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arXiv 2024年

作者： Khan, Arif Ali Akbar, Muhammad Azeem Lahtinen, Valtteri Paavola, Marko Niazi, Mahmood Alatawi, Mohammed Naif Alotaibi, Shoayee Dlaim M3S Empirical Software Engineering Research Unit University of Oulu Oulu90014 Finland Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta53851 Finland Quanscient Oy Tampere Finland VTT Technical Research Ctr of Finland Ltd Oulu Finland Department of Information and Computer Science King Fahd University of Petroleum and Minerals Saudi Arabia Interdisciplinary Research Centre for Intelligent Secure Systems King Fahd University of Petroleum and Minerals Saudi Arabia Information Technology Department Faculty of Computers and Information Technology University of Tabuk Saudi Arabia Department of Artificial Intelligence and Data Science College of Computer Science and Engineering University of Hail Hail Saudi Arabia

Context: Quantum software systems represent a new realm in software engineering, utilizing quantum bits (Qubits) and quantum gates (Qgates) to solve the complex problems more efficiently than classical counterparts. Agile software development approaches are considered to address many inherent challenges in quantum software development, but their effective integration remains unexplored. Objective: This study investigates key causes of challenges that could hinders the adoption of traditional agile approaches in quantum software projects and develop an Agile-Quantum Software Project Success Prediction Model (AQSSPM). Methodology: Firstly, we identified 19 causes of challenging factors discussed in our previous study, which are potentially impacting agile-quantum project success. Secondly, a survey was conducted to collect expert opinions on these causes and applied Genetic Algorithm (GA) with Naive Bayes Classifier (NBC) and Logistic Regression (LR) to develop the AQSSPM. Results: Utilizing GA with NBC, project success probability improved from 53.17% to 99.68%, with cost reductions from 0.463% to 0.403%. Similarly, GA with LR increased success rates from 55.52% to 98.99%, and costs decreased from 0.496% to 0.409% after 100 iterations. Both methods result showed a strong positive correlation (rs=0.955) in causes ranking, with no significant difference between them (t=1.195, p=0.240>0.05). Conclusion: The AQSSPM highlights critical focus areas for efficiently and successfully implementing agile-quantum projects considering the cost factor of a particular project. © 2024, CC BY.

关键词： Genetic algorithms

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Collective Learning Mechanism based Optimal Transport Generative Adversarial Network for Non-parallel Voice Conversion

arXiv

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arXiv 2025年

作者： Dhar, Sandipan Akhter, Md. Tousin Jana, Nanda Dulal Das, Swagatam Department of Computer Science and Engineering National Institute of Technology Durgapur India Department of Computer Science and Engineering Indian Institute of Technology Bombay India Electronics and Communication Sciences Unit Indian Statistical Institute Kolkata India

After demonstrating significant success in image synthesis, Generative Adversarial Network (GAN) models have likewise made significant progress in the field of speech synthesis, leveraging their capacity to adapt the precise distribution of target data through adversarial learning processes. Notably, in the realm of State-Of-The-Art (SOTA) GAN-based Voice Conversion (VC) models, there exists a substantial disparity in naturalness between real and GAN-generated speech samples. Furthermore, while many GAN models currently operate on a single generator discriminator learning approach, optimizing target data distribution is more effectively achievable through a single generator multi-discriminator learning scheme. Hence, this study introduces a novel GAN model named Collective Learning Mechanism-based Optimal Transport GAN (CLOT-GAN) model, incorporating multiple discriminators, including the Deep Convolutional Neural Network (DCNN) model, Vision Transformer (ViT), and conformer. The objective of integrating various discriminators lies in their ability to comprehend the formant distribution of mel-spectrograms, facilitated by a collective learning mechanism. Simultaneously, the inclusion of Optimal Transport (OT) loss aims to precisely bridge the gap between the source and target data distribution, employing the principles of OT theory. The experimental validation on VCC 2018, VCTK, and CMU-Arctic datasets confirms that the CLOT-GAN-VC model outperforms existing VC models in objective and subjective assessments. © 2025, CC BY.

关键词： Generative adversarial networks

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Robust Classification of High-Dimensional Data using Data-Adaptive Energy Distance

arXiv

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arXiv 2023年

作者： Choudhury, Jyotishka Ray Saha, Aytijhya Roy, Sarbojit Dutta, Subhajit School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta United States Indian Statistical Institute Kolkata India Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Saudi Arabia Applied Statistics Unit Indian Statistical Institute Kolkata India Department of Mathematics and Statistics Indian Institute of Technology Kanpur India

Classification of high-dimensional low sample size (HDLSS) data poses a challenge in a variety of real-world situations, such as gene expression studies, cancer research, and medical imaging. This article presents the development and analysis of some classifiers that are specifically designed for HDLSS data. These classifiers are free of tuning parameters and are robust, in the sense that they are devoid of any moment conditions of the underlying data distributions. It is shown that they yield perfect classification in the HDLSS asymptotic regime, under some fairly general conditions. The comparative performance of the proposed classifiers is also investigated. Our theoretical results are supported by extensive simulation studies and real data analysis, which demonstrate promising advantages of the proposed classification techniques over several widely recognized methods. © 2023, CC BY.

关键词： Data assimilation

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A Survey of Artificial Intelligence Solutions in Response to the COVID-19 Pandemic in Saudi Arabia

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Procedia computer science 2021年 194卷 190-201页

作者： Jailan Aljizawi Dina Dalloul Layal Ghryani Shaymaa AlDabbagh Tayeb Brahimi Computer Science Department College of Engineering Effat University Jeddah Saudi Arabia. Energy and Technology Research Center Natural Science Mathematics and Technology Unit College of Engineering Effat University Jeddah Saudi Arabia.

Artificial Intelligence (AI) has a significant impact on the course of the COVID-19 pandemic from various aspects. This paper aims to explore the trends of AI applications and how they help predict and prevent the progress of COVID-19 in Saudi Arabia. The method used in this study is based on a narrative review of recent literature on AI and COVID-19 and on a survey conducted on 211 participants. Results show that AI is a crucial element to overcoming COVID 19. The use of COVID-19 related Apps that helped reduce the pandemic spread was more common among the age group participants ranging from 15 - 30. The study concluded that COVID-19 made a positive impact on everyone, and by the use of technology, things got much more comfortable to help in enduring any long-term health consequences.

关键词： Artificial Intelligence COVID-19 pandemic

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Effects of Low-Geometrical-Precision PCB Manufacturing on mmWave Passive Metasurfaces

Effects of Low-Geometrical-Precision PCB Manufacturing on mm...

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2023 International Technical Conference on Circuits/Systems, computers, and Communications, ITC-CSCC 2023

作者： La-Aiddee, Panithan Sangwongngam, Paramin Pongpaibool, Pornanong Tancharoen, Datchakorn Wuttisittikulkij, Lunchakorn Vanichchanunt, Pisit Chulalongkorn University Wireless Communication Ecosystem Research Unit Faculty of Engineering Department of Electrical Engineering 254 Phayathai Road Pathumwan Bangkok10330 Thailand 111 Thailand Science Park Phahonyothin Road Khlong Neung Khlong Luang Pathum Thani12120 Thailand 111 Thailand Science Park Phahonyothin Road Khlong Neung Khlong Luang Pathum Thani12120 Thailand Panyapiwat Institute of Management Faculty of Engineering and Technology 85/1 Moo 2 Chaengwattana Rd. Bang-Talat Pakkred Nonthaburi11120 Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering 1518 Pracharat 1 RoadWongsawang Bangkok10800 Thailand

ISBN: (纸本)9798350326413

As millimeter-wave (mmWave) signals suffer from high path loss and blocking, passive metasurface reflectors that can be fabricated with conventional PCB manufacturing become a promising cost-effective candidate for enhancing mmWave mobile service coverage. In this work, we show how PCB fabrication geometrical precision affects the performance of passive reflectors that are used in 5G and beyond mmWave networks. With the broad beamwidth of PCB-based metasurfaces, the dimension tolerance of PCB manufacturing can be traded off between cost benefits and key communications performance. From our numerical results based on full-wave simulations, the received power at the design anomalous angle is not significantly affected by the geometrical precision while high errors can cause higher interference at specular and mirror angles. However, the fabrication error in unit cell shape rotation does not influence the received power at the design anomalous, specular and mirror angles. © 2023 IEEE.

关键词： Millimeter waves

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A Meta-Analysis of Artificial Intelligence Applications for Tracking COVID-19: The Case of the U.A.E

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Procedia computer science 2021年 194卷 180-189页

作者： Hala Haneya Dhekra AlKaf Faigah Bajammal Tayeb Brahimi Computer Science Department College of Engineering Effat University Jeddah Saudi Arabia. Energy and Technology Research Center Natural Science Mathematics and Technology Unit College of Engineering Effat University Jeddah Saudi Arabia.

Coronavirus disease (COVID-19) is an infectious respiratory disease that was first found in Wuhan, China, on December 31, 2019. It is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of November 10, 2020, more than fifty million cases have been confirmed, and more than one million deaths have been reported globally. This situation has created a serious challenge for all countries to institute a variety of control measures to track and slow down the spread of the virus and prevent the increasing number of deaths. In recent years, there has been an ongoing interest in using Artificial Intelligence (A.I.) in healthcare to create new treatments and detecting diseases. The objective of this study is to analyze the application and the impact of A.I. on the breakout of COVID-19 and discuss the contribution of A.I. to the fight against the pandemic based on the most recent applications used in the united Arab Emirates, including Dubai Police Movement Restriction Monitoring System, Taxis Preventive Measures Compliance System, Mobile App "Wai-Eye," Smart Helmets, Virtual Doctor, and The Department of Health - Abu Dhabi (DoH) Remote Healthcare App. The method used in this study is based on a meta-analysis of recent COVID-19 studies from various databases such as scienceDirect, Sage Journals, SpringerLink, ResearchGate, Emerald Open Research, and IEEE Xplore. The COVID-19 data was based on Johns Hopkins University Center for Systems science and Engineering (JHU CCSE). Results showed that A.I. applications provided the necessary prevention of the spread of COVID-19, assisted in monitoring restrictions and preventive measures violations, and provided remote healthcare, which directly impacted the number of hospital visits amidst the lockdown. The study concluded that A.I. has proven to be effective in supporting governments in fighting the pandemic.

关键词： Artificial Intelligence COVID-19 applications detection prevention

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AI-driven system for non-contact continuous nocturnal blood pressure monitoring using fiber optic ballistocardiography

Communications Engineering

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Communications Engineering 2024年第1期3卷 1-11页

作者： Huang, Yandao Chen, Lin Li, Chenggao Peng, Junyao Hu, Qingyong Sun, Yu Ren, Hao Lyu, Weimin Jin, Wen Tian, Junzhang Yu, Changyuan Cheng, Weibin Wu, Kaishun Zhang, Qian Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong The Hong Kong University of Science and Technology (Guangzhou) Guangdong Guangzhou China Department of Cardiac Intensive Care Unit Guangdong Second Provincial General Hospital Guangdong Guangzhou China Institute for Healthcare Artificial Intelligence Guangdong Second Provincial General Hospital Guangdong Guangzhou China Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong

Continuous monitoring of nocturnal blood pressure is crucial for hypertension management and cardiovascular risk assessment. However, current clinical methods are invasive and discomforting, posing challenges. These traditional techniques often disrupt sleep, impacting patient compliance and measurement accuracy. Here we introduce a non-contact system for continuous monitoring of nocturnal blood pressure, utilizing ballistocardiogram signals. The key component of this system is the utilization of advanced, flexible fiber optic sensors designed to capture medical-grade ballistocardiogram signals accurately. Our artificial intelligence model extracts deep learning and fiducial features with physical meanings and implements an efficient, lightweight personalization scheme on the edge device. Furthermore, the system incorporates a crucial algorithm to automatically detect the user’s sleeping posture, ensuring accurate measurement of nocturnal blood pressure. The model underwent rigorous evaluation using open-source and self-collected datasets comprising 158 subjects, demonstrating its effectiveness across various blood pressure ranges, demographic groups, and sleep states. This innovative system, suitable for real-world unconstrained sleeping scenarios, allows for enhanced hypertension screening and management and provides new insights for clinical research into cardiovascular complications. © The Author(s) 2024.

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DCT-phase statistics for forged IMEI numbers and air ticket detection

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Expert Systems with Applications 2021年 164卷 114014-114014页

作者： Nandanwar, Lokesh Shivakumara, Palaiahnakote Kanchan, Swati Basavaraja, V. Guru, D.S. Pal, Umapada Lu, Tong Blumenstein, Michael Faculty of Computer Science and Information Technology University of Malaya Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Department of Studies in Computer Science University of Mysore Karnataka India National Key Lab for Novel Software Technology Nanjing University China Sydney Australia

New tools have been developing with the intention of having more flexibility and greater user-friendliness for editing the images and documents in digital technologies, but, unfortunately, they are also being used for manipulating and tampering information. Examples of such crimes include creating forged International Mobile Equipment Identity (IMEI) numbers which are embedded on mobile packages and inside smart mobile cases for illicit activities. Another example of such crimes is altering the name or date on air tickets for breaching security at the airport. This paper presents a new expert system for detecting forged IMEI numbers as well as altered air ticket images. The proposed method derives the phase spectrum using the Discrete Cosine Transform (DCT) to highlight the suspicious regions;it is unlike the phase spectrum from a Fourier transform, which is ineffective due to power spectrum noise. From the phase spectrum, our method extracts phase statistics to study the effect of distortions introduced by forgery operations. This results in feature vectors, which are fed to a Support Vector Machine (SVM) classifier for detection of forged IMEI numbers and air ticket images. Experimental results on our dataset of forged IMEI numbers (which is created by us for this work), on altered air tickets, on benchmark datasets of video caption text (which is tampered text), and on altered receipts of the ICPR 2018 FDC dataset, show that the proposed method is robust across different datasets. Furthermore, comparative studies of the proposed method with the existing methods on the same datasets show that the proposed method outperforms the existing methods. The dataset created will be available freely on request to the authors. © 2020 Elsevier Ltd

关键词： Discrete cosine transforms

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Machine Learning-Assisted Protein Structure Prediction: An AI Approach for Biochemical Insights

Machine Learning-Assisted Protein Structure Prediction: An A...

引用

International Conference on science technology Engineering and Management (ICONSTEM)

作者： S. Radha Mahendran Akriti Dogra Dinesh Mendhe S. B G Tilak Babu Shriniket Dixit Surya Pratap Singh Department of Bioinformatics Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai India Department of Oral & Maxillofacial Surgery Manav Rachna Dental College Faridabad Haryana Office of Research Computing Rutgers State University of New Jersey New Brunswick New Jersey Department of ECE Aditya Engineering College Surampalem School of Computer Science Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Multi-Disciplinary Research Unit UPUMS Etawah UP

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

Recently, machine learning has changed the field of bioinformatics by making it possible to identify and understand protein structures in ways that were not possible before. This paper gives a thorough examination of a unique way for guessing protein structure. It uses complex machine learning algorithms to give you deep biological information. We use cutting-edge deep learning models along with traditional computer methods to improve the accuracy and usefulness of predicting protein structures. The suggested AI-assisted method uses a variety of protein structures and attention processes to find complex spatial connections and long-range interactions within protein sequences. It does this by using convolutional neural networks (CNNs), recurrent neural networks (RNNs), and CNNs. Using machine learning to predict protein structures can be very useful, and this work shows how it can effectively connect computational biology and experimental research. Our method not only makes predictive modeling better, but it also gives researchers a useful way to learn more about the complex links between protein structure and function as the field grows. The study's results could speed up the process of finding new drugs and help us learn more about how different diseases work at the molecular level.

关键词： Proteins Drugs Deep learning Recurrent neural networks Machine learning algorithms Computational modeling Predictive models

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Distribution Regression with Sliced Wasserstein Kernels

arXiv

引用

arXiv 2022年

作者： Meunier, Dimitri Pontil, Massimiliano Ciliberto, Carlo Gatsby Computational Neuroscience Unit University College London London United Kingdom Italian Institute of Technology Genoa Italy Department of Computer Science University College London London United Kingdom

The problem of learning functions over spaces of probabilities - or distribution regression - is gaining significant interest in the machine learning community. A key challenge behind this problem is to identify a suitable representation capturing all relevant properties of the underlying functional mapping. A principled approach to distribution regression is provided by kernel mean embeddings, which lifts kernel-induced similarity on the input domain at the probability level. This strategy effectively tackles the two-stage sampling nature of the problem, enabling one to derive estimators with strong statistical guarantees, such as universal consistency and excess risk bounds. However, kernel mean embeddings implicitly hinge on the maximum mean discrepancy (MMD), a metric on probabilities, which may fail to capture key geometrical relations between distributions. In contrast, optimal transport (OT) metrics, are potentially more appealing. In this work, we propose an OT-based estimator for distribution regression. We build on the Sliced Wasserstein distance to obtain an OT-based representation. We study the theoretical properties of a kernel ridge regression estimator based on such representation, for which we prove universal consistency and excess risk bounds. Preliminary experiments complement our theoretical findings by showing the effectiveness of the proposed approach and compare it with MMD-based estimators. Copyright © 2022, The Authors. All rights reserved.

关键词： Embeddings

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