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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Xu, Xiaolong Ding, Qun Lin, Licheng Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory Big Data Security and Intelligent Processing Nanjing210023 China

ISBN: (纸本)9798350331684

With the increase of car ownership in China, the problem of difficult parking in cities has become more and more serious. In large parking garage, finding an ideal parking space has become a daily problem for people;especially during the peak usage period of parking garage, a large number of vehicles will be driven into the parking garage, which will bring congestion, and at the same time, the demand of a large number of users in the area will lead to network congestion under 4G network, and users cannot access the network to check and find parking spaces. Therefore, this paper proposes a global optimal navigation mechanism with 5G network based on the above mentioned problems by analyzing the congestion situation during the peak usage of the parking garage. This mechanism uses an optimal parking space selection model and a global optimal scheduling model that can avoid congestion. Based on the model proposed in this paper, we construct a navigation planning system based on mobile, front-end and back-end. With the ultra-low latency, ultrahigh transmission efficiency and reliability of 5G network, the mobile port can select the optimal parking space according to the user's preference, and the back-end can show the parking space and congestion in the parking garage in real time according to the actual situation of the parking garage. Finally, the performance of the model and system proposed in this paper is verified through experiments. © 2023 IEEE.

关键词： 5G mobile communication systems

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ConvTrans-TPS: A Convolutional Transformer Model for Disk Failure Prediction in Large-Scale Network Storage systems 26

ConvTrans-TPS: A Convolutional Transformer Model for Disk Fa...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Xu, Shicheng Xu, Xiaolong Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing210023 China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China

ISBN: (纸本)9798350331684

Disk failure is one of the most important reliability problems in large-scale network storage systems. Disk failure may lead to serious data loss and even disastrous consequences if the missing data cannot be recovered. Therefore, predicting disk failures is an important means of ensuring storage security in network storage systems. However, since the fault data in the fast degradation stage is smaller than the healthy data in the normal state, the mixture of healthy data and faulty data leads to extremely unbalanced data, which brings great challenges to finding hidden fault information, thus making fault prediction more accurate. Aiming at the above problems, a convolutional transformer model ConvTrans-TPS model for disk failure prediction in large-scale network storage systems is proposed. The ConvTrans-TPS model acquires dependencies between long-term sequence data through transformers and uses convolutional projections for attention computation to enhance attention to local contextual information. data augmentation to predict failures in the next 7 days. Validated by the analysis on the Backblaze dataset, the F1 is 0.96 and the Matthews correlation coefficient (MCC) is 0.92. Compared with the popular CNN-LSTM model in recent years, our proposed method improves F1 and MCC by 4% and 5%, respectively, improving the prediction accuracy. © 2023 IEEE.

关键词： Convolution

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An SGX and Blockchain-Based system for Federated Learning in Classifying COVID-19 Images 21

An SGX and Blockchain-Based System for Federated Learning in...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Qin, Sihang Gu, Xianjun Jun, Xing Li, Haitao Huang, Mengqi Dai, Weiqi Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China State Grid Wuhan Power Supply Company China

ISBN: (纸本)9798350329223

Federated learning, an emerging distributed learning framework, has gained prominence recently. However, two challenges arise when applying federated learning to medical data. The first challenge stems from the inability of model-generating nodes to ensure computational fairness. The second challenge involves the potential leakage of private information through gradient differences. This paper introduces a federated learning system that leverages Software Guard Extensions (SGX) and blockchain technology to enhance the fairness and security of federated learning for analyzing chest imaging data. The modeling of imaging factors for chest radiography data can effectively regulate the data quality of semi-trusted nodes. A blockchain smart contract is employed for data quality-aware proof, and the quality-aware block undergoes a two-stage rewriting process across distinct nodes. A notary membership model achieves notarized cluster consensus for imaging informatics data. The multi-channel layer model quantifies the distribution of computational workload across participating nodes within the medical consortium. Upon successful verification of the enclave command in the aggregation cycle, traceable and packaged signatures are generated to support computational fairness. To ensure robust information security, SGX remote authentication is used between downstream and upstream nodes. The federated learning system, built upon SGX and blockchain, has been implemented using the Fabric federated chain platform. The ResNet model underwent training through cross-sectional federated learning to effectively classify novel coronavirus lung medical image data. Following this, multiple experimental control and security analyses were conducted, revealing that the time loss incurred due to crossing channels is approximately 21.31%, while the time overhead of SGX remains within 5.2%, as allowed by the number of nodes. © 2023 IEEE.

关键词： Blockchain

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Towards Stricter Black-box Integrity Verification of Deep Neural Network Models 24

Towards Stricter Black-box Integrity Verification of Deep Ne...

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32nd ACM International Conference on Multimedia, MM 2024

作者： He, Chaoxiang Bai, Xiaofan Ma, Xiaojing Zhu, Bin B. Hu, Pingyi Fu, Jiayun Jin, Hai Zhang, Dongmei Huazhong University of Science and Technology Hubei Wuhan China Shanghai Jiao Tong University Shanghai China Microsoft Corporation Beijing China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Huazhong University of Science and Technology China 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 China

ISBN: (纸本)9798400706868

Cloud-based machine learning services offer significant advantages but also introduce the risk of tampering with cloud-deployed deep neural network (DNN) models. Black-box integrity verification (BIV) allows model owners and end-users to determine if a cloud-deployed DNN model has been tampered with by examining only the top-1 label responses. Fingerprinting generates fingerprint samples to query the model, achieving BIV with no impact on the model's accuracy. In this paper, we present BIVBench, the first comprehensive benchmark for BIV of DNN models. BIVBench covers 16 types of model modifications, providing extensive coverage of practical modification scenarios. Our analysis reveals that existing fingerprinting methods, which are typically focused on significant tampering, lack the sensitivity needed to effectively detect subtle yet common and potentially severe modifications. To address this limitation, we propose MiSentry (Model Integrity Sentry), a novel fingerprinting method that leverages meta-learning. MiSentry strategically incorporates a few subtly modified models into the meta-learning model zoo and maximizes the divergence of output predictions between the target model and the modified models in the model zoo to generate highly sensitive, generalizable, and effective fingerprint samples. Extensive evaluations using BIVBench demonstrate that MiSentry outperforms existing state-of-the-art methods overall and significantly surpasses them in detecting subtle modifications. The BIVBench and supplementary materials are available at: https://***/CGCL-codes/BIVBench. © 2024 ACM.

关键词： Deep neural networks

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A Real-time Demonstration of Bidirectional Multiformat/Rate-adjustable Integrated Laser Communication/Ranging for Satellite Communication

A Real-time Demonstration of Bidirectional Multiformat/Rate-...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Wang, Yizhou Wang, Yuanxiang Luo, Zhen Zhong, Linsheng Zou, Yang Ao, Xueyuan Liu, Jiale Fan, Feng Li, Xiaoliang Dai, Xiaoxiao Yang, Qi Liu, Deming National Engineering Laboratory for Next Generation Internet Access System School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Beijing Research Institute of Telemetry Beijing100094 China Jinyinhu Laboratory Wuhan430040 China

A multi-format/rate-adjustable integrated laser communication/ranging system is proposed, achieving adjustable rates between 0.625-10Gbit/s. Real-time experiment shows that at 1e-4 BER the ranging error is only 0.29% ... 详细信息

ISBN: (纸本)9781957171395

关键词： Laser theory

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A Real-time Demonstration of Bidirectional Multiformat/Rate-adjustable Integrated Laser Communication/Ranging for Satellite Communication

A Real-time Demonstration of Bidirectional Multiformat/Rate-...

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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Wang, Yizhou Wang, Yuanxiang Luo, Zhen Zhong, Linsheng Zou, Yang Ao, Xueyuan Liu, Jiale Fan, Feng Li, Xiaoliang Dai, Xiaoxiao Yang, Qi Liu, Deming National Engineering Laboratory for Next Generation Internet Access System School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science andEngineering Huazhong University of Science and Technology Wuhan430074 China Beijing Research Institute of Telemetry Beijing100094 China Jinyinhu Laboratory Wuhan430040 China

关键词： Satellite communication systems

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Reinforcement Learning Methods for Fixed-Wing Aircraft Control 9

Reinforcement Learning Methods for Fixed-Wing Aircraft Contr...

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9th International Conference on systems and Informatics, ICSAI 2023

作者： Li, Lun Zhang, Xuebo Wang, Yang Qian, Chenxu Zhao, Minghui Nankai University Institute of Robotics and Automatic Information System Tianjin Key Laboratory of Intelligent Robotics College of Artificial Intelligence Tianjin China Tianjin Municipal Data Management Center Tianjin China

ISBN: (纸本)9798350383706

In recent years, reinforcement learning (RL) has been widely researched for fixed-wing aircraft control, offering transformative potential for more adaptive, efficient, and autonomous flight operations. This paper presents a comprehensive analysis of the current state, advantages, challenges, and future prospects of employing RL in fixed-wing aircraft control. The advantages of RL include model-free, adaptive decision-making, and effectively solving complex nonlinear constraints, leading to increased efficiency and maneuverability in high-dynamic environments. However, significant challenges, such as the guarantee of stability and optimization, interpretability, and sim-to-real transferring, highlight areas necessitating further attention and development. Overcoming these challenges requires collaborative efforts among aviation experts, control theory experts, and machine learning researchers to ensure safe, reliable integration of RL in flight systems. As research and advancements continue, the RL holds promise for providing a more efficient, maneuverable, and autonomous strategy for controlling fixed-wing aircraft in the future. © 2023 IEEE.

关键词： Reinforcement learning

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A multi-source signals separation algorithm for identifying the threatening signals applied in Fiber-Optic Distributed Acoustic Sensor

A multi-source signals separation algorithm for identifying ...

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2023 Optical Fiber Communications Conference and Exhibition, OFC 2023

作者： He, Tao Zhang, Shixiong Li, Hao Yan, Zhijun Liu, Deming Sun, Qizhen School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Wuhan430074 China Optics Valley Laboratory Huazhong University of Science and Technology School of Optical and Electronic Information National Laboratory for Optoelectronics National Engineering Laboratory for Next Generation Internet Access System Wuhan430074 China

A multi-source signals separation algorithm is proposed to identify the potential threatening signals submerged in the strong background noises. Finally, the recognition rate of the mixed signals is improved from 62.8... 详细信息

ISBN: (纸本)9781957171180

关键词： Source separation

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Classifying Galaxy Morphologies with Few-shot Learning

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Research in Astronomy and Astrophysics 2022年第5期22卷 9-20页

作者： Zhirui Zhang Zhiqiang Zou Nan Li Yanli Chen College of Computer Nanjing University of Posts and Telecommunications Nanjing 210023.China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing 210023China Key Laboratory of Optical Astronomy National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China

The taxonomy of galaxy morphology is critical in astrophysics as the morphological properties are powerful tracers of galaxy *** the upcoming Large-scale Imaging Surveys,billions of galaxy images challenge astronomers to accomplish the classification task by applying traditional methods or human ***,machine learning,in particular supervised deep learning,has been widely employed to classify galaxy morphologies recently due to its exceptional automation,efficiency,and ***,supervised deep learning requires extensive training sets,which causes considerable workloads;also,the results are strongly dependent on the characteristics of training sets,which leads to biased outcomes *** this study,we attempt Few-shot Learning to bypass the two *** research adopts the data set from the Galaxy Zoo Challenge Project on Kaggle,and we divide it into five categories according to the corresponding truth *** classifying the above data set utilizing few-shot learning based on Siamese Networks and supervised deep learning based on AlexNet,VGG_16,and ResNet_50 trained with different volumes of training sets separately,we find that few-shot learning achieves the highest accuracy in most cases,and the most significant improvement is 21%compared to AlexNet when the training sets contain 1000 *** addition,to guarantee the accuracy is no less than 90%,few-shot learning needs~6300 images for training,while ResNet_50 requires~13,000 *** the advantages stated above,foreseeably,few-shot learning is suitable for the taxonomy of galaxy morphology and even for identifying rare astrophysical objects,despite limited training sets consisting of observational data only.

关键词： Galaxies-Galaxy morphological classification-Method neural networks

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Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need 38

Unlearnable 3D Point Clouds: Class-wise Transformation Is Al...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Wang, Xianlong Li, Minghui Liu, Wei Zhang, Hangtao Hu, Shengshan Zhang, Yechao Zhou, Ziqi Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China

Traditional unlearnable strategies have been proposed to prevent unauthorized users from training on the 2D image data. With more 3D point cloud data containing sensitivity information, unauthorized usage of this new type data has also become a serious concern. To address this, we propose the first integral unlearnable framework for 3D point clouds including two processes: (i) we propose an unlearnable data protection scheme, involving a class-wise setting established by a category-adaptive allocation strategy and multi-transformations assigned to samples;(ii) we propose a data restoration scheme that utilizes class-wise inverse matrix transformation, thus enabling authorized-only training for unlearnable data. This restoration process is a practical issue overlooked in most existing unlearnable literature, i.e., even authorized users struggle to gain knowledge from 3D unlearnable data. Both theoretical and empirical results (including 6 datasets, 16 models, and 2 tasks) demonstrate the effectiveness of our proposed unlearnable framework. Our code is available at https://***/CGCL-codes/UnlearnablePC. © 2024 Neural information processing systems foundation. All rights reserved.

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