Nitrogen dioxide (NO2) is an important precursor of atmospheric aerosol. Forecasting urban NO2 concentration vital for effective control air pollution. This paper proposes a hybrid deep learning model predicting daily average concentrations on the next day, based pollutants, meteorological data, and historical data during 2014 to 2020 in five coastal cities Shandong peninsula, northern China. A...

Rainfall–runoff modeling is a complex hydrological issue that still has room for improvement. This study developed coupled bidirectional long short-term memory (LSTM) with sequence-to-sequence (Seq2Seq) learning (BiLSTM-Seq2seq) model to simulate multi-step-ahead runoff flood events. The LSTM Seq2Seq (LSTM-Seq2Seq) and multilayer perceptron (MLP) was set as benchmarks. results show that: (1) ro...

With the rapid development of network technology and entertainment creation, types movies have become more diverse, which makes users wonder how to choose type movies. In order improve selection efficiency, recommend Algorithm came into being. Deep learning is a research field that has received extensive attention from scholars in recent years. Due characteristics its deep architecture, models ...

Sequence-to-sequence (Seq2Seq) models with attention have excelled at tasks which involve generating natural language sentences such as machine translation, image captioning and speech recognition. Performance has further been improved by leveraging unlabeled data, often in the form of a language model. In this work, we present the Cold Fusion method, which leverages a pre-trained language mode...

This study presents a method to reconstruct a high-resolution image using a deep convolution neural network. We propose a deep model, entitled Deep Block Super Resolution (DBSR), by fusing the output features of a deep convolutional network and a shallow convolutional network. In this way, our model benefits from high frequency and low frequency features extracted from deep and shallow networks...

An accurate and reliable forecast for traffic flow is regarded as one of the foundational functions in an intelligent transportation system. In this paper, a new model forecasting, named EnGS-DGR, designed based on ensemble learning graph convolutional network (GCN), sequence-to-sequence (Seq2Seq) model, dynamic reconfiguration (DGR) algorithm. At first stage, instead employing entire nodes net...

Social media accumulates vast amounts of online conversations that enable datadriven modeling of chat dialogues. It is, however, still hard to utilize the neural network-based SEQ2SEQ model for dialogue modeling in spite of its acknowledged success in machine translation. The main challenge comes from the high degrees of freedom of outputs (responses). This paper presents neural conversational ...

The cascades prediction aims to predict the possible information diffusion path in future based on of social network. Recently, existing researches deep learning have achieved remarkable results, which indicates great potential support cascade task. However, most prior arts only considered either features or user relationship network cascade, leads performance limitation because lack unified mo...

Abstract The pandemic has forced young people to stay away from school and friends, complete online learning at home live home. Therefore, various mental illnesses such as anxiety depression occur more frequently. Chatbot is a communication method that acceptable people. This paper proposes multi-modal chatbot seq2seq framework, which divides the state of into different types through informatio...

Recently, open-domain dialogue systems have attracted growing attention. Most of them use the sequence-to-sequence (Seq2Seq) architecture to generate responses. However, traditional Seq2Seq-based models tend generic and safe responses, which are less informative, unlike human In this paper, we propose a simple but effective Keywords-guided Sequence-to-sequence model (KW-Seq2Seq) uses keywords i...