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Proceedings of the International Conference on Data Science, Machine Learning and Artificial Intelligence

作者： Vivek Tiwari Krutika Bapat Kushashwa R. Shrimali Saurabh K. Singh Basant Tiwari Swati Jain Hemant Kumar Sharma Comp. Sci. & Engg. DSPM IIIT Naya Raipur Chhattisgarh India Computer Science Hawassa University Hawassa Ethiopia Computer Science Govt. J. Yoganandam Chhattisgarh College Raipur (CG) India DKS PGI & Research Center Raipur (CG) India

ISBN: (纸本)9781450387637

Generating medical reports manually is a difficult task, especially in rural areas and in urgent medical cases, where there is an emergency. It can also be error-prone for inexperienced physicians to generate a medical report. There are various deep learning methodologies such as Image captioning, image classification that has been implemented earlier to solve this problem. Generating a medical report automatically is a difficult task, considering the less amount of open-source data available and the paired data which contains medical Images and the report is also limited. One of the challenging tasks is data bias in medical Imaging. A generative encoder-decoder model is suggested to solve this problem in an efficient way. There are various other challenges. First, the medical report itself contains various heterogeneous information such as paragraphs, tags, keywords. Secondly, it is also difficult to identify the abnormal regions in medical images. To solve this problem, a multi-task framework is built, which can perform tag generation and paragraph generation. LSTM (Long Short Term Memory) is built to generate long heterogeneous paragraphs in the medical report. The model working is demonstrated on Chest X-Ray dataset and also on pathology dataset.

关键词： Deep Learning LSTM X-Ray encoder-decoder medical imaging

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Semi-supervised Trajectory Understanding with POI Attention for End-to-End Trip Recommendation

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ACM TRANSACTIONS ON SPATIAL ALGORITHMS AND SYSTEMS 2020年第2期6卷 1–25页

作者： Zhou, Fan Wu, Hantao Trajcevski, Goce Khokhar, Ashfaq Zhang, Kunpeng Univ Elect Sci & Technol China 4Sect 2North Jianshe Rd Chengdu 610054 Sichuan Peoples R China Iowa State Univ Ames IA 50011 USA Univ Maryland College Pk MD 20742 USA

Trip planning/recommendation is an important task for a plethora of applications in urban settings (e.g., tourism, transportation, social outings), relying on services provided by Location-Based Social Networks (LBSN). To provide greater context-awareness in trajectory planning, LBSNs combine historical trajectories of users for generating various hand-crafted features-e.g., geo-tags of photos taken by tourists and textual characteristics derived from reviews. Those features are used to learn tourists' preferences, which are then used to generate a travel plan recommendation. However, many such features are extracted based on prior knowledge or empirical analysis specific to particular datasets, rendering the corresponding solutions not to be generalizable to diverse data sources. Thus, one important question for managing mobility is how to learn an accurate tour planning model based solely on POI visits or user check-ins and without the efforts of hand-crafted feature engineering. Inspired by recent successes of deep learning in sequence learning, we develop a solution to the tour planning problem based on the semi-supervised learning paradigm. An important aspect of our solution is that it does not involve any feature engineering. Specifically, we propose the Trip Recommendation method via trajectory encoder and decoder-a novel end-to-end approach encoding historical trajectories into vectors, while capturing both the intrinsic characteristics of individual POIs and the transition patterns among POIs. We also incorporate historical attention mechanism in our sequence-to-sequence trip recommendation task to improve the effectiveness. Experiments conducted on multiple publicly available LBSN datasets demonstrate significantly superior performance of our method.

关键词： Trip recommendation semi-supervised learning encoder-decoder recurrent neural networks attention mechanism

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Image inspired Chinese couplet generation

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WEB INTELLIGENCE 2020年第3期18卷 217-227页

作者： Yuan, Shengqiong Zhong, Luo Li, Lin Wuhan Univ Technol Wuhan Peoples R China

Chinese couplets, as one of the traditional Chinese culture, is the treasure of Chinese civilization and the inheritance of Chinese history. Given a sentence (namely an antecedent clause), people reply with another sentence (namely a subsequent clause) equal in length. Because of the complexity of the semantic and grammatical rules of couplet, it is not easy to create a suitable couplet that meets the requirements of sentence pattern, context, and flatness. With the development of neural models and natural language processing, automatic generation of Chinese couplets has drawn significant attention due to its artistic and cultural value, most of these works mainly focus on generating couplet by given text information, while visual inspirations for couplet generation have been rarely explored. In this paper, we design a Chinese couplet generation model based on NIC (Neural Image Caption), which can compose a piece of couplet suitable to the artistic conception in an image. At first, we use the improved VGG16 model to predict the input image. The content of the image can be automatically recognized and the corresponding description are generated and translated into Chinese keywords. Then, the encoder-decoder framework is used repeatedly to process these keywords, and finally the couplet can be generated. Moreover, to satisfy special characteristics of couplets, we incorporate the attention mechanism into the encoding-decoding process, which greatly improves the accuracy of couplets generated automatically.

关键词： Convolution neural network machine translation text summary generation encoder-decoder couplet

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General and robust voxel feature learning with Transformer for 3D object detection

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Journal of Measurement Science and Instrumentation 2022年第1期13卷 51-60页

作者： LI Yang GE Hongwei Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Wuxi 214122 China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi 214122 China

The self-attention networks and Transformer have dominated machine translation and natural language processing fields,and shown great potential in image vision tasks such as image classification and object *** by the great progress of Transformer,we propose a novel general and robust voxel feature encoder for 3D object detection based on the traditional *** first investigate the permutation invariance of sequence data of the self-attention and apply it to point cloud *** we construct a voxel feature layer based on the self-attention to adaptively learn local and robust context of a voxel according to the spatial relationship and context information exchanging between all points within the ***,we construct a general voxel feature learning framework with the voxel feature layer as the core for 3D object *** voxel feature with Transformer(VFT)can be plugged into any other voxel-based 3D object detection framework easily,and serves as the backbone for voxel feature *** results on the KITTI dataset demonstrate that our method achieves the state-of-the-art performance on 3D object detection.

关键词： 3D object detection self-attention networks voxel feature with Transformer(VFT) point cloud encoder-decoder

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An autoencoder-based deep learning approach for clustering time series data

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SN APPLIED SCIENCES 2020年第5期2卷 937页

作者： Tavakoli, Neda Siami-Namini, Sima Khanghah, Mahdi Adl Soltani, Fahimeh Mirza Namin, Akbar Siami Georgia Inst Technol Dept Comp Sci Atlanta GA 30332 USA Texas Tech Univ Dept Math & Stat Lubbock TX 79409 USA Univ Debrecen Dept Comp Sci Debrecen Hungary Texas Tech Univ Dept Comp Sci Lubbock TX 79409 USA

This paper introduces a two-stage deep learning-based methodology for clustering time series data. First, a novel technique is introduced to utilize the characteristics (e.g., volatility) of the given time series data in order to create labels and thus enable transformation of the problem from an unsupervised into a supervised learning. Second, an autoencoder-based deep learning model is built to model both known and hidden non-linear features of time series data. The paper reports a case study in which the selected financial and stock time series data of over 70 stock indices are clustered into distinct groups using the introduced two-stage procedure. The results show that the proposed methodology is capable of achieving 87.5% accuracy in clustering and predicting the labels for unseen time series data. The paper also reports an important finding in which it is observed that the performance of both techniques (i.e., autoencoder and Kmeans) are comparable. However, there are a few instances of time series data that are classified differently by the autoencoder-based methodology compared to the Kmeans algorithm. The results may indicate that the proposed deep learning-based approach is taking into account additional hidden features that might be overlooked by conventional Kmeans. The finding raises the question whether the explicit features of data should be analyzed for clustering or more advanced techniques such as deep learning need to be adapted by which hidden features and relationships are explored for clustering purposes.

关键词： Kmeans clustering Financial data analysis Time series clustering Deep learning encoder-decoder Unsupervised learning Supervised learning encoder-decoder Multi-layer perceptron

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4G-VOS: Video Object Segmentation using guided context embedding

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KNOWLEDGE-BASED SYSTEMS 2021年 231卷

作者： Fiaz, Mustansar Zaheer, Muhammad Zaigham Mahmood, Arif Lee, Seung-Ik Jung, Soon Ki Mohamed Bin Zayed Univ Artificial Intelligence Comp Vis Dept Abu Dhabi U Arab Emirates Kyungpook Natl Univ Sch Comp Sci & Engn Daegu South Korea Univ Sci & Technol Daejeon South Korea Elect & Telecommunicat Res Inst Daejeon South Korea Informat Technol Univ Dept Comp Sci Lahore Pakistan

Video Object Segmentation (VOS) is a fundamental task required in many high-level real-world computer vision applications. VOS becomes challenging due to the presence of background distractors as well as to object appearance variations. Many existing VOS approaches use online model updates to capture the appearance variations which incurs high computational cost. Template matching and propagation-based VOS methods, although cost-effective, suffer from performance degradation under challenging scenarios such as occlusion and background clutter. In order to tackle these challenges, we propose a network architecture dubbed 4G-VOS to encode video context for improved VOS performance to tackle these challenges. To preserve long term semantic information, we propose a guided transfer embedding module. We employ a global instance matching module to generate similarity maps from the initial image and the mask. Besides, we use a generative directional appearance module to estimate and dynamically update the foreground/background class probabilities in a spherical embedding space. Moreover, during feature refinement, existing approaches may lose contextual information. Therefore, we propose a guided pooled decoder to exploit the global and local contextual information during feature refinement. The proposed framework is an end-to-end learning architecture that is trained in an offline fashion. Evaluations over three VOS benchmark datasets including DAVIS2016, DAVIS2017, and YouTube-VOS have demonstrated outstanding performance of the proposed algorithm compared to 40 existing state-of-the-art methods. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Video Object Segmentation Feature transfer and matching Spherical embedding Feature refinement Channel convolutional neural networks encoder-decoder

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基于深度学习的瓦斯浓度预测方法研究

基于深度学习的瓦斯浓度预测方法研究

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作者：徐宁可安徽理工大学

学位级别：硕士

瓦斯事故是影响煤矿井下安全工作的主要原因,对瓦斯浓度进行实时精确的监测是防治瓦斯事件的主要措施。但是,常规的瓦斯浓度预测方法存在很大的局限性。因此,建立精确、高效、实时的瓦斯浓度预测方法,对加强国家煤矿产能,改善行业管理水... 详细信息

瓦斯事故是影响煤矿井下安全工作的主要原因,对瓦斯浓度进行实时精确的监测是防治瓦斯事件的主要措施。但是,常规的瓦斯浓度预测方法存在很大的局限性。因此,建立精确、高效、实时的瓦斯浓度预测方法,对加强国家煤矿产能,改善行业管理水平,提高企业经济效益,保护职工生命安全都具有重要的理论价值与现实意义。为了提高瓦斯浓度的预测精度,本文提出了基于深度学习的瓦斯浓度预测方法。大量实验表明本文提出的方法相比于传统的预测算法能够进一步提高模型的整体预测精度。本研究主要做了如下工作:(1)在数据选择方面,本研究首先以瓦斯数据的时空特性为出发点,采用深度学习算法构建瓦斯浓度预测模型。传统的瓦斯浓度预测方法的输入端只考虑到瓦斯浓度序列的时间特性,缺乏了对空间信息的考虑。本文通过将时间序列因子和空间拓扑因子二者相结合,明确输入数据的属性要求,在提高模型预测精度的同时也大大增强了模型的稳定性。(2)在瓦斯浓度预测模型构建方面,本研究通过改进长短期记忆神经网络(LSTM)的门控单元,增强各个门单元之间的相关性,从而提高提取长期序列特征的能力。本文为了避免传统长短期记忆神经网络在有限的存储空间内提取过多的瓦斯浓度和其他监测数据的冗余特征信息,利用改进后的长短期记忆网络进行相关特征提取,并基于此构建encoder-decoder模型对瓦斯浓度进行长期序列预测。并利用改进的蛇优化算法(SO)简化神经网络的调参过程,避免在训练过程中模型出现局部最优解的情况。(3)在残差序列修正方面,本文利用自适应噪声完备集合经验模态分解(CEEMDAN)算法对残差序列进行分解,接着将各本征模函数作为新一轮的输入数据,然后通过改进的变权组合方法将多个本征模函数的预测值组合起来,作为初始预测值的残差修正序列,进一步提高模型的预测精度。为了验证本文提出方法的有效性,本研究采用矿井真实数据集上进行了实验。实验结果表明,本文提出的encoder-decoder框架下的改进长短期记忆神经网络预测模型优于其他对比模型,能够实现长期准确瓦斯浓度预测。图[26]表[4]参[80]

关键词：深度学习 SO encoder-decoder ILSTM 残差修正瓦斯浓度预测

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Domain Adapted Abstractive Summarization of Dialogue using Transfer Learning 21

Domain Adapted Abstractive Summarization of Dialogue using T...

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Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence

作者： Rohit Sroch Course5 AI Labs Course5 Intelligence Pvt. Ltd. India India

ISBN: (纸本)9781450385053

Recently, the abstractive dialogue summarization task has been gaining a lot of attention from researchers. Also, unlike news articles and documents with well-structured text, dialogue differs in the sense that it often comes from two or more interlocutors, exchanging information with each other and having an inherent hierarchical structure based on the sequence of utterances by different speakers. This paper proposes a simple but effective hybrid approach that consists of two modules and uses transfer learning by leveraging pretrained language models (PLMs) to generate an abstractive summary. The first module highlights important utterances, capturing the utterance level relationship by adapting an auto-encoding model like BERT based on the unsupervised or supervised method. And then, the second module generates a concise abstractive summary by adapting encoder-decoder models like T5, BART, and PEGASUS. Experiment results on benchmark datasets show that our approach achieves a state-of-the-art performance by adapting to dialogue scenarios and can also be helpful in low-resource settings for domain adaptation.

关键词： PLMs abstractive summary T5 PEGASUS encoder-decoder BERT dialogue summarization BART

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Inter-Level Feature Balanced Fusion Network for Street Scene Segmentation

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SENSORS 2021年第23期21卷 7844-7844页

作者： Li, Dongqian Fan, Cien Zou, Lian Zuo, Qi Jiang, Hao Liu, Yifeng Wuhan Univ Sch Elect Informat Wuhan 430072 Peoples R China Natl Engn Lab Risk Percept & Prevent NEL RPP Beijing 100041 Peoples R China

Semantic segmentation, as a pixel-level recognition task, has been widely used in a variety of practical scenes. Most of the existing methods try to improve the performance of the network by fusing the information of high and low layers. This kind of simple concatenation or element-wise addition will lead to the problem of unbalanced fusion and low utilization of inter-level features. To solve this problem, we propose the Inter-Level Feature Balanced Fusion Network (IFBFNet) to guide the inter-level feature fusion towards a more balanced and effective direction. Our overall network architecture is based on the encoder-decoder architecture. In the encoder, we use a relatively deep convolution network to extract rich semantic information. In the decoder, skip-connections are added to connect and fuse low-level spatial features to restore a clearer boundary expression gradually. We add an inter-level feature balanced fusion module to each skip connection. Additionally, to better capture the boundary information, we added a shallower spatial information stream to supplement more spatial information details. Experiments have proved the effectiveness of our module. Our IFBFNet achieved a competitive performance on the Cityscapes dataset with only finely annotated data used for training and has been greatly improved on the baseline network.

关键词： semantic segmentation encoder-decoder feature balanced fusion Cityscapes

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MUMC: Minimizing uncertainty of mixture of cues

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IMAGE AND VISION COMPUTING 2021年 115卷 104280-104280页

作者： Patro, Badri N. Kurmi, Vinod K. Kumar, Sandeep Namboodiri, Vinay P. Indian Inst Technol Kanpur Dept Elect Engn Kanpur Uttar Pradesh India Indian Inst Technol Kanpur Dept Comp Sci & Engn Kanpur Uttar Pradesh India Indian Inst Technol Kanpur Dept Math & Stat Kanpur Uttar Pradesh India

Generating natural questions from an image is a semantic task that requires using vision and language modalities to learn multimodal representations. Images can have multiple visual and language cues such as places, captions, and tags. In this paper, we propose a principled deep Bayesian learning framework that combines these cues to produce natural questions. We observe that with the addition of more cues and by minimizing uncertainty in the among cues, the Bayesian network becomes more confident. We propose a Minimizing Uncertainty of Mixture of Cues (MUMC), that minimizes uncertainty present in a mixture of cues experts for generating probabilistic questions. This is a Bayesian framework and the results show a remarkable similarity to natural questions as validated by a human study. Ablation studies of our model indicate that a subset of cues is inferior at this task and hence the principled fusion of cues is preferred. Further, we observe that the proposed approach substantially improves over state-of-the-art benchmarks on the quantitative metrics (BLEU-n, METEOR, ROUGE, and CIDEr). Here, we provide project link for Deep Bayesian VQG: https://***/BVQG/. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Uncertainty estimation Mixture of cues Visual Question Answering Paraphrase Visual Question Generation LSTM CNN encoder-decoder

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