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arXiv 2022年

作者： Upama, Paramita Basak Faruk, Jobair Hossain Nazim, Mohammad Masum, Mohammad Shahriar, Hossain Uddin, Gias Barzanjeh, Shabir Ahamed, Sheikh Iqbal Rahman, Akond Department of Computer Science and Engineering Eastern University Bangladesh Department of Software Engineering and Game Development Kennesaw State University United States Department Computer Science Kennesaw State University United States Department of Information Technology Kennesaw State University United States Schulich School of Engineering University of Calgary Canada Department of Physics and Astronomy University of Calgary Canada Department of Mathematics Statistics and Computer Marquette University United States Department of Computer Science Tennessee Tech university United States

Quantum Computing (QC) refers to an emerging paradigm that inherits and builds with the concepts and phenomena of Quantum Mechanic (QM) with the significant potential to unlock a remarkable opportunity to solve complex and computationally intractable problems that scientists could not tackle previously. In recent years, tremendous efforts and progress in QC mark a significant milestone in solving real-world problems much more efficiently than classical computing technology. While considerable progress is being made to move quantum computing in recent years, significant research efforts need to be devoted to move this domain from an idea to a working paradigm. In this paper, we conduct a systematic survey and categorize papers, tools, frameworks, platforms that facilitate quantum computing and analyze them from an application and Quantum Computing perspective. We present quantum Computing Layers, Characteristics of Quantum Computer platforms, Circuit Simulator, Open-source Tools Cirq, TensorFlow Quantum, ProjectQ that allow implementing quantum programs in Python using a powerful and intuitive syntax. Following that, we discuss the current essence, identify open challenges and provide future research direction. We conclude that scores of frameworks, tools and platforms are emerged in the past few years, improvement of currently available facilities would exploit the research activities in the quantum research community. © 2022, CC BY.

关键词： Circuit simulation

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BG-GAN: Generative AI Enable Representing Brain Structure-Function Connections for Alzheimer's Disease

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IEEE Transactions on Consumer Electronics 2025年

作者： Zhou, Tong Ding, Chen Jing, Changhong Liu, Feng Hung, Kevin Pham, Hieu Mahmud, Mufti Lyu, Zhihan Qiao, Sibo Wang, Shuqiang Tsang, Kim-Fung Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen518055 China University of Chinese Academy of Sciences Beijing100864 China Stevens Institute of Technology Department of Systems and Enterprises United States Hong Kong Metropolitan University School of Science and Technology Hong Kong Hong Kong VinUniversity College of Engineering and Computer Science VinUni-Illinois Smart Health Center Viet Nam King Fahd University of Petroleum and Minerals Interdisciplinary Research Center for Biosystems and Machines Department of Information and Computer Science SDAIA-KFUPM Joint Research Center for AI Dhahran Saudi Arabia Uppsala University Department of Game Design Faculty of Arts Sweden Tiangong University School of Software Tianjin300387 China

The relationship between brain structure and function is critical for revealing the pathogenesis of brain disorders, including Alzheimer's disease (AD). However, mapping brain structure to function connections is a very challenging task. In this work, a bidirectional graph generative adversarial network (BG-GAN) is proposed to represent brain structure-function connections. Specifically, by designing a module incorporating inner graph convolution network (InnerGCN), the generators of BG-GAN can employ features of direct and indirect brain regions to learn the mapping function between the structural domain and the functional domain. Besides, a new module named Balancer is designed to counterpoise the optimization between generators and discriminators. By introducing the Balancer into BG-GAN, both the structural generator and functional generator can not only alleviate the issue of mode collapse but also learn complementarity of structural and functional features. Experimental results using Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset show that both generated structure and function connections can improve the identification accuracy of AD. The experimental findings suggest that the relationship between brain structure and function is not a complete one-to-one correspondence. They also suggest that brain structure is the basis of brain function, and the strong structural connections are majorly accompanied by strong functional connections. © 2025 IEEE.

关键词： Generative adversarial networks

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Discrete Bare Bones Particle Swarm Optimization Algorithm for Solving Multidimensional Knapsack Problem 4

Discrete Bare Bones Particle Swarm Optimization Algorithm fo...

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2019 4th International Conference on Intelligent Computing and Signal Processing, ICSP 2019

作者： Liu, Shengjian Yang, Yan Luo, Lin Department of Game Design South China Institute of Software Engineering Guangzhou University Guangzhou GuangDong510990 China

Aiming at the characteristics of multi-dimensional knapsack problem, the widely used bare bones particle swarm optimization algorithm is discretized, and the traditional objective function based on large penalty parameters is improved. The infeasible solution is transformed into a feasible solution by using the repair mechanism. The typical examples of 10 multi-dimensional knapsack problems are simulated and compared with four intelligent optimization algorithms. The simulation results show that the discrete bare bones particle swarm optimization algorithm has good convergence efficiency, high precision and good robustness when solving multi-dimensional knapsack problem. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Combinatorial optimization

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Time-series regeneration with convolutional recurrent generative adversarial network for remaining useful life estimation

arXiv

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arXiv 2021年

作者： Zhang, Xuewen Qin, Yan Yuen, Chau Jayasinghe, Lahiru Liu, Xiang The Department of Software and Microelectronics Peking University Peking100871 China The Engineering Product Development Pillar of Singapore University of Technology and Design 8 Somapah Road Singapore487372 Singapore

For health prognostic task, ever-increasing efforts have been focused on machine learning-based methods, which are capable of yielding accurate remaining useful life (RUL) estimation for industrial equipment or components without exploring the degradation mechanism. A prerequisite ensuring the success of these methods depends on a wealth of run-to-failure data, however, run-to-failure data may be insufficient in practice. That is, conducting a substantial amount of destructive experiments not only is high costs, but also may cause catastrophic consequences. Out of this consideration, an enhanced RUL framework focusing on data self-generation is put forward for both non-cyclic and cyclic degradation patterns for the first time. It is designed to enrich data from a data-driven way, generating realistic-like time-series to enhance current RUL methods. First, high-quality data generation is ensured through the proposed convolutional recurrent generative adversarial network (CR-GAN), which adopts a two-channel fusion convolutional recurrent neural network. Next, a hierarchical framework is proposed to combine generated data into current RUL estimation methods. Finally, the efficacy of the proposed method is verified through both non-cyclic and cyclic degradation systems. With the enhanced RUL framework, an aero-engine system following non-cyclic degradation has been tested using three typical RUL models. State-of-art RUL estimation results are achieved by enhancing capsule network with generated time-series. Specifically, estimation errors evaluated by the index score function have been reduced by 21.77%, and 32.67% for the two employed operating conditions, respectively. Besides, the estimation error is reduced to zero for the Lithium-ion battery system, which presents cyclic degradation. © 2021, CC BY-SA.

关键词： Convolution

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A comparative study on HIPAA technical safeguards assessment of android mHealth applications

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Smart Health 2022年 26卷 100349页

作者： Mia, Md Raihan Shahriar, Hossain Valero, Maria Sakib, Nazmus Saha, Bilash Barek, Md Abdul Faruk, Md Jobair Hossain Goodman, Ben Khan, Rumi Ahmed Ahamed, Sheikh Iqbal Department of Computer Science at Marquette University WI United States Department of Information Technology at Kennesaw State University GA United States Department of Software Engineering and Game Development at Kennesaw State University GA United States 4A Security & Compliance PA United States Dell Medical School at University of Texas Austin TX United States

Protecting personal health records is becoming increasingly important as more people use Mobile Health applications (mHealth apps) to improve their health outcomes. These mHealth apps enable consumers to monitor their health-related problems, store, manage, and share health records, medical conditions, treatment, and medication. With the increase of mHealth apps accessibility and usability, it is crucial to create, receive, maintain or transmit protected health information (PHI) on behalf of a covered entity or another business associate. The Health Insurance Portability and Accountability Act (HIPAA) provides guidelines to the app developers so that the apps must be compliant with required and addressable Technical Safeguards. However, most mobile app developers, including mHealth apps are not aware of HIPAA security and privacy regulations. Therefore, a research opportunity has emerged to develop an analytical framework to assist the developer to maintain a secure and HIPAA-compliant source code and raise awareness among consumers about the privacy and security of sensitive and personal health information. We proposed an Android source code analysis framework that evaluates twelve HIPAA Technical Safeguards to check whether a mHealth application is HIPAA compliant or not. The implemented meta-analysis and data-flow analysis algorithms efficiently identify the risk and safety features of mHealth apps that violate HIPAA regulations. Furthermore, we addressed API level checking for secure data communication mandated by recent CMS guidelines between third-party mobile health apps and EHR systems. Experimentally, a web-based tool has been developed for evaluating the efficacy of analysis techniques and algorithms. We have investigated 200 top popular Medical and Health & Fitness category Android apps collected from Google Play Store. We identified from the comparative analysis of the HIPAA rules assessment results that authorization to access sensitive resources, data

关键词： Android Data flow analysis HIPAA technical safeguards Metadata analysis mHealth Privacy Security Static analysis

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Bayesian Hyperparameter Optimization for Deep Neural Network-Based Network Intrusion Detection

arXiv

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arXiv 2022年

作者： Masum, Mohammad Shahriar, Hossain Haddad, Hisham Faruk, Md Jobair Hossain Valero, Maria Khan, Md Abdullah Rahman, Mohammad A. Adnan, Muhaiminul I. Cuzzocrea, Alfredo Wu, Fan School of Data Science Kennesaw State University United States Department of Information Technology Kennesaw State University United States Department of Software Engineering and Game Development Kennesaw State University United States Department Computer Science Kennesaw State University United States Department of Electrical and Computer Engineering Florida International University United States Institute of Natural Sciences United International University United States IDEA Lab University of Calabria Rende Italy LORIA Nancy France Department of Computer Science Tuskegee University United States

Traditional network intrusion detection approaches encounter feasibility and sustainability issues to combat modern, sophisticated, and unpredictable security attacks. Deep neural networks (DNN) have been successfully applied for intrusion detection problems. The optimal use of DNN-based classifiers requires careful tuning of the hyper-parameters. Manually tuning the hyperparameters is tedious, time-consuming, and computationally expensive. Hence, there is a need for an automatic technique to find optimal hyperparameters for the best use of DNN in intrusion detection. This paper proposes a novel Bayesian optimization-based framework for the automatic optimization of hyperparameters, ensuring the best DNN architecture. We evaluated the performance of the proposed framework on NSL-KDD, a benchmark dataset for network intrusion detection. The experimental results show the framework's effectiveness as the resultant DNN architecture demonstrates significantly higher intrusion detection performance than the random search optimization-based approach in terms of accuracy, precision, recall, and f1-score. © 2022, CC BY.

关键词： Deep neural networks

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Vacancy-Type Defects in HfO2 Layers and Their Role in Amorphous-to-Crystalline Transition Studied by Monoenergetic Positron Beams

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IEEE Transactions on Semiconductor Manufacturing 2025年

作者： Uedono, Akira Kawakami, Kazuya Kuribara, Tatsunori Hasunuma, Ryu Harashima, Yosuke Shigeta, Yasuteru Ni, Zeyuan Yan, Ruolin Nakamura, Hideo Notake, Akira Moriya, Tsuyoshi Michishio, Koji Ishibashi, Shoji University of Tsukuba Faculty of Pure and Applied Science Tsukuba Ibaraki305-8573 Japan Nara Institute of Science and Technology Graduate School of Science and Technology Ikoma Nara630-0192 Japan Tokyo Electron Technology Solutions Ltd S-Technology Development Center Nirasaki Yamanashi650 Japan Tokyo Electron Technology Solutions Ltd Development Department1 Nirasaki Yamanashi407-0192 Japan Tokyo Electron Ltd Digital Design Center Sapporo Hokkaido060-0003 Japan Tokyo Electron Technology Solutions Ltd Software and Data Engineering Division Nirasaki Yamanashi407-0192 Japan National Institute of Advanced Industrial Science and Technology Research Institute for Measurement and Analytical Instrumentation Tsukuba Ibaraki305-8568 Japan National Institute of Advanced Industrial Science and Technology Research Center for Computational Design of Advanced Functional Materials Tsukuba Ibaraki305-8568 Japan

The annealing properties of vacancies in HfO2 layers deposited on Si substrates and their role in amorphous-to-crystalline transition were studied with monoenergetic positron beams. HfO2 layers with a thickness of 4.30 nm were fabricated by the atomic layer deposition technique. The major vacancytype defects in these layers were identified as a Hf vacancy (VHf) coupled with multiple oxygen vacancies (VOs) and larger vacancy clusters. After annealing at 500°C, the concentration of vacancy clusters started to increase, which was attributed to the agglomeration of intrinsic open spaces in the amorphous phase upon the phase transition from amorphous to monoclinic crystalline phases. The amorphous-crystalline transition started near the interface between the HfO2 layer and bottom electrodes (TiN). After the crystallization, the concentration of vacancy clusters decreased as the annealing temperature increased. © 2025 IEEE.

关键词： Hafnium oxides

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Image Privacy Protection by Particle Swarm Optimization Based Pivot Pixel Modification 1

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5th International Conference on Internet of Things, ICIOT 2020, held as part of the Services Conference Federation, SCF 2020

作者： Yang, Jishen Huang, Yan Pang, Junjie Xie, Zhenzhen Li, Wei Department of Computer Science Georgia State University Atlanta30303 United States Department of Software Engineering and Game Development Kennesaw State University Atlanta30144 United States College of Computer Science and Technology Qingdao University Qingdao266000 China College of Computer Science and Technology Jilin University Changchun130012 China

ISBN: (数字)9783030596156

ISBN: (纸本)9783030596149

The image classification models based on neural networks recently have outperformed most of the traditional models, and rapidly been developed and implemented by industry because of the capability of qualifying various computer vision tasks. Hence, the exposure of users image data to unauthorized powerful models causes more information leak in a shorter time. Through experiments, we find that for each input image, the change of the images prediction scores by each pixels RGB value change is different. Also, the pattern of the sensitivity on each pixel is highly related to the category and composition of the input image. By utilizing this feature, we present Pivot Pixel Noise Generator by Particle Swarm Optimization to generate noise points on original images to lower the target models accuracy of correctly predicting the target images label, so to protect the information contained in the target image from the image classification models. The model performs in a semi-black-box manner and balances the number of queries to the target and total number of modified points. We also propose an initialization strategy for the model, PSO Knowledge Transfer, which initializes the models parameters with experience learned from previous runs to further reduce the number of query times and noise points. The model is evaluated using the image classification benchmark model ResNet50 and shows an advantage compared to the baseline algorithm. © 2020, Springer Nature Switzerland AG.

关键词： Image classification

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Vacancy-Type Defects in Thin HfO2 Layers Probed by Monoenergetic Positron Beams

Vacancy-Type Defects in Thin HfO2 Layers Probed by Monoenerg...

引用

Joint e-Manufacturing and design Collaboration Symposium (eMDC) & International Symposium on Semiconductor Manufacturing (ISSM)

作者： Akira Uedono Kazuya Kawakami Tatsunori Kuribara Ryu Hasunuma Yosuke Harashima Yasuteru Shigeta Zeyuan Ni Ruolin Yan Hideo Nakamura Akira Notake Tsuyoshi Moriya Koji Michishio Shoji Ishibashi Faculty of Pure and Applied Science University of Tsukuba Tsukuba Ibaraki Japan Graduate School of Science and Technology Nara Institute of Science and Technology Ikoma Nara Japan S-Technology Development Center Tokyo Electron Technology Solutions Ltd. Nirasaki Yamanashi Japan Development Department 1 Tokyo Electron Technology Solutions Ltd. Nirasaki Yamanashi Japan Digital Design Center Tokyo Electron Ltd. Sapporo Hokkaido Japan Software and Data Engineering Division Tokyo Electron Technology Solutions Ltd. Nirasaki Yamanashi Japan Research Institute for Measurement and Analytical Instrumentation National Institute of Advanced Industrial Science and Technology Tsukuba Ibaraki Japan Research Center for Computational Design of Advanced Functional Materials National Institute of Advanced Industrial Science and Technology Tsukuba Ibaraki Japan

ISBN: (数字)9798331510589

ISBN: (纸本)9798331510596

Behaviors of vacancies during the amorphous-to-crystalline transition of thin HfO 2 layers were studied by monoenergetic positron beams. The HfO 2 layers with 4‒30 nm thickness were fabricated on TiN/Si templates by atomic layer deposition technique. The major vacancy-type defects in the layers were identified as (i) Hf vacancies (V Hf ) coupled with oxygen vacancies (V O s) and (ii) vacancy clusters. After annealing at 500°C, the concentration of vacancy clusters increased, which was attributed to the agglomeration of intrinsic open spaces in the amorphous phase upon the phase transition from amorphous to crystalline phases. The phase transition started near the interface between the HfO 2 layer and bottom electrodes (TiN). After the crystallization, the concentration of vacancy clusters decreased as the annealing temperature increased.

关键词： Electrodes Annealing Atomic layer deposition Positrons Crystallization Tin Semiconductor device manufacture Hafnium compounds

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Communication efficiency in federated learning: Achievements and challenges

arXiv

引用

arXiv 2021年

作者： Shahid, Osama Pouriyeh, Seyedamin Parizi, Reza M. Sheng, Quan Z. Srivastava, Gautam Zhao, Liang Department of Information Technology Kennesaw State University MariettaGA United States Department of Software Engineering and Game Development Kennesaw State University MariettaGA United States Department of Computing Macquarie University Sydney Australia Department of Math and Computer Science Brandon University Canada Research Centre for Interneural Computing China Medical University Taichung Taiwan

Federated Learning (FL) is known to perform Machine Learning tasks in a distributed manner. Over the years, this has become an emerging technology especially with various data protection and privacy policies being imposed FL allows performing machine learning tasks whilst adhering to these challenges. As with the emerging of any new technology, there are going to be challenges and benefits. A challenge that exists in FL is the communication costs, as FL takes place in a distributed environment where devices connected over the network have to constantly share their updates this can create a communication bottleneck. In this paper, we present a survey of the research that is performed to overcome the communication constraints in an FL setting. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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