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7th International Conference on Learning Representations, ICLR 2019

作者： Xu, Bingbing Shen, Huawei Cao, Qi Qiu, Yunqi Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China

We present graph wavelet neural network (GWNN), a novel graph convolutional neural network (CNN), leveraging graph wavelet transform to address the shortcomings of previous spectral graph CNN methods that depend on graph Fourier transform. Different from graph Fourier transform, graph wavelet transform can be obtained via a fast algorithm without requiring matrix eigendecomposition with high computational cost. Moreover, graph wavelets are sparse and localized in vertex domain, offering high efficiency and good interpretability for graph convolution. The proposed GWNN significantly outperforms previous spectral graph CNNs in the task of graph-based semi-supervised classification on three benchmark datasets: Cora, Citeseer and Pubmed. © 7th International Conference on Learning Representations, ICLR 2019. All Rights Reserved.

关键词： Wavelet transforms

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Privacy-preserving data outsourcing with integrity auditing for lightweight devices in cloud computing 14th

Privacy-preserving data outsourcing with integrity auditing ...

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14th International Conference on Information Security and Cryptology, Inscrypt 2018

作者： Liu, Dengzhi Shen, Jian Chen, Yuling Wang, Chen Zhou, Tianqi Wang, Anxi Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China Guizhou Provincial Key Laboratory of Public Big Data Guiyang China

ISBN: (纸本)9783030142339

The cloud can provide unlimited storage space to users via the Internet. Unlike locally data storing, users will lose the direct control of the data after outsourcing it to the cloud. Moreover, the cloud is an untrusted entity. It is possible that the cloud may try to extract, discard and destroy users’ data due to benefits. Hence, the data security in cloud computing needs to be well guaranteed. In this paper, we propose a privacy-preserving data outsourcing scheme with integrity auditing for lightweight devices in cloud computing. On the one hand, the blind signature is used in the proposed scheme to delegate the generation of users’ data signatures to the TPA. On the other hand, based on the property of the BLS signature, the blinded signatures received from the TPA can be verified by the user and the data integrity stored in the cloud can be audited by the TPA. In addition, the proposed scheme supports batch operation. Security analysis shows that the proposed scheme achieves the properties of correctness, privacy-preserving and non-forgeability. Performance analysis indicates that the proposed scheme can be performed with high efficiency. © 2019, Springer Nature Switzerland AG.

关键词： Outsourcing

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Improving preparation and luminescence properties of Eu and dy doped Sr2MgSi2O7 phosphors by adding excess silica

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Scientific reports 2025年第1期15卷 18367页

作者： Chi Zhang Liuming Wei Qin Liu Ruirui Cui Chaoyong Deng Jingyu Li College of Electrical Engineering Henan University of Technology Zhengzhou 450001 China. chizhang@***. Department of Network Security Henan Police College Zhengzhou 450046 China. Anqiu Qingyun Bilingual School Weifang China. Key Laboratory of Functional Composite Materials of Guizhou Province College of Big Data and Information Engineering Guizhou University Guiyang 550025 China. Key Laboratory of Functional Composite Materials of Guizhou Province College of Big Data and Information Engineering Guizhou University Guiyang 550025 China. cydeng@***. Spallation Neutron Source Science Center Dongguan 523803 China. ***@***.

Solid state reaction method is most commonly used to synthesize SrMgSiO: Eu, Dy phosphors. In order to avoid the energy waste caused by high sintering temperature, we systematically studied and optimized the preparation process of SrMgSiO: Eu, Dy long afterglow phosphors by increasing the Si/Sr ratio in the raw materials. The appropriate addition of silica not only facilitates the preparation of single phase SrMgSiO: Eu, Dy phosphor but also enhances the rate of solid state reaction, so as to achieve the purpose of reducing the sintering temperature. The photoluminescence performance of the sample with a Si/Sr ratio of 1.1 sintered at 1350 °C is the highest, followed by that of the sample sintered at 1400 °C with a Si/Sr ratio of 1.0. Notably, the photoluminescence properties of the Si/Sr ratio of 1.3 sample sintered at 1300 °C are comparable to those of the Si/Sr ratio of 1.0 sample sintered at 1400 °C. However, in terms of afterglow properties, the former exhibits an enhancement by a factor of 3.5 compared to the latter. This finding suggests that strategically increasing the Si/Sr ratio can reduce the sintering temperature by 100 °C while maintaining or even enhancing the luminescence and afterglow properties of the samples. Additionally, the electronic structures of the SrMgSiO host, the doped luminescence center Eu, the oxygen vacancies and the co-doped activator Dy have been methodically investigated through first-principles calculations. By integrating the findings on the distribution of the dopants and defects levels, we predict and propose the photoluminescence and afterglow mechanisms of SrMgSiO: Eu, Dy. These studies suggest an efficient, cheap and energy-saving strategy for the preparation of SrMgSiO: Eu, Dy long afterglow phosphor, and reveal the roles of oxygen vacancies and co-doped Dy in the afterglow luminescence.

关键词： Electron traps First-principles calculation Optimizing synthesis Persistent luminescence Sr2MgSi2O7

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An Improved YOLOv5s-Based Smoke Detection System for Outdoor Parking Lots

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Computers, Materials & Continua 2024年第8期80卷 3333-3349页

作者： Ruobing Zuo Xiaohan Huang Xuguo Jiao Zhenyong Zhang State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou310027China Key Laboratory of Computing Power Network and Information Security Ministry of EducationQilu University of Technology(Shandong Academy of Sciences)Jinan250353China Text Computing&Cognitive Intelligence Engineering Research Center of National Education Ministry Guizhou UniversityGuiyang550025China

In the rapidly evolving urban landscape,outdoor parking lots have become an indispensable part of the city’s transportation *** growth of parking lots has raised the likelihood of spontaneous vehicle combus-tion,a significant safety hazard,making smoke detection an essential preventative ***,the complex environment of outdoor parking lots presents additional challenges for smoke detection,which necessitates the development of more advanced and reliable smoke detection *** paper addresses this concern and presents a novel smoke detection technique designed for the demanding environment of outdoor parking ***,we develop a novel dataset to fill the gap,as there is a lack of publicly available *** dataset encompasses a wide range of smoke and fire scenarios,enhanced with data augmentation to ensure robustness against diverse outdoor ***,we utilize an optimized YOLOv5s model,integrated with the Squeeze-and-Excitation network(SENet)attention mechanism,to significantly improve detection accuracy while maintaining real-time processing ***,this paper implements an outdoor smoke detection system that is capable of accurately localizing and alerting in real time,enhancing the effectiveness and reliability of emergency *** show that the system has a high accuracy in terms of detecting smoke incidents in outdoor scenarios.

关键词： YOLOv5s smoke detection deep learning SENet

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Encrypted network Traffic Detection Based on Blockchain and Federated Learning 4

Encrypted Network Traffic Detection Based on Blockchain and ...

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4th International Conference on Blockchain technology and Information Security, ICBCTIS 2024

作者： Liu, Wei Cui, Wentao She, Wei Tian, Zhao School of Cyber Science and Engineering Zhengzhou University Henan Key Laboratory of Network Cryptography Technology Zhengzhou Key Laboratory of Blockchain and Data Intelligence Zhengzhou China School of Cyber Science and Engineering Zhengzhou University Zhengzhou China School of Cyber Science and Engineering Zhengzhou University Zhengzhou Key Laboratory of Blockchain and Data Intelligence Zhengzhou China

ISBN: (纸本)9798331528324

In recent years, encrypted malicious traffic has significantly threatened network security. Deep learning offers a viable solution for feature extraction, but its accuracy depends on data volume, and traffic data varies across organizations, leading to data silos. This paper proposes a blockchain-based federated learning framework, utilizing smart contracts for aggregation and sharing. A novel aggregation algorithm is introduced, selectively aggregating based on each round's local model score to enhance global model performance. Evaluations on public datasets show that this framework effectively shares encrypted malicious traffic data, achieving high detection accuracy and resolving single points of failure. © 2024 IEEE.

关键词： Federated learning

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Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation

arXiv

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arXiv 2025年

作者： Cheng, Zihan Guo, Jintao Zhang, Jian Qi, Lei Zhou, Luping Shi, Yinghuan Gao, Yang Shanghai Jiao Tong University School of Medicine Shanghai200025 China National Institute of Healthcare Data Science Nanjing University Jiangsu Nanjing210093 China State Key Laboratory for Novel Software Technology the National Institute of Healthcare Data Science Nanjing University Jiangsu Nanjing210093 China School of Computer Science and Engineering Key Lab of Computer Network and Information Integration Southeast University Jiangsu Nanjing211189 China School of Electrical and Information Engineering The University of Sydney SydneyNSW2006 Australia

To segment medical images with distribution shifts, domain generalization (DG) has emerged as a promising setting to train models on source domains that can generalize to unseen target domains. Existing DG methods are mainly based on CNN or ViT architectures. Recently, advanced state space models, represented by Mamba, have shown promising results in various supervised medical image segmentation. The success of Mamba is primarily owing to its ability to capture long-range dependencies while keeping linear complexity with input sequence length, making it a promising alternative to CNNs and ViTs. Inspired by the success, in the paper, we explore the potential of the Mamba architecture to address distribution shifts in DG for medical image segmentation. Specifically, we propose a novel Mamba-based framework, Mamba-Sea, incorporating global-to-local sequence augmentation to improve the model’s generalizability under domain shift issues. Our Mamba-Sea introduces a global augmentation mechanism designed to simulate potential variations in appearance across different sites, aiming to suppress the model’s learning of domain-specific information. At the local level, we propose a sequence-wise augmentation along input sequences, which perturbs the style of tokens within random continuous sub-sequences by modeling and resampling style statistics associated with domain shifts. To our best knowledge, Mamba-Sea is the first work to explore the generalization of Mamba for medical image segmentation, providing an advanced and promising Mamba-based architecture with strong robustness to domain shifts. Remarkably, our proposed method is the first to surpass a Dice coefficient of 90% on the Prostate dataset, which exceeds previous SOTA of 88.61%. The code is available at https://***/orange-czh/Mamba-Sea. Copyright © 2025, The Authors. All rights reserved.

关键词： Image segmentation

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Scale‐wise interaction fusion and knowledge distillation network for aerial scene recognition

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CAAI Transactions on Intelligence technology 2023年第4期8卷 1178-1190页

作者： Hailong Ning Tao Lei Mengyuan An Hao Sun Zhanxuan Hu Asoke K.Nandi School of Computer Science and Technology Xi'an University of Posts and TelecommunicationsShaanxi Key Laboratory of Network Data Analysis and Intelligent ProcessingXi'anChina Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'anChina School of Electronic Information and Artificial Intelligence Shaanxi University of Science and TechnologyXi'anChina School of Computer Central China Normal UniversityWuhanChina Department of Electronic and Electrical Engineering Brunel University LondonLondonUK Xi'an Jiaotong University Xi'anChina

Aerial scene recognition(ASR)has attracted great attention due to its increasingly essential *** of the ASR methods adopt the multi‐scale architecture because both global and local features play great roles in ***,the existing multi‐scale methods neglect the effective interactions among different scales and various spatial locations when fusing global and local features,leading to a limited ability to deal with challenges of large‐scale variation and complex background in aerial scene *** addition,existing methods may suffer from poor generalisations due to millions of to‐belearnt parameters and inconsistent predictions between global and local *** tackle these problems,this study proposes a scale‐wise interaction fusion and knowledge distillation(SIF‐KD)network for learning robust and discriminative features with scaleinvariance and background‐independent *** main highlights of this study include two *** the one hand,a global‐local features collaborative learning scheme is devised for extracting scale‐invariance features so as to tackle the large‐scale variation problem in aerial scene ***,a plug‐and‐play multi‐scale context attention fusion module is proposed for collaboratively fusing the context information between global and local *** the other hand,a scale‐wise knowledge distillation scheme is proposed to produce more consistent predictions by distilling the predictive distribution between different scales during *** experimental results show the proposed SIF‐KD network achieves the best overall accuracy with 99.68%,98.74%and 95.47%on the UCM,AID and NWPU‐RESISC45 datasets,respectively,compared with state of the arts.

关键词： deep learning image analysis image classification information fusion

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A Distributed Anomaly Filtering Algorithm for Heterogeneous data Based on City Computing 20

A Distributed Anomaly Filtering Algorithm for Heterogeneous ...

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6th International Conference on Computing and Artificial Intelligence, ICCAI 2020

作者： Wang, Shiwei Li, Yangyang Xu, Xiaobin Yue, Guijie Future Network Innovation Center Beijing University of Technology Beijing China National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data China Academy of Electronics and Information Technology Beijing China

ISBN: (纸本)9781450377089

In modern cities, numerous urban perception devices collect and release urban data all the time, but urban data may become abnormal due to environmental interference or artificial tampering. In view of the problem that urban data will face data anomalies, this paper designs a distributed gauss membership anomaly data filtering algorithm, and defines a set of extraction protocols suitable for heterogeneous data. Simulation results show that this algorithm can filter abnormal data in real time, improve the efficiency of urban computing and reduce the cost of network. © 2020 ACM.

关键词： Internet of things

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Mixture-of-Experts for Distributed Edge Computing with Channel-Aware Gating Function

arXiv

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arXiv 2025年

作者： Song, Qiuchen Jing, Shusen Zhang, Shuai Zhang, Songyang Huang, Chuan Shenzhen Future Network of Intelligence Institute School of Science and Engineering Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Shenzhen518172 China Department of Radiation Oncology University of California San FranciscoCA94118 United States Department of Data Science New Jersey Institute of Technology NewarkNJ07102 United States Department of Electrical and Computer Engineering University of Louisiana at Lafayette LafayetteLA70504 United States School of Science and Engineering Shenzhen Future Network of Intelligence Institute Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Shenzhen518172 China

In a distributed mixture-of-experts (MoE) system, a server collaborates with multiple specialized expert clients to perform inference. The server extracts features from input data and dynamically selects experts based on their areas of specialization to produce the final output. Although MoE models are widely valued for their flexibility and performance benefits, adapting distributed MoEs to operate effectively in wireless networks has remained unexplored. In this work, we introduce a novel channel-aware gating function for wireless distributed MoE, which incorporates channel conditions into the MoE gating mechanism. To train the channel-aware gating, we simulate various signal-to-noise ratios (SNRs) for each expert’s communication channel and add noise to the features distributed to the experts based on these SNRs. The gating function then utilizes both features and SNRs to optimize expert selection. Unlike conventional MoE models which solely consider the alignment of features with the specializations of experts, our approach additionally considers the impact of channel conditions on expert performance. Experimental results demonstrate that the proposed channel-aware gating scheme outperforms traditional MoE models. Copyright © 2025, The Authors. All rights reserved.

关键词： Edge computing

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Aggregation-based dual heterogeneous task allocation in spatial crowdsourcing

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Frontiers of Computer Science 2024年第6期18卷 215-227页

作者： Xiaochuan LIN Kaimin WEI Zhetao LI Jinpeng CHEN Tingrui PEI College of Information Science and Technology&Cyberspace Security Jinan UniversityGuangzhou 510632China National&Local Joint Engineering Research Center of Network Security Detection and Protection Technology Guangzhou 510632China Guangdong Provincial Key Laboratory of Data Security and Privacy Protection Guangzhou 510632China School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876China

Spatial crowdsourcing(SC)is a popular data collection paradigm for numerous *** the increment of tasks and workers in SC,heterogeneity becomes an unavoidable difficulty in task *** researches only focus on the single-heterogeneous task ***,a variety of heterogeneous objects coexist in real-world SC *** dramatically expands the space for searching the optimal task allocation solution,affecting the quality and efficiency of data *** this paper,an aggregation-based dual heterogeneous task allocation algorithm is put *** investigates the impact of dual heterogeneous on the task allocation problem and seeks to maximize the quality of task completion and minimize the average travel *** problem is first proved to be ***,a task aggregation method based on locations and requirements is built to reduce task ***,a time-constrained shortest path planning is also developed to shorten the travel distance in a *** that,two evolutionary task allocation schemes are ***,extensive experiments are conducted based on real-world datasets in various *** with baseline algorithms,our proposed schemes enhance the quality of task completion by up to 25% and utilize 34% less average travel distance.

关键词： task allocation aggregation shortest path dual heterogeneous spatial crowdsourcing

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