The article studies the interrelation of Languages of Colored Petri Nets and Traditional formal languages. The author constructed the graph of Colored Petri Net, which generates L* Context-free language. This language...The article studies the interrelation of Languages of Colored Petri Nets and Traditional formal languages. The author constructed the graph of Colored Petri Net, which generates L* Context-free language. This language may not be modeled using standard Petri Nets [1]. The Venn graph and diagram that the author modified [1], show the interrelation between languages of Colored Petri Nets and some Traditional languages. Thus the class of languages of Colored Petri Nets is supposed to include an entire class of Context-free languages.展开更多
In bilingual translation,attention-based Neural Machine Translation(NMT)models are used to achieve synchrony between input and output sequences and the notion of alignment.NMT model has obtained state-of-the-art perfo...In bilingual translation,attention-based Neural Machine Translation(NMT)models are used to achieve synchrony between input and output sequences and the notion of alignment.NMT model has obtained state-of-the-art performance for several language pairs.However,there has been little work exploring useful architectures for Urdu-to-English machine translation.We conducted extensive Urdu-to-English translation experiments using Long short-term memory(LSTM)/Bidirectional recurrent neural networks(Bi-RNN)/Statistical recurrent unit(SRU)/Gated recurrent unit(GRU)/Convolutional neural network(CNN)and Transformer.Experimental results show that Bi-RNN and LSTM with attention mechanism trained iteratively,with a scalable data set,make precise predictions on unseen data.The trained models yielded competitive results by achieving 62.6%and 61%accuracy and 49.67 and 47.14 BLEU scores,respectively.From a qualitative perspective,the translation of the test sets was examined manually,and it was observed that trained models tend to produce repetitive output more frequently.The attention score produced by Bi-RNN and LSTM produced clear alignment,while GRU showed incorrect translation for words,poor alignment and lack of a clear structure.Therefore,we considered refining the attention-based models by defining an additional attention-based dropout layer.Attention dropout fixes alignment errors and minimizes translation errors at the word level.After empirical demonstration and comparison with their counterparts,we found improvement in the quality of the resulting translation system and a decrease in the perplexity and over-translation score.The ability of the proposed model was evaluated using Arabic-English and Persian-English datasets as well.We empirically concluded that adding an attention-based dropout layer helps improve GRU,SRU,and Transformer translation and is considerably more efficient in translation quality and speed.展开更多
1 Introduction and main contributions Finite automata are dynamical systems with discrete inputs and outputs, which belong to the domain of logical systems and have a wide range of applications. In engineering, due to...1 Introduction and main contributions Finite automata are dynamical systems with discrete inputs and outputs, which belong to the domain of logical systems and have a wide range of applications. In engineering, due to the excellent hardware qualities of simple structure, low power consumption and low electromagnetic noise, etc., finite automata are used in avionics and nuclear engineering, where the environment is bad and require strict safety. In science, finite automata serve as one of the main molding tools for discrete event dynamic systems (DEDS)(others are Petri nets, Markov chains and queuing networks, etc.). Studying DEDS is one of the major ways to study the cyber physical systems (CPS) which is the core content of Industry 4.0.展开更多
文摘The article studies the interrelation of Languages of Colored Petri Nets and Traditional formal languages. The author constructed the graph of Colored Petri Net, which generates L* Context-free language. This language may not be modeled using standard Petri Nets [1]. The Venn graph and diagram that the author modified [1], show the interrelation between languages of Colored Petri Nets and some Traditional languages. Thus the class of languages of Colored Petri Nets is supposed to include an entire class of Context-free languages.
基金This work was supported by the Institute for Big Data Analytics and Artificial Intelligence(IBDAAI),Universiti Teknologi Mara,Shah Alam,Selangor.Malaysia.
文摘In bilingual translation,attention-based Neural Machine Translation(NMT)models are used to achieve synchrony between input and output sequences and the notion of alignment.NMT model has obtained state-of-the-art performance for several language pairs.However,there has been little work exploring useful architectures for Urdu-to-English machine translation.We conducted extensive Urdu-to-English translation experiments using Long short-term memory(LSTM)/Bidirectional recurrent neural networks(Bi-RNN)/Statistical recurrent unit(SRU)/Gated recurrent unit(GRU)/Convolutional neural network(CNN)and Transformer.Experimental results show that Bi-RNN and LSTM with attention mechanism trained iteratively,with a scalable data set,make precise predictions on unseen data.The trained models yielded competitive results by achieving 62.6%and 61%accuracy and 49.67 and 47.14 BLEU scores,respectively.From a qualitative perspective,the translation of the test sets was examined manually,and it was observed that trained models tend to produce repetitive output more frequently.The attention score produced by Bi-RNN and LSTM produced clear alignment,while GRU showed incorrect translation for words,poor alignment and lack of a clear structure.Therefore,we considered refining the attention-based models by defining an additional attention-based dropout layer.Attention dropout fixes alignment errors and minimizes translation errors at the word level.After empirical demonstration and comparison with their counterparts,we found improvement in the quality of the resulting translation system and a decrease in the perplexity and over-translation score.The ability of the proposed model was evaluated using Arabic-English and Persian-English datasets as well.We empirically concluded that adding an attention-based dropout layer helps improve GRU,SRU,and Transformer translation and is considerably more efficient in translation quality and speed.
基金This work was supported by the National Natural Science Foundation of China (Grant Nos. U 1804150, 61573199)the 2018 Henan Province Science and Technique Foundation (182102210045).
文摘1 Introduction and main contributions Finite automata are dynamical systems with discrete inputs and outputs, which belong to the domain of logical systems and have a wide range of applications. In engineering, due to the excellent hardware qualities of simple structure, low power consumption and low electromagnetic noise, etc., finite automata are used in avionics and nuclear engineering, where the environment is bad and require strict safety. In science, finite automata serve as one of the main molding tools for discrete event dynamic systems (DEDS)(others are Petri nets, Markov chains and queuing networks, etc.). Studying DEDS is one of the major ways to study the cyber physical systems (CPS) which is the core content of Industry 4.0.
