Translation Algorithm Research Articles

System Architecture for Brain-Computer Interface based on Machine Learning and Internet of Things

Brain functions are required to be read for curing neurological illness. Brain-Computer Interface (BCI) connects the brain to the digital world for brain signals receiving, recording, processing, and comprehending. With a Brain-Computer Interface (BCI), the information from the user’s brain is fed into actuation devices, which then carry out the actions programmed into them. The Internet of Things (IoT) has made it possible to connect a wide range of everyday devices. Asynchronous BCIs can benefit from an improved system architecture proposed in this paper. Individuals with severe motor impairments will particularly get benefit from this feature. Control commands were translated using a rule-based translation algorithm in traditional BCI systems, which relied only on EEG recordings of brain signals. Examining BCI technology’s various and cross-disciplinary applications, this argument produces speculative conclusions about how BCI instruments combined with machine learning algorithms could affect the forthcoming procedures and practices. Compressive sensing and neural networks are used to compress and reconstruct ECoG data presented in this article. The neural networks are used to combine the classifier outputs adaptively based on the feedback. A stochastic gradient descent solver is employed to generate a multi-layer perceptron regressor. An example network is shown to take a 50% compression ratio and 89% reconstruction accuracy after training with real-world, medium-sized datasets as shown in this paper.

American Sign Language Words Recognition Using Spatio-Temporal Prosodic and Angle Features: A Sequential Learning Approach

Most of the available American Sign Language (ASL) words share similar characteristics. These characteristics are usually during sign trajectory which yields similarity issues and hinders ubiquitous application. However, recognition of similar ASL words confused translation algorithms, which lead to misclassification. In this paper, based on fast fisher vector (FFV) and bi-directional Long-Short Term memory (Bi-LSTM) method, a large database of dynamic sign words recognition algorithm called bidirectional long-short term memory-fast fisher vector (FFV-Bi-LSTM) is designed. This algorithm is designed to train 3D hand skeletal information of motion and orientation angle features learned from the leap motion controller (LMC). Each bulk features in the 3D video frame is concatenated together and represented as an high-dimensional vector using FFV encoding. Evaluation results demonstrate that the FFV-Bi-LSTM algorithm is suitable for accurately recognizing dynamic ASL words on basis of prosodic and angle cues. Furthermore, comparison results demonstrate that FFV-Bi-LSTM can provide better recognition accuracy of 98.6% and 91.002% for randomly selected ASL dictionary and 10 pairs of similar ASL words, in leave-one-subject-out cross-validation on the constructed dataset. The performance of our FFV-Bi-LSTM is further evaluated on ASL data set, leap motion dynamic hand gestures data set (LMDHG), and Semaphoric hand gestures contained in the Shape Retrieval Contest (SHREC) dataset. We improve the accuracy of the ASL data set, LMDHG, and SHREC data sets by 2%, 2%, and 3.19% respectively.

To understand the relationship between Machine learning and Artificial intelligence in large and diversified business organisations

In the modern era, Machine learning and Artificial intelligence remain the most remarkable IT application, a technology that has seen unprecedented progress in recent decades. AI and machine learning are all closely linked with using a computer to simulate intelligent behaviour with little human interaction. It has been noticed that many large organisations are implementing technologies such as ML and AI to improve their performance and productivity in the business. Companies can create better, more customised and engaging campaigns as AI becomes increasingly adept at collecting massive data faster than ever before. As AI and ML technologies become more widely used, being a technology-powered company will undoubtedly be a must for survival.AI and ML are guiding on everything from production to delivering products and services to customers. Machine translation, chatbots, and self-learning algorithms are examples of AI technology that may help people better comprehend their surroundings and respond appropriately. Organisations have been embracing artificial intelligence technology advancements to create and maximising their strategic and competitive advantages. Hence, businesses rely heavily on AI to enhance their performance and develop new services to boost productivity and generate new offerings. In addition, they are fundamentally reshaping companies' business and operation processes to better meet customers’ requirements and expectations, resulting in increased efficiency.Machine learning and artificial intelligence are broad concepts that will be examined further in this article. The report aims to understand the relationship between Machine Learning and Artificial Intelligence in large and diversified business organisations. The researcher used a study methodology oriented toward a more inclusive and comprehensive approach to better account for the intangible aids of ML and AI within organisations.

Research on Intelligent English Translation Method Based on the Improved Attention Mechanism Model

The use of neural machine algorithms for English translation is a hot topic in the current research. English translation using the traditional sequential neural framework, which is too poor at capturing long-distance information, has its own major limitations. However, the current improved frameworks, such as recurrent neural network translation, are not satisfactory either. In this paper, we establish an attention coding and decoding model to address the shortcomings of traditional machine translation algorithms, combine the attention mechanism with a neural network framework, and implement the whole English translation system based on TensorFlow, thus improving the translation accuracy. The experimental test results show that the BLUE values of the algorithm model built in this paper are improved to different degrees compared with the traditional machine learning algorithms, which proves that the performance of the proposed algorithm model is significantly improved compared with the traditional model.

Parallel Algorithm of Hierarchical Phrase Machine Translation Based on Distributed Network Memory

Machine translation has developed rapidly. But there are some problems in machine translation, such as good reading, unable to reflect the mood and context, and even some language machines can not recognize. In order to improve the quality of translation, this paper uses the SSCI method to improve the quality of translation. It is found that the translation quality of hierarchical phrases is significantly improved after using the parallel algorithm of machine translation, which is about 9% higher than before, and the problem of context free grammar is also solved. The research also found that the use of parallel algorithm can effectively reduce the network memory occupation, the original 10 character content, after using the parallel algorithm, only need to occupy 8 characters, the optimization reaches 20%. This means that the parallel algorithm of hierarchical phrase machine translation based on distributed network memory can play a very important role in machine translation.

Automatic RadLex coding of Chinese structured radiology reports based on text similarity ensemble

BackgroundStandardized coding of plays an important role in radiology reports’ secondary use such as data analytics, data-driven decision support, and personalized medicine. RadLex, a standard radiological lexicon, can reduce subjective variability and improve clarity in radiology reports. RadLex coding of radiology reports is widely used in many countries, but translation and localization of RadLex in China are far from being established. Although automatic RadLex coding is a common way for non-standard radiology reports, the high-accuracy cross-language RadLex coding is hardly achieved due to the limitation of up-to-date auto-translation and text similarity algorithms and still requires further research.MethodsWe present an effective approach that combines a hybrid translation and a Multilayer Perceptron weighting text similarity ensemble algorithm for automatic RadLex coding of Chinese structured radiology reports. Firstly, a hybrid way to integrate Google neural machine translation and dictionary translation helps to optimize the translation of Chinese radiology phrases to English. The dictionary is made up of 21,863 Chinese–English radiological term pairs extracted from several free medical dictionaries. Secondly, four typical text similarity algorithms are introduced, which are Levenshtein distance, Jaccard similarity coefficient, Word2vec Continuous bag-of-words model, and WordNet Wup similarity algorithms. Lastly, the Multilayer Perceptron model has been used to synthesize the contextual, lexical, character and syntactical information of four text similarity algorithms to promote precision, in which four similarity scores of two terms are taken as input and the output presents whether the two terms are synonyms.ResultsThe results show the effectiveness of the approach with an F1-score of 90.15%, a precision of 91.78% and a recall of 88.59%. The hybrid translation algorithm has no negative effect on the final coding, F1-score has increased by 21.44% and 8.12% compared with the GNMT algorithm and dictionary translation. Compared with the single similarity, the result of the MLP weighting similarity algorithm is satisfactory that has a 4.48% increase compared with the best single similarity algorithm, WordNet Wup.ConclusionsThe paper proposed an innovative automatic cross-language RadLex coding approach to solve the standardization of Chinese structured radiology reports, that can be taken as a reference to automatic cross-language coding.

Trkic G00gle: Why and How Users Game Translation Algorithms

Individuals interact with algorithms in various ways. Users even game and circumvent algorithms so as to achieve favorable outcomes. This study aims to come to an understanding of how various stakeholders interact with each other in tricking algorithms, with a focus towards online review communities. We employed a mixed-method approach in order to explore how and why users write machine non-translatable reviews as well as how those encrypted messages are perceived by those receiving them. We found that users are able to find tactics to trick the algorithms in order to avoid censoring, to mitigate interpersonal burden, to protect privacy, and to provide authentic information for enabling the formation of informative review communities. They apply several linguistic and social strategies in this regard. Furthermore, users perceive encrypted messages as both more trustworthy and authentic. Based on these findings, we discuss implications for online review community and content moderation algorithms.

Automatic Calibration Algorithm for English Text Translation Based on Semantic Features

At present, the existing methods of English article flip calibration neglect to extract English semantic features, which leads to errors in English flip results and has a great impact on the accuracy and time consumption of translation sentence calibration. Therefore, a semantic feature-based automatic text flipping calibration algorithm is proposed. According to the features of semantic information in machine translation, a semantic grammar tree is constructed to complete the machine turning of English articles. The CART decision tree attribute is obtained, and the random forest method is introduced to extract the input matrix and output matrix of the corpus feature as samples to determine the spatial attribute feature of the mistranslated sentences. Choose 10000 English sentences about human body parts as the experimental object and design the simulation experiment. The experimental results show that the minimum and maximum accuracy rates are 95.4% and 100.0%, respectively. The proposed algorithm is time-consuming, and the KSMR value is lower than that of the traditional method. It is proved that the error rate of English article flipping is significantly reduced.

Graph-Based Shape Analysis for Heterogeneous Geometric Datasets: Similarity, Retrieval and Substructure Matching

Practically all existing shape analysis and processing algorithms have been developed for specific geometric representations of 3D models. However, the product development process always involves a large number of often incompatible geometric representations tailored to specific computational tasks that take place during this process. Consequently, a substantial effort has been expended to develop robust geometric data translation and conversion algorithms, but the existing methods have well known limitations.The Maximal Disjoint Ball Decomposition (MDBD) was recently defined as a unique and stable geometric construction and used to define universal shape descriptors based on the contact graph associated with MDBD. In this paper, we demonstrate that by applying graph analysis tools to MDBD in conjunction with graph convolutional neural networks and graph kernels, one can effectively develop methods to perform similarity, retrieval and substructure matching from geometric models regardless of their native geometric representation. We show that our representation-agnostic approach achieves comparable performance with state-of-the-art geometric processing methods on standard yet heterogeneous benchmark datasets while supporting all valid geometric representations.

Research on Intelligent Calibration of English Long Sentence Translation Based on Corpus

With the continuous promotion and development of the new curriculum reform, English teaching is becoming more practical and comprehensive. As an indispensable part of daily English topics, the use rate and scope of English attributive clauses are extensive. Moreover, due to English attributive provisions, the length of the whole English sentence will inevitably increase; therefore, we can accurately understand and translate sentences by mastering the translation and understanding details of attributive clauses. In addition, there are noticeable differences between English and Chinese in attributive clauses. Chinese will not add a variety of modifiers like English but will directly put them in front of them as attributives, so we should pay attention to this in translation. This process increases the difficulty of the English translation. Therefore, this paper proposes a Corpus-based intelligent calibration of English long sentence translation. Based on the construction of the English long sentence Corpus, an intelligent calibration algorithm for English long sentence translation is designed, and experiments verify the effectiveness of this method.

Virtual restoration of the colored paintings on weathered beams in the Forbidden City using multiple deep learning algorithms

The colored paintings on the surfaces of ancient Chinese buildings have suffered from the wind and the sun and produced many defects such as paint loss, blurring, and color distortion. Previous methods have not been able to virtually repair them well. Therefore, this paper proposed a virtual restoration method for the weathered beams in the Forbidden City using multiple deep learning algorithms. Instead of using only one technology to restore paintings, this paper divided the painting into 3 parts, i.e., the background, the golden edges, and the dragon patterns, and restored them in different technology. For the background, this paper transformed the problem of unrecognizable color restoration into a semantic segmentation problem using U-Net MobileNet. For the golden edges, this paper used traditional image processing technology to obtain them from the color maps generated by the semantic segmentation algorithm. For the dragon patterns, after sketching the skeletons according to the dragon patterns, the image translation algorithm Pix2pix was applied to generate a realistic dragon pattern. Finally, the three repair results are superimposed to complete the repair. The virtually restored paintings can provide reference and guidance for traditional manual restoration and help the restorers to imagine what they looked like before oxidation, thus alleviating some repetitive work and reducing the complexity of restoration. Besides, each step of the method could form a layer, so that the restorers could overlay or modify them as they wish.

Technical note: A fast and robust integrator of delay differential equations in DCM for electrophysiological data

Dynamic causal models (DCMs) of electrophysiological data allow, in principle, for inference on hidden, bulk synaptic function in neural circuits. The directed influences between the neuronal elements of modeled circuits are subject to delays due to the finite transmission speed of axonal connections. Ordinary differential equations are therefore not adequate to capture the ensuing circuit dynamics, and delay differential equations (DDEs) are required instead. Previous work has illustrated that the integration of DDEs in DCMs benefits from sophisticated integration schemes in order to ensure rigorous parameter estimation and correct model identification. However, integration schemes that have been proposed for DCMs either emphasize speed (at the possible expense of accuracy) or robustness (but with computational costs that are problematic in practice).In this technical note, we propose an alternative integration scheme that overcomes these shortcomings and offers high computational efficiency while correctly preserving the nature of delayed effects. This integration scheme is available as open-source code in the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) toolbox and can be easily integrated into existing software (SPM) for the analysis of DCMs for electrophysiological data. While this paper focuses on its application to the convolution-based formalism of DCMs, the new integration scheme can be equally applied to more advanced formulations of DCMs (e.g. conductance based models). Our method provides a new option for electrophysiological DCMs that offers the speed required for scientific projects, but also the accuracy required for rigorous translational applications, e.g. in computational psychiatry.

HTN Problem Spaces: Structure, Algorithms, Termination

For HTN planning, we formally characterize and classify four kinds of problem spaces in which each node represents a planning problem or subproblem. Two of the problem spaces are searched by current HTN planning algorithms; the other two problem spaces are new.This enables us to provide:Sufficient (and in one case, necessary) conditions for finiteness of each kind of problem space. The conditions can be evaluated up-front to see if an HTN planning problem is finite.Loop-detection tests that can be used in HTN planners to ensure termination when the problem space is finite.A way to compute the correct value for an upper-bound parameter in an HTN-to-PDDL translation algorithm published in IJCAI-2009.Planning algorithms that utilize the two new problem spaces to guarantee termination on broader classes of planning problems than previous HTN planning algorithms.

A Survey in Implementation and Applications of Electroencephalograph (EEG)-Based Brain-Computer Interface

A Brain-Computer Interface (BCI) is an external system that controls activities and processes in the physical world based on brain signals. In Passive BCI, artificial signals are automatically generated by a computer program without any input from nerves in the body. This is useful for individuals with mobility issues. Traditional BCI has been dependent only on recording brain signals with Electroencephalograph (EEG) and has used a rule-based translation algorithm to generate control commands. These systems have developed very accurate translation systems. This paper is about the different methods for adapting the signals from the brain. It has been mentioned that various kinds of surveys in the past to serve the purpose of the present research. This paper shows a simple and easy analysis of each technique and its respective benefits and drawbacks, including signal acquisition, signal pre-processing, feature classification and classification. Finally, discussed is the application of EEG-based BCI.

Context-aware Retrieval-based Deep Commit Message Generation

Commit messages recorded in version control systems contain valuable information for software development, maintenance, and comprehension. Unfortunately, developers often commit code with empty or poor quality commit messages. To address this issue, several studies have proposed approaches to generate commit messages from commit diffs . Recent studies make use of neural machine translation algorithms to try and translate git diffs into commit messages and have achieved some promising results. However, these learning-based methods tend to generate high-frequency words but ignore low-frequency ones. In addition, they suffer from exposure bias issues, which leads to a gap between training phase and testing phase. In this article, we propose CoRec to address the above two limitations. Specifically, we first train a context-aware encoder-decoder model that randomly selects the previous output of the decoder or the embedding vector of a ground truth word as context to make the model gradually aware of previous alignment choices. Given a diff for testing, the trained model is reused to retrieve the most similar diff from the training set. Finally, we use the retrieval diff to guide the probability distribution for the final generated vocabulary. Our method combines the advantages of both information retrieval and neural machine translation. We evaluate CoRec on a dataset from Liu et al. and a large-scale dataset crawled from 10K popular Java repositories in Github. Our experimental results show that CoRec significantly outperforms the state-of-the-art method NNGen by 19% on average in terms of BLEU.

Linnea

The translation of linear algebra computations into efficient sequences of library calls is a non-trivial task that requires expertise in both linear algebra and high-performance computing. Almost all high-level languages and libraries for matrix computations (e.g., Matlab, Eigen) internally use optimized kernels such as those provided by BLAS and LAPACK; however, their translation algorithms are often too simplistic and thus lead to a suboptimal use of said kernels, resulting in significant performance losses. To combine the productivity offered by high-level languages, and the performance of low-level kernels, we are developing Linnea, a code generator for linear algebra problems. As input, Linnea takes a high-level description of a linear algebra problem; as output, it returns an efficient sequence of calls to high-performance kernels. Linnea uses a custom best-first search algorithm to find a first solution in less than a second, and increasingly better solutions when given more time. In 125 test problems, the code generated by Linnea almost always outperforms Matlab, Julia, Eigen, and Armadillo, with speedups up to and exceeding 10×.

Research on the Efficiency of Intelligent Algorithm for English Speech Recognition and Sentence Translatione

Machine translation has been gradually widely used to improve the efficiency of English translation. This paper briefly introduced the English speech recognition algorithm based on the back-propagation (BP) neural network algorithm and the machine translation algorithm based on the long short-term memory-recurrent neural network (LSTM-RNN) algorithm. Then, the machine translation algorithm was simulated and compared with BP-RNN and RNN-RNN algorithms. The results showed that the BP neural network algorithm had a lower word error rate and shorter recognition time than manual recognition; the LSTM-RNN-based machine translation algorithm had the lowest error rate of the translated words in the speech recognition results, and the translation gained the highest evaluation score from ten professional translators.

TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry.

Psychiatry faces fundamental challenges with regard to mechanistically guided differential diagnosis, as well as prediction of clinical trajectories and treatment response of individual patients. This has motivated the genesis of two closely intertwined fields: (i) Translational Neuromodeling (TN), which develops “computational assays” for inferring patient-specific disease processes from neuroimaging, electrophysiological, and behavioral data; and (ii) Computational Psychiatry (CP), with the goal of incorporating computational assays into clinical decision making in everyday practice. In order to serve as objective and reliable tools for clinical routine, computational assays require end-to-end pipelines from raw data (input) to clinically useful information (output). While these are yet to be established in clinical practice, individual components of this general end-to-end pipeline are being developed and made openly available for community use. In this paper, we present the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) software package, an open-source collection of building blocks for computational assays in psychiatry. Collectively, the tools in TAPAS presently cover several important aspects of the desired end-to-end pipeline, including: (i) tailored experimental designs and optimization of measurement strategy prior to data acquisition, (ii) quality control during data acquisition, and (iii) artifact correction, statistical inference, and clinical application after data acquisition. Here, we review the different tools within TAPAS and illustrate how these may help provide a deeper understanding of neural and cognitive mechanisms of disease, with the ultimate goal of establishing automatized pipelines for predictions about individual patients. We hope that the openly available tools in TAPAS will contribute to the further development of TN/CP and facilitate the translation of advances in computational neuroscience into clinically relevant computational assays.

Accessing OMOP Common Data Model Repositories with the i2b2 Webclient - Algorithm for Automatic Query Translation.

In the era of translational research, data integration and clinical data warehouses are important enabling technologies for clinical researchers. The OMOP common data model is a wide-spread choice as a target for data integration in medical informatics. It's portability of queries and analyses across different institutions and data are ideal also from the viewpoint of the FAIR principles. Yet, the OMOP CDM lacks a simple and intuitive user interface for untrained users to run simple queries for feasibility analysis. Aim of this study is to provide an algorithm to translate any given i2b2 query to an equivalent query which can then be run on the OMOP CDM database. The provided algorithm is able to convert queries created in the i2b2 webclient to SQL statements which can be executed on a standard OMOP CDM database programmatically.

Research on the Application of Computer Intelligent Proofreading System in College English Teaching

With the rapid development of computer technology, people have higher requirements for English Teaching (hereinafter referred to as ET) tools. However, in ET, teachers need to correct students’ homework, which will take a lot of time. Therefore, we can develop intelligent proofreading system (hereinafter referred to as IPS), which can improve the translation and proofreading of English syntax and phrases. Through corpus and other databases, we can calibrate English phrases and syntax with high accuracy, which will improve phrase translation and poor sentence coherence. By creating semantic ontology model and phrase translation combination translation algorithm, we can create semantic ontology model, which will better realize online collaborative translation and intelligent school peer-to-peer work, which will play an important role in ET. Through the IPS, we can display the situation in a graphical way, which will better carry out teaching activities. Firstly, this paper analyzes the positioning of IPS for College ET. Then, this paper analyzes the working principle of the IPS. Finally, some suggestions are put forward.