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IEEE World Symposium on Communication engineering (WSCE)

作者： Konstantinos D. Stergiou Yutaka Ishibashi Sotirios K. Goudos Thomas Lagkas Panagiotis Sarigiannidis Konstantinos E. Psannis Department of Applied Informatics University of Macedonia Thessaloniki Greece Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan Department of Physics Aristotle University of Thessaloniki Thessaloniki Greece Department of Computer Science International Hellenic University Kavala Greece Department of Informatics and Telecommunications Engineering University of Western Macedonia Kozani Greece

This paper discusses the use of federated learning as a method for optimizing decision-making in communication systems. Federated learning is a machine learning technique that enables the training of models on decentralized data, allowing for the collection and analysis of data from multiple sources while maintaining the privacy and security of the data. This approach is particularly useful in communication systems, as it allows for the optimization of decision-making across a wide range of devices and networks. The paper examines the advantages of federated learning, including the ability to collect a large amount of data from a diverse range of devices, the protection of sensitive data, and the ability to adapt to changing conditions in real-time. The paper also provides specific examples of how federated learning can be used in the optimization of mobile networks and content delivery. The conclusion highlights the growing importance of federated learning in improving communication systems.

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CincyMedIR at TREC 2021 Clinical Trial Track 30

CincyMedIR at TREC 2021 Clinical Trial Track

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30th Text REtrieval Conference, TREC 2021

作者： Vu, Hoang Wu, Danny T.Y. Department of Biomedical Informatics University of Cincinnati College of Medicine CincinnatiOH United States Department of Electrical Engineering and Computer Science College of Engineering and Applied Science University of Cincinnati CincinnatiOH United States

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An overview of data integration principles for heterogeneous databases

An overview of data integration principles for heterogeneous...

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Proceedings of the International Convention MIPRO

作者： Aleksandar Stojanović Marko Horvat Ž eljko Kovač ević Department of Informatics and Computer Science Zagreb University of Applied Sciences Zagreb Croatia Department of Applied Computing University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia

ISBN: (数字)9789532331035

ISBN: (纸本)9781665484343

Modern large-scale information systems often use multiple database management systems, not all of which are necessarily relational. In recent years, NoSQL databases have gained acceptance in certain domains while relational databases remain de facto standard in many others. Many "legacy" information systems also use relational databases. Unlike relational database systems, NoSQL databases do not have a common data model or query language, making it difficult for users to access data in a uniform manner when using a combination of relational and NoSQL databases or simply several different NoSQL database systems. Therefore, the need for uniform data access from such a variety of data sources becomes one of the central problems for data integration. In this paper we provide an overview of the main problems, methods, and solutions for data integration between relational and NoSQL databases, as well as between different NoSQL databases. We focus mainly on the problems of structural, syntactic, and semantic heterogeneity and on proposed solutions for uniform data access, emphasizing some of the more recent proposals.

关键词： NoSQL databases Soft sensors Semantics Data integration Relational databases Writing Syntactics

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Experimental Learning in Electrical engineering with the Erasmus+ DIG-SENSING Blended Intensive Program

Experimental Learning in Electrical Engineering with the Era...

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EAEEIE Annual Conference,

作者： Grindei Laura Vicent Lorente Juan-Luis Posadas-Yagüe Ian Grout Thomas Fischer Department of Electrotechnics and Measurements Technical University of Cluj-Napoca Cluj-Napoca Romania Department of Computer Engineering School of Informatics Universitat Politècnica de València Valencia Spain Department of Electronic and Computer Engineering University of Limerick Limerick Ireland Department Engineering University of Applied Sciences Campus Vienna Vienna Austria

This paper presents the Erasmus+ DIG-SENSING project which is a Blended Intensive Program (BIP) that has the main objective to increase the motivation of students in achieving knowledge and practical experience in the field of electronic circuits. This is to be achieved through projects that integrate the design of electronic circuits, and the programming of microcontrollers through an initial online series of Webinars and local activities within the partner institutions from March to May 2023 that are then followed by a physical Summer School in July 2023. The structure and implementation of DIG-SENSING, with the benefits for students, academic staff, and partner universities will be presented. In this project, the participating students must develop and tackle a challenge that brings together Information and Communication Technologies in the framework of solving socio-environmental *** paper also presents and evaluation of students perception after the first webinar.

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ML_Recognition: A Robust Model for Handwritten Digit Recognition Using Machine Learning Classifiers 3

ML_Recognition: A Robust Model for Handwritten Digit Recogni...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Abdelminaam, Diaa Salama Madbouly, Magda M. Zeid, Magdy Abd-Elghany Abualigah, Laith Misr International University Faculty of Computer Science Cairo Egypt Benha University Faculty of Computers and Artificial Intelligence Benha Egypt Alexandria University Faculty of Computer and Data Science Alexandria Egypt Alobour High Institute for Computer and Informatics Ibrahim Abd Elatif Gomaa Computer Science Department Cairo Egypt Lebanese American University Department of Electrical and Computer Engineering Byblos13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Middle East University Meu Research Unit Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia

ISBN: (纸本)9798350306231

Handwritten digit recognition is a branch of machine learning in which a computer is taught to recognize hand-written numbers. Classification and regression are applied using deep learning and machine learning algorithms. In this paper, we discussed the efficiency of different algorithms: Random Forest, KNN, Naive Bayes, SVM, CNN, and Decision Tree Algorithm. Those algorithms were applied in the handwritten digit recognition process using the MINIST data set. By comparing each algorithm by training every one of them on the data set, then by testing, we got the best algorithm to get the optimal results with the highest accuracy. After comparing the results of each algorithm, it has shown that the neural network has the best results with 97.3% classification accuracy followed by K- the nearest neighbor with 97.2%. © 2023 IEEE.

关键词： Support vector machines

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Multi-Criteria Decision-Making Approach for Laptop Selection: A Case Study

Multi-Criteria Decision-Making Approach for Laptop Selection...

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Innovation in Technology (ASIANCON), Asian Conference on

作者： Harpreet Kaur Channi Ramandeep Sandhu Mohammad Faiz Sardar M. N. Islam Department of Electrical Engineering Chandigarh University Gharuan Mohali Punjab India School of Computer Science and Engineering Lovely Professional University Jalandhar Punjab India Applied Informatics Research Victoria University Melbourne Australia

This article uses the well-known multi-criteria decision-making (MCDM) theory to determine which laptop is the best among those that are currently on the market. To illustrate the importance of decision-making in a class of 25 students, data on laptop features was gathered for this case study. The following eight factors were considered while choosing an option: price, weight, RAM, camera megapixels, screen size, clock speed, operating system, and laptop appearance. SAW and WPM determine the optimal laptop alternative. To rank laptop qualities, identical weights are applied. The suggested work displays laptop rankings derived by SAW and WPM methods. Future engineers and management may benefit significantly from this straightforward decision-making process. More important attributes/criteria and alternatives may be taken into account, and there are a variety of weighting techniques from which to choose. The study emphasizes the importance of incorporating user preferences and real-time market data to ensure personalized and up-to-date laptop recommendations. Through this approach, users can make well-informed decisions that align with their specific needs and environmental considerations.

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Communicative Robot Signals: Presenting a New Typology for Human-Robot Interaction

arXiv

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arXiv 2023年

作者： Holthaus, Patrick Schulz, Trenton Lakatos, Gabriella Soma, Rebekka University of Hertfordshire School of Physics Engineering and Computer Science Hatfield United Kingdom Norwegian Computing Center Department of Applied Research in ICT Oslo Norway University of Oslo Department of Informatics Oslo Norway

We present a new typology for classifying signals from robots when they communicate with humans. For inspiration, we use ethology, the study of animal behaviour and previous efforts from literature as guides in defining the typology. The typology is based on communicative signals that consist of five properties: the origin where the signal comes from, the deliberateness of the signal, the signal’s reference, the genuineness of the signal, and its clarity (i.e., how implicit or explicit it is). Using the accompanying worksheet, the typology is straightforward to use to examine communicative signals from previous human-robot interactions and provides guidance for designers to use the typology when designing new robot behaviours. © 2023, CC BY.

关键词： Human robot interaction

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On the existence of highly organized communities in networks of locally interacting agents

arXiv

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arXiv 2023年

作者： Liagkou, V. Nastou, P.E. Spirakis, P. Stamatiou, Y.C. Computer Technology Institute and Press - "Diophantus" University of Patras Campus 26504 Greece Department of Computer Science University of Liverpool UK and Computer Engineering and Informatics Department University of Patras 26504 Greece Department of Business Administration University of Patras 26504 Greece University of Ioannina Department of Informatics and Telecommunications Kostakioi Arta47100 Greece Department of Mathematics University of the Aegean Applied Mathematics and Mathematical Modeling Laboratory Samos Greece Center for Applied Optimization University of Florida Gainesville United States

In this paper we investigate phenomena of spontaneous emergence or purposeful formation of highly organized structures in networks of related agents. We show that the formation of large organized structures requires exponentially large, in the size of the structures, networks. Our approach is based on Kolmogorov, or descriptional, complexity of networks viewed as finite size strings. We apply this approach to the study of the emergence or formation of simple organized, hierarchical, structures based on Sierpinski Graphs and we prove a Ramsey type theorem that bounds the number of vertices in Kolmogorov random graphs that contain Sierpinski Graphs as subgraps. Moreover, we show that Sierpinski Graphs encompass close-knit relationships among their vertices that facilitate fast spread and learning of information when agents in their vertices are engaged in pairwise interactions modelled as two person games. Finally, we generalize our findings for any organized structure with succinct representations. Our work can be deployed, in particular, to study problems related to the security of networks by identifying conditions which enable or forbid the formation of sufficiently large insider-subnetworks with malicious common goal to overtake the network or cause disruption of its operation. © 2023, CC BY.

关键词： Graphic methods

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Proximal methods for structured nonsmooth optimization over Riemannian submanifolds

arXiv

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arXiv 2024年

作者： Li, Qia Zhang, Na Yan, Hanwei Feng, Junyu School of Computer Science and Engineering Guangdong Province Key Laboratory of Computational Science Sun Yat-sen University Guangzhou China Department of Applied Mathematics College of Mathematics and Informatics South China Agricultural University Guangzhou China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

In this paper, we consider a class of structured nonsmooth optimization problems over an embedded submanifold of a Euclidean space, where the first part of the objective is the sum of a difference-of-convex (DC) function and a smooth function, while the remaining part is the square of a weakly convex function over a smooth function. This model problem has many important applications in machine learning and scientific computing, for example, the sparse generalized eigenvalue problem. We propose a manifold proximal-gradient-subgradient algorithm (MPGSA) and show that under mild conditions any accumulation point of the solution sequence generated by it is a critical point of the underlying problem. By assuming the Kurdyka-Lojasiewicz property of an auxiliary function, we further establish the convergence of the full sequence generated by MPGSA under some suitable conditions. When the second component of the DC function involved is the maximum of finite continuously differentiable convex functions, we also propose an enhanced MPGSA with guaranteed subsequential convergence to a lifted B-stationary points of the optimization problem. Finally, some preliminary numerical experiments are conducted to illustrate the efficiency of the proposed *** Codes 90C26, 90C30, 65K05 Copyright © 2024, The Authors. All rights reserved.

关键词： Discriminant analysis

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Multi-Parameter Analysis of Biosignals in Long-Term Control Medical Devices Management

Multi-Parameter Analysis of Biosignals in Long-Term Control ...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Serhii Holdobin Natalia Brynza Olha Tiutiunyk Olena Serhiienko Ihor Sosnov Pavlo Samus Industrial and Biomedical Electronics Department Kharkiv polytechnic institute National Technical University Kharkiv Ukraine Department of Informatics and Computer Technology Simon Kuznets Kharkiv National University of Economics Kharkiv Ukraine Department of Applied Mathematics Kharkiv National University of Radio Electronics Kharkiv Ukraine Department of Informatics and Computer Engineering Simon Kuznets Kharkiv National University of Economics Kharkiv Ukraine Business Trade and Logistics Department Kharkiv Polytechnic Institute National Technical University Kharkiv Ukraine

ISBN: (数字)9798331510886

ISBN: (纸本)9798331510893

The development of data processing technologies, microelectronics and sensor systems allows for high-precision multiparametric analysis of biosignals in real time. The paper considers the problem of automating medical processes to reduce the influence of the human factor and increase the accuracy of diagnostics. An improved method of multiparametric analysis of biosignals is proposed for long-term monitoring of the state of the cardiovascular system using modern sensor devices, data processing algorithms and artificial intelligence technologies. The research is aimed at improving the methods of collecting, transmitting and analyzing biosignals, which contributes to the creation of personalized medical devices and effective prediction of cardiovascular pathologies. The issues of classification of devices and biosignals, as well as their mathematical modeling to increase the accuracy of diagnostics, are considered.

关键词： Cardiovascular system Wireless sensor networks Accuracy Medical devices Heuristic algorithms Robot sensing systems Prediction algorithms Real-time systems Classification algorithms Monitoring

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