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arXiv 2023年

作者： Guo, Shunxin Wang, Hongsong Geng, Xin MOE Key Laboratory of Computer Network and Information Integration China School of Computer Science and Engineering Southeast University Nanjing210096 China

Federated learning shows promise as a privacy-preserving collaborative learning technique. Existing heterogeneous federated learning mainly focuses on skewing the label distribution across clients. However, most approaches suffer from catastrophic forgetting and concept drift, mainly when the global distribution of all classes is extremely unbalanced and the data distribution of the client dynamically evolves over time. In this paper, we study the new task, i.e., Dynamic Heterogeneous Federated Learning (DHFL), which addresses the practical scenario where heterogeneous data distributions exist among different clients and dynamic tasks within the client. Accordingly, we propose a novel federated learning framework named Federated Multi-Level Prototypes (FedMLP) and design federated multi-level regularizations. To mitigate concept drift, we construct prototypes and semantic prototypes to provide fruitful generalization knowledge and ensure the continuity of prototype spaces. To maintain the model stability and consistency of convergence, three regularizations are introduced as training losses, i.e., prototype-based regularization, semantic prototype-based regularization, and federated inter-task regularization. Extensive experiments show that the proposed method achieves state-of-the-art performance in various settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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A firmware upgrade solution based on hardware unique ID and SM4 cryptographic algorithm

A firmware upgrade solution based on hardware unique ID and ...

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2023 International Conference on computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2023

作者： Gao, Shaofeng Wang, Kun Chen, Xinze Fu, Yong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250000 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

ISBN: (纸本)9781510677685

This article presents and implements a firmware upgrade solution for embedded systems, which aims to address the vulnerability of using the same file that can be easily copied or stolen. When upgrading firmware, a txt file is sent by the upper-level software, which is encrypted using the SM4 algorithm. This encryption is based on a hardware-unique ID and a pre-stored upper-level software-unique ID, which act as encryption keys. During the upgrade, a secure downloader utilizes its hardware-unique ID and a pre-stored upper-level software-unique ID as decryption keys to decrypt the file. If the decryption key does not match the encryption key, the decrypted txt file will not be readable. Once decrypted, the firmware is downloaded to the target device using BSL (Bootstrap Loader) technology. Practical tests have shown that this method can achieve "one file, one key,"effectively preventing unauthorized use of the upgraded firmware. © 2024 SPIE.

关键词： Firmware

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Comprehensive Perturbation Consistency for Semi-Supervised Change Detection in Remote Sensing Images

Comprehensive Perturbation Consistency for Semi-Supervised C...

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

作者： Zan Mao Xin Li Ze Luo Yingjuan Tang Dongmei Jiang Computer Network Information Center Chinese Academy of Sciences University of the Chinese Academy of Sciences PengCheng Laboratory National Key Laboratory of Advanced Vehicle Integration and Control Beijing Institute of Technology

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Currently, many change detection (CD) methods rely on supervised learning, which necessitates extensive manually annotated data, resulting in significant labor and time requirements. Recently, semi-supervised (SS) approaches have emerged in the CD community, which exploit large amounts of unlabeled data by utilizing consistency regularization. However, these methods do not consider the broader perturbation consistency to confer better generalization of the model. In this paper, we propose a novel SS CD framework with a comprehensive perturbation consistency called CPC, which extends perturbation consistency to the entire learning period. Specifically, our CPC combines the input, feature, and network perturbations for comprehensive perturb space. And, we design two distinct structures in practice, decoupled CPC and coupled CPC. Furthermore, we propose a change-aware input perturbation that introduces expensive annotation information to further expand the input perturb space. Extensive experiments conducted on the WHUCD, and GZ-CD datasets demonstrate that the proposal performs favorably against the state-of-the-art methods.

关键词： Codes Annotations Perturbation methods Supervised learning Signal processing Acoustics Proposals Labeling Speech processing Remote sensing

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MMSFD: Multi-grained and Multi-modal Fusion for Short Video Fake News Detection 7

MMSFD: Multi-grained and Multi-modal Fusion for Short Video ...

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7th International Conference on Data Science and information Technology, DSIT 2024

作者： Ren, Shuai Liu, Yahui Zhu, Yaping Bing, Wanlong Ma, Hongliang Wang, Wei Shihezi University School of Information Science and Technology Shihezi China Xinjiang Production and Construction Corps Key Laboratory of Computing Intelligence and Network Information Security Shihezi China Beijing Jiaotong University School of Computer Science and Information Technology Beijing China

ISBN: (纸本)9798350384093

Short video platforms have become an essential part of people's daily lives, while also facing the challenge of the rapid spread of fake news. Therefore, the detection of fake news in short videos has attracted increasing attention. Existing works mostly focus on the integration of textual and visual content, but fail to fully exploit multi-modal information at both fine-grained and coarse-grained levels. Moreover, multi-modal fake news detection is vulnerable to modality ambiguity due to potential conflicts or weak correlations between different modalities. In this paper, we propose a Multi-grained and Multi-modal fusion for Short video Fake news Detection (MMSFD). We encode textual and visual content using pre-trained models at both fine-grained and coarse-grained levels and fuse them through distinct branches. To tackle the ambiguity issue, we design a multi-modal fusion branch based on similarity weighting, as well as separate single-modal branches. Additionally, this paper presents a cross-modal attention mechanism for adaptively aggregating and reweighting features. The experimental results on a real-world short video dataset demonstrate that the proposed method outperforms state-of-the-art short video fake news detection methods. © 2024 IEEE.

关键词： Fake detection

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Sea-Land Segmentation of Remote Sensing Images Based on SDW-UNet

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computer Systems Science & Engineering 2023年第5期45卷 1033-1045页

作者： Tianyu Liu Pengyu Liu Xiaowei Jia Shanji Chen Ying Ma Qian Gao The Information Department Beijing University of TechnologyBeijing 100124China School of Physics and Electronic Information Engineering Qinghai Minzu UniversityXining810000China Advanced Information Network Beijing Laboratory Beijing100124China Computational Intelligence and Intelligent Systems Beijing key Laboratory Beijing100124China Department of Computer Science University of Pittsburgh15260USA

Image segmentation of sea-land remote sensing images is of great importance for downstream applications including shoreline extraction,the monitoring of near-shore marine environment,and near-shore target *** mitigate large number of parameters and improve the segmentation accuracy,we propose a new Squeeze-Depth-Wise UNet(SDW-UNet)deep learning model for sea-land remote sensing image *** proposed SDW-UNet model leverages the squeeze-excitation and depth-wise separable convolution to construct new convolution modules,which enhance the model capacity in combining multiple channels and reduces the model *** further explore the effect of position-encoded information in NLP(Natural Language Processing)domain on sea-land segmentation *** have conducted extensive experiments to compare the proposed network with the mainstream segmentation network in terms of accuracy,the number of parameters and the time cost for *** test results on remote sensing data sets of Guam,Okinawa,Taiwan China,San Diego,and Diego Garcia demonstrate the effectiveness of SDW-UNet in recognizing different types of sea-land areas with a smaller number of parameters,reduces prediction time cost and improves performance over other mainstream segmentation *** also show that the position encoding can further improve the accuracy of model segmentation.

关键词： Sea-land segmentation UNet depth-wise separable convolution squeeze-excitation position encoding

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StarMFND: Star Topology Multi-Domain Fake News Detection 7

StarMFND: Star Topology Multi-Domain Fake News Detection

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7th International Conference on Data Science and information Technology, DSIT 2024

作者： Gu, Pengfei Chen, Yibing Bing, Wanlong Liu, Yahui Ma, Hongliang Wang, Wei Shihezi University School of Information Science and Technology Shihezi China Xinjiang Production and Construction Corps Key Laboratory of Computing Intelligence and Network Information Security Shihezi China Beijing Jiaotong University School of Computer Science and Information Technology Beijing China

ISBN: (纸本)9798350384093

The proliferation of the Internet and the emergence of social media platforms have greatly accelerated the speed and scope of information dissemination, while also introducing the peril of fake news propagation, particularly in political, economic, health, and other domains. Many scholars have conducted research on fake news detection, and most of this research focuses on domain-specific single-domain fake news detection or mixed-domain detection without considering domain information. Most existing multi-domain fake news detection approaches model the domains of fake news implicitly and do not pay attention to the domain seesaw phenomenon. To solve the above problems, this paper proposes a Star Topology Multi-Domain Fake News Detection Model (StarMFND), which explicitly models the domains of fake news using a star topology, and simultaneously leverages the data from all the domains to obtain the common and specific features associated with fake news across different domains. Experiments on Chinese and English datasets demonstrated the good performance of StarMFND for multi-domain fake news detection. © 2024 IEEE.

关键词： Fake News Detection Multi-Domain Learning Multi-Task Learning Social Media

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CoinSeg: Contrast Inter- and Intra- Class Representations for Incremental Segmentation

CoinSeg: Contrast Inter- and Intra- Class Representations fo...

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IEEE/CVF International Conference on computer Vision (ICCV)

作者： Zhang, Zekang Gao, Guangyu Jiao, Jianbo Liu, Chi Harold Wei, Yunchao Beijing Institute of Technology School of Computer Science China University of Birmingham School of Computer Science United Kingdom Beijing Jiaotong University WEI Lab Institute of Information Science China Beijing Key Laboratory of Advanced Information Science and Network China

ISBN: (纸本)9798350307184

Class incremental semantic segmentation aims to strike a balance between the models stability and plasticity by maintaining old knowledge while adapting to new concepts. However, most state-of-the-art methods use the freeze strategy for stability, which compromises the models plasticity. In contrast, releasing parameter training for plasticity could lead to the best performance for all categories, but this requires discriminative feature representation. Therefore, we prioritize the models plasticity and propose the Contrast inter- and intra-class representations for Incremental Segmentation (CoinSeg), which pursues discriminative representations for flexible parameter tuning. Inspired by the Gaussian mixture model that samples from a mixture of Gaussian distributions, CoinSeg emphasizes intra-class diversity with multiple contrastive representation centroids. Specifically, we use mask proposals to identify regions with strong objectness that are likely to be diverse instances/centroids of a category. These mask proposals are then used for contrastive representations to reinforce intra-class diversity. Meanwhile, to avoid bias from intra-class diversity, we also apply category-level pseudo-labels to enhance category-level consistency and inter-category diversity. Additionally, CoinSeg ensures the models stability and alleviates forgetting through a specific flexible tuning strategy. We validate CoinSeg on Pascal VOC 2012 and ADE20K datasets with multiple incremental scenarios and achieve superior results compared to previous state-of-the-art methods, especially in more challenging and realistic long-term scenarios. Code is available at https://***/zkzhang98/CoinSeg.

关键词： Training Adaptation models computer vision Semantic segmentation Gaussian distribution Stability analysis Proposals

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ABCD-HN: An Artificial network Benchmark for Community Detection on Heterogeneous networks 18th

ABCD-HN: An Artificial Network Benchmark for Community Dete...

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18th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2023

作者： Liu, Junjie Guo, Kun Wu, Ling Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China College of Computer and Data Science/College of Software Fuzhou University Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819996360

Community detection is essential for identifying cohesive groups in complex networks. Artificial benchmarks are critical for evaluating community detection algorithms, offering controlled environments with known community structures. However, existing benchmarks mainly focus on homogeneous networks and overlook the unique characteristics of heterogeneous networks. This paper proposes a novel artificial benchmark, called ABCD-HN (Artificial network Benchmark for Community Detection on Heterogeneous networks), for community detection in heterogeneous networks. This benchmark enables the generation of artificial heterogeneous networks with controllable community quantity, node quantity, and community complexity. Additionally, an evaluation framework for artificial heterogeneous networks is proposed to assess their effectiveness. Experimental results demonstrate the effectiveness and usability of ABCD-HN as a benchmark for artificial heterogeneous networks. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Heterogeneous networks

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A Novel Classical-Quantum Transfer Learning Framework for Image Recognition

SSRN

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SSRN 2024年

作者： Ruan, Banyao Liu, Zhihao Li, Xi School of Computer Science and Engineering Southeast University Nanjing211189 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing211189 China

With the advent of the era of Noisy Intermediate-Scale quantum (NISQ), quantum processors have demonstrated clear quantum supremacy, marking a new milestone in the development of quantum computing. At the same time, classical deep learning algorithms have been widely used in many fields such as image recognition and natural language processing. In this context, the Classical-Quantum transfer learning (CQ-TL) method, which combines the advantages of quantum computing and classical deep learning algorithms, emerged. In view of the problem of unclear quantum advantage in the existing CQ-TL framework, this paper proposes a novel CQ-TL framework based on amplitude encoding and effective learning algorithm is given. It not only has the flexibility of the existing framework, but also reduces the number of classical parameters, and can better demonstrate quantum advantages. Based on the CQ-TL framework, we designed and implemented two CQ-TL neural network models: named Transfer Learning Quantum Neural network (TLQNN) and Transfer Learning Quantum Convolutional Neural network (TLQCNN). TLQNN is structurally similar to a multi-layer feedforward neural network, and it achieves feature extraction through repeated quantum unit layers (ansatz). TLQCNN, on the other hand, employs quantum convolution and quantum pooling operations, which can effectively alleviate the effects of the barren plateau phenomenon, making it highly trainable and possessing great potential for practical applications. In cross-experiments with three benchmark datasets (MNIST, Fashion-MNIST and CIFAR10) and three source models (ResNet18, ResNet50 and DenseNet121), the two models have exceeded the benchmark classical classifier in many performance measures such as accuracy, convergence, stability and generalization capability. Our research shows that the proposed novel CQ-TL framework and its models have potential quantum advantages in the NISQ era, providing strong support for the practical application of qu

关键词： Learning algorithms

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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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