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Conference on Lasers and Electro-Optics Europe (CLEO EUROPE)

作者： Stavros Deligiannidis Nikos Argyris Stefanos Dris Dimitrios Kalavrouziotis Paraskevas Bakopoulos Charis Mesaritakis Adonis Bogris Department of Informatics and Computer Engineering University of West Attica Athens Greece NVIDIA Athens Greece Dept. Information and Communication Systems Engineering University of the Aegean Karlovasi Samos Greece

ISBN: (纸本)9798350345995

The design of photonic/opto-electronic structures is a crucial field of research due to the wide range of applications in which these devices are utilized, such as communications, sensing and imaging. The traditional approach to design, which involves numerical simulation and iterative adjustments, is both time-consuming and computationally intensive. The ability to design-emulate photonic structures quickly and efficiently could bring new technologies to market at a faster pace and can also facilitate the process of extracting critical performance metrics from complex fabricated devices. The Vertical Cavity Surface Emitting Laser (VCSEL) and its use as a transmitter broadly used in data center interconnects is an example of a rather complex nonlinear system also exhibiting bandwidth limitation mechanisms attributed to its frequency response and parasitic effects, and thus its numerical simulation based on conventional rate equations can only partially approximate its behavior. When it comes to modelling fabricated devices, such physical models are notoriously difficult to parameterize in order to fit to observed laboratory data; in contrast, the emulation of the experimentally recorded behavior with the use of nonlinear models based on neural networks provides a much more efficient path to modelling, and with potentially higher prediction accuracy [1]. In this work, two types of bidirectional recurrent neural networks (RNN), namely Long Short-Term Memory (bi-LSTM) and Vanilla (bi-VRNN) were used to simulate the dynamic behavior of an experimentally characterized VCSEL-based, PAM-4 transmitter, exhibiting a high prediction accuracy approaching 100%.

关键词： Interpolation Multimode fibers Neural networks Numerical simulation Optical systems Vertical cavity surface emitting lasers

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The Classification of Timor Weaving Using Artificial Neural Network

The Classification of Timor Weaving Using Artificial Neural ...

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International Conference on informatics and Computing (ICIC)

作者： Mendarissan Aritonang Adi Setiawan Krismiyati Theophilus Wellem Dept of Computer Science Faculty of Information Technology Satya Wacana Christian University Salatiga Indonesia Dept. Mathematics and Data Science Faculty of Science and Mathematics Satya Wacana Christian University Salatiga Indonesia Dept. Informatics & Computer Engineering Education Faculty of Information Technology Satya Wacana Christian University Salatiga Indonesia Department of Informatics Satya Wacana Christian University Salatiga Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

The number of woven fabric on the island of Timor is very large and varied, making it difficult to distinguish between types and origins. Many woven fabric motifs appear similar but represent different types. Therefore, this classification was used to perform pattern recognition of woven fabric in the system. The process allowed the system’s ability to be tested by applying the pattern recognition algorithm to woven images. In this context, the feature extraction method used was gray level co-occurrence matrix (GLCM), while the method used for classification was artificial neural network (ANN). The results showed an accuracy of $87.5 \%$ in the system. Finally, GUI system was developed, enabling tests to be performed on woven images for the identification of Timor Weaving image patterns.

关键词： Training Accuracy Social networking (online) Neurons Artificial neural networks Feature extraction Weaving Fabrics Pattern recognition Web sites

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Context-aware Container Orchestration in Serverless Edge Computing

arXiv

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arXiv 2024年

作者： Guan, Peiyuan Chen, Chen Chen, Ziru Cai, Lin X. Hao, Xing Taherkordi, Amir Dept. of Informatics University of Oslo Oslo Norway Dept. of Computer Science and Technology University of Cambridge Cambridge United Kingdom Dept. of Electrical and Computer Engineering Illinois Institute of Technology Chicago United States School of Information Science and Technology Northwest Univesity Xi'An China

Adopting serverless computing to edge networks benefits end-users from the pay-as-you-use billing model and flexible scaling of applications. This paradigm extends the boundaries of edge computing and remarkably improves the quality of services. However, due to the heterogeneous nature of computing and bandwidth resources in edge networks, it is challenging to dynamically allocate different resources while adapting to the burstiness and high concurrency in serverless workloads. This article focuses on serverless function provisioning in edge networks to optimize end-to-end latency, where the challenge lies in jointly allocating wireless bandwidth and computing resources among heterogeneous computing nodes. To address this challenge, We devised a context-aware learning framework that adaptively orchestrates a wide spectrum of resources and jointly considers them to avoid resource fragmentation. Extensive simulation results justified that the proposed algorithm reduces over 95% of converge time while the end-to-end delay is comparable to the state of the art. © 2024, CC BY.

关键词： Quality of service

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Exploring Aspects Regarding Reasoning in Neuro-Symbolic Rules and Connectionist Expert Systems 12

Exploring Aspects Regarding Reasoning in Neuro-Symbolic Rule...

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12th International Conference on Information, Intelligence, Systems and Applications, IISA 2021

作者： Prentzas, Jim Hatzilygeroudis, Ioannis Democritus University of Thrace Dept. of Education Sciences in Early Childhood Alexandroupolis Greece University of Patras Dept. of Computer Engineering and Informatics Patras Greece

ISBN: (纸本)9781665400329

Neuro-symbolic approaches combine neural and symbolic methods. This paper explores aspects regarding the reasoning mechanisms of two neuro-symbolic approaches, that is, neurules and connectionist expert systems. Both of them provide reasoning and explanation facilities. Neurules are a type of neuro-symbolic rules tightly integrating the neural and symbolic components and giving pre-eminence to the symbolic component. Connectionist expert systems give pre-eminence to the connectionist component. The aspects explored in this paper have not been previously addressed. As far as neurules are concerned, an aspect playing a role in conflict resolution (i.e., order of neurules) is explored. Experimental results show an improvement in reasoning efficiency. As far as connectionist expert systems are concerned, variations of the reasoning mechanism are explored. Experimental results show that none of the alternative versions of the reasoning mechanism outperforms the others in all cases. © 2021 IEEE.

关键词： Expert systems

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Comparative Analysis of the Queueing Systems E2/M/1 and M/E2/1 with Shifted Distribution Laws

Comparative Analysis of the Queueing Systems E2/M/1 and M/E2...

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2020 IEEE International Conference on Problems of Infocommunications Science and Technology, PIC S and T 2020

作者： Bakhareva, Nadezhda Tarasov, Veniamin Povolzhskiy State University of Telecommunications and Informatics Dept. of Info. and Computer Engineering and Software and Management in Technical Systems Department Samara Russia

ISBN: (纸本)9781728191775

The objective of this work is to the output the solution in closed form for the average waiting time in a queue for queueing systems with shifted Erlangian and exponential input distributions and comparative analysis of their results with the results for conventional systems. As is known from queuing theory, the average waiting time for requirements in a queue is the main characteristic of a queuing system. To solve this problem, we used the classical method of spectral decomposition of the solution of Lindley integral equation, which allows one to obtain a solution for average the waiting time for systems under consideration in a closed form. The method of spectral decomposition of the solution of Lindley integral equation plays an important role in the theory of systems G/G/1. For the practical application of the results obtained, the well-known method of moments of probability theory is used. The introduction of the time shift parameter in the laws of input flow distribution and service time for the systems under consideration turns them into systems with a delay with a shorter waiting time. This is because the time shift operation reduces the coefficient of variation in the intervals between the receipts of the requirements and their service time, and as is known from queuing theory, the average wait time of requirements is related to these coefficients of variation by a quadratic dependence. The obtained results expand and supplement the well-known incomplete formula of the queuing theory for the average waiting time of requirements in the queue for G/G/1 systems. © 2020 IEEE.

关键词： Integral equations

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AIANTAS) for IoT Cyberspace - An Upcoming Cloud-based Security Architecture for Police Authorities 9

AIANTAS) for IoT Cyberspace - An Upcoming Cloud-based Securi...

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9th IEEE International Conference on Information, Communication and Networks, ICICN 2021

作者： Memos, Vasileios A. Psannis, Konstantinos E. Ishibashi, Yutaka University of Macedonia School of Information Sciences Dept of Applied Informatics Thessaloniki Greece Nagoya Institute of Technology School of Engineering Dept of Computer Science Nagoya Japan

ISBN: (纸本)9780738113456

In this research work, we investigate the effects of cybercrime and focus on concerns about cybercrime expansion. Since black-hat hackers are constantly one step ahead of white-hat hackers and exploit new sophisticated hack-tools to intrude to devices and networks to commit cybercrime for different reasons, the utilization of the available emerging technologies can provide better privacy and protection level, while they can be used to combat cybercriminal attacks. This shift to new technologies and methods is regarded to be an urgent necessity today. Therefore, we demonstrate a potential novel security mechanism for Police Authorities, named 'Artificial Intelligence ANTi-Attack System' or simply 'AIANTAS' that aspires to improve the detection rate of cybercriminals and lead to their faster arrest. This model can be integrated into the other National Security defense mechanisms both in Greece and worldwide. © 2021 IEEE.

关键词： Data Analytics

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An Iterated Local Search Algorithm for Commuting Bus Routing Problem with Latest Arrival Time Constraint

An Iterated Local Search Algorithm for Commuting Bus Routing...

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2022 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2022

作者： Hashimoto, H. Hu, Y. Sugiura, T. Takada, Y. Yagiura, M. Tokyo University of Marine Science and Technology Dept. Logistics and Information Engineering Tokyo Japan Tokyo University of Science Dept. Applied Mathematics Tokyo Japan Nagoya University Graduate School of Information Science Dept. Computer Science and Mathematical Informatics Nagoya Japan

ISBN: (数字)9781665486873

ISBN: (纸本)9781665486873

Given a set of bus stops and a set of employees, the commuting bus routing problem with latest arrival time constraint (CBRP-LATC) aims to determine routes of buses to carry every employee from the company to one of the bus stops at which he/she wants to get off. Each employee walks to his/her destination after getting off the bus and has a desired time when he/she wants to arrive there, which is called the latest arrival time. The objective is to find a set of routes with the minimum total traveling cost that covers all employees' destinations under bus capacity and latest arrival time constraints. We propose an iterated local search algorithm in which we use two kinds of neighborhood operations called set-del/1-ins and VRP-OPT∗. The set-del/1ins operation consists of three neighborhood operations, setdel, 1-ins and set-del-ins to remove or assign employees from or to a bus route. The VRP-OPT∗ is a local search algorithm to improve the distances of routes based on four neighborhood operations, 2-opt, 2-opt∗, relocate and cross-exchange. Computational results show the effectiveness of the two neighborhood operations. © 2022 IEEE.

关键词： Personnel

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Detection of Deep-Morphed Deepfake Images to Make Robust Automatic Facial Recognition Systems 19

Detection of Deep-Morphed Deepfake Images to Make Robust Aut...

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19th Orissa Information Technology Society International Conference on Information Technology, OCIT 2021

作者： Mitra, Alakananda Mohanty, Saraju P. Corcoran, Peter Kougianos, Elias University of North Texas Dept. of Computer Science And Engineering United States National University of Ireland School of Engineering And Informatics Galway Ireland University of North Texas Dept. of Electrical Engineering United States

ISBN: (纸本)9781665416641

Face Morphing has emerged as a pervasive attack of Facial Recognition Systems. The rapid growth of Generative Adversarial Networks takes it to a complete new level. Deepfake or deep neural network based face morphing, a.k.a deep-morph attack, presents a significant threat to Facial Recognition System. In this paper, we propose a novel Convolutional Neural Network based detection method of deep morphed deepfake images which is suitable for IoT environments in smart cities. A high accuracy of 94.83% has been achieved for the DeepfakeTIMIT HQ dataset. This lightweight and fast network is a natural choice for IoT environments. © 2021 IEEE.

关键词： Smart city

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Predicting Fish-Mortality: Artificial Neural Networks vs Symbolic Regression

Predicting Fish-Mortality: Artificial Neural Networks vs Sym...

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International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Theofanis Aravanis Aristeidis Ilias Ioannis Hatzilygeroudis Georgios Spiliopoulos Dept of Mechanical Engineering University of the Peloponnese Patras Greece Dept of Computer Engin. and Informatics University of Patras Patras Greece Kefalonia Fisheries S.A. Kefalonia Greece

In this paper, we present a comparison between a collection of Artificial Neural Networks (ANNs) —specifically, Multilayer Perceptrons— and a collection of Symbolic Regression (SR) models, all developed for predicting fish-deaths due to infectious diseases in aquaculture. The implemented Machine-Learning models are able to identify patterns that indicate an increased risk of fish-deaths, and provide early warning-alerts to fish-farmers and other stakeholders, enabling them to take necessary measures to prevent or minimize losses of fishes. The models were trained using real-world data acquired from a large Greek fish-farming unit, and evaluated in a validation dataset (distinct from the training dataset) based on their Mean Absolute Error (MAE) performance. The study found that, for each disease considered, the corresponding ANN model outperformed the respective SR model in terms of MAE. However, the ANNs sacrificed interpretability for higher performance, while the developed SR models, despite lower performance, managed to produce transparent and understandable mathematical expressions for estimating fish-deaths. Overall, this study not only provides a valuable method for generating early warning-alerts of pathogenetic circumstances to the interested stakeholders, improving the efficiency and sustainability of fish-farming operations, but it also sheds light on the strengths and limitations of the developed ANN and SR models.

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Information Storage and Retrieval System for a Molecular Dynamics Based Digital Twin of Materials

Information Storage and Retrieval System for a Molecular Dyn...

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2022 IEEE International Multi-Conference on engineering, computer and Information Sciences, SIBIRCON 2022

作者： Topolsky, Dmitry Belyakov, Alexander Pochinskaia, Veronica Topolskaya, Irina Yumagulov, Nikolay Fedorov, Dmitry South Ural State University Dept. of Electronic Computing Machines Chelyabinsk Russia South Ural State University Dept. of Energy and Power Engineering Chelyabinsk Russia Nizhnevartovsk State University Dept. for Education in Physics and Mathematics Name of Organization Nizhnevartovsk Russia Surgut State University Dept. of Informatics and Computer Engineering Surgut Russia

ISBN: (纸本)9781665464802

Digital twin technology becomes an appropriate respond to the new challenge of rapid development in science and industry. Chemical data include the information about structure and properties of materials and compounds. Machine learning technology can help to predict properties of innovative compounds and material, but it takes enormous amount of raw data. The implementation of digital twin technology in spectral and molecular dynamics helps to reduce risks introducing the innovations. Large systems technologies are used in multiscale modeling of materials with computer programs. The world experience of chemical data aggregation and storage is based on specialized databases creation. Disunity of data sources demands a single multiscale data model to operate raw data and amend the accuracy of prediction. To collect and analyze unstructured data full-text search and distributed computing were applied. In respond to perpetual raw data expansion a Data Lake technology is used to store, analyze and search for information of atomic and molecular systems. © 2022 IEEE.

关键词： Big data

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