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Procedia Computer Science 2019年 147卷 124-130页

作者： Huzaifa Rashid Nauman Zafar M Javed Iqbal Hassan Dawood Hussain Dawood Department of Software Engineering University of Engineering and Technology Taxila Pakistan Faculty of Computing and Information Technology University of Jeddah Jeddah Saudi Arabia

Haze is a natural phenomenon in which the dust, smoke and other particles alter the vision of the sky to reduce the visibility. Hazy images cause various visibility problems for traffic user, tourists everywhere, especially in hilly areas where haze and fog are very common. In this paper, a method for single image dehazing using convolutional neural network is proposed. Outdoor images have been used on which particular filters are applied to find the haze in image. Hazy images contain small value in only one-color alpha channel from Red, Blue, green RGB channel. The intensity of these pixels is mainly bestowed by air light depth map. Estimating these low value points of haze transmission map are useful to obtain a high quality dehazed image. An end-to-end encoder-decoder training model is utilized to achieve a high quality dehazed image. The approach is validated on datasets which consists of around 1500 outdoor images. The method also gives transmission map of the hazy image which can further be used to enhance visibility of the scene.

关键词： Image Dehazing Guided filter Transmission map Depth map Atmospheric light encoder-decoder

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A Comparative Analysis of Generative Neural Attention-based Service Chatbot

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第8期13卷 742-751页

作者： Suhaili, Sinarwati Mohamad Salim, Naomie Jambli, Mohamad Nazim Pre Univ Kota Samarahan Sarawak Malaysia Univ Teknol Malaysia Fac Comp Skudai 81310 Johor Malaysia Univ Teknol Malaysia Ibnu Sina Inst Sci & Ind Res UTM Big Data Ctr Skudai 81310 Johor Malaysia Univ Malaysia Sarawak Fac Comp Sci & Informat Technol Kota Samarahan Sarawak Malaysia

Companies constantly rely on customer support to deliver pre-and post-sale services to their clients through websites, mobile devices or social media platforms such as Twitter. In assisting customers, companies employ virtual service agents (chatbots) to provide support via communication devices. The primary focus is to automate the generation of conversational chat between a computer and a human by constructing vir-tual service agents that can predict appropriate and automatic responses to customers' queries. This paper aims to present and implement a seq2seq-based learning task model based on encoder-decoder architectural solutions by training generative chatbots on customer support Twitter datasets. The model is based on deep Recurrent Neural Networks (RNNs) structures which are uni-directional and bi-directional encoder types of Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU). The RNNs are augmented with an attention layer to focus on important information between input and output sequences. Word level embedding such as Word2Vec, GloVe, and FastText are employed as input to the model. Incorporating the base architecture, a comparative analysis is applied where baseline models are compared with and without the use of attention as well as different types of input embedding for each experi-ment. Bilingual Evaluation Understudy (BLEU) was employed to evaluate the model's performance. Results revealed that while biLSTM performs better with Glove, biGRU operates better with FastText. Thus, the finding significantly indicated that the attention-based, bi-directional RNNs (LSTM or GRU) model significantly outperformed baseline approaches in their BLEU score as a promising use in future works.

关键词： Sequence-to-sequence encoder-decoder service chatbot attention-based encoder-decoder Recurrent Neural Network (RNN) Long Short-Term Memory (LSTM) Gated Recurrent Unit (GRU) word embedding

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DMMFnet: A Dual-Branch Multimodal Medical Image Fusion Network Using Super Token and Channel-Spatial Attention

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第8期15卷 696-705页

作者： Zhang, Yukun Wang, Lei Tahir, Muhammad Huang, Zizhen Han, Yaolong Yang, Shanliang Liu, Shilong Saeed, Muhammad Imran Shandong Univ Technol Sch Comp Sci & Technol Zibo 255000 Peoples R China Mohammad Ali Jinnah Univ Dept Comp Sci Block-6 Karachi 75400 Sindh Pakistan

-Multimodal medical image fusion leverages the correlation between different modal images to enhance the information contained within a single medical image. Existing fusion methods often fail to effectively extract multiscale features from medical images and establish long-distance relationships between deep feature blocks. To address these issues, we propose DMMFnet, an encoder-decoder fusion network that utilizes shared and private encoders to extract shared and private features. DMMFnet is based on super token sampling and channel-spatial attention. The shared encoder and decoder use a transformer structure with super token sampling technology to effectively integrate information from different modalities, improving processing efficiency and enhancing the ability to capture key features. The private encoder consists of invertible neural networks and transformer modules, designed to extract local and global features, respectively. A novel transformer module refines attention distribution and feature aggregation to capture superpixel-level global correlations, ensuring that the network effectively captures essential global information, thereby enhancing the quality of the fused image. Experimental results, comparing DMMFnet with nine leading fusion methods, indicate that DMMFnet significantly improves various evaluation metrics and achieves superior visual effects, demonstrating its advanced fusion capability.

关键词： Medical image fusion channel-spatial attention super token sampling encoder-decoder

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Pseudo dense counterfactual augmentation for aspect-based sentiment analysis

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NEUROCOMPUTING 2023年 561卷

作者： Ouyang, Jihong Feng, Shi Wang, Bing Yang, Zhiyao Jilin Univ Coll Comp Sci & Technol Changchun 130022 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130022 Peoples R China

Aspect-based sentiment analysis (ABSA) is a fine-grained text classification task, and the cutting-edge ABSA models have achieved outstanding performance. Unfortunately, the robustness of these ABSA models is neglected. ABSA models must face numerous challenges to be robust, and we concentrate on one of these challenges caused by negation words, such as "not'', "un-". In the actual context, these negation words intuitively result in two problems: negative sensitivity and spurious correlation. First, a negation word tends to reverse the sentiment polarity of a sentence. Meanwhile, in the ABSA datasets, most sentences containing negation words express Negative polarities, which will lead the predictive model to learn the spurious correlation between negation words and polarities. To resolve these ambiguous issues, we are inspired by causal inference and propose a novel data augmentation framework, namely Pseudo Dense Counterfactual Augmentation (PDCaug) for ABSA. Specifically, we initialize a pseudo sequence and employ a multi-head multi layer attention network to achieve counterfactual augmentation for a vanilla sentence in the hidden space. This pseudo sequence will be adversarially trained. PDCaug is a plug-and-play method for various ABSA models, so we evaluate it on discriminative models and generative prompt-based models. Our extensive experiments show that our PDCaug can significantly and consistently outperform several data augmentation methods and ABSA models.

关键词： Aspect-based sentiment analysis encoder-decoder Data augmentation

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Large-scale 3D point-cloud semantic segmentation of urban and rural scenes using data volume decomposition coupled with pipeline parallelism

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AUTOMATION IN CONSTRUCTION 2022年第0期133卷 103995-103995页

作者： Chew, Alvin Wei Ze Ji, Ankang Zhang, Limao Bentley Syst Res Off 1 Harbourfront PlHarbourFront Tower One Singapore 098633 Singapore Harbin Inst Technol Sch Management Harbin 150001 Peoples R China Nanyang Technol Univ Sch Civil & Environm Engn 50 Nanyang Ave Singapore 639798 Singapore

This study proposes a generic approach which performs a series of systematic analyses by first introducing a data volume decomposition method to generate useful data features for performing semantic segmentation analysis involving 3D point-cloud data. Pipeline parallelism protocol is then implemented to accelerate the deep learning model's training phase. Our proposed approach is verified by decomposing around 2.0 billion point-cloud data points, as extracted from an open-source Semantic3D dataset, into many 3D regular structures with defined numbers of voxels. Each derived 3D structure has imposed normality in their data distribution of the respective label classes. Using the optimal hyperparameters for model training, the resulting trained model achieves average overall accuracy (mOA) and average intersection over union (mIOU) values of 0.984 and 0.752, respectively, on a testing dataset having close to 800 million point-cloud data points. The results are comparable with that of other state-of-the-art models in the literature.

关键词： Deep learning Image segmentation analysis encoder-decoder Pipeline parallelism 3D point-cloud data

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Contextual label sensitive gated network for biomedical event trigger extraction

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JOURNAL OF BIOMEDICAL INFORMATICS 2019年第0期95卷 103221-000页

作者： Li, Lishuang Huang, Mengzuo Liu, Yang Qian, Shuang He, Xinyu Dalian Univ Technol Sch Comp Sci & Technol Dalian Peoples R China

Biomedical events play a key role in improving biomedical research. Event trigger identification, extracting the words describing the event types, is a crucial and prerequisite step in the pipeline process of biomedical event extraction. There exist two main problems in previous methods: (1) The association among contextual trigger labels which can provide significant clues is ignored. (2)The weight between word embeddings and contextual features needs to be adjusted dynamically according to the trigger candidate. In this paper, we propose a novel contextual label sensitive gated network for biomedical event trigger extraction to solve the above two problems, which can mix the two parts dynamically and capture the contextual label clues automatically. Furthermore, we also introduce the dependency-based word embeddings to represent dependency-based semantic information as well as attention mechanism to get more focused representations. Experimental results show that our approach advances state-of-the-arts and achieves the best F1-score on the commonly used Multi-Level Event Extraction (MLEE) corpus.

关键词： Biomedical event trigger detection encoder-decoder Bi-GRU Gated mechanism

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2D Semantic Segmentation of the Prostate Gland in Magnetic Resonance Images using Convolutional Neural Networks

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IFAC-PapersOnLine 2021年第15期54卷 394-399页

作者： Silvia P. Vacacela Marco E. Benalcázar Artificial Intelligence and Computer Vision Research Lab Department of Computer Science and Informatics Escuela Politécnica Nacional Quito Ecuador

Convolutional Neural Networks is one of the most commonly used methods for automatic prostate segmentation. However, few studies focus on the segmentation of the two main zones of the prostate: the central gland and the peripheral zone. This work proposes and evaluates two models for 2D semantic segmentation of these two zones of the prostate. The first model (Model-A) uses an encoder-decoder architecture based on the global U-net and the local U-net architectures. The global U-net segments the whole prostate, whereas the local U-net segments the central gland. The peripheral zone is obtained by subtracting the central gland from the whole prostate. On the other hand, the second model (Model-B) uses an encoder-classifier architecture based on the VGG16 network. Model-B performs segmentation by classifying each pixel of a Magnetic Resonance Image (MRI) into three categories: background, central gland, and peripheral zone. Both models are tested using MRIs from the dataset NCI-ISBI 2013 Challenge. The experimental results show a superior segmentation performance for Model-A, encoder-decoder architecture, (DSC = 96.79% ± 0.15% and IoU = 93.79% ± 0.29%) compared to Model-B, encoder-classifier architecture, (DSC = 92.50%± 1.19% and IoU = 86.13% ±2.02%).

关键词： Convolutional Neural Networks Prostate Segmentation Central Gland Peripheral Zone MRIs encoder-decoder U-net encoder-Classifier VGG16 NCI-ISBI 2013

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Deep joint modelling of mixed asynchronous streams - Proof of concept for data-driven simulation of a digital patient under anaesthesia

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Procedia Computer Science 2024年 246卷 2597-2606页

作者： Julien Paris Christine Sinoquet Fadoua Taia-Alaoui Corinne Lejus-Bourdeau CR2TI/UMR 1064 Inserm Nantes University Hospital France LS2N/UMR CNRS 6004 (Digital Science Laboratory at Nantes) Nantes University France GRICAD (Grenoble Alpe Research - Scientific Computing and Data Infrastructure) Grenoble France LE SiMU (Experimental Intensive Care Medicine Simulation Laboratory at Nantes University)/Nantes University Hospital France

In this paper, we investigate the use of an encoder-decoder neural architecture for unsupervised representation learning of mixed asynchronous data, and we introduce the JMETTS (Joint Modelling of Event Traces and Time Series) model. Our aim is to perform short-term forecasts for multivariate time series contextualized by events. As a proof of concept, we focus on a real-world case related to digitally assisted training in anaesthesiology. With a maximal prediction error percentage around 5.5%, the high predictive performance of JMETTS is comparable to its only competitor published to date. The source code is publicly available.

关键词： Joint modelling multivariate time series multivariate event traces artificial neural network encoder-decoder time series forecasting contextualized prediction simulation digital patient training in anaesthesiology

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ED-GAN:基于改进生成对抗网络的法律文本生成模型

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小型微型计算机系统 2019年第5期40卷 1020-1025页

作者：康云云彭敦陆陈章刘丛上海理工大学光电信息与计算机工程学院上海200093

法律文本的自动生成能缓解我国法律服务行业中的人力资源不足的问题,对抗生成网络模型的出现为法律文本的自动生成提供了新思路.本文提出一种基于对抗生成网络的文本自动生成模型——ED-GAN(Generative Adversarial Networks based on E... 详细信息

法律文本的自动生成能缓解我国法律服务行业中的人力资源不足的问题,对抗生成网络模型的出现为法律文本的自动生成提供了新思路.本文提出一种基于对抗生成网络的文本自动生成模型——ED-GAN(Generative Adversarial Networks based on encoder-decoder).在该模型的生成器中,首先将案情要素的关键词序列输入至编码器encoder阶段的LSTM中编码成一隐含层向量,再将这个隐含层向量输入到解码器decoder的LSTM中,并结合其各时间步的输出生成下一时间步的隐含层向量,进而得到各时间步的输出,生成文本序列.模型最后采用CNN网络来鉴别生成文本和真实文本之间的差距.实验验证表明,采用所提模型能够生成较理想的法律文本.

关键词：案情要素 GAN 文本自动生成 LSTMs encoder-decoder CNN

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Model-Based Underwater Image Simulation and Learning-Based Underwater Image Enhancement Method

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INFORMATION 2022年第4期13卷 187-187页

作者： Liu, Yidan Xu, Huiping Zhang, Bing Sun, Kelin Yang, Jingchuan Li, Bo Li, Chen Quan, Xiangqian Chinese Acad Sci Inst Deep Sea Sci & Engn Sanya 572000 Peoples R China

Due to the absorption and scattering effects of light in water bodies and the non-uniformity and insufficiency of artificial illumination, underwater images often present various degradation problems, impacting their utility in underwater applications. In this paper, we propose a model-based underwater image simulation and learning-based underwater image enhancement method for coping with various degradation problems in underwater images. We first derive a simplified model for describing various degradation problems in underwater images, then propose a model-based image simulation method that can generate images with a wide range of parameter values. The proposed image simulation method also comes with an image-selection part, which helps to prune the simulation dataset so that it can serve as a training set for learning to enhance the targeted underwater images. Afterwards, we propose a convolutional neural network based on the encoder-decoder backbone to learn to enhance various underwater images from the simulated images. Experiments on simulated and real underwater images with different degradation problems demonstrate the effectiveness of the proposed underwater image simulation and enhancement method, and reveal the advantages of the proposed method in comparison with many state-of-the-art methods.

关键词： underwater image simulation underwater image enhancement modeling of underwater image degradation encoder-decoder deep learning

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