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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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Open Architecture for computer-Aided control engineering

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IEEE control Systems 1993年第2期13卷 17-27页

作者： Barker, H. Anthony Chen, Min Grant, Philip W. Jobling, Chris P. Townsend, Peter Control and Computer-Aided Engineering Research Group University of Wales Swansea SA2 8PP Singleton Park United Kingdom

Open systems are currently being used successfully in many areas of information technology. Some guiding principles for the design of open systems suitable for computer-aided control engineering are presented. Some possible architectures are considered and an assessment is made of the available technologies which might be suitable for the basis of an open environment for computer-aided control engineering. An analogy is made with the requirements for computer-aided software engineering, for which a reference model has already been developed, and it is concluded that such a model might well provide a clear definition of what is needed in computer-aided control engineering. © 1993 IEEE

关键词： computer architecture control engineering computing control engineering Packaging machines Open systems Software packages User interfaces Bridges Error correction Information technology

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Physical Layer Security Scheme With AoI-Awareness for Industrial IoT Based on Covert Communications

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 276-278页

作者： Yaping Li Zhi-Xin Liu Jia-Wei Su Ya-Zhou Yuan the Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University

Dear Editor,Industrial Internet of things(IIoT) is a typical application of cyberphysical system(CPS). In the IIoT, wireless communication is an inevitable trend to replace the deployment-limited wired transmission for cases with large-scale and mobile devices. However, wireless communication gives rise to critical issues related to physical security, such as malicious detections and attacks [1].

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Privacy-preserving filtering, control and optimization for industrial cyber-physical systems

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Science China(Information Sciences) 2025年第4期68卷 267-283页

作者： Derui DING Qing-Long HAN Xiaohua GE Xian-Ming ZHANG Jun WANG Department of Control Science and Engineering University of Shanghai for Science and Technology School of Engineering Swinburne University of Technology Department of Computer Science City University of Hong Kong

Industrial cyber-physical systems closely integrate physical processes with cyberspace, enabling real-time exchange of various information about system dynamics, sensor outputs, and control decisions. The connection between cyberspace and physical processes results in the exposure of industrial production information to unprecedented security risks. It is imperative to develop suitable strategies to ensure cyber security while meeting basic performance *** the perspective of control engineering, this review presents the most up-to-date results for privacy-preserving filtering,control, and optimization in industrial cyber-physical systems. Fashionable privacy-preserving strategies and mainstream evaluation metrics are first presented in a systematic manner for performance evaluation and engineering *** discussion discloses the impact of typical filtering algorithms on filtering performance, specifically for privacy-preserving Kalman filtering. Then, the latest development of industrial control is systematically investigated from consensus control of multi-agent systems, platoon control of autonomous vehicles as well as hierarchical control of power systems. The focus thereafter is on the latest privacy-preserving optimization algorithms in the framework of consensus and their applications in distributed economic dispatch issues and energy management of networked power systems. In the end, several topics for potential future research are highlighted.

关键词： industrial cyber-physical systems privacy preservation distributed control distributed optimization power systems

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Enhancing Social Experiences in Immersive Virtual Reality with Artificial Facial Mimicry

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IEEE Transactions on Visualization and computer Graphics 2025年第5期31卷 3325-3335页

作者： Visconti, Alessandro Calandra, Davide Giorgione, Federica Lamberti, Fabrizio Politecnico di Torino Department of Control and Computer Engineering Italy Politecnico di Torino Italy

The growing availability of affordable Virtual Reality (VR) hardware and the increasing interest in the Metaverse are driving the expansion of Social VR (SVR) platforms. These platforms allow users to embody avatars in immersive social virtual environments, enabling real-time interactions using consumer devices. Beyond merely replicating real-life social dynamics, SVR platforms offer opportunities to surpass real-world constraints by augmenting these interactions. One example of such augmentation is Artificial Facial Mimicry (AFM), which holds significant potential to enhance social experiences. Mimicry, the unconscious imitation of verbal and non-verbal behaviors, has been shown to positively affect human-agent interactions, yet its role in avatar-mediated human-to-human communication remains under-explored. AFM presents various possibilities, such as amplifying emotional expressions, or substituting one emotion for another to better align with the context. Furthermore, AFM can address the limitations of current facial tracking technologies in fully capturing users' emotions. To investigate the potential benefits of AFM in SVR, an automated AM system was developed. This system provides AFM, along with other kinds of head mimicry (nodding and eye contact), and it is compatible with consumer VR devices equipped with facial tracking. This system was deployed within a test-bench immersive SVR application. A between-dyads user study was conducted to assess the potential benefits of AFM for interpersonal communication while maintaining avatar behavioral naturalness, comparing the experiences of pairs of participants communicating with AFM enabled against a baseline condition. Subjective measures revealed that AFM improved interpersonal closeness, aspects of social attraction, interpersonal trust, social presence, and naturalness compared to the baseline condition. These findings demonstrate AFM's positive impact on key aspects of social interaction and highlight its pote

关键词： Virtual environments

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Real-time drone detection framework based on advanced texture feature extraction and pattern recognition model using GUI

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Neural Computing and Applications 2025年第5期37卷 3435-3454页

作者： Hussen, Noha Salem, Mofreh Eldesouky, Ali I. Sakr, Noha Elghamrawy, Sally Computers and Control Systems Engineering Department Mansoura University Mansoura Egypt Computer Engineering Department Misr Higher Institute for Engineering and Technology Mansoura Egypt

The increasing prevalence of drones has raised significant concerns regarding their potential for misuse in activities such as smuggling, terrorism, and unauthorized access to restricted airspace. Consequently, the development of robust and efficient drone detection systems has become paramount. Traditional detection methods such as radar and acoustic sensors have limitations in detecting small drones and can be costly to implement. This can lead to lower detection rates and increased security breaches and safety concerns. However, prevailing methodologies often falter in achieving real-time detection with requisite precision and efficacy, particularly within complex operational environments. Motivated by the imperative to surmount these challenges, our research endeavors to introduce a pioneering real-time drone detection framework. Our study encapsulates several seminal advancements. Firstly, we proffer a groundbreaking framework that synergizes advanced integration technique based on texture feature extraction and pattern recognition techniques for real-time drone detection to increase accuracy to detect drones in different conditions such as bad weather and low resolution. Secondly, we introduce an intuitive graphical user interface, enhancing the usability and accessibility of the system in real-time scenario. Lastly, through exhaustive evaluation and comparative analysis, we substantiate the superior performance of our framework in terms of accuracy, precision, and real-time detection capabilities compared to conventional DDS methodologies. Its stability and effectiveness render it a compelling solution for security-focused entities, notably those within air force and military systems. Our experimental results reveal a commendable accuracy rate of 97%, affirming the reliability and precision 98% and recall parameter 98% of our framework in accurately identifying and detecting drones, thus surpassing recent models in the field. © The Author(s), under exclusive

关键词： Graphical user interfaces

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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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Self-Supervised Pretraining and Quantization for Fault Tolerant Neural Networks: Friend or Foe?

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IEEE Access 2025年 13卷 75546-75562页

作者： Milazzo, Rosario Fosson, Sophie M. Morra, Lia Sterpone, Luca Politecnico di Torino Department of Control and Computer Engineering Turin10129 Italy

Deep neural networks (DNNs) are increasingly being applied in critical domains such as healthcare and autonomous driving. However, their predictive capabilities can degrade in the presence of transient hardware faults, which can lead to potentially catastrophic and unpredictable errors. Consequently, various techniques have been proposed to enhance DNN fault tolerance by modifying the network structure or training procedure, thereby reducing the need for costly hardware redundancy. However, there are design or training choices whose impact on fault propagation has been overlooked in the literature. Specifically, self-supervised learning (SSL) as a pre-training technique has been shown to enhance the robustness of learned features, resulting in improved performance on downstream tasks. This study investigates the error tolerance of different DNN and SSL techniques in image classification and segmentation tasks, including those relevant to Earth Observation. Experimental results suggest that SSL pretraining, whether used alone or in combination with error mitigation techniques, generally enhances DNN fault tolerance. We complement these findings with an in-depth analysis of the fault tolerance of quantized networks. In this context, the use of standard SSL techniques leads to a decrease in accuracy. However, this issue can be partially addressed by employing methods that, during the pretraining phase, incorporate the quantization error into the loss function. © 2013 IEEE.

关键词： Self-supervised learning

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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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Aerial Robots Carrying Flexible Cables: Dynamic Shape Optimal control via Spectral Method Model

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IEEE Transactions on Robotics 2025年 41卷 3162-3182页

作者： Shen, Yaolei Franchi, Antonio Gabellieri, Chiara Faculty AE Enschede7500 Netherlands Sapienza University of Rome Department of Computer Control and Management Engineering Rome00185 Italy

In this work, we present a model-based optimal boundary control design for an aerial robotic system composed of a quadrotor carrying a flexible cable. The whole system is modeled by partial differential equations combined with boundary conditions described by ordinary differential equations. The proper orthogonal decomposition (POD) method is adopted to project the original infinite-dimensional system on a finite low-dimensional space spanned by orthogonal basis functions. Based on such a reduced-order model, nonlinear model predictive control is implemented online to realize both position and shape trajectory tracking of the flexible cable in an optimal predictive fashion. The proposed POD-based reduced modeling and optimal control paradigms are verified in simulation using an accurate high-dimensional finite difference method-based model and experimentally using a real quadrotor and a cable. The results show the viability of the POD-based predictive control approach (allowing to close the control loop on the full system state) and its superior performance compared to an optimally tuned proportional–integral–derivative (PID) controller (allowing to close the control loop on the quadrotor state only). © 2004-2012 IEEE.

关键词： Orthogonal functions

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