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Regional prediction of ground-level ozone using a hybrid sequence-to-sequence deep learning approach

JOURNAL OF CLEANER PRODUCTION 2020年 253卷 119841-119841页

作者： Wang, Hong-Wei Li, Xiao-Bing Wang, Dongsheng Zhao, Juanhao He, Hong-di Peng, Zhong-Ren Shanghai Jiao Tong Univ Ctr Intelligent Transportat Syst & Unmanned Aeria Sch Naval Architecture Ocean & Civil Engn State Key Lab Ocean Engn Shanghai 200240 Peoples R China Jinan Univ Inst Environm & Climate Res Guangzhou 510632 Peoples R China Univ Southern Calif Viterbi Sch Engn Dept Comp Sci Los Angeles CA 90089 USA Univ Florida Coll Design Construct & Planning Int Ctr Adaptat Planning & Design iAdapt Sch Landscape Architecture & Planning POB 115706 Gainesville FL 32611 USA

Ozone is one of the most important greenhouse gases and air pollutants in urban areas, and has significantly negative impacts both on the climate change and human health. In addition to alert the public for health concerns, accurate ozone prediction is also crucial to understand the formation mechanisms of surface ozone episodes, and has significant implications for making emission control strategies of ozone precursors such as methane, carbon monoxide, and volatile organic compounds. However, existing methods of ozone concentration prediction could not effectively capture temporal dependency, and most neglect spatial correlations. In this study, a hybrid sequence to sequence model embedded with the attention mechanism is proposed for predicting regional ground-level ozone concentration. In the proposed model, the inherent spatiotemporal correlations in air quality monitoring network are simultaneously extracted, learned and incorporated, and auxiliary air pollution and meteorological information are adaptively involved. The hourly data collected from 35 air quality monitoring stations and 651 meteorological girds in Beijing, China are used to validate the present model. The results demonstrate that the spatiotemporal correlations in the monitoring network present enormous advantages for the regional ozone prediction. Auxiliary data and time lags matching day-of-week or diurnal periods of ozone are confirmed to benefit the improvement of prediction accuracy. Monitoring stations in urban areas exhibit better prediction performances than stations in remote areas. The addressed model outperforms the baseline models, and is proven to have excellent performance in all monitoring station categories of Beijing and different months with significant disparity of ozone concentrations. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Ozone prediction Spatiotemporal correlation Air quality monitoring network sequence to sequence model Deep learning

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Arabic Optical Character Recognition Using Attention Based Encoder-Decoder Architecture 2

Arabic Optical Character Recognition Using Attention Based E...

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3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC)

作者： Sobhi, Mohamed Hifny, Yasser Elkaffas, Saleh Mesbah Arab Acad Sci Technol & Maritime Transport Coll Comp & IT Kerdasa Egypt Univ Helwan Cairo Egypt

ISBN: (纸本)9781450389266

Optical character recognition (OCR) systems are used to convert scanned documents into text. Arabic OCR is an active area of research where high accuracy is demanding. This paper focuses on building a model for converting images that contain Arabic text into their corresponding text using a deep learning approach. This model does not require any knowledge of the underlying language and it is simply trained end-to-end on the KAFD dataset. It combines several standard neural components from vision and natural language processing. Features are extracted from images using Convolutional Neural Networks (CNNs) where the features are arranged in a grid. Each row is then encoded using a Recurrent Neural Networks (RNNs). An RNN decoder with a visual attention mechanism is used to generate the output text. Our preliminary experiments show that the presented approach is effective. The overall obtained accuracy is 89.82%. However, the individual results for some fonts are higher than this score.

关键词： sequence to sequence model Encoder-Decoder Architecture Arabic OCR Convolutional Neural Networks (CNNs) Recurrent Neural Network (RNN) Attention Mechanism Long Short-Term Memory (LSTM)

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Automated Smartification of Notices to Airmen 7

Automated Smartification of Notices to Airmen

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7th Swiss Conference on Data Science (SDS)

作者： Bravin, Marc Pfaffli, Daniel Mazumder, Sita Pouly, Marc Univ Lucerne Appl Sci & Arts Sch IT Rotkreuz Switzerland

ISBN: (纸本)9781728171777

Notices to airmen (NOTAMs) are text messages targeted to pilots to make them aware of short term events and obstacles. Typical examples are military actions in airspaces or closed runways. NOTAMs are written in a strongly compressed format and vocabulary, making the information hard to access for non-trained readers. Smart NOTAM is a commercial service offered by Skyguide that filters irrelevant messages for a specific route or period. Additionally, it converts the messages into a standardized phraseology and removes irrelevant information. Having access to thousands of NOTAMs together with their smartified version, a transformer model was trained and evaluated to automate the currently manual smartification process. NOTAMs contain a large number of factual details such as coordinates or radio frequencies, making the decoding process vulnerable to the inaccurate generation of such details. Therefore, the model was extended by a pointer-network that allows copying words directly from the source text. It achieves a BLEU score above 86% on unseen test data. Visual inspection of fails reveals many ground truth messages written in other languages than English or containing factual details added from other sources.

关键词： notice to airmen machine learning natural language processing sequence to sequence model transformer pointer-generator

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Neigbourhood Embedding for Improving Diffusion based Short Term Traffic Flow Prediction 10

Neigbourhood Embedding for Improving Diffusion based Short T...

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IEEE 10th Symposium on Computer Applications and Industrial Electronics (ISCAIE)

作者： Chan, Yin Hei Lui, Andrew Kwok-Fai Ng, Sin-Chun Open Univ Hong Kong Sch Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9781728150338

This paper describes a short-term traffic flow forecasting approach that combines efficient probability based diffusion mechanism and embedding of surrounding information to handle both ordinary situation and abnormal situations. A discrete diffusion model is insufficient in handle Spatial temporal traffic flow data sampled at regular time interval as there may be information loss during volatile environment. The hybrid model utilize DCRNN as discrete diffusion model and GRU as embedding, combining both for accurate prediction. Preliminary results shows improvement in both microscopic and macroscopic scales indicating the potential of hybrid approach towards accurate and efficient short term traffic flow forecasting

关键词： traffic flow forecasting diffusion convolution gated recurrent unit sequence to sequence model deep learning

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Review Response Generation in E-Commerce Platforms with External Product Information 19

Review Response Generation in E-Commerce Platforms with Exte...

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World Wide Web Conference (WWW)

作者： Zhao, Lujun Song, Kaisong Sun, Changlong Zhang, Qi Huang, Xuanjing Liu, Xiaozhong Fudan Univ Shanghai Peoples R China Alibaba Grp Hangzhou Zhejiang Peoples R China Indiana Univ Bloomington IN 47405 USA Alibaba Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781450366748

"User reviews" are becoming an essential component of e-commerce. When buyers write a negative or doubting review, ideally, the sellers need to quickly give a response to minimize the potential impact. When the number of reviews is growing at a frightening speed, there is an urgent need to build a response writing assistant for customer service providers. In order to generate high-quality responses, the algorithm needs to consume and understand the information from both the original review and the target product. The classical sequence-to-sequence (Seq2Seq) methods can hardly satisfy this requirement. In this study, we propose a novel deep neural network model based on the Seq2Seq framework for the review response generation task in e-commerce platforms, which can incorporate product information by a gated multi-source attention mechanism and a copy mechanism. Moreover, we employ a reinforcement learning technique to reduce the exposure bias problem. To evaluate the proposed model, we constructed a large-scale dataset from a popular e-commerce website, which contains product information. Empirical studies on both automatic evaluation metrics and human annotations showthat the proposed model can generate informative and diverse responses, significantly outperforming state-of-the-art text generation models.

关键词： Review Response Generation Neural Network sequence to sequence model Gated Multi-Source Attention Mechanism Reinforcement Learning

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INTER- AND INTRA-PATIENT ECG HEARTBEAT CLASSIFICATION FOR ARRHYTHMIA DETECTION: A sequence TO sequence DEEP LEARNING APPROACH 44

INTER- AND INTRA-PATIENT ECG HEARTBEAT CLASSIFICATION FOR AR...

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44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Mousavi, Sajad Afghah, Fatemeh No Arizona Univ Sch Informat Comp & Cyber Syst Flagstaff AZ 86011 USA

ISBN: (纸本)9781479981311

Electrocardiogram (ECG) signal is a common and powerful tool to study heart function and diagnose several abnormal arrhythmias. While there have been remarkable improvements in cardiac arrhythmia classification methods, they still cannot offer acceptable performance in detecting different heart conditions, especially when dealing with imbalanced datasets. In this paper, we propose a solution to address this limitation of current classification approaches by developing an automatic heartbeat classification method using deep convolutional neural networks and sequence to sequence models. We evaluated the proposed method on the MIT-BIH arrhythmia database, considering the intra-patient and inter-patient paradigms, and the AAMI EC57 standard. The evaluation results for both paradigms show that our method achieves the best performance in the literature (a positive predictive value of 96.46% and sensitivity of 100% for the category S, and a positive predictive value of 98.68% and sensitivity of 97.40% for the category F for the intra-patient scheme;a positive predictive value of 92.57% and sensitivity of 88.94% for the category S, and a positive predictive value of 99.50% and sensitivity of 99.94% for the category V for the inter-patient scheme.).

关键词： ECG analysis heartbeat classification deep learning sequence to sequence model RNNs

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Automated Thematic and Emotional Modern Chinese Poetry Composition 8th

Automated Thematic and Emotional Modern Chinese Poetry Compo...

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8th CCF International Conference on Natural Language Processing and Chinese Computing (NLPCC)

作者： Guo, Xiaoyu Chen, Meng Song, Yang He, Xiaodong Zhou, Bowen JD AI Beijing Peoples R China

ISBN: (纸本)9783030322335;9783030322328

Topic and emotion are two essential elements in poetry creation, and also have critical impact on the quality of poetry. Inspired by this motivation, we propose a novel model to inject rich topics and emotions into modern Chinese poetry generation simultaneously in this paper. For this purpose, our model leverages three novel mechanisms including (1) learning specific emotion embeddings and incorporate them into decoding process;(2) mining latent topics and encode them via a joint attention mechanism;and (3) enhancing content diversity by encouraging coverage scores in beam search process. Experimental results show that our proposed model can not only generate poems with rich topics and emotions, but can also improve the poeticness of generated poems significantly.

关键词： Poetry generation Deep learning sequence to sequence model

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Improvement of Chatbot in Trading System for SMEs by Using Deep Neural Network 4

Improvement of Chatbot in Trading System for SMEs by Using D...

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4th IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)

作者： Prasomphan, Sathit King Mongkuts Univ Technol North Bangkok Dept Comp & Informat Sci Fac Appl Sci 1518 Pracharat 1 Rd Bangkok 10800 Thailand

ISBN: (纸本)9781728114101

This research presents a method for developing chatbots to serve their users. In general, these chatbots are used for answering questions in many businesses, providing customer information, providing train schedules, helping customer reservations, virtual assistants;serve as call centers to serve ten million customers automatically. A deep learning based conversational artificial intelligence technique was used as tools for learning conversation between machine and customer. In addition, the steps required are the technique used in conjunction with the convolution neural network technique by using Tensorflow training to improve the accuracy of these chatbots. From the experimental results, using deep learning for chatbots learning, the accuracy is better than the traditional model.

关键词： NLU NLG Word Embedding Tensorflow RNN LSTM sequence to sequence model chatbots

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Semi-Automatic Formatting of Spelled Out Numbers 27

Semi-Automatic Formatting of Spelled Out Numbers

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27th Signal Processing and Communications Applications Conference (SIU)

作者： Guser, Abdullah Samil Erden, Mustafa Arslan, M. Levent Sestek Ses & Iletisim Bilgisayar Teknol Istanbul Turkey Bogazici Univ Elekt Elekt Muhendisligi Istanbul Turkey

ISBN: (纸本)9781728119045

In this paper, we present a semi-automatic method to transform Turkish numeric entities such as telephone numbers, monetary amounts, time and date written in spoken form into their grammatical format using deep neural networks. The method consists of two stages. The first stage consists of sequence labeling model which labels the parts that need to be formatted in a given text and the second stage is a sequence to sequence model which transforms these parts into their numerical format. Given a text normalizer that can transform numerical entities into their spoken format, our method is able to learn reverse transform automatically, without requiring a predetermined set of transform rules. To the best of our knowledge, this is the first work that is conducted on Turkish language. Resulting transformation accuracy of the model in the test set is 92.1%.

关键词： text denormalization deep neural networks sequence labeling model sequence to sequence model long short-term memory

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Context Influence on sequence to sequence Turkish Spelling Correction 27

Context Influence on Sequence to Sequence Turkish Spelling C...

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27th Signal Processing and Communications Applications Conference (SIU)

作者： Buyuk, Osman Erden, Mustafa Arslan, Levent M. Kocaeli Univ Elekt & Haberlesme Muhendisligi Bolumu Kocaeli Turkey Bogazici Univ Elekt & Elekt Muhendisligi Bolumu Istanbul Turkey Sestek Ses & Iletisim Bilgisayar Teknol AS Istanbul Turkey

ISBN: (纸本)9781728119045

In this paper, we implement a Turkish spelling correction system using sequence to sequence (seq2seq) deep neural networks (DNN). In the baseline system, the letters of the misspelled and reference words are fed into the seq2seq model as input and outputs, respectively. In order to improve the performance, we utilize right and left context of the misspelled words. The context words are coded into three letter sequences. The first three consonants of context words are taken when the length of the word is longer than three. The seq2seq models are trained using a large text corpus collected from internet. We randomly add substitution, deletion and insertion spelling errors to the words in the training set. In the experiments, we observe that, the context-dependent model improves the correction performance by 9.2% absolute when compared to the baseline seq2seq model in which no context is utilized.

关键词： sequence to sequence model Turkish spelling correction context words

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