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

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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Feature Aggregating Network with Inter-Frame Interaction for Efficient Video Super-Resolution

Feature Aggregating Network with Inter-Frame Interaction for...

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IEEE International Conference on Data Mining (ICDM)

作者： Yawei Li Zhao Zhang Suiyi Zhao Jicong Fan Haijun Zhang Mingliang Xu School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Kunming China Shenzhen Research Institute of Big Data Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China Department of Computer Science Harbin Institute of Technology Shenzhen China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

Video super-resolution (VSR) on mobile devices aims to restore high-resolution frames from their low-resolution counterparts, satisfying the requirements of performance, FLOPs and latency. On one hand, partial feature processing, as a classic and acknowledged strategy, is developed in current studies to reach an appropriate trade-off between FLOPs and accuracy. However, the splitting of partial feature processing strategy are usually performed in a blind manner, thereby reducing the computational efficiency and performance gains. On the other hand, current methods for mobile platforms primarily treat VSR as an extension of single-image super-resolution to reduce model calculation and inference latency. However, lacking inter-frame information interaction in current methods results in a suboptimal latency and accuracy trade-off. To this end, we propose a novel architecture, termed Feature Aggregating Network with Inter-frame Interaction (FANI), a lightweight yet considering frame-wise correlation VSR network, which could achieve real-time inference while maintaining superior performance. Our FANI accepts adjacent multi-frame low-resolution images as input and generally consists of several fully-connection-embedded modules, i.e., Multi-stage Partial Feature Distillation (MPFD) for capturing multi-level feature representations. Moreover, considering the importance of inter-frame alignment, we further employ a tiny Attention-based Frame Alignment (AFA) module to promote inter-frame information flow and aggregation efficiently. Extensive experiments on the well-known dataset and real-world mobile device demonstrate the superiority of our proposed FANI, which means that our FANI could be well adapted to mobile devices and produce visually pleasing results.

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Information Leakage from Optical Emanations

arXiv

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arXiv 2023年

作者： Loughry, Joe Umphress, David A. Lockheed Martin Space Systems Dept. 3740 Mail Stop X3741 P.O. Box 179 DenverCO80201 United States Auburn University Department of Computer Science and Software Engineering 215 Dunstan Hall AL36849 United States

A previously unknown form of compromising emanations has been discovered. LED status indicators on data communication equipment, under certain conditions, are shown to carry a modulated optical signal that is significantly correlated with information being processed by the device. Physical access is not required;the attacker gains access to all data going through the device, including plaintext in the case of data encryption systems. Experiments show that it is possible to intercept data under realistic conditions at a considerable distance. Many different sorts of devices, including modems and Internet Protocol routers, were found to be vulnerable. A taxonomy of compromising optical emanations is developed, and design changes are described that will successfully block this kind of "Optical Tempest" attack. Categories and Subject Descriptors: C.2.0 [computer systems Organization]: computer-COMMUNICATION NETWORKS—General, Security and protection (e.g., firewalls);D.4.6 [software]: OPERATING systems—Security and Protection, Invasive software (e.g., viruses, worms, Trojan horses);E.3 [Data]: DATA ENCRYPTION—Code breaking;K.6.5 [Computing Milieux]: MANAGEMENT OF COMPUTING AND INFORMATION systems—Security and Protection, Unauthorized Access (e.g., hacking, phreaking) Copyright © 2023, The Authors. All rights reserved.

关键词： Light emitting diodes

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Accelerating Real-Time Imaging for Radiotherapy: Leveraging Multi-GPU Training with PyTorch 22

Accelerating Real-Time Imaging for Radiotherapy: Leveraging ...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Obe, Ruth Kaufmann, Brandt Baird, Kaelen Kadel, Sam Soltani, Yasmin Cham, Mostafa Gobbert, Matthias K. Barajas, Carlos A. Jiang, Zhuoran Sharma, Vijay R. Ren, Lei Peterson, Stephen W. Poif, Jerimy C. U. of Houston-Clear Lake Dept. of Computer Science and of Software Engineering United States U. of San Francisco Dept. of Mathematics and Statistics United States Skidmore College Dept. of Computer Science and of Mathematics United States Mount Holyoke College Dept. of Computer Science and of Psychology United States U. of Houston Dept. of Biomedical Engineering United States U. of Maryland Dept. of Information Systems Baltimore County United States U. of Maryland Dept. of Mathematics and Statistics Baltimore County United States Medical Physics Graduate Program Duke University United States U. of Maryland School of Medicine Dept. of Radiation Oncology United States U. of Maryland School of Medicine Department of Radiation Oncology United States U. of Cape Town Dept. of Physics South Africa H3D Inc. United States

ISBN: (纸本)9798350345346

Proton beam therapy is an advanced form of cancer radiotherapy that uses high-energy proton beams to deliver precise and targeted radiation to tumors. This helps to mit-igate unnecessary radiation exposure in healthy tissues. Real-time imaging of prompt gamma rays with Compton cameras has been suggested to improve therapy efficacy. However, the camera's non-zero time resolution leads to incorrect interaction classifications and noisy images that are insufficient for accurately assessing proton delivery in patients. To address the challenges posed by the Compton camera's image quality, machine learning techniques are employed to classify and refine the generated data. These machine-learning techniques include recurrent and feedforward neural networks. A PyTorch model was designed to improve the data captured by the Compton camera. This decision was driven by PyTorch's flexibility, powerful capabilities in handling sequential data, and enhanced G PU usage. This accelerates the model's computations on large-scale radiotherapy data. Through hyperparameter tuning, the validation accuracy of our PyTorch model has been improved from an initial 7 % to over 60 %. Moreover, the PyTorch Distributed Data Parallelism strategy was used to train the RNN models on multiple G PU s, which significantly reduced the training time with a minor impact on model accuracy. © 2023 IEEE.

关键词： Clas-sification Compton camera Distributed Data Parallelism Proton beam therapy PyTorch Recurrent neural network

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An Adaptive Decision-Making Approach for Better Selection of a Blockchain Platform for Health Insurance Frauds Detection with Smart Contracts: Development and Performance Evaluation

arXiv

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arXiv 2023年

作者： Kaafarani, Rima Ismail, Leila Zahwe, Oussama ICCS-Lab Computer Science Department American University of Culture and Education Beirut1507 Lebanon Laboratory School of Computing and Information Systems The University of Melbourne Melbourne Australia Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Abu Dhabi United Arab Emirates National Water and Energy Center United Arab Emirates University Abu Dhabi United Arab Emirates

Blockchain technology has piqued the interest of businesses of all types, while consistently improving and adapting to developers and business owners requirements. Therefore, several blockchain platforms have emerged, making it challenging to select a suitable one for a specific type of business. This paper presents a classification of over one hundred blockchain platforms. We develop smart contracts for detecting healthcare insurance frauds using two blockchain platforms selected based on our proposed decision-making map approach for the selection of the top two suitable platforms for healthcare insurance frauds detection application, followed by an evaluation of their performances. Our classification shows that the largest percentage of blockchain platforms could be used for all types of application domains, and the second biggest percentage is to develop financial services only, even though generic platforms can be used, while a small number is for developing in other specific application domains. Our decision-making map revealed that Hyperledger Fabric is the best blockchain platform for detecting healthcare insurance frauds. The performance evaluation of the top two selected platforms indicates that Fabric surpassed Neo in all *** Codes 6804, 68M11, 68U35 © 2023, CC BY-NC-ND.

关键词： Smart contract

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Need for Computational and Psycho-linguistics Models in Natural language Processing for Web Documents 4

Need for Computational and Psycho-linguistics Models in Natu...

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4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020

作者： Naqvi, Muhammad Raza Shahzad, Syed Khuram Iqbal, Muhammad Waseem Ahmed, Muhammad Tahir, Muhammad Usman Khan, Babar Ayub Superior College Department of Computer Science Lahore Pakistan University of Management Technology Department of Informatics and Systems Lahore Pakistan Superior College Department of Software Engineering Lahore Pakistan

ISBN: (纸本)9781728190907

Natural language Processing (NLP) is an argument that provokes territory of figural manner for motorized scrutiny and depiction of humane language. Research in it unfolded from the term of punch cards till the age of intelligent examination engines which takes less than a second to Figure out millions of web pages and digital documents. In the web, pieces of evidence and end-users produced content to have formerly stimulated text. The obligation of advantageous and astute content driving is decisive forthwith. Consequently, the web is being treated as a foremost source of evidence brought about the extensive extent of a probe to perceive the compass of the machine. Considering the democratization of online content formulation, it has skeptically influenced the information resurgence and eradication. This paper pulls contemporary developments in the NLP probe to perceive the scope of machine learning in a modern era to avoid ramification of adverse instructions. © 2020 IEEE.

关键词： Computational and psycholinguistics models Cortana Google Assistant ALO Natural language processing Siri Web documents

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Deep Learning-based Synthetic High-Resolution In-Depth Imaging Using an Attachable Dual-element Endoscopic Ultrasound Probe

arXiv

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arXiv 2023年

作者： Lew, Hah Min Kim, Jae Seong Lee, Moon Hwan Park, Jaegeun Youn, Sangyeon Kim, Hee Man Kim, Jihun Hwang, Jae Youn Department of Electrical Engineering and Computer Science DGIST Daegu42988 Korea Republic of Deep Learning Research Team KLleon R&D Center Seoul04637 Korea Republic of Department of Information and Communication Engineering DGIST Daegu42988 Korea Republic of Software Team ALPINION MEDICAL SYSTEMS Anyang-si14117 Korea Republic of Department of Health Promotion Center Yonsei University College of Medicine Gangnam Severance Hospital Seoul06273 Korea Republic of Department of Electrical Engineering and Computer Science The Interdisciplinary Studies of Artificial Intelligence DGIST Daegu42988 Korea Republic of

Endoscopic ultrasound (EUS) imaging has a trade-off between resolution and penetration depth. By considering the in-vivo characteristics of human organs, it is necessary to provide clinicians with appropriate hardware specifications for precise diagnosis. Recently, super-resolution (SR) ultrasound imaging studies, including the SR task in deep learning fields, have been reported for enhancing ultrasound images. However, most of those studies did not consider ultrasound imaging natures but rather they were conventional SR techniques based on downsampling of ultrasound images. In this study, we propose a novel deep learning-based high-resolution in-depth imaging probe capable of offering low- and high-frequency ultrasound image pairs. We developed an attachable dual-element EUS probe with customized low- and high-frequency ultrasound transducers under small hardware constraints. We also designed a special geared structure to enable the same image plane. The proposed system was evaluated with a wire phantom and a tissue-mimicking phantom. After the evaluation, 442 ultrasound image pairs from the tissue-mimicking phantom were acquired. We then applied several deep learning models to obtain synthetic high-resolution in-depth images, thus demonstrating the feasibility of our approach for clinical unmet needs. Furthermore, we quantitatively and qualitatively analyzed the results to find a suitable deep-learning model for our task. The obtained results demonstrate that our proposed dual-element EUS probe with an image-to-image translation network has the potential to provide synthetic high-frequency ultrasound images deep inside tissues. Copyright © 2023, The Authors. All rights reserved.

关键词： Phantoms

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A Survey on Large language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions

arXiv

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arXiv 2024年

作者： Boateng, Gordon Owusu Sami, Hani Alagha, Ahmed Elmekki, Hanae Hammoud, Ahmad Mizouni, Rabeb Mourad, Azzam Otrok, Hadi Bentahar, Jamal Muhaidat, Sami Talhi, Chamseddine Dziong, Zbigniew Guizani, Mohsen Department of Software and IT Engineering Ecole de Technologie Superieure Montreal Canada Artificial Intelligence & Cyber Systems Research Center Department of CSM Lebanese American University Beirut Lebanon Department of Electrical Engineering Ecole de Technologie Superieure Montreal Canada University Beirut Lebanon Department of CS Khalifa University Abu Dhabi United Arab Emirates KU 6G Research Center Department of CS Khalifa University United Kingdom Concordia Institute for Information Systems Engineering Concordia University MontrealQC Canada KU 6G Research Center Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada Department of ML Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

The rapid evolution of communication networks in recent decades has intensified the need for advanced Network and Service Management (NSM) strategies to address the growing demands for efficiency, scalability, enhanced performance, and reliability of these networks. Large language Models (LLMs) have received tremendous attention due to their unparalleled capabilities in various Natural language Processing (NLP) tasks and generating context-aware insights, offering transformative potential for automating diverse communication NSM tasks. Contrasting existing surveys that consider a single network domain, this survey investigates the integration of LLMs across different communication network domains, including mobile networks and related technologies, vehicular networks, cloud-based networks, and fog/edge-based networks. First, the survey provides foundational knowledge of LLMs, explicitly detailing the generic transformer architecture, general-purpose and domain-specific LLMs, LLM model pre-training and fine-tuning, and their relation to communication NSM. Under a novel taxonomy of network monitoring and reporting, AI-powered network planning, network deployment and distribution, and continuous network support, we extensively categorize LLM applications for NSM tasks in each of the different network domains, exploring existing literature and their contributions thus far. Then, we identify existing challenges and open issues, as well as future research directions for LLM-driven communication NSM, emphasizing the need for scalable, adaptable, and resource-efficient solutions that align with the dynamic landscape of communication networks. We envision that this survey serves as a holistic roadmap, providing critical insights for leveraging LLMs to enhance NSM. © 2024, CC BY-NC-SA.

关键词： Mobile telecommunication systems

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Experimental Researches of Biometric Authentication Using Convolutional Neural Networks and Histograms of Oriented Graphs 4

Experimental Researches of Biometric Authentication Using Co...

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4th IEEE International Conference on Advanced Information and Communication Technologies, AICT 2021

作者： Kuznetsov, Alexandr Datsenko, Serhii Gorbenko, Yuriy Chupilko, Tetiana Korneyev, Maxim Klym, Victoria V.N. Karazin Kharkiv National University Department of Information Systems and Technologies Security Kharkiv Ukraine University of Customs and Finance Department of Computer Science and Software Engineering Dnipro Ukraine University of Customs and Finance Faculty of Innovative Technologies Dnipro Ukraine University of Customs and Finance Department of Cybersecurity and Information Technologies Dnipro Ukraine

ISBN: (纸本)9781665406185

User authentication technologies are an important component of modern cybersecurity. These technologies are designed to verify the correctness and validity, for example, by verifying the authenticity of entered passwords, identifiers, certain biometric features, and so on. We consider biometric authentication techniques for the human face. Two techniques are used to process biometric features in our work: convolutional neural networks and histograms of directed gradients. We conduct experimental research to study these techniques and compare the results of both in terms of authentication accuracy and the speed of processing biometric images. The obtained results will be useful in the development and implementation of cybersecurity applications that use biometric authentication © 2021 IEEE.

关键词： Authentication

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Forecasting COVID-19 Infections in Gulf Cooperation Council (GCC) Countries using Machine Learning

arXiv

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arXiv 2023年

作者： Ismail, Leila Materwala, Huned Hennebelle, Alain Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates Independent Data Scientist and Engineer Al Ain Abu Dhabi United Arab Emirates

COVID-19 has infected more than 68 million people worldwide since it was first detected about a year ago. Machine learning time series models have been implemented to forecast COVID-19 infections. In this paper, we develop time series models for the Gulf Cooperation Council (GCC) countries using the public COVID-19 dataset from Johns Hopkins. The dataset set includes the one-year cumulative COVID-19 cases between 22/01/2020 to 22/01/2021. We developed different models for the countries under study based on the spatial distribution of the infection data. Our experimental results show that the developed models can forecast COVID-19 infections with high *** Codes 68-04, 68T05, 68T07, 68T20 © 2023, CC BY-NC-ND.

关键词： Coronavirus

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