficient and Nusselt number are computed numerically and presented through *** is noticed from the obtained results that entropy generation rate and Bejan number have similar effects versus diffusion *** entropy generation rate is more against the higher Brinkman number.

关键词： Darcy-Forchheimer porous medium Titanium dioxide and graphene oxide nanoparticles Second order velocity slip Convective boundary condition Activation energy Heat generation/absorption

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ETHACA - A Container-Based Cyber Range for Research and Education

ETHACA - A Container-Based Cyber Range for Research and Educ...

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IEEE International Workshop on computer-Aided Modeling, Analysis, and Design of Communication Links and Networks, CAMAD

作者： Nestoras Chouliaras Ioanna Kantzavelou Grammati Pantziou Leandros Maglaras Panagiota Chouliara-Sidera Antonis Adamakos Panagiotis Chalatsakos Department of Informatics and Computer Engineering University of West Attica Athens Greece School of Computing Edinburgh Napier University Edinburgh UK Department of Computer Science and Biomedical Infromatics University of Thessaly Lamia Greece Senior Software Engineer at *** Athens Greece Faculty of Pure and Applied Sciences Open University of Cyprus Nicosia Cyprus

ISBN: (数字)9798350377644

ISBN: (纸本)9798350377651

The advent of Industry 4.0 and the Industrial Internet of Things (IIoT) has revolutionized manufacturing and production systems, enhancing efficiency and communication through the integration of advanced technologies such as IoT devices and AI. However, this increased digitization has also led to heightened cybersecurity risks, with a notable rise in the frequency and complexity of cyber-attacks. Cyber Ranges emerges as a critical solution in addressing these challenges, offering a controlled environment for simulating cyber-attack scenarios, training cybersecurity teams, and developing robust defense strategies. The escalating demand for cybersecurity professionals has highlighted the crucial need for effective training solutions. This article presents an open-source Cyber Range constructed on container-based architecture, meticulously engineered for efficient operation with minimal resource consumption. In response to the scarcity of cybersecurity experts, this platform prioritizes extensive scalability and robust isolation between exercises, ensuring secure learning environments, and also integrates a complex developed exercise series aimed at strengthening cybersecurity learning experiences. These exercises encompass a variety of scenarios and challenges, catering to professionals at different skill levels. Each exercise replicates real-world cyber threats, offering practical, hands-on learning opportunities. Additionally, a comprehensive questionnaire accompanies these exercises, designed to gather feedback on participants' experiences.

关键词： Training Production systems Scalability Conferences Computational modeling computer architecture Fourth Industrial Revolution Complexity theory Cyberattack Industrial Internet of Things

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Single Loop Control of a Common DC-Bus-Configured Traction Motor Emulator Using State Feedback Linearization Method

Single Loop Control of a Common DC-Bus-Configured Traction M...

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2021 IEEE Southern Power Electronics Conference, SPEC 2021

作者： Kadam, Arvind H. Williamson, Sheldon S. Group Faculty of Engineering and Applied Science Department of Electrical Computer and Software Engineeirng 2000 Simcoe Street North OshawaONL1G 0C5 Canada

ISBN: (纸本)9781665436236

Nowadays, the LCL (inductor-capacitor-inductor) filter is an integral part of every power electronic converter system operating on current control. Though higher order, the LCL filter due to its high harmonic suppression capability are widely adopted in back-to-back converter systems or grid connected converter systems. Normally, with LCL filter, the control system follows double closed loop with outer loop controlling DC voltage and inner loop controlling AC current. However, for some special applications like common DC-bus-configured motor emulator (ME) system, the DC bus voltage is not required to be controlled. In addition, the presence of LCL filter introduces cross coupling of flux and torque component in case of AC motor emulation. The double closed loop control is generally adopted to decouple torque and flux components, however the controller design is more complex process. Therefore, this paper presents a single loop control of a common DC-bus-configured ME system with LCL filter based on state feedback linearization method. The decoupling equations in direct and quadrature axis are derived here. The simulation results presented for a 2.0 kW PMSM motor validate the stability as well as closed loop operation of the system with single loop control. © 2021 IEEE.

关键词： Synchronous motors

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Gcl-Psd: Graph Contrastive Learning for Phishing Scams Detection in Ethereum Networks

SSRN

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SSRN 2023年

作者： Sui, Hao Zhang, Jiale Sun, Xiaobing Chen, Bing Meng, Weizhi Yu, Shui School of Information Engineering Yangzhou University Yangzhou225009 China Yangzhou University China Department of Applied Mathematics and Computer Science Technical University of Denmark Copenhagen Denmark School of Computing Science University of Technology Sydney UltimoNSW2007 Australia

Currently, Ethereum has become a public blockchain platform for developers to build and deploy smart contracts and decentralized applications (DApps). However, the fact that accounts of the user are able to trade directly only through their addresses in Ethereum, phishing scam attackers can easily disguise themselves as legitimate accounts to defraud large amounts of money. At present, phishing scam detection methods mainly focus on improving the detection accuracy by continuously extracting the account's feature information. Another issue is data imbalance, since the current publicly available URLs for labeling phishing scam data are no longer updated, it becomes an incomplete understanding of the data by supervised learning methods. To address these challenges, we present a self-supervised phishing scams detection method based on graph contrastive learning, named GCL-PSD, which can effectively handle the unavailability of labeled data. GCL-PSD can learn features between accounts by constructing positive and negative samples without labeling. To further enhance the inconspicuous feature representation in contrastive learning, our method leverages ``node versus local subgraph" as contrastive instances, and learns feature information of neighboring nodes through the contrastive training between nodes and subgraphs. Additionally, our extensive experimental results on real-world datasets demonstrate that GCL-PSD can effectively detect phishing scams without relying on training labels and outperforms baseline methods (97% Precision, and 95% F1 score). © 2023, The Authors. All rights reserved.

关键词： Learning systems

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Deep Reinforcement Learning Optimized Intelligent Resource Allocation in Active RIS-Integrated TN-NTN Networks

arXiv

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arXiv 2025年

作者： Mohsin, Muhammad Ahmed Rizwan, Hassan Jazib, Muhammad Iqbal, Muhammad Bilal, Muhammad Ashraf, Tabinda Khan, Muhammad Farhan Pan, Jen-Yi Dept. of Electrical Engineering Stanford University United States Dept. of Electrical Engineering University of California Riverside United States Dept. of Electrical Engineering Pakistan Institute of Engineering and Applied Sciences Pakistan Dept. of Communication Engineering National Chung Cheng University Taiwan Dept. of Electrical Engineering University of California Irvine United States School of Computer Science and Information Technology University College Cork Ireland

This work explores the deployment of active reconfigurable intelligent surfaces (A-RIS) in integrated terrestrial and non-terrestrial networks (TN-NTN) while utilizing coordinated multipoint non-orthogonal multiple access (CoMP-NOMA). Our system model incorporates a UAV-assisted RIS in coordination with a terrestrial RIS which aims for signal enhancement. We aim to maximize the sum rate for all users in the network using a custom hybrid proximal policy optimization (H-PPO) algorithm by optimizing the UAV trajectory, base station (BS) power allocation factors, active RIS amplification factor, and phase shift matrix. We integrate edge users into NOMA pairs to achieve diversity gain, further enhancing the overall experience for edge users. Exhaustive comparisons are made with passive RIS-assisted networks to demonstrate the superior efficacy of active RIS in terms of energy efficiency, outage probability, and network sum rate. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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