Translational Algorithms: The Heart of a Brain Computer Interface

Singh, Harsimrat; Daly, Ian

doi:10.1007/978-3-319-10978-7_4

Cited by 4 publications

(3 citation statements)

References 72 publications

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“…Unlike traditional statistical machine translation, which uses multiple modules to complete the translation task, neural machine translation uses an encoder-decoder architecture [15][16][17][18][19]. e encoder-decoder architecture was first proposed by Kalchbrenner and Blunsom at the University of Oxford in 2013.…”

Section: Neural Network Structure For Chinese-english Translationmentioning

confidence: 99%

A Study of English Translation Theory Based on Multivariate Statistical Analysis of Random Matrix

2022

Mathematical Problems in Engineering

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With the continuous advancement of artificial intelligence in natural language processing technology, machine translation based on machine learning technology has been fully transformed from traditional machine translation methods to neural network machine translation methods. In particular, the tremendous development of large families has made data-driven a reality. With deep learning as the research and design background, the neural network structure of random matrix multivariate statistical analysis is designed according to the language characteristics of English. The model was tested on a Chinese-English panning model, and the optimal model fused with a Bleu value of 39.53. The model results were applied to a real system to achieve language detection, multidirectional language translation, and manual correction of results to be able to learn long dependencies and overcome the limitations of recurrent neural networks to translate long sentences more fluently.

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Section: Neural Network Structure For Chinese-english Translationmentioning

confidence: 99%

A Study of English Translation Theory Based on Multivariate Statistical Analysis of Random Matrix

2022

Mathematical Problems in Engineering

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“…However, the aforementioned methods have a greater problem of contextual interference in conducting large-scale business English translation, resulting in poor accuracy of translation. To address this problem, this paper proposes a design method for a teaching platform for business English translation based on the logistic model, which uses contextual feature matching and adaptive semantic variable finding methods for automated lexical feature analysis of business English translation, and carries out differential correction of translation in specific business contexts to improve the accuracy of English translation [5].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Analysis of Intelligent Translation Systems and Evaluation Systems for Business English

Chen

2022

Journal of Mathematics

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In order to improve the accuracy of automatic translation of business English, an optimized design of business English translation teaching platform is proposed based on the logistic model combined with deep learning. After using the logistic model to analyze the semantic features of business English translation, the deep learning model is used to segment and mine English images, and the automated lexical feature analysis of business English translation is carried out by using contextual feature matching and adaptive semantic variable finding methods to extract the amount of correlation features between words and vocabulary and to correct the differences in translation in a specific business context to improve the accuracy of English translation. The software design of the platform is carried out under the logistic model, and the platform is mainly divided into a vocabulary database module, an English information processing module, a web interface module, and a human-computer interaction interface module. The test results show that the accuracy of business English translation using this method is good, and the automatic translation capability is strong.

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“…Pilot studies with a similar setting to the real HRI application, if possible, are recommended to confirm the validity of the chosen neural signatures. A typical BCI system consists of multiple component stages such as data acquisition, preprocessing, dimensionality reduction, feature extraction, classification, and application [87]. To construct a BCI based on the selected features, we recommend classical handbooks on BCI application (e.g., [88,89]) for technical details.…”

Section: Developing Bci Systems For Hri Evaluation and Optimizationmentioning

confidence: 99%

Inspiring Real-Time Evaluation and Optimization of Human–Robot Interaction with Psychological Findings from Human–Human Interaction

2023

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The increasingly central role of robotic agents in daily life requires effective human–robot interaction (HRI). For roboticists to optimize interaction design, it is crucial to understand the potential effects of robotic agents on human performance. Yet a systematic specification of contributing factors is lacking, and objective measures of HRI performance are still limited. In these regards, the findings of research on human–human interaction can provide valuable insights. In this review, we break down the complex effects of robotic agents on interacting humans into some basic building blocks based on human–human interaction findings, i.e., the potential effects of physical presence, motor actions, and task co-representation in HRI. For each effect, we advise on future directions regarding its implication. Furthermore, we propose that the neural correlates of these effects could support real-time evaluation and optimization of HRI with electroencephalograph (EEG)-based brain–computer interface (BCI).

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Translational Algorithms: The Heart of a Brain Computer Interface

Cited by 4 publications

References 72 publications

A Study of English Translation Theory Based on Multivariate Statistical Analysis of Random Matrix

A Study of English Translation Theory Based on Multivariate Statistical Analysis of Random Matrix

[Retracted] Analysis of Intelligent Translation Systems and Evaluation Systems for Business English

Inspiring Real-Time Evaluation and Optimization of Human–Robot Interaction with Psychological Findings from Human–Human Interaction

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