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Joint Feature Encoding and Task Alignment Mechanism for Emotion-Cause Pair Extraction

computers, Materials & Continua 2025年第1期82卷 1069-1086页

作者： Shi Li Didi Sun College of Computer and Control Engineering Northeast Forestry UniversityHarbin150040China

With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant ***-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying *** comprehension is crucial for making informed strategic decisions in various business and societal ***,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause *** address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment *** model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause ***,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among *** proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 *** results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its *** research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-c

关键词： Emotion-cause pair extraction interactive information enhancement joint feature encoding label consistency task alignment mechanisms

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Using join learning of multi-level features for automated essay scoring

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Neural Computing and Applications 2025年 1-18页

作者： Li, Shi Gao, Yan College of Computer and Control Engineering Northeast Forestry University Wenzheng Street Heilongjiang Province Harbin150006 China

Recently, the approach of extracting features of essays at different levels for joint learning and scoring using hybrid models has achieved excellent results. However, there are still some issues that need to be improved: (1) Splitting the essays into sentences of the same length when extracting sentence-level features may result in the truncation of sentence-level semantics;(2) multi-level representations of essays obtained through pre-trained language models (PLMs) are not sufficiently comprehensive and in depth to provide limited improvement in model performance;and (3) in extracting features from multi-level essay representations, finer-grained local features in sentences and dependencies among features are ignored. To address these issues, we propose an improved method for joint learning of multi-level features based on PLMs. Specifically, we first split the essays according to different semantic scales through a text splitter to maximize the retention of semantic and structural features at sentence level;secondly, we integrate the essay representations derived from the PLM and mine more comprehensive and deeper multi-level features from it;and finally, we aggregate sentence-level features and construct dependencies among them by a feature extraction fusion method. The value of average quadratic weighted kappa (QWK) on the Kaggle ASAP competition dataset is 0.809, which validates the effectiveness of our proposed method in the automated essay scoring (AES) task. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Semantics

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Harnessing Trend Theory to Enhance Distributed Proximal Point Algorithm Approaches for Multi-Area Economic Dispatch Optimization

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computers, Materials & Continua 2025年第3期82卷 4503-4533页

作者： Yaming Ren Xing Deng College of Mechanical and Control Engineering Guilin University of TechnologyGuilin541006China School of Computer Jiangsu University of Science and TechnologyZhenjiang212003China

The exponential growth in the scale of power systems has led to a significant increase in the complexity of dispatch problem resolution,particularly within multi-area interconnected power *** complexity necessitates the employment of distributed solution methodologies,which are not only essential but also highly *** the realm of computational modelling,the multi-area economic dispatch problem(MAED)can be formulated as a linearly constrained separable convex optimization *** proximal point algorithm(PPA)is particularly adept at addressing such mathematical constructs *** study introduces parallel(PPPA)and serial(SPPA)variants of the PPA as distributed algorithms,specifically designed for the computational modelling of the *** PPA introduces a quadratic term into the objective function,which,while potentially complicating the iterative updates of the algorithm,serves to dampen oscillations near the optimal solution,thereby enhancing the convergence ***,the convergence efficiency of the PPA is significantly influenced by the parameter *** address this parameter sensitivity,this research draws on trend theory from stock market analysis to propose trend theory-driven distributed PPPA and SPPA,thereby enhancing the robustness of the computational *** computational models proposed in this study are anticipated to exhibit superior performance in terms of convergence behaviour,stability,and robustness with respect to parameter selection,potentially outperforming existing methods such as the alternating direction method of multipliers(ADMM)and Auxiliary Problem Principle(APP)in the computational simulation of power system dispatch *** simulation results demonstrate that the trend theory-based PPPA,SPPA,ADMM and APP exhibit significant robustness to the initial value of parameter c,and show superior convergence characteristics compared to the residual balancing ADMM.

关键词： Multi-area economic dispatch problem proximal point algorithm trend theory

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Minimum Initial Marking Estimation in Labeled Petri Nets Using Minimum Token Number Prediction

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IEEE Transactions on Automatic control 2025年第5期70卷 3296-3302页

作者： Yue, Hao Xu, Yakun Hu, Hesuan Wu, Weimin Li, Lingxi College of Computer Science and Technology Qingdao266580 China Xidian University School of Electro-Mechanical Engineering Xi'an710071 China Nanyang Technological University School of Computer Science and Engineering College of Engineering 639798 Singapore Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Zhejiang University Institute of Cyber-Systems and Control Hangzhou310027 China Purdue University Elmore Family School of Electrical and Computer Engineering College of Engineering IndianapolisIN46202 United States

This article proposes an approach to addressing the problem of minimum initial marking (MuIM) estimation for labeled Petri nets (LPNs). We introduce the important concept of a label synthesis net for LPNs and develop a method for predicting the minimum number of tokens. By using this prediction method, we develop an algorithm that has polynomial complexity in the length of the observed label sequence for estimating MuIMs. An illustrative example is provided to show the effectiveness and efficiency of our proposed approach. Moreover, experimental results demonstrate its advantage over existing work. Finally, we provide a comparison of some representative studies in the literature for MuIM estimation. © 1963-2012 IEEE.

关键词： Discrete event simulation

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Neuroadaptive Containment control for Nonlinear Multiagent Systems With Input Saturation: An Event-Triggered Communication Approach

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3163-3173页

作者： Wang, Xin Zhang, Sen Li, Huaqing Zhang, Wei Li, Hongyi Huang, Tingwen Southwest University College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

In this article, a neuroadaptive event-triggered containment control strategy combined with the dynamic surface control (DSC) approach is proposed for nonlinear multiagent systems (MASs) with input saturation. Based on the event-triggered communication mechanisms, the updates of neural network weight and controllers are implemented solely under triggering conditions of violation, which markedly reduces unnecessary communication resources and minimizes inefficient control costs compared with the traditional control method. Radial basis function neural networks (RBF NNs) are employed to handle the nonlinear uncertainties of MASs. Simultaneously, an adaptive compensatory mechanism is incorporated within the backstepping process to address the nonlinear effect posed by input saturation. Additionally, we demonstrate the system stability by extending the Lyapunov theorem to jump and continuous scenarios while excluding Zeno behavior, realizing that all followers can enter the convex hull constructed by leaders. Finally, the effectiveness of the proposed methodology is verified through application simulations. © 2013 IEEE.

关键词： Radial basis function networks

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Data-driven output regulation control for constrained linear systems

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Science China(Information Sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

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A stacking-based model for the spread of Botryosphaeria laricina

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Journal of Forestry Research 2025年第1期36卷 427-443页

作者： Hongwei Zhou Shibo Zhang Meng Xie Xiaodong Li Yifan Chen Wenhao Dai College of Computer and Control Engineering Northeast Forestry UniversityHarbin 150040People’s Republic of China Key Laboratory of National Forestry and Grassland Administration On Forest and Grassland Pest Monitoring and Warning Center for Biological Disaster Prevention and ControlNational Forestry and Grassland AdministrationShenyang 110034People’s Republic of China

Botryosphaeria laricina(larch shoot blight)was first identified in 1973 in Jilin Province,*** disease spread rapidly and caused considerable damage because its pathogenesis was unknown at the time and there were no effective controls or quarantine *** present,it shows a spreading trend,but most research can only conduct physiological analyses within a relatively short period,combining individual influencing ***,methods such as neural network models,ensemble learning algorithms,and Markov models are used in pest and disease prediction and ***,there may be fitting issues or inherent limitations associated with these *** study obtained *** data at the county level from 2003 to *** dataset was augmented using the SMOTE algorithm,and then algorithms such as XGBoost were used to select the significant features from a combined set of 12 features.A new stacking fusion model has been proposed to predict the status of *** model is based on random forest,gradient boosted decision tree,CatBoost and logistic regression *** accuracy,recall,specificity,precision,F_(1) value and AUC of the model reached 90.9%,91.6%,90.4%,88.8%,90.2%and 96.2%.The results provide evidence of the strong performance and stability of the *** is mainly found in the northeast and this study indicates that it is spreading *** means should be used promptly to prevent further damage and spread.

关键词： Botryosphaeria laricina Data augmentation Feature selection Stacking Status of spread

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Frequency Attacks Based on Invertible Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 292-300页

作者： Shao, Ming-Wen Yang, Jian-Xin Meng, Ling-Zhuang Hu, Zhi-Yong China University of Petroleum School of Computer Science and Technology East China Qingdao266580 China China University of Petroleum College of Control Science and Engineering East China Qingdao266580 China

Adversarial attacks reveal the vulnerability of classifiers based on deep neural networks to well-designed perturbations. Most existing attack methods focus on adding perturbations directly to the pixel space. However, the perturbations generated by these methods may be easily perceived by humans. To alleviate the aforementioned problem, we propose a novel high-frequency attack based on invertible neural networks (HA-INN) that relies on INNs to adds perturbations to the high-frequency space of the image instead of the pixel space. In this way, we can fool the classifier while the perturbations are not easily detected by humans. Specifically, we introduce INNs to separate the high-frequency and low-frequency components of the image. And the low-frequency components are guaranteed to be reconstructed as the original image, while the high-frequency components are replaced with resampled high-frequency latent variables with additional adversarial information. Then, the low-frequency components and the high-frequency components with adversarial information are inversely fed into the INN to generate effective adversarial examples. Extensive experiments on two datasets (CIFAR-10 and CIFAR-100) show that our method can generate misleading and transferable cross-architectural adversarial examples with greatly reduced computational resource requirements. Under the white-box setting, the attack success rate of CIFAR10 and CIFAR100 is 99.8% and 99.74%, respectively. Further, under the black-box setting, the adversarial examples generated by our method are more effective than the other methods. © 2020 IEEE.

关键词： Deep neural networks

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Invariant Set Analysis and control Synthesis of Multi-Equilibrium Switched Systems under control Constraints 13

Invariant Set Analysis and Control Synthesis of Multi-Equili...

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13th International Conference on Intelligent control and Information Processing, ICICIP 2025

作者： Han, Yuejiang Zhang, Lixian Ding, Yihang Dong, Yifei Liang, Ye School of Astronautics Harbin Institute of Technology Harbin China College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (纸本)9798331516147

This paper investigates the problems of invariant set analysis and control synthesis for multi-equilibrium switched systems under control constraints. A control strategy based on the invariant set method is proposed, accompanied by criteria formulated as linear matrix inequalities, ensuring that the state remains within the mode-dependent invariant sets. Furthermore, considering the control constraints in practical scenarios, a multi-equilibrium switched controller with control constraints (MESCC controller) is designed. Compared to existing works, the proposed method incorporates mode-dependent dwell time conditions and mode-dependent invariant sets, thereby offering enhanced design flexibility. An example of an aero-engine control system is presented to demonstrate the effectiveness and potential of the theoretical results. © 2025 IEEE.

关键词： control system synthesis

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IMC Based PID controller for Spherical Tank Liquid Level control 1

IMC Based PID Controller for Spherical Tank Liquid Level Con...

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1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Kadu, C.B. Bhosale, V.P. Vikhe, P.S. Mandhare, V.V. Pravara Rural Engineering College Department of Instrumentation & Control Engineering Loni India Pravara Rural Engineering College Department of Computer Engineering Loni India

ISBN: (纸本)9798350355611

In the process industries, it's hard to control a non-linear process. Nonlinear behavior is frequently seen in real processes. The challenging problem of controlling a spherical tank is result of its nonlinearity caused by the continuously varying cross section. An IMC-based PID controller is employed to improve efficiency of controlling the level of spherical tanks which exhibit nonlinear dynamics. For processes up to second-order with dead time and first-order numerator dynamics, a single tuning parameter will be offered. The process model is determined employing mathematical modeling and standard step response-based system identification. The laboratory model of an interacting and non-interacting nonlinear system is introduced, which is capable of performing more complex tasks. To verify the effectiveness of the suggested IMC based PID controller, results of real-time experiments and a simulation study on spherical tank level control are incorporated. © 2025 IEEE.

关键词： Three term control systems

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