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Antigen epitopes of animal coronaviruses:a mini-review

Animal Diseases 2024年第1期4卷 19-26页

作者： Mingjun Su Guanghui Zheng Xiangwen Xu Houhui Song Key Laboratory of Applied Technology On Green-Eco-Healthy Animal Husbandry of Zhejiang Province Zhejiang Provincial Engineering Research Center for Animal Health Diagnostics&Advanced TechnologyZhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health ManagementChina-Australia Joint Laboratory for Animal Health Big Data AnalyticsCollege of Animal Science and Technology&College of Veterinary Medicine of ZhejiangA&F University666 Wusu StreetLin'an DistrictHangzhou 311300Zhejiang ProvinceChina

Coronaviruses are widespread in nature and can infect mammals and poultry,making them a public health ***,prevention and control of emerging and re-emerging animal coronaviruses is a great *** mecha-nisms of virus-mediated immune responses have important implications for research on virus prevention and *** antigenic epitope is a chemical group capable of stimulating the production of antibodies or sensitized lympho-cytes,playing an important role in antiviral immune ***,it can shed light on the development of diagnos-tic methods and novel ***,we have reviewed advances in animal coronavirus antigenic epitope research,aiming to provide a reference for the prevention and control of animal and human coronaviruses.

关键词： Animal coronavirus Antigen epitope B-cell epitope T-cell epitope Immune responses Vaccines

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HiLPD-GEE:high spatial resolution land productivity dynamicscal culation tool using Landsat and MODIS data

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International Journal of Digital Earth 2023年第1期16卷 671-690页

作者： Tong Shen Xiaosong Li Yang Chen Yuran Cui Qi Lu Xiaoxia Jia Jin Chen Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijingPeople’s Republic of China International Research Center of Big Data for Sustainable Development Goals BeijingPeople’s Republic of China College of Resources and Environment University of Chinese Academy of SciencesBeijingPeople’s Republic of China State Key Laboratory of Earth Surface Processes and Resource Ecology Institute of Remote Sensing Science and EngineeringFaculty of Geographical ScienceBeijing Normal UniversityBeijingPeople’s Republic of China State Key Laboratory of Remote Sensing Science Faculty of Geographical ScienceBeijing Normal UniversityBeijingPeople’s Republic of China Institute of Desertification Studies Chinese Academy of ForestryBeijingPeople’s Republic of China g United Nations Convention to Combat Desertification BonnGermany

Land productivity is one of the sub-indicators for measuring SDG *** Productivity Dynamics(LPD)is the most popular approach for reporting this indicator at the global scale.A major limitation of existing products of LPD is the coarse spatial resolution caused by remote sensing data input,which cannot meet the requirement offine-scale land degradation *** resolve this problem,this study developed a tool(HiLPD-GEE)to calculate 30 m LPD by fusing Landsat and MODIS data based on Google Earth Engine(GEE).The tool generates high-quality fused Normalized Difference Vegetation Index(NDVI)dataset for LPD calculation through gapfilling and Savitzky–Golayfiltering(GF-SG)and then uses the method recommended by the European Commission Joint Research Centre(JRC)to calculate *** tool can calculate 30 m LPD in any spatial range within any time window after 2013,supporting global land degradation *** demonstrate the applicability of this tool,the LPD product was produced for African Great Green Wall(GGW)*** analysis proves that the 30 m LPD product generated by HiLPD-GEE could reflect the land productivity change effectively and reflect more spatial *** results also provide an important insight for the GGW initiative.

关键词： SDG 15.3.1 land productivity dynamics GF-SG Great Green Wall Google Earth Engine

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A multi-target UAV fault diagnosis method based on feature discriminant dimension reduction under small samples

A multi-target UAV fault diagnosis method based on feature d...

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Computer Technology and Information Science (ISCTIS), International Symposium on

作者： Xue An Yizong Zhang Shaobo Li Xuan Xiong School of Mechanical Engineering Guizhou University Guiyang China State Key Laboratory of Public Big Data Guizhou University Guiyang China College of Computer Science and Technology Guizhou University Guiyang China

ISBN: (数字)9798350354560

ISBN: (纸本)9798350354577

In recent years, deep learning has provided many efficient and accurate solutions to UAV fault diagnosis, but most of the current deep learning can only deal with a single task, whereas the UAV system is a complex coupled system, which needs to perform several different fault position diagnostics (FPD) and fault degree diagnostics (FDD) at the same time. Therefore, this paper proposed a multi-target UAV fault diagnosis method based on feature discriminant dimensionality reduction (MFD-FDDR) under small samples. Firstly, We connect specific features of different tasks with the same spatial definition through the channel dimension. Then, we perform feature discriminant dimensionality reduction (FDDR) for each task feature, and the output characteristics satisfy the input size of the following layer of LSTM. Finally, we validate the effectiveness of our proposed method with extensive experiments on real UAV flight data.

关键词： Dimensionality reduction Fault diagnosis Deep learning Information science Accuracy Autonomous aerial vehicles Long short term memory

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Global Feature Enhancing and Fusion Framework for Strain Gauge Status Recognition 25

Global Feature Enhancing and Fusion Framework for Strain Gau...

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Companion Proceedings of the ACM on Web Conference 2025

作者： Xu Zhang Peng Wang Chen Wang Zhe Xu Xiaohua Nie Wei Wang Shanghai Key Laboratory of Data Science School of Computer Science and Technology Fudan University Shang Hai China Shanghai Key Laboratory of Data Science School of Computer Science and Technology Fudan University Shanghai China National Engineering Research Center for Big Data Software Tsinghua University Beijing China School of Software Tsinghua University Beijing China National Key Laboratory of Strength and Structural Integrity Aircraft Strength Research Institute of China Xi'an Shaanxi China

ISBN: (纸本)9798400713316

Strain Gauge Status (SGS) recognition is crucial in the field of intelligent manufacturing based on the Internet of Things, as accurate identification helps timely detection of failed mechanical components, avoiding accidents. The loading and unloading sequences generated by strain gauges can be identified through time series classification (TSC) algorithms. Recently, deep learning models, e.g., convolutional neural networks (CNNs) have shown remarkable success in the TSC task, as they can extract discriminative local features from the subsequences to identify the time series. However, we observe only the local features may not be sufficient for expressing the time series, especially when the local sub-sequences between different time series are very similar, e.g., SGS data of aircraft wings in static strength experiments. Nevertheless, CNNs suffer from the limitation in extracting global features due to the nature of convolution operations. For extracting global features to more comprehensively represent the SGS time series, we propose two insights: (i) Constructing global features through feature engineering. (ii) Learning high-order relationships between local features to capture global features. To realize and utilize them, we propose a hypergraph-based global feature learning and fusion framework, which learns and fuses global features for semantic consistency to enhance the representation of SGS time series, thereby improving recognition accuracy. Our method designs are validated on industrial SGS and public UCR datasets, showing better generalization for unseen data in SGS recognition.

关键词： deep learning models

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An Intelligent Design Method Based on Case-based Reasoning and Reinforcement Learning

An Intelligent Design Method Based on Case-based Reasoning a...

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IEEE International Conference on Industrial engineering and engineering Management

作者： Yu Huang Ru Wang Zhuqin Wei Guoxin Wang Key Laboratory of Industry Knowledge & Data Fusion Technology and Application Ministry of Industry and Information Technology School of Machanical Engineering Beijing Institute of Technology Beijing China

Enterprises currently face the challenge of reducing production cycles and costs and utilizing existing cases for making changes and iterations has emerged as a viable solution. However, the acquisition and modification of historical cases present their challenges. To address this, the present paper proposes an intelligent design method based on reinforcement learning that aims to meet the demand for efficient and high-quality design solutions in the field of engineering design. This method comprises four key steps: case characterization, matching, retrieval, and selection. By employing case characterization and matching, users can acquire sets of similar cases that align closely with their specific requirements. Building upon this foundation incorporates a combination of reinforcement learning and weight order cross-reconstruction to generate more proposals. Subsequently, the multi-attribute decision-making method is utilized to select the extended set of design schemes. The effectiveness of the proposed method is demonstrated through its successful application to a radar design case.

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MalWuKong: Towards Fast, Accurate, and Multilingual Detection of Malicious Code Poisoning in OSS Supply Chains

MalWuKong: Towards Fast, Accurate, and Multilingual Detectio...

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IEEE International Conference on Automated Software engineering (ASE)

作者： Ningke Li Shenao Wang Mingxi Feng Kailong Wang Meizhen Wang Haoyu Wang 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 Wuhan China

In the face of increased threats within software registries and management systems, we address the critical need for effective malicious code detection. In this paper, we propose an innovative approach that integrates source code slicing, inter-procedural analysis, and cross-file inter-procedural analysis, thereby enhancing the detection precision and reducing false positives. This approach has been encapsulated within a multi-analysis-based framework for automatic detection of malicious code in real-world software packages. In its application to major third-party software registries like PyPI and NPM, our framework has proven effective, identifying 130 malicious packages from a total of 169,640 monitored over a continuous period of five weeks. This work advances the current state-of-the-art solution to malicious code detection, demonstrating significant practical impact in strengthening the software supply chain defense.

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Image Denoising Method Based on Channel Compression and Split-Attention Mechanism

Image Denoising Method Based on Channel Compression and Spli...

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Computer engineering and Intelligent Control (ICCEIC) International Conference on

作者： Hao Sun Xiaoyan Qiao Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion School of Computer Science and Technology Shandong Technology and Business University Technology and Evaluation Shandong Engineering Research Center Yantai Shandong

ISBN: (数字)9798331507992

ISBN: (纸本)9798331508005

In the image restoration task, how to make full use of spatial and channel feature information to improve the reconstruction quality of the model without significantly increasing the computational complexity is an important research issue. To address this challenge, this paper proposes a CS module (Channel Compression and Split-Attention) specifically for optimizing image denoising tasks. The module first compresses the features through a preliminary convolution operation, then introduces the split attention convolution mechanism to split the features by channel and dynamically weight them, thereby enhancing the model's adaptive attention to different feature channels and improving feature expression capabilities. Through experiments on multiple image denoising datasets, the results show that the CS module significantly improves the image reconstruction quality, especially in terms of peak signal-to-noise ratio (PSNR). At the same time, the introduction of the CS module does not lead to a significant increase in computational complexity, maintaining a high computational efficiency, which verifies the practicality and superiority of the module in image denoising tasks.

关键词： Image coding PSNR Convolution Computational modeling Refining Image restoration Computational efficiency Computational complexity Image reconstruction Image denoising

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BadToken: Token-level Backdoor Attacks to Multi-modal Large Language Models

arXiv

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arXiv 2025年

作者： Yuan, Zenghui Shi, Jiawen Zhou, Pan Gong, Neil Zhenqiang Sun, Lichao 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 China Duke University United States Lehigh University United States

Multi-modal large language models (MLLMs) extend large language models (LLMs) to process multi-modal information, enabling them to generate responses to image-text inputs. MLLMs have been incorporated into diverse multimodal applications, such as autonomous driving and medical diagnosis, via plug-and-play without fine-tuning. This deployment paradigm increases the vulnerability of MLLMs to backdoor attacks. However, existing backdoor attacks against MLLMs achieve limited effectiveness and stealthiness. In this work, we propose BadToken, the first token-level backdoor attack to MLLMs. BadToken introduces two novel backdoor behaviors: Token-substitution and Token-addition, which enable flexible and stealthy attacks by making token-level modifications to the original output for backdoored inputs. We formulate a general optimization problem that considers the two backdoor behaviors to maximize the attack effectiveness. We evaluate BadToken on two open-source MLLMs and various tasks. Our results show that our attack maintains the model’s utility while achieving high attack success rates and stealthiness. We also show the real-world threats of BadToken in two scenarios, i.e., autonomous driving and medical diagnosis. Furthermore, we consider defenses including fine-tuning and input purification. Our results highlight the threat of our attack. Copyright © 2025, The Authors. All rights reserved.

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VERO: Verification and Zero-Shot Feedback Acquisition for Few-Shot Multimodal Aspect-Level Sentiment Classification 39

VERO: Verification and Zero-Shot Feedback Acquisition for Fe...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Sun, Kai Wu, Hao Shi, Bin Mensah, Samuel Liu, Peng Dong, Bo Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University China School of Computer Science and Technology Xi’an Jiaotong University China School of computer science and engineering Guangxi Normal University Guilin China Department of Computer Science University of Sheffield Sheffield United Kingdom School of Continuing Education Xi’an Jiaotong University China

ISBN: (纸本)157735897X

Deep learning approaches for multimodal aspect-level sentiment classification (MALSC) often require extensive data, which is costly and time-consuming to obtain. To mitigate this, current methods typically fine-tune small-scale pretrained models like BERT and BART with few-shot examples. While these models have shown success, Large Vision-Language Models (LVLMs) offer significant advantages due to their greater capacity and ability to understand nuanced language in both zero-shot and few-shot settings. However, there is limited work on fine-tuning LVLMs for MALSC. A major challenge lies in selecting few-shot examples that effectively capture the underlying patterns in data for these LVLMs. To bridge this research gap, we propose an acquisition function designed to select challenging samples for the few-shot learning of LVLMs for MALSC. We compare our approach, Verification and ZERO-shot feedback acquisition (VERO), with diverse acquisition functions for few-shot learning in MALSC. Our experiments show that VERO outperforms prior methods, achieving an F1 score improvement of up to 6.07% on MALSC benchmark datasets. © 2025, Association for the Advancement of Artificial.

关键词： Zero-shot learning

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Research on Penetration Strategy of UAV

Research on Penetration Strategy of UAV

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2020 International Conference on Cyberspace Innovation of Advanced Technologies, CIAT 2020

作者： Han, Daojun Xiang, Hongzhen Chen, Haolin Xie, Yi Institute of Data and Knowledge Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

ISBN: (纸本)9781450387828

Taking a military operation as an example, this paper makes an in-depth study of what tactics a single UAV should adopt when facing the interception of multiple enemy UAV groups. According to the analysis of the impact diagram, the aerial range of the UAV standoff defines a threat area, that is, when the UAV arrives at the threat area, it needs to adopt a strategy for penetration, or it will be intercepted. The strategy adopted by UAV in the threat area is analyzed, and the decision control method is made. © 2020 ACM.

关键词： Unmanned aerial vehicles (UAV)

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