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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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An Improved YOLOv8-Based Method for Real-Time Detection of Harmful Tea Leaves in Complex Backgrounds

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Phyton-International Journal of Experimental Botany 2024年第11期93卷 2963-2981页

作者： Xin Leng Jiakai Chen Jianping Huang Lei Zhang Zongxuan Li College of Computer and Control Engineering Northeast Forestry UniversityHarbin150040China

Tea,a globally cultivated crop renowned for its uniqueﬂavor proﬁle and health-promoting properties,ranks among the most favored functional beverages ***,diseases severely jeopardize the production and quality of tea leaves,leading to signiﬁcant economic *** early and accurate identiﬁcation coupled with the removal of infected leaves can mitigate widespread infection,manual leaves removal remains time-con-suming and *** robots for pruning can signiﬁcantly enhance efﬁciency and reduce ***-ever,the accuracy of object detection directly impacts the overall efﬁciency of pruning *** complex tea plantation environments,complex image backgrounds,the overlapping and occlusion of leaves,as well as small and densely harmful leaves can all introduce interference *** algorithms perform poorly in detecting small and densely packed *** address these challenges,this paper collected a dataset of 1108 images of harmful tea leaves and proposed the YOLO-DBD *** model excels in efﬁciently identifying harmful tea leaves with various poses in complex backgrounds,providing crucial guidance for the posture and obstacle avoidance of a robotic arm during the pruning *** improvements proposed in this study encompass the Cross Stage Partial with Deformable Convolutional Networks v2(C2f-DCN)module,Bi-Level Routing Atten-tion(BRA),Dynamic Head(DyHead),and Focal Complete Intersection over Union(Focal-CIoU)Loss function,enhancing the model’s feature extraction,computation allocation,and perception *** to the baseline model YOLOv8s,mean Average Precision at IoU 0.5(mAP0.5)increased by 6%,and Floating Point Operations Per second(FLOPs)decreased by 3.3 G.

关键词： Harmful tea leaves YOLO-DBD Focal-CIoU Loss dynamic head Bi-Level Routing Attention

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Drug-Target Interactions Prediction Based on Signed Heterogeneous Graph Neural Networks

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Chinese Journal of Electronics 2024年第1期33卷 231-244页

作者： Ming CHEN Yajian JIANG Xiujuan LEI Yi PAN Chunyan JI Wei JIANG College of Information Science and Engineering Hunan Normal University School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Computer Science Department BNU-HKBU United International College

Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.

关键词： Drug-target interactions Signed heterogeneous network Link sign prediction Graph neural networks

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A calibration and compensation method for an industrial robot with high accuracy harmonic reducers

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Science China(Technological Sciences) 2024年第3期67卷 725-739页

作者： ZHAO YiYang LIU Xun CAO JiaBin LIU JinYu XU Yan YAN WeiXin School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghai 200240China College of Computer and Control Engineering Northeast Forestry UniversityHarbin 150040China

Industrial serial robots need high stiffness to keep absolute pose accuracy and meet the requirements in practical applications. However, the weak stiffness feature of robot joints and the payloads affected on robot end-effector, which will also increase the pose error of robot. Especially, the existing calibration methods often consider under no-payload condition without discussing the payload state. In this paper, we report a new industrial serial robot composed by a new harmonic reducer: Model-Y, based on high accuracy and high stiffness, and a kinematic parameter calibration algorithm which is based on a harmonic reducer forcedeformation model. To decrease the accuracy effects of payload, an iterative calibration method for kinematic parameters with payload situation was proposed. Simulation and experiments are conducted to verify the effectiveness of the proposed calibration method using the self-developed industrial serial robot. The results show a remarkably improved accuracy in absolute position and orientation with the robot's payload range. The position mean error has 70% decreased to 0.1 mm and the orientation mean error diminished to less than 0.01° after calibration with compensation. Additionally, online linear and circular tests are carried out to evaluate the position error of the robot during large-scale spatial and low-speed continuous movement. The accuracy is consistent with the previous calibration results, indicating the effectiveness and advantages of the proposed strategy in this article.

关键词： industrial serial robot Model-Y harmonic calibration stiffness model online compensation

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A multi-lesion segmentation method for diabetic retinopathy based on location information guidance

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Multimedia Tools and Applications 2025年第15期84卷 15205-15222页

作者： Chen, Yu Xu, Shibao Long, Jun Xie, Yining College of Computer and Control Engineering Northeast Forestry University Harbin150040 China College of Mecheanical and Electrical Engineering Northeast Forestry University Harbin150040 China

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) segmentation model. Firstly, the lesions in the image are detected and reconstructed using the lesions information descriptor extraction module to preserve the location, scale and features of the lesions information (Lesion information descriptor). Secondly, the lesions information descriptors are combined with the downsampling part of the semantic segmentation module, so that the lesions information descriptors can play a role in guiding the location and features in the downsampling process of the model, in order for the segmentation module to pay more attention to the regions of the image where lesions exist. Compared with recent representative studies Ffu-net, the evaluation metrics of each lesions were improved by more than 2%. The problem of the model losing lesions information are effectively solved, and the performance of the DR semantic segmentation model is improved. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Location

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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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Research on incentive mechanism and evaluation of cross-enterprise distributed research and development resource sharing under networked collaborative design mode

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Soft Computing 2024年第21期28卷 12543-12559页

作者： Hu, Yongqing Su, Weixing Chen, Hanning He, Maowei Liu, Fang College of Control Science and Engineering Tiangong University Tianjin China College of Computer Science and Technology Tiangong University Tianjin China

The promotion and application of model-based systems engineering (MBSE) suffer from the lack of effective sharing of research and design (R&D) resources among enterprises in the networked collaborative design environment. This paper establishes a cross-enterprise R&D resource sharing incentive evolutionary game model, which considers multiple factors about resource sharers, sharing process and shared resource value information. The model analyses how various factors influence the evolution trend of sharing behavior. In addition, in order to realize the dynamic multidimensional evaluation and feedback of shared R&D resources and promote the positive development of resource sharing, this paper establishes a new dynamic double-objective resource evaluation model. The digital information resource in R&D resources is taken as an example to carry out experimental simulation and verification. Compared with the traditional evaluation methods, the new dynamic double-objective evaluation model established in this paper owns better performance on improving the cross-enterprise R&D resources collaborative sharing and further promoting the deep integration between R&D resources and R&D process under the networked collaborative design and MBSE development mode than others. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Evolutionary game model Incentive mechanism Multi-objective evaluation Networked collaborative design Resource sharing

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A Practical Byzantine Fault Tolerance Algorithms Based on Randomized Mean Clustering, Trust and Credibility 8th

A Practical Byzantine Fault Tolerance Algorithms Based on R...

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8th International Conference on Data Mining and Big Data, DMBD 2023

作者： Zhai, Haonan Tong, Xiangrong Yantai University College of Computer and Control Engineering Yantai China

ISBN: (纸本)9789819708369

The Practical Byzantine Fault Tolerance (PBFT) consensus protocol, while robust, faces efficiency challenges with the escalating number of network nodes. Various enhancement algorithms employ grouping strategies to bolster performance in large-scale networks. However, as nodes consolidate into smaller clusters, their capacity to accommodate Byzantine nodes diminishes markedly, prompting the need for a systematic approach to distributing Byzantine nodes evenly across consensus regions. In order to address the above issues, this paper proposes a Practical Byzantine fault tolerant algorithm based on Randomized Mean Clustering, Trust, and Credibility (CTPBFT). Nodes possess two key attributes: trust and credibility. Trust determines the priority of node cluster allocation, while credibility evaluations ensure cluster uniformity. A customized random equalization algorithm assigns nodes within the federated chain to diverse clusters. Post-partitioning, the consensus process is streamlined, with each cluster autonomously conducting consensus activities. A well-designed reward function incentivizes nodes to execute the consensus protocol, aiding the system’s smooth and efficient operation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Clustering algorithms

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A Practical Byzantine Fault Tolerant Algorithm Based on Credit Value and Dynamic Grouping 1

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2nd International Conference on Cognitive Computation and Systems, ICCCS 2023

作者： Zhai, Haonan Tong, Xiangrong College of Computer and Control Engineering Yantai University Yantai China

ISBN: (数字)9789819708857

ISBN: (纸本)9789819708840

The Practical Byzantine Fault Tolerance (PBFT) consensus algorithm plays a pivotal role in the efficiency of blockchain networks. Despite several enhancements, optimized PBFT still grapples with issues, notably incomplete credit assessments, and uneven node groupings. While existing trust models predominantly focus on objectively evaluating nodes within the system, the subjective assessment of inter-node interactions remains relatively underexplored. Furthermore, post-grouping, the reduction in nodes within each group leads to a diminished capacity for Byzantine nodes. This poses a notable challenge to the algorithm’s effectiveness. In this paper, a practical Byzantine fault-tolerant algorithm (CDGPBFT) based on credit value and dynamic grouping is proposed to solve these problems. First, credit attributes are extended for nodes. The design of the node model introduces a dual judgment mechanism, including dynamic evaluation of node interactions and static system evaluation. Subsequently, nodes are categorized into three groups based on their credit scores: high, medium, and reserve node groups. Next, formulas are designed to enhance the master node election scheme and simplify the consensus process. Finally, a reward function based on node computational resources, network bandwidth, and storage capacity is proposed. Nodes that effectively complete the consensus process receive tokens as rewards, thus promoting accurate and rapid participation. Experimental validation shows that the enhanced algorithm has lower latency and communication overheads compared to PBFT, while also improving message throughput. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fault tolerance

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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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