Generating Questions Based on Semi-Automated and End-to-End Neural Network

With the emergence of large-scale knowledge base, how to use triple information to generate natural questions is a key technology in question answering systems. The traditional way of generating questions require a lot of manual intervention and produce lots of noise. To solve these problems, we propose a joint model based on semi-automated model and End-to-End neural network to automatically generate questions. The semi-automated model can generate question templates and real questions combining the knowledge base and center graph. The End-to-End neural network directly sends the knowledge base and real questions to BiLSTM network. Meanwhile, the attention mechanism is utilized in the decoding layer, which makes the triples and generated questions more relevant. Finally, the experimental results on SimpleQuestions demonstrate the effectiveness of the proposed approach.     

Fractional order battery modelling methodologies for electric vehicle applications: Recent advances and perspectives

Science China Technological Sciences - Accurate modelling of lithium ion batteries is crucial for battery management in electric vehicles. Recent studies have revealed the fractional order nature...     

A Preamble Re-utilizing Access Scheme for Machine-Type Communications with Optimal Access Class Barring

Wireless Personal Communications - The existing long term evolution networks originally designed for human-to-human communications are hard to tackle numerous and bursty random access requests from...