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SSRN 2023年

作者： Wen, Jie Zhang, Jingbo Zhang, Zhixia Cui, Zhihua Cai, Xingjuan Chen, Jinjun The Shanxi Key Laboratory of Advanced Control and Equipment intelligence Taiyuan University of Science and Technology Shanxi Taiyuan China The Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and Technology Shanxi Taiyuan China The State Key Lab for Novel Software Technology Nanjing University China Department of Computing Technologies Swinburne University of Technology Melbourne Australia

Federated learning (FL), as a safe distributed training mode, provides strong support for the edge intelligence of the Internet of Vehicles (IoV) to realize efficient collaborative control and safe data sharing. However, due to the resource limitation and the instability of training environment in the complex IoV, ideal performance of FL cannot be achieved. Since considering the actual resource constraints and federated task requirements, the diversified device selection criteria make the resource-aware vehicle selection problem become a multi-criteria selection problem. To effectively support FL for IoV, the resource-aware multi-criteria vehicle selection problem was described as a many-objective optimization problem, and proposed a resource-aware many-objective vehicle selection model (RA-MaOVSM) to optimize resource efficiency. The RA-MaOVSM considering heterogeneous resources (like computation resources, communication resources, energy resources and data resources) of on-board devices in IoV, and realizes the joint optimization of learning efficiency, energy cost and global performance. Additionally, a novel probability distribution combination game strategy is applied to many-objective evolutionary algorithm (MaOEA) for improving the model solving performance. Simulation results demonstrate that RA-MaOVSM can effectively optimize the IoV resources and FL model performance, and the designed algorithm exhibits good convergence and distribution, achieving a good balance among multiple device selection criteria. © 2023, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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GraphMI: Extracting private graph data from graph neural networks

arXiv

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arXiv 2021年

作者： Zhang, Zaixi Liu, Qi Huang, Zhenya Wang, Hao Lu, Chengqiang Liu, Chuanren Chen, Enhong Anhui Province Key Lab. of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Alibaba Group University of Tennessee Knoxville

As machine learning becomes more widely used for critical applications, the need to study its implications in privacy turns to be urgent. Given access to the target model and auxiliary information, model inversion attack aims to infer sensitive features of the training dataset, which leads to great privacy concerns. Despite its success in grid-like domains, directly applying model inversion techniques on non-grid domains such as graph achieves poor attack performance due to the difficulty to fully exploit the intrinsic properties of graphs and attributes of nodes used in Graph Neural Networks (GNN). To bridge this gap, we present Graph Model Inversion attack (GraphMI), which aims to extract private graph data of the training graph by inverting GNN, one of the state-of-the-art graph analysis tools. Specifically, we firstly propose a projected gradient module to tackle the discreteness of graph edges while preserving sparsity and smoothness of graph features. Then we design a graph auto-encoder module to efficiently exploit graph topology, node attributes, and target model parameters for edge inference. With the proposed methods, we study the connection between model inversion risk and edge influence and show that edges with greater influence are more likely to be recovered. Extensive experiments over several public datasets demonstrate the effectiveness of our method. We also show that differential privacy in its canonical form can hardly defend our attack while preserving decent utility. Copyright © 2021, The Authors. All rights reserved.

关键词： Graph neural networks

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ProtGNN: Towards self-explaining Graph Neural Networks

arXiv

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arXiv 2021年

作者： Zhang, Zaixi Liu, Qi Wang, Hao Lu, Chengqiang Lee, Cheekong Anhui Province Key Lab. of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China Tencent America United States

Despite the recent progress in Graph Neural Networks (GNNs), it remains challenging to explain the predictions made by GNNs. Existing explanation methods mainly focus on post-hoc explanations where another explanatory model is employed to provide explanations for a trained GNN. The fact that post-hoc methods fail to reveal the original reasoning process of GNNs raises the need of building GNNs with built-in interpretability. In this work, we propose Prototype Graph Neural Network (ProtGNN), which combines prototype learning with GNNs and provides a new perspective on the explanations of GNNs. In ProtGNN, the explanations are naturally derived from the case-based reasoning process and are actually used during classification. The prediction of ProtGNN is obtained by comparing the inputs to a few learned prototypes in the latent space. Furthermore, for better interpretability and higher efficiency, a novel conditional subgraph sampling module is incorporated to indicate which part of the input graph is most similar to each prototype in ProtGNN+. Finally, we evaluate our method on a wide range of datasets and perform concrete case studies. Extensive results show that ProtGNN and ProtGNN+ can provide inherent interpretability while achieving accuracy on par with the non-interpretable counterparts. © 2021, CC BY.

关键词： Graph neural networks

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Integrating Sensing, Communication, and Computation in the Sky

Integrating Sensing, Communication, and Computation in the S...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yao Tang Guangxu Zhu Wei Xu Man Hon Cheung Tat-Ming Lok Shuguang Cui Department of Information Engineering The Chinese University of Hong Kong Hong Kong Shenzhen Research Institute of Big Data Shenzhen China Peng Cheng Laboratory Shenzhen China National Mobile Communications Research Lab Southeast University Nanjing China Department of Computer Science City University of Hong Kong Hong Kong School of Science and Engineering (SSE) Future Network of Intelligence Institute (FNii) The Chinese University of Hong Kong (Shenzhen) Shenzhen China

Unmanned Aerial Vehicle (UAV)-mounted edge devices are particularly advantageous for federated edge learning (FEEL) due to their flexibility and mobility in efficient data collection. In UAV-assisted FEEL, sensing, computation, and communication are coupled and compete for limited onboard resources, and UAV deployment also affects sensing and communication performance. Therefore, the joint design of UAV deployment and resource allocation is crucial to achieving the optimal training performance. In this paper, we address the problem of joint UAV deployment design and resource allocation for FEEL via a concrete case study of human motion recognition based on wireless sensing. Due to the nonideal sensing channels, we consider the probabilistic sensing model. Then, we derive the upper bound of the FEEL training loss as a function of the sensing probability. We formulate a training time minimization problem by jointly optimizing UAV deployment, integrated sensing, computation, and communication (ISCC) resources under a desirable optimality gap constraint. To solve this challenging mixed-integer non-convex problem, we propose our algorithm based on the alternating optimization technique. Simulation results demonstrate that our algorithm outperforms other baselines regarding convergence rate and testing accuracy.

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DVS: Deep Visibility Series and its Application in Construction Cost Index Forecasting

arXiv

引用

arXiv 2021年

作者： Zhan, Tianxiang He, Yuanpeng Li, Hanwen Xiao, Fuyuan College of Computer and Information Science College of Software Southwest University Chongqing400715 China School of Big Data and Software Engineering Chongqing University Chongqing401331 China National Eng. Lab. for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China

Time series forecasting is a hot spot in recent years. Visibility Graph (VG) algorithm is used for time series forecasting in previous research, but the forecasting effect is not as good as deep learning prediction methods such as methods based on Artificial Neural Network (ANN), Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM). The visibility graph generated from specific time series contains abundant network information, but the previous forecasting method did not effectively use the network information to forecast, resulting in relatively large prediction errors. To optimize the forecasting method based on VG, this article proposes the Deep Visibility Series (DVS) module through the bionic design of VG and the expansion of the past research. By applying the bionic design of biological vision to VG, DVS has obtained superior forecasting accuracy. At the same time, this paper applies the DVS forecasting method to the construction cost index forecast, which has practical significance. Copyright © 2021, The Authors. All rights reserved.

关键词： Visibility

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EFFICIENT ONLINE labEL CONSISTENT HASHING FOR LARGE-SCALE CROSS-MODAL RETRIEVAL

EFFICIENT ONLINE LABEL CONSISTENT HASHING FOR LARGE-SCALE CR...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Yi, Jinhan Liu, Xin Cheung, Yiu-Ming Xu, Xing Fan, Wentao He, Yi Department of Computer Science and Technology Huaqiao University Xiamen361021 China Xiamen Key Lab. of Computer Vision and Pattern Recognition Fujian Key Lab. of Big Data Intelligence and Security China Department of Computer Science Hong Kong Baptist University Kowloon Hong Kong School of Computer Science and Engineering University of Electronic Science and Technology of China China Provincial Key Laboratory for Computer Information Processing Technology Soochow University China

ISBN: (纸本)9781665438643

Existing cross-modal hashing still faces three challenges: (1) Most batch-based methods are unsuitable for processing large-scale and streaming data. (2) Current online methods often suffer from insufficient semantic association, while lacking flexibility to learn the hash functions for varying streaming data. (3) Existing supervised methods always require much computation time or accumulate large quantization loss to learn hash codes. To address above challenges, we present an efficient Online label Consistent Hashing (OLCH) for cross-modal retrieval, which aims to incrementally learn hash codes for the current arriving data, while updating the hash functions at a streaming manner. To be specific, an online semantic representation learning framework is designed to adaptively preserve the semantic similarity across different modalities, and a mini-batch online gradient descent approach associated with forward-backward splitting is developed to optimize the hash functions. Accordingly, the hash codes are adaptively learned online with the high discriminative capability, while avoiding high computation complexity to process the streaming data. Experimental results show its outstanding performance in comparison with the-state-of-arts. © 2021 IEEE Computer Society. All rights reserved.

关键词： Hash functions

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An Efficient data Prefetch Strategy for Deep Learning Based on Non-volatile Memory 15th

An Efficient Data Prefetch Strategy for Deep Learning Based ...

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15th International Conference on Green, Pervasive, and Cloud Computing, GPC 2020

作者： Jiang, Wenbin Liu, Pai Jin, Hai Peng, Jing National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783030642426

Deep learning (DL) systems usually utilize asynchronous prefetch to improve data reading performance. However, the efficiency of the data transfer path from hard disk to DRAM is still limited by disk performance. The emerging non-volatile memory (NVRAM) provides a novel solution for this problem, while few existing researches have considered it. We propose a novel efficient data prefetch strategy for DL based on a heterogeneous memory system combining NVRAM with DRAM. Benefitting from the large capacity and fast reading speed of NVRAM, the strategy uses an asynchronous reading method named sliding NVRAM cache (SNC) to improve the performance of the data transfer paths. A sliding window is applied to map the data from disk to NVRAM and continuously update the data, while non-ideal writing performance of NVRAM can be remitted to a large extent in this strategy. Experiments show that SNC can improve the time performance of diverse deep neural networks training by more than 30%. © 2020, Springer Nature Switzerland AG.

关键词： Dynamic random access storage

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Hyperbolic geometric latent diffusion model for graph generation 24

Hyperbolic geometric latent diffusion model for graph genera...

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Proceedings of the 41st International Conference on Machine Learning

作者： Xingcheng Fu Yisen Gao Yuecen Wei Qingyun Sun Hao Peng Jianxin Li Xianxian Li Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Institute of Artificial Intelligence Beihang University Beijing China and Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin 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.

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Hfcyclegan: High-Frequency Information Guided Cycle-Consistent Adversarial Network for Fundus Image Enhancement

SSRN

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SSRN 2025年

作者： Liu, Shaopeng Wang, Kai Wu, Xiaohang Xu, Fabao Zhou, Ying Wang, Xinpeng Wang, Qinkang Cen, Haoran Guo, Jianhua Lu, Xu School of Computer Science Guangdong Polytechnic Normal University Guangzhou510665 China Zhongshan Ophthalmic Center State Key Laboratory of Ophthalmology Sun Yat-Sen University Guangdong Guangzhou China Qilu Hospital Shandong University Jinan China Shenzhen Institute of Information Technology Shenzhen China Guangdong Provincial Key Laboratory of Intellectual Property & Big Data Guangzhou510665 China Pazhou Lab Guangzhou510330 China

The development of fundus imaging techniques has greatly supported the diagnosis of ophthalmic *** advancements, poor imaging quality, such as artifacts or insufficient illumination, poses challenges for disease *** clinical, it is often difficult to obtain paired data for fundus image enhancement. For unpaired data, CycleGAN-based methods are commonly used to learn the style transfer from low-quality images to high-quality images for enhancement, improving the aforementioned transfer process through brightness loss or regularization ***, fundus images are quite complex, and high-quality images can also exhibit uneven *** methods are not efficient in modeling the characteristics of fundus *** wavelet analysis, we found that the texture information represented by vessels is expressed in the high-frequency components of the ***, we designed a high-frequency information guided cycle-consistent generative adversarial network for unpaired fundus image enhancement, termed *** introduces a novel high-frequency guided generator (HFGen) and a corresponding frequency-aware discriminator (FADis).Specifically, HG uses the proposed high-frequency feature extraction module (HFM) to extract high-frequency features and inject them into the decoding process, outside of the main branches of the *** concatenate the input image with the frequency components obtained from wavelet transformation and input them into *** embedded channel-wise attention blocks of discriminator enable HFCycleGAN to focus on the frequency information differences between low-quality and high-quality fundus *** experiments on two real-world datasets and one synthetic dataset verify that HFcycleGAN outperforms existing *** conclusion, the HFcycleGAN effectively enhances important structures such as vessels in fundus images, significantly impacting fundus image analysis. © 2025, The Author

关键词： Image segmentation

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Comparison of research on social media in china and foreign countries based on bibliometric analysis 3

Comparison of research on social media in china and foreign ...

引用

2020 3rd International Symposium on big data and Applied Statistics, ISBDAS 2020

作者： Yunan, Peng Zongshui, Wang Yueyang, Feng Shengqin, Yang School of Economics and Management Beijing Information Science and Technology University Beijing100192 China Beijing Key Lab of Green Development and Decision Based on Big Data Beijing100192 China

This paper aims to compare the similarities and differences of Chinese and foreign research on social media. Cluster analysis, co-word analysis and social network analysis have been used to compare the research trends, content relevance and network structure of research on social media. (1) Similarities: Social media is a hot topic for scholars both in China and foreign countries;the development of social network technology leads to many new areas;social media research tends to study privacy, commerciality and service;hotspots of social media research in China and foreign countries both include social media, platform and social network. (2) Difference: Chinese research mainly focuses on big data, Wechat and Weibo, while foreign research mainly focuses on Facebook, Twitter and other kinds of social network media;Chinese research is clustered while foreign research is extensive;Chinese research pays more attention on macroscopic perspectives, such as media convergence, big data and libraries;foreign research tends to microcosmic perspectives, such as emotional analysis, crisis information dissemination, social media marketing. © Published under licence by IOP Publishing Ltd.

关键词： big data

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