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Distributed optimal consensus control for multiagent systems based on event-triggered and prioritized experience replay strategies

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Science China(Information Sciences) 2025年第1期68卷 306-321页

作者： Cuijuan ZHANG Lianghao JI Shasha YANG Xing GUO Huaqing LI Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University College of Electronic and Information Engineering Southwest University

This study uses event-triggered(ET) and reinforcement learning methods to investigate the optimal consensus control problem for cooperative-competitive multiagent systems. It proposes a novel distributed ET control strategy, which relies on a prioritized experience replay(PER) policy. This strategy not only conserves communication resources but also ensures acceptable system performance. To implement the proposed method, actor-critic(AC) dual-structured neural networks(NNs) are used to approximate the value function and control policy. In the AC NNs, the weight estimates for the NNs are updated at the moment of event triggering, resulting in a nonperiodic weight adjustment pattern. This approach decreases the computational cost in comparison with the traditional ET mechanism. The PER-based ET mechanism makes full use of valid historical data and effectively establishes a balance between system performance and communication resource ***, it does not require the following two conditions in most existing studies:(1) requirement of the system dynamics model to be known, and(2) persistent excitation. In addition, Zeno behavior is excluded from this study. Finally, a simulation is conducted to confirm the validity of the suggested approach.

关键词： multiagent systems event-triggered mechanism prioritized experience replay dual-structured neural networks optimal consensus control

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RRHF-V: Ranking Responses to Mitigate Hallucinations in Multimodal Large Language Models with Human Feedback 31

RRHF-V: Ranking Responses to Mitigate Hallucinations in Mult...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Chen, Guoqing Zhang, Fu Lin, Jinghao Lu, Chenglong Cheng, Jingwei School of Computer Science and Engineering Northeastern University China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University China

ISBN: (纸本)9798891761964

Multimodal large language models (MLLMs) demonstrate strong capabilities in multimodal understanding, reasoning, and interaction but still face the fundamental limitation of hallucinations, where they generate erroneous or fabricated information. To mitigate hallucinations, existing methods annotate pair-responses (one non-hallucination vs one hallucination) using manual methods or GPT-4V, and train alignment algorithms to improve the correspondence between images and text. More critically, an image description often involve multiple dimensions (e.g., object attributes, posture, and spatial relationships), making it challenging for the model to comprehensively learn multidimensional information from pair-responses. To this end, in this paper, we propose RRHF-V, which is the first using rank-responses (one non-hallucination vs multiple ranking hallucinations) to mitigate multimodal hallucinations. Instead of using pair-responses to train the model, RRHF-V expands the number of hallucinatory responses, so that the responses with different scores in a rank-response enable the model to learn rich semantic information across various dimensions of the image. Further, we propose a scene graph-based approach to automatically construct rank-responses in a cost-effective and automatic manner. We also design a novel training objective based on rank loss and margin loss to balance the differences between hallucinatory responses within a rank-response, thereby improving the model's image comprehension. Experiments on two MLLMs of different sizes and four widely used benchmarks demonstrate that RRHF-V is effective in mitigating hallucinations and outperforms the DPO method based on pair-responses. © 2025 Association for Computational Linguistics.

关键词： Semantics

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Re-Cent: A Relation-Centric Framework for Joint Zero-Shot Relation Triplet Extraction 31

Re-Cent: A Relation-Centric Framework for Joint Zero-Shot Re...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Li, Zehan Zhang, Fu Lyu, Kailun Cheng, Jingwei Peng, Tianyue School of Computer Science and Engineering Northeastern University China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University China

ISBN: (纸本)9798891761964

Zero-shot Relation Triplet Extraction (ZSRTE) aims to extract triplets from the context where the relation patterns are unseen during training. Due to the inherent challenges of the ZSRTE task, existing extractive ZSRTE methods often decompose it into named entity recognition and relation classification, which overlooks the interdependence of two tasks and may introduce error propagation. Motivated by the intuition that crucial entity attributes might be implicit in the relation labels, we propose a Relation-Centric joint ZSRTE method named Re-Cent. This approach uses minimal information, specifically unseen relation labels, to extract triplets in one go through a unified model. We develop two span-based extractors to identify the subjects and objects corresponding to relation labels, forming span-pairs. Additionally, we introduce a relation-based correction mechanism that further refines the triplets by calculating the relevance between span-pairs and relation labels. Experiments demonstrate that Re-Cent achieves state-of-the-art performance with fewer parameters and does not rely on synthetic data or manual labor. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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SGMEA: Structure-Guided Multimodal Entity Alignment 31

SGMEA: Structure-Guided Multimodal Entity Alignment

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31st International Conference on Computational Linguistics, COLING 2025

作者： Cheng, Jingwei Guo, Mingxiao Zhang, Fu School of Computer Science and Engineering Northeastern University China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University China

ISBN: (纸本)9798891761964

Multimodal Entity Alignment (MMEA) aims to identify equivalent entities across different multimodal knowledge graphs (MMKGs) by integrating structural information, entity attributes, and visual data, thereby promoting knowledge sharing and deep multimodal data integration. However, existing methods often overlook the deeper connections between multimodal data. They primarily focus on the interactions between neighboring entities in the structural modality while neglecting the interactions between entities in the visual and attribute modalities. To address this, we propose a structure-guided multimodal entity alignment method (SGMEA), which prioritizes structural information from knowledge graphs to enhance the visual and attribute modalities. By fusing multimodal representations, SGMEA improves the accuracy of entity alignment. Experimental results demonstrate that SGMEA achieves state-of-the-art performance across multiple datasets, validating its effectiveness and superiority in practical applications. © 2025 Association for Computational Linguistics.

关键词： Knowledge graph

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CE-DA: Custom Embedding and Dynamic Aggregation for Zero-Shot Relation Extraction 31

CE-DA: Custom Embedding and Dynamic Aggregation for Zero-Sho...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zhang, Fu Liu, He Li, Zehan Cheng, Jingwei School of Computer Science and Engineering Northeastern University China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University China

ISBN: (纸本)9798891761964

Zero-shot Relation Extraction (ZSRE) aims to predict novel relations from sentences with given entity pairs, where the relations have not been encountered during training. Prototype-based methods, which achieve ZSRE by aligning the sentence representation and the relation prototype representation, have shown great potential. However, most existing works focus solely on improving the quality of prototype representations, neglecting sentence representations, and lacking interaction between different types of relation side information. In this paper, we propose a novel ZSRE framework named CE-DA, which includes two modules: Custom Embedding and Dynamic Aggregation. We employ a two-stage approach to obtain customized embeddings of sentences. In the first stage, we train a sentence encoder through unsupervised contrastive learning, and in the second stage, we highlight the potential relations between entities in sentences using carefully designed entity emphasis prompts to further enhance sentence representations. Additionally, our dynamic aggregation method assigns different weights to different types of relation side information through a learnable network to enhance the quality of relation prototype representations. In contrast to traditional methods that treat the importance of all side information equally, our dynamic aggregation method further strengthens the interaction between different types of relation side information. Our method demonstrates competitive performance across various metrics on two ZSRE datasets. © 2025 Association for Computational Linguistics.

关键词： Embeddings

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Speaker Extraction with Verification of Present and Absent Target Speakers

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Journal of Shanghai Jiaotong University (Science) 2025年 1-6页

作者： Zhang, Ke Borsdorf, Marvin Liu, Tianchi Wang, Shuai Wei, Yangjie Li, Haizhou Key Laboratory of Intelligent Computing in Medical Image Northeastern University Shenyang110819 China Shenzhen Guangdong518000 China Machine Listening Lab University of Bremen Bremen28359 Germany Department of Electrical and Computer Engineering National University of Singapore Singapore119077 Singapore

Target speaker extraction (TSE) models are expected to extract the target speech from a cocktail party mixture signal. When only trained with present target speaker samples (PT), these models output noise in the absence of the target speaker (AT). One may enhance the TSE quality by providing the information about the PT and AT. However, the detection of the target speaker is not perfect. In this paper, we propose a new model, TSEV, which performs target speaker extraction and speaker verification simultaneously. The TSEV model outputs an extracted speech and generates two speaker embeddings per inference to detect the target speaker. By sharing the speaker encoder and low-level modules, the speaker verification task can be performed in low signal-to-noise ratio scenarios. We train the TSEV model on multi-talker PT and AT conditions with fully overlapped speech. Experiments verify the superiority of jointly performing two tasks in the proposed model. The TSEV model achieves better verification performance without degrading the extraction performance compared with the baseline. © Shanghai Jiao Tong University 2025.

关键词： Speech recognition

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An Abnormal Audio Generation Method for Fault Diagnosis of Power Transformers

An Abnormal Audio Generation Method for Fault Diagnosis of P...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ben Niu Yangjie Wei Ke Zhang Zhuoran Yu Key Laboratory of Intelligent Computing in Medical Image Northeastern University Shenyang China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Existing deep learning-based models can achieve a prompt diagnosis of operational anomalies by analyzing the audios emitted from power transformers. However, the practical abnormal data are insufficient for model training, resulting in limited diagnostic performance. To address this problem, we propose an abnormal audio generation method based on the improved cycle generative adversarial networks (ImCycleGAN) to augment the limited training dataset. In the ImCycleGAN, the generator and discriminator are redesigned and adversarially trained to generate the realistic-like audios of six abnormal statuses. Moreover, we combine adversarial, cycle consistency and identity mapping losses to optimize the training process of ImCycleGAN and enhance its ability to capture nonlinear features of audios. Finally, the generated data is evaluated in terms of similarity and fault classification. Experimental results show that our method can generate abnormal audios with high similarity to the real ones, and significantly improve the classification accuracy of existing fault diagnosis models.

关键词： Fault diagnosis Training Support vector machines Accuracy Signal processing Generative adversarial networks Generators Power transformers Speech processing Long short term memory

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An Abnormal Audio Generation Method for Fault Diagnosis of Power Transformers

An Abnormal Audio Generation Method for Fault Diagnosis of P...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Niu, Ben Wei, Yangjie Zhang, Ke Yu, Zhuoran Key Laboratory of Intelligent Computing in Medical Image Northeastern University Shenyang China

ISBN: (纸本)9798350368741

关键词： abnormal audio generation fault diagnosis improved cycle generative adversarial networks

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DAEA: Enhancing Entity Alignment in Real-World Knowledge Graphs Through Multi-Source Domain Adaptation 31

DAEA: Enhancing Entity Alignment in Real-World Knowledge Gra...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Yang, Linyan Zhou, Shiqiao Cheng, Jingwei Zhang, Fu Wan, Jizheng Wang, Shou Lee, Mark School of Computer Science and Engineering Northeastern University China School of Computer Science University of Birmingham United Kingdom Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University China

ISBN: (纸本)9798891761964

Entity Alignment (EA) is a critical task in Knowledge Graph (KG) integration, aimed at identifying and matching equivalent entities that represent the same real-world objects. While EA methods based on knowledge representation learning have shown strong performance on synthetic benchmark datasets such as DBP15K, their effectiveness significantly decline in real-world scenarios which often involve data that is highly heterogeneous, incomplete, and domain-specific, as seen in datasets like DOREMUS and AGROLD. Addressing this challenge, we propose DAEA, a novel EA approach with Domain Adaptation that leverages the data characteristics of synthetic benchmarks for improved performance in real-world datasets. DAEA introduces a multi-source KGs selection mechanism and a specialized domain adaptive entity alignment loss function to bridge the gap between real-world data and optimal benchmark data, mitigating the challenges posed by aligning entities across highly heterogeneous KGs. Experimental results demonstrate that DAEA outperforms state-of-the-art models on real-world datasets, achieving a 29.94% improvement in Hits@1 on DOREMUS and a 5.64% improvement on AGROLD. Code is available at https://***/yangxiaoxiaoly/DAEA. © 2025 Association for Computational Linguistics.

关键词： Knowledge graph

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Complementary Learning Subnetworks towards Parameter-Efficient Class-Incremental Learning

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IEEE Transactions on Knowledge and Data Engineering 2025年第6期37卷 3240-3252页

作者： Li, Depeng Zeng, Zhigang Dai, Wei Suganthan, Ponnuthurai Nagaratnam Huazhong University of Science and Technology School of Artificial Intelligence and Automation China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Qatar University KINDI Center for Computing Research College of Engineering Doha Qatar

In the scenario of class-incremental learning (CIL), deep neural networks have to adapt their model parameters to non-stationary data distributions, e.g., the emergence of new classes over time. To mitigate the catastrophic forgetting phenomenon, typical CIL methods either cumulatively store exemplars of old classes for retraining model parameters from scratch or progressively expand model size as new classes arrive, which, however, compromises their practical value due to little attention paid to parameter efficiency. In this paper, we contribute a novel solution, effective control of the parameters of a well-trained model, by the synergy between two complementary learning subnetworks. Specifically, we integrate one plastic feature extractor and one analytical feed-forward classifier into a unified framework amenable to streaming data. In each CIL session, it achieves non-overwritten parameter updates in a cost-effective manner, neither revisiting old task data nor extending previously learned networks;Instead, it accommodates new tasks by attaching a tiny set of declarative parameters to its backbone, in which only one matrix per task or one vector per class is kept for knowledge retention. Experimental results on a variety of task sequences demonstrate that our method achieves competitive results against state-of-the-art CIL approaches, especially in accuracy gain, knowledge transfer, training efficiency, and task-order robustness. Furthermore, a graceful forgetting implementation on previously learned trivial tasks is empirically investigated to make its non-growing backbone (i.e., a model with limited network capacity) suffice to train on more incoming tasks. © 2025 IEEE. All rights reserved.

关键词： Contrastive Learning

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