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International Conference on Autonomous Robots and Agents, ICARA

作者： Gulnara A.Abitova Miras Abalkanov Gulnara A. Abitova Gulnur Shuteyeva Elvira Aitmukhanbetova Korlan Kulniyazova Dep. Computer Engineeering Astana IT University Astana Kazakhstan Dept. Electical Engineering and Computer Science State Univerity of New York at Binghamton Binghamton USA Dept. System Anslysis and Managemnt Eurasian National University Astana Kazakhstan

ISBN: (数字)9798350394245

ISBN: (纸本)9798350394252

The paper presents a comprehensive analysis of the integration of Cloud AI in real-time fraud detection, highlighting significant advancements and practical applications. It is identifying a key challenge such as data privacy and the need for scalable solutions, while also exploring the potential of innovative AI algorithms and strategic partnerships to enhance fraud detection capabilities. Through detailed case studies, the work is demonstrated the effective application of Cloud AI across various sectors. The study concludes that Cloud AI, despite not being a complete solution, plays a crucial role in strengthening fraud detection systems. Our findings suggest that strategic implementation of Cloud AI can lead to more adaptive and efficient systems, thereby significantly contributing to the economic sector's resilience against fraudulent activities. This paper underscores the transformative impact of Cloud AI in fraud detection, offering insights into its future potential and laying the groundwork for further advancements in this field.

关键词： Economics Training Data privacy Technological innovation Scalability Real-time systems Fraud

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Zero-Shot Classification by Large-Margin Distance Learning 10

Zero-Shot Classification by Large-Margin Distance Learning

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10th International Conference on computer and Knowledge engineering, ICCKE 2020

作者： Reza Zarei, Mohammad Taheri, Mohammad Shiraz University Dep. of Computer Science and Engineering Shiraz Iran

ISBN: (纸本)9781728185668

Zero-Shot Learning (ZSL) has increasingly attained a lot of attentions due to the scalability it provides to recognition models for classifying instances from new classes of which no training data have been seen before. This scalability is achieved by providing semantic information about new classes, which could be obtained remarkably easier, with a lower cost, in comparison with gathering a new training set. In other words, ZSL can be seen as a subset of transfer learning. In this paper, a distance function is learnt in order to discriminate the classes with a customized large-margin loss function. We also propose a nonlinear prototype learning approach by defining a theoretical-based, but simple mapping function to generate the class prototypes from associated semantic information. The instances are compared with the generated prototypes for classification considering the learnt distance function. The evaluations on five widely-used ZSL datasets, illustrate the effectiveness and superiority of the proposed method in comparison with the state-of-the-art ZSL approaches, despite of its simplicity. © 2020 IEEE.

关键词： Distance education

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Prediction of Electrical Characteristics of a-IGZO TFT Based on Transfer Learning-Based Variational Autoencoder

Prediction of Electrical Characteristics of a-IGZO TFT Based...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Khean-Thye Bea Shih-Shin Hu Wei-Hsuan Lin Da-Zheng Lin Hsin-Hui Hu Xiu-Zhi Chen Ting-Ru Lin Yen-Lin Chen Kun-Ming Chen Wai Khuen Cheng Dep. of Electronic Engineering National Taipei University of Technology Taipei Taiwan Dep. of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan Taiwan Semiconductor Research Institute Hsinchu Taiwan Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Kampar Malaysia

In this study, we proposed a transfer-learning based variational autoencoder model for predicting the electrical characteristics in the parameter tuning process of a-IGZO TFT structure design. The result achieve a high R2 score of 0.9704 with a low-computing-power hardware-friendly method that reduced time consumption significantly compared to prior approaches. The findings have practical implications for mitigating the time-consuming nature of TCAD simulations, and the method can expand to various types of input data while ensuring high performance and generalization. We demonstrated significant improvement in generalization and accuracy through a k-fold validation.

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Algorithms for Enhanced Indoor Positioning and Tracking based on a 60-GHz Radar Platform

Algorithms for Enhanced Indoor Positioning and Tracking base...

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Workshop on Metrology for Industry 4.0 and IoT

作者： F. Shamsfakhr M. Corrà A. Ferrari D. Macii L. Palopoli D. Fontanelli Dep. of Industrial Engineering University of Trento Italy Tretec S.r.L. Trento Italy VNG Ingegneria Trento Italy Dep. of Information Engineering and Computer Science University of Trento Italy

ISBN: (数字)9781665410939

ISBN: (纸本)9781665410946

Wireless technologies are increasingly adopted for indoor localisation and tracking. Among them, radars based on 60 GHz phased-array transceivers are becoming a viable and low-cost solution, which delivers high accuracy with a low computational cost while preserving people privacy. In this paper, we propose two techniques for radar-based people tracking and localisation that tackle important effects such as delayed and spurious detection. We also offer a thorough and scientifically sound experimental analysis of the performance of the proposed techniques using an off-the-shelf phased-array device (i.e., the System-on-Chip (SOC) TI IWR6843).

关键词： Wireless communication Performance evaluation Privacy Target tracking Radar measurements Radar Radar tracking

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Discrete cosine transform as universal sentence encoder

arXiv

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arXiv 2021年

作者： Almarwani, Nada Diab, Mona Dep. of Computer Science The George Washington University Dep. of Computer Science College of Computer Science and Engineering Taibah University Facebook AI Research

Modern sentence encoders are used to generate dense vector representations that capture the underlying linguistic characteristics for a sequence of words, including phrases, sentences, or paragraphs. These kinds of representations are ideal for training a classifier for an end task such as sentiment analysis, question answering and text classification. Different models have been proposed to efficiently generate general purpose sentence representations to be used in pretraining protocols. While averaging is the most commonly used efficient sentence encoder, Discrete Cosine Transform (DCT) was recently proposed as an alternative that captures the underlying syntactic characteristics of a given text without compromising practical efficiency compared to averaging. However, as with most other sentence encoders, the DCT sentence encoder was only evaluated in English. To this end, we utilize DCT encoder to generate universal sentence representation for different languages such as German, French, Spanish and Russian. The experimental results clearly show the superior effectiveness of DCT encoding in which consistent performance improvements are achieved over strong baselines on multiple standardized datasets. Copyright © 2021, The Authors. All rights reserved.

关键词： Discrete cosine transforms

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Usage of Blockchain Technology for Improving computer Security

Usage of Blockchain Technology for Improving Computer Securi...

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Information Technology (ACIT)

作者： Adai Almomani Al Refai Mohammed N Ahmad Aburomman Omar Khair Alla Alidmat Qotadeh Saber Fuad Alshariedeh Mohammed Khouj Department of Computer Science Zarqa University Zarqa Jordan Software Engineering Dep. Zarqa University Zarqa Jordan Department of Data Science and A Zarqa University Zarqa Jordan Department of Electronic Enginnering Univeristy of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Cyber threats have grown increasingly sophisticated, surpassing the capabilities of traditional security measures. Blockchain technology, with its decentralized and immutable structure, offers a potential solution to these challenges. This study examines the role of blockchain tools such as smart contracts, decentralized identity management, and secure data sharing in enhancing cybersecurity. A review of key literature and analysis of survey data from cybersecurity professionals demonstrate blockchain's effectiveness in improving data integrity, access control, and secure communications. The results show significant correlations between the adoption of blockchain technologies and the mitigation of threats like data breaches and unauthorized access. While blockchain presents promising advancements, scalability, regulatory barriers, and energy consumption remain challenges. The study provides valuable insights for researchers, practitioners, and policymakers on how blockchain can be integrated into modern cybersecurity strategies to create more secure digital environments.

关键词： Access control Industries Proof of stake Scalability Smart contracts Data protection Blockchains computer security Identity management systems Business

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Stabilization of THz waves using chaos supremacy

Stabilization of THz waves using chaos supremacy

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2023 International Conference on Smart-Green Technology in Electrical and Information Systems, ICSGTEIS 2023

作者： Kuwashima, Fumiyoshi Jarrahi, Mona Cakmakyapan, Semih Wada, Kenji Haraguchi, Masanobu Kawakami, Yuki Moriyasu, Takeshi Morikawa, Osamu Kurihara, Kazuyoshi Kitahara, Hideaki Furuya, Takashi Nakajima, Makoto Tani, Masahiko Fukui University of Technology Dep. of Electr. and Electron. Eng. Fukui Japan Ucla Electrical and Computer Eng. Los AngelesCA90095 United States Osaka Metropolitan University Dep. of Physics and Electronics Osaka Japan Tokushima Univ. Industrial and Social Science Grad. Sch. of Tech. Tokushima Japan Fukui Col. Natl. Inst. of Tech. Dep. of Elec. and Inf. Eng. Fukui Japan University of Fukui Faculty of Engineering Fukui Japan Japan Coast Guard Academy Liberal Arts Kure Japan University. of Fukui School of Education Fukui Japan FIR-UF University of Fukui Fukui Japan Osaka Univ. Inst. of Laser Engineering Osaka Japan

ISBN: (纸本)9798350382822

We apply the chaos supremacy of laser chaos system to the high stabilization of THz waves. Stability was demonstrated by down converting the optical beats between laser longitudinal modes in the THz domain to the radio frequency domain and comparing a conventional free running CW laser diode to a chaotically oscillating laser diode. Because of their exceptional stability, chaotically oscillating lasers are attractive options for optical pump sources in terahertz time-domain spectroscopy systems. © 2023 IEEE.

关键词： Stabilization

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Investigation and Study of the Performance of a DVB over Satellite Link Using M-Ary PSK 2

Investigation and Study of the Performance of a DVB over Sat...

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2nd IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer engineering, MI-STA 2022

作者： Bishi, Hager Alshepane, Somya H. Alhaddar, Kheriya F. Alnour, Saaida E. Electronic Technology Collage Communication Dep. Tripoli Libya Higher Institute of Science and Technology EE Department Al Khomes Libya Al-Mergib University Department of Electrical and Computer Engineering Al Khomes Libya Al-Gharefa College of Technical Engineering EE Department Al-Gharefa Libya

ISBN: (数字)9781665479189

ISBN: (纸本)9781665479189

In this paper, the performance of a Digital Video Broadcasting Over Satellite Link, a digital Video second-generation for satellite broadcasting (DVB-S2), was investigated and studied as a case study. The simulation was done by using Matlab in order to evaluate DVB-S2 performance developments in channel coding (LDPC codes) combined with modulation schemes (QPSK and 8-PSK ), code rates, forward error correction (FEC) and transmission channels. The obtained simulation results were graphically presented, where the bit error rate as a function of the signal to noise ratio (S/N) was evaluated as well. © 2022 IEEE.

关键词： Signal to noise ratio

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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 interdep.ndency between FL and backhaul wireless communications. © 2023 ACM.

关键词： 5G mobile communication systems

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Speech Emotion Recognition Using Deep Learning Techniques and Augmented Features

Speech Emotion Recognition Using Deep Learning Techniques an...

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International Conference on Electrical engineering, computer science and Informatics (EECSI)

作者： Shahed Mohammadi Niloufar Hemati Ali Hashemi Haniye Zandiye Computer Science & Systems Engineering Dep. Ayandegan University Mazandaran Iran Islamic Azad University Central Tehran Branch Tehran Iran Qom University of Technology Qom Iran

This paper presents a study on speech emotion recognition using deep learning techniques. The focus is on utilizing the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) and the Toronto Emotional Speech Set (TESS) datasets. The proposed approach involves preprocessing the audio signals by converting them into a sequence of numbers and normalizing them using max normalization. Furthermore, the signals are augmented using three methods: noise with a 0.015 noise rate, pitch adjustment, and a combination of noise with a 0.03 noise rate and pitch adjustment. The extraction of audio features includes a combination of two features, namely Mel Frequency Cepstrum Coefficients (MFCC) and Root Mean Square (RMS) energy, which provide distinct information from each other. These features are then used to train a CNN for emotion classification. Experimental results demonstrate the effectiveness of the proposed approach in accurately recognizing emotions from speech signals. The proposed approach achieves an accuracy of 93.6% on the RAVDESS dataset and 99.9% on the TESS dataset. These results highlight the potential of the proposed approach in practical applications such as speech therapy and human-computer interaction.

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