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2022 Global Information Infrastructure and Networking Symposium, GIIS 2022

作者： Kouris, Michael Kokkinos, Vasileios Gkamas, Apostolos Pouyioutas, Philippos Bouras, Christos University of Patras Computer Engineering and Informatics Department Patras Greece University Ecclesiastical Academy of Vella Ioannina Greece University of Nicosia Computer Science Department Nicosia Cyprus

ISBN: (数字)9781665490955

ISBN: (纸本)9781665490955

With the advancement of wireless networks the data needs of the wireless internet have become so great, and the use of 4G and 5G so ubiquitous, that challenges arise in the availability and distribution of resources. In this paper we examine an application of Machine Learning and subsequently Neural Networks to offer a solution to this problem. The, now more than ever, profound availability of processing power greatly empowers and makes the deployment of these tools easier than ever, even in problems where in the past their application would not be feasible. The use of machine learning techniques to distribute resources in wireless networks is investigated and contrasted to a traditional algorithmic method. Scenarios are also entertained in which such approaches might be applicable. © 2022 IEEE.

关键词： Wireless networks

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A Novel Inception Deep Learning model for Mobile AI Stroke Prediction system: AI Research and Industry Perspectives for Connected Health 6

A Novel Inception Deep Learning model for Mobile AI Stroke P...

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6th International Conference on Computing and informatics, ICCI 2024

作者： Elbagoury, Bassant M. Maskeliuans, Rytis Zaghow, Marwa Kamel, Nabil Faculty of Computer and Information Sciences Ain Shams University Computer Science and Artificial Intelligence Programs Egypt Faculty of Computer Science and Engineering King Salman International University KSIU Egypt Kaunas University of Technology Faculty of Informatics Department of Multimedia Engineering Lithuania Dell Technologies EMEA Egypt

ISBN: (纸本)9798350373875

Artificial intelligence technologies for the mobile health and smart hospitals are important, by applying predictive Analytics and Deep Learning algorithms and developing new models. The main objective is applying artificial Intelligence methods in the medical field, especially for heart/brain stroke diseases diagnosis and prediction for emergency patients' cases. Thus, to save patients' lives, also through the integration with IoT and wearable technologies, which are integrated with AI and DL algorithms that help to make sense of bio signals predictive analytics such as biomedical sensors processing. and complex diseases predictions and early detection like heart and stroke diseases. Also dealing with EMG sensors for stroke prediction. Moreover, Intelligent Mobile Health based on our previously introduced project of AI smart hospital and Mobile AI stroke prediction system for connected health and stroke emergencies. In this paper, a new deep learning model has been built and tested for Stroke EMG signal prediction by modifying the Inception-v3 architecture and other deep learning models. Also, this paper compares current results of Mobile AI health Inception model with our previous developed Mobile AI stroke engine that depends on hybrid LSTM deep learning for EMG signals prediction. Both models have achieved high accuracies. Moreover, the inception model is more stable and higher average accuracies that reaches 98%. Moreover, AI research and future industry implementations for Generative AI Dell Technologies servers is discussed. © 2024 IEEE.

关键词： Learning systems

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A Game Theoretic Approach for Efficient Resource Allocation in 5G Networks 14

A Game Theoretic Approach for Efficient Resource Allocation ...

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14th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, ICUMT 2022

作者： Aspasia, Koukouvela Christos, Bouras Vasileios, Kokkinos Apostolos, Gkamas Philippos, Pouyioutas University of Patras Computer Engineering and Informatics Department Patras Greece University Ecclesiastical Academy of Vella Ioannina Greece University of Nicosia Department of Computer Science Nicosia Cyprus

ISBN: (数字)9798350398663

ISBN: (纸本)9798350398663

Heterogeneous Networks (HetNets), which enable rapid expansion in mobile traffic, have been hailed as a critical technology for 5G communications. HetNets could increase the network's capacity and enable it to support additional users. However, interference between different cells makes Quality of Service (QoS) more challenging, and thus innovative protocols, and emerging trends should be incorporated to provide Resource Allocation efficiency (RA). Concepts provided from Game Theory (GT) have been widely applied to a variety of problems where the actions of one may influence or even conflict with those of others. As a result, game formulations are utilized, and the idea of equilibrium is applied to find a stable solution for all participants. In this paper, we compare two algorithms used for RA, the first being a classical RA algorithm and the second being a game theoretic one. The game implemented is called 'Tragedy of the Commons' and is applied in a non-cooperative network. © 2022 IEEE.

关键词： Game theory

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Inertia-Aware Microgrid Investment Planning Using Tractable Decomposition Algorithms

arXiv

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arXiv 2023年

作者： Nakiganda, Agnes Marjorie Dehghan, Shahab Aristidou, Petros The Department of Electrical Engineering Technical University of Denmark Denmark The School of Engineering Newcastle University United Kingdom The Department of Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Cyprus

The integration of the frequency dynamics into Micro-Grid (MG) investment and operational planning problems is vital in ensuring the security of the system in the post-contingency states. However, the task of including transient security constraints in MG planning problems is non-trivial. This is due to the highly non-linear and non-convex nature of the analytical closed form of the frequency metrics (e.g., frequency nadir) and power flow constraints. To handle this issue, this paper presents two algorithms for decomposing the MG investment planning problem into multiple levels to enhance computational tractability and optimality. Furthermore, the sensitivity of the decisions made at each level is captured by corresponding dual cutting planes to model feasible secure regions. This, in turn, ensures both the optimal determination and placement of inertia services and accelerates the convergence of the proposed decomposition algorithms. The efficient and effective performance of the proposed algorithms is tested and verified on an 18-bus Low Voltage (LV) network and a 30-bus Medium Voltage (MV) network under various operating scenarios. © 2023, CC BY.

关键词： Investments

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Emerging IoT Applications and Architectures for Smart Maritime Container Terminals

Emerging IoT Applications and Architectures for Smart Mariti...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Sheraz Aslam Herodotos Herodotou Eduardo Garro Álvaro MartÎnez-Romero María A. Burgos Alessandro Cassera George Papas Petros Dias Michalis P. Michaelides Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Prodevelop S.L VALENCIA Spain Eurogate Container Terminal Limassol Cyprus

ISBN: (数字)9798350311617

ISBN: (纸本)9798350311624

The maritime industry has recently seen a boom of Internet of Things (loT) technologies, which are successfully digitalizing maritime transportation. The loT-enabled maritime transportation has led to the introduction of the Internet of Ships (loS) paradigm, where maritime objects are interconnected with the goal of boosting the maritime industry. Therefore, this study discusses loS, its architecture, and its elements, in combination with other emerging technologies, such as machine learning and computer vision, which lead to the creation of smart maritime container terminals (SMCTs). In particular, two specific use cases for SMCTs are presented, namely predictive maintenance and container damage/seal inspection. In addition, specific architectures are proposed and the key benefits of integrating IoT with ML and CV for the above use cases are highlighted.

关键词： Industries Reviews Transportation computer architecture Containers Inspection Predictive models

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A Comparative Analysis of Transformer Models in Zero-Shot Text Classification 15

A Comparative Analysis of Transformer Models in Zero-Shot Te...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Kyritsis, Konstantinos Liapis, Charalampos M. Spatiotis, Nikolaos Perikos, Isidoros Paraskevas, Michael University of Peloponnese Electrical & Computer Engineering Dept. Patras26334 Greece University of Patras Mathematics Dept. Patras26504 Greece Computer Technology Institute & Press 'Diophantus' Patras26504 Greece University of Patras Computer Engineering & Informatics Dept. Patras26504 Greece

ISBN: (纸本)9798350368833

Ahstract-Zero-shot text classification leverages pre-trained transformer models to categorize texts without the need for task-specific training. This paper analyzes a variety of transformer-based pre-trained methods on the task of zero-shot text classi-fication. Specifically, we present a deep comparative analysis of various transformer models, such as BART, DeBerta, DistiIBART, RoBERTa, and their variants, and evaluate their performance in various zero-shot text classification schemes. Furthermore, we examine the models' generalization capabilities. The findings highlight key strengths and weaknesses of each model, providing insights into their suitability for different text categorization tasks. Our research contributes to the broader understanding of transformer-based approaches and offers guidance for selecting models for zero-shot text classification. © 2024 IEEE.

关键词： Zero-shot learning

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Gamification of cyber ranges in cybersecurity education 20

Gamification of cyber ranges in cybersecurity education

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20th IEEE International Conference on Emerging eLearning Technologies and Applications, ICETA 2022

作者： Jelo, Martin Helebrandt, Pavol Slovak University of Technology Institute of Computer Engineering and Applied Informatics Bratislava Slovakia

ISBN: (数字)9798350320336

ISBN: (纸本)9798350320336

There is a lack of cybersecurity experts, and this problem is getting worse with the increasing number and importance of IT systems together with rising number and sophistication of attacks. Rapid development in cybersecurity necessitates constant training and practice to keep up with the latest threats. Studying IT, and especially cybersecurity is considered difficult and there is an urgent need to encourage interest of students in this field. One promising avenue to improving the situation is gamification. Gamification has been used in education to improve enthusiasm of students, increasing their numbers, information uptake and retention. Leveraging social connections and human competitiveness can further motivate students to take part and engage in the learning activities they would otherwise not consider. Virtualized environments offer means to scale up the practical training as well as share latest teaching methods. Cyber ranges used by educational institutions often are such open platforms that provide such practical cybersecurity training. Moreover, with some thoughtful planning they can prove to be popular serious games. In this paper, we explore opportunities for hands-on cybersecurity education by using gamified scenarios in cyber range virtualized environment. We focus on cyber testing environments, what their types are, and what it includes. We place emphasis on creating basic game scenario close to real-world situations. © 2022 IEEE.

关键词： Students

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Internal Validity Index for Fuzzy Clustering Based on Relative Uncertainty

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computers, Materials & Continua 2022年第8期72卷 2909-2926页

作者： Refik Tanju Sirmen Burak Berk Üstündag Graduate School of Science Engineering&Technology Istanbul Technical UniversityIstanbul34469Turkey Faculty of Computer&Informatics Istanbul Technical UniversityIstanbul34469Turkey

Unsupervised clustering and clustering validity are used as essential instruments of data *** clustering being realized under uncertainty,validity indices do not deliver any quantitative evaluation of the uncertainties in the suggested ***,validity measures may be biased towards the underlying clustering ***,neglecting a confidence requirement may result in *** the absence of an error estimate or a confidence parameter,probable clustering errors are forwarded to the later stages of the ***,having an uncertainty margin of the projected labeling can be very fruitful for many applications such as machine ***,the validity issue was approached through estimation of the uncertainty and a novel low complexity index proposed for fuzzy *** involves only uni-dimensional membership weights,regardless of the data dimension,stipulates no specific distribution,and is independent of the underlying similarity *** tests and comparisons returned that it can reliably estimate the optimum number of partitions under different data distributions,besides behaving more robust to over ***,in the comparative correlation analysis between true clustering error rates and some known internal validity indices,the suggested index exhibited the highest strong *** relationship has been also proven stable through additional statistical acceptance *** the provided relative uncertainty measure can be used as a probable error estimate in the clustering as ***,it is the only method known that can exclusively identify data points in dubiety and is adjustable according to the required confidence level.

关键词： Machine learning data science clustering validity fuzzy clustering uncertainty intelligent systems data analytics

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Covert Channel Detection Methods 20

Covert Channel Detection Methods

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20th IEEE International Conference on Emerging eLearning Technologies and Applications, ICETA 2022

作者： Ondov, Adrian Helebrandt, Pavol Institute of Computer Engineering and Applied Informatics Slovak University of Technology Bratislava Slovakia

ISBN: (数字)9798350320336

ISBN: (纸本)9798350320336

The modern networking world is being exposed to many risks more frequently every day. Most of systems strongly rely on remaining anonymous throughout the whole endpoint exploitation process. Covert channels represent risk since they ex-ploit legitimate communications and network protocols to evade typical filtering. This firewall avoidance sees covert channels frequently used for malicious communication of intruders with systems they compromised, and thus a real threat to network security. While there are commercial tools to safeguard computer networks, novel applications such as automotive connectivity and V2X present new challenges. This paper focuses on the analysis of the recent ways of using covert channels and detecting them, but also on the state-of-the-art possibilities of protection against them. We investigate observing the timing covert channels behavior simulated via injected ICMP traffic into standard network communications. Most importantly, we concentrate on enhancing firewall with detection and prevention of such attack built-in features. The main contribution of the paper is design for detection timing covert channel threats utilizing detection methods based on statistical analysis. These detection methods are combined and implemented in one program as a simple host-based intrusion detection system (HIDS). As a result, the proposed design can analyze and detect timing covert channels, with the addition of taking preventive measures to block any future attempts to breach the security of an end device. © 2022 IEEE.

关键词： Statistical methods

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Market Conditions and their Effect on Market Clearing Price Forecasting 2

Market Conditions and their Effect on Market Clearing Price ...

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2nd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2022

作者： Loizidis, Stylianos Konstantinidis, Georgios Theocharidis, Spyros Kyprianou, Andreas Georghiou, George E. University of Cyprus Pv Technology Laboratory Foss Research Centre for Sustainable Energy Department of Electrical and Computer Engineering Nicosia Cyprus University of Cyprus Pv Technology Laboratory Foss Research Centre for Sustainable Energy Department of Mechanical and Manufacturing Engineering Nicosia Cyprus

ISBN: (数字)9781665461078

ISBN: (纸本)9781665461078

High volatility in deregulated electricity markets is that characteristic that exposes its participants to higher risks. Volatility is due to many, and most of the times unpredictable, factors, ranging from fuel prices, production from renewable energy sources, electricity demand to Covid-19 and energy crisis. This article describes the initial stages of the work that combines signalling with market conditions in order to analyse the factors affecting the clearing prices of the day ahead market in view of enhancing the forecasting of these prices. The proposed forecasting methodology is based on the extreme learning machine (ELM) and it is tested on the German and Finnish markets. © 2022 IEEE.

关键词： Forecasting

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