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作者： Hammad, Mohamed Plawiak, Pawel Wang, Kuanquan Acharya, Udyavara Rajendra School of Computer Science and Technology Harbin Institute of Technology Harbin China Faculty of Computers and Information Menoufia University Menoufia Egypt Department of Information and Communications Technology Faculty of Computer Science and Telecommunications Cracow University of Technology Krakow Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Gliwice Poland Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Singapore Department of Biomedical Engineering School of Science and Technology Singapore School of Social Sciences Singapore Singapore Department of Bioinformatics and Medical Engineering Asia University Taichung Taiwan

Authentication is the process of verifying the claimed identity of the user. Recently, traditional authentication methods such as passwords, tokens, and so on are no longer used for authentication as they are more prone to theft and different types of violations. Therefore, new authentication approaches based on biometric modalities such as heartbeat pattern obtained from electrocardiogram (ECG) signals are considered. Unlike other biometrics, ECG provides the assurance that the person is alive, and is considered as one of the most accurate recent methods for authentication. In this article, two end-to-end deep neural network models for ECG-based authentication are proposed. In the first model, a convolutional neural network (CNN) is developed and in the second model, a residual convolutional neural network (ResNet) with attention mechanism called ResNet-Attention is designed for human authentication. We have used 2-s duration ECG signals obtained from two ECG databases (Physikalisch-Technische Bundesanstalt [PTB] and Check Your Bio-signals Here initiative [CYBHi]) for authentication. Our proposed ResNet-Attention algorithm achieved an accuracy of 98.85 and 99.27% using PTB and CYBHi, respectively. The results obtained by our developed model show that the performance is better than existing algorithms and can be used in real-time authentication systems after the validation with more diverse ECG data. © 2020 John Wiley & Sons, Ltd

关键词： Electrocardiograms

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Cactus Mechanisms: Optimal Differential Privacy Mechanisms in the Large-Composition Regime

Cactus Mechanisms: Optimal Differential Privacy Mechanisms i...

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IEEE International Symposium on Information Theory

作者： Wael Alghamdi Shahab Asoodeh Flavio P. Calmon Oliver Kosut Lalitha Sankar Fei Wei School of Engineering and Applied Science Harvard University Department of Computing and Software McMaster University School of Electrical Computer and Energy Engineering Arizona State University

ISBN: (数字)9781665421591

ISBN: (纸本)9781665421607

Most differential privacy mechanisms are applied (i.e., composed) numerous times on sensitive data. We study the design of optimal differential privacy mechanisms in the limit of a large number of compositions. As a consequence of the law of large numbers, in this regime the best privacy mechanism is the one that minimizes the Kullback-Leibler divergence between the conditional output distributions of the mechanism given two different inputs. We formulate an optimization problem to minimize this divergence subject to a cost constraint on the noise. We first prove that additive mechanisms are optimal. Since the optimization problem is infinite dimensional, it cannot be solved directly; nevertheless, we quantize the problem to derive nearoptimal additive mechanisms that we call "cactus mechanisms" due to their shape. We show that our quantization approach can be arbitrarily close to an optimal mechanism. Surprisingly, for quadratic cost, the Gaussian mechanism is strictly suboptimal compared to this cactus mechanism. Finally, we provide numerical results which indicate that cactus mechanisms outperform Gaussian and Laplace mechanisms for a finite number of *** full proofs can be found in the extended version at [1]. This paper is Part I in a pair of papers, where Part II is [2].

关键词： Differential privacy Privacy Costs Additives Quantization (signal) Shape Optimization

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Simulation and measurement of long-term polarization processes on PET films in a DC stressed bushing

Simulation and measurement of long-term polarization process...

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23rd International Symposium on High Voltage engineering (ISH 2023)

作者： Martin Scheler Michael Rossner Frank Berger Udo Prucker Department of Electrical Engineering and Computer Science Coburg University of Applied Sciences and Arts Coburg Germany Department of Electrical Apparatus and Switchgear Technische Universität Ilmenau Ilmenau Germany Research and Development Trench Germany GmbH Bamberg Germany

ISBN: (数字)9781839539923

The increasing importance of high voltage DC transmissions requires to investigate the DC behaviour of the used materials. For plastics, long-term polarization processes are observed under DC stress, which may last several weeks. This leads to the question of suitable tests or simulation scenarios to take these processes into account with sufficient accuracy. In this paper it is presented how FEM simulations can be adapted to consider long-term polarization processes. Temperature and field strength dependencies on polarization and DC conductivity is considered and discussed. The simulations can be performed for DC and any other voltage waveforms such as polarity reversals. The influence of stationary or dynamic temperature gradients can also be considered. The given material parameters of the plastic films are verified with measurements on an experimental setup. To get sufficient results, side effects like surface conductivity and parasitic capacities are considered. The results of the simulation program are compared with potential distribution measurements on an indoor 170 kV bushing with defined temperature gradient and an outdoor 660 kV bushing. It is evaluated and discussed in context of engineering aspects.

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GPT (Generative Pre-trained Transformer) - A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directions

arXiv

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arXiv 2023年

作者： Yenduri, Gokul Ramalingam, M. Selvi, Chemmalar G. Supriya, Y. Srivastava, Gautam Maddikunta, Praveen Kumar Reddy Raj, Deepti G. Jhaveri, Rutvij H. Prabadevi, B. Wang, Weizheng Vasilakos, Athanasios V. Gadekallu, Thippa Reddy The School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India The Dept. of Math and Computer Science Brandon University Canada The Research Centre for Interneural Computing China Medical University Taichung Taiwan Dept. of Computer Science and Math Lebanese American University Beirut Lebanon The Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University India The Department of Computer Science City University of Hong Kong Hong Kong Grimstad Norway Lovely Professional University Phagwara India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Jiaxing University Jiaxing314001 China Zhongda Group 314312 China

The Generative Pre-trained Transformer (GPT) represent a notable breakthrough in the domain of natural language processing, which is propelling us toward the development of machines that can understand and communicate using language in a manner that closely resembles that of humans. GPT is based on the transformer architecture, a deep neural network designed for natural language processing tasks. Due to their impressive performance on natural language processing tasks and ability to effectively converse, GPT have gained significant popularity among researchers and industrial communities, making them one of the most widely used and effective models in natural language processing and related fields, which motivated to conduct this review. This review provides a detailed overview of the GPT, including its architecture, working process, training procedures, enabling technologies, and its impact on various applications. In this review, we also explored the potential challenges and limitations of a GPT. Furthermore, we discuss potential solutions and future directions. Overall, this paper aims to provide a comprehensive understanding of GPT, enabling technologies, their impact on various applications, emerging challenges, and potential solutions. © 2023, CC BY.

关键词： Deep neural networks

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A Framework for Autonomous Vehicle Testing Using Semantic Models

A Framework for Autonomous Vehicle Testing Using Semantic Mo...

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IEEE International Conference on Artificial Intelligence Testing (AITest)

作者： Yejun He Muslim Razi Jerry Gao Chuanqi Tao Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing University of Aeronautics and Astronautics Nanjing Jiangsu China Computer Engineering Department and Applied Data Science Department San Jose State University San Jose USA

With the rapid development of big data and artificial intelligence, autonomous vehicles (AV) have achieved great success in diverse application domains. Although the current development of autonomous driving technology has been greatly improved, the accident rate of driverless vehicles still rises. Therefore, how to ensure the quality of AVs using different testing technologies has become an important issue. Traditional testing approaches such as decision tables and state charts struggle with diversified test inputs. Hence, there is a strong demand for new test models for AV testing. In this paper, we present a model-based testing framework that utilizes semantic trees and 3-dimensional (3D) test tables to model driving scenarios into individual test cases. Using the proposed framework, we generate fine-grained examples corresponding to the real world. Also, we evaluate the behavior of the Apollo AV system in 30 scenarios in SVL simulator. The results show that the scenarios generated by our framework can cover most conditions occurring, thus addressing the challenge of testing AVs for safety and reliability.

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A New Approach to Enhance Learning Data Sets for Object Classification

A New Approach to Enhance Learning Data Sets for Object Clas...

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Circuits, Systems, Communications and computers (CSCC), International Conference on

作者： Andrzej Dziech Jakob Wassermann Michael Windisch Remigiusz Baran Andrzej Zeja Piotr Bogacki Institute of Telecommunications AGH University of Science and Technology Krakǒw Poland Department of Electronic Engineering University of Applied Sciences Technikum-Wien Wien Austria Department of Computer Science Electronics and Electrical Engineering Kielce University of Technology Kielce Poland Department of Teleinformatics University of Computer Engineering and Telecommunications Kielce Poland

ISBN: (纸本)9781665481878

In this paper, we propose a novel solution to the problem of insufficient training data in complex image classification tasks. The presented method efficiently recommends new learning samples with the use of contour-based descriptors and Scalable Vocabulary Tree (SVT) technique. The SVT is built from SIFT features and may be used for identification and localization of objects of interest in the analyzed query images. In conditions where the training data set is not large enough, the SVT's classification accuracy is satisfactory and comparable to that achieved by the dedicated CNN network. However, the SVT approach surpasses the classic convnet approaches due to its incremental learning capability. The classification certainty is further increased thanks to contour-based analysis using Point Distance Histogram (PDH) descriptor. At this stage, the PDH representation of the analyzed object is matched with the information stored in the pattern database. This database contains a set of 360 shape outlines for each object that are extracted from its 3D model. This leads to higher classification accuracy of the presented approach.

关键词： Vocabulary Solid modeling Three-dimensional displays Databases Shape Computational modeling Training data

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Ranking of Different States in India Based on Sustainable Women Empowerment Using MCDM Methodology Under Uncertain Environment

Journal of Uncertain Systems

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Journal of Uncertain Systems 2024年第4期17卷

作者： Adhikari, Doli Gazi, Kamal Hossain Sobczak, Anna Giri, Bibhas Chandra Salahshour, Soheil Mondal, Sankar Prasad Department of Mathematics Jadavpur University Department of Applied Mathematics Maulana Abul Kalam Azad University of Technology Faculty of Economics The Jacob of Paradies University Faculty of Engineering and Natural Sciences Istanbul Okan University Faculty of Engineering and Natural Sciences Bahcesehir University Department of Computer Science and Mathematics Lebanese American University

In this paper, women's empowerment in different states of India is considered. Based on considered data sets, the states of India are ranked with Decision-Making (MCDM) methodology. Here, Generalized Triangular Intuitionistic Fuzzy Numbers (GTIFNs) are considered. Here, GTIFNs are taken to deal with the uncertainty and we introduce a new de-fuzzification method for converting the GTIFNs to corresponding crisp values. Here, we have applied two MCDM techniques namely the Entropy-weighted method and Vlekriterijumsko KOmpromisno Rangiranje (VIKOR) method. The entropy-weighted method is used for evaluating the criteria weights and the VIKOR method is applied to rank the alternatives. Last, to verify the stability and vagueness of the system, we perform sensitivity and comparative analysis. © 2024 World Scientific Publishing Company.

关键词： entropy gender equality GTIFN VIKOR Women empowerment

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Sources of optical radiation based on ZnTe/ZnSe/ZnS heterostructures

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Materials Today: Proceedings 2022年 62卷 5763-5766页

作者： Slyotov, Mykhailo Mazur, Tetiana Prokopiv, Volodymyr Slyotov, Oleksii Mazur, Myroslav Department of Optics Physical Publishing and Printing/Technical and Computer Science Institute Yuriy Fedkovych Chernivtsi National University 2 Kotsubinsky Str. Chernivtsi 58012 Ukraine Department of General and Applied Physics/Institute of Architecture Construction and Power Engineering Ivano-Frankisvsk National Technical University of Oil and Gas 15 Karpatska Str. Ivano-Frankivsk 76019 Ukraine Department of Solid State Physics and Chemistry/Faculty of Physics and Technology Vasyl Stefanyk Precarpathian National University 57 Shevchenko Str. Ivano-Frankivsk 76019 Ukraine

The heterostructures of zinc chalcogenides β-ZnTe/ZnSe/ZnS were obtained by isovalent substitution. The regimes for obtaining materials that ensured the stability of the cubic lattice of heterolayers of zinc chalcogenides β-ZnSe and β-ZnS were determined. The isovalent elements Se and S determined the solid-phase substitution and determined the formation of heterolayers in the lattice of the surface of the base crystal. The optical transmission, reflection, and photoluminescence spectra of the heterolayers are studied. The analysis of the results of studies of the energy structure of the obtained heterolayers was carried out, which indicates the correspondence of these heterolayers to the compounds of zinc selenide and zinc sulfide obtained by classical technological processes. They are also confirmed by the results of studies of the optical transmission of β-ZnSe and β-ZnS films specially obtained on transparent substrates under the same growing conditions as compared to isovalent substitution. © 2022

关键词： Light transmission

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Fed-Haul: A Federated Learning Dual Band Point Multi-Point Backhaul Requirements in 5G Evolution and Beyond 23

Fed-Haul: A Federated Learning Dual Band Point Multi-Point B...

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6th International Conference on Networking, Intelligent Systems and Security, NISS 2023

作者： Chehri, Abdellah Chaibi, Hasna Mettiti, Abderrahmane Saadane, Rachid Jeon, Gwanggil Dep. of Math. and Computer Science Royal Military College of Canada Kingston Canada Supmti Genius Laboratory Hassan Rabat Morocco It Rabat Center Faculty of Sciences Mohammed v University Rabat Morocco SIRC-LAGES-EHTP Hassania School of Public Works Casalanca Morocco Department of Embedded Systems Engineering Incheon National University Korea Republic of

ISBN: (纸本)9798400700194

The rapid adoption of smartphones and the explosive growth of data traffic due to these devices have been phenomenal. As the world anticipates more connected devices - the Internet of Things (IoT), vehicle-to-vehicle (V2V) communications, and wearable devices - and more value-added applications and services (ultra-high-definition video, 360° video, virtual reality, smart cars, etc.), leading industry experts are calling for the sixth generation (6G) networks. Federated learning is a common distributed machine learning framework. Through the training of the global model, the problems of large communication overhead and data privacy protection in traditional centralized machine learning are solved. Federated learning (FL) is essential in optimizing wireless communication networks' resources. On the other hand, wireless communications are crucial for FL. Therefore, the purpose of this survey paper is to bridge this gap in the literature by discussing the interdependency between FL and backhaul wireless communications. © 2023 ACM.

关键词： 5G mobile communication systems

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Adaptation of cluster analysis methods to optimize a biomechanical motion model of humans in a nursing bed 28

Adaptation of cluster analysis methods to optimize a biomech...

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28th European Signal Processing Conference, EUSIPCO 2020

作者： Demmer, Julia Kitzig, Andreas Stockmanns, Gudrun Naroska, Edwin Viga, Reinhard Grabmaier, Anton Hochschule Niederrhein University of Applied Science Faculty of Electrical Engineering and Computer Science Krefeld Germany University of Duisburg-Essen Department of Electronic Components and Circuits EBS Duisburg Germany Fraunhofer IMS University of Duisburg-Essen Department EBS Duisburg Germany

ISBN: (纸本)9789082797053

The paper considers the optimization of a Hidden-Markov Model (HMM) based method for the generation of averaged motion sequences. To create averaged motion sequences, motion sequences of different test persons were originally recorded with a motion capture system (MoCap system) and then averaged using an HMM approach. The resulting averaged data sets, however, partly showed serious motion artifacts and uncoordinated intermediate movements, especially in the extremities. The aim of this work was to combine only movements with similar courses in the extremities by a suitable cluster analysis. For each test person, model body descriptions of 21 body elements are available, each of which is represented in three-dimensional time series. For optimization, the MoCap data are first compared using time warp edit distance (TWED) and clustered using an agglomerative hierarchical procedure. Finally, the data of the resulting clusters are used to generate new averaged motion sequences using the HMM approach. The resulting averaged data can be used, for example, in a simulation in a multilevel biomechanical model. © 2021 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Hidden Markov models

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