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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Li, Jiawei Wang, Dawei Zhang, Jiankang Alfarraj, Osama He, Yixin Al-Rubaye, Saba Yu, Keping Mumtaz, Shahid Northwestern Polytechnical University School of Electronics and Information Xi'an710072 China Bournemouth University Department of Computing and Informatics United Kingdom Jiaxing University College of Information Science and Engineering Jiaxing314001 China Cranfield University School of Aerospace Transport and Manufacturing United Kingdom King Saud University Community College Department of Computer Science Riyadh11437 Saudi Arabia Hosei University School of Science and Engineering Tokyo184-8584 Japan Silesian University of Technology Department of Applied Informatics Gliwice44-100 Poland Nottingham Trent University Departement of Computer Sciences United Kingdom

ISBN: (纸本)9798350351255

This paper investigates the secure transmissions in the Unmanned Aerial Vehicle (UAV) communication network facilitated by a Reconfigurable Intelligent Surface (RIS). In this network, the RIS acts as a relay, forwarding sensitive information to the legitimate receiver while preventing eavesdropping. We optimize the positions of the UAV at different time slots, which gives another degree to protect the privacy information. For the proposed network, a secrecy rate maximization problem is formulated. The non-convex problem is solved by optimizing the RIS's phase shifts and UAV trajectory. The RIS phase shift optimization problem is converted into a series of subproblems, and a non-linear fractional programming approach is conceived to solve it. Furthermore, the first-order taylor expansion is employed to transform the UAV trajectory optimization into convex function, and then we use the deep Q-network (DQN) method to obtain the UAV's trajectory. Simulation results show that the proposed scheme enhances the secrecy rate by 18.7% compared with the existing approaches. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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How we can use text classification in the Back-Office environment of a bank as 'business as usual' solution 17

How we can use text classification in the Back-Office enviro...

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17th IEEE International Symposium on applied Computational Intelligence and informatics, SACI 2023

作者： Krutilla, Zsolt Kovari, Attila Óbuda University Doctoral School of Applied Informatics and Applied Mathematics Budapest Hungary Informatics Institution University of Dunaújváros Dunaújváros Hungary Institute of Engineering Alba Regia Technical Faculty buda University Budapest Hungary Institute of Digital Technology Faculty of Informatics Eszterházy Károly Catholic University Eger Hungary University of Dunaújváros Institute of Engineering Department of Natural Science Dunaújváros Hungary John von Neumann University Gamf Faculty of Engineering and Computer Science Kecskemét Hungary

ISBN: (纸本)9798350321104

Natural Language Processing nowadays provides scientists with many research areas and opportunities, but as with most applied sciences, our goal in Natural Language Processing is to refine the underlying science and technology until it can be used reliably for business purposes. Transformer models, such as GPT or BERT, are currently showing outstanding results in the field of natural-language processing, but they require huge computational power and data to teach, but these conditions can only be met by larger research centers and large companies, and their inaccuracy makes them unsuitable for use as a 'business as usual' (BAU) solution. In this paper, we present a solution that is able to overcome these problems by focusing on accuracy and usability, and that can also bring a new perspective to the process of teaching deep learning models. © 2023 IEEE.

关键词： Information retrieval systems

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The Applications of Federated Learning Algorithm in the Federated Cloud Environment: A Systematic Review

The Applications of Federated Learning Algorithm in the Fede...

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

作者： Ademolu Ajao Oluranti Jonathan Emmanuel Adetiba Department of Computer and Information Sciences Covenant Applied and Informatics Africa Center of Excellence (CApIC-ACE) Covenant University Ota Nigeria Department of Computer and Information Sciences Covenant University Covenant University Ota Nigeria Department of Electrical & Information Engineering Covenant Applied Informatics and Communications (CApIC-ACE) Covenant University Ota Nigeria

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

Federated learning (FL) refers to a system in which a central aggregator coordinates the efforts of several clients to solve the issues of machine learning. This setting allows the training data to be dispersed to protect the privacy of each device. This paper provides an overview of federated learning systems and how it can be applied in the federated cloud environment its frameworks, architectures, and applications, we were able to identify from various literature the Federated Learning technology was most used in the Federated Cloud/Multi-Cloud Environment, we also identified the use cases, additionally, we were able to identify the future directions Federated Learning could lean towards.

关键词： Performance evaluation Cloud computing Federated learning Smart contracts Training data Blockchains Internet of Things

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Psychological Stress Classification Using Extreme Gradient Boosting Algorithm 15

Psychological Stress Classification Using Extreme Gradient B...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Sakanti, Maria Mahardini Siniaev, Viacheslav Amaris, Aurelia Luo, Win-Jet Suhartono Kuncoro, C. Bambang Dwi Graduate Institute of Precision Manufacturing National Chin-Yi University of Technology Taichung41170 Taiwan Institute of Applied Computer Science Itmo University St. Petersburg19710 Russia Universitas Brawijaya Faculty of Computer Science Department of Informatics Engineering Malang65145 Indonesia National Chin-Yi University of Technology Taichung41170 Taiwan National Chin-Yi University of Technology Department of Refrigeration Air Conditioning and Energy Engineering Taichung41170 Taiwan

ISBN: (纸本)9798350364637

Stress, as a reaction to threatening situations, can raise heart rate and result in serious conditions that might cause significant damage or even be life-threatening. Traditional methods for evaluating stress, which rely on subjective self-reporting and clinical assessments, often suffer from biases and inconsistencies. Artificial intelligence models have been explored to predict stress levels more accurately. This paper investigates the application of Extreme Gradient Boosting in classifying psychological stress using the WESAD dataset, which includes parameters such as acceleration, electrocardiogram, electromyography, electrodermal activity, temperature, and respiration. The dataset was balanced and sampled to create a manageable subset for experimental. Extreme Gradient Boosting was chosen for its efficiency and scalability in handling complex datasets. The model was trained and validated, achieving a 95% accuracy in predicting stress levels. This study highlights the potential of integrating Extreme Gradient Boosting models into wearable devices for real-time stress monitoring. Future work involves optimizing the model to utilize fewer sensors without decreasing accuracy, ensuring it can be integrated into portable/wearable systems using tiny microcontrollers. © 2024 IEEE.

关键词： Electromyography

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An Open-Source Experimentation Framework for the Edge Cloud Continuum

An Open-Source Experimentation Framework for the Edge Cloud ...

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Georgios Koukis Sotiris Skaperas Ioanna Angeliki Kapetanidou Vassilis Tsaoussidis Lefteris Mamatas Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece Athena Research and Innovation Center Greece Department of Applied Informatics University of Macedonia Thessaloniki Greece

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

The CODECO Experimentation Framework is an open-source solution designed for the rapid experimentation of Kubernetes-based edge cloud deployments. It adopts a microservice-based architecture and introduces innovative abstractions for (i) the holistic deployment of Kubernetes clusters and associated applications, starting from the VM allocation level; (ii) declarative cross-layer experiment configuration; and (iii) automation features covering the entire experimental process, from the configuration up to the results visualization. We present proof-of-concept results that demonstrate the above capabilities in three distinct contexts: (i) a comparative evaluation of var-ious network fabrics across different edge-oriented Kubernetes distributions; (ii) the automated deployment of EdgeNet, which is a complex edge cloud orchestration system; and (iii) an assessment of anomaly detection (AD) workflows tailored for edge environments.

关键词： Visualization Cross layer design Automation Image edge detection Conferences Microservice architectures computer architecture

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An Open-Source Experimentation Framework for the Edge Cloud Continuum

arXiv

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arXiv 2024年

作者： Koukis, Georgios Skaperas, Sotiris Kapetanidou, Ioanna Angeliki Tsaoussidis, Vassilis Mamatas, Lefteris Athena Research and Innovation Center Greece Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece Department of Applied Informatics University of Macedonia Thessaloniki Greece

The CODECO Experimentation Framework is an open-source solution designed for the rapid experimentation of Kubernetes-based edge cloud deployments. It adopts a microservice-based architecture and introduces innovative abstractions for (i) the holistic deployment of Kubernetes clusters and associated applications, starting from the VM allocation level;(ii) declarative cross-layer experiment configuration;and (iii) automation features covering the entire experimental process, from the configuration up to the results visualization. We present proof-of-concept results that demonstrate the above capabilities in three distinct contexts: (i) a comparative evaluation of various network fabrics across different edge-oriented Kubernetes distributions;(ii) the automated deployment of EdgeNet, which is a complex edge cloud orchestration system;and (iii) an assessment of anomaly detection (AD) workflows tailored for edge environments. © 2024, CC BY-NC-ND.

关键词： Anomaly detection

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Measurement of Synthetic-Dimension Band Structures in Silicon Coupled Ring Resonators 16

Measurement of Synthetic-Dimension Band Structures in Silico...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Zhang, Ruming Li, Liucun Kamata, Mikiya Baba, Toshihiko Ozawa, Tomoki Ota, Yasutomo Iwamoto, Satoshi Research Center for Advanced Science and Technology The University of Tokyo Tokyo153-8904 Japan Yokohama National University Department of Electrical and Computer Engineering Yokohama240-8501 Japan Advanced Institute for Materials Research Tohoku University Sendai980-8577 Japan Keio University Department of Applied Physics and Physico-Informatics Yokohama223-8533 Japan

We fabricated integrated silicon coupled-ring resonators and measured photonic band structures for synthetic frequency lattices with different configurations. A phase difference between two modulation signals introduc... 详细信息

ISBN: (纸本)9798350372076

关键词： Silicon photonics

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Performance Evaluation of Kubernetes Networking Approaches across Constraint Edge Environments

Performance Evaluation of Kubernetes Networking Approaches a...

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IEEE Symposium on computers and Communications (ISCC)

作者： Georgios Koukis Sotiris Skaperas Ioanna Angeliki Kapetanidou Lefteris Mamatas Vassilis Tsaoussidis Athena Research and Innovation Center Greece Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece Department of Applied Informatics University of Macedonia Thessaloniki Greece

ISBN: (数字)9798350354232

ISBN: (纸本)9798350354249

Kubernetes (K8s) serves as a mature orchestration system for the seamless deployment and management of containerized applications spanning across cloud and edge environments. In this context, optimizing Kubernetes networking to achieve high-performance connectivity and minimal resource utilization is crucial for its applicability and effectiveness at the edge. This paper contributes to this effort, by conducting a qualitative and quantitative performance evaluation of diverse Container Network Interface (CNI) plugins within different K8s environments, incorporating lightweight implementations designed for the Edge. Our experimental assessment was conducted in two distinct (intra- and inter-host) scenarios, revealing interesting insights and tradeoffs for both researchers and practitioners. For example, the deployment of plugins across lightweight distributions does not necessarily lead to resource utilization improvements, e.g., in terms of CPU/memory or throughput, while, in contrast, their impact on lifecycle metrics such as pod readiness times, is significant.

关键词： Performance evaluation computers Containers Throughput Resource management Network interfaces

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Performance Evaluation of Kubernetes Networking Approaches across Constraint Edge Environments

arXiv

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arXiv 2024年

作者： Koukis, Georgios Skaperas, Sotiris Kapetanidou, Ioanna Angeliki Mamatas, Lefteris Tsaoussidis, Vassilis Athena Research and Innovation Center Greece Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece Department of Applied Informatics University of Macedonia Thessaloniki Greece

Kubernetes (K8s) serves as a mature orchestration system for the seamless deployment and management of containerized applications spanning across cloud and edge environments. Since high-performance connectivity and minimal resource utilization become critical factors as we approach the edge, evaluating the performance of K8s networking in this context is essential. This paper contributes to this effort, by conducting a qualitative and quantitative performance evaluation of diverse Container Network Interface (CNI) plugins within different K8s environments, incorporating lightweight implementations designed for the Edge. Our experimental assessment was conducted in two distinct (intra- and inter-host) scenarios, revealing interesting insights for both researchers and practitioners. For example, the deployment of plugins across lightweight distributions does not necessarily lead to resource utilization improvements, e.g., in terms of CPU/memory or throughput. © 2024, CC BY.

关键词： Containers

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The Virtual Research Environment: towards a comprehensive analysis platform

arXiv

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arXiv 2023年

作者： Gazzarrini, Elena Garcia, Enrique Gosein, Domenic Moya, Alba Vendrell Kounelis, Agisilaos Espinal, Xavier Meyrin1211 Switzerland Mannheim University of Applied Sciences Germany Computer Engineering and Informatics Department University of Patras Greece

The Virtual Research Environment is an analysis platform developed at CERN serving the needs of scientific communities involved in European Projects. Its scope is to facilitate the development of end-to-end physics workflows, providing researchers with access to an infrastructure and to the digital content necessary to produce and preserve a scientific result in compliance with FAIR principles. The platform’s development is aimed at demonstrating how sciences spanning from High Energy Physics to Astrophysics could benefit from the usage of common technologies, initially born to satisfy CERN’s exabyte-scale data management needs. The Virtual Research Environment’s main components are (1) a federated distributed storage solution (the Data Lake), providing functionalities for data injection and replication through a Data Management framework (Rucio), (2) a computing cluster supplying the processing power to run full analyses with Reana, a re-analysis software, (3) a federated and reliable Authentication and Authorization layer and (4) an enhanced notebook interface with containerised environments to hide the infrastructure’s complexity from the user. The deployment of the Virtual Research Environment is open-source and modular, in order to make it easily reproducible by partner institutions;it is publicly accessible and kept up to date by taking advantage of state of the art IT-infrastructure technologies. © 2023, CC BY.

关键词： Information management

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