Deep learning models have been shown to have great advantages in answer selection tasks.The existing models,which employ encoder-decoder recurrent neural network(RNN),have been demonstrated to be effective.However,the...Deep learning models have been shown to have great advantages in answer selection tasks.The existing models,which employ encoder-decoder recurrent neural network(RNN),have been demonstrated to be effective.However,the traditional RNN-based models still suffer from limitations such as 1)high-dimensional data representation in natural language processing and 2)biased attentive weights for subsequent words in traditional time series models.In this study,a new answer selection model is proposed based on the Bidirectional Long Short-Term Memory(Bi-LSTM)and attention mechanism.The proposed model is able to generate the more effective question-answer pair representation.Experiments on a question answering dataset that includes information from multiple fields show the great advantages of our proposed model.Specifically,we achieve a maximum improvement of 3.8%over the classical LSTM model in terms of mean average precision.展开更多
Radio-Frequency IDentification(RFID)technology is an essential enabler of a multitude of intelligent applications.The robust authentication of RFID system components is critical in providing trustworthy data delivery ...Radio-Frequency IDentification(RFID)technology is an essential enabler of a multitude of intelligent applications.The robust authentication of RFID system components is critical in providing trustworthy data delivery from/to tags.In this paper,we propose an authentication protocol based on monitoring the transmissions between readers and tags in the system.The proposed authentication scheme is based on injecting decoys within the exchanged communications(between RFID readers and tags)and is used in the authentication process.Furthermore,the proposed authentication scheme is mathematically modeled and validated using extensive simulation.The simulations results show that the proposed scheme provides a 100%confidence level in the authentication of tags and detection of compromised readers.展开更多
基金This work was supported in part by the National Natural Science Foundation of China under Grant 61572326,and Grant 61802258the Natural Science Foundation of Shanghai under Grant 18ZR1428300the Shanghai Committee of Science and Technology under Grant 17070502800 and Grant 16JC1403000.
文摘Deep learning models have been shown to have great advantages in answer selection tasks.The existing models,which employ encoder-decoder recurrent neural network(RNN),have been demonstrated to be effective.However,the traditional RNN-based models still suffer from limitations such as 1)high-dimensional data representation in natural language processing and 2)biased attentive weights for subsequent words in traditional time series models.In this study,a new answer selection model is proposed based on the Bidirectional Long Short-Term Memory(Bi-LSTM)and attention mechanism.The proposed model is able to generate the more effective question-answer pair representation.Experiments on a question answering dataset that includes information from multiple fields show the great advantages of our proposed model.Specifically,we achieve a maximum improvement of 3.8%over the classical LSTM model in terms of mean average precision.
基金This material is based on the project supported by the National Science Foundation,CISE/CNS Trustworthy Computing program,under grant No.CNS-1053286.
文摘Radio-Frequency IDentification(RFID)technology is an essential enabler of a multitude of intelligent applications.The robust authentication of RFID system components is critical in providing trustworthy data delivery from/to tags.In this paper,we propose an authentication protocol based on monitoring the transmissions between readers and tags in the system.The proposed authentication scheme is based on injecting decoys within the exchanged communications(between RFID readers and tags)and is used in the authentication process.Furthermore,the proposed authentication scheme is mathematically modeled and validated using extensive simulation.The simulations results show that the proposed scheme provides a 100%confidence level in the authentication of tags and detection of compromised readers.
