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IEEE 802.15.6 Relaying Protocol for MBANs 1

IEEE 802.15.6 Relaying Protocol for MBANs

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1st Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2021

作者： Waheed, Tabassum Aqeel-Ur-Rehman Shaikh, Faisal Karim Hamdard University Dept. of Computer Engineering Karachi Pakistan Sir Syed University of Engineering Technology Dept. of Software Engineering Karachi Pakistan Mehran University of Engieneering Technology Dept. of Telecommmunication Engineering Jamshoro Pakistan

ISBN: (纸本)9781665424134

During the recent COVID-19 pandemic, Medical Body area networks (MBANs) emerged as an essential tool for the early diagnosis and treatment of infected patients. MBANs played an instrumental role in the remote treatment of COVID patients through early diagnosis using machine learning and IoT (Internet of Things). MBANs have paved their way in the health care industry and are being used in remote monitoring, emergency handling, and elderly assistance. The availability of the IEEE 802.15.6 standard has opened doors for new wearable consumer devices and IoMT (Internet of Medical Things) enabled applications to facilitate disease management. This standard was developed for wireless communication at a short-range, around the human body with low power requirements. This standard allows 2hop star topology within an intra-MBAN so that when direct communication is not possible, alternate routes are available through relays. This paper proposes a protocol for an intra-MBAN topology, which is a novel T-Relay mechanism that implements the 2-hop star topology. To validate the proposed protocol, it was tested on OMNeT ++ framework with Castalia Simulator for various scenarios. The simulation results show that the T-Relay mechanism enhances the PDR when the channel has variations. © 2021 IEEE.

关键词： Wireless local area networks (WLAN)

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Automated Boundary Identification for Machine Learning Classifiers

Automated Boundary Identification for Machine Learning Class...

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Search-Based and Fuzz Testing (SBFT), IEEE/ACM International Workshop on

作者： Felix Dobslaw Robert Feldt Dept. of Quality Mngmt Communication and Inf. Systems Mid Sweden University Östersund Sweden Dept. of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden

ISBN: (数字)9798400705625

ISBN: (纸本)9798350376012

AI and Machine Learning (ML) models are increasingly used as (critical) components in software systems, even safety-critical ones. This puts new demands on the degree to which we need to test them and requires new and expanded testing methods. Recent boundary-value identification methods have been developed and shown to automatically find boundary candidates for traditional, non-ML software: pairs of nearby inputs that result in (highly) differing outputs. These can be shown to developers and testers, who can judge if the boundary is where it is supposed to ***, we explore how this method can identify decision boundaries of ML classification models. The resulting ML Boundary Spanning Algorithm (ML-BSA) is a search-based method extending previous work in two main ways. We empirically evaluate ML-BSA on seven ML datasets and show that it better spans and thus better identifies the entire classification boundary(ies). The diversity objective helps spread out the boundary pairs more broadly and evenly. This, we argue, can help testers and developers better judge where a classification boundary actually is, compare to expectations, and then focus further testing, validation, and even further training and model refinement on parts of the boundary where behaviour is not ideal.

关键词： Training Conferences Machine learning Fuzzing software systems Classification algorithms Testing

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Enhancing Cybersecurity Through Advanced Fraud and Anomaly Detection Techniques: A Systematic Review

Enhancing Cybersecurity Through Advanced Fraud and Anomaly D...

引用

Science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Oluwambo Tolulope Olowe Ayodele A. Adebiyi Adebiyi O. Marion Olowe Mofiyinfoluwa Tobi Deborah Olaniyan Julius Olaniyan Aderemi Emmanuel Kayode Akindeji Dept of Computer Science Landmark University Omuaran Nigeria Dept. of Industrial & Systems Engineering University of Pretoria Pretoria South Africa Dept. of Power Electric Engineering Durban University of Technology Durban South Africa

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

Fraud and anomaly detection in cybersecurity have become critical areas of research, prompting a comprehensive review of recent developments and model effectiveness. This study investigates prevalent trends and challenges in fraud and anomaly detection, focusing on data science approaches within e-commerce and smart cities. Through the analysis, key factors influencing detection efficacy, such as deep learning models and hybrid approaches, are examined. Findings highlight the multidimensional nature of cyber threats across various domains and underscore the importance of proactive security measures, including robust authentication, network fortification, and employee training. This paper contributes to enhancing awareness and understanding of evolving cyber threats while offering actionable insights for bolstering detection capabilities and mitigating risks.

关键词： Deep learning Training Systematics Authentication Data science Market research software

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Unveiling Disparities: NLP Analysis of software Industry and Vocational Education Gaps

Unveiling Disparities: NLP Analysis of Software Industry and...

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Natural Language-Based software engineering (NLBSE), IEEE/ACM International Workshop on

作者： Emil Bäckstrand Rasmus Djupedal Lena-Maria Öberg Francisco Gomes De Oliveira Neto Dept. of Quality Management Communication and Inf. Systems Mid Sweden University Östersund Sweden Dept. of Computer Science and Engineering Chalmers|University of Gothenburg Gothenburg Sweden

ISBN: (数字)9798400705762

ISBN: (纸本)9798350352559

The rapid growth of software industry highlights the importance of the education system in producing competent professionals to meet industry demands. Previous research has identified a gap between industry needs and the content of educational programs. This study presents Vocational Education and Labour Market Analyser (VELMA), a tool designed to extract information from job ads and educational curricula (both from Sweden), utilising topic modelling to identify the diverse technologies and skills in demand within the industry and those covered by professional education. Particularly, we use Latent Dirichlet Allocation (LDA) to categorise keywords into cohesive themes for document frequency analysis. Our findings highlight industry demand for skills in cloud and embedded technologies, security engineering, and software architecture. In contrast, the Higher Vocational Education (HVE) curricula emphasise the education of web developers and general object-oriented programming languages. CCS CONCEPTS • Applied computing $\rightarrow$ Document management and text processing; Education; $\cdot$ software and its engineering $\rightarrow$ Designing software.

关键词： Industries Analytical models Text analysis Object oriented modeling Education software Natural language processing

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Combining Simulation and Recurrent Neural Networks for Model-Based Condition Monitoring of Machines 24

Combining Simulation and Recurrent Neural Networks for Model...

引用

Proceedings of the Winter Simulation Conference

作者： Alexander Wuttke Markus Rabe Joachim Hunker Jan-Philipp Diepenbrock Dept. IT in Production and Logistics TU Dortmund University Dortmund GERMANY Fraunhofer Institute for Software and Systems Engineering Dortmund GERMANY IVA Schmetz GmbH Menden GERMANY

ISBN: (纸本)9798331534202

Maintenance is pivotal in industry, with condition-based maintenance emerging as a key strategy. This involves monitoring the machine condition through sensor data analysis. Model-based approaches compare observed data with expected values from models, which requires high-quality models. An established method is to use simulation models, which in many cases produce good results but may lack precision due to uncertainties. Alternatively, models created by machine learning can detect patterns directly from data. This paper proposes combining simulation models with machine learning models, leveraging the simulation's a-priori knowledge and machine learning's data patterns to enhance models for condition monitoring. Recurrent neural networks are suggested as the machine learning method. The paper outlines a systematic approach and demonstrates its application in an industrial use case, which investigates vacuum processes in industrial furnaces.

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Multiband Microstrip Patch Antenna Operating at Five Distinct 5G mm-Wave Bands

Multiband Microstrip Patch Antenna Operating at Five Distinc...

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2021 International Conference of Technology, Science and Administration, ICTSA 2021

作者： Saeed, Osama Y. A. Saeed, Ahmed A. A. Gaid, Abdulguddoos S. A. Aoun, Amjad M. H. Sallam, Amer A. Taiz University Faculty of Engineering and IT Dept. of Communication and Computer Engineering Taiz Yemen Taiz University Faculty of Engineering and IT Dept. O F Computer Networks and Distributed Systems Taiz Yemen

ISBN: (纸本)9781665428620

A novel mm-Wave multiband microstrip patch antenna for 5G wireless communication is designed and presented in this paper. The proposed design is an H-shaped with inset feed and a rectangular slit in the upper side of the radiating patch. The 5G mm-Wave antenna resonates at frequencies of 23.2, 40.3, 59.3, 86.9 and 104.3 GHz with a maximum bandwidth of 1.1, 2.8, 18.1, 7.7 and 10.7 GHz respectively. The antenna has sufficient realized gains of 6.4882, 7.1198, 7.9062, 8.1804 and 9.8156 dBi at the resonant frequencies respectively. Furthermore, the antenna covers the most interest 5G mm-Wave spectrum with a total bandwidth of 40.4 GHz. The proposed multiband antenna is designed using microstrip technology with features such as light weight, low cost, low profile, high gain and efficiency. The 5G antenna is designed using a high frequency structure simulator software (HFSS). It has a small form factor of 7.5 × 8.5 × 0.55 mm3. The antenna is compact enough to incorporate into smart devices and used for various 5G applications. © 2021 IEEE.

关键词： Bandwidth

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Does Sri Lanka Have a Good Maritime Strategy to Stop Cyber-Terrorist Attacks

Does Sri Lanka Have a Good Maritime Strategy to Stop Cyber-T...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： Samaraweera S. A. R. P Siriwardana S. M. D. N W. D. V. Fernando D. N. Rajakarunaratne Dept. of Computer Systems Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

Maritime cyber-terrorist attacks have become a major concern to the entire world in recent decades. Everyone should be more aware of marine strategies to prevent cyberterrorist attacks, both locally and globally. This study investigates how Sri Lanka and the Sri Lanka Navy (SLN) may successfully address maritime security concerns by utilizing current resources and developing a marine strategy to counter such maritime cyber-terrorism assaults. The literature review in this thesis primarily evaluates the level of understanding in Sri Lanka concerning when marine cyber terrorism strikes may occur. Furthermore, this thesis investigates if Sri Lanka has any maritime rules and regulations. The use of such laws and regulations will concentrate on how they can increase their expertise.

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Ontological View-driven Intensional Semantic Integration for Information systems in a Decentralized Environment 14

Ontological View-driven Intensional Semantic Integration for...

引用

14th International Conference on Knowledge engineering and Ontology Development, KEOD 2022 as part of 14th International Joint Conference on Knowledge Discovery, Knowledge engineering and Knowledge Management, IC3K 2022

作者： Ali Adhnouss, Fateh Mohamed Ali El-Asfour, Husam M. McIsaac, Kenneth El-Feghia, Idris Aburukba, Raafat Wahaishi, Abdulmutalib Dept. of Electrical & Computer Engineering University of Western London Canada Faculty of Information Technology Misurata University Libya Department of Computer Science and Engineering American University of Sharjah Sharjah United Arab Emirates Software Engineering Rochester Institute of Technology New York United States

ISBN: (纸本)9789897586149

Ontologies are an essential component of semantic integration approaches for information systems . In a decentralized environment, each specification of the domain reflects an Ontological view. However, the semantics characterization of information systems in such decentralized environment poses a significant issue related to their integration. Information systems are viewed as independent intensional entities with their own beliefs, distinct from those held by others. Such autonomy is distorted by traditional extensional semantics. Other entities’ beliefs are introduced into a given entity, thus affecting their beliefs. Additionally, the information that one entity provides to another entity may not be consistent with the information known by the latter. We need an alternative semantics for information integration, which is not dependent on the extension, but rather on the underlying conceptualization. This paper proposes a classification of the environment where the information systems lives and a novel modelling paradigm for information integration using intensional logic to model the ontological views. The model comprises a formal modeling approach for the conceptualization as well as for the semantic integration process. Copyright © 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Semantics

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Auto Disturbance Rejection Control Based on A Neural Network Predictive State Observer

Auto Disturbance Rejection Control Based on A Neural Network...

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International Conference on Intelligent Human-Machine systems and Cybernetics, IHMSC

作者： Shengzhi Du BJ van Wyk NFS Nahri Chunling Tu Dept. Electrical Engineering Tshwane University of Technology Pretoria South Africa Dept. Computer Systems Engineering Tshwane University of Technology Pretoria South Africa

This paper addresses the auto disturbance rejection control (ADRC) for systems with time delay. ADRC is an effective control for most linear or nonlinear systems with noises and disturbances, because of its advantage on disturbance estimation and compensation. The extended state observer (ESO) is a critical component of ADRC. However, this observer is usually designed for systems without time delay. The time delay in a system could lead the ADRC unstable when the phase delay of the response cannot be ignored. This paper proposes a neural network based predictive state observer (NNPSO) to predict the response of systems with time delay. The simulation results validate the proposed method when systems with time delay are controlled.

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Collaborative User Mobility Prediction in Distributed Edge Computing Framework

Collaborative User Mobility Prediction in Distributed Edge C...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor systems (MASS)

作者： Sardar Jaffar Ali Syed M. Raza Hyunseung Choo Dept. of AI Systems Engineering Sungkyunkwan University Suwon South Korea James Watt School of Engineering University of Glasgow Glasgow United Kingdom Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

User attachment forecasting with Deep Learning (DL) is an effective tool for proactive mobility management in dense Beyond 5G (B5G)/6G deployments with reduced cell sizes. However, collecting user data in a central cloud to facilitate DL model learning causes extensive overhead and privacy concerns. Distributed edge cloud-based federated learning solves these issues, but it faces challenges in handling out-of-distribution data from decentralized edges at the network periphery, and the model biases due to data heterogeneity. This paper addresses these limitations by proposing a fully distributed Collaborative User Mobility Prediction (CUMP) framework that mitigates the out-of-distribution data issue through collaboration among initial layers of DL models in edges that are selected using inter-edge mobility rates. The remaining part of each model only trains on local data, preserving biases towards their respective edges. This enhances the generalization, robustness, and predictive performance of the DL models. Results show that CUMP outperforms conventional global learning and state-of-the-art distributed personalized federated learning and cyclic incremental institutional learning by 63%, 12%, and 10% in predicting the next Point of Attachment (PoA) of a user and by 70%, 22%, and 28% in predicting user dwell time in current PoA, respectively. Thus, CUMP improves prediction performance while reducing network and storage overheads while preserving privacy.

关键词： Privacy Federated learning Microprocessors Distributed databases Collaboration Predictive models Smart systems Data models Robustness Forecasting

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