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A Lightweight Convolutional Neural Network with Representation Self-challenge for Fingerprint Liveness Detection

computers, Materials & Continua 2022年第10期73卷 719-733页

作者： Jie Chen Chengsheng Yuan Chen Cui Zhihua Xia Xingming Sun Thangarajah Akilan School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Ministry of Public SecurityZhejiang Police CollegeHangzhou310053China Jiangsu Yuchi Blockchain Research Institute Nanjing210044China Department of Software Engineering Lakehead UniversityThunder BayON P7B 5E1Canada

Fingerprint identification systems have been widely deployed in many occasions of our daily ***,together with many advantages,they are still vulnerable to the presentation attack(PA)by some counterfeit *** address challenges from PA,fingerprint liveness detection(FLD)technology has been proposed and gradually attracted people’s *** vast majority of the FLD methods directly employ convolutional neural network(CNN),and rarely pay attention to the problem of overparameterization and over-fitting of models,resulting in large calculation force of model deployment and poor model *** at filling this gap,this paper designs a lightweight multi-scale convolutional neural network method,and further proposes a novel hybrid spatial pyramid pooling block to extract abundant features,so that the number of model parameters is greatly reduced,and support multi-scale true/fake fingerprint ***,the representation self-challenge(RSC)method is used to train the model,and the attention mechanism is also adopted for optimization during execution,which alleviates the problem of model over-fitting and enhances generalization of detection ***,experimental results on two publicly benchmarks:LivDet2011 and LivDet2013 sets,show that our method achieves outstanding detection results for blind materials and *** size of the model parameters is only 548 KB,and the average detection error of cross-sensors and cross-materials are 15.22 and 1 respectively,reaching the highest level currently available.

关键词： FLD lightweight multi-scale RSC blind materials

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Adversarial defense based on distribution transfer

arXiv

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arXiv 2023年

作者： Chen, Jiahao Yan, Diqun Dong, Li Faculty of Electrical Engineering and Computer Science Ningbo University Zhejiang Ningbo315211 China College of Computer Science and Technology Zhejiang University Zhejiang Hangzhou310027 China Key Laboratory of Mobile Network Application Technology of Zhejiang Province Zhejiang Ningbo315211 China

The presence of adversarial examples poses a significant threat to deep learning models and their applications. Existing defense methods provide certain resilience against adversarial examples, but often suffer from decreased accuracy and generalization performance, making it challenging to achieve a trade-off between robustness and generalization. To address this, our paper interprets the adversarial example problem from the perspective of sample distribution and proposes a defense method based on distribution shift, leveraging the distribution transfer capability of a diffusion model for adversarial defense. The core idea is to exploit the discrepancy between normal and adversarial sample distributions to achieve adversarial defense using a pretrained diffusion model. Specifically, an adversarial sample undergoes a forward diffusion process, moving away from the source distribution, followed by a reverse process guided by the protected model (victim model) output to map it back to the normal distribution. Experimental evaluations on CIFAR10 and ImageNet30 datasets are conducted, comparing with adversarial training and input preprocessing methods. For infinite-norm attacks with 8/255 perturbation, accuracy rates of 78.1% and 83.5% are achieved, respectively. For 2-norm attacks with 128/255 perturbation, accuracy rates are 74.3% and 82.5%. Additional experiments considering perturbation amplitude, diffusion iterations, and adaptive attacks also validate the effectiveness of the proposed method. Results demonstrate that even when the attacker has knowledge of the defense, the proposed distribution-based method effectively withstands adversarial examples. It fills the gaps of traditional approaches, restoring high-quality original samples and showcasing superior performance in model robustness and generalization. Copyright © 2023, The Authors. All rights reserved.

关键词： Normal distribution

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Effect of the Mixing Structure Parameters of a Self-reflux Burner on Combustion Characteristics and NO_(x)Emission

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Journal of Thermal Science 2021年第4期30卷 1224-1236页

作者： XU Qian SHEN Meng SHI Kejian LIU Zhihui AKKURT Nevzat XIONG Yaxuan LIU Lin FENG Junxiao WANG Jiulong School of energy and environmental engineering Shunde Graduate SchoolUniversity of Science and Technology BeijingBeijing 100083China Beijing Key Laboratory of Energy Conservation and Emission Reduction for Metallurgical Industry Beijing 100083China Department of Mechanical Engineering Munzur UniversityTunceli 62000Turkey Key Laboratory of HVAC Beijing University of Civil Engineering and ArchitectureBeijing 100044China School of Mathematics and Physics University of Science and Technology BeijingBeijing 100083China Department of Big Data Technology and Application Development Computer Network Information CenterChinese Academy of SciencesBeijing 100190China

To solve the problem of low efficiency of NO_(x)emission reduction in self-reflux burners,this study is based on the concept of coordinated control of self-reflux burner structural and thermal *** completing the structural design and optimization of thermal parameters,we continue to adjust the two key structural parameters:the nozzle axis distance and the length of the cylindrical section,to minimize NO_(x)*** are the two parameters that chiefly affect the mixing of flue gas and fuel *** results show that increasing nozzle axis distance can delay the mixing of gas and air and create a more uniform oxygen concentration field for the combustion *** maximum combustion temperature is reduced from 1973.65 K to 1935.88 K and the volume fraction of NO_(x)in the flue gas is reduced from 188.08×10^(–6)to 143.47×10^(–6).However,compared with the nozzle axis distance,the length of the cylindrical section of the burner has little effect on the mixing of the flow *** different cylindrical section lengths,the maximum combustion temperature does not change more than 3 K,and the volume fraction of NO_(x)in the flue gas changes within 5×10~(–6).

关键词： self-reflux structure structural parameter control thermal parameter combustion characteristic NO_(x)emission

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Deep-sea Nodule Mineral Image Segmentation Algorithm Based on Pix2PixHD

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computers, Materials & Continua 2022年第10期73卷 1449-1462页

作者： Wei Song Haolin Wang Xinping Zhang Jianxin Xia Tongmu Liu Yuxi Shi School of Information and Engineering Minzu University of ChinaBeijing100081China Key Laboratory of Marine Environmental Survey Technology and Application Ministry of Natural ResourceGuangzhou510300China National Language Resource Monitoring&Research Center of Minority Languages Beijing100081China School of Ocean Science China University of GeosciencesBeijing100191China Department of Buoy Engineering South China Sea Marine Survey and Technology CenterGuangzhou510300China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNew Jersey07102USA

Deep-sea mineral image segmentation plays an important role in deep-sea mining and underwater mineral resource monitoring and *** application of artificial intelligence technology to deep-sea mining projects can effectively improve the quality and efficiency of *** existing deep learning-based underwater image segmentation algorithms have problems such as the accuracy rate is not high enough and the running time is slightly *** order to improve the segmentation performance of underwater mineral images,this paper uses the Pix2PixHD(Pixel to Pixel High Definition)algorithm based on Conditional Generative Adversarial Network(CGAN)to segment deep-sea mineral *** model uses a coarse-to-fine generator composed of a global generation network and two local enhancement networks,and multiple multi-scale discriminators with same network structures but different input pictures to generate highquality *** test results on the deep-sea mineral datasets show that the Pix2PixHD algorithm can identify more target minerals under certain other *** evaluation index shows that the Pix2PixHD algorithm effectively improves the accuracy rate and the recall rate of deep-sea mineral image segmentation compared with the CGAN algorithm and the U-Net *** is important for expanding the application of deep learning techniques in the field of deep-sea exploration and mining.

关键词： Deep-sea mineral image segmentation generative adversarial network

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Boosting LLMS with Ontology-Aware Prompt for Ner Data Augmentation

Boosting LLMS with Ontology-Aware Prompt for Ner Data Augmen...

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

作者： Zhizhao Luo Youchen Wang Wenjun Ke Rui Qi Yikai Guo Peng Wang Beijing Institute of Computer Technology and Application Beijing China School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) Ministry of Education Nanjing China China Life Property & Casualty Insurance Company Limited Changsha China

Named Entity Recognition (NER) data augmentation (DA) aims to improve the performance and generalization capabilities of NER models by generating scalable training data. The key challenge lies in ensuring the generated samples maintain contextual diversity while preserving label consistency. However, existing dominant methods fail to simultaneously satisfy both criteria. Inspired by the extensive generative capabilities of large language models (LLMs), we propose ANGEL, a frAmework integrating the oNtoloGy structure and instructivE prompting within LLMs. Specifically, the hierarchical ontology structure guides prompt ranking, while instructive prompting enhances LLMs’ mastery of domain knowledge, empowering synthetic sample generation and annotation. Experiments show ANGEL surpasses state-of-the-art (SOTA) baselines, conferring absolute F1 increases of 2.86% and 0.93% on two benchmark datasets, respectively.

关键词：

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Backdoor Defense via Deconfounded Representation Learning

arXiv

引用

arXiv 2023年

作者： Zhang, Zaixi Liu, Qi Wang, Zhicai Lu, Zepu Hu, Qingyong Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence Anhui Hefei China Hong Kong University of Science and Technology Hong Kong University of Science and Technology of China China

Deep neural networks (DNNs) are recently shown to be vulnerable to backdoor attacks, where attackers embed hidden backdoors in the DNN model by injecting a few poisoned examples into the training dataset. While extensive efforts have been made to detect and remove backdoors from backdoored DNNs, it is still not clear whether a backdoor-free clean model can be directly obtained from poisoned datasets. In this paper, we first construct a causal graph to model the generation process of poisoned data and find that the backdoor attack acts as the confounder, which brings spurious associations between the input images and target labels, making the model predictions less reliable. Inspired by the causal understanding, we propose the Causality-inspired Backdoor Defense (CBD), to learn deconfounded representations for reliable classification. Specifically, a backdoored model is intentionally trained to capture the confounding effects. The other clean model dedicates to capturing the desired causal effects by minimizing the mutual information with the confounding representations from the backdoored model and employing a sample-wise re-weighting scheme. Extensive experiments on multiple benchmark datasets against 6 state-of-the-art attacks verify that our proposed defense method is effective in reducing backdoor threats while maintaining high accuracy in predicting benign samples. Further analysis shows that CBD can also resist potential adaptive attacks. The code is available at https://***/zaixizhang/CBD. © 2023, CC BY-NC-SA.

关键词： Deep neural networks

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Formation of precipitates and their effects on the mechanical properties in a Fe–Mn–Al–C austenitic low-density steel

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Journal of Materials Research and technology 2025年 36卷 9422-9431页

作者： Yongxuan Shang Xue Cao Wenqing Jiang Lixin Sun Shuyong Jiang Zhongwu Zhang College of Materials Science and Chemical Engineering Harbin Engineering University Harbin 150001 China College of Computer Science and Technology Harbin Engineering University Harbin 150001 China State Key Laboratory of Metal Material for Marine Equipment and Application Iron & Steel Research Institute of Ansteel Group Corporation Anshan 114009 Liaoning China

κ-carbides serve as a strengthening phase in Fe–Mn–Al–C austenitic low-density steels, enhancing yield strength but reducing the work hardening rate due to slip plane softening effect. To address this issue, this study introduces strong carbide-forming elements Ti and V to regulate carbon distribution and then control κ-carbide precipitation. Without Ti and V addition, κ-carbides uniformly precipitate in the matrix after aging, with an average size of 10.8 ± 6.2 nm and a volume fraction of 7.4 ± 3.4 %. The addition of Ti and V leads to the formation of (Ti, V)C carbides at high temperatures, which act as carbon sinks, reducing the matrix carbon content and thereby suppressing the homogeneous precipitation of κ-carbides. As a result, the κ-carbides precipitate at the edge of the (Ti, V)C carbides, with an average radius of 9.7 ± 8.5 nm and a volume fraction of 3.2 ± 2.8 %. Compared to the uniform precipitation of κ-carbides in low-density steel without Ti/V additions, this modified precipitation behavior preserves the work-hardening rate and increases the tensile strength from 1004 MPa to 1096 MPa. TEM analysis reveals that both steels exhibit planar slip deformation, but the Ti/V-modified steel shows narrower slip band spacing and more intense plastic deformation. This study establishes a novel microalloying strategy for optimizing precipitate distribution and mechanical properties in low-density steels.

关键词： Low-density steels κ-carbides Precipitation Strengthening mechanisms Work-hardening

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Sequence-to-Action: Grammatical Error Correction with Action Guided Sequence Generation

arXiv

引用

arXiv 2022年

作者： Li, Jiquan Guo, Junliang Zhu, Yongxin Sheng, Xin Jiang, Deqiang Ren, Bo Xu, Linli Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology China Tencent YouTu Lab China

The task of Grammatical Error Correction (GEC) has received remarkable attention with wide applications in Natural Language Processing (NLP) in recent years. While one of the key principles of GEC is to keep the correct parts unchanged and avoid over-correction, previous sequence-to-sequence (seq2seq) models generate results from scratch, which are not guaranteed to follow the original sentence structure and may suffer from the over-correction problem. In the meantime, the recently proposed sequence tagging models can overcome the over-correction problem by only generating edit operations, but are conditioned on human designed language-specific tagging labels. In this paper, we combine the pros and alleviate the cons of both models by proposing a novel Sequence-to-Action (S2A) module. The S2A module jointly takes the source and target sentences as input, and is able to automatically generate a token-level action sequence before predicting each token, where each action is generated from three choices named SKIP, COPY and GENerate. Then the actions are fused with the basic seq2seq framework to provide final predictions. We conduct experiments on the benchmark datasets of both English and Chinese GEC tasks. Our model consistently outperforms the seq2seq baselines, while being able to significantly alleviate the over-correction problem as well as holding better generality and diversity in the generation results compared to the sequence tagging models. © 2022, CC BY.

关键词： Error correction

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Reinforced Label Denoising for Weakly-Supervised Audio-Visual Video Parsing

arXiv

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arXiv 2024年

作者： Gao, Yongbiao Sun, Xiangcheng Lv, Guohua Yu, Deng Niu, Sijiu Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Application [Southeast University Ministry of Education China School of Artificial Intelligence Shandong University China Shandong Key Laboratory of Ubiquitous Intelligent Computing Jinan China

Audio-visual video parsing (AVVP) aims to recognize audio and visual event labels with precise temporal boundaries, which is quite challenging since audio or visual modality might include only one event label with only the overall video labels available. Existing label denoising models often treat the denoising process as a separate preprocessing step, leading to a disconnect between label denoising and AVVP tasks. To bridge this gap, we present a novel joint reinforcement learning-based label denoising approach (RLLD). This approach enables simultaneous training of both label denoising and video parsing models through a joint optimization strategy. We introduce a novel AVVP-validation and soft inter-reward feedback mechanism that directly guides the learning of label denoising policy. Extensive experiments on AVVP tasks demonstrate the superior performance of our proposed method compared to label denoising techniques. Furthermore, by incorporating our label denoising method into other AVVP models, we find that it can further enhance parsing results. © 2024, CC BY.

关键词： Self-supervised learning

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application of AmsGradP optimizer in fault diagnosis of bearing with few samples

Application of AmsGradP optimizer in fault diagnosis of bear...

引用

Internet of Things, Automation and Artificial Intelligence (IoTAAI), International Conference on

作者： Jinfa Fang Qiong Liang Xiangjun Dong Qi Wang Jianbin Xiong School of Automation Guangdong Polytechnic Normal University Guangzhou China The Key Laboratory of Marine Environmental Survey Technology and Application Ministry of Natural Resources P.R.China School of Computer Guangdong Polytechnic Normal University Guangzhou China

ISBN: (数字)9798350386974

ISBN: (纸本)9798350386981

Bearing fault diagnosis methods based on deep learning usually require a large number of training samples. However, obtaining large amounts of faulty bearing data can be challenging. How to diagnose faults with limited or few samples becomes a research problem. In order to solve the problem of bearing fault diagnosis with few samples, a rolling bearing fault diagnosis method based on improved Adam (Adaptive Moment Estimation) is proposed. First, it combines the advantages of both Adam and AmsGrad optimizers. Second, similar to AdamP, AmsGradP (Adaptive Gradient Optimizer with Penalty Parameters) introduces the penalty term of the parameter norm to mitigate the problem of premature decay of the learning rate. Finally, by penalizing the norm of the parameter during optimization, AmsGradP can prevent excessive parameter growth, thus further stabilizing the learning rate. The experimental results show that when only 1.58% of the data is used as the training dataset, the average accuracy of 98.26% can be achieved by using the AmsGradP optimizer, which is 6.68% higher than that by using the Adam optimizer.

关键词： Fault diagnosis Training Deep learning Automation Accuracy Rolling bearings Estimation Internet of Things Artificial intelligence Optimization

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