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 trig...
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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
In order to solve the problem that the segmentation model does not pay enough attention to the lesion information and loses the lesion information, this paper proposes the MLS-Net (Multiple-Lesions-Segmentation-Net) s...
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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 impro...
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In wireless sensor networks(WSNs), data aggregation effectively reduces network traffic, thereby reducing energy consumption and improving network life cycle. Nevertheless, in the process of data aggregation schedulin...
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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 t...
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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.
作者:
Yue, HaoXu, YakunHu, HesuanWu, WeiminLi, 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
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