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11th International Symposium on Computing and Networking, CANDAR 2023

作者： Chen, Chunlu Wang, Kevin I-Kai Li, Peng Sakurai, Kouichi Kyushu University Graduate School of Information Science and Electrical Engineering Fukuoka Japan The University of Auckland Dept. of Electrical Computer and Software Engineering Auckland New Zealand The University of Aizu School of Computer Science and Engineering Aizuwakamatsu Japan Kyushu University Faculty of Information Science and Electrical Engineering Fukuoka Japan

ISBN: (纸本)9798350306705

Federated Learning (FL) and the Internet of Things (IoT) have revolutionized data processing and analysis, overcoming the traditional limitations of cloud computing. However, traditional machine learning strategies lead to increased costs and catastrophic forgetting due to model retraining with new datasets. Continual learning has been proposed to counter this, enabling models to adapt to new data while preserving previous knowledge, which is beneficial for dynamic edge environments. Despite these advantages, the retention of previous knowledge during the continual learning process may lead to the information leakage. To address the inherent challenges of multitask scenarios, we present a Federated Continual Learning (FCL) framework that integrates the privacy-preserving benefits of Federated Learning (FL) into a continual learning system, ensuring both continual learning and privacy preservation in edge computing data processing and analysis. Specifically, our architecture introduces dedicated fully-connected layers for each task. This architecture ensures that distinctive features pertinent to each task are not only captured but also preserved throughout the model's lifespan. Within our framework, data is processed via task-specific layers. Subsequently, the final label is determined by associating it with the paramount prediction value, thus capitalizing on the model's comprehensive knowledge reservoir to bolster prediction accuracy. We subjected our FCL framework to rigorous validation using two benchmark datasets: MNIST and CFAR-10. Experimental outcomes unequivocally substantiate the efficacy of our proposed methodology. © 2023 IEEE.

关键词： Learning systems

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Domain Contrastive Learning for Multi-Site Alzheimer's Disease Classification 20

Domain Contrastive Learning for Multi-Site Alzheimer's Disea...

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20th International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Fiasam, Linda Delali Rao, Yunbo Sey, Collins Aggrey, Esther Stacy E. B. Kodjiku, Seth Larweh Browne, Judith Ayekai Danso, Juliana Mantebea Ukwuoma, Chiagoziem C. Gyarteng, Emmanuel S. A. School of Information and Software Engineering University of Electronic Science and Technology of China China School of Computer Science and Technology Zhejiang Gongshang University China School of Computer Science and Technology University of Electronic Science and Technology of China University China College of Nuclear Technology and Automation Engineering Chengdu University of Technology China

ISBN: (纸本)9798350318982

In the field of medical imaging, accurately classifying Alzheimer's Disease (AD) poses a substantial challenge, primarily due to the inherent variability in imaging data across different clinical sites. This paper introduces a novel approach, Domain Contrastive Learning (DCL), to address this challenge by enhancing the robustness and transferability of AD classification models using multi-site Magnetic Resonance Imaging (MRI) data. DCL leverages a unique contrastive learning technique within an autoencoder's latent space, enabling the extraction of domain-invariant features essential for accurate diagnosis. We rigorously evaluate the proposed network on brain images from three distinct imaging sites, employing a Domain Generalization (DG) training protocol to ensure robustness and generalizability across varying data sources. Our model is evaluated on three binary classification tasks: AD vs. Normal Control (NC), AD vs. Mild Cognitive Impairment (MCI), and MCI vs. NC. Results demonstrate that DCL outperforms existing methods, especially in AD vs. NC classification, achieving higher accuracy, sensitivity, and specificity with 0.51%, 1.54% and 1.68% increments respectively. This underscores DCL's potential as a robust and generalizable method for enhanced AD diagnosis, marking a significant step forward in medical imaging challenges in a multi-site context. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Image segmentation algorithm using maximum dependency of variable precision rough set

Image segmentation algorithm using maximum dependency of var...

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2024 International Conference on computer Application and Information Security, ICCAIS 2024

作者： Xiaoling, Wu Xuefei, Liu Zewen, Gao School of Software Engineering Wuhan University of Bioengineering Hubei Wuhan430415 China School of Mathematics and Computer Science Guangxi Minzu Normal University Guangxi Chongzuo532200 China

ISBN: (纸本)9781510689312

Image processing is an important way to obtain information and is widely used in important fields such as military, medical and transportation. Image segmentation plays an important role in image processing. In view of the inherent complexity and correlation of the image itself, how to deal with the uncertainty in the image segmentation process is the main work to obtain a more accurate image segmentation result. We propose an image single-threshold segmentation algorithm using the maximum dependency of variable precision rough set (VPRS). The algorithm uses VPRS to represent the image, and uses the maximum dependency and particle swarm optimization to solve the optimal image segmentation threshold, which effectively handles the uncertainty in image segmentation. The experiments show that the single-threshold segmentation algorithm has certain practicability and flexibility, the segmentation effect is better than the maximum average information entropy method, and the segmentation efficiency is significantly higher than the ordinary iterative method. © The Authors.

关键词： Particle swarm optimization (PSO)

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Knowledge Graph and Knowledge Reasoning:A Systematic Review

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Journal of Electronic science and Technology 2022年第2期20卷 159-186页

作者： Ling Tian Xue Zhou Yan-Ping Wu Wang-Tao Zhou Jin-Hao Zhang Tian-Shu Zhang Kashi Institute of Electronics and Information Industry Kashi 844099 Shenzhen Institute of Information Technology Shenzhen 518172 the School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 610054 the School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054

The knowledge graph(KG) that represents structural relations among entities has become an increasingly important research field for knowledge-driven artificial intelligence. In this survey, a comprehensive review of KG and KG reasoning is provided. It introduces an overview of KGs, including representation, storage, and essential technologies. Specifically, it summarizes several types of knowledge reasoning approaches, including logic rules-based, representation-based, and neural network-based methods. Moreover, this paper analyzes the representation methods of knowledge hypergraphs. To effectively model hyper-relational data and improve the performance of knowledge reasoning, a three-layer knowledge hypergraph model is proposed. Finally, it analyzes the advantages of three-layer knowledge hypergraphs through reasoning and update algorithms which could facilitate future research.

关键词： methods. representation reasoning

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Cyber security skills development in South Africa: Addressing the Gender Gap in the Industry 8

Cyber security skills development in South Africa: Addressin...

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8th Conference on Information Communication Technology and Society, ICTAS 2024

作者： Ramonyai, Tebogo Motlatso Mpekoa, Noluntu Tom, Sheethal University of Johannesburg Academy of Computer Science & Software Engineering Cape Town South Africa IIE Varsity College School of Information Technology Cape Town South Africa

ISBN: (纸本)9798350314892

This paper investigates the gender gap in South Africa's cybersecurity sector and its effects on both the sector and the nation's economic growth. There is a significant shortage of skilled workers in the sector, and women are notably underrepresented. The main research question examines how the gender gap affects the industry's development, innovation, and national security posture, emphasizing the importance of utilizing the nation's talent pool to its fullest capacity to combat the evolving cyber threats. Insights and perspectives on the gender disparity in the cybersecurity sector were gathered through a questionnaire circulated amongst cybersecurity professionals and academics. These participants were chosen due to their knowledge and direct engagement in the subject, which offers valuable insights into the obstacles that women encounter in their workplace. The study highlights a number of problems, including gender stereotypes, a lack of access to education and training, and a non-inclusive workplace culture, that contribute to the gender gap. The paper makes the case that in order to solve these problems and advance diversity and inclusivity in the workplace, all stakeholders must work together to close the gender gap. In order to overcome the gender gap in cybersecurity in South Africa, the study suggests special programs aimed at expanding access to education and training opportunities, promoting gender diversity and inclusivity, and creating a more hospitable and equal work environment. This will boost the country's economic growth and strengthen security against cyberattacks. © 2024 IEEE.

关键词： Cybersecurity

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MORPSO_ECD+ELM: A Unified Framework for Gene Selection and Cancer Classification

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IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4571-4584页

作者： Sumet Mehta Fei Han Qinghua Ling Muhammad Sohail Arfan Nagra School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China School of Computer Science Jiangsu University of Science and Technology Zhenjiang China Department of Computer Software Engineering Military College of Signals NUST Islamabad Pakistan School of Computer Science Lahore Garrison University Lahore Pakistan

Gene selection and cancer classification are inherently multi-objective tasks that require balancing competing objectives, such as maximizing classification accuracy while minimizing irrelevant or redundant genes. Existing methods often optimize a single objective or treat gene selection and classification independently, limiting their overall effectiveness. This study proposes a unified framework, MORPSO_ECD+ELM, which formulates gene selection and classification as a multimodal multi-objective optimization problem (MMOP) to optimize both objectives simultaneously. The framework introduces two key innovations: (1) an enhanced crowding distance (ECD) metric to improve diversity preservation and (2) an advanced multi-objective particle swarm optimization variant (MORPSO_ECD) that incorporates ECD and ring topography to effectively explore the MMOP solution space. Integrated with the Extreme Learning Machine (ELM), this framework achieves robust and efficient cancer classification. Extensive experimental validations demonstrate that the proposed approach achieves high classification accuracy while identifying biologically meaningful gene subsets, providing a powerful solution to bridge the gap between gene selection and cancer classification.

关键词： Cancer Accuracy Optimization Particle swarm optimization Measurement Training Surfaces Extreme learning machines Functional magnetic resonance imaging Electronic mail

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Self-supervised Distillation and Multi-scale Network for Parkinson's Disease Classification

Self-supervised Distillation and Multi-scale Network for Par...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Lei, Haijun Lei, Shuyuan Liang, Chengjia Lei, Baiying Zhang, Yuchen Shenzhen University College of Computer Science & Software Engineering Shenzhen China Harbin University of Commerce College of Computer & Information Engineering Harbin China Shenzhen University Medical School Shenzhen University School of Biomedical Engineering Shenzhen China

ISBN: (纸本)9798350337488

Parkinson's disease (PD) is a common and irreversible neurodegenerative disease that the earlier it is diagnosed, the easier and better it can be controlled. This paper proposes a self-supervised distillation and multi-scale dynamic convolutional network for PD classification. Experiments indicate that our proposed method has outperformed most SOTA methods. © 2023 IEEE.

关键词： Multi-scale network Parkinson's disease Self-supervised distillation

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Degenerate asymmetric quantum concatenated codes for correcting biased quantum errors

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Chinese Physics B 2021年第12期30卷 206-213页

作者： Ji-Hao Fan Jun Li Han-Wu Chen Wen-Jie Liu School of Electronic and Optical Engineering Nanjing University of Science and TechnologyNanjing 210094China School of Computer Science and Engineering Southeast UniversityNanjing 211189China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China

In most practical quantum mechanical systems,quantum noise due to decoherence is highly biased towards *** quantum state suffers from phase flip noise much more seriously than from the bit flip *** this work,we construct new families of asymmetric quantum concatenated codes(AQCCs)to deal with such biased quantum *** construction is based on a novel concatenation scheme for constructing AQCCs with large asymmetries,in which classical tensor product codes and concatenated codes are utilized to correct phase flip noise and bit flip noise,*** generalize the original concatenation scheme to a more general case for better correcting degenerate ***,we focus on constructing nonbinary AQCCs that are highly *** to previous literatures,AQCCs constructed in this paper show much better parameter performance than existed ***,we design the specific encoding circuit of the *** is shown that our codes can be encoded more efficiently than standard quantum codes.

关键词： asymmetric quantum codes concatenated code quantum channel degenerate code

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Evaluating LoRaWAN performance in intentional and unintentional DoS attacks by legacy 900MHz network devices 6

Evaluating LoRaWAN performance in intentional and unintentio...

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6th International Conference on Advanced Communication Technologies and Networking, CommNet 2023

作者： Bang, Seokhyeon Jang, Junyoung Ro, Minju Choi, Yuseon Kwon, Doyong Lee, Kangyeon Smith, Anthony Chung-Ang University School of Computer Science and Engineering Seoul Korea Republic of Chonnam National University Department of Software Engineering Gwangju Korea Republic of Kyungpook National University School of Computer Science and Engineering Daegu Korea Republic of Purdue University Department of Computer and Information Technology IN United States

ISBN: (纸本)9798350329391

The deployment of LoRaWAN on the Internet of Things (IoT) has increased since its advent and LoRaWAN now predominates the IoT market over other Low Powered Wide Area Networks (LPWAN). However, since LoRaWAN uses Chirp Spread Spectrum (CSS), it is susceptible to wideband jamming attacks. In this paper, we demonstrate with experiments and concrete numerical results that jamming US915 LoRaWAN frequency is possible by the usual data transmission and reception process of 900MHz Canopy, one of the legacy 900MHz network device. Intentional attack is possible in the same manner. The experiments emulate the real-world environment operated in medical and agriculture industries, in outdoor and indoor conditions, respectively. In addition, this paper introduces and utilizes the novel metric, Jamming Effect (JE), that indicates the network performance of wireless networks that spread the data on air. © 2023 IEEE.

关键词： Risk assessment

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Deciphering urban traffic impacts on air quality by deep learning and emission inventory

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Journal of Environmental sciences 2023年第2期124卷 745-757页

作者： Wenjie Du Lianliang Chen Haoran Wang Ziyang Shan Zhengyang Zhou Wenwei Li Yang Wang School of Software Engineering University of Science and Technology of ChinaHefei 230026China Suzhou Institute for Advanced Research University of Science and Technology of ChinaSuzhou 215123China Alibaba Inc. Hangzhou 310052China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230026China CAS Key Laboratory of Urban Pollutant Conversion Department of environmental science and EngineeringUniversity of Science and Technology of ChinaHefei 230026China USTC-CityU Joint Advanced Research Center Suzhou 215123China

Air pollution is a major obstacle to future sustainability,and traffic pollution has become a large drag on the sustainable developments of future ***,combined with the large volume of real-time monitoring data,we propose a deep learning model,iDeepAir,to predict surface-level PM2.5 concentration in Shanghai megacity and link with MEIC emission inventory creatively to decipher urban traffic impacts on air *** model exhibits high-fidelity in reproducing pollutant concentrations and reduces the MAE from 25.355μg/m^(3) to 12.283μg/m^(3) compared with other *** identifies the ranking of major factors,local meteorological conditions have become a nonnegligible ***-wise relevance propagation(LRP)is used here to enhance the interpretability of the model and we visualize and analyze the reasons for the different correlation between traffic density and PM_(2.5) concentration in various regions of ***,As the strict and effective industrial emission reduction measurements implementing in China,the contribution of urban traffic to PM_(2.5) formation calculated by combining MEIC emission inventory and LRP is gradually increasing from 18.03%in 2011 to 24.37% in 2017 in Shanghai,and the impact of traffic emissions would be ever-prominent in 2030 according to our *** also infer that the promotion of vehicular electrification would achieve further alleviation of PM_(2.5) about 8.45% by 2030 *** insights are of great significance to provide the decision-making basis for accurate and high-efficient traffic management and urban pollution control,and eventually benefit people’s lives and high-quality sustainable developments of cities.

关键词： PM_(2.5)concentration forecast Traffic emissions Deep learning Attention mechanism New energy vehicles

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