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5th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2021

作者： Li, Hang Wang, Li Xia, Chengyi Tianjin University of Technology Tianjin300382 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Beijing China

ISBN: (纸本)9789811939976

Commonly abstracted as a set of units, the complex systems in the real world are interconnected with each other using the specific dynamical methods. However, in practice, there is nothing else but the performances of the system known, nor are the network structure and the dynamical rules observed. In order to solve this problem, a research has been conducted on network reconstruction, so as to recover the potential network structure and network dynamics from the observed time series. In this paper, a new method is proposed, namely, slope difference similarity (SDS), which is a way of thinking in reverse. Based on the trend of change in nodes, the coupling degree between nodes is judged and the network is reconstructed. By comparing the SDS and Kendall rank correlation method with experiments, the experimental results are obtained in this paper to demonstrate the effectiveness of SDS network reconstruction. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Time series

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Complex-Valued Gabor-Attention Residual Fusion Network for Iris Recognition 27

Complex-Valued Gabor-Attention Residual Fusion Network for I...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Li, Zhuoru Xiao, Jian Bai, Xiaowei Wang, Xiaodong Li, Yingxi Fang, Zhenyu Xie, Liang Yan, Ye Yin, Erwei College of Intelligence and Computing Tianjin University Tianjin300450 China Beijing100071 China Intelligent Game and Decision Laboratory Beijing100071 China Tianjin300450 China College of Engineering Peking University Beijing100071 China School of Software Northwestern Polytechnical University Xian710000 China

ISBN: (纸本)9781643685489

Iris recognition has gained significant attention in identity verification due to the unique, stable texture patterns in iris. Successfully extracting these patterns is essential for quick and precise identification. Although deep learning methods have automated the iris recognition, they predominantly rely on real-valued networks that overlook the complex-valued representation of iris texture. This means they cannot effectively process phase and amplitude information, and fail to integrate domain-specific knowledge of iris, thereby not fully capturing the intricate details of the iris texture. Inspired by classical manual methods that efficiently harness the complex-valued representation of the iris to extract both amplitude and phase information. We integrate Gabor filters with complex-valued neural networks, propose a Complex-Valued Gabor-Attention Residual Fusion Network (GRFN) tailored for iris recognition, aiming to comprehensively capture the iris texture's multi-scale and multi-orientation phase and amplitude features. The GRFN incorporates adaptive Gabor Complex-Valued Convolution Kernels (GCVK) to introduce a Gabor attention mechanism focused on iris biometric characteristics. Furthermore, we propose a novel residual feature fusion approach that selects and merges local and global features across multiple directions and scales, mitigating model degradation and enhancing the network's ability to extract iris texture features effectively. Extensive experiments show that the proposed network outperforms the state-of-the-art performance on two benchmark datasets. © 2024 The Authors.

关键词： Gabor filters

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BMDP:Blockchain-Based Multi-Cloud Storage Data Provenance 26

BMDP:Blockchain-Based Multi-Cloud Storage Data Provenance

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

作者： Wang, Feiyu Zhou, Jian-Tao Guo, Xu Inner Mongolia University College of Computer Science Inner Mongolia Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Engineering Research Center of Ecological Big Data Ministry of Education Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software China

ISBN: (纸本)9798350331684

In a multi-cloud storage system, provenance data records all operations and ownership during its lifecycle, which is critical for data security and audibility. However, recording provenance data also poses some challenging security and storage issues. In this paper, we present a secure and efficient multi-cloud storage data source scheme, BMDP. We use blockchain technology to ensure the secure storage of provenance data and design a smart contract to utilize the provenance data to ensure the proper operation of the multi-cloud storage system. Finally, we analyze the scheme's safety and do simulation experiments to show that the scheme has practicality. © 2023 IEEE.

关键词： Smart contract

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Blockchain-Based Multi-Cloud Data Storage System Disaster Recovery

Blockchain-Based Multi-Cloud Data Storage System Disaster Re...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wang, Feiyu Zhou, Jian-Tao College of Computer Science Inner Mongolia University Inner Mongolia Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

ISBN: (纸本)9798350337020

Cloud storage services have been used by most businesses and individual users. However, data loss, service interruptions and cyber attacks often lead to cloud storage services not being provided properly, and these incidents have caused financial losses to users. Second, traditional and single-cloud model disaster recovery services are no longer suitable for the current complex cloud storage systems. Therefore, a scheme to provide disaster recovery for cloud storage services in a multi-cloud storage environment is needed in real production. In this paper, we propose a disaster recovery scheme based on blockchain technology. The proposed scheme outlined in this study aims to address the issue of data availability within the cloud storage landscape. The proposed scheme achieves this goal by dividing data into hot and cold categories, verifying the integrity of copy data via blockchain technology, and utilizing blockchain networks to manage multi-cloud storage systems. Experimental findings demonstrate that the proposed scheme yields superior results in terms of computation and time overheads. © 2023 IEEE.

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FL-DP: Differential Private Federated Neural Network 4th

FL-DP: Differential Private Federated Neural Network

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4th EAI International Conference on Security and Privacy in New computing Environments, SPNCE 2021

作者： Irfan, Muhammad Maaz Wang, Lin Ali, Sheraz Jing, Shan Zhao, Chuan School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network-based Intelligent Computing University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Software Engineering Jinan China

ISBN: (纸本)9783030967901

The rapid development of the Internet and machine learning has brought convenience and comfort to users’ lives. However, due to various attacks and sensitive data leaks, the large amount of data used in machine learning training has made the issue of personal privacy a growing concern as well. In the era of big data, anyone’s information can be stolen, which makes many people feel uneasy. We propose a new approach called FL-DP (Federated Learning Based on Differential Privacy). Based on differential privacy, this approach can effectively restrict the adversary’s access to the client model, which has the ability to limit data leakage. In this framework, we use the DP Laplace mechanism. It is ensured that all operations (including the server-side aggregation process) are secure and do not leak any information about the training data. Also, we consider adding multiple noises to the preprocessing process so that the client-side data is secure during the training process. Our approach not only provides client-level privacy, but also balances efficiency and privacy. After evaluation, our approach is highly scalable and can be applied to most machine learning based applications. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Machine learning

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Learning with Open-world Noisy Data via Class-independent Margin in Dual Representation Space

arXiv

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arXiv 2025年

作者： Pan, Linchao Gao, Can Zhou, Jie Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China

Learning with Noisy Labels (LNL) aims to improve the model generalization when facing data with noisy labels, and existing methods generally assume that noisy labels come from known classes, called closed-set noise. However, in real-world scenarios, noisy labels from similar unknown classes, i.e., open-set noise, may occur during the training and inference stage. Such open-world noisy labels may significantly impact the performance of LNL methods. In this study, we propose a novel dual-space joint learning method to robustly handle the open-world noise. To mitigate model overfitting on closed-set and open-set noises, a dual representation space is constructed by two networks. One is a projection network that learns shared representations in the prototype space, while the other is a One-Vs-All (OVA) network that makes predictions using unique semantic representations in the class-independent space. Then, bi-level contrastive learning and consistency regularization are introduced in two spaces to enhance the detection capability for data with unknown classes. To benefit from the memorization effects across different types of samples, class-independent margin criteria are designed for sample identification, which selects clean samples, weights closed-set noise, and filters open-set noise effectively. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods and achieves an average accuracy improvement of 4.55% and an AUROC improvement of 6.17% on CIFAR80N. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Big-Moe: Bypassing Isolated Gating For Generalized Multimodal Face Anti-Spoofing

Big-Moe: Bypassing Isolated Gating For Generalized Multimoda...

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

作者： Yingjie Ma Zitong Yu Xun Lin Weicheng Xie Linlin Shen College of Computer Science and Software Engineering Shenzhen University Great Bay University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the domain of facial recognition security, multimodal Face Anti-Spoofing (FAS) is essential for countering presentation attacks. However, existing technologies encounter challenges due to modality biases and imbalances, as well as domain shifts. Our research introduces a Mixture of Experts (MoE) model to address these issues effectively. We identified three limitations in traditional MoE approaches to multimodal FAS: (1) Coarse-grained experts’ inability to capture nuanced spoofing indicators; (2) Gated networks’ susceptibility to input noise affecting decision-making; (3) MoE’s sensitivity to prompt tokens leading to overfitting with conventional learning methods. To mitigate these, we propose the Bypass Isolated Gating MoE (BIG-MoE) framework, featuring: (1) Fine-grained experts for enhanced detection of subtle spoofing cues; (2) An isolation gating mechanism to counteract input noise; (3) A novel differential convolutional prompt bypass enriching the gating network with critical local features, thereby improving perceptual capabilities. Extensive experiments on four benchmark datasets demonstrate significant generalization performance improvement in multimodal FAS task. The code is released at https://***/murInJ/BIG-MoE.

关键词： Convolutional codes Sensitivity Convolution Face recognition Noise Logic gates Feature extraction Security Speech processing Overfitting

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Learning with Open-world Noisy Data via Class-independent Margin in Dual Representation Space 39

Learning with Open-world Noisy Data via Class-independent Ma...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

ISBN: (纸本)157735897X

关键词： Contrastive Learning

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Deep User and Item Inter-matching Network for CTR Prediction 28th

Deep User and Item Inter-matching Network for CTR Predicti...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Yuan, Zhiyang Xiao, Yingyuan Yang, Peilin Hao, Qingbo Wang, Hongya Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin300384 China College of Computer Science and Engineering Donghua University Shanghai China

ISBN: (纸本)9783031306716

CTR prediction plays an important role in increasing company revenue and user experience, and many efforts start with historical behavior to uncover user interest. There are two main problems with previous works: (1) When most previous works mined interests from users’ historical behaviors, they only focus on implicit or explicit interests. (2) When most previous works mined user interests through the relationship between target users and similar users, the representation of target users was not rich and accurate enough, resulting in less accurate item recommendations. Therefore the Deep User and Item Inter-Matching Network (DUIIN) is proposed in this paper to solve the above problems. First, we design Item-to-Item Network (IIN), using two different sub-networks Evolving Interest Network (EIN) and Feature Interaction Network (FIN) to mine the hidden interests and explicit interests shown by users’ historical behaviors, respectively. Then the User-to-User Network (UUN) is designed to mine user interests through the relationship between target users and similar users after representing the target users more accurately and richly. The experimental results show that the DUIIN model proposed in this paper performs better than other state-of-the-art models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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LinNet: Linear Network for Efficient Point Cloud Representation Learning 38

LinNet: Linear Network for Efficient Point Cloud Representat...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Deng, Hao Jing, Kunlei Cheng, Shengmei Liu, Cheng Ru, Jiawei Bo, Jiang Wang, Lin State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services School of Information Science and Technology Northwest University China Shaanxi Key Laboratory of Higher Education Institution of Generative Artificial Intelligence and Mixed Reality China School of Software Engineering Xi'an Jiaotong University China Department of Computing The Hong Kong Polytechnic University Hong Kong

Point-based methods have made significant progress, but improving their scalability in large-scale 3D scenes is still a challenging problem. In this paper, we delve into the point-based method and develop a simpler, faster, stronger variant model, dubbed as LinNet. In particular, we first propose the disassembled set abstraction (DSA) module, which is more effective than the previous version of set abstraction. It achieves more efficient local aggregation by leveraging spatial anisotropy and channel anisotropy separately. Additionally, by mapping 3D point clouds onto 1D space-filling curves, we enable parallelization of downsampling and neighborhood queries on GPUs with linear complexity. LinNet, as a purely point-based method, outperforms most previous methods in both indoor and outdoor scenes without any extra attention, and sparse convolution but merely relying on a simple MLP. It achieves the mIoU of 73.7%, 81.4%, and 69.1% on the S3DIS Area5, NuScenes, and SemanticKITTI validation benchmarks, respectively, while speeding up almost 10x times over PointNeXt. Our work further reveals both the efficacy and efficiency potential of the vanilla point-based models in large-scale representation learning. Our code will be available at https://***/DengH293/LinNet. © 2024 Neural information processing systems foundation. All rights reserved.

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