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2nd International Conference on Artificial Intelligence, Systems and Network Security, AISNS 2024

作者： Li, Haili Wang, Xiaodong Zhou, Yunyan Liu, Weijie Pan, Shilong College of Computer Science and Technology National University of Defense Technology Hunan Changsha China Unit 63893 of PLA Henan Luoyang China Unit 63891 of PLA Henan Luoyang China

ISBN: (纸本)9798400711237

Event extraction is a fundamental and complex task in information extraction, aiming at automatically identifying and extracting structured event information from unstructured text. However, the same word may have different meanings in different contexts, necessitating semantic disambiguation during the classification process. The polysemy of triggers presents a significant challenge to accurate EE. Existing surveys primarily focus on different domains, fields, or technical paradigms, but none provide a systematic summary of the semantic disambiguation techniques employed. This study classifies these techniques into three categories based on the underlying technical frameworks and offers a comprehensive overview of the most advanced methods in each category. Finally, we summarize the application scenarios, strengths, and limitations of existing approaches, and discuss potential directions for future research. © 2024 Copyright held by the owner/author(s).

关键词： Deep neural networks

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EBCFL: An Efficient BlockChain-based Federated Learning Framework for Internet of Vehicles

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IEEE Network 2025年

作者： Cao, Dun Liao, Yonghui Alqahtani, Fayez Wang, Jin School of Computer and Communication Engineering Changsha University of Science and Technology China Changsha University of Science and Technology China Software Engineering Department College of Computer and Information Sciences King Saud University Saudi Arabia Hunan University of Science and Technology China

Federated Learning (FL) can train a shared model while protecting vehicle data privacy, which has significant advantages in improving driving safety and efficiency. However, there are still many challenges in applying FL to Internet of Vehicles (IoV). First, the varying computing capabilities of moving vehicles result in slow convergence speed due to underperforming vehicles. Second, malicious vehicles may tamper with model parameters before uploading them to the central server, which is itself vulnerable to single-point failure. To solve the above challenges, we design an efficient and decentralized blockchain FL framework (Efficient BlockChain-based Federated Learning Framework, EBCFL). This framework effectively prevents possible malicious behaviors in the vehicular network nodes and improves the efficiency of FL through a well-designed security verification mechanism. The experimental results show that EBCFL outperforms other schemes in terms of average test accuracy and average total time cost, indicating that EBCFL significantly improves the learning efficiency while bearing an acceptable additional time cost for blockchain communication, demonstrating its strong ability to guard against malicious behaviours and ensure robustness. © 1986-2012 IEEE.

关键词： Blockchain Federated learning IoV Security verification

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Modeling Multilevel Business Process Monitoring via BPMN Extension

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Concurrency and Computation: Practice and Experience 2025年第12-14期37卷

作者： Kang, Guosheng Wang, Zhen Cheng, Hangyu Liu, Jianxun Wen, Yiping Peng, Jun School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology Hunan University of Science and Technology Xiangtan China School of Computer Science and Technology Xi'an Jiaotong University Xi'an China School of Computer Science Fudan University Shanghai China

Business process monitoring involves real-time supervision of a series of activities carried out by an organization to achieve specific objectives. Process event monitoring points (PEMP) are used to pinpoint specific locations within a process model where expected events are anticipated to occur. However, in general business process modeling languages, such as business process model and notation (BPMN), there is a lack of explicit modeling for PEMPs. This article proposes a method for modeling pairwise event monitoring points in business process models to track the execution status of specific activities or process segments via BPMN extension. Specifically, process monitoring points are designed by expanding the modeling element of sequence flow. Moreover, the business process model is decomposed using the refined process structure tree (RPST) to verify the soundness of the designed pairwise monitoring points. The proposed method allows flexible monitoring at the process segment level. And the process monitoring data could be used for a clear understanding of business progress, especially useful in heterogeneous business process model to support decision-making. Through case study from real-world business processes, the effectiveness and usefulness of the proposed process monitoring modeling method is validated. © 2025 John Wiley & Sons Ltd.

关键词： Electronic trading

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Discrete-time Physics-Informed Neural Networks for Two-Phase Flow Interface Capturing 24

Discrete-time Physics-Informed Neural Networks for Two-Phase...

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8th International Conference on computer science and Application engineering, CSAE 2024

作者： Zhang, Sen Zhong, Yanxu Yang, Xi Wang, Wei Zhang, Zhuo Guo, Xiao-Wei Yang, Canqun College of Computer Science and Technology National University of Defense Technology Hunan Changsha China National SuperComputer Center in Tianjin Tianjin China National SuperComputer Center in Tianjin Tianjin China

ISBN: (纸本)9798400718090

In two-phase flow, accurately capturing sharp interfaces is essential. Physics-Informed Neural Networks (PINNs) provide a new way to capture interfaces. This paper introduces a new framework that uses Discrete-time Physics-Informed Neural Networks (DtPINNs) to solve the volume of fluid (VOF) advection equation, offering a new approach to interface capturing in two-phase flows. With this framework, we propose an adaptive collocation-point refinement algorithm, which improves the precision of capturing sharp interfaces, reducing errors and diffusion. Experiments, including cases with translation and deformation, show that the DtPINNs method maintains sharp interfaces, outperforming traditional numerical methods. In a 2D single-vortex deformation case, DtPINNs achieved a relative L2 error of 0.0760, much lower than the 5th-order WENO-JS scheme (0.5534) and the 1st-order Upwind scheme (0.8879). This shows that DtPINNs are better at capturing complex interface shapes while keeping accuracy and minimizing numerical diffusion. © 2024 Copyright held by the owner/author(s).

关键词： Two phase flow

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Construction of Digital Anatomy Experiment Teaching System Based on VR technology

Construction of Digital Anatomy Experiment Teaching System B...

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2024 International Conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Yu, Penghui Li, Qiao Cai, Shichang Pan, Meisen College of Basic Medicine Hunan University of Medicine Huaihua418000 China College of Computer and Electrical Engineering Hunan University of Arts and Science Hunan Changde415000 China Huaihua Key Laboratory of Digital Anatomy 3D Printing for Clinical Translational Research Hunan Huaihua418000 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Currently, VR technology is constantly developing and widely applied in the field of medical education. Many experiments in medical courses have evolved from simple animated demonstrations to virtual simulation experiments. The current lack of intuitive clarity and ease of operation in teaching human anatomy experiments is a common problem. The 3D anatomical structure teaching model has the advantages of three-dimensional and highly simulated teaching, which can effectively reproduce the three-dimensional structure and three-dimensional entity information of tissues and organs. This article introduces the basic principles of VR technology and its application in anatomy experimental teaching. By comparing the traditional teaching methods and VR integrated teaching methods of 60 students in the anatomy course, the advantages of applying VR anatomical structure teaching mode to human anatomy experimental teaching were analyzed. As a result, the combination of VR anatomical structure teaching and traditional experimental entity specimen teaching solved many difficulties in the teaching process of this specialized course, and gave full play to their respective advantages, effectively overcame the shortcomings of traditional experimental teaching and VR anatomical structure teaching model, and greatly mobilized students' subjective initiative. Make it easier for students to master the complex knowledge of human anatomy. © 2025 SPIE.

关键词： Digital elevation model

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Periodic-Enhanced Informer Model for Short-Term Wind Power Forecasting Using SCADA Data

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IEEE Transactions on Sustainable Energy 2025年

作者： Liu, Zhao-Hua Li, Long-Wei Wei, Hua-Liang Li, Ming Lv, Ming-Yang Zhang, Ying-Jie Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Supervisory Control and Data Acquisition (SCADA) systems can collect abundant information about wind farm operation and environment. To better make use of SCADA data, a periodic-enhanced informer model for short-term wind power forecasting using scada data is proposed. Firstly, to effectively filter out noise in SCADA data, a v-p curve-based method is adopted by incorporating a quartile approach to remove sparse outliers;a density-based spatial clustering of applications with noise (DBSCAN) algorithm is then employed to eliminate stacked outliers from the power curve. Secondly, a multi-feature input set selection method based on Maximization Information Coefficient is introduced to make better use of the SCADA system data by reducing the number of features. Thirdly, a Temporal Convolutional Network (TCN) is designed to extract the scalar projection of the input set, followed by fusing the local time stamp and global time stamp to build the periodic information enhanced prediction model embedding layer. Subsequently, the enhanced input set is fed into an informer model to predict future wind power. Finally, considering the multiple temporal scales structure characteristics present in the dataset, a multi-scale deep fusion module is established in the informer model to deeply integrate the features of different scales. It can simultaneously avoid the resource waste and overfitting problems caused by increasing the network depth. The experimental results, which are obtained from several deep learning methods on real SCADA data, demonstrate that the suggested approach has good predictive capability. © 2010-2012 IEEE.

关键词： Wind forecasting

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“A Task Offloading and Edge Computing Framework for Enhanced Energy Efficiency and Reduced Latency in IoT Image Recognition Applications”

“A Task Offloading and Edge Computing Framework for Enhance...

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Innovative Research in Applied science, engineering and technology (IRASET),Conference on

作者： Juan Luo Baabou Mohammed College of Computer Science and Technology Hunan University Changsha China

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

The rapid growth of the Internet of Things (IoT) has revolutionized many areas, but it also presents new challenges, particularly in energy efficiency. IoT devices are often small, with limited battery life, making energy-saving solutions crucial. Edge computing, along with task offloading, has become a key strategy to improve energy efficiency while maintaining good performance. This paper explores some offloading methods, like edge computing (EC), and cloud computing, to reduce energy use while addressing latency and network traffic issues. We present a mathematical model that evaluates the balance between energy, speed, and bandwidth, supported by extensive simulations. The results show that edge computing offloading can significantly reduce energy consumption and lower latency in the IoT ecosystems.

关键词： Cloud computing Energy consumption Computational modeling Ecosystems Telecommunication traffic Energy efficiency Mathematical models Internet of Things Time factors Edge computing

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Robust Znncn Protective Layer with High Zincphilicity and Ionic Conductivity for Ultra-Stable Zinc Metal Anodes

SSRN

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SSRN 2025年

作者： Dong, Yaoyong Liu, Fangzhong Song, Ting Pei, Yong Wang, Xianyou Wu, Xiongwei Chen, Lijuan Long, Bei University of Macau China School of Electromechanical Engineering Guangdong University of Technology Guangzhou510003 China School of Chemistry Xiangtan University Xiangtan411105 China School of Chemistry and Materials Science Hunan Agricultural University Changsha410128 China College of Intelligent Science and Engineering Hunan Institute of Engineering Hunan Xiangtan411104 China

Artificial solid electrolyte interphase (SEI) layers effectively address Zn dendrite formation and side reactions in zinc-ion batteries (ZIBs). However, the low ionic conductivities in conventional SEI layers often result in increased battery polarization voltages and reduced electrochemical performances in ZIBs. Herein, we report a highly zincophilic ZnNCN protective layer with superior ionic conductivity (2.50 mS cm−1) that enables a highly stable Zn anode with enhanced reaction kinetics. Compared to common ZnF2 (0.90 mS cm−1) and Zn3(PO4)2 (1.71 mS cm−1) protective layers, the ZnNCN layer also exhibits lower Zn2+ diffusion barriers (0.46 eV) and activation energy (17.28 kJ mol−1), which improves Zn2+ deposition kinetics. Additionally, theoretical calculations reveal the strong binding energy between ZnNCN and hydrated Zn2+, which facilitates the Zn2+ desolvation process. Consequently, ZnNCN@Zn symmetric cells achieve outstanding cycling stability of 2300 h at 0.5 mA cm−2 and 0.25 mA h cm−2. Moreover, ZnNCN@Zn||MnOOH full batteries deliver an initial discharge capacity of 125 mA h g−1 at 0.5 A g−1 with nearly 100% capacity retention after 500 cycles. This work provides valuable insights into developing high-stability ZIBs through high ionic conductivity artificial SEI layers. © 2025, The Authors. All rights reserved.

关键词： Activation energy

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Joint Optimization of Offloading and Caching in Full-Duplex-Enabled Edge Computing Networks

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IEEE Transactions on Mobile Computing 2025年

作者： Dai, Xingxia Tian, Shujuan Liu, Haolin Li, Zhetao Jiang, Hongbo Deng, Qingyong Xiangtan University School of Computer Science School of Cyberspace Security Xiangtan411105 China Jinan University College of Information Science and Technology Guangzhou510632 China Hunan University College of Computer Science and Electronic Engineering Changsha Hunan410082 China Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin541004 China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin541004 China

Edge computing (EC) reduces task processing and content download delay by providing computation and caching resources directly to task offloading (TO) users and content request (CR) users. However, existing studies often focus exclusively on either TO users or CR users within EC networks, neglecting the interaction between these two groups. To address this gap, we investigate the offloading and caching decision-making in scenarios where TO and CR users coexist. Furthermore, we employ full-duplex (FD) technology to enhance spectral utilization for edge-end transmissions. Specifically, we jointly optimize offloading and caching in FD-enabled EC networks. To accomplish this, we decompose the formulated optimization problem into three sub-problems using the alternating optimization (AO) method. We then propose a three-subproblem alternating iterative delay minimization algorithm to effectively tackle the challenges of offloading and caching. Additionally, we analyze the convergence and complexity of our proposed algorithm. Finally, we conduct extensive simulations to evaluate the effectiveness of our approach. The simulation results demonstrate that the delay reduction achieved by our algorithm is between 24.78% and 89.23% greater than that of comparative algorithms. © 2002-2012 IEEE.

关键词： Computation offloading

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A-Site Deficiency-Mediated Creation of Oxygen Vacancies in LaMnO3-δ Nanofibers for Efficient Nitrate Reduction

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ACS Catalysis 2025年第10期15卷 8094-8102页

作者： Lv, Yaxin Ren, Jing Jiang, Min Wang, Ran Guo, Haoran Sun, Xuping Guo, Xiaodong Li, Tingshuai School of Materials and Energy University of Electronic Science and Technology of China Sichuan Chengdu611731 China Department of Chemistry Key Laboratory of Environmentally Friendly Chemistry and Applications Ministry of Education Xiangtan University Hunan Xiangtan411105 China Center for High Altitude Medicine West China Hospital of Sichuan University Sichuan Chengdu610041 China College of Chemical Engineering Sichuan University Sichuan Chengdu610065 China

Electrocatalysis of nitrate to ammonia is retarded by a large dissociation energy and sluggish kinetics, which requires high-efficiency catalysts to reduce the reaction barrier. Herein, creation of oxygen vacancies (Ovs) in LaMnO3−δ (LMO) nanofibers with A-site La deficiency is gradiently mediated through copper doping and hydrogen etching. Both the Faradaic efficiency (FE) and ammonia yield of the etched La0.9Mn0.5Cu0.5O3−δ (R-LMCO) with the most Ovs are significantly increased to 90.1% and 28.1 mg h-1 mgcat.-1. In situ electrochemical Raman spectroscopy verifies the reduction of nitrate, and differential electrochemical mass spectrometry (DEMS) reveals the processes of deoxygenation and hydrogenation at the surface of the catalyst. Density DFT calculations confirm a negative nitrate adsorption free energy of −0.14 eV at R-LMCO, and the d-band centers for La and Cu reach −1.97 and −2.66 eV, respectively, suggesting a strong interaction between intermediates and active sites. This study presents a potential strategy for regulating the Ov concentration to enhance the catalytic activity of nitrate reduction. © 2025 American Chemical Society.

关键词： Electrolytic reduction

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