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A Survey of the Application of Neural Networks to Event Extraction

Tsinghua science and technology 2025年第2期30卷 748-768页

作者： Jianye Xie Yulan Zhang Huaizhen Kou Xiaoran Zhao Zhikang Feng Lekang Song Weiyi Zhong College of Computer Science and Technology China University of Petroleum(East China)Qingdao 266000China Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyWeifang 261000China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210000China School of Computer Science Qufu Normal UniversityRizhao 276800China

Event extraction is an important part of natural language information extraction,and it’s widely employed in other natural language processing tasks including question answering and machine reading ***,there is a lack of recent comprehensive survey papers on event *** the past few years,numerous high-quality and innovative event extraction methods have been proposed,making it necessary to consolidate these new developments with previous work in order to provide a clear overview for researchers and serve as a reference for future *** addition,event detection is a fundamental sub-task in event extraction,previous survey papers have often overlooked the related work on event ***,this paper aims to bridge these gaps by presenting a comprehensive survey of event extraction,including recent advancements and an analysis of previous research on event *** resources for event extraction are first introduced in this research,and then the numerous neural network models currently employed in event extraction tasks are divided into four types:word sequence-based methods,graph-based neural network methods,external knowledge-based approaches,and prompt-based *** compare and contrast them in depth,pointing out the flaws and difficulties with existing ***,we discuss the future of event extraction development.

关键词： event extraction natural language processing event extraction methods graph neural network prompt-based learning

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An Encoding-Based Privacy-Preserving Security Control Strategy Under Stealthy Attacks

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 208-217页

作者： Liu, Jian Wang, Shuailong Zha, Lijuan Liu, Jinliang Tian, Engang Nanjing University of Finance and Economics School of Computer and Artificial Intelligence Nanjing210023 China Nanjing Forestry University School of Science Nanjing210037 China Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China

This paper focuses on the privacy-preserving security control issue for cyber-physical systems (CPSs) via an encoding-decoding communication scheme (EDCS). To considerably contemplate the delayed data acquisition and analysis, the integral measurement model is established, which is governed by the function related to system states within a certain period. The EDCS designed has a certain resistant to the non-ideal communication network with limited bandwidth capacity and fading channels. Then the controller is constructed by utilizing the decoding data. Furthermore, in the controller-to-actuator channel, assuming the stealthy false data injection attacks occur randomly and the injected pseudo data is associated with transmission data that has been eavesdropped. By virtue of deriving sufficient conditions, the CPS with EDCS can achieve input-to-state stability as well as the corresponding controller and observer gain parameters are acquired. Eventually, the effectiveness of the designed EDCS and control algorithm is illustrated by a case study involving an autonomous underwater vehicle system. © 2023 IEEE.

关键词： Encoding (symbols)

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Asynchronous Control for Discrete-Time Markov Jump Systems with Multi-Node Round-Robin Protocol

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IAENG International Journal of Applied Mathematics 2025年第1期55卷 126-133页

作者： Fang, Qi He, Lanlan Liu, Jie Jiang, Taiping School of Computer Science and Technology Anhui University of Technology Ma'anshan243032 China School of Computer Science and Technology Anhui University of Technology Ma'anshan243032 China School of Computer Science and Technology Anhui University of Technology Ma'anshan243032 China School of Computer Science and Technology Anhui University of Technology Ma'anshan243032 China

This paper considers the asynchronous control for discrete-time Markov jump systems (MJSs) using a multi-node round-robin protocol (MNRRP). Compared to the traditional round-robin protocol, MNRRP increases the number of nodes updated at each transmission time, thereby improving system performance. In addition, a hidden Markov model is constructed to address the asynchronous behavior between the controlled object and the controller. Using Lyapunov functions and several inequalities, a criterion is provided to ensure the stochastic stability of MJS and joint L2 – L1 and H1 performance. A required asynchronous controller design approach is then presented based on the scheduling signal. Finally, a numerical example is given to verify the feasibility and applicability of the theoretical results. © (2025), (International Association of Engineers). All rights reserved.

关键词： Hidden Markov models

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Rendering acceleration method based on JND and sample gradient

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Optoelectronics Letters 2025年第3期21卷 177-182页

作者： ZHANG Ripei CHEN Chunyi School of Computer Science and Technology Qinghai Normal UniversityXining810016China School of Computer Science and Technology Changchun University of Science and TechnologyChangchun130022China

Currently,the main idea of iterative rendering methods is to allocate a fixed number of samples to pixels that have not been fully rendered by calculating the completion *** is obvious that this strategy ignores the c... 详细信息

关键词： iterative rendering pixel values allocate fixed number samples completion rateit iterative rendering methods changes pixel values iterative operations completion rate

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Neural Network Based on Inter-layer Perturbation Strategy for Text Classification

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Machine Intelligence Research 2025年第1期22卷 176-188页

作者： Nai Zhou Nianmin Yao Qibin Li School of Computer Science and Technology Dalian University of TechnologyDalian116024China

Recently,many researches have created adversarial samples to enrich the diversity of training data for improving the text classification performance via reducing the loss incurred in the neural network ***,existing studies have focused solely on adding perturbations to the input,such as text sentences and embedded representations,resulting in adversarial samples that are very similar to the original *** adversarial samples can not significantly improve the diversity of training data,which restricts the potential for improved classification *** alleviate the problem,in this paper,we extend the diversity of generated adversarial samples based on the fact that adding different disturbances between different layers of neural network has different *** propose a novel neural network with perturbation strategy(PTNet),which generates adversarial samples by adding perturbation to the intrinsic representation of each hidden layer of the neural ***,we design two different perturbation ways to perturb each hidden layer:1)directly adding a certain threshold perturbation;2)adding the perturbation in the way of adversarial *** above settings,we can get more perturbed intrinsic representations of hidden layers and use them as new adversarial samples,thus improving the diversity of the augmented training *** validate the effectiveness of our approach on six text classification datasets and demonstrate that it improves the classification ability of the *** particular,the classification accuracy on the sentiment analysis task improved by an average of 1.79%and on question classification task improved by 3.2%compared to the BERT baseline,respectively.

关键词： Perturbation strategy adversarial training machine learning text classification adversarial samples

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RandoMix: a mixed sample data augmentation method with multiple mixed modes

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Multimedia Tools and Applications 2025年第8期84卷 4343-4359页

作者： Liu, Xiaoliang Shen, Furao Zhao, Jian Nie, Changhai National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China School of Artificial Intelligence Nanjing University Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing China

Data augmentation plays a crucial role in enhancing the robustness and performance of machine learning models across various domains. In this study, we introduce a novel mixed-sample data augmentation method called RandoMix. RandoMix is specifically designed to simultaneously address robustness and diversity challenges. It leverages a combination of linear and mask mixed modes, introducing flexibility in candidate selection and weight adjustments. We evaluate the effectiveness of RandoMix on diverse datasets, including CIFAR-10/100, Tiny-ImageNet, ImageNet, and Google Speech Commands. Our results demonstrate its superior erformance compared to existing techniques such as Mixup, CutMix, Fmix, and ResizeMix. Notably, RandoMix excels in enhancing model robustness against adversarial noise, natural noise, and sample occlusion. The comprehensive experimental results and insights into parameter tuning underscore the potential of RandoMix as a versatile and effective data augmentation method. Moreover, it seamlessly integrates into the training pipeline. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Data augmentation Diversity Mixed-sample RandoMix Robustness

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Series-parallel Sequence Impedance Models of Multi-loop Grid-forming Converters

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Journal of Modern Power Systems and Clean Energy 2025年第1期13卷 29-41页

作者： Xiaokuan Jin Jianhua Wang Han Yan Xijun Ni Zhendong Ji Baojian Ji Ding Wan School of Electrical Engineering Southeast UniversityNanjing 210096China School of Electric Power Engineering Nanjing Institute of TechnologyNanjing 211167China School of Automation Nanjing University of Science and TechnologyNanjing 210094China

The gradual penetration of grid-forming(GFM)converters into new power systems with renewable energy sources may result in the emergence of small-signal instability *** issues can be elucidated using sequence impedance models,which offer a more tangible and meaningful interpretation than dq-domain impedance models and state-space ***,existing research has primarily focused on the impact of power loops and inner control loops in GFM converters,which has not yet elucidated the precise physical interpretation of inner voltage and current loops of GFM converters in *** paper derives series-parallel sequence impedance models of multi-loop GFM converters,demonstrating that the voltage loop can be regarded as a parallel impedance and the current loop as a series ***,the corresponding small-signal stability characteristics can be identified through Bode diagrams of sequence impedances or by examining the physical meanings of impedances in series and in *** results indicate that the GFM converter with a single power loop is a candidate suitable for application in new power systems,given its reduced number of control parameters and enhanced low-frequency performance,particularly in weak *** results of PLECS simulations and corresponding prototype experiments verify the accuracy of the analytical analysis under diverse grid conditions.

关键词： Grid-forming(GFM)converter sequence impedance renewable energy source small-signal stability

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Absorbing Time Synchronization Errors Using Shadow Queues in Time-Sensitive Networking

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IEEE Internet of Things Journal 2025年第9期12卷 12912-12926页

作者： Yang, Hao Zhang, Tong Feng, Xiaoqin Ma, Yanying Zhang, Liwei Ren, Fengyuan Lanzhou University School of Information Science and Engineering Lanzhou730000 China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China Tsinghua University Department of Computer Science and Technology Beijing100084 China

Time-sensitive networking (TSN) is widely used in industrial automation and automotive applications due to its ability to provide deterministic transmission. To meet the stringent deterministic requirements of time-sensitive traffic, the mainstream traffic management mechanism time-aware shaper (TAS) relies on time synchronization among network devices. TAS schedules periodic traffic transmission using preallocated time windows. However, in real networks, time-sensitive traffic may not be precisely forwarded as scheduled, thus failing to achieve the expected performance. A fundamental reason is that the statically planned transmission schemes cannot accommodate dynamic time synchronization errors. To address this problem, we propose a novel TSN traffic scheduling strategy called robust TAS (RTAS), which utilizes a dual-queue gating structure to define a new scheduling rule for absorbing time synchronization errors to guarantee the real-time performance of scheduled traffic (ST). We conducted extensive simulations on OMNeT++ to study the performance of RTAS in industrial automation scenarios. The results show that RTAS enables deterministic transmission with imperfectly synchronized clocks and minimizes the impact of time synchronization errors on ST. © 2014 IEEE.

关键词： Queueing theory

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Rendered image denoising method with filtering guided by lighting information

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Optoelectronics Letters 2025年第4期21卷 242-248页

作者： MA Minghui HU Xiaojuan ZHANG Ripei CHEN Chunyi YU Haiyang School of Computer Science and Technology Changchun University of Science and TechnologyChangchun 130022China

The visual noise of each light intensity area is different when the image is drawn by Monte Carlo ***,the existing denoising algorithms have limited denoising performance under complex lighting conditions and are easy to lose detailed *** we propose a rendered image denoising method with filtering guided by lighting ***,we design an image segmentation algorithm based on lighting information to segment the image into different illumination ***,we establish the parameter prediction model guided by lighting information for filtering(PGLF)to predict the filtering parameters of different illumination *** different illumination areas,we use these filtering parameters to construct area filters,and the filters are guided by the lighting information to perform sub-area ***,the filtering results are fused with auxiliary features to output denoised images for improving the overall denoising effect of the *** the physically based rendering tool(PBRT)scene and Tungsten dataset,the experimental results show that compared with other guided filtering denoising methods,our method improves the peak signal-to-noise ratio(PSNR)metrics by 4.2164 dB on average and the structural similarity index(SSIM)metrics by 7.8%on *** shows that our method can better reduce the noise in complex lighting scenesand improvethe imagequality.

关键词： establish paramet rendered image denoising Monte Carlo method filtering guided lighting information denoising algorithms image segmentation algorithm rendered image denoising method monte carlo methodhoweverthe

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Learning Hierarchical Adaptive Code Clouds for Neural 3D Shape Representation

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Machine Intelligence Research 2025年第2期22卷 304-323页

作者： Yuanxun Lu Xinya Ji Hao Zhu Xun Cao State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China School of Electronic Science and Engineering Nanjing UniversityNanjing 210023China School of Intelligence Science and Technology Nanjing UniversitySuzhou 215163China

Neural implicit representation(NIR)has attracted significant attention in 3D shape representation for its efficiency,generalizability,and flexibility compared with traditional explicit *** works usually parameterize shapes with neural feature grids/volumes,which prove to be inefficient for the discrete position constraints of the *** recent advances make it possible to optimize continuous positions for the latent codes,they still lack self-adaptability to represent various kinds of shapes *** this paper,we introduce a hierarchical adaptive code cloud(HACC)model to achieve an accurate and compact implicit 3D shape ***,we begin by assigning adaptive influence fields and dynamic positions to latent codes,which are optimizable during training,and propose an adaptive aggregation function to fuse the contributions of candidate latent codes with respect to query *** addition,these basic modules are stacked hierarchically with gradually narrowing influence field thresholds and,therefore,heuristically forced to focus on capturing finer structures at higher *** formulations greatly improve the distribution and effectiveness of local latent codes and reconstruct shapes from coarse to fine with high *** qualitative and quantitative evaluations both on single-shape reconstruction and large-scale dataset representation tasks demonstrate the superiority of our method over state-of-the-art approaches.

关键词： Representation learning shape analysis deep implicit function 3D reconstruction 3D modeling

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