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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Yang, Ziqi Peng, Zhaopeng Wang, Zihui Qi, Jianzhong Chen, Chaochao Pan, Weike Wen, Chenglu Wang, Cheng Fan, Xiaoliang Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen University China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China School of Computing and Information Systems The University of Melbourne Australia College of Computer Science and Technology Zhejiang University Hangzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Cross-domain recommendation (CDR) offers a promising solution to the data sparsity problem by enabling knowledge transfer between source and target domains. However, many recent CDR models overlook crucial issues such as privacy as well as the risk of negative transfer (which negatively impact model performance), especially in multi-domain settings. To address these challenges, we propose FedGCDR, a novel federated graph learning framework that securely and effectively leverages positive knowledge from multiple source domains. First, we design a positive knowledge transfer module that ensures privacy during inter-domain knowledge transmission. This module employs differential privacy-based knowledge extraction combined with a feature mapping mechanism, transforming source domain embeddings from federated graph attention networks into reliable domain knowledge. Second, we design a knowledge activation module to filter out potential harmful or conflicting knowledge from source domains, addressing the issues of negative transfer. This module enhances target domain training by expanding the graph of the target domain to generate reliable domain attentions and fine-tunes the target model for improved negative knowledge filtering and more accurate predictions. We conduct extensive experiments on 16 popular domains of the Amazon dataset, demonstrating that FedGCDR significantly outperforms state-of-the-art methods. We open source the code at https://***/LafinHana/FedGCDR. © 2024 Neural information processing systems foundation. All rights reserved.

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Correction to: Improving time and energy efficiency in multi-UAV coverage operations by optimizing the UAVs’ initial positions (International Journal of Intelligent Robotics and Applications, (2024), 10.1007/s41315-024-00333-2)

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International Journal of Intelligent Robotics and Applications 2024年第1期9卷 365-373页

作者： Stefanopoulou, Aliki Raptis, Emmanuel K. Apostolidis, Savvas D. Gkelios, Socratis Kapoutsis, Athanasios Ch. Chatzichristofis, Savvas A. Vrochidis, Stefanos Kosmatopoulos, Elias B. Information Technologies Institute The Centre for Research & ampTechnology Hellas 6th km Harilaou - Thermis Thessaloniki57001 Greece Department of Electrical and Computer Engineering Democritus University of Thrace Kimmeria Campus Xanthi67100 Greece Intelligent Systems Laboratory Department of Computer Science Neapolis University Pafos 2 Danais Avenue PafosCY 8042 Cyprus

In the original publication, the author has found few errors in the figures 2, 4, 11 and 12 which significantly impact both the paper's quality and the reputation of the journal. These issues undermine the clarity of the methodology and the novelty of the research. 1. Each column represents a different case in the experiment, corresponding to the number of UAVs in both the obstacle-free and obstacle areas. However, in Fig. 2, the first row contains identical subfigures across all columns, while the subfigures in the remaining rows are identical to each other The incorrect Fig. 2 appears as below: (Figure presented.) However the corrected Fig. 2 should appear as below. (Figure presented.) 2. In Fig. 4, every row showcase the average QoF for the testing scenarios (obstacle-free and obstacle area). However the first row has the exact same figures with the second row. The incorrect Fig. 4 appears as below: (Figure presented.) However the corrected Fig. 4 should appear as below. (Figure presented.) 3. Figures 11 and 12 showcase the distribution of each Quality of Flight (QoF) metric value for every UAV in the conducted trials during the testing scenarios. However, Fig. 11 is the same with Fig. 12. The incorrect Figs. 11 and 12 appears as below: (Figure presented.) However the corrected Figs. 11 and 12 should appear as below. (Figure presented.) Distribution of each Quality of Flight metric value for every UAV in the conducted trials during the coverageof the obstacle-free area Distribution of each Quality of Flight metric value for every UAV in the conducted trials during the coverageof the obstacle area The original article has been corrected. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

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GraphFed: A Personalized Subgraph Federated Learning Framework for Non-IID Graphs 20

GraphFed: A Personalized Subgraph Federated Learning Framewo...

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20th IEEE International Conference on Mobile Ad Hoc and Smart systems, MASS 2023

作者： Deng, Pan Liu, Xuefeng Niu, Jianwei Hu, Chunming Zhongguancun Laboratory Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9798350324334

Recently, federated graph learning has attracted significant attention, as subgraphs of a global graph may often distribute across different institutions and are subject to privacy restrictions. However, inevitable data heterogeneity among sub-graphs can lead to poor performance of Graph Neural Networks (GNNs) trained by vanilla federated learning algorithm, which has been neglected before. To address this problem we look at subgraphs and find that there are usually overlapping nodes among them. Inspired by the common overlapping nodes among subgraphs, we propose GraphFed, a subgraph federated learning framework which solve the challenge in terms of both data augmentation and personalized federated learning. GraphFed uses common overlapping nodes among subgraphs to locate lost node information and applies data augmentation by retrieving lost nodes and forming common node sets that mitigates data distribution difference. Furthermore, GraphFed treats the overlapping nodes as bridges to calculate the distance among different subgraphs, which implies the similarity of the graph data distribution. Then, we use the distance to perform personalized federated learning. Empirical results and analysis on three real-world graph datasets with graph federated learning settings demonstrate the effectiveness of our proposed framework. © 2023 IEEE.

关键词： Graph neural networks

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Super-resolution of Solar Magnetograms Using Deep Learning

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Research in Astronomy and Astrophysics 2022年第8期22卷 218-229页

作者： Fengping Dou Long Xu Zhixiang Ren Dong Zhao Xinze Zhang State Key Laboratory of Space Weather National Space Science CenterChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Peng Cheng National Laboratory Shenzhen 518000China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Currently,data-driven models of solar activity forecast are investigated extensively by using machine *** model training,it is highly demanded to establish a large database which may contain observations coming from different instruments with different spatio-temporal *** this paper,we employ deep learning models for super-resolution(SR)of magnetogram of Michelson Doppler Imager(MDI)in order to achieve the same spatial resolution of Helioseismic and Magnetic Imager(HMI).First,a generative adversarial network(GAN)is designed to transfer characteristics of MDI onto downscaled HMI,getting low-resolution HMI magnetogram in the same domain as ***,with the paired low-resolution and high-resolution HMI magnetograms,another GAN is trained in a supervised learning way,which consists of two streams,one is for generating high-fidelity image content,the other is explicitly optimized for generating elaborate image ***,these two streams work together to guarantee both high-fidelity and photorealistic super-resolved *** results demonstrate that the proposed method can generate super-resolved magnetograms with perceptual-pleasant visual ***,the best PSNR,LPIPS,RMSE,comparable SSIM and CC are obtained by the proposed *** source code and data set can be accessed via https://***/filterbank/SPSR.

关键词： techniques:image processing Sun:magnetic fields Sun:atmosphere

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Thematic Event Extraction Based on Event-Related Sentence Detection 1

Thematic Event Extraction Based on Event-Related Sentence De...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Zhang, Xing Guo, Yucan Jin, Xiaolong Liang, Haozhe Guan, Saiping School of Computer Science and Technology University of Chinese Academy of Sciences China Institute of Computing Technology Chinese Academy of Sciences Cas Key Laboratory of Network Data Science and Technology China Institute of Systems Engineering Academy of Military Science Australia

ISBN: (纸本)9798350358780

Existing works in event extraction typically extract event arguments within the sentence scope. However, besides the sentence level, events may also be naturally presented at the document level. A document-level event usually reflects, to some extent, the theme (i.e., the main content) of the document (e.g., electronic medical records and news articles), which is thus referred to as the thematic event. Thematic Event Extraction (TEE) aims to extract the arguments of thematic events. TEE faces a major challenge, i.e., the sparsity and dispersion of arguments, which means that the arguments of a thematic event are dispersed in different sentences of the document. To overcome this challenge, we propose an Event-related Sentence Detection based TEE model, called ESDTEE, which first detects the sentences related to the thematic event and then extracts the arguments only within these detected sentences using existing models. Extensive experiments with comprehensive analyses demonstrate the effectiveness of ESDTEE. © 2023 IEEE.

关键词： Medical computing

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A Chaotic Encryption Algorithm with Dynamic Key Enabled by Power-Division Multiplexed Transmission

A Chaotic Encryption Algorithm with Dynamic Key Enabled by P...

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2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Ding, Yinchen Lei, Yi Lu, Qi Guo, Zhongyi Chen, Bin School of Computer Science and Information Engineering Hefei University of Technology Hefei China Hefei University of Technology Intelligent Interconnected Systems Laboratory of Anhui Province Hefei China Shanghai Jiao Tong University State Key Laboratory of Advanced Optical Communication Systems and Networks China

Using power-division multiplexing, a chaotic encryption method is proposed based on dynamic key generated from the instantaneously transmitted information. The influence of power allocation is analyzed and the securit... 详细信息

ISBN: (纸本)9798350312614

关键词： chaotic encryption dynamic key Optical fiber communication physical layer security power-division mul-tiplexing

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UAV-Assisted Data Synchronization for Digital-Twin-Enabled Vehicular Networks

UAV-Assisted Data Synchronization for Digital-Twin-Enabled V...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Wu, Guoquan Nie, Jiangtian Tang, Jianhang Zhang, Yang Xiao, Jiang Xiong, Zehui Yanshan University School of Information Science and Engineering China Nanyang Technological University School of Computer Science and Engineering Singapore Guizhou University State Key Laboratory of Public Big Data China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China Huazhong University of Science and Technology School of Computer Science and Technology China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore

ISBN: (纸本)9798350345384

Digital twin (DT) is becoming a promising solution for vehicular networks to improve the interoperability of distributed autonomous driving systems. Mobile edge computing (MEC) has been introduced to provide low-latency services for DT-enabled vehicular networks at the edge of the network. However, it is hard to obtain the dynamic network topology for moving vehicles by the ground-based MEC system, which may deteriorate the service quality for DT synchronization. In this paper, we propose a novel unmanned aerial vehicle (UAV)-assisted synchronization framework for DT-enabled vehicular networks. With the proposed framework, an intelligent resource allocation algorithm is developed to improve UAV resource utilization and maximize the synchronization completion ratio. By leveraging an advantage actor-critic (A2C) algorithm, the synchronization decisions are obtained with low time complexity. Experiment results demonstrate that the proposed algorithm can reduce the synchronization latency and improve the synchronization completion ratio effectively. © 2023 IEEE.

关键词： Resource allocation

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An automatic isotropic/anisotropic hybrid grid generation technique for viscous flow simulations based on an artificial neural network

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Chinese Journal of Aeronautics 2022年第4期35卷 102-117页

作者： Peng LU Nianhua WANG Xinghua CHANG Laiping ZHANG Yadong WU School of Information Engineering Southwest University of Science and TechnologyMianyang 621010China School of Intelligent Manufacturing Engineering Chongqing University of Arts and ScienceChongqing 402160China Stake Key Laboratory of Aerodynamics China Aerodynamics Research and Development CenterMianyang 621000China Unmanned Systems Research Center National Innovation Institute of Defense TechnologyBeijing 100071China School of Computer Science and Technology Sichuan University of Science&EngineeringYibin 644005China

Based on the author’s previous research, a novel hybrid grid generation technique is developed by introducing an Artificial Neural Network(ANN) approach for realistic viscous flow simulations. An initial hybrid grid over a typical geometry with anisotropic quadrilaterals in the boundary layer and isotropic triangles in the off-body region is generated by the classical mesh generation method to train two ANNs on how to predict the advancing direction of the new point and to control the grid size. After inputting the initial discretized fronts, the ANN-based Advancing Layer Method(ALM) is adopted to generate the anisotropic quadrilaterals in boundary layers. When the high aspect ratio of the anisotropic grid reaches a specified value, the ANN-based Advancing Front Method(AFM) is adopted to generate isotropic triangles in the off-body computational *** initial isotropic triangles are smoothed to further improve the grid quality. Three typical cases are tested and compared with experimental data to validate the effectiveness of grids generated by the ANN-based hybrid grid generation method. The experimental results show that the two ANNs can predict the advancing direction and the grid size very well, and improve the adaptability of the isotropic/anisotropic hybrid grid generation for viscous flow simulations.

关键词： Advancing front method Advancing layer method Anisotropic quadrilateral grid generation Artificial neural network Isotropic triangular grid generation Machine learning

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Binary Bleed: Fast Distributed and Parallel Method for Automatic Model Selection

Binary Bleed: Fast Distributed and Parallel Method for Autom...

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2024 IEEE High Performance Extreme Computing Conference, HPEC 2024

作者： Barron, Ryan Eren, Maksim E. Bhattarai, Manish Boureima, Ismael Matuszek, Cynthia Alexandrov, Boian S. Theoretical Division Los Alamos National Laboratory Los Alamos United States University of Maryland Baltimore County Department of Computer Science and Electrical Engineering MD United States Advanced Research in Cyber Systems Los Alamos National Laboratory Los Alamos United States

ISBN: (纸本)9798350387131

In several Machine Learning (ML) clustering and dimensionality reduction approaches, such as nonnegative matrix factorization (NMF), RESCAL, and K-Means clustering, users must select a hyper-parameter k to define the number of clusters or components that yield an ideal separation of samples or clean clusters. This selection, while difficult, is crucial to avoid overfitting or underfitting the data. Several ML applications use scoring methods (e.g., Silhouette and Davies Boulding scores) to evaluate the cluster pattern stability for a specific k. The score is calculated for different trials over a range of k, and the ideal k is heuristically selected as the value before the model starts overfitting, indicated by a drop or increase in the score resembling an elbow curve plot. While the grid-search method can be used to accurately find a good k value, visiting a range of k can become time-consuming and computationally resource-intensive. In this paper, we introduce the Binary Bleed method based on binary search, which significantly reduces the k search space for these grid-search ML algorithms by truncating the target k values from the search space using a heuristic with thresholding over the scores. Binary Bleed is designed to work with single-node serial, single-node multi-processing, and distributed computing resources. In our experiments, we demonstrate the reduced search space gain over a naive sequential search of the ideal k and the accuracy of the Binary Bleed in identifying the correct k for NMFk, K-Means pyDNMFk, and pyDRESCALk with Silhouette and Davies Boulding scores. We make our implementation of Binary Bleed for the NMF algorithm available on GitHub. © 2024 IEEE.

关键词： Dimensionality reduction

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Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization 12

Cyber-Security Knowledge Graph Generation by Hierarchical No...

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Barron, Ryan Eren, Maksim E. Bhattarai, Manish Wanna, Selma Solovyev, Nicholas Rasmussen, Kim Alexandrov, Boian S. Nicholas, Charles Matuszek, Cynthia Los Alamos National Laboratory Theoretical Division Los Alamos United States Los Alamos National Laboratory Advanced Research in Cyber Systems Los Alamos United States University of Maryland Department of Computer Science and Electrical Engineering Baltimore CountyMD United States

ISBN: (纸本)9798350330366

Much of human knowledge in cybersecurity is encapsulated within the ever-growing volume of scientific papers. As this textual data continues to expand, the importance of document organization methods becomes increasingly crucial for extracting actionable insights hidden within large text datasets. Knowledge Graphs (KGs) serve as a means to store factual information in a structured manner, providing explicit, interpretable knowledge that includes domain-specific information from the cybersecurity scientific literature. One of the challenges in constructing a KG from scientific literature is the extraction of ontology from unstructured text. In this paper, we address this topic and introduce a method for building a multi-modal KG by extracting structured ontology from scientific papers. We demonstrate this concept in the cybersecurity domain. One modality of the KG represents observable information from the papers, such as the categories in which they were published or the authors. The second modality uncovers latent (hidden) patterns of text extracted through hierarchical and semantic non-negative matrix factorization (NMF), such as named entities, topics or clusters, and keywords. We illustrate this concept by consolidating more than two million scientific papers uploaded to arXiv into the cyber-domain, using hierarchical and semantic NMF, and by building a cyber-domain-specific KG. © 2024 IEEE.

关键词： Cybersecurity

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