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Enhanced Multimodal Sentiment Analysis via Integrated Spatial Position Encoding and Fusion Embedding 认领 引用 被引量：1
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作者 Chenquan Gan Xu Liu +3 位作者 Yu Tang Xianrong Yu Qingyi Zhu Deepak Kumar Jain 《Computers, Materials & Continua》 SCIE EI 2025年第12期5399-5421,共23页
Multimodal sentiment analysis aims to understand emotions from text,speech,and video data.However,current methods often overlook the dominant role of text and suffer from feature loss during integration.Given the vary... Multimodal sentiment analysis aims to understand emotions from text,speech,and video data.However,current methods often overlook the dominant role of text and suffer from feature loss during integration.Given the varying importance of each modality across different contexts,a central and pressing challenge in multimodal sentiment analysis lies in maximizing the use of rich intra-modal features while minimizing information loss during the fusion process.In response to these critical limitations,we propose a novel framework that integrates spatial position encoding and fusion embedding modules to address these issues.In our model,text is treated as the core modality,while speech and video features are selectively incorporated through a unique position-aware fusion process.The spatial position encoding strategy preserves the internal structural information of speech and visual modalities,enabling the model to capture localized intra-modal dependencies that are often overlooked.This design enhances the richness and discriminative power of the fused representation,enabling more accurate and context-aware sentiment prediction.Finally,we conduct comprehensive evaluations on two widely recognized standard datasets in the field—CMU-MOSI and CMU-MOSEI to validate the performance of the proposed model.The experimental results demonstrate that our model exhibits good performance and effectiveness for sentiment analysis tasks. 展开更多
关键词 Multimodal sentiment analysis spatial position encoding fusion embedding feature loss reduction
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Position Encoding Based Convolutional Neural Networks for Machine Remaining Useful Life Prediction 认领 引用 被引量：7
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作者 Ruibing Jin Min Wu +3 位作者 Keyu Wu Kaizhou Gao Zhenghua Chen Xiaoli Li 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2022年第8期1427-1439,共13页
Accurate remaining useful life(RUL)prediction is important in industrial systems.It prevents machines from working under failure conditions,and ensures that the industrial system works reliably and efficiently.Recentl... Accurate remaining useful life(RUL)prediction is important in industrial systems.It prevents machines from working under failure conditions,and ensures that the industrial system works reliably and efficiently.Recently,many deep learning based methods have been proposed to predict RUL.Among these methods,recurrent neural network(RNN)based approaches show a strong capability of capturing sequential information.This allows RNN based methods to perform better than convolutional neural network(CNN)based approaches on the RUL prediction task.In this paper,we question this common paradigm and argue that existing CNN based approaches are not designed according to the classic principles of CNN,which reduces their performances.Additionally,the capacity of capturing sequential information is highly affected by the receptive field of CNN,which is neglected by existing CNN based methods.To solve these problems,we propose a series of new CNNs,which show competitive results to RNN based methods.Compared with RNN,CNN processes the input signals in parallel so that the temporal sequence is not easily determined.To alleviate this issue,a position encoding scheme is developed to enhance the sequential information encoded by a CNN.Hence,our proposed position encoding based CNN called PE-Net is further improved and even performs better than RNN based methods.Extensive experiments are conducted on the C-MAPSS dataset,where our PE-Net shows state-of-the-art performance. 展开更多
关键词 Convolutional neural network(CNN) deep learning position encoding remaining useful life prediction
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Beyond Classical Positional Encodings:A Learnable QFT-Inspired Framework for Transformer Language Models 认领 引用
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作者 Sara Tehsin Tallha Akram +2 位作者 Syed Rameez Naqvi Meshal Alharbi Abdulrahman Alabduljabbar 《Computers, Materials & Continua》 SCIE EI 2026年第9期159-182,共24页
Transformers have become the dominant architecture for sequence modeling in natural language processing;however,their effectiveness critically depends on how positional information is encoded.Conventional positional e... Transformers have become the dominant architecture for sequence modeling in natural language processing;however,their effectiveness critically depends on how positional information is encoded.Conventional positional encodings,while effective,may have limited structural flexibility for capturing complex global sequence relationships.Recent quantum-inspired approaches have sought to address this limitation,yetmany either oversimplify quantum principles or introduce substantial computational or hardware overhead.We introduce a novel Quantum Fourier Transform(QFT)-inspired positional encoding scheme for transformers,motivated by the structured frequency representation of the QFT.Unlike prior approaches that either emulate quantum operations superficially or require complex circuit constructions,the proposed method provides a learnable hybrid encoding that preserves quantuminspired structure while remaining aligned with hardware-efficient circuit primitives and structurally compatible with future near-term quantum implementations.Experiments on WikiText-103 indicate that the proposed encoding achieves competitive perplexity,improved robustness to input scrambling,and stable training behavior relative to alternative quantum-inspired baselines under the evaluated settings.Preliminary circuit-level simulations further suggest favorable noise resilience of the associated encoding primitives.These findings support the potential utility of incorporating quantum-inspired design principles into deep learning architectures and provide a foundation for future exploration at the interface of quantum computing and transformer-based natural language processing(NLP). 展开更多
关键词 LLMs positional encoding Quantum Fourier Transform positional embeddings hybrid quantumclassical models near-term quantum devices quantum transformer
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Bidirectional Transformer with absolute-position aware relative position encoding for encoding sentences 认领 引用 被引量：2
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作者 Le QI Yu ZHANG Ting LIU 《Frontiers of Computer Science》 SCIE EI CSCD 2023年第1期63-71,共9页
Transformers have been widely studied in many natural language processing (NLP) tasks, which can capture the dependency from the whole sentence with a high parallelizability thanks to the multi-head attention and the ... Transformers have been widely studied in many natural language processing (NLP) tasks, which can capture the dependency from the whole sentence with a high parallelizability thanks to the multi-head attention and the position-wise feed-forward network. However, the above two components of transformers are position-independent, which causes transformers to be weak in modeling sentence structures. Existing studies commonly utilized positional encoding or mask strategies for capturing the structural information of sentences. In this paper, we aim at strengthening the ability of transformers on modeling the linear structure of sentences from three aspects, containing the absolute position of tokens, the relative distance, and the direction between tokens. We propose a novel bidirectional Transformer with absolute-position aware relative position encoding (BiAR-Transformer) that combines the positional encoding and the mask strategy together. We model the relative distance between tokens along with the absolute position of tokens by a novel absolute-position aware relative position encoding. Meanwhile, we apply a bidirectional mask strategy for modeling the direction between tokens. Experimental results on the natural language inference, paraphrase identification, sentiment classification and machine translation tasks show that BiAR-Transformer achieves superior performance than other strong baselines. 展开更多
关键词 Transformer relative position encoding bidirectional mask strategy sentence encoder
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PCATNet: Position-Class Awareness Transformer for Image Captioning 认领 引用
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作者 Ziwei Tang Yaohua Yi +1 位作者 Changhui Yu Aiguo Yin 《Computers, Materials & Continua》 SCIE EI 2023年第6期6007-6022,共16页
Existing image captioning models usually build the relation between visual information and words to generate captions,which lack spatial infor-mation and object classes.To address the issue,we propose a novel Position... Existing image captioning models usually build the relation between visual information and words to generate captions,which lack spatial infor-mation and object classes.To address the issue,we propose a novel Position-Class Awareness Transformer(PCAT)network which can serve as a bridge between the visual features and captions by embedding spatial information and awareness of object classes.In our proposal,we construct our PCAT network by proposing a novel Grid Mapping Position Encoding(GMPE)method and refining the encoder-decoder framework.First,GMPE includes mapping the regions of objects to grids,calculating the relative distance among objects and quantization.Meanwhile,we also improve the Self-attention to adapt the GMPE.Then,we propose a Classes Semantic Quantization strategy to extract semantic information from the object classes,which is employed to facilitate embedding features and refining the encoder-decoder framework.To capture the interaction between multi-modal features,we propose Object Classes Awareness(OCA)to refine the encoder and decoder,namely OCAE and OCAD,respectively.Finally,we apply GMPE,OCAE and OCAD to form various combinations and to complete the entire PCAT.We utilize the MSCOCO dataset to evaluate the performance of our method.The results demonstrate that PCAT outperforms the other competitive methods. 展开更多
关键词 Image captioning relative position encoding object classes awareness
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Efficient Iris Recognition via Polar Representation and Radial Stripe Attention 认领 引用
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作者 Trong-Thua Huynh De-Thu Huynh +3 位作者 Cong-Sang Duong Hong-Son Nguyen Quoc H.Nguyen Lam-Thanh Tu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期1171-1189,共19页
Deep iris recognition models are often trained on Cartesian grids,whereas iris texture follows a concentric structure with angular periodicity.This representational mismatch can weaken rotation robustness and limit pu... Deep iris recognition models are often trained on Cartesian grids,whereas iris texture follows a concentric structure with angular periodicity.This representational mismatch can weaken rotation robustness and limit pupil-to-limbus context modeling,while many pipelines still rely on accurate segmentation masks.We propose RadialFormer,an efficient mask-free iris recognition framework that performs representation learning directly in the polar domain.The pipeline first estimates pupil/iris parameters(cx,cy,rin,rout)using a percentile radial-gradient operator with anatomical constraints,and then applies a crop-based polar transform to obtain a compact 64×512unwrapped iris map.To better match polar geometry,we introduce Learnable Polar Position Encoding(LPPE)with separable radial-angular embeddings,where Fourier terms in the angular branch enforce continuity atθ=0/2π.We further propose Radial Stripe Window Attention(RSWA),which computes self-attention within full-height radial stripes and uses modular angular shifting to preserve circular consistency.Trained end-to-end with batch-hard triplet loss under P×K sampling,RadialFormer achieves 99.04%TPR@1%FPR with 0.48%EER on CASIA-V4-Lamp,and 93.63%TPR@1%FPR with 2.92%EER on CASIA-V4-Interval.Ablation and cross-dataset evaluations further validate the contributions of polar processing,LPPE,and RSWA and demonstrate robust generalization across acquisition conditions.Under the same input resolution,RadialFormer reduces computation by about 3.5x compared with a standard transformer baseline while maintaining competitive recognition accuracy. 展开更多
关键词 Iris recognition polar unwrapping vision transformer positional encoding window attention metric learning
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ViT-LPATA:a vision transformer model for autism detection in children using facial images 认领 引用
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作者 Li DENG Wenqiu ZHU Yingbo WU 《Optoelectronics Letters》 EI 2026年第6期379-384,共6页
To address the difficulty in recognizing subtle differences in facial biomarkers in children with autism,a learnable positional encoding enhancement(LPEE)module was combined with the adaptive token aggregation(ATA)mod... To address the difficulty in recognizing subtle differences in facial biomarkers in children with autism,a learnable positional encoding enhancement(LPEE)module was combined with the adaptive token aggregation(ATA)module.The vision transformer with learnable positional encoding and adaptive token aggregation(Vi T-LPATA),a predictive model for autism,was proposed.The model leverages the LPEE module to dynamically capture facial geometric deformation features and integrates the ATA module to enhance the feature representation capability of pathological regions,thereby establishing precise mappings of biomarker differences.Experiments on a publicly available autism facial dataset demonstrated that the Vi T-LPATA achieved optimal performance,with 99.2%accuracy and an area under the curve(AUC)value of 0.940. 展开更多
关键词 facial geometric deformation features predictive model enhance feature adaptive token aggregation vi learnable positional encoding recognizing subtle differences vision transformer adaptive token
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Graph transformer with disease subgraph positional encoding for improved comorbidity prediction 认领 引用
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作者 Xihan Qin Li Liao 《Quantitative Biology》 CAS CSCD 2025年第4期91-100,共10页
Comorbidity,the co-occurrence of multiple medical conditions in a single patient,profoundly impacts disease management and outcomes.Understanding these complex interconnections is crucial,especially in contexts where ... Comorbidity,the co-occurrence of multiple medical conditions in a single patient,profoundly impacts disease management and outcomes.Understanding these complex interconnections is crucial,especially in contexts where comorbidities exacerbate outcomes.Leveraging insights from the human interactome and advancements in graph-based methodologies,this study introduces transformer with subgraph positional encoding(TSPE)for disease comorbidity prediction.Inspired by biologically supervised embedding,TSPE employs transformer's attention mechanisms and subgraph positional encoding(SPE)to capture interactions between nodes and disease associations.Our proposed SPE proves more effective than Laplacian positional encoding,as used in Dwivedi et al.'s graph transformer,underscoring the importance of integrating clustering and disease-specific information for improved predictive accuracy.Evaluated on real clinical benchmark datasets(RR0 and RR1),TSPE demonstrates substantial performance enhancements over the state-of-the-art method,achieving up to 28.24%higher ROC AUC(receiver operating characteristic-area under the curve)and 4.93%higher accuracy.This method shows promise for adaptation to other complex graph-based tasks and applications.The source code is available at GitHub website(xihan-qin/TSPE-GraphTransformer). 展开更多
关键词 comorbidity graph embedding graph transformer human interactome subgraph positional encoding
Sea-Land Segmentation of Remote Sensing Images Based on SDW-UNet 认领 引用 被引量：1
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作者 Tianyu Liu Pengyu Liu +3 位作者 Xiaowei Jia Shanji Chen Ying Ma Qian Gao 《Computer Systems Science & Engineering》 SCIE EI 2023年第5期1033-1045,共13页
Image segmentation of sea-land remote sensing images is of great importance for downstream applications including shoreline extraction,the monitoring of near-shore marine environment,and near-shore target recognition.... Image segmentation of sea-land remote sensing images is of great importance for downstream applications including shoreline extraction,the monitoring of near-shore marine environment,and near-shore target recognition.To mitigate large number of parameters and improve the segmentation accuracy,we propose a new Squeeze-Depth-Wise UNet(SDW-UNet)deep learning model for sea-land remote sensing image segmentation.The proposed SDW-UNet model leverages the squeeze-excitation and depth-wise separable convolution to construct new convolution modules,which enhance the model capacity in combining multiple channels and reduces the model parameters.We further explore the effect of position-encoded information in NLP(Natural Language Processing)domain on sea-land segmentation task.We have conducted extensive experiments to compare the proposed network with the mainstream segmentation network in terms of accuracy,the number of parameters and the time cost for prediction.The test results on remote sensing data sets of Guam,Okinawa,Taiwan China,San Diego,and Diego Garcia demonstrate the effectiveness of SDW-UNet in recognizing different types of sea-land areas with a smaller number of parameters,reduces prediction time cost and improves performance over other mainstream segmentation models.We also show that the position encoding can further improve the accuracy of model segmentation. 展开更多
关键词 Sea-land segmentation UNet depth-wise separable convolution squeeze-excitation position encoding
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Function prediction of grain proteins based on graph transformer and protein interaction network 认领 引用
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作者 Jing Liu Jing Ju +1 位作者 Kai Huang Xiao Guan 《Food Bioscience》 SCIE 2025年第11期690-705,共16页
Grain proteins are essential for human health and food security.However,accurately predicting their biological functions remains challenging due to the difficulty of simultaneously capturing both local structural deta... Grain proteins are essential for human health and food security.However,accurately predicting their biological functions remains challenging due to the difficulty of simultaneously capturing both local structural details and global positional information from protein data.To address this issue,a novel graph-based deep learning model,SPE-GTN(Structural and Positional Encoding Graph Transformer Network),is proposed for grain protein function prediction.In this model,structural and positional encodings are embedded into a protein complex graph to enable the joint extraction of local and global features.Graph Convolutional Networks(GCNs)are employed to aggregate neighborhood information,whereas Transformer mechanisms are used to model longrange dependencies among protein nodes.The proposed SPE-GTN model is evaluated on four datasets comprising proteins from wheat,soybean,maize,and indica rice,representing a diverse range of grain types.Experimental results demonstrate that SPE-GTN achieves a 13.6%improvement in prediction accuracy and a 9.4%enhancement in F1-score.Compared to state-of-the-art methods.Theoretical analysis further validates its capacity to effectively capture complex relationships within protein interaction networks.These findings highlight the effectiveness and generalizability of SPE-GTN in real-world grain protein function prediction tasks and provide novel insights into protein bioinformatics and agricultural genomics. 展开更多
关键词 Grain protein function prediction GNN Transformer Positional encoding Structural encoding
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