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17th International Conference on Brain Informatics, BI 2024

作者： Doborjeh, Zohreh lavin, Daniel Hunter, Kirsty Heym, Nadja Heasman, Bryony Doborjeh, Maryam Kasabov, Nikola Gibson, Glen Sumich, Alexander School of Engineering Computer and Mathematical Sciences Knowledge Engineering and Discovery Research Institute Auckland University of Technology Auckland New Zealand School of Population Health Faculty of Medical and Health Sciences The University of Auckland Auckland New Zealand Department of Sport Science School of Science and Technology Nottingham Trent University Nottingham United Kingdom NTU Psychology School of Social Sciences Nottingham Trent University Nottingham United Kingdom School of Software Engineering Dalian University Dalian China Institute for Information and Communication Technologies Bulgarian Academy of Sciences Sofia Bulgaria Department of Food and Nutritional Sciences The University of Reading Whiteknights Reading United Kingdom Department of Psychology and Neuroscience Auckland University of Technology Auckland New Zealand

ISBN: (纸本)9789819632930

The immune and enteric systems have been implicated in psychopathology, including depressive disorders. However, the precise neurocognitive mechanisms remain unclear. The present study uses Brain-Inspired Spiking Neural Network (SNN) technique to model electroencephalographic (EEG) data as a function of gut-microbiota, inflammation, and depressive symptoms. Forty-two participants (nonclinical cohort) were assessed for Bacteroides-Prevotella (BAC) colonization in faecal samples, Interleukin 6 (IL-6) in peripheral blood, depressive symptoms (self-report of cognitive and non-cognitive symptoms) and brain function (resting state EEG). SNN models were applied to EEG data to generate connection weight values across scalp regions. Connection weights were visualized as a function of 1) depression;2) inflammation;3) gut microbiome. Lower depression and higher BAC were associated with higher frontal connection weights (cognitive depression = bilateral, non-cognitive and BAC = left hemisphere only). Lower cognitive depression and lower IL-6 were associated with higher posterior connection weights. The SNN models achieved higher accuracy for theta and alpha EEG sub-bands, highlighting their relevance in differentiating between groups. These findings emphasize the potential of SNNs in predicting parameters from brain functional connectivity data and developing targeted diagnostic and intervention strategies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electroencephalography

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DeepRicci: Self-supervised Graph Structure-Feature Co-Refinement for Alleviating Over-squashing

DeepRicci: Self-supervised Graph Structure-Feature Co-Refine...

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IEEE International Conference on Data Mining (ICDM)

作者： Li Sun Zhenhao Huang Hua Wu Junda Ye Hao Peng Zhengtao Yu Philip S. Yu School of Control and Computer Engineering North China Electric Power University Beijing China School of Computer Science Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Department of Computer Science University of Illinois at Chicago IL USA

Graph Neural Networks (GNNs) have shown great power for learning and mining on graphs, and Graph Structure Learning (GSL) plays an important role in boosting GNNs with a refined graph. In the literature, most GSL solutions either primarily focus on structure refinement with task-specific supervision (i.e., node classification), or overlook the inherent weakness of GNNs themselves (e.g., over-squashing), resulting in suboptimal performance despite sophisticated designs. In light of these limitations, we propose to study self-supervised graph structure-feature co-refinement for effectively alleviating the issue of over-squashing in typical GNNs. In this paper, we take a fundamentally different perspective of the Ricci curvature in Riemannian geometry, in which we encounter the challenges of modeling, utilizing and computing Ricci curvature. To tackle these challenges, we present a self-supervised Riemannian model, DeepRicci. Specifically, we introduce a latent Riemannian space of heterogeneous curvatures to model various Ricci curvatures, and propose a gyrovector feature mapping to utilize Ricci curvature for typical GNNs. Thereafter, we refine node features by geometric contrastive learning among different geometric views, and simultaneously refine graph structure by backward Ricci flow based on a novel formulation of differentiable Ricci curvature. Finally, extensive experiments on public datasets show the superiority of DeepRicci, and the connection between backward Ricci flow and over-squashing. Codes of our work are given in https://***/RiemanGraph/.

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Using Orthogonal Signals to Hide Information in Images 4

Using Orthogonal Signals to Hide Information in Images

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

作者： Kuznetsov, Alexandr Lokotkova, Iryna Smirnov, Oleksii Kuznetsova, Tetiana Florov, Serhii Lebid, Oksana Vn Karazin Kharkiv National University Department of Information Systems and Technologies Security Kharkiv Ukraine Central Ukrainian National Technical University Cybersecurity and Software Academic Department Kropivnitskiy Ukraine Dnipro University of Technology Department of Information Security and Telecommunications Dnipro Ukraine University of Customs and Finance Department of Computer Science and Software Engineering Dnipro Ukraine

ISBN: (纸本)9781665406185

Spread-Spectrum Steganography (SSS) technology is used to hide information messages in image containers. For this purpose, propagating discrete signals are used, in specially formed pseudo-random sequences (PRS) with special correlation properties. This technology is similar to the direct-sequence spread spectrum (DSSS) used in telecommunications systems to reduce the effects of random noise in information channels. The properties of expansion PRS directly affect the quality of communication in DSSS and, accordingly, the efficiency of SSS. This article investigates various methods of PRS formation and conducts experiments to assess the intensity of bit errors and the quality of the image container. We show that it is most appropriate to use orthogonal discrete Walsh-Hadamard signals that provide the best performance. © 2021 IEEE.

关键词： Signal to noise ratio

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PUBLICLY VERIFIABLE SECRET SHARING: GENERIC CONSTRUCTIONS AND LATTICE-BASED INSTANTIATIONS IN THE STANDARD MODEL

arXiv

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arXiv 2025年

作者： Minh, Pham Nhat Nguyen, Khoa Susilo, Willy Nguyen-An, Khuong Department of Computer Science Faculty of Computer Science and Engineering 268 Ly Thuong Kiet Street District 10 Ho Chi Minh City740500 Viet Nam Vietnam National University Ho Chi Minh City Linh Trung Ward Thu Duc City Ho Chi Minh City721400 Viet Nam Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2500 Australia

Publicly verifiable secret sharing (PVSS) allows a dealer to share a secret among a set of shareholders so that the secret can be reconstructed later from any set of qualified participants. In addition, any public verifier should be able to check the correctness of the sharing and reconstruction process. PVSS has been demonstrated to yield various applications, such as e-voting, distributed key generation, decentralized random number generation protocols and multi-party computation. Although many concrete PVSS protocols have been proposed, their security is either proven in the random oracle model or relies on quantum-vulnerable assumptions such as factoring or discrete logarithm. In this work, we put forward a generic construction for PVSS, that can be instantiated in the standard model under the Learning With Errors (LWE) assumption. Our instantiation provides the first post-quantum PVSS in the standard model, with a reasonable level of asymptotic *** Codes 94A60 Copyright © 2025, The Authors. All rights reserved.

关键词： Quantum cryptography

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Dual-View Pyramid Pooling in Deep Neural Networks for Improved Medical Image Classification and Confidence Calibration

arXiv

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arXiv 2024年

作者： Zhang, Xiaoqing Nie, Qiushi Xiao, Zunjie Zhao, Jilu Wu, Xiao Guo, Pengxin Li, Runzhi Liu, Jin Wei, Yanjie Pan, Yi Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Statistics and Actuarial Science The University of Hong Kong 999077 Hong Kong Cooperative Innovation Center of Internet Healthcare Zhengzhou University Zhengzhou450001 China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi830046 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

Spatial pooling (SP) and cross-channel pooling (CCP) operators have been applied to aggregate spatial features and pixel-wise features from feature maps in deep neural networks (DNNs), respectively. Their main goal is to reduce computation and memory overhead without visibly weakening the performance of DNNs. However, SP often faces the problem of losing the subtle feature representations, while CCP has a high possibility of ignoring salient feature representations, which may lead to both miscalibration of confidence issues and suboptimal medical classification results. To address these problems, we propose a novel dual-view framework, the first to systematically investigate the relative roles of SP and CCP by analyzing the difference between spatial features and pixel-wise features. Based on this framework, we propose a new pooling method, termed dual-view pyramid pooling (DVPP), to aggregate multi-scale dual-view features. DVPP aims to boost both medical image classification and confidence calibration performance by fully leveraging the merits of SP and CCP operators from a dual-axis perspective. Additionally, we discuss how to fulfill DVPP with five parameter-free implementations. Extensive experiments on six 2D/3D medical image classification tasks show that our DVPP surpasses state-of-the-art pooling methods in terms of medical image classification results and confidence calibration across different DNNs. © 2024, CC BY.

关键词： Deep neural networks

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Access Control Based on Self-Sovereign Identity

Access Control Based on Self-Sovereign Identity

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Stefania Loredana Nita Marius Iulian Mihailescu Valentina Marascu Marius Rogobete Department of Computers and Cybersecurity Faculty of Information Systems and Cyber Security Military Technical Academy “Ferdinand I” Bucharest Romania Scientific Research Center in Mathematics and Informatics Faculty of Engineering and Computer Science “Spiru Haret” University Bucharest Romania Low Temperature Plasma Department National Institute for Laser Plasma and Radiation Physics Bucharest Romania Telematics Harman International Bucharest Romania

ISBN: (数字)9798350371154

ISBN: (纸本)9798350371161

In the rapidly growing interest in digital identity management, there is an important shift toward adopting verifiable credentials, often based on blockchain technology. This transition highlights a growing interest in the concept of Self-Sovereign Identity (SSI), a paradigm that empowers individuals with control over their own digital identities through the use of verifiable credentials. This paper introduces a novel access control framework that leverages the principles of SelfSovereign Identity to significantly enhance the security of system access based on the users’ credentials. By integrating the decentralized and user-centric nature of SSI, our proposed framework aims to address existing vulnerabilities in traditional access control systems. It provides a robust, scalable, and controlled mechanism that ensures higher levels of trust and security in digital interactions. This approach aligns with the broader digital identity trends favoring transparency, user autonomy, and trust.

关键词： Access control computers Privacy Computational modeling computer architecture Market research Blockchains

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The applications of machine learning techniques in medical data processing based on distributed computing and the Internet of Things

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computer Methods and Programs in Biomedicine 2023年第1期241卷 107745-107745页

作者： Aminizadeh, Sarina Heidari, Arash Toumaj, Shiva Darbandi, Mehdi Navimipour, Nima Jafari Rezaei, Mahsa Talebi, Samira Azad, Poupak Unal, Mehmet Medical Faculty of Islamic Azad University of Tabriz Tabriz Iran Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Software Engineering Haliç University Istanbul Turkey Urmia University of Medical Sciences Urmia Iran Department of Electrical and Electronic Engineering Eastern Mediterranean University Gazimagusa99628 Turkey Department of Computer Engineering Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan Tabriz University of Medical Sciences Faculty of Surgery Tabriz Iran Department of Computer Science University of Texas at San Antonio TX United States Department of Computer Science University of Manitoba Winnipeg Canada Department of Computer Engineering Nisantasi University Istanbul Turkey

Medical data processing has grown into a prominent topic in the latest decades with the primary goal of maintaining patient data via new information technologies, including the Internet of Things (IoT) and sensor technologies, which generate patient indexes in hospital data networks. Innovations like distributed computing, Machine Learning (ML), blockchain, chatbots, wearables, and pattern recognition can adequately enable the collection and processing of medical data for decision-making in the healthcare era. Particularly, to assist experts in the disease diagnostic process, distributed computing is beneficial by digesting huge volumes of data swiftly and producing personalized smart suggestions. On the other side, the current globe is confronting an outbreak of COVID-19, so an early diagnosis technique is crucial to lowering the fatality rate. ML systems are beneficial in aiding radiologists in examining the incredible amount of medical images. Nevertheless, they demand a huge quantity of training data that must be unified for processing. Hence, developing Deep Learning (DL) confronts multiple issues, such as conventional data collection, quality assurance, knowledge exchange, privacy preservation, administrative laws, and ethical considerations. In this research, we intend to convey an inclusive analysis of the most recent studies in distributed computing platform applications based on five categorized platforms, including cloud computing, edge, fog, IoT, and hybrid platforms. So, we evaluated 27 articles regarding the usage of the proposed framework, deployed methods, and applications, noting the advantages, drawbacks, and the applied dataset and screening the security mechanism and the presence of the Transfer Learning (TL) method. As a result, it was proved that most recent research (about 43%) used the IoT platform as the environment for the proposed architecture, and most of the studies (about 46%) were done in 2021. In addition, the most popular utilized DL

关键词： Deep learning

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Conventional/Phase Shift Dual Drive Mach-Zehnder Modulation Measured Type Based Radio over Fiber Systems

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Journal of Optical Communications 2024年第s1期45卷 s123-s131页

作者： Amiri, I.S. Rashed, Ahmed Nabih Zaki Rahman, Ziaur Paul, Bikash Kumar Ahmed, Kawsar Computational Optics Research Group Advanced Institute of Materials Science Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Applied Sciences Ton Duc Thang University Ho Chi Minh City Viet Nam Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32951 Egypt School of Computer Science and Software Engineering RMIT University 414-418 Swanston Street MelbourneVIC3000 Australia Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail1902 Bangladesh Group of Bio-photomatiχ Mawlana Bhashani Science and Technology University Santosh Tangail1902 Bangladesh Department of Software Engineering Daffodil International University Sukrabad Dhanmondi Dhaka1207 Bangladesh

This work clarified the conventional/phase shift dual Mach-Zehnder modulation measured technique for upgrading radio over fiber (ROF) systems. Four levels of pulse amplitude modulation and non-return to zero code scheme are employed in different previous studies for upgrading ROF systems. The possible transmission distance is extended to 150 km and a bit rate of 40 Gb/s for achieved acceptable max. Q factor of 13.7498 and minimum bit error rates of 1.82×10-43. The total optical received power is measured at fiber cable length for maximum distance. Also, in the same way, the signal power levels are measured after fiber optic cable and avalanche photo detector optical receiver. The proposed model has presented the enhancement percentage ratio of 13.654% over the previous models in signal quality enhancement. © 2020 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Q factor measurement

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Machine learning for Autopsy reports Forensic using Text Classification Techniques 2

Machine learning for Autopsy reports Forensic using Text Cla...

引用

2nd International Conference on Computing and Information Technology, ICCIT 2022

作者： Shahid, Muhammad Rehman Munir, Asim Ifraheem, Layeba Aldabbas, Hamza Wadood, Abdul Alwada'n, Tariq University of Faisalabad Departmental of Computational Sciences Faislabad Pakistan International Islamic University Department of Computer Science and Software Engineering Islamabad Pakistan Al-Balqa Applied University Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology Al-Salt Jordan School of Computing Engineering Digital Technologies Teesside University Middlesbrough United Kingdom

ISBN: (纸本)9781665436052

A forensic autopsy is a surgical process in which experts collect a deceased body's internal and external information. These death certificates are the source of timely warnings of an increase in disease activity. It's only helpful if accurate and quantitative data is available. Therefore, the Classification of plain text medical autopsy reports reduces the time consumption and irregularities. The motive is to design an automatic text classification system that classifies plain text autopsy reports. Therefore, a methodology proposes using different Automatic Text Classification Techniques (ATC). This technique has embedded Feature Extraction, Feature Representation, and Feature Reduction techniques. These techniques use for the construction of classification models that classify the text of autopsy reports. Data sets collected from these types will be helpful in future experiments. Finally, the performance of the classifier measures by using different Evaluation parameters. These Evaluation Measures are Precision, Recall, Accuracy, and F-measure. © 2022 IEEE.

关键词： Support vector machines

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Heterogeneous Multi-commodity Network Flows over Time 1

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17th International computer science Symposium in Russia, CSR 2022

作者： Li, Yifen Bei, Xiaohui Qiao, Youming Tao, Dacheng Chen, Zhiya School of Traffic and Transportation Engineering Central South University Changsha China School of Physical and Mathematical Sciences Nanyang Technological University Singapore Singapore Centre for Quantum Software and Information University of Technology Sydney Ultimo Australia UBTECH Sydney AI Centre School of Computer Science Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia

ISBN: (数字)9783031095740

ISBN: (纸本)9783031095733

In the 1950’s, Ford and Fulkerson introduced dynamic flows by incorporating the notion of time into the network flow model (Oper. Res., 1958). In this paper, motivated by real-world applications including route planning and evacuations, we extend the framework of multi-commodity dynamic flows to the heterogeneous commodity setting by allowing different transit times for different commodities along the same edge. We first show how to construct the time-expanded networks, a classical technique in dynamic flows, in the heterogeneous setting. Based on this construction, we give a pseudopolynomial-time algorithm for the quickest flow problem when there are two heterogeneous commodities. We then present a fully polynomial-time approximation scheme when the nodes have storage for any number of heterogeneous commodities. The algorithm is based on the condensed time-expanded network technique introduced by Fleischer and Skutella (SIAM J. Comput., 2007). © 2022, Springer Nature Switzerland AG.

关键词： Polynomial approximation

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