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Extinction Chains Reveal Intermediate Phases Between the Safety and Collapse in Mutualistic Ecosystems

Engineering 2024年第12期43卷 89-98页

作者： Guangwei Wang Xueming Liu Ying Xiao Ye Yuan Linqiang Pan Xiaohong Guan Jianxi Gao Hai-Tao Zhang The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Digital Manufacturing Equipment and TechnologySchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Guangdong HUST Industrial Technology Research Institute Huazhong University of Science and TechnologyDongguan 523808China Guangdong Provincial Engineering Technology Center of Autonomous Unmanned Vessels Dongguan 523808China MOE Key Laboratory of Intelligent Networks and Network Security&State Key Laboratory of Manufacturing Systems Xi’an Jiaotong UniversityXi’an 710049China Tsinghua National Laboratory of Information Science and Technology Department of AutomationTsinghua UniversityBeijing 100084China Department of Computer Science Rensselaer Polytechnic InstituteTroyNY 12180USA

Ecosystems are undergoing unprecedented persistent deterioration due to unsustainable anthropogenic human activities,such as overfishing and deforestation,and the effects of such damage on ecological stability are *** recent advances in experimental and theoretical studies on regime shifts and tipping points,theoretical tools for understanding the extinction chain,which is the sequence of species extinctions resulting from overexploitation,are still lacking,especially for large-scale nonlinear networked *** this study,we developed a mathematical tool to predict regime shifts and extinction chains in ecosystems under multiple exploitation situations and verified it in 26 real-world mutualistic networks of various sizes and *** discovered five phases during the exploitation process:safe,partial extinction,bistable,tristable,and collapse,which enabled the optimal design of restoration strategies for degraded or collapsed *** validated our approach using a 20-year dataset from an eelgrass restoration ***,we also found a specific region in the diagram spanning exploitation rates and competition intensities,where exploiting more species helps increase *** computational tool provides insights into harvesting,fishing,exploitation,or deforestation plans while conserving or restoring the biodiversity of mutualistic ecosystems.

关键词： Complex system network science Overexploitation Regime shift Metastability

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Android Secure Cloud Storage System Based on SM Algorithms 10th

Android Secure Cloud Storage System Based on SM Algorithms

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10th International Conference on computer Engineering and networks, CENet 2020

作者： Wang, Zhiqiang Yu, Kunpeng Wang, Wenbin Yu, Xinyue Kang, Haoyue Lv, Xin Li, Yang Yang, Tao State Information Center Beijing China Beijing Electronic Science and Technology Institute Beijing China Key Lab of Information Network Security Ministry of Public Security Shanghai China

ISBN: (纸本)9789811584619

With the rapid development of mobile Internet, the methods of data storage have been greatly enriched. However, many problems exist in mobile internets' current information storage, such as plain-text storage, weak and nondomestic encryption algorithms. This paper designs a secure cloud storage platform with SM algorithms based on the Android platform. The platform uses a combination of encryption and authentication methods that use multiple SM algorithms (SM2, SM3, SM4), which realizes hybrid encryption and authentication of information storage. Finally, lots of experiments are done, and the results show the security and reliability of our platform, the time about regular operations of our platform is in the range of acceptance given performance and security. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cloud storage

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Joint Optimization of UAV Deployment and Task Computation Offloading Decision in UAV-assisted Edge Computing network

Joint Optimization of UAV Deployment and Task Computation Of...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Yichuan Liu Jinbin Tu Yun Wang Key Lab of Computer Network and Information Integration MOE School of Computer Science and Engineering Southeast University Nanjing China

The UAVs' deployment decision and task computation offloading decision in the UAV-assisted edge computing network significantly impact the operating efficiency of edge network. On the basis of this, the Optimization Model for UAV Cluster Deployment and Computation Offloading Decision (OMUCDCOD) is established. The model jointly optimizes the number, location of UAV s, and task computation offloading decision. Different from previous studies, this model regards the terminal devices in the edge network as virtual MEC servers, and introduces the collaborative computing mode. The task computation offloading decision made can effectively utilize the computing capabilities offered by the edge network. Considering that the two problems of UAV deployment decision and task computation offloading decision are intricately interconnected, we propose a two-layer optimization algorithm combining K-Means and ant colony algorithm (ToKmAc) to solve OMUCDCOD. ToKmAc is divided into upper and lower layers to solve this optimization problem. The upper layer uses K-Means to solve the UAV deployment decision, that is, the number and location of UAVS; the lower layer employs ant colony algorithm to solve computation offloading decision. Finally, extensive experiments verify the effectiveness of OMUCDCOD and ToKmAc.

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T-Sorokin: A General Mobility Model in Opportunistic networks

T-Sorokin: A General Mobility Model in Opportunistic Network...

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International Conference on computer Supported Cooperative Work in Design

作者： Jinbin Tu Qing Li Yun Wang School of Computer Science and Engineering Southeast University Key Lab of Computer Network and Information Integration MOE Nanjing China

The opportunistic networks are a kind of ad hoc networks that rely on the chance of nodes meeting to transmit messages. Acting as an effective supplement to 4G and 5G networks in some special scenarios where hardware devices are limited, the opportunistic networks have a significant application in health monitoring, warning broadcasting, disaster relief, and so on. The mobility model is one of the research focuses on the opportunistic networks. On the basis of the social mobility theory proposed by Sorokin, a general mobility model, which is suited for various scenarios, called T-Sorokin is proposed. This model is described as a seven-tuple and implemented on the Opportunistic network Environment simulator and fits both Infocom06 and Rome taxi data set, which includes different areas ranging from hotel to city and different mobile units ranging from person to taxi. The results of experiments demonstrate that the T-Sorokin model has the advantage of generality, simplicity, and accuracy. It can simply establish movement tracks close to real data under different scenarios.

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LAGNet: A Hybrid Deep Learning Model for Automatic Modulation Recognition

LAGNet: A Hybrid Deep Learning Model for Automatic Modulatio...

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IEEE Conference on Wireless Communications and networking

作者： Zhuo Li Guangyue Lu Yuxin Li Hao Zhou Huan Li Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications Xi'an China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Automatic Modulation Recognition (AMR) is becoming increasingly crucial in the industrial internet, and it ensures more reliable communication for devices within this vast and intricate network. Although recent application of neural networks, notably Graph Convolutional network (GCN) in the AMR domain shows promising results, it fails to account for temporal features within modulation signals, resulting in a loss of recognition precision. Given that modulation signals possess both time and space features, this paper proposes a hybrid deep learning model named LAGNet, which combines Long Short-Term Memory (LSTM) and GCN. The model uses an attention mechanism to map the LSTM outputs into a graph and then employs the GCN to extract the spatial features of the signal. Subsequently, the temporal features and the spatial features are combined together for modulation signal classification. Experimental results show that LAGNet not only outperforms several advanced models in classification accuracy across all signal-to-noise ratios, but also requires fewer learnable parameters.

关键词： Deep learning Accuracy Neural networks Modulation Pattern classification Feature extraction Reliability

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A DoS-attack Resilient Control Approach for AIOT-enabled Systems

A DoS-attack Resilient Control Approach for AIOT-enabled Sys...

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2023 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2023

作者： Cao, Xiaoya Chen, Zhenya Liu, Weisheng Liu, Yuanlong Wang, Wenting Wang, Xin Yang, Ming Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China State Grid Shandong Electric Power Company Jinan China State Grid Shandong Electric Power Research Institute Jinan China

ISBN: (纸本)9798350312270

This paper presents a novel controller design for dealing with the challenges posed by Denial-of-Service (DoS) attacks in the context of Artificial Intelligence of Things (AIOT). The proposed design employs a prediction approach that utilizes uniform quantization for both encoder and decoder, aiming to effectively mitigate data loss during data transmission in scenarios involving DoS attacks. Furthermore, the study investigates the application of dynamic system design as a means to enhance the security defense mechanisms of AIOT, consequently bolstering the overall resilience of the system. The effectiveness and practicality of the proposed approach are validated through comprehensive simulations, providing empirical evidence of its efficacy. © 2023 IEEE.

关键词： System stability

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FDSC-YOLOv8:Advancements in Automated Crack Identification for Enhanced Safety in Underground Engineering

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computer Modeling in Engineering & Sciences 2024年第9期140卷 3035-3049页

作者： Rui Wang Zhihui Liu Hongdi Liu Baozhong Su Chuanyi Ma Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan250353China School of Qilu Transportation Shandong UniversityJinan250100China Geotechnical and Structural Engineering Center Shandong UniversityJinan250061China Shandong Lairong High-Speed Railway Co. Ltd.Weihai264200China Shandong High-Speed Group Co. Ltd.Jinan250014China

In underground engineering,the detection of structural cracks on tunnel surfaces stands as a pivotal task in ensuring the health and reliability of tunnel ***,the dim and dusty environment inherent to under-ground engineering poses considerable challenges to crack *** paper proposes a crack segmentation algorithm termed as Focused Detection for Subsurface Cracks YOLOv8(FDSC-YOLOv8)specifically designed for underground engineering structural ***,to improve the extraction of multi-layer convolutional features,the fixed convolutional module is replaced with a deformable convolutional ***,the model’s receptive field is enhanced by introducing a multi-branch convolutional module,improving the extraction of shallow features for small ***,the Dynamic Snake Convolution module is incorporated to enhance the extraction capability for slender and weak ***,the Convolutional Block Attention Module(CBAM)module is employed to achieve better target *** FDSC-YOLOv8s algorithm’s mAP50 and mAP50-95 reach 96.5%and 66.4%,according to the testing data.

关键词： Crack segmentation improved YOLOv8 engineering applications feature extraction

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Attribute-Consistent Knowledge Graph Representation Learning for Multi-Modal Entity Alignment 23

Attribute-Consistent Knowledge Graph Representation Learning...

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2023 World Wide Web Conference, WWW 2023

作者： Li, Qian Guo, Shu Luo, Yangyifei Ji, Cheng Wang, Lihong Sheng, Jiawei Li, Jianxin School of Computer Science and Engineering Bdbc Beihang University Beijing China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing China Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security Ucas Beijing China Scse Beihang University Zhongguancun Lab Beijing China

ISBN: (纸本)9781450394161

The multi-modal entity alignment (MMEA) aims to find all equivalent entity pairs between multi-modal knowledge graphs (MMKGs). Rich attributes and neighboring entities are valuable for the alignment task, but existing works ignore contextual gap problems that the aligned entities have different numbers of attributes on specific modality when learning entity representations. In this paper, we propose a novel attribute-consistent knowledge graph representation learning framework for MMEA (ACK-MMEA) to compensate the contextual gaps through incorporating consistent alignment knowledge. Attribute-consistent KGs (ACKGs) are first constructed via multi-modal attribute uniformization with merge and generate operators so that each entity has one and only one uniform feature in each modality. The ACKGs are then fed into a relation-aware graph neural network with random dropouts, to obtain aggregated relation representations and robust entity representations. In order to evaluate the ACK-MMEA facilitated for entity alignment, we specially design a joint alignment loss for both entity and attribute evaluation. Extensive experiments conducted on two benchmark datasets show that our approach achieves excellent performance compared to its competitors. © 2023 ACM.

关键词： Knowledge graph

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A Web Attack Detection Method Based on DistilBERT and Feature Fusion for Power Micro-Application Server 2

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

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2nd International Conference on Advanced Electronics, Electrical and Green Energy, AEEGE 2023

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

ISBN: (纸本)9798350322866

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. © 2023 IEEE.

关键词： Cybersecurity

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Using Problem Frames Approach for key information Extraction from Natural Language Requirements 23

Using Problem Frames Approach for Key Information Extraction...

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23rd IEEE International Conference on Software Quality, Reliability, and security Companion, QRS-C 2023

作者： Tang, Shangzhi Chen, Xuan Xiao, Hongbin Wei, Jiahao Li, Zhi School of Computer Science and Engineering Guangxi Normal University Guilin China Guangxi Normal University Key Lab of Education Blockchain Intelligent Technology Ministry of Education Guilin China Guangxi Normal University Guangxi Key Laboratory of Multi-source Information Mining and Security Guilin China

ISBN: (纸本)9798350359398

The correct capturing and understanding of requirements play a crucial role in the software development process. However, the ambiguity in requirements described using natural language (NL) poses a challenge. Existing methods often require the use of specific requirements templates to rewrite system requirements, which increases efforts in analyzing requirements. Additionally, frequent requirement changes during the development process can lead to extra costs. In this paper, we propose a visualization method that allows user to extract key information from NL requirements, aiming to mitigate the difficulty and efforts in analyzing NL requirements. We also argue that more attention should be paid to the problems that need to be solved in the development process, especially the security problems. Application of our method to practical requirements sentences demonstrates its effectiveness in extracting the problem concerns from the requirements, leading to the stepwise derivation of solutions. We envision that our approach may have the potential to support iterative updates and analysis of NL requirements automatically. © 2023 IEEE.

关键词： Requirements engineering

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