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14th International Conference on Communications, COMM 2022

作者： Degaru, Radu Mirica, Adrian-Dumitru Dogaru, Ioana Natural Computing Laboratory University 'Politehnica' of Bucharest Dept. of Applied Electronics and Information Eng. Bucharest Romania Doctoral School of Electronics Telecommunications and Information Technology University 'Politehnica' of Bucharest Bucharest Romania

ISBN: (纸本)9781665494854

Comparative synapses are proposed and investigated in the context of convolutional neural networks as replacements for the traditional, multiplier-based synapses. A comparative synapse is an operator inspired from the min() operator used in fuzzy-logic a replacement for product to implement AND function. Its implementation complexity is linear in the number of bits unlike multipliers, requiring quadratic complexity. In effect, using a typical resolution of 8 bits the use of comparative synapse would reduce 8 times the number of hardware resources allocated for the operator. A C-CNN model was constructed to support comparative synapses and their update and error propagation rules. GPU acceleration of the C-CNN model was achieved using CUPY. The model was trained with several widely known image recognition datasets including MNIST, CIFAR and USPS. It turns out that functional performance (accuracy) is not dramatically affected in C-CNN against a similar traditional CNN model with multiplicative operators, thus opening an interesting implementation perspective, particularly for the TinyML and HW-oriented solutions with significant reduction in energy, silicon area and costs. The approach is scalable to more sophisticated CNN models providing adequate optimized operators adapted to this new synaptic model. © 2022 IEEE.

关键词： Convolutional neural networks

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U-Slot Microstrip Patch Array Antenna for UHF RFID Reader 6th

U-Slot Microstrip Patch Array Antenna for UHF RFID Reader

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6th International Conference on Electrical, Control and Computer Engineering, InECCE 2021

作者： Ariff, Mohd Hisyam Mohd Saifullah, Imran Jusoh, Rahimah Shakib, Mohammed Nazmus Abas, Mohammad Fadhil Faculty of Electrical & Electronics Engineering Technology Universiti Malaysia Pahang Pahang Pekan26600 Malaysia Centre for Mathematical Sciences College of Computing & Applied Sciences Universiti Malaysia Pahang Pahang Gambang26300 Malaysia

ISBN: (纸本)9789811686894

This paper aims to design and analyze a microstrip patch array antenna for the application of Radio Frequency Identification (RFID). Array antennas are widely used in the RFID applications as it offers high gain and directivity to allow long distance read range. The microstrip patch is arranged in 2 × 2 array and is printed on FR-4 materials. In compliance with the Malaysian RFID regulated range of frequency of 919 to 923 MHz, the antenna is designed to meet its specifications. The operating frequency of the microstrip patch antenna array is 921 MHz. The FR-4 substrate with a dielectric constant of 4.7 and height of 0.16 cm. Theoretical studies and calculations on this topic have been done in order to design the microstrip patch antenna array with the correct dimensions. By using the CST Microwave Studio 2014 as the primary software to model and simulate the results, there are a few parameters that are going to be analyze which includes reflection coefficient, Voltage Standing Ratio (VSWR), gain, directivity, radiation pattern and bandwidth. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Radio frequency identification (RFID)

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Remaining Useful Life for Aircraft Engine Forecasting Based on Data-driven

Remaining Useful Life for Aircraft Engine Forecasting Based ...

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2021 International Conference on Mechatronics, Automation and Intelligent Control, MAIC 2021

作者： Niu, Wei Huang, Wei Wang, Hao Cheng, Juan Wang, Sen Xi'An Aeronautics Computing Technique Research Institute AVIC Xi'an China Xi'An North Jierui Opto-electronic Technology CO. LT Xi'an China Xi'An Institute of Applied Optics Xi'an China

As for the low forecasting precision problem about time series in avionics complex system, a time series combined forecasting model of complex systems was established based on GM (grey model is referred to as GM). However, using the traditional GM(1, 1) model to predict complex system failures might result in huge forecasting errors. To solve the drawbacks of the traditional GM(1, 1) model, this work developed the traditional GM(1, 1) model (abbreviated as DGM(1, 1)), which can predict non-geometric sequences more correctly. To reduce such mistakes, a PS-DGM (1, 1) forecast model was created by combining DGM(1, 1) with the particle-swarm optimization process. The DGM(1, 1) model was utilized to generate the forecasting sequence, and then the particle-swarm optimization approach was employed to further alter them, resulting in the degradation trend prediction of avionics complex system characteristic parameters. The experimental results show that the PS-DGM(1, 1) model's validity was confirmed by tracking and forecasting avionics equipment experiment data in a real-world setting, and that the suggested model achieves higher precision than the old one. © Published under licence by IOP Publishing Ltd.

关键词： Time series

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GAF-GAN: Gated Attention Feature Fusion Image Inpainting Network Based on Generative Adversarial Network 29

GAF-GAN: Gated Attention Feature Fusion Image Inpainting Net...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Qiu, Ran Liang, Hu Liu, Yuchen Zhao, Shengrong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

Image inpainting, which aims to reconstruct reasonably clear and realistic images from known pixel information, is one of the core problems in computer vision. However, due to the complexity and variability of the underwater environment, the inability to extract valid pixel points and insufficient correlation between feature information in existing image inpainting techniques lead to blurring in the generated images. Therefore, a novel gated attention feature fusion image inpainting network based on generative adversarial networks (GAF-GAN) is proposed. The accuracy of feature similarity matching depends heavily on the validity of the information contained in the features. On the one hand, gating values are dynamically generated by gated convolution to reduce the interference of invalid information. On the other hand, semantic information at distant locations in an image is accurately acquired by the attention mechanism. For these reasons, we designed an improved gated attention mechanism. Gated attention mechanism make the network focus on effective information such as high-frequency texture and color fidelity of restored images. In addition, the dense feature fusion module is added to expand the overall receptive field of the network to fully learn the image features. Experimental results show that the proposed method can effectively repair defective images with complex texture structures and improve the reality and integrity of image details and structures. © 2023 IEEE.

关键词： Dense feature fusion module Gated attention mechanism Image inpainting Underwater environment

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Malware analysis and multi-label category detection issues: Ensemble-based approaches 3

Malware analysis and multi-label category detection issues: ...

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3rd International Conference on Intelligent Data Science Technologies and Applications, IDSTA 2022

作者： Alsmadi, Izzat Al-Ahmad, Bilal Alsmadi, Mohammad Department of Computing and Cyber Security Texas A&m San Antonio United States The University of Jordan Computer Information Systems Department Aqaba Jordan Applied Science University Information Technology Amman Jordan

ISBN: (数字)9781665499606

ISBN: (纸本)9781665499606

Detection of malware and security attacks is a complex process that can vary in its details and analysis activities. As part of the detection process, malware scanners try to categorize a malware once it is detected under one of the known malware categories (e.g. worms, spywares, viruses, etc.). However, many studies and researches indicate problems with scanners categorizing or identifying a particular malware under more than one malware category. This paper, and several others, show that machine learning can be used for malware detection especially with ensemble base prediction methods. In this paper, we evaluated several custom-built ensemble models. We focused on multi-label malware classification as individual or classical classifiers showed low accuracy in such *** paper showed that recent machine models such as ensemble and deep learning can be used for malware detection with better performance in comparison with classical models. This is very critical in such a dynamic and yet important detection systems where challenges such as the detection of unknown or zero-day malware will continue to exist and evolve. © 2022 IEEE.

关键词： Scanning

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Components for COVID19 Outbreak Control Model: A System Dynamics Perspective 7

Components for COVID19 Outbreak Control Model: A System Dyna...

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7th International Conference on Software Engineering and Computer Systems and 4th International Conference on Computational Science and Information Management, ICSECS-ICOCSIM 2021

作者： Ibrahim, Aisyah Abdul Kadir, Tuty Asmawaty Daniyal, Hamdan Kamaludin, Adzhar Faculty of Computing College of Computing Applied Sciences Universiti Malaysia Pahang Pahang Pekan Malaysia Faculty of Electrical and Electronic Engineering Technology Universiti Malaysia Pahang Pahang Pekan Malaysia

ISBN: (纸本)9781665414074

The world is facing a massive challenge as the COVID-19 outbreak strikes across the globe. Many efforts have been made to detect, control and contain the coronavirus proactively and aggressively before a further catastrophe occurs. Indeed, ending the global COVID-19 pandemic is not a simple task. It requires adequate planning and implementation of sustainable strategies and interventions to control COVID-19 from keep spreading globally. One way to address this issue is using System Dynamics (SD). With this aim in mind, this paper presents an initial COVID-19 modelling work in the formulation stage of SD methodology. A literature review was carried out on published and unpublished papers to understand the essential outbreak model design structure. Within this process, a total of 15 COVID-19 models in SD were gathered and analysed. As the outcome, this paper highlights the components of the conceptual representation model for the COVID-19 outbreak, which later can serve as the core basis for modelling complex COVID19 outbreak dynamics and interventions for future development. As an implication, a comprehensive model can be developed to support decision making. © 2021 IEEE.

关键词： COVID-19

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Body Fat Prediction using Various Regression Techniques 2

Body Fat Prediction using Various Regression Techniques

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2nd IEEE International Conference on Advances in computing, Communication and applied Informatics, ACCAI 2023

作者： Mahesh, Nikhil Pati, Peeta Basa Deepa, K. Yanan, Suresh Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electrical and Electronics Engineering Bengaluru India University of Technology and Applied Sciences-Al Musannah Sultanate of Oman Department of Engineering Oman

ISBN: (纸本)9798350315905

Predicting body fat percentage is essential for addressing the obesity problem. This paper compares the performance of several machine learning models based on Regression, to predict the body fat percentage. Using a dataset of 252 participants with information on age, weight, height, and fat percentage, the models were assessed based on multiple performance criteria, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Squared Error(MSE). The results demonstrates that Random Forest Regressor surpass other models with a lower RMSE of 0.276. These findings suggest that machine learning models can be a valuable tool for precise BFP, the use of machine learning provides a faster and more precise method for predicting body fat percentage. Overall, the study's results suggest that machine learning models can be valuable tool for accurate body fat percentage prediction. © 2023 IEEE.

关键词： Forecasting

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An overview of common emotion models in computer systems

An overview of common emotion models in computer systems

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Proceedings of the International Convention MIPRO

作者： M. Horvat A. Stojanović Ž . Kovač ević Department of Applied Computing Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Department of Computer Science and Information Technology Zagreb University of Applied Sciences Zagreb Croatia

ISBN: (数字)9789532331035

ISBN: (纸本)9781665484343

Emotions are an omnipresent and important factor in the interaction and communication between people. Since emotions are an indispensable part of human life, it would accelerate the progress of artificial intelligence and other fields of science that require data about emotions if they could be adequately described by computer systems. Today there are many different theories of affect, but few of them are used in affective computing. Other areas of computing also benefit from structured and expressive data models of the affective domain, such as human-computer interaction and brain-computer interfaces. Typical tasks include automated recognition and analysis of emotional states, mental fatigue, individual motivation, vigilance and stress resilience. In this paper four often used models of emotion and cognitive behavior are listed and their properties explained: discrete, dimensional, appraisal and action tendency models. For each model, algorithms are provided for similarity measures that can be used to determine the relatedness between different stimulation and estimation artefacts in their respective emotion spaces. The goal of this article is to help professionals find the optimal emotion model for their research and quickly become familiar with data modelling of affective states.

关键词： Emotion recognition Computational modeling Estimation Human factors Brain modeling Fatigue Extraterrestrial measurements

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Fake Twitter Followers Detection using Machine Learning Approach 2

Fake Twitter Followers Detection using Machine Learning Appr...

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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Zeshan Shabbir, Muhammad Naseer, Iftikhar Akhter, Shamim Abubakar, Muhammad Issa, Ghassan F. Hassaan Mehmood, Muhammad Comsats University Faculty of Information and Technology Islamabad Pakistan The Superior University Faculty of Computer Science & Information Technology Lahore Pakistan Minhaj University School of Library & Information Sciences Lahore Pakistan Fast School of Computing National University of Computer and Emerging Sciences Lahore Pakistan Skyline University College School of Information Technology University City Sharjah Sharjah1797 United Arab Emirates NCBA&E School of Computer Science Lahore Pakistan Applied Science Private Universit Applied Science Research Center Amman11937 Jordan

ISBN: (纸本)9798350335644

Fake user accounts on social media are a serious threat. Artificial intelligence can play an important role in getting rid of fake user accounts. Twitter has a problem with fake accounts that are run by automated bots. These bots are used for bad things like spamming, sabotaging trends, and getting more followers. In this research, publicly available information about Twitter users, such as their activity patterns, profile information, and tweets, to figure out how real each one is. Twitter's API and other ways of getting information will be used to get user data. The proposed model consists of a machine learning technique, and it detects whether the user accounts are real or fake. Various algorithms such as logistic regression, long short-term memory, K-mean, and random forest are applied to evaluate the proposed model. The experimental results show that the random forest algorithm has obtained the highest accuracy 0.7557, a precision of 0.7277, and F1 of 0.7943 among all other algorithms. © 2023 IEEE.

关键词： Logistic regression

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Algorithmic Fault Tolerance for Fast Quantum computing

arXiv

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arXiv 2024年

作者： Zhou, Hengyun Zhao, Chen Cain, Madelyn Bluvstein, Dolev Duckering, Casey Hu, Hong-Ye Wang, Sheng-Tao Kubica, Aleksander Lukin, Mikhail D. QuEra Computing Inc. 1284 Soldiers Field Road BostonMA02135 United States Department of Physics Harvard University CambridgeMA02138 United States AWS Center for Quantum Computing PasadenaCA91125 United States California Institute of Technology PasadenaCA91125 United States Department of Applied Physics Yale University New HavenCT06511 United States

Fast, reliable logical operations are essential for the realization of useful quantum computers [1–3], as they are required to implement practical quantum algorithms at large scale. By redundantly encoding logical qubits into many physical qubits and using syndrome measurements to detect and subsequently correct errors, one can achieve very low logical error rates. However, for most practical quantum error correcting (QEC) codes such as the surface code, it is generally believed that due to syndrome extraction errors, multiple extraction rounds—on the order of the code distance d—are required for fault-tolerant computation [4–14]. Here, we show that contrary to this common belief, fault-tolerant logical operations can be performed with constant time overhead for a broad class of QEC codes, including the surface code with magic state inputs and feed-forward operations, to achieve "algorithmic fault tolerance". Through the combination of transversal operations [7] and novel strategies for correlated decoding [15], despite only having access to partial syndrome information, we prove that the deviation from the ideal measurement result distribution can be made exponentially small in the code distance. We supplement this proof with circuit-level simulations in a range of relevant settings, demonstrating the fault tolerance and competitive performance of our approach. Our work sheds new light on the theory of quantum fault tolerance, potentially reducing the space-time cost of practical fault-tolerant quantum computation by orders of magnitude. Copyright © 2024, The Authors. All rights reserved.

关键词： Fault tolerance

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