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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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Human Observation-Inspired Trajectory Prediction for Autonomous Driving in Mixed-Autonomy Traffic Environments

arXiv

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arXiv 2024年

作者： Liao, Haicheng Liu, Shangqian Li, Yongkang Li, Zhenning Wang, Chengyue Li, Yunjian Li, Shengbo Eben Xu, Chengzhong State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China Department of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China State Key Laboratory of Internet of Things for Smart City Department of Civil and Environmental Engineering and Computer and Information Science University of Macau China State Key Laboratory of Internet of Things for Smart City Department of Civil and Environmental Engineering University of Macau China Faculty of Innovation Engineering Macau University of Science and Technology China School of Vehicle and Mobility Tsinghua University Beijing China

In the burgeoning field of autonomous vehicles (AVs), trajectory prediction remains a formidable challenge, especially in mixed autonomy environments. Traditional approaches often rely on computational methods such as time-series analysis. Our research diverges significantly by adopting an interdisciplinary approach that integrates principles of human cognition and observational behavior into trajectory prediction models for AVs. We introduce a novel "adaptive visual sector" mechanism that mimics the dynamic allocation of attention human drivers exhibit based on factors like spatial orientation, proximity, and driving speed. Additionally, we develop a "dynamic traffic graph" using Convolutional Neural Networks (CNN) and Graph Attention Networks (GAT) to capture spatio-temporal dependencies among agents. Benchmark tests on the NGSIM, HighD, and MoCAD datasets reveal that our model (GAVA) outperforms state-of-the-art baselines by at least 15.2%, 19.4%, and 12.0%, respectively. Our findings underscore the potential of leveraging human cognition principles to enhance the proficiency and adaptability of trajectory prediction algorithms in AVs. The code for the proposed model is available at our Github. © 2024, CC BY-NC-SA.

关键词： Time series analysis

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Overlooked Aspects of Data Governance: Workflow Framework For Enterprise Data Deduplication

Overlooked Aspects of Data Governance: Workflow Framework Fo...

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Intelligent Computing, Communication, Networking and Services (ICCNS), International Conference on

作者： Otmane Azeroual Anastasija Nikiforova Kewei Sha German Centre for Higher Education Research and Science Studies (DZHW) Berlin Germany Faculty of Science and Technology Institute of Computer Science Chair of Software Engineering University of Tartu Tartu Estonia Task Force “FAIR Metrics and Data Quality” European Open Science Cloud Brussels Belgium College of Science and Engineering University of Houston Clear Lake Houston USA

Data quality in companies is decisive and critical to the benefits their products and services can provide. However, in heterogeneous IT infrastructures where, e.g., different applications for Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), product management, manufacturing, and marketing are used, duplicates, e.g., multiple entries for the same customer or product in a database or information system, occur. There can be several reasons for this, but the result of non-unique or duplicate records is a degraded data quality. This ultimately leads to poorer, inefficient, and inaccurate data-driven decisions. For this reason, in this paper, we develop a conceptual data governance framework for effective and efficient management of duplicate data, and improvement of data accuracy and consistency in large data ecosystems. We present methods and recommendations for companies to deal with duplicate data in a meaningful way.

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Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service

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Artificial Intelligence in Medicine 2024年 149卷 102779-102779页

作者： Aminizadeh, Sarina Heidari, Arash Dehghan, Mahshid Toumaj, Shiva Rezaei, Mahsa Jafari Navimipour, Nima Stroppa, Fabio Unal, Mehmet Medical Faculty Tabriz Branch Islamic Azad University Tabriz Iran Department of Software Engineering Haliç University Istanbul34060 Turkey Tabriz University of Medical Sciences Faculty of Medicine Tabriz Iran Urmia University of Medical Sciences Urmia Iran Tabriz University of Medical Sciences Faculty of Surgery Tabriz Iran Future Technology Research Center National Yunlin University of Science and Technology Douliou64002 Taiwan Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Department of Mathematics School of Engineering and Natural Sciences Bahçeşehir University Istanbul Turkey

The healthcare sector, characterized by vast datasets and many diseases, is pivotal in shaping community health and overall quality of life. Traditional healthcare methods, often characterized by limitations in disease prevention, predominantly react to illnesses after their onset rather than proactively averting them. The advent of Artificial Intelligence (AI) has ushered in a wave of transformative applications designed to enhance healthcare services, with Machine Learning (ML) as a noteworthy subset of AI. ML empowers computers to analyze extensive datasets, while Deep Learning (DL), a specific ML methodology, excels at extracting meaningful patterns from these data troves. Despite notable technological advancements in recent years, the full potential of these applications within medical contexts remains largely untapped, primarily due to the medical community's cautious stance toward novel technologies. The motivation of this paper lies in recognizing the pivotal role of the healthcare sector in community well-being and the necessity for a shift toward proactive healthcare approaches. To our knowledge, there is a notable absence of a comprehensive published review that delves into ML, DL and distributed systems, all aimed at elevating the Quality of Service (QoS) in healthcare. This study seeks to bridge this gap by presenting a systematic and organized review of prevailing ML, DL, and distributed system algorithms as applied in healthcare settings. Within our work, we outline key challenges that both current and future developers may encounter, with a particular focus on aspects such as approach, data utilization, strategy, and development processes. Our study findings reveal that the Internet of Things (IoT) stands out as the most frequently utilized platform (44.3 %), with disease diagnosis emerging as the predominant healthcare application (47.8 %). Notably, discussions center significantly on the prevention and identification of cardiovascular diseases (29.

关键词： Quality of service

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Fault Diagnosis Model of Switching Power Supply Based on Improved DBN 16

Fault Diagnosis Model of Switching Power Supply Based on Imp...

引用

16th IEEE International Conference on Intelligent Systems and Knowledge engineering, ISKE 2021

作者： Zhou, Lei Yu, Cheng Liu, Hu Li, Xin Li, Fenfen Zhang, Haiyan National-Local Joint Engineering Lab of System Credibility Automatic Verification Huaiyin Institute of Technology huai'An Faculty of Computer and Software Engineering Research Center for Logic and Intelligent Computation Jiangsu China Nanjing University of Technology Nanjing College of Computer Science and Technology Jiangsu211816 China Huaiyin Institute of Technology huai'An Faculty of Chemical Engineering Jiangsu China

ISBN: (纸本)9781665405539

Switching power supply has wide application in various electrical equipment. The failure of high-voltage filter capacitors is the main cause of frequent faults, accounting for more than 60% of switching power supply failures. In order to realize the real-time detection of switching power supply failure without disassembling the power supply, a fault diagnosis model for improving the deep belief network (DTDBN) is proposed. The model uses the data acquisition device to isolate, filter, and collect data on the grid voltage of the switching power supply, judge the failure ratio of the filter capacitor according to the degree of voltage distortion of the power supply. Experimental results show that the proposed fault detection method achieves a 100% recognition rate for the filter capacitor normal state, 50% and 90% state recognition rate is 90%. The superiority of this method for the classification and early warning of power capacitor faults is proved. © 2021 IEEE.

关键词： Fault detection

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Hierarchical Reverse Games for the Resource Ecosystem in Cloud-Edge Computing

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IEEE Transactions on Consumer Electronics 2024年

作者： Pei, Manlin Wang, Chenyu Wang, Xia Zheng, Zhigao Huang, Jianhui Wang, Shengling Guo, Yufei School of Computer and Information Technology Beijing Jiaotong University Beijing China Department of Computer Science Georgia State University United States Beijing University of Technology School of Statistics and Data Science Faculty of Science Beijing China Institute of Artificial Intelligence School of Computer Science National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Hubei Luojia Laboratory Wuhan University Wuhan China Chinese Academy of Sciences Institute of Computing Technology Beijing China Beijing Normal University School of Artificial Intelligence Beijing China George Washington University Department of Statistics United States

Edge computing has emerged as a killer technology for a hyper-connected world due to its distributed architecture and customer-proximity property. Combined edge nodes with the cloud data center, a cloud-edge computing paradigm is formed, whose resource ecosystem calls for competitive pricing and optimal resource allocation. However, it is not feasible to realize global information-based optimization since some information is kept private to their owners. To solve this challenge, this paper develops the user-independent hierarchical reverse game and the user-assisted hierarchical reverse game to depict the relationship among the resource provider, the resource tenant, and the resource user. The aim is to seek the optimal strategies for players without revealing any private information. The common trait of both games is to force the players to report the optimal strategies based on their actual private information. This salient trait can produce privacy-preservation optimization since there is no requirement for the players to release their individual information directly, and the optimal strategies of other players can be deduced naturally. The difference between the user-independent hierarchical reverse game and the user-assisted one is that the game rules are stipulated entirely by the provider and the tenants (edge nodes) in the former, while some game rules can be customized according to the advice of users in the latter. This leads the user-independent hierarchical reverse game is more concise and easy to guarantee the incentive compatibility constraint, while the user-assisted hierarchical reverse game can attract more users since the game rules can adapt to their economic status, requirements, and preferences. © 1975-2011 IEEE.

关键词： Cloud platforms

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A Comprehensive Part-of-Speech Tagging to Standardize Central-Kurdish Language: A Research Guide for Kurdish Natural Language Processing Tasks

arXiv

引用

arXiv 2025年

作者： Sabr, Shadan Shukr Mustafa, Nazira Sabr Omar, Talar Sabah Rasool, Salah Hwayyiz Omer, Nawzad Anwer Hamad, Darya Sabir Shams, Hemin Abdulhameed Kareem, Omer Mahmood Abdullah, Rozhan Noori Abdullah, Khabat Atar Mohammad, Mahabad Azad Al-Raghefy, Haneen Asaad, Safar M. Mohammed, Sara Jamal Ali, Twana Saeed Shawrow, Fazil Maghdid, Halgurd S. Department of Kurdish Language Faculty of Education Koya University Danielle Mitterrand Boulevard Kurdistan Region KoyaKOY45 Iraq Department of Computer Science Faculty of Health and Science Koya University Koya Kurdistan Region KOY45 Iraq Department of Software Engineering Faculty of Engineering Koya University Danielle Mitterrand Boulevard Kurdistan Region KoyaKOY45 Iraq Department of Computer Engineering College of Engineering Knowledge University Erbil44001 Iraq Department of English language Faculty of Education Koya University Danielle Mitterrand Boulevard Kurdistan Region KoyaKOY45 Iraq Department of Computer Engineering College of Engineering University of Sulaimani Region Kurdistan Iraq Department of Engineering Research Center Deanship of R&D Centers Koya University Danielle Mitterrand Boulevard Kurdistan Region KoyaKOY45 Iraq

The field of natural language processing (NLP) has dramatically expanded within the last decade. Many human-being applications are conducted daily via NLP tasks, starting from machine translation, speech recognition, text generation and recommendations, Part-of-Speech tagging (POS), and Named-Entity Recognition (NER). However, low-resourced languages, such as the Central-Kurdish language (CKL), mainly remain unexamined due to shortage of necessary resources to support their development. The POS tagging task is the base of other NLP tasks;for example, the POS tag set has been used to standardized languages to provide the relationship between words among the sentences, followed by machine translation and text recommendation. Specifically, for the CKL, most of the utilized or provided POS tagsets are neither standardized nor comprehensive. To this end, this study presented an accurate and comprehensive POS tagset for the CKL to provide better performance of the Kurdish NLP tasks. The article also collected most of the POS tags from different studies as well as from Kurdish linguistic experts to standardized part-of-speech tags. The proposed POS tagset is designed to annotate a large CKL corpus and support Kurdish NLP tasks. The initial investigations of this study via comparison with the Universal Dependencies framework for standard languages, show that the proposed POS tagset can streamline or correct sentences more accurately for Kurdish NLP tasks. © 2025, CC BY.

关键词： Calibration

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Simulation and Modeling of NLOS P2P 5G and Future Wireless Communication Systems in FR2 within an Urban Environment 5

Simulation and Modeling of NLOS P2P 5G and Future Wireless C...

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5th IEEE International Conference on Electro-Computing Technologies for Humanity, NIGERCON 2024

作者： Faruk, Nasir Alozie, Emmanuel Abdulkarim, Abubakar Usman, Aliyu D. Imam-Fulani, Yusuf Olayinka Garba, Salisu Adewole, Kayode S. Imoize, Agbotiname Lucky Oloyede, Abdulkarim A. Baba, Bashir Abdullahi Olagunju, Hawau I. Sule Lamido University Department of Information Technology PMB 048 Kafin Hausa Nigeria Kampala International University Department of Electrical and Telecommunications Engineering Uganda Ahmadu Bello University Department of Electronics and Telecommunication Engineering Zaria 810107 Nigeria University of Ilorin Department of Telecommunication Science Ilorin 240003 Nigeria Sule Lamido University Department of Software Engineering PMB 048 Kafin Hausa Nigeria University of Ilorin Department of Computer Science Ilorin 240003 Nigeria University of Lagos Faculty of Engineering Department of Electrical and Electronics Engineering Nigeria Sule Lamido University Department of Cyber Security PMB 048 Kafin Hausa Nigeria

ISBN: (纸本)9798331542559

Signal behavior varies across different environments due to various parameters and factors, including terrain features, clutter, and atmospheric conditions. The International Telecommunication Union-Radio Sector (ITU-R) categorized the globe into different rain regions and provided classifications based on important parameters to understand signal behavior and the impact of atmospheric-based attenuation. In Nigeria, four regions have been designated, with different states categorized under each region. This paper critically analyses Region N (Lagos State) by utilizing the EDX Signal Pro software® to simulate typical Point-to-Point (P2P) links for the 5G communication networks considering the Non-Line-of-Sight (NLOS) scenario. The objective is to estimate the total and excess losses as well as the flat fading across different frequencies and distances. Our results demonstrate that losses and fading increase with higher frequencies and distances, attributable to the presence of obstacles and the level of rainfall rate in the location. © 2024 IEEE.

关键词： 5G Excess Path Loss Fading Lagos Nigeria NLOS Path Loss

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Groove Gap Waveguide Crossover for Butler Matrices and Beamforming in Millimeter-Wave Satellite Antenna Systems

Groove Gap Waveguide Crossover for Butler Matrices and Beamf...

引用

International Conference on Radioelektronika

作者： Mohammad Alibakhshikenari Peiman Parand Ali Zidour Bal S. Virdee Lida Kouhalvandi Patrick Longhi Takfarinas Saber Ernesto Limiti Electronics Engineering Department University of Rome “Tor Vergata” Rome Italy Department of Electrical Systems Engineering Faculty of Technology University of M'hamed Bougara Boumerdes Algeria Center for Communications Technology London Metropolitan University London UK Department of Electrical and Electronics Engineering Dogus University Istanbul Turkiye Lero Science Foundation Ireland Centre for Software School of Computer Science University of Galway Galway Ireland

ISBN: (数字)9798331544478

ISBN: (纸本)9798331544485

This paper presents an innovative H-plane crossover based on groove gap-waveguide (GGW) technology for high-performance millimeter-wave (mm-wave) circuits. The design facilitates the development of key transmission components, such as Butler matrices (BMs) and beamforming feeding networks (BFNs), for multi-beam antenna systems operating in the V-band spectrum (40–50 GHz). The proposed crossover is built by cascading two identical 3-dB/90° hybrid couplers. Each coupler is designed with GGW unit-cells constructed from metallic pins spaced less than a quarter-wavelength apart. This configuration creates a wide stopband of 20–57 GHz, ensuring minimal signal interference and strong impedance matching. The coupler achieves 90° phase shift, 50 dB isolation, and low insertion loss of 0.02 dB at 45 GHz, with a fractional bandwidth of 22.22%. The crossover demonstrates excellent performance over the entire V-band, making it suitable for advanced antenna systems in satellite communications and space applications. The design reduces complexity, cost, and losses typically associated with 3D and multilayer crossover technologies, providing a compact and efficient solution for mm-wave networks.

关键词： Butler matrices Satellite antennas Millimeter wave circuits Three-dimensional displays Array signal processing Impedance matching Transmitting antennas Gap waveguide Couplers Millimeter wave communication

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Transition Role of Entangled Data in Quantum Machine Learning

arXiv

引用

arXiv 2023年

作者： Wang, Xinbiao Du, Yuxuan Tu, Zhuozhuo Luo, Yong Yuan, Xiao Tao, Dacheng Institute of Artificial Intelligence School of Computer Science Wuhan University Hubei430072 China National Engineering Research Center for Multimedia Software Wuhan University Hubei430072 China JD Explore Academy Beijing101111 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Computer Science Faculty of Engineering University of Sydney NSW2008 Australia Center on Frontiers of Computing Studies Peking University Beijing100871 China School of Computer Science Peking University Beijing100871 China

Entanglement serves as the resource to empower quantum computing. Recent progress has highlighted its positive impact on learning quantum dynamics, wherein the integration of entanglement into quantum operations or measurements of quantum machine learning (QML) models leads to substantial reductions in training data size, surpassing a specified prediction error threshold. However, an analytical understanding of how the entanglement degree in data affects model performance remains elusive. In this study, we address this knowledge gap by establishing a quantum no-free-lunch (NFL) theorem for learning quantum dynamics using entangled data. Contrary to previous findings, we prove that the impact of entangled data on prediction error exhibits a dual effect, depending on the number of permitted measurements. With a sufficient number of measurements, increasing the entanglement of training data consistently reduces the prediction error or decreases the required size of the training data to achieve the same prediction error. Conversely, when few measurements are allowed, employing highly entangled data could lead to an increased prediction error. The achieved results provide critical guidance for designing advanced QML protocols, especially for those tailored for execution on early-stage quantum computers with limited access to quantum resources. © 2023, CC BY.

关键词： Machine learning

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