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arXiv 2023年

作者： Yang, Gang Cao, Xiangyong Xiao, Wenzhe Zhou, Man Liu, Aiping Chen, Xun Meng, Deyu University of Science and Technology of China China Xi’an Jiaotong University China School of Computer Science and Technology Xi’an Jiaotong University China Ministry of Education Key Lab For Intelligent Networks and Network Security Xi’an Jiaotong University China Nanyang Technological University Singapore

Pan-sharpening aims to generate a high-resolution multispectral (HRMS) image by integrating the spectral information of a low-resolution multispectral (LRMS) image with the texture details of a high-resolution panchromatic (PAN) image. It essentially inherits the ill-posed nature of the super-resolution (SR) task that diverse HRMS images can degrade into an LRMS image. However, existing deep learning-based methods recover only one HRMS image from the LRMS image and PAN image using a deterministic mapping, thus ignoring the diversity of the HRMS image. In this paper, to alleviate this ill-posed issue, we propose a flow-based pan-sharpening network (PanFlowNet) to directly learn the conditional distribution of HRMS image given LRMS image and PAN image instead of learning a deterministic mapping. Specifically, we first transform this unknown conditional distribution into a given Gaussian distribution by an invertible network, and the conditional distribution can thus be explicitly defined. Then, we design an invertible Conditional Affine Coupling Block (CACB) and further build the architecture of PanFlowNet by stacking a series of CACBs. Finally, the PanFlowNet is trained by maximizing the log-likelihood of the conditional distribution given a training set and can then be used to predict diverse HRMS images. The experimental results verify that the proposed PanFlowNet can generate various HRMS images given an LRMS image and a PAN image. Additionally, the experimental results on different kinds of satellite datasets also demonstrate the superiority of our PanFlowNet compared with other state-of-the-art methods both visually and quantitatively. Copyright © 2023, The Authors. All rights reserved.

关键词： Mapping

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Species-Based Differential Evolution with Migration for Multimodal Optimization 14th

Species-Based Differential Evolution with Migration for Mult...

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14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Li, Wei Fan, Yaochi Jiang, Qiaoyong School of Computer Science and Engineering Xi’an University of Technology Xi’an710048 China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an710048 China

ISBN: (纸本)9789811534249

Due to the fact that many realistic problems are multimodal optimization problems (MMOPs), multimodal optimization has attracted a growing interest. In this paper, a Species-based differential evolution with migration algorithm is proposed for MMOPs. First, a migration strategy is developed to improve the exploration ability of species with less individuals. Then, an improved operation of creating local individuals is introduced to help the algorithm to find high-quality solutions. Finally, an archive mechanism is applied to preserve the best solutions found during previous generations and avoid the loss of the peaks. Experimental results on CEC2013 test problems confirm the effectiveness of the proposed algorithm compared to several well-known multimodal optimization algorithms. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Optimization

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Mind Reasoning Manners: Enhancing Type Perception for Generalized Zero-shot Logical Reasoning over Text

arXiv

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arXiv 2023年

作者： Xu, Fangzhi Liu, Jun Lin, Qika Zhao, Tianzhe Zhang, Jian Zhang, Lingling School of Computer Science and Technology Xi’an Jiaotong University Xi’an China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&D National Engineering lab for Big Data Analytics Xi’an China

Logical reasoning task involves diverse types of complex reasoning over text, based on the form of multiple-choice question answering. Given the context, question and a set of options as the input, previous methods achieve superior performances on the full-data setting. However, the current benchmark dataset has the ideal assumption that the reasoning type distribution on the train split is close to the test split, which is inconsistent with many real application scenarios. To address it, there remain two problems to be studied: (1) How is the zero-shot capability of the models (train on seen types and test on unseen types)? (2) How to enhance the perception of reasoning types for the models? For problem 1, we propose a new benchmark for generalized zero-shot logical reasoning, named ZsLR. It includes six splits based on the three type sampling strategies. For problem 2, a type-aware model TaCo is proposed. It utilizes both the heuristic input reconstruction and the contrastive learning to improve the type perception in the global representation. Extensive experiments on both the zero-shot and full-data settings prove the superiority of TaCo over the state-of-the-art methods. Also, we experiment and verify the generalization capability of TaCo on other logical reasoning dataset. © 2023, CC BY.

关键词： Benchmarking

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SAR Image Scene Classification Based on Metric Learning and CNNs

SAR Image Scene Classification Based on Metric Learning and ...

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Information Communication and Software Engineering (ICICSE), IEEE International Conference on

作者： Yaozhong Wang Zhiqiang Zhao Lei Wang School of Computer Science and Engineering and the Shaanxi Key Laboratory of Network Computing and Security Technology Xi‘an University of Technology Xi'an China School of Mathematics and Computer Science Shaanxi University of Technology Hanzhong China

ISBN: (纸本)9781665430302

With the development of synthetic aperture radar (SAR) imaging techniques, many high-quality images are acquired for exactly earth observation. As a result of heavy speckle, it is difficult to extract effective representation of different scene images to distinguish them accurately. In this paper, a metric-learning based convolutional neural network (CNN) is proposed to characterize SAR images. Specifically, a regularization term is proposed, which is constrained by the Mahalanobis distance. The regularization term learns a new metric space based on CNN, where the similar images should be close and those of dissimilar images should be separated. Comparing with the other approaches, experimental results of the proposed approach illustrate a superior classification for different scenes.

关键词： Earth Image analysis Conferences Speckle Extraterrestrial measurements Radar polarimetry Convolutional neural networks

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Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural networks

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Dong, Xuewen Li, Jiachen Li, Shujun You, Zhichao Qu, Qiang Kholodov, Yaroslav Shen, Yulong The School of Computer Science and Technology Xidian University The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education China The Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China The School of Computing Kent Interdisciplinary Research Centre in Cyber Security University of Kent CanterburyCT2 7NF United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China The Intelligent Transportation Systems Lab Innopolis University Innopolis Russia The School of Computer Science and Technology Xidian University China

Recent studies show that graph neural networks (GNNs) are vulnerable to backdoor attacks. Existing backdoor attacks against GNNs use fixed-pattern triggers and lack reasonable trigger constraints, overlooking individual graph characteristics and rendering insufficient evasiveness. To tackle the above issues, we propose ABARC, the first Adaptive Backdoor Attack with Reasonable Constraints, applying to both graph-level and node-level tasks in GNNs. For graph-level tasks, we propose a subgraph backdoor attack independent of the graph’s topology. It dynamically selects trigger nodes for each target graph and modifies node features with constraints based on graph similarity, feature range, and feature type. For node-level tasks, our attack begins with an analysis of node features, followed by selecting and modifying trigger features, which are then constrained by node similarity, feature range, and feature type. Furthermore, an adaptive edge-pruning mechanism is designed to reduce the impact of neighbors on target nodes, ensuring a high attack success rate (ASR). Experimental results show that even with reasonable constraints for attack evasiveness, our attack achieves a high ASR while incurring a marginal clean accuracy drop (CAD). When combined with the state-of-the-art defense randomized smoothing (RS) method, our attack maintains an ASR over 94%, surpassing existing attacks by more than 7%. © 2004-2012 IEEE. All rights reserved.

关键词： Graph neural networks

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A Prior Embedding-Driven Architecture for Long Distance Blind Iris Recognition

arXiv

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arXiv 2024年

作者： Xiong, Qi Zhang, Xinman Shen, Jun International College Hunan University of Arts and Sciences Changde415000 China School of Automation Science and Engineering Faculty of Electronic and Information Engineering MOE Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Shaanxi Xi'An710049 China School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia

Blind iris images, which result from unknown degradation during the process of iris recognition at long distances, often lead to decreased iris recognition rates. Currently, little existing literature offers a solution to this problem. In response, we propose a prior embedding-driven architecture for long distance blind iris recognition. We first proposed a blind iris image restoration network called Iris-PPRGAN. To effectively restore the texture of the blind iris, Iris-PPRGAN includes a Generative Adversarial network (GAN) used as a Prior Decoder, and a DNN used as the encoder. To extract iris features more efficiently, we then proposed a robust iris classifier by modifying the bottleneck module of InsightFace, which called Insight-Iris. A low-quality blind iris image is first restored by Iris-PPRGAN, then the restored iris image undergoes recognition via Insight-Iris. Experimental results on the public CASIA-Irisdistance dataset demonstrate that our proposed method significantly superior results to state-of-the-art blind iris restoration methods both quantitatively and qualitatively, Specifically, the recognition rate for long-distance blind iris images reaches 90% after processing with our methods, representing an improvement of approximately ten percentage points compared to images without restoration. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Haplotype-resolved Chinese male genome assembly based on high-fidelity sequencing

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Fundamental Research 2022年第6期2卷 946-953页

作者： Xiaofei Yang Xixi Zhao Shoufang Qu Peng Jia Bo Wang Shenghan Gao Tun Xu Wenxin Zhang Jie Huang Kai Ye Genome Institute the First Affiliated Hospital of Xi'an Jiaotong UniversityXi'an 710061ShaanxiChina School of Computer Science and Technology Faculty of Electronic and Information EngineeringXi'an Jiaotong UniversityXi'an 710049ShaanxiChina MOE Key Lab for Intelligent Networks&Networks Security Faculty of Electronic and Information EngineeringXi'an Jiaotong UniversityXi'an 710049ShaanxiChina National Institutes for food and drug Control(NIFDC) No.2Tiantan XiliDongcheng DistrictBeijing 102629China School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi'an Jiaotong UniversityXi'an 710049ShaanxiChina School of Life Science and Technology Xi'an Jiaotong UniversityXi'an 710049ShaanxiChina Faculty of Science Leiden UniversityLeidenThe Netherlands

The advantages of both the length and accuracy of high-fidelity(HiFi)reads enable chromosome-scale haplotype-resolved genome *** this study,we sequenced a cell line named HJ,established from a Chinese Han male individual by using HiFi and *** assembled two high-quality haplotypes of the HJ genome(haplotype 1(H1):3.1 Gb,haplotype 2(H2):2.9 Gb).The continuity(H1:contig N50=28.2 Mb,H2:contig N50=25.9 Mb)and completeness(BUSCO:H1=94.9%,H2=93.5%)are substantially better than those of other Chinese genomes,for example,HX1,NH1.0,and *** comparing HJ genome with GRCh38,we reported the mutation landscape of HJ and found that 176 and 213 N-gaps were filled in H1 and H2,*** addition,we detected 12.9 Mb and 13.4 Mb novel sequences containing 246 and 135 protein-coding genes in H1 and H2,*** results demonstrate the advantages of HiFi reads in haplotype-resolved genome assembly and provide two high-quality haplotypes of a potential Chinese genome as a reference for the Chinese Han population.

关键词： Genome assembly HiFi reads Human genome Haplotype-resolved Chinese Han Mutation landscape

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A Web Attack Detection Method Based on DistilBERT and Feature Fusion for Power Micro-Application Server

A Web Attack Detection Method Based on DistilBERT and Featur...

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Advanced Electronics, Electrical and Green Energy (AEEGE), International Conference on

作者： Li Nige Chen Lu Zhao Lei Teng Zhenning Wang Zhiqiang Shao Yiyang Gui Xiaolin State Grid Smart Grid Research Institute Co. Ltd. State Grid Laboratory of Power cyber-Security Protection and Monitoring Technology Nanjing China Shaanxi Province Key Laboratory of Computer Network Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an China

The widespread use of power micro-applications has significantly expanded the attack surface of their server side, thus increasing the risk of vulnerability attacks such as SQL injection, XSS, and CRLF injection. To ensure the security of power systems, these attacks must be detected precisely and timely. Therefore, this paper proposes a server-side web attack detection method based on DistilBERT and feature fusion for HTTP and HTTPS requests. The method treats HTTP and HTTPS requests as text data, specifically, extracts deep semantic features using DistilBERT, and fuses them with well-designed empirical features to comprehensively characterize and classify the request. Consequently, anomalous requests can be detected. The experimental results show that the accuracy, precision, recall, and F1 score of the method on HTTP CSIC 2010 and FWAF datasets are close to or higher than 99%. Compared with other methods as far as we known, this method has better performance and efficiency.

关键词：

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A Driving Risk Prediction Approach Based on Generative Adversarial networks and VANET for Autonomous Trams

A Driving Risk Prediction Approach Based on Generative Adver...

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International Conference on networking and network Applications (NaNA)

作者： Wenjiang Ji Jiangcheng Yang Yichuan Wang Lei Zhu Yuan Qiu Xinhong Hei College of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

Driving safety is an essential prerequisite to the rapid development of autonomous trams. However, the relationship of driving risk factors is nonlinear, which makes modeling difficult. To improve the accuracy of driving risk prediction, a data driven approach based on Generative Adversarial networks was proposed. First of all, a communication and alarming scenario of Vehicular Ad-hoc networks was demonstrated, in which the original data sets can be collected and transmitted by the help of sensors and Road Side Units. Then the RFE feature selection algorithm was used to keep the key features. To deal the sample asymmetry problem, a DCGAN model was designed for sparse samples expansion. At last, the XGBoost algorithm was used to classification and output the risk prediction result. During the experiment implemented with the public and real data sets, the risk prediction accuracy of proposed approach can up to 97.24%, for which takes the advantages in generating of the sparse samples.

关键词： Road side unit Transportation Prediction methods Prediction algorithms Generative adversarial networks Classification algorithms Sensors

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ConvCatb: An Attention-based CNN-CATBOOST Risk Prediction Model for Driving Safety

ConvCatb: An Attention-based CNN-CATBOOST Risk Prediction Mo...

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International Conference on networking and network Applications (NaNA)

作者： Xinhong Hei Hao Zhang Wenjiang Ji Yichuan Wang Lei Zhu Yuan Qiu College of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

Risk prediction is one of the most important tasks in assistant and automatic driving. In recent years, by the help of VANET and various sensors in the cars, the status of cars and roads can be collected in real time and used for data-driven based driving risk prediction. However, it is challenging to predict the driving risk due to the complex relationship between multiple environment factors like location, weather, time etc. Thus, a deep learning model ConvCatb was proposed in this paper, which improves the attention mechanism to the traditional Convolutional Neural networks, and combines the CatBoost algorithm to predict the current driving safety. The main idea is to emphasize the combination relationship between driving environment features through Non-local attention mechanism, and then use CatBoost to replace the softmax of CNN for classification. Finally, the experiment results show that the ConvCatb achieved superiorities in accuracy and F1-score, compared with existing schemes.

关键词： Machine learning algorithms Roads Vehicular ad hoc networks Prediction algorithms Real-time systems Classification algorithms Safety

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