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arXiv 2024年

作者： Fu, Xingcheng Gao, Yisen Wei, Yuecen Sun, Qingyun Peng, Hao Li, Jianxin Li, Xianxian Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Institute of Artificial Intelligence Beihang University Beijing China School of Software Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China

Diffusion models have made significant contributions to computer vision, sparking a growing interest in the community recently regarding the application of them to graph generation. Existing discrete graph diffusion models exhibit heightened computational complexity and diminished training efficiency. A preferable and natural way is to directly diffuse the graph within the latent space. However, due to the non-Euclidean structure of graphs is not isotropic in the latent space, the existing latent diffusion models effectively make it difficult to capture and preserve the topological information of graphs. To address the above challenges, we propose a novel geometrically latent diffusion framework HypDiff. Specifically, we first establish a geometrically latent space with interpretability measures based on hyperbolic geometry, to define anisotropic latent diffusion processes for graphs. Then, we propose a geometrically latent diffusion process that is constrained by both radial and angular geometric properties, thereby ensuring the preservation of the original topological properties in the generative graphs. Extensive experimental results demonstrate the superior effectiveness of HypDiff for graph generation with various topologies. Copyright © 2024, The Authors. All rights reserved.

关键词： Topology

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FedAL: Black-Box Federated Knowledge Distillation Enabled by Adversarial Learning

arXiv

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arXiv 2023年

作者： Han, Pengchao Shi, Xingyan Huang, Jianwei The School of Information Engineering Guangdong University of Technology Guangzhou510006 China The School of Data Science The Chinese University of Hong Kong Shenzhen China The School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network CSIJRI Joint Research Centre on Smart Energy Storage The Chinese University of Hong Kong Guangdong Shenzhen518172 China

Knowledge distillation (KD) can enable collaborative learning among distributed clients that have different model architectures and do not share their local data and model parameters with others. Each client updates its local model using the average model output/feature of all client models as the target, known as federated KD. However, existing federated KD methods often do not perform well when clients’ local models are trained with heterogeneous local datasets. In this paper, we propose Federated knowledge distillation enabled by Adversarial Learning (FedAL) to address the data heterogeneity among clients. First, to alleviate the local model output divergence across clients caused by data heterogeneity, the server acts as a discriminator to guide clients’ local model training to achieve consensus model outputs among clients through a min-max game between clients and the discriminator. Moreover, catastrophic forgetting may happen during the clients’ local training and global knowledge transfer due to clients’ heterogeneous local data. Towards this challenge, we design the less-forgetting regularization for both local training and global knowledge transfer to guarantee clients’ ability to transfer/learn knowledge to/from others. Experimental results show that FedAL and its variants achieve higher accuracy than other federated KD baselines. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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Case Discrimination: Self-supervised Feature Learning for the Classification of Focal Liver Lesions 9th

Case Discrimination: Self-supervised Feature Learning for th...

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9th KES International Conference on Innovation in Medicine and Healthcare, KES-InMed 2021

作者： Dong, Haohua Iwamoto, Yutaro Han, Xianhua Lin, Lanfen Hu, Hongjie Cai, Xiujun Chen, Yen-Wei College of Information Science and Engineering Ritsumeikan University Shiga Japan Artificial Intelligence Research Center Yamaguchi University Yamaguchi Japan College of Computer Science and Technology Zhejiang University Hangzhou China Sir Run Run Shaw Hospital Hangzhou China Zhejiang Lab Research Center for Healthcare Data Science Hangzhou China

ISBN: (纸本)9789811630125

Deep Learning provides exciting solutions to problems in medical image analysis and is regarded as a key method for future applications. However, only a few annotated medical image datasets exist compared to numerous natural images. A solution to this problem is transfer learning using ImageNet. However, because the domain of ImageNet is different from that of medical images, the results of transfer learning are not always good. Therefore, we propose a model to investigate transfer learning by self-supervised learning using medical images. It is widely known that the results of Computerized Tomography (CT) scan are 3D volume images. There are lots of slices in CT or Magnetic Resonance Imaging scan images. So why not make these slices to a class? It is imperative to formulate this intuition as a self-supervised feature learning at the case-level. The results of our experiment demonstrated that, under self-supervised feature learning settings, our method surpasses the transfer learning method that uses ImageNet for classification. By experimenting with unannotated datasets, our method is remarkable for consistently improving test performance with a few annotated data. By fine-tuning the learned features, we obtained competitive results for self-supervised learning and classification tasks. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image classification

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Exact Fit Attention in Node-Holistic Graph Convolutional Network for Improved EEG-Based Driver Fatigue Detection

arXiv

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arXiv 2025年

作者： Xu, Meiyan Chen, Qingqing Chen, Duo Ding, Yi Wang, Jingyuan Gu, Peipei Pan, Yijie Huang, Deshuang Guo, Jiayang Zhang, Xun Department of Computer Science The Institute of Human Factors Engineering Minnan Normal University Zhangzhou363000 China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing210023 China College of Computing and Data Science Nanyang Technological University Singapore School of Digital Media and Design Arts Beijing University of Posts and Telecommunications Beijing100088 China College of Software Engineering Zhengzhou University of Light Industry Zhengzhou450000 China Department of Computer Science and Technology Tsinghua University Beijing100084 China Eastern Institute for Advanced Study Eastern Institute of Technology Ningbo315200 China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo315200 China National Institute for Data Science in Health and Medicine Xiamen University Xiamen361005 China

EEG-based fatigue monitoring can effectively reduce the incidence of related traffic accidents. In the past decade, with the advancement of deep learning, convolutional neural networks (CNN) have been increasingly used for EEG signal processing. However, due to the data’s non-Euclidean characteristics, existing CNNs may lose important spatial information from EEG, specifically channel correlation. Thus, we propose the node-holistic graph convolutional network (NHGNet), a model that uses graphic convolution to dynamically learn each channel’s features. With exact fit attention optimization, the network captures inter-channel correlations through a trainable adjacency matrix. The interpretability is enhanced by revealing critical areas of brain activity and their interrelations in various mental states. In validations on two public datasets, NHGNet outperforms the SOTAs. Specifically, in the intra-subject, NHGNet improved detection accuracy by at least 2.34% and 3.42%, and in the inter-subjects, it improved by at least 2.09% and 15.06%. Visualization research on the model revealed that the central parietal area plays an important role in detecting fatigue levels, whereas the frontal and temporal lobes are essential for maintaining vigilance. Copyright © 2025, The Authors. All rights reserved.

关键词： Electroencephalography

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Investigating the Effectiveness of Birthday Attack Strategies in Cryptography Network Security

Investigating the Effectiveness of Birthday Attack Strategie...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Gurunath T. Chavan Shalu Agrawal Nitin N. Sakhare Dipannita Mondal M. Vigenesh Vijayalakshmi G Department of Information Technology Vishwakarma Institute of Information Technology Pune India Department of Management Mangalayatan University Aligarh India Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Artificial Intelligence And Data Science Department Dr. D.Y.Patil College Of Engineering And Innovation Talegaon India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India

Cryptography is a crucial factor of community safety because it involves growing and decoding mystery codes to guard touchy statistics from unauthorized right of entry. As the era advances, community protection professionals must continuously evaluate the effectiveness of numerous cryptographic techniques and techniques. One such method is the birthday attack, a form of brute-pressure attack that takes benefit of the birthday paradox – the chance that in a group of people, two people will proportion an equal birthday. In cryptography, this attack is used to discover collisions in a hashing function, wherein different enter messages produce identical output. This have a look at examines the effectiveness of various birthday attack strategies in a cryptography community protection context. specially, the studies observes the numerous algorithms and scenarios concerning the use of birthday attack methods and how they're being utilized in network safety structures. moreover, the have a look at strives to apprehend how the boom within the sophistication of the cryptography networks has brought about an boom in the prevalence and effectiveness of the birthday attack. The research seeks to cope with the diverse problems and challenges faced by using cryptography networks using the birthday attack approach, inclusive of reducing the reliability of certain factors of the cryptography network and exposing the ability of introducing vulnerabilities into the device. The examine additionally examines the usage of multi-layered birthday attack strategies, which include utilising a couple of algorithms and using a mixture of various methods, as well as exploring the capacity of any new threat vectors which may be used. in the end, the studies will explore whether or not there are any first-rate practices or methods which may be used to lessen the chance associated with any a hit birthday assault tries. The consequences of this take a look at could be used to gain a deeper knowledg

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SVM-based Predictive Modeling of Drowsiness in Hospital Staff for Occupational Safety Solution via IoT Infrastructure

SVM-based Predictive Modeling of Drowsiness in Hospital Staf...

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Computer, Communication and Control (IC4), International Conference on

作者： B. Jai Ganesh P. Vijayan V. Vaidehi S. Murugan R. Meenakshi M. Rajmohan Department of Electronics and Communication Engineering R.M.D. Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai Tamil Nadu India Department of Computer Science Vinayaka Mission’s Kirupananda Variyar Arts and Science College Vinayaka Mission’s Research Foundation (DU) Salem Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

This research proposes an efficient approach to improve hospital worker safety on the job by detecting and preventing drowsiness. The system incorporates data from hospital-deployed Internet of Things (IoT) infrastructure and using Support Vector Machines (SVM) as its primary predictive modeling tool. data pertaining to ambient factors, work habits, and physiological indications of employees are collected in real-time by the IoT infrastructure. To identify patterns linked to sleepiness, the SVM model is trained on an extensive dataset. The next step is to implement the model in a healthcare setting so that it can detect signs of sleepiness and act accordingly in a timely manner. With the help of the IoT the predictive model can be dynamically adjusted and monitored in real-time, making it useful in a wide range of situations. The recommended approach is to enhance workplace safety by reducing the likelihood of accidents caused by sleepiness among healthcare workers. The SVM-based prediction model can correctly detect and forecast cases of sleepiness, the research subjected it to extensive testing and validation. This system's adoption helps with the creation of data-driven, proactive methods to improve hospital occupational safety.

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Low-Latency Layer-Aware Proactive and Passive Container Migration in Meta Computing

arXiv

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arXiv 2024年

作者： Liu, Mengjie Li, Yihua Mou, Fangyi Tang, Zhiqing Lou, Jiong Guo, Jianxiong Jia, Weijia Institute of Artificial Intelligence and Future Networks Beijing Normal University China Faculty of Arts and Sciences Beijing Normal University China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College China Department of Computer Science and Engineering Shanghai Jiao Tong University China Department of Informatics University of Zurich Zurich Switzerland

关键词： Containers

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NTIRE 2023 Challenge on Image Super-Resolution (×4): Methods and Results

NTIRE 2023 Challenge on Image Super-Resolution (×4): Method...

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Zhang, Yulun Zhang, Kai Chen, Zheng Li, Yawei Timofte, Radu Zhang, Junpei Zhang, Kexin Peng, Rui Ma, Yanbiao Jiao, Licheng Huang, Huaibo Zhou, Xiaoqiang Ai, Yuang He, Ran Qiu, Yajun Zhu, Qiang Li, Pengfei Li, Qianhui Zhu, Shuyuan Zhang, Dafeng Li, Jia Wang, Fan Li, Chunmiao Kim, TaeHyung Kil, Jungkeong Kim, Eon Yu, Yeonseung Lee, Beomyeol Lee, Subin Lim, Seokjae Chae, Somi Choi, Heungjun Huang, ZhiKai Chen, YiChung Chiang, YuanChun Yang, HaoHsiang Chen, WeiTing Chang, HuaEn Chen, I-Hsiang Hsieh, ChiaHsuan Kuo, SyYen Choi, Ui-Jin Conde, Marcos V. Khowaja, Sunder Ali Yoon, Jiseok Lee, Ik Hyun Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Zhang, Zhao Li, Baiang Zheng, Huan Zhao, Suiyi Gao, Yangcheng Wei, Yanyan Ren, Jiahuan Wei, Jiayu Li, Yanfeng Sun, Jia Cheng, Zhanyi Li, Zhiyuan Yao, Xu Wang, Xinyi Li, Danxu Cui, Xuan Cao, Jun Li, Cheng Zheng, Jianbin Sarvaiya, Anjali Prajapati, Kalpesh Patra, Ratnadeep Barik, Pragnesh Rathod, Chaitanya Upla, Kishor Raja, Kiran Ramachandra, Raghavendra Busch, Christoph Computer Vision Lab Eth Zurich Switzerland Shanghai Jiao Tong University China University of Würzburg Germany Xidian University China Mais&cripac Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China University of Science and Technology of China China Beijing Institute of Technology China School of Information Science and Technology ShanghaiTech University China School of Information and Communication Engineering University of Electronic Science and Technology of China China China Lotte Data Communication Company Seoul Korea Republic of Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States MegaStudyEdu Korea Republic of Computer Vision Lab Caidas University of Würzburg Germany University of Sindh Pakistan Iklab Inc. Tech University of Korea Siheung-Si Korea Republic of Micro-Nano Electronics Department Shanghai Jiao Tong University China Electrical Engineering Department Faculty of Engineering Assiut University Egypt Hefei University of Technology China Beijing Jiaotong University China South China University of Technology Guangdong Guangzhou China Sardar Vallabhbhai National Institute of Technology India Norwegian University of Science and Technology Norway

ISBN: (纸本)9798350302493

This paper reviews the NTIRE 2023 challenge on image super-resolution (×4), focusing on the proposed solutions and results. The task of image super-resolution (SR) is to generate a high-resolution (HR) output from a corresponding low-resolution (LR) input by leveraging prior information from paired LR-HR images. The aim of the challenge is to obtain a network design/solution capable to produce high-quality results with the best performance (e.g., PSNR). We want to explore how high performance we can achieve regardless of computational cost (e.g., model size and FLOPs) and data. The track of the challenge was to measure the restored HR images with the ground truth HR images on DIV2K testing dataset. The ranking of the teams is determined directly by the PSNR value. The challenge has attracted 192 registered participants, where 15 teams made valid submissions. They achieve state-of-the-art performance in single image super-resolution. © 2023 IEEE.

关键词： Statistical tests

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Designing Resilient Network Architectures for Disaster Recovery

Designing Resilient Network Architectures for Disaster Recov...

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： Parikshit N. Mahalle A. Amudha Anubhav Bhalla Jagtej Singh Janakiraman V Srinivasa Rao K S Department of Artificial Intelligence & Data Science Vishwakarma Institute of Technology Pune India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India Department of Mech Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Adjunct Faculty School of Mangement-PG Management JAIN Deemed to be University Bangalore Karnataka India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

Digital infrastructure is always changing, therefore secure network designs are essential to keep things running amid natural catastrophes. The Dynamic Resilience Framework (DRF) uses ARO, DDI, and ERA to strengthen networks. Many prominent methods are used to compare the proposed framework against new ones. Hierarchical network design, SDN, distributed cloud architecture, and active-active redundancy are examples. The experiment simulates disaster situations to examine each network architecture's downtime, data recovery, and resource use. DDI scores reveal how well components interact; ARO scores show how repetition varies with difficulty. ERA scores measure resource efficiency and how to scale them with demand. You may analyze qualitative and quantitative statistics, user comments, and qualitative assessments. DRF outperforms other methods in ARO, DDI, and ERA. Statistics show that these performance variances are considerable, proving that the proposed structure is strong and adaptive. This research provides a comprehensive catastrophic recovery strategy, adding to the resilient network architecture discussion. The results will help companies determine the best ways to protect their digital assets against unexpected challenges.

关键词： Technological innovation Disasters Scalability Redundancy Network architecture Signal processing Resource management Software defined networking Resilience Testing

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LogisticsVISTA: 3D Terminal Delivery Services With UAVs, UGVs and USVs Based on Foundation Models and Scenarios engineering

LogisticsVISTA: 3D Terminal Delivery Services With UAVs, UGV...

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2024 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2024

作者： Tian, Yonglin Lin, Fei Zhang, Xinyuan Ge, Jingwei Wang, Yutong Dai, Xingyuan Lv, Yisheng Wang, Fei-Yue University of Chinese Academy of Sciences Beijing100049 China Tsinghua University Department of Automation Beijing100084 China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems State Key Laboratory for Management and Control of Complex Systems Beijing100190 China Macau University of Science and Technology Faculty of Innovation Engineering Department of Engineering Science 999078 China

Intelligent logistics with autonomous vehicles is promising to improve the efficiency of rapidly expanding delivery systems. However, complex environments, diverse packages, and irregular human interaction have imposed challenges to the application of unmanned delivery vehicles. Combining various forms of vehicles and developing knowledge-driven delivery services have been the key to further facilitating the upgrading of logistics systems. In this paper, we propose an integrated framework named LogisticsVISTA (Logistics on Vehicles with Intelligent Systems for Transport Automation) comprised of UAVs (Unmanned Aerial Vehicles), UGVs (Unmanned Ground Vehicles), and USVs (Unmanned Surface Vehicles) to provide timely and flexible 3D delivery services. In LogisticsVISTA, we design Large language models (LLMs) and large multimodal models (LMMs) for task planning, active perception, autonomous decision, and human-machine interaction. Besides, virtual-real integrated parallel scenarios are constructed to provide the playing ground for embodied agents efficiently and flexibly. © 2024 IEEE.

关键词： Drones

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