Neural Processing Letters最新文献_第5页

Multichannel Multimodal Emotion Analysis of Cross-Modal Feedback Interactions Based on Knowledge Graph 基于知识图谱的跨模态反馈互动的多通道多模态情感分析

IF 3.1 4区计算机科学

Neural Processing Letters Pub Date : 2024-05-29 DOI: 10.1007/s11063-024-11641-w

Shaohua Dong, Xiaochao Fan, Xinchun Ma

{"title":"Multichannel Multimodal Emotion Analysis of Cross-Modal Feedback Interactions Based on Knowledge Graph","authors":"Shaohua Dong, Xiaochao Fan, Xinchun Ma","doi":"10.1007/s11063-024-11641-w","DOIUrl":"https://doi.org/10.1007/s11063-024-11641-w","url":null,"abstract":"Multimodal sentiment analysis is a downstream branch task of sentiment analysis with high attention at present. Previous work in multimodal sentiment analysis have focused on the representation and fusion of modalities, capturing the underlying semantic relationships between modalities by considering contextual information. While this approach is feasible for simple contextual comments, more complex comments require the integration of external knowledge to obtain more accurate sentiment information. However, incorporating external knowledge into sentiment analysis to enhance information complementarity has not been thoroughly investigated. To address this, we propose a multichannel cross-modal feedback interaction model that incorporates the knowledge graph into multimodal sentiment analysis. Our proposed model consists of two main components: the cross-modal feedback recurrent interaction module and the external knowledge module for capturing latent information. The cross-modal interaction employs a self-feedback mechanism during network training, extracting feature representations of each modality and using these representations to mask sensory inputs, allowing the model to perform feedback-based feature masking. The external knowledge graph captures potential semantic information representations in the textual data through knowledge graph embedding. Finally, a global feature fusion module is employed for multichannel multimodal information integration. On two publicly available datasets, our method demonstrates good performance in terms of accuracy and F1 scores, compared to state-of-the-art models and several baselines.","PeriodicalId":51144,"journal":{"name":"Neural Processing Letters","volume":"52 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141170614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Quantized Iterative Learning Bipartite Containment Tracking Control for Unknown Nonlinear Multi-agent Systems 针对未知非线性多代理系统的量化迭代学习双方包含跟踪控制

IF 3.1 4区计算机科学

Neural Processing Letters Pub Date : 2024-05-29 DOI: 10.1007/s11063-024-11649-2

Ruikun Zhang, Shangyu Sang, Jingyuan Zhang, Xue Lin

引用次数: 0

Neural Circuit Policies for Virtual Character Control 虚拟字符控制的神经回路策略

IF 3.1 4区计算机科学

Neural Processing Letters Pub Date : 2024-05-28 DOI: 10.1007/s11063-024-11640-x

Waleed Razzaq, Kashif Raza

{"title":"Neural Circuit Policies for Virtual Character Control","authors":"Waleed Razzaq, Kashif Raza","doi":"10.1007/s11063-024-11640-x","DOIUrl":"https://doi.org/10.1007/s11063-024-11640-x","url":null,"abstract":"The development of high-stakes decision-making neural agents that interact with complex environments, such as video games, is an important aspect of AI research with numerous potential applications. Reinforcement learning combined with deep learning architectures (DRL) has shown remarkable success in various genres of games. The performance of DRL is heavily dependent upon the neural networks resides within them. Although these algorithms perform well in offline testing but the performance deteriorates in noisy and sub-optimal conditions, creating safety and security issues. To address these, we propose a hybrid deep learning architecture that combines a traditional convolutional neural network with worm brain-inspired neural circuit policies. This allows the agent to learn key coherent features from the environment and interpret its dynamics. The obtained DRL agent was not only able to achieve an optimal policy quickly, but it was also the most noise-resilient with the highest success rate. Our research indicates that only 20 control neurons (12 inter-neurons and 8 command neurons) are sufficient to achieve competitive results. We implemented and analyzed the agent in the popular video game Doom, demonstrating its effectiveness in practical applications.\u0000","PeriodicalId":51144,"journal":{"name":"Neural Processing Letters","volume":"44 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141170874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Sample-Adaptive Classification Inference Network 样本自适应分类推理网络

IF 3.1 4区计算机科学

Neural Processing Letters Pub Date : 2024-05-28 DOI: 10.1007/s11063-024-11629-6

Juan Yang, Guanghong Zhou, Ronggui Wang, Lixia Xue

{"title":"Sample-Adaptive Classification Inference Network","authors":"Juan Yang, Guanghong Zhou, Ronggui Wang, Lixia Xue","doi":"10.1007/s11063-024-11629-6","DOIUrl":"https://doi.org/10.1007/s11063-024-11629-6","url":null,"abstract":"Existing pre-trained models have yielded promising results in terms of computational time reduction. However, these models only focus on pruning simple sentences or less salient words, while neglecting the treatment of relatively complex sentences. It is frequently these sentences that cause the loss of model accuracy. This shows that the adaptation of the existing models is one-sided. To address this issue, in this paper, we propose a sample-adaptive training and inference model. Specifically, complex samples are extracted from the training datasets and a dedicated data augmentation module is trained to extract global and local semantic information of complex samples. During inference, simple samples can exit the model via the Sample Adaptive Exit Mechanism, Normal samples pass through the whole backbone model before inference, while complex samples are processed by the Characteristic Enhancement Module after passing through the backbone model. In this way, all samples are processed adaptively. Our extensive experiments on classification tasks datasets in the field of Natural Language Processing demonstrate that our method enhances model accuracy and reduces model inference time for multiple datasets. Moreover, our method is transferable and can be applied to multiple pre-trained models.","PeriodicalId":51144,"journal":{"name":"Neural Processing Letters","volume":"23 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141170615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

An Observer-Based Topology Identification and Synchronization in Finite Time for Fractional Singularly Perturbed Complex Networks via Dynamic Event-Triggered Control 基于观测器的拓扑识别和有限时间内通过动态事件触发控制实现分数奇异扰动复杂网络的同步

IF 3.1 4区计算机科学

Neural Processing Letters Pub Date : 2024-05-28 DOI: 10.1007/s11063-024-11648-3

Lingyan Wang, Huaiqin Wu, Jinde Cao

引用次数: 0

Dual-Channel Autoencoder with Key Region Feature Enhancement for Video Anomalous Event Detection 利用关键区域特征增强的双通道自动编码器进行视频异常事件检测

IF 3.1 4区计算机科学

Neural Processing Letters Pub Date : 2024-05-28 DOI: 10.1007/s11063-024-11634-9

Qing Ye, Zihan Song, Yuqi Zhao, Yongmei Zhang

{"title":"Dual-Channel Autoencoder with Key Region Feature Enhancement for Video Anomalous Event Detection","authors":"Qing Ye, Zihan Song, Yuqi Zhao, Yongmei Zhang","doi":"10.1007/s11063-024-11634-9","DOIUrl":"https://doi.org/10.1007/s11063-024-11634-9","url":null,"abstract":"Video anomaly event detection is crucial for analyzing surveillance videos. Existing methods have limitations: frame-level detection fails to remove background interference, and object-level methods overlook object-environment interaction. To address these issues, this paper proposes a novel video anomaly event detection algorithm based on a dual-channel autoencoder with key region feature enhancement. The goal is to preserve valuable information in the global context while focusing on regions with a high anomaly occurrence. Firstly, a key region extraction network is proposed to perform foreground segmentation on video frames, eliminating background redundancy. Secondly, a dual-channel autoencoder is designed to enhance the features of key regions, enabling the model to extract more representative features. Finally, channel attention modules are inserted between each deconvolution layer of the decoder to enhance the model’s perception and discrimination of valuable information. Compared to existing methods, our approach accurately locates and focuses on regions with a high anomaly occurrence, improving the accuracy of anomaly event detection. Extensive experiments are conducted on the UCSD ped2, CUHK Avenue, and SHTech Campus datasets, and the results validate the effectiveness of the proposed method.","PeriodicalId":51144,"journal":{"name":"Neural Processing Letters","volume":"130 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141170612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Enhancing Document Information Selection Through Multi-Granularity Responses for Dialogue Generation 通过多粒度响应加强对话生成中的文件信息选择

IF 3.1 4区计算机科学

Neural Processing Letters Pub Date : 2024-05-28 DOI: 10.1007/s11063-024-11633-w

Meiqi Wang, Kangyu Qiao, Shuyue Xing, Caixia Yuan, Xiaojie Wang

{"title":"Enhancing Document Information Selection Through Multi-Granularity Responses for Dialogue Generation","authors":"Meiqi Wang, Kangyu Qiao, Shuyue Xing, Caixia Yuan, Xiaojie Wang","doi":"10.1007/s11063-024-11633-w","DOIUrl":"https://doi.org/10.1007/s11063-024-11633-w","url":null,"abstract":"Document information selection is an essential part of document-grounded dialogue tasks, and more accurate information selection results can provide more appropriate dialogue responses. Existing works have achieved excellent results by employing multi-granularity of dialogue history information, indicating the effectiveness of multi-level historical information. However, these works often focus on exploring the hierarchical information of dialogue history, while neglecting the multi-granularity utilization in response, important information that holds an impact on the decoding process. Therefore, this paper proposes a model for document information selection based on multi-granularity responses. By integrating the document selection results at the response word level and semantic unit level, the model enhances its capability in knowledge selection and produces better responses. For the division at the semantic unit level of the response, we propose two semantic unit division methods, static and dynamic. Experiments on two public datasets show that our models combining static or dynamic semantic unit levels significantly outperform baseline models.","PeriodicalId":51144,"journal":{"name":"Neural Processing Letters","volume":"236 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141170972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

A Point-Cluster-Partition Architecture for Weighted Clustering Ensemble 加权聚类组合的点-聚类-分区架构

IF 3.1 4区计算机科学

Neural Processing Letters Pub Date : 2024-05-27 DOI: 10.1007/s11063-024-11618-9

Na Li, Sen Xu, Heyang Xu, Xiufang Xu, Naixuan Guo, Na Cai

{"title":"A Point-Cluster-Partition Architecture for Weighted Clustering Ensemble","authors":"Na Li, Sen Xu, Heyang Xu, Xiufang Xu, Naixuan Guo, Na Cai","doi":"10.1007/s11063-024-11618-9","DOIUrl":"https://doi.org/10.1007/s11063-024-11618-9","url":null,"abstract":"Clustering ensembles can obtain more superior final results by combining multiple different clustering results. The qualities of the points, clusters, and partitions play crucial roles in the consistency of the clustering process. However, existing methods mostly focus on one or two aspects of them, without a comprehensive consideration of the three aspects. This paper proposes a three-level weighted clustering ensemble algorithm namely unified point-cluser-partition algorithm (PCPA). The first step of the PCPA is to generate the adjacency matrix by base clusterings. Then, the central step is to obtain the weighted adjacency matrix by successively weighting three layers, i.e., points, clusters, and partitions. Finally, the consensus clustering is obtained by the average link method. Three performance indexes, namely F, NMI, and ARI, are used to evaluate the accuracy of the proposed method. The experimental results show that: Firstly, as expected, the proposed three-layer weighted clustering ensemble can improve the accuracy of each evaluation index by an average value of 22.07% compared with the direct clustering ensemble without weighting; Secondly, compared with seven other methods, PCPA can achieve better clustering results and the proportion that PCPA ranks first is 28/33.","PeriodicalId":51144,"journal":{"name":"Neural Processing Letters","volume":"38 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141170545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Hierarchical Reinforcement Learning from Demonstration via Reachability-Based Reward Shaping 通过基于可达性的奖励塑造，从示范中进行分层强化学习

IF 3.1 4区计算机科学

Neural Processing Letters Pub Date : 2024-05-27 DOI: 10.1007/s11063-024-11632-x

Xiaozhu Gao, Jinhui Liu, Bo Wan, Lingling An

{"title":"Hierarchical Reinforcement Learning from Demonstration via Reachability-Based Reward Shaping","authors":"Xiaozhu Gao, Jinhui Liu, Bo Wan, Lingling An","doi":"10.1007/s11063-024-11632-x","DOIUrl":"https://doi.org/10.1007/s11063-024-11632-x","url":null,"abstract":"Hierarchical reinforcement learning (HRL) has achieved remarkable success and significant progress in complex and long-term decision-making problems. However, HRL training typically entails substantial computational costs and an enormous number of samples. One effective approach to tackle this challenge is hierarchical reinforcement learning from demonstrations (HRLfD), which leverages demonstrations to expedite the training process of HRL. The effectiveness of HRLfD is contingent upon the quality of the demonstrations; hence, suboptimal demonstrations may impede efficient learning. To address this issue, this paper proposes a reachability-based reward shaping (RbRS) method to alleviate the negative interference of suboptimal demonstrations for the HRL agent. The novel HRLfD algorithm based on RbRS is named HRLfD-RbRS, which incorporates the RbRS method to enhance the learning efficiency of HRLfD. Moreover, with the help of this method, the learning agent can explore better policies under the guidance of the suboptimal demonstration. We evaluate the proposed HRLfD-RbRS algorithm on various complex robotic tasks, and the experimental results demonstrate that our method outperforms current state-of-the-art HRLfD algorithms.","PeriodicalId":51144,"journal":{"name":"Neural Processing Letters","volume":"23 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141170611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Hyperbolic Tangent-Type Variant-Parameter and Robust ZNN Solutions for Resolving Time-Variant Sylvester Equation in Preassigned-Time 用于解决预分配时间内时变西尔维斯特方程的双曲切变参数和稳健 ZNN 解决方案

IF 3.1 4区计算机科学

Neural Processing Letters Pub Date : 2024-05-13 DOI: 10.1007/s11063-024-11482-7

Jiawei Luo, Lei Yu, Bangshu Xiong

{"title":"Hyperbolic Tangent-Type Variant-Parameter and Robust ZNN Solutions for Resolving Time-Variant Sylvester Equation in Preassigned-Time","authors":"Jiawei Luo, Lei Yu, Bangshu Xiong","doi":"10.1007/s11063-024-11482-7","DOIUrl":"https://doi.org/10.1007/s11063-024-11482-7","url":null,"abstract":"To solve a general time-variant Sylvester equation, two novel zeroing neural networks (ZNNs) solutions are designed and analyzed. In the foregoing ZNN solutions, the design convergent parameters (CPs) before the nonlinear stimulated functions are very pivotal because CPs basically decide the convergent speeds. Nonetheless, the CPs are generally set to be constants, which is not feasible because CPs are generally time-variant in practical hardware conditions particularly when the external noises invade. So, a lot of variant-parameter ZNNs (VP-ZNNs) with time-variant CPs have been come up with. Comparing with fixed-parameter ZNNs, the foregoing VP-ZNNs have been illustrated to own better convergence, the downside is that the CPs generally increases over time, and will be probably infinite at last. Obviously, infinite large CPs would lead to be non-robustness of the ZNN schemes, which are not permitted in reality when the exterior noises inject. Moreover, even though VP-ZNNs are convergent over time, the growth of CPs will waste tremendous computing resources. Based on these factors, 2 hyperbolic tangent-type variant-parameter robust ZNNs (HTVPR-ZNNs) have been proposed in this paper. Both the convergent preassigned-time of the HTVPR-ZNN and top-time boundary of CPs are theoretically investigated. Many numerical simulations substantiated the admirable validity of the HTVPR-ZNN solutions.","PeriodicalId":51144,"journal":{"name":"Neural Processing Letters","volume":"26 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140942150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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