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IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)

作者： Ding, Xiaofei Jiang, Wenjun He, Jiawei Hunan Univ Coll Comp Sci & Elect Engn Changsha Hunan Peoples R China

ISBN: (纸本)9781538693803

Many consumers resort to others' assessments in product reviews for decision making, while their time is limited to deal with many reviews. Therefore, an expert's review which contains all important features in user-generated reviews, is strongly expected. In this paper, we study "how to generate expert's review from a large number of user-generated reviews (i.e., the crowds)." It can be implemented by text summarization, which mainly has two types of the extractive and the abstractive approaches. However, the former may generate redundant and incoherent summaries, while the latter cannot deal with long sequences. Moreover, both approaches usually neglect the sentiment information. To address the above issues, we propose a novel Expert Review Generation model to integrate a multi-attention mechanism with the encoder-decoder framework. We design a comprehensive preprocess strategy to identify the important sentences while keeping users' sentiment in original reviews, and use them as the input of the encoder-decoder generation model, so as to generate non-redundant and coherent summaries. Experimental results in two real world data sets (Idebate and Rotten Tomatoes) demonstrate that our model performs well in expert review generation.

关键词： product review expert review generation text summarization multi-attention mechanism encoder-decoder framework

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Parallel encoder-decoder framework for image captioning

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KNOWLEDGE-BASED SYSTEMS 2023年 282卷

作者： Saeidimesineh, Reyhane Adibi, Peyman Karshenas, Hossein Darvishy, Alireza Univ Isfahan Fac Comp Engn Artificial Intelligence Dept Esfahan Iran Zurich Univ Appl Sci ZHAW Sch Engn Zurich Switzerland

Recent progress in deep learning has led to successful utilization of encoder-decoder frameworks inspired by machine translation in image captioning models. The stacking of layers in encoders and decoders has made it possible to use several modules in encoders and decoders. However, just one type of module in encoder or decoder has been used in stacked models. In this research, we propose a parallel encoder-decoder framework that aims to take advantage of multiple of types modules in encoders and decoders, simultaneously. This framework contains augmented parallel blocks, which include stacking modules or non-stacked ones. Then, the results of the blocks are integrated to extract higher-level semantic concepts. This general idea is not limited to image captioning and can be customized for many applications that utilize encoder-decoder frameworks. We evaluated our proposed method on the MS-COCO dataset and achieved state-of-the-art results. We got 149.92 for CIDEr-D metric outperforming state-of-the-art image captioning models.

关键词： Parallelization encoder-decoder framework Image captioning Natural language processing

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PML-ED: A method of partial multi-label learning by using encoder-decoder framework and exploring label correlation

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INFORMATION SCIENCES 2024年 661卷

作者： Wang, Zhenwu Liu, Fanghan Han, Mengjie Tang, Hongjian Wan, Benting China Univ Min & Technol Beijing Sch Artificial Intelligence Beijing 100083 Peoples R China Dalarna Univ Sch Informat & Engn S-79131 Falun Sweden Jiangxi Univ Finance & Econ Sch Software & IoT Engn Nanchang 330013 Peoples R China

Partial multi-label learning (PML) addresses problems where each instance is assigned a candidate label set and only a subset of these candidate labels is correct. The major challenge of PML is that the training procedure can be easily misguided by noisy labels. Current studies on PML have revealed two significant drawbacks. First, most of them do not sufficiently explore complex label correlations, which could improve the effectiveness of label disambiguation. Second, PML models heavily rely on prior assumptions, limiting their applicability to specific scenarios. In this work, we propose a novel method of PML based on the encoder-decoder framework (PML-ED) to address the drawbacks. PML-ED initially achieves the distribution of label probability through a KNN label attention mechanism. It then adopts Conditional Layer Normalization (CLN) to extract the high-order label correlation and relaxes the prior assumption of label noise by introducing a universal encoder-decoder framework. This approach makes PML-ED not only more efficient compared to the state-of-the-art methods, but also capable of handling the data with large noisy labels across different domains. Experimental results on 28 benchmark datasets demonstrate that the proposed PML-ED model, when benchmarked against nine leading-edge PML algorithms, achieves the highest average ranking across five evaluation criteria.

关键词： Partial multi-label learning Label correlation Label disambiguation encoder-decoder framework Conditional layer normalization

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A variable fidelity approach for predicting aerodynamic wall quantities of hypersonic vehicles using the ConvNeXt encoder-decoder framework

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AEROSPACE SCIENCE AND TECHNOLOGY 2024年 155卷

作者： Yang, Yuxin Yao, Shaobo Xue, Youtao Zhao, Wenwen Wu, Changju Zhejiang Univ Sch Aeronaut & Astronaut Hangzhou 310027 Zhejiang Peoples R China Zhejiang Univ Huanjiang Lab Zhuji 311800 Peoples R China

Computational fluid dynamics (CFD) simulations for obtaining three-dimensional hypersonic vehicle aerodynamic characteristics are resource-intensive. Deep learning offers a promising alternative for predicting aerodynamic wall quantities but typically requires a large dataset, which conflicts with the intention to reduce CFD costs. To address this, we propose a novel prediction model leveraging variable fidelity data to alleviate computational demands. The model utilizes an encoder-decoder architecture with ConvNeXt blocks as operators, processing variable fidelity data as inputs and outputs. We also developed a U-Net with residual blocks (ResUNet) for performance comparison. Transformation and topology patching techniques were applied to tackle the challenges posed by large grid volumes and complex topologies in three-dimensional vehicles. Results demonstrate that the ConvNeXt encoder-decoder predicts peak regions more accurately than ResUNet and aligns closely with CFD in low-value areas. The ConvNeXt encoder-decoder maintains maximum heat flux errors below 1 % and viscous drag errors below 3.81 % across varying angles of attack. It exhibits superior fitting performance with minimal deviation from CFD compared to ResUNet. Prediction accuracy decreases under multiple inflow changes compared to the angle of attack variations alone. Heat flux predictions show high consistency with CFD, with relative errors below 6.38 %, whereas friction predictions exhibit higher errors with 10.28 % maximum error and 0.28 % minimum error. The model accurately predicts friction variation trends in peak regions at the blunt edge's central plane but performs poorly in low-value areas. In summary, the ConvNeXt encoder-decoder accurately predicts the wall quantities under varying multiple inflow conditions.

关键词： Variable fidelity data Aerodynamic wall quantities prediction ConvNeXt block encoder-decoder framework Deep learning

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ED-ACNN: Novel attention convolutional neural network based on encoder-decoder framework for human traffic prediction

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APPLIED SOFT COMPUTING 2020年第PartA期97卷 106688-106688页

作者： Pu, Bin Liu, Yuan Zhu, Ningbo Li, Kenli Li, Keqin Hunan Univ Coll Comp Sci & Elect Engn Changsha Hunan Peoples R China SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

Accurate human traffic prediction, as a vital component of an intelligent transportation system (ITS), can not only reduce traffic congestion and resource consumption, but also provide a foundation for other tasks, such as risk assessment and public safety. Owing to the rapid development of computing power, massive data storage, and parallelization, deep-learning techniques, especially convolutional neural networks (CNNs), have become a powerful tool for traffic-flow forecasting. However, most of these methods in the literature over-emphasize the accuracy of traffic-flow forecasting and ignore its efficiency. It is often beneficial to develop smaller models (e.g., fewer model parameters) to improve efficiency. In this work, taking into account the efficiency and accuracy of the prediction, a novel attention CNN based on an encoder-decoder framework, called ED-ACNN, is proposed. First, the convolutional layer is considered the coding layer to extract spatial and temporal correlations. Then, the deconvolution layer as a decoding layer is expertly designed to reconstruct the future traffic-flow image. Next, the attention mechanism is introduced into the proposed model to capture the correlation between the spatial traffic-flow images' channels. Finally, for the three characteristics of closeness, period, and trend, it is concluded that the closeness feature is the most significant for human traffic prediction in the proposed approach. An extensive experimental evaluation of two types of real-world crowd flow (Beijing and New York City) is presented, and the results show that the proposed method can be very competitive with state-of-the-art baselines. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Attention mechanism encoder-decoder framework Spatio-temporal data mining Traffic-flow prediction

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Exploring EfficientNet Variants for Image Encoding in Auxiliary Signal Guided Knowledge encoder-decoder framework

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Procedia Computer Science 2024年 245卷 391-398页

作者： Matthew Martianus Henry Nur Adhianti Heryanto Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

The growing number of medical images has led to radiologist burnout, which seriously impacts the radiologist's performance. To address the previously mentioned issue, an Auxiliary Signal Guided Knowledge (ASGK) multimodal encoder-decoder framework was designed to automatically generate the medical report based on the proposed medical graph and natural language decoder. It utilizes DenseNet-121 as the image encoder. With DenseNet-121 lack of computational and memory efficiency, this study aims to explore the potential of EfficientNetB0 to EfficientNetB4 as an ASGK image encoder substitute. The framework is trained with IU X-Ray dataset for 30 epochs, with Adam optimizer, a learning rate of 0.01 with 0.8 decay rate, binary cross entropy loss for the medical tags, and cross-entropy loss for the generated medical captions. During the framework training process with each image encoder, the parameter that achieves the highest CIDEr score on the validation set is considered the best image encoder parameter and will be used on the test set. On the test set, EfficientNetB3 as an ASGK image encoder has been shown to increase the CIDEr score to 0.35, a significant increase from the 0.28 CIDEr score obtained by the ASGK using DenseNet-121. This score is only a 1% decrease from the best validation score. It suggests that not only EfficientNetB3 increases the framework's performance, it is also less prone to overfitting. This study has demonstrated that EfficientNetB3 is a potential image encoder substitute for DenseNet-121 in the ASGK framework.

关键词： Automated medical report generation DenseNet-121 EfficientNet encoder-decoder framework

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Nonautoregressive encoder-decoder Neural framework for End-to-End Aspect-Based Sentiment Triplet Extraction

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第9期34卷 5544-5556页

作者： Fei, Hao Ren, Yafeng Zhang, Yue Ji, Donghong Wuhan Univ Sch Cyber Sci & Engn Wuhan 430072 Peoples R China Guangdong Univ Foreign Studies Lab Language & Artificial Intelligence Guangzhou 510420 Peoples R China Westlake Univ Sch Engn Hangzhou 310024 Peoples R China

Aspect-based sentiment triplet extraction (ASTE) aims at recognizing the joint triplets from texts, i.e., aspect terms, opinion expressions, and correlated sentiment polarities. As a newly proposed task, ASTE depicts the complete sentiment picture from different perspectives to better facilitate real-world applications. Unfortunately, several major challenges, such as the overlapping issue and long-distance dependency, have not been addressed effectively by the existing ASTE methods, which limits the performance of the task. In this article, we present an innovative encoder-decoder framework for end-to-end ASTE. Specifically, the ASTE task is first modeled as an unordered triplet set prediction problem, which is satisfied with a nonautoregressive decoding paradigm with a pointer network. Second, a novel high-order aggregation mechanism is proposed for fully integrating the underlying interactions between the overlapping structure of aspect and opinion terms. Third, a bipartite matching loss is introduced for facilitating the training of our nonautoregressive system. Experimental results on benchmark datasets show that our proposed framework significantly outperforms the state-of-the-art methods. Further analysis demonstrates the advantages of the proposed framework in handling the overlapping issue, relieving long-distance dependency and decoding efficiency.

关键词： Task analysis Decoding Sentiment analysis Predictive models Labeling Analytical models Transformers Bipartite matching loss encoder-decoder framework natural language processing (NLP) nonautoregressive decoding pointer network sentiment analysis

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ENDE-GNN: An encoder-decoder GNN framework for Sketch Semantic Segmentation

ENDE-GNN: An Encoder-decoder GNN Framework for Sketch Semant...

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IEEE International Conference on Visual Communications and Image Processing (VCIP)

作者： Zheng, Yixiao Xie, Jiyang Sain, Aneeshan Ma, Zhanyu Song, Yi-Zhe Guo, Jun Beijing Univ Posts & Telecommun Sch Artificial Intelligence PRIS Beijing Peoples R China Univ Surrey SketchX CVSSP Guildford Surrey England

ISBN: (纸本)9781665475921

Sketch semantic segmentation serves as an important part of sketch interpretation. Recently, some researchers have obtained significant results using graph neural networks (GNN) for this task. However, existing GNN-based methods usually neglect the drawing order of sketches thus missing out the sequence information inherent to sketches. Towards solving this problem to achieve better performance on sketch semantic segmentation, we propose an encoder-decoder GNN framework named ENDE-GNN. Working with an auxiliary decoder, our ENDE-GNN guides the GNN backbone network to not only extract the inter-stroke and intra-stroke features, but also pays attention to the drawing order of sketches. This decoder acts during training only, preventing any additional overhead during testing. The proposed ENDE-GNN obtains state-of-the-art performances on three public sketch semantic segmentation datasets, namely SPG, SketchSeg-150K, and CreativeSketch. We further evaluate the effectiveness of ENDE-GNN via ablation studies and visualizations. Codes are available at https://***/PRIS- CV/ENDE For SSS.

关键词： Sketch Semantic Segmentation Graph Neural Network encoder-decoder framework

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VMDI-LSTM-ED: A novel enhanced decomposition ensemble model incorporating data integration for accurate non-stationary daily streamflow forecasting

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JOURNAL OF HYDROLOGY 2025年 653卷

作者： Liu, Jiadong Xu, Teng Lu, Chunhui Hohai Univ Natl Key Lab Water Disaster Prevent Nanjing Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing Peoples R China Hohai Univ Yangtze Inst Conservat & Dev Nanjing Peoples R China

Accurate daily streamflow forecasting is crucial for effective flood control and water management. However, the non-stationary nonlinearity in actual streamflow poses a challenge to accurate forecasting. While decomposition ensemble models can address non-stationary nonlinear streamflow, they still suffer from low forecast accuracy when dealing with highly non-stationary streamflow. Recent studies have shown that incorporating lagged streamflow into long short-term memory (LSTM) networks, known as data integration (DI), represents an effective approach for streamflow forecasting. Nevertheless, existing decomposition ensemble models do not fully leverage the benefits of recent observations. To enhance the precision of non-stationary streamflow forecasting, we propose an improved decomposition ensemble model based on DI called VMDI-LSTM-ED, which utilizes recent observations to improve prediction while processing the subsignals of Variational mode decomposition (VMD) decomposition using LSTM with encoder-decoder framework (LSTM-ED). In order to evaluate the reliability and applicability of VMDI-LSTM-ED and demonstrate its superiority, we conducted model tests in six different basins in the United States and compared VMDI-LSTM-ED with VMD-LSTM, Transformer, and LSTMED. The results indicate that VMDI-LSTM-ED yields the best streamflow forecast result across all tested basins, with an average Nash-Sutcliffe Efficiency (NSE) of 0.880 for 1-day ahead forecasts over the six basins;whereas NSE values for VMD-LSTM, Transformer, and LSTM-ED are only 0.687, 0.556, and 0.368 respectively. In addition, VMDI-LSTM-ED is good not only for high-streamflow areas but also for low-streamflow areas, and the prediction effect of peak streamflow is the best.

关键词： Decomposition ensemble model Deep learning Non-stationary streamflow Data integration encoder-decoder framework

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Triple confidence-aware encoder-decoder model for commonsense knowledge graph completion

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2025年第3期16卷 2073-2091页

作者： Chen, Hongzhi Zhang, Fu Li, Qinghui Li, Xiang Ding, Yifan Zhang, Daqing Cheng, Jingwei Wang, Xing Northeastern Univ Sch Comp Sci & Engn Shenyang 110169 Peoples R China Linyi Univ Sch Informat Sci & Engn Linyi 276005 Peoples R China

Commonsense knowledge is essential for performing inference and retrieval in many artificial intelligence applications, including those in natural language processing and expert system. However, a large amount of valuable commonsense knowledge exists implicitly or is missing in commonsense knowledge graphs (KGs). In this case, commonsense knowledge graph completion (CKGC) is proposed to solve this incomplete problem by inferring missing parts of commonsense triples, e.g., (?, HasPrerequisite, turn computer on) or (get onto web, HasPrerequisite, ?). Some existing methods attempt to learn as much entity semantic information as possible by exploiting the structural and semantic context of entities for improving the performance of CKGC. However, we found that the existing models only pay attention to entities and relations of the commonsense triples and ignore the important confidence (weight) information related to the commonsense triples. In this paper we innovatively introduce commonsense triple confidence into CKGC and propose a confidence-aware encoder-decoder CKGC model. In the encoding stage, we propose a method to incorporate the commonsense triple confidence into RGCN (relational graph convolutional network), so that the encoder can learn a more accurate semantic representation of a triple by considering the triple confidence constraints. Moreover, the commonsense KGs are usually sparse, because there are a large number of entities with an in-degree of 1 in the commonsense triples. Therefore, we propose to add a new relation (called similar edge) between two similar entities for compensating the sparsity of commonsense KGs. In the decoding stage, considering that entities in the commonsense triples are sentence-level entities (e.g., the tail entity turn computer on mentioned above), we propose a joint decoding model by fusing effectively the existing InteractE and ConvTransE models. Experiments show that our new model achieves better performance compared to the

关键词： Commonsense knowledge graph Commonsense knowledge graph completion (CKGC) Triple confidence encoder-decoder framework

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