Neurocomputing最新文献_第10页

Neural network-based distributed adaptive fault-tolerant containment control 基于神经网络的分布式自适应容错控制

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2025-06-16 DOI: 10.1016/j.neucom.2025.130643

Ziying Fang , Xiaojian Yi , Tao Xu , Xiaoguang Wang

{"title":"Neural network-based distributed adaptive fault-tolerant containment control","authors":"Ziying Fang , Xiaojian Yi , Tao Xu , Xiaoguang Wang","doi":"10.1016/j.neucom.2025.130643","DOIUrl":"10.1016/j.neucom.2025.130643","url":null,"abstract":"<div><div>In practical applications, multi-agent systems (MASs) often face challenges arising from incomplete knowledge of system dynamics, and agent actuators may suffer from faults such as partial failures or biased inputs. This paper investigates the fault-tolerant containment control problem for nonlinear MASs subject to actuator faults and proposes a neural network-based control approach. The system model is assumed to involve unknown nonlinearities, and the follower agents may experience actuator faults. Neural networks are employed to approximate the unknown nonlinear dynamics, and adaptive parameters are introduced and updated online based on the system evolution. An adaptive distributed fault-tolerant control protocol is developed by integrating neural network approximations, adaptive parameter adjustments, and relative state errors between neighboring agents. By dynamically tuning the control effort through the adaptive parameters, the proposed protocol effectively compensates for system nonlinearities and ensures the achievement of the containment control objective, even in the presence of actuator faults. Simulation results are presented to demonstrate the effectiveness of the proposed control strategy.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"648 ","pages":"Article 130643"},"PeriodicalIF":5.5,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interest transfer graph convolutional networks for multi-behavior recommendation 多行为推荐的兴趣转移图卷积网络

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2025-06-16 DOI: 10.1016/j.neucom.2025.130659

Minjie Fan, Yongquan Fan, Yajun Du, Xianyong Li

{"title":"Interest transfer graph convolutional networks for multi-behavior recommendation","authors":"Minjie Fan, Yongquan Fan, Yajun Du, Xianyong Li","doi":"10.1016/j.neucom.2025.130659","DOIUrl":"10.1016/j.neucom.2025.130659","url":null,"abstract":"<div><div>Multi-behavior recommendation is an effective approach to address the problem of data sparsity. Graph-based multi-behavior recommendation is one of the most promising branches. Most research on multi-behavior recommendation uses Graph Convolutional Networks (GCNs) to model user features, as GCNs can capture high-order relationships and global features between nodes. However, the existing approaches suffer from multi-level user interests and noisy interactions. To address this issue, we propose a novel Interest Transfer Graph Convolutional Networks (ITGCN) for multi-behavior recommendation. Specifically, to model multi-level user interests, we designed a multi-level GCN by removing multi-layer aggregation operations to capture high-order relationships between nodes. In addition, to address the issue of noisy interactions, we propose a multi-behavior interest transfer method. This approach uses similarity-based comparison to reduce the impact of noisy interactions. It makes both target and auxiliary behaviors more robust to noise. At the same time, it transfers interests from auxiliary behaviors into the semantic space of the target behavior. Experiments on four datasets demonstrated the effectiveness of ITGCN.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"648 ","pages":"Article 130659"},"PeriodicalIF":5.5,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RoSiLC-RS:A Robust Similar Legal Case Recommender System Empowered by Large Language Model and Step-Back Prompting RoSiLC-RS：基于大语言模型和退步提示的鲁棒相似案例推荐系统

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2025-06-16 DOI: 10.1016/j.neucom.2025.130660

Guohang Zeng , George Tian , Guangquan Zhang , Jie Lu

{"title":"RoSiLC-RS:A Robust Similar Legal Case Recommender System Empowered by Large Language Model and Step-Back Prompting","authors":"Guohang Zeng , George Tian , Guangquan Zhang , Jie Lu","doi":"10.1016/j.neucom.2025.130660","DOIUrl":"10.1016/j.neucom.2025.130660","url":null,"abstract":"<div><div>Legal case recommendation systems face significant challenges in the era of Large Language Models (LLMs). While LLMs offer unprecedented opportunities for understanding legal texts, they also introduce risks through AI-generated false legal content. Our survey reveals concerning gaps in public awareness: 41% of respondents incorrectly believe AI never generates false legal information, while only 6% understand potential legal liabilities. To address these issues, we propose RoSiLC-RS, a Robust Similar Legal Case Recommender System that guides LLMs to understand legal concepts at a higher abstraction level. Our system employs four key components: (1) abstraction processing to extract core legal elements, (2) semantic matching to identify similar case features, (3) LLM-powered explanation generation to provide detailed recommendation rationales, enhancing system explainability, and (4) a specialized detection module to identify and filter AI-generated false content. Comprehensive experiments on real-world legal datasets demonstrate that our method significantly outperforms traditional retrieval approaches in precision, relevance, explainability, and resistance to AI-generated content interference. This research provides both technological solutions and insights for the safe application of LLMs in legal domains.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"648 ","pages":"Article 130660"},"PeriodicalIF":5.5,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning referee evaluation and assessing action quality from coarse to fine in diving sport 学习裁判员评价，评价跳水运动中由粗到精的动作质量

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2025-06-16 DOI: 10.1016/j.neucom.2025.130664

Hong-Ming Qiu , Hong-Bo Zhang , Qing Lei , Jing-Hua Liu , Ji-Xiang Du

{"title":"Learning referee evaluation and assessing action quality from coarse to fine in diving sport","authors":"Hong-Ming Qiu , Hong-Bo Zhang , Qing Lei , Jing-Hua Liu , Ji-Xiang Du","doi":"10.1016/j.neucom.2025.130664","DOIUrl":"10.1016/j.neucom.2025.130664","url":null,"abstract":"<div><div>Intelligently assessing the quality of athletic performances in sports scenarios remains a fascinating challenge in computer vision. However, unraveling the subtle distinctions between two similar actions in videos and mapping those video representations to quality scores remain significant obstacles. To address these challenges, this work redefines the paradigm of quality score estimation from traditional relative quality score prediction to relative referee score prediction. To make this shift, a cross-feature fusion module rooted in Transformer-based video representation is introduced, to improve pairwise video feature learning in the realm of action quality assessment. Then, a novel contrastive action parsing decoder module generates mid-level representations to effectively connect visual features with detailed quality scores. Both modules utilize cross-attention mechanisms; the former refines the pairwise video features to represent the differences between video pairs, while the latter updates the input queries corresponding to each referee’s evaluation. Finally, to achieve precise quality score estimation, we introduce a meticulous coarse-to-fine decision process, integrating a score classifier and offset regressor. After validation on challenging diving datasets, including MTL-AQA, FineDiving, and TASD-2, the experimental results show that the proposed approach demonstrates effectiveness and feasibility when compared with state-of-the-art methods.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"648 ","pages":"Article 130664"},"PeriodicalIF":5.5,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Language-guided Alignment and Distillation for Source-free Domain Adaptation 面向无源域自适应的语言引导对齐和精馏

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2025-06-16 DOI: 10.1016/j.neucom.2025.130501

Jiawen Peng, Jiaxin Chen, Rong Pan, Andy J. Ma

{"title":"Language-guided Alignment and Distillation for Source-free Domain Adaptation","authors":"Jiawen Peng, Jiaxin Chen, Rong Pan, Andy J. Ma","doi":"10.1016/j.neucom.2025.130501","DOIUrl":"10.1016/j.neucom.2025.130501","url":null,"abstract":"<div><div>Source-free domain adaptation (SFDA) is a practical problem in which a pre-trained source model is adapted to an unlabeled target domain without accessing the labeled source data. Although recent studies have successfully incorporated vision–language models (VLMs) like CLIP into SFDA frameworks, the performance of existing methods may be limited due to their reliance on coarse-grained class prompts, in which fine-grained textual knowledge has not been fully exploited. To overcome this limitation, we develop a novel framework of Language-guided Alignment and Distillation (LAD) by integrating visual features with fine-grained textual descriptions generated by pre-trained captioning models. Our method consists of two innovative designs, i.e., category-aware modality alignment (CMA) and language-guided knowledge distillation (LKD). CMA aligns cross-modal feature representations with a gating function to filter out high-confidence same-class samples from negatives to preserve intra-class similarity. LKD better adapts the vision encoder to the target domain through adaptive modality fusion and dual-level distillation guided by both visual and textual modalities. Extensive experiments on five benchmarks, including <em>both image and video recognition</em>, demonstrate that our method consistently outperforms the state of the arts for SFDA, e.g., +2.1% in Office-Home and +4.3% in UCF-HMDB.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"648 ","pages":"Article 130501"},"PeriodicalIF":5.5,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MTR-MSE: Motion-Text Retrieval Method Based on Motion Semantics Expansion 基于运动语义扩展的运动文本检索方法

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2025-06-16 DOI: 10.1016/j.neucom.2025.130632

Daoliang Xu , Tianyou Zheng , Yang Zhang , Xiaodong Yang , Weiwei Fu

引用次数: 0

Digital-twin-based AGV cluster dynamic scheduling for solar cell production workshop using deep reinforcement learning 基于深度强化学习的太阳能电池生产车间数字孪生AGV集群动态调度

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2025-06-15 DOI: 10.1016/j.neucom.2025.130772

Zhuo Zhou , Liyun Xu , Yiyang Chen , Liqiang Liao , Zhun Xu

{"title":"Digital-twin-based AGV cluster dynamic scheduling for solar cell production workshop using deep reinforcement learning","authors":"Zhuo Zhou , Liyun Xu , Yiyang Chen , Liqiang Liao , Zhun Xu","doi":"10.1016/j.neucom.2025.130772","DOIUrl":"10.1016/j.neucom.2025.130772","url":null,"abstract":"<div><div>In recent years, the demand for renewable energy sources, notably solar energy, has rapidly increased. As the most essential photovoltaic module, solar cells with high cleanliness and fragility rely on automated guided vehicles (AGVs) for transportation between various processes. However, the solar cell production workshop with massive AGVs has the characteristics of high dynamics, complexity, and uncertainty, which makes the traditional AGV scheduling methods unable to meet the dynamic scheduling requirements. Therefore, this paper proposes a digital-twin-based (DT-based) AGV cluster dynamic scheduling method using deep reinforcement learning (DRL)<strong>.</strong> Firstly, a DT-based AGV cluster dynamic scheduling framework is constructed, ensuring operational synergy among DT, decision-making model formulation, and real-world application. Secondly, an AGV cluster dynamic scheduling mathematical model that minimizes the average waiting time is established. Thirdly, the problem of AGV cluster dynamic scheduling is transformed into a Markov Decision Process (MDP) with detailed descriptions. Moreover, an improved soft actor-critic (ISAC) DRL algorithm, adding the Softmax function to the actor network and introducing a multi-stage sample selection strategy, is implemented to resolve the established MDP model. Finally, the six cases derived from real-world solar cell production workshops are studied, and the results demonstrate the effectiveness of the proposed methodology.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"648 ","pages":"Article 130772"},"PeriodicalIF":5.5,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144364899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MSHRT-Net: Multi-scale hierarchical residual transfer network for image manipulation detection and localization MSHRT-Net：用于图像处理检测和定位的多尺度分层残差传递网络

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2025-06-15 DOI: 10.1016/j.neucom.2025.130788

Xiao Yang , Xiuli Chai , Zhihua Gan , Lvchen Cao , Yushu Zhang

{"title":"MSHRT-Net: Multi-scale hierarchical residual transfer network for image manipulation detection and localization","authors":"Xiao Yang , Xiuli Chai , Zhihua Gan , Lvchen Cao , Yushu Zhang","doi":"10.1016/j.neucom.2025.130788","DOIUrl":"10.1016/j.neucom.2025.130788","url":null,"abstract":"<div><div>The proliferation of malicious image tampering has triggered a trust crisis in the authenticity of visual content, posing potential risks. However, existing methods have limitations when dealing with complex tampering. With the rapid development of forgery techniques and the increasing stealthiness of tampering methods, these methods are gradually becoming ineffective, struggling to effectively detect and accurately locate the tampered areas in images. To address this issue, we have developed the Multi-Scale Hierarchical Residual Transfer Network (MSHRT-Net), which focuses on edge texture and multi-scale information extraction for efficient image tampering detection and localization. Specifically, the Adaptive Gabor Texture Extractor (AGTE) employs a dual-stream-like structure with edge texture and spatial features extracted in parallel. To enhance the expressiveness of the extracted features, we presented the Multi-Scale Hierarchical Residual Module (MSHRM) as the encoder-decoder layer of the backbone network, which captured global and local information via three parallel branches at distinct scales. Subsequently, the Detail-Preserving Skip Module (DPSM), constructed with skip connections, further improves the network’s feature-capturing capability. Additionally, to address inconsistencies between features at different scales, we designed a Dual-Dimensional Attention Module (DAM), which filtered critical information from coarse feature maps while suppressing irrelevant content. Finally, to address tampering-type imbalance in training data, we proposed a class of loss functions that improved the model’s ability to detect and localize various types of tampering. Extensive experimental validation on multiple datasets demonstrates that our model surpasses previous methods, particularly on the DSO dataset with realistic scenarios (pixel-level AUC and IoU: 0.993, 0.966).</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"648 ","pages":"Article 130788"},"PeriodicalIF":5.5,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FATL: Frozen-feature augmentation transfer learning for few-shot long-tailed sonar image classification FATL：基于定格特征增强的多镜头长尾声纳图像分类迁移学习

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2025-06-15 DOI: 10.1016/j.neucom.2025.130652

Zhongyu Bai, Hongli Xu, Qichuan Ding, Xiangyue Zhang

{"title":"FATL: Frozen-feature augmentation transfer learning for few-shot long-tailed sonar image classification","authors":"Zhongyu Bai, Hongli Xu, Qichuan Ding, Xiangyue Zhang","doi":"10.1016/j.neucom.2025.130652","DOIUrl":"10.1016/j.neucom.2025.130652","url":null,"abstract":"<div><div>Current deep learning-based approaches for sonar image classification have demonstrated effectiveness in scenarios with abundant and balanced samples. However, real-world applications often face challenges such as few-shot learning and long-tailed distribution due to the inherent limitations of underwater sonar image acquisition. In this work, a Frozen-feature Augmentation Transfer Learning (FATL) framework is proposed for few-shot long-tailed sonar image classification. The feature extractor and classifier are independently frozen during training to improve generalization capability. A frozen-feature augmentation module with contrastive learning is introduced to enrich the representational capacity of the feature space in both value and channel dimensions. In addition, a balanced sampling strategy that divides the training set into multiple balanced subsets is employed to mitigate sample bias during classifier training. Extensive experiments conducted on the three public sonar image datasets (KLSG, FLSMDD, and NKSID) indicate that the proposed FATL framework achieves superior performance and outperforms existing approaches in the few-shot long-tailed sonar image classification task.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"648 ","pages":"Article 130652"},"PeriodicalIF":5.5,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144364845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LitANFIS: Literal-aware Adaptive Neuro-Fuzzy Inference System to learn Conjunctive Normal Form LitANFIS：文字感知自适应神经模糊推理系统，用于学习合词范式

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2025-06-14 DOI: 10.1016/j.neucom.2025.130658

Mohammad Mahdi Parchamijalal, Armin Salimi-Badr

{"title":"LitANFIS: Literal-aware Adaptive Neuro-Fuzzy Inference System to learn Conjunctive Normal Form","authors":"Mohammad Mahdi Parchamijalal, Armin Salimi-Badr","doi":"10.1016/j.neucom.2025.130658","DOIUrl":"10.1016/j.neucom.2025.130658","url":null,"abstract":"<div><div>In this paper, a novel neuro-fuzzy inference system able to learn fuzzy rules based on using both positive and negative predicates (literals) is proposed. We propose a novel fuzzy neuron named <em>Fuzzy Positive–Negative Neuron</em> to decide whether a predicate or its negated form should be considered in a fuzzy rule. Consequently, to exclude a lingual value, there is no need for many fuzzy rules to cover the whole universe of discourse, except that value. Instead, the proposed method can consider its negated lingual value. Moreover, the proposed unit can relax the effect of a variable in forming a fuzzy rule that leads to unstructured fuzzy rules. Since the proposed method can consider literals (positive and negative predicates), it can calculate the <em>Peirce’s arrow</em> which is functionally complete and also forms <em>Conjunctive Normal Form</em> (CNF). Moreover, considering literals improves the interpretability of neuro-fuzzy systems by decreasing the number of fuzzy rules along with making the inference closer to the human’s. Considering the presence of negated predicates, to initialize the rules’ parameters we propose a learning scheme based on minimizing the classification error along with the reconstruction one instead of applying usual clustering approaches. Moreover, dropout regularization is also applied during the training process to extract independent fuzzy rules. The performance and number of fuzzy rules of the proposed method have been compared with state-of-the-art studies, and based on these comparisons, it has the best performance along with the most parsimonious structure.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"648 ","pages":"Article 130658"},"PeriodicalIF":5.5,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0