检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Wu, Chengpei Lou, Yang Wang, Lin Li, Junli Li, Xiang Chen, Guanrong The College of Computer Science Sichuan Normal University Chengdu610066 China The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China The Department of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

This paper addresses the robustness of a network to sustain its connectivity and controllability against malicious attacks. This kind of network robustness is typically measured by the time-consuming attack simulation, which returns a sequence of values that record the remaining connectivity and controllability after a sequence of node- or edge-removal attacks. For improvement, this paper develops an efficient framework for network robustness prediction, the spatial pyramid pooling convolutional neural network (SPP-CNN). The new framework installs a spatial pyramid pooling layer between the convolutional and fully-connected layers, overcoming the common mismatch issue in the CNN-based prediction approaches and extending its generalizability. Extensive experiments are carried out by comparing SPP-CNN with three state-of-the-art robustness predictors, namely a CNN-based and two graph neural networks-based frameworks. Synthetic and real-world networks, both directed and undirected, are investigated. Experimental results demonstrate that the proposed SPP-CNN achieves better prediction performances and better generalizability to unknown datasets, with significantly lower time-consumption, than its counterparts. © 2023, CC BY.

关键词： Complex networks

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Editorial: Dependability and security

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IEEE Transactions on Dependable and Secure Computing 2008年第1期5卷 1-3页

作者： Iyer, Ravishankar K. Gligor, Virgil D. Kalbarczyk, Zbigniew T. Kanoun, Karama Katzenbeisser, Stefan Mukherjee, Shubu Shieh, Shiuhpyng Zorian, Yervant Department of Electrical and Computer Engineering Carnegie Mellon University Microsoft's Trusted Computing Academic Advisory Board Center for Reliable and High-Performance Computing Coordinated Science Laboratory University of Illinois Urbana-Champaign United States IEEE IEEE Computer Society LAAS CNRS IFIP Working Group 10.4 on Dependable Computing and Fault Tolerance Special Interest Group on Dependability Benchmarking IFIP Working Group 10.4 French SEE Technical Committee on Trustworthy Computer Systems SEE Working Group Design and Validation for Dependability Technical University Munich Germany Darmstadt University of Technology ACM IACR Intel's SER Tech Council Intel's Architecture Patent Committee Taiwan Information Security Center NCTU IEEE Reliability Society Taipei and Tainan IACR United States Virage Logic Corp. AT and T Bell Laboratories Logic Vision Inc. University of British Columbia IEEE Test Technology Technical Council

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Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services

arXiv

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arXiv 2023年

作者： Xu, Minrui Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Mao, Shiwen Han, Zhu Jamalipour, Abbas Kim, Dong In Shen, Xuemin Leung, Victor C.M. Poor, H. Vincent The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States The Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada The College of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China The Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Artificial Intelligence-Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This survey paper focuses on the deployment of AIGC applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile AIGC networks, that provide personalized and customized AIGC services in real time while maintaining user privacy. We begin by introducing the background and fundamentals of generative models and the lifecycle of AIGC services at mobile AIGC networks, which includes data collection, training, fine-tuning, inference, and product management. We then discuss the collaborative cloud-edge-mobile infrastructure and technologies required to support AIGC services and enable users to access AIGC at mobile edge networks. Furthermore, we explore AIGC-driven creative applications and use cases for mobile AIGC networks. Additionally, we discuss the implementation, security, and privacy challenges of deploying mobile AIGC networks. Finally, we highlight some future research directions and open issues for the full realization of mobile AIGC networks. © 2023, CC BY.

关键词： Life cycle

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A Scalable Deep Attention Mechanism of Instance Segmentation for the Investigation of Chromosome

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SLAS technology 2025年 100306页

作者： Neelam Umbreen Sara Ali Hasan Sajid Yasar Ayaz Shrooq Alsenan Yunyoung Nam So Yeon Kim Muhammad Baber Sial National University of Sciences and Technology (NUST) Islamabad Pakistan. Electronic address: neelamumbreen.phd@smme.edu.pk. National University of Sciences and Technology (NUST) Islamabad Pakistan Suzhou Industrial Park (SIP) Monash Research Institute of Science and Technology Monash University 8Q9C+R6W Suzhou Jiangsu China. School of Mechanical and Manufacturing Engineering (SMME) National University of Sciences and Technology (NUST) Islamabad NUST-COVENTRY Human-Robot Interaction (NC-HRI) Laboratory Pakistan Intelligent Field Robotics Laboratory (IFRL) National Center for Artificial Intelligence (NCAI) National University of Sciences and Technology (NUST) Islamabad Pakistan. Electronic address: sarababer@smme.nust.edu.pk. National University of Sciences and Technology (NUST) Islamabad Pakistan. Electronic address: hasan.sajid@smme.nust.edu.pk. Intelligent Field Robotics Laboratory (IFRL) National Center for Artificial Intelligence (NCAI) National University of Sciences and Technology (NUST) Islamabad Pakistan. Electronic address: yasar@smme.nust.edu.pk. Information Systems Department College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia. Electronic address: shalsenan@pnu.edu.sa. Emotional and Intelligent Child Care Convergence Research Center Soonchunhyang University Asan 31538 Korea. Electronic address: ynam@sch.ac.kr. Department of Artificial Intelligence Department of Software and Computer Engineering Ajou University. Electronic address: jebi1771@ajou.ac.kr. School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China. Electronic address: babersial@***.

Chromosome segmentation in metaphase images is a critical yet challenging task in cytogenetics and genomics due to the inherent complexity, variability in chromosome shapes, and the scarcity of high-quality annotated datasets. This study proposes a robust instance segmentation framework that integrates an automated annotation pipeline with an enhanced deep learning architecture to address these challenges. A novel dataset is introduced, comprising metaphase images and corresponding karyograms, annotated with precise instance segmentation information across 24 chromosome classes in COCO format. To overcome the labor-intensive manual annotation process, a feature-based image registration technique leveraging SIFT and homography is employed, enabling the accurate mapping of chromosomes from karyograms to metaphase images and significantly improving annotation quality and segmentation performance. The proposed framework includes a custom Mask R-CNN model enhanced with an Attention-based Feature Pyramid Network (AttFPN), spatial attention mechanisms, and a LastLevelMaxPool block for superior multi-scale feature extraction and focused attention on critical regions of the image. Experimental evaluations demonstrate the model's efficacy, achieving a mean average precision (mAP) of 0.579 at IoU = 0.50:0.95, surpassing the baseline Mask R-CNN and Mask R-CNN with AttFPN by 3.94% and 5.97% improvements in mAP and AP50, respectively. Notably, the proposed architecture excels in segmenting small and medium-sized chromosomes, addressing key limitations of existing methods. This research not only introduces a state-of-the-art segmentation framework but also provides a benchmark dataset, setting a new standard for chromosome instance segmentation in biomedical imaging. The integration of automated dataset creation with advanced model design offers a scalable and transferable solution, paving the way for tackling similar challenges in other domains of biomedical and cytogenetic imagi

关键词： Chromosome segmentation Instance segmentation, cytogenetics Mask R-CNN attention mechanism biomedical imaging multi-scale feature fusion

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CEBRA Method: Decoding Brain Activity for Advanced Brain-computer Interface Technology 46

CEBRA Method: Decoding Brain Activity for Advanced Brain-Com...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Yang, Jingcheng Kulwa, Frank Liu, Xuanwei Lu, Yiqing Fu, Yufa Li, Guanglin Huai, Yaping Zhang, Xin Li, Yongcheng Shenzhen Dapeng New District Nanao People's Hospital Guangdong Shenzhen518000 China Shenzhen Longhua District Central Hospital Department of Rehabilitation Medicine Guangdong Shenzhen518000 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Kunming University of Science and Technology Brain Cognition and Brain-Computer Intelligence Integration Group Kunming650500 China Cas Key Laboratory of Human-Machine Intelligence Synergy Systems Shenzhen518055 China University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Shenzhen518055 China Shandong Zhongke Advanced Technology Co. Ltd. Jinan250014 China

ISBN: (纸本)9798350371499

The emerging neurorehabilitation technology, Brain-computer Interface (BCI), provides a novel prospect for stroke recovery. However, decoding brain activity during the movement present substantial challenges, and feature extraction is crutial to build a better decoder. In this study the CEBRA method were employed to extract features first based on electroencephalogram(EEG) data during Motor Execution(ME) and Motor lmagery (Ml) tasks for 20 participants (including 10 stroke patients). The results revealed that, in MI tasks, CEBRA-RF (Random Forests) achieved an average classification accuracy of 91.33%, with an average F1-score of 91.19%, and CEBRA-SVM (Support Vector Machine) achieved an average classification accuracy of 91.32%, with an average F1-score of 90.83%. Compared to other conventional feature extraction methods, CEBRA shows significantly higher accuracy (t-test, p © 2024 IEEE.

关键词： Patient rehabilitation

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Generative AI for Space-Air-Ground Integrated Networks

arXiv

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arXiv 2023年

作者： Zhang, Ruichen Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Zhang, Ping Kim, Dong In The College of Computing and Data Science Nanyang Technological University Singapore The Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The University of Sydney SydneyNSW2006 Australia The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Recently, generative AI technologies have emerged as a significant advancement in artificial intelligence field, renowned for their language and image generation capabilities. Meantime, space-air-ground integrated network (SAGIN) is an integral part of future B5G/6G for achieving ubiquitous connectivity. Inspired by this, this article explores an integration of generative AI in SAGIN, focusing on potential applications and case study. We first provide a comprehensive review of SAGIN and generative AI models, highlighting their capabilities and opportunities of their integration. Benefiting from generative AI’s ability to generate useful data and facilitate advanced decision-making processes, it can be applied to various scenarios of SAGIN. Accordingly, we present a brief survey on their integration, including channel modeling and channel state information (CSI) estimation, joint air-space-ground resource allocation, intelligent network deployment, semantic communications, image extraction and processing, security and privacy enhancement. Next, we propose a framework that utilizes a generative diffusion model (GDM) to construct channel information map to enhance quality of service for SAGIN. Simulation results demonstrate the effectiveness of the proposed framework. Finally, we discuss potential research directions for generative AI-enabled SAGIN. © 2023, CC BY.

关键词： Generative adversarial networks

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ICSGD-Momentum: SGD Momentum Based on Inter-gradient Collision

ICSGD-Momentum: SGD Momentum Based on Inter-gradient Collisi...

引用

IEEE International Conference on Industrial Informatics (INDIN)

作者： Weidong Zou Weipeng Cao Yuanqing Xia Bineng Zhong Dachuan Li School of Automation Beijing Institute of Technology Beijing China Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Deep neural networks (DNNs) are widely used in fields like computer vision and natural language processing. A key component of DNN training is the optimizer. SGD-Momentum is popular in many DNN methodologies, such as ResNet and DenseNet, due to its simplicity and effectiveness. However, its slow convergence rate limits its use. To overcome this, we introduce inter-gradient collision into SGD-Momentum, inspired by the elastic collision model in physics. This new method, called ICSGD-Momentum, aims to improve convergence. We provide theoretical proof of convergence and establish a regret bound for ICSGD-Momentum. Experiments on benchmarks including function optimization, CIFAR-100, ImageNet, Penn Treebank, COCO, and YCB-Video show that ICSGD-Momentum accelerates training and enhances the generalization performance of DNNs compared to optimizers like SGD-Momentum, Adam, Radam, Adabound, and AdaBelief.

关键词： Training computer vision Artificial neural networks Benchmark testing Natural language processing Informatics Physics Optimization Convergence

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The Age of Generative AI and AI-Generated Everything

arXiv

引用

arXiv 2023年

作者： Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Zhang, Ping Cui, Shuguang Shen, Xuemin Mao, Shiwen Han, Zhu Jamalipour, Abbas Vincent Poor, H. Kim, Dong In School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Generative AI (GAI) has emerged as a significant advancement in artificial intelligence, renowned for its language and image generation capabilities. This paper presents "AIGenerated Everything" (AIGX), a concept that extends GAI beyond mere content creation to real-time adaptation and control across diverse technological domains. In networking, AIGX collaborates closely with physical, data link, network, and application layers to enhance real-time network management that responds to various system and service settings as well as application and user requirements. Networks, in return, serve as crucial components in further AIGX capability optimization through the AIGX lifecycle, i.e., data collection, distributed pre-training, and rapid decision-making, thereby establishing a mutually enhancing interplay. Moreover, we offer an in-depth case study focused on power allocation to illustrate the interdependence between AIGX and networking systems. Through this exploration, the article analyzes the significant role of GAI for networking, clarifies the ways networks augment AIGX functionalities, and underscores the virtuous interactive cycle they form. This article paves the way for subsequent future research aimed at fully unlocking the potential of GAI and networks. © 2023, CC BY.

关键词： Decision making

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EDOLAB: An Open-Source Platform for Education and Experimentation with Evolutionary Dynamic Optimization algorithms

arXiv

引用

arXiv 2023年

作者： Peng, Mai Yazdani, Delaram She, Zeneng Yazdani, Danial Luo, Wenjian Li, Changhe Branke, Juergen Nguyen, Trung Thanh Gandomi, Amir H. Yang, Shengxiang Jin, Yaochu Yao, Xin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Research Institute Faculty of Engineering and Technology Liverpool John Moores University United Kingdom School of Computer Science and Technology Harbin Institute of Technology China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Peng Cheng Laboratory China School of Artificial Intelligence Anhui University of Sciences & Technology China Information Systems Management and Analytics in Warwick Business School University of Warwick United Kingdom Faculty of Engineering & Information Technology University of Technology Sydney Australia Obuda University Hungary School of Computer Science and Informatics De Montfort University United Kingdom Department of Artificial Intelligence School of Engineering Westlake University China School of Computer Science University of Birmingham United Kingdom

Many real-world optimization problems exhibit dynamic characteristics, posing significant challenges for traditional optimization techniques. Evolutionary Dynamic Optimization algorithms (EDOAs) are designed to address these challenges effectively. However, in existing literature, the reported results for a given EDOA can vary significantly. This inconsistency often arises because the source codes for many EDOAs, which are typically complex, have not been made publicly available, leading to error-prone re-implementations. To support researchers in conducting experiments and comparing their algorithms with various EDOAs, we have developed an open-source MATLAB platform called the Evolutionary Dynamic Optimization laboratory (EDOLAB). This platform not only facilitates research but also includes an educational module designed for instructional purposes. The education module allows users to observe: a) a 2-dimensional problem space and its morphological changes following each environmental change, b) the behaviors of individuals over time, and c) how the EDOA responds to environmental changes and tracks the moving optimum. The current version of EDOLAB features 25 EDOAs and four fully parametric benchmark generators. © 2023, CC BY-NC-SA.

关键词： Optimization algorithms

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Low-Overhead Channel Estimation via 3D Extrapolation for TDD mmWave Massive MIMO systems Under High-Mobility Scenarios

arXiv

引用

arXiv 2024年

作者： Zhou, Binggui Yang, Xi Ma, Shaodan Gao, Feifei Yang, Guanghua School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai200241 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau 999078 China Department of Automation Tsinghua University Beijing100084 China School of Intelligent Systems Science and Engineering Guangdong International Cooperation Base of Science and Technology for GBA Smart Logistics Jinan University Zhuhai519070 China

In time division duplexing (TDD) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, downlink channel state information (CSI) can be obtained from uplink channel estimation thanks to channel reciprocity. However, under high-mobility scenarios, frequent uplink channel estimation is needed due to channel aging. Additionally, large amounts of antennas and subcarriers result in high-dimensional CSI matrices, aggravating pilot training overhead. To address this, we propose a three-domain (3D) channel extrapolation framework across spatial, frequency, and temporal domains. First, considering the effectiveness of traditional knowledge-driven channel estimation methods and the marginal effects of pilots in the spatial and frequency domains, a knowledge- and-data driven spatial-frequency channel extrapolation network (KDD-SFCEN) is proposed for uplink channel estimation via joint spatial-frequency channel extrapolation to reduce spatial-frequency domain pilot overhead. Then, leveraging channel reciprocity and temporal dependencies, we propose a temporal uplink-downlink channel extrapolation network (TUDCEN) powered by generative artificial intelligence for slot-level channel extrapolation, aiming to reduce the tremendous temporal domain pilot overhead caused by high mobility. Numerical results demonstrate the superiority of the proposed framework in significantly reducing the pilot training overhead by 16 times and improving the system’s spectral efficiency under high-mobility scenarios compared with state-of-the-art channel estimation/extrapolation methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Frequency estimation

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