Sea clutter is a kind of ubiquitous interference in sea-detecting radars, which will definitely influence target detection. An accurate sea prediction method supposed to be beneficial while existing methods are based on the one-step-ahead prediction. In this paper, network (SCPNet) proposed achieve k-step-ahead characteristics clutter. The SCPNet takes sequence-to-sequence (Seq2Seq) structure a...

Recently, neural sequence-to-sequence (Seq2Seq) models have been applied to the problem of grapheme-to-phoneme (G2P) conversion. These models offer a straightforward way of modeling the conversion by jointly learning the alignment and translation of input to output tokens in an end-to-end fashion. However, until now this approach did not show improved error rates on its own compared to traditio...

Recent studies showed that the sequenceto-sequence (seq2seq) model is a promising approach for morphological reinflection. At the CoNLL-SIGMORPHON 2017 Shared Task for universal morphological reinflection, we basically followed the approach with some minor variations. The results were remarkable in a certain sense. In high-resource scenarios our system achieved 91.46% accuracy (only modestly be...

Water quality prediction plays a crucial role in both enterprise management and government environmental management. However, due to the variety water data, inconsistent frequency of data acquisition, inconsistency organization, volatility sparsity predicting accurately efficiently has become key problem. This paper presents recurrent neural network method based on sequence-to-sequence (seq2seq...

This paper describes our participation in STC-2 Chinese subtask of NTCIR-13. All runs are submitted for both two tasks, namely generation-based task and retrieval-based task. Various methods based on Seq2Seq framework were used to generate the responses. Interaction-focused method based on deep learning models is used to deal with relevance between queries and comments. As for generation-based ...

Task-oriented dialogue systems can efficiently serve a large number of customers and relieve people from tedious works. However, existing task-oriented dialogue systems depend on handcrafted actions and states or extra semantic labels, which sometimes degrades user experience despite the intensive human intervention. Moreover, current user simulators have limited expressive ability so that deep...

Human interactions and human-computer interactions are strongly influenced by style as well as content. Adding a persona to a chatbot makes it more human-like and contributes to a better and more engaging user experience. In this work, we propose a design for a chatbot that captures the style of Star Trek by incorporating references from the show along with peculiar tones of the fictional chara...

The recently proposed Sequence-to-Sequence (seq2seq) framework advocates replacing complex data processing pipelines, such as an entire automatic speech recognition system, with a single neural network trained in an end-to-end fashion. In this contribution, we analyse an attention-based seq2seq speech recognition system that directly transcribes recordings into characters. We observe two shortc...

Operators in the main control room of a nuclear power plant have crucial role supervising all operations, and any human error can be fatal. By providing operators with information regarding future trends safety-critical parameters based on their actions, errors detected prevented timely manner. This paper proposed Sequence-to-Sequence (Seq2Seq)-based Long Short-Term Memory (LSTM) model to predi...

Neural Network Elements. Deep learning is the name we use for “stacked neural networks”; that is, networks composed of several layers. The layers are made of nodes. A node is just a place where computation happens, loosely patterned on a neuronin the human brain, which fires when it encounters sufficient stimuli. Deep Neural Networks (DNNs) are powerful models that have achieved excellent perfo...