Real-time Translation Research Articles

Using eye-tracking for real-time translation: a new approach to improving reading experience

Using eye-tracking for real-time translation: a new approach to improving reading experience

Philosophy of future: analytical overview of interaction between education, science, and artificial intelligence in the context of contemporary challenges

Currently, artificial intelligence is changing the future landscape of technology and digitalization and transforming the way knowledge is generated and shared in education and science. The aim of the article is to interpret the future through interactions between education, science, and AI and to outline their potential challenges when digital transformations occur. While answering the research questions the mixed-methods approach was applied. For the study both quantitative and qualitative data was collected through descriptive and empirical survey. The empirical survey included computer-assisted web interview to assess the most relevant concepts according to the participants responses. The study involved 42 participants that were selected randomly. The selection criteria concerned educational background, previous experience, representation of diverse perspectives, theories, or approaches within the philosophy, active engagement in educational practice, and wiliness to participate. To analyse the data statistical software NVivo was applied. Approximately 70 recent scientific works were studied to present the problem through multifaceted approach. The findings show the participants’ anticipations are related to the cognitive load theory, constructivist theory, and socio-cultural theory. A number of challenges threatening innovative developments arise within the educational and scientific environments. They include ethical concerns, misinformation, digital divide, unequal infrastructure, lack of regulation, lack of digital skills, resistance to change, technology integration, and limited digital pedagogy. Ethics plays a crucial role in shaping digital transformations in education and science. The category of academic virtue is closely connected to ethical and responsible use of Ai. The paradigm of academic virtue includes ethical of knowledge, collaboration, responsibility, honesty, accuracy, adaptability, openness. AI brings automated content creation, language processing opportunities, real-time translation, and enhanced accessibility and, therefore, changes communication in the field of education and science. The research showed that the principles of effective implementation of AI in education and science include philosophical, educational, and ethical dimensions.

Arts Premium Collection

ProQuest’s Arts Premium Collection database provides access to three cross-searchable databases across thousands of journals in specialized subject areas of the arts. The primary audience of this resource comprises academic scholars and students. Access to these resources is provided by the standardized search bar layout, which is consistent across ProQuest products. A robust Advanced Search tool allows users to construct Boolean searches, apply facets, and recombine searches to efficiently access the available resources. All search results have a Details display, which provides hyperlinked metadata to help users expand their discovery. Full text and PDFs are provided, and real-time translation is available for full-text resources. Each item also has suggested resources and citation counts/bibliography for works cited in other ProQuest collections.

Effective application of multimodal discourse analysis in Russian translation

Abstract Based on ELAN multimodal discourse analysis software, this paper constructs a multimodal Russian translation model based on the machine translation model with visual grammar and multimodal discourse analysis as the theoretical basis. To address the issue of missing semantics caused by insufficient input information at the source of real-time translation, the model uses images as auxiliary modalities. The real-time Russian translation model is constructed using the wait-k strategy and the concept of multimodal self-attention. Experiments and analysis are carried out on the Multi30k training set, and the generalization ability and translation effect of the model are finally evaluated with the test set. The results show that by applying multimodal discourse analysis to Russian translation, the three translation evaluation indexes of BLEU, METEOR, and TER are improved by 1.3, 1.0, and 1.4 percentage points, respectively, and the phenomenon of phantom translation is effectively reduced.

Sign Language Recognition using Spiking Neural Networks

In recent years, research in automatic Sign Language Recognition (SLR) has undergone significant progress, serving as a founda-tional base for developing applications that aim to promote the integration of deaf individuals into society. Most of this progress is owed to the recent developments in deep learning. However, the deployment of conventional Artificial Neural Networks (ANNs) can be hindered by their requirements in terms of computational power and energy consumption. Therefore, to improve the ef-ciency of current SLR systems, in this work, we propose the use of the increasingly popular Spiking Neural Networks (SNNs), which, on the one hand, provide more energy-efficient computations than conventional ANNs and, on the other hand, are able to process temporal sequences with simpler architectures thanks to their temporal dynamics. To evaluate our method, we utilize WLASL300, the 300-word (300 classes of signs) dataset from Word-Level American Sign Language, and achieve an improvement in accuracy with the SNN (+2.70%) over the previous state-of-the-art, when working with energy-efficient spiking neurons. Furthermore, we construct a non-spiking version of the same network and evaluate it in a similar manner. Our results demonstrate how the SNN has sparser activations (25% less), thanks to the use of spiking neurons, and therefore can be implemented with a lower power requirement than an ANN version of the same architecture. This work thus demonstrates the possibility of performing SLR in a very effective and efficient way, thus opening up the development of applications that span from the automatic real-time translation of dynamic signs to remote control utilizing sign languages.

A Language Modeling-Based Approach to Real-Time Language Translation Quality Enhancement in ESL Education

Abstract With the development of ESL education in recent years, real-time language translation has gradually become a research hotspot in the field of natural language communication. In this paper, the data processing technology of real-time translation is introduced, and then a Transducer (RNN-T) model based on real-time language translation is constructed based on the BPE subword segmentation method under the condition of probability gradient calculation algorithm and transcription network. Secondly, this model determines and tests the content involved in the process of real-time language translation and finally obtains results to improve the quality of real-time language translation. The experimental results show that the accuracy of character translation among the six language models is 85.8%, 86.2%, 88%, 93.4%, 94.03%, and 94.36%, respectively. In addition, the error rate of inserting, deleting, and replacing characters is < 20%. Compared with the other four models, the word error rate of the RNN-Transducer algorithm is reduced by 1.59%, 3.94%, 3.86%, and 9.01%, and the RTF is reduced by 3.91%, 10.96%, 10.09%, and 7.94%, respectively, which reflects the superiority of the RNN-Transducer method proposed in this paper.

Augmented Reality (AR) And Virtual Reality (VR) Applications In Tourism: Embracing Emerging Technologies For Improved Tourist Experiences In Malaysian Tourism Industry

This paper investigates the impact of Augmented Reality (AR) and Virtual Reality (VR) on the Malaysian tourism business. AR and VR have emerged as revolutionary forces, transforming a wide range of industries, with tourism at the forefront of this digital transition. Augmented Reality complements real-world experiences by superimposing digital information on top of the physical world, whereas Virtual Reality immerses users in simulated environment. AR and VR have completely transformed the tourist experience, including planning, booking, on-site exploration, and post-trip comments. AR applications enrich the tourist experience by providing interactive maps, historical insights, and real-time translations, whereas virtual reality (VR) technologies enable deep interaction in a variety of fascinating activities and provide a fascinating insight into the destination's attractions.

AR 기술을 이용한 부산박물관 체험을 통한 발전방안 연구

Traditional museums face limitations in providing an optimal viewing experience, while Augmented Reality (AR) technology continues to advance. This study, using Busan Museum as a case example, aims to enhance the viewing experience and visitor satisfaction, ultimately attracting a larger audience. Employing literature review, case analysis, and questionnaire survey methods, the research establishes the rationale for introducing AR technology to Busan Museum.Through investigation, nine AR functionalities applicable to Busan Museum are identified. Utilizing the Kano model, the study summarizes visitor satisfaction and prioritizes functionality, thereby confirming future development strategies for Busan Museum. Additionally, the research explores the future development of traditional museums by integrating AR technology to deepen visitors' cultural experiences. This includes aspects such as virtual exhibitions, remote tours, real-time translation and interpretation, interactive exhibits, digital collection management, and the fusion of art and technology. These enhancements aim to elevate the overall visitor experience and contribute to the development of traditional museums.

Smart Glove for Sign Language Translation

Sign language is a vital mode of communication for deaf people, yet it presents a significant barrier when interacting with those who do not understand it. The advent of technology has paved the way for innovative solutions to bridge this communication gap. This abstract explores the development and implications of a smart glove designed for sign language translation (SLT). The primary aim of this study is to create a wearable device, the Smart Glove, capable of recognizing and translating sign language gestures into text or speech. Key objectives include designing a lightweight and ergonomic glove prototype, developing machine learning algorithms for sign language recognition, implementing real-time translation capabilities, evaluating the glove's accuracy and usability, and assessing the potential impact on facilitating communication for deaf people. The Smart Glove utilizes only one sensor, flex sensors, to capture hand movements and gestures. These data inputs are processed through a custom-built machine learning model trained on a comprehensive sign language dataset. Preliminary results indicate a high accuracy rate in recognizing sign language gestures, with an average recognition rate of over 90% across a diverse set of signs. While challenges such as expanding gesture recognition and refining translation algorithms remain, this technology offers a promising solution to break down communication barriers and enhance the quality of life for those who rely on sign language.

Analysis of the influence of selected audio pre-processing stages on accuracy of speaker language recognition

The subject matter of the study is the analysis of the influence of pre-processing stages of the audio on the accuracy of speaker language regognition. The importance of audio pre-processing has grown significantly in recent years due to its key role in a variety of applications such as data reduction, filtering, and denoising. Taking into account the growing demand for accuracy and efficiency of audio information classification methods, evaluation and comparison of different audio pre-processing methods becomes important part of determining optimal solutions. The goal of this study is to select the best sequence of stages of pre-processing audio data for use in further training of a neural network for various ways of converting signals into features, namely, spectrograms and mel-cepstral characteristic coefficients. In order to achieve the goal, the following tasks were solved: analysis of ways of transforming the signal into certain characteristics and analysis of mathematical models for performing an analysis of the audio series by selected characteristics were carried out. After that, a generalized model of real-time translation of the speaker's speech was developed and the experiment was planned depending on the selected stages of pre-processing of the audio. To conclude, the neural network was trained and tested for the planned experiments. The following methods were used: mel-cepstral characteristic coefficients, spectrogram, time mask, frequency mask, normalization. The following results were obtained: depending on the selected stages of pre-processing of voice information and various ways of converting the signal into certain features, it is possible to achieve speech recognition accuracy up to 93%. The practical significance of this work is to increase the accuracy of further transcription of audio information and translation of the formed text into the chosen language, including artificial laguages. Conclusions: In the course of the work, the best sequence of stages of pre-processing audio data was selected for use in further training of the neural network for different ways to convert signals into features. Mel-cepstral characteristic coefficients are better suited for solving our problem. Since the neural network strongly depends on its structure, the results may change with the increase in the volume of input data and the number of languages. But at this stage, it was decided to use only mel-cepstral characteristic coefficients with normalization.

INNOVATIONS IN TOURISM

Tourism has evolved significantly in recent years, driven by advancements in technology, changing consumer preferences, and a growing awareness of the need for sustainable and responsible travel. Innovative tourism represents a dynamic and transformative sector with vast potential. This essay explores the prospects for the development of innovative tourism and its role in shaping the future of travel. Innovations in technology have been at the forefront of the tourism industry's transformation. The advent of the internet, smartphones, and apps has revolutionized the way travelers plan, book, and experience their journeys. Virtual reality and augmented reality are enhancing pre-trip experiences, allowing travelers to “preview” destinations. Moreover, block chain technology is enhancing security and transparency in bookings and transactions. The prospects for further technological innovations are promising. Artificial intelligence can personalize travel recommendations, making them more relevant to individual preferences. The Internet of Things can enable smart and connected destinations, enhancing convenience for tourists. As 5G networks become more widespread, real-time translation services and immersive experiences will become more accessible, changing how tourists engage with their surroundings.Sustainable and Responsible Tourism:
 Innovative tourism places a strong emphasis on sustainability and responsible practices. Travelers are increasingly conscious of their ecological footprint and seek eco-friendly options. Eco-tourism, community-based tourism, and wildlife conservation programs are gaining traction. Eco-friendly accommodations, transportation alternatives, and renewable energy sources are becoming standard offerings. The prospects for sustainable tourism are encouraging. As climate change becomes a pressing concern, the tourism industry has an opportunity to lead by example. Innovations like electric and hydrogen-powered vehicles, sustainable architectural designs, and green energy solutions are driving the industry towards a more sustainable future. Responsible tourism practices are essential for preserving the world's natural and cultural treasures.
 Authentic and Experiential Travel: Modern travelers are seeking more meaningful and immersive experiences. They desire connections with local cultures, traditions, and people. As a result, the prospects for authentic and experiential travel are bright. Innovative tourism encourages travelers to participate in hands-on experiences, such as cooking classes, artisan workshops, and cultural exchanges. Tourism has embraced the sharing economy, with platforms like Airbnb and local experience marketplaces, allowing travelers to live like locals. This trend is expected to grow, leading to more diversified and personalized travel experiences. As tourists increasingly seek unique and transformative experiences, destinations must adapt to meet these evolving demands. Health and wellness tourism has seen remarkable growth, driven by the pursuit of physical, mental, and spiritual well-being. Innovations in this sector include wellness retreats, spa resorts, yoga and meditation tourism, and medical tourism. In a post-pandemic world, travelers are placing greater emphasis on health and safety. The prospects for the health and wellness tourism industry are excellent. As global health concerns persist, travelers will continue to prioritize destinations and services that promote well-being. Innovations such as health-focused apps, telemedicine services, and wellness technologies will continue to shape this growing niche.
 Innovative tourism holds great promise for the future of the industry. The rapid pace of technological advancements, coupled with evolving traveler preferences, is reshaping the way we experience the world. Tourism's sustainable and responsible future, combined with the emphasis on authentic, experiential, and wellness travel, is paving the way for a more inclusive and enriching global tourism landscape. To realize these prospects, industry stakeholders must adapt, invest in innovation, and prioritize responsible and sustainable practices. As the world continues to change, the tourism sector will remain at the forefront of innovation and progress.
 Keywords: tourism, innovative tourism, using innovations in tourism.

Democratizing neural machine translation with OPUS-MT

This paper presents the OPUS ecosystem with a focus on the development of open machine translation models and tools, and their integration into end-user applications, development platforms and professional workflows. We discuss our ongoing mission of increasing language coverage and translation quality, and also describe work on the development of modular translation models and speed-optimized compact solutions for real-time translation on regular desktops and small devices.

Turkish sign language recognition using fuzzy logic asisted ELM and CNN methods

This work aimed to develop a data glove for the real-time translation of Turkish sign language. In addition, a novel Fuzzy Logic Assisted ELM method (FLA-ELM) for hand gesture classification is proposed. In order to acquire motion information from the gloves, 12 flexibility sensors, two inertial sensors, and 10 Hall sensors were employed. The NVIDIA Jetson Nano, a small pocketable minicomputer, was used to run the recognition software. A total of 34 signal information was gathered from the sensors, and feature matrices were generated in the form of time series for each word. In addition, an algorithm based on Euclidean distance has been developed to detect end-points between adjacent words in a sentence. In addition to the proposed method, CNN and classical ANN methods, whose model was created by us, were used in sign language recognition experiments, and the results were compared. For each classified word, samples were collected from 25 different signers, and 3000 sample data were obtained for 120 words. Furthermore, the dataset’s size was reduced using PCA, and the results of the newly created datasets were compared to the reference results. In the performance tests, single words and three-word sentences were translated with an accuracy of up to 96.8% and a minimum 2.4 ms processing time.

Wearable multifunctional organohydrogel-based electronic skin for sign language recognition under complex environments

Language barrier is the main cause of disagreement. Sign language, which is a common language in all the worldwide language families, is difficult to be entirely popularized due to the high cost of learning as well as the technical barrier in real-time translation. To solve these problems, here, we constructed a wearable organohydrogel-based electronic skin (e-skin) with fast self-healing, strong adhesion, extraordinary anti-freezing and moisturizing properties for sign language recognition under complex environments. The e-skin was obtained by using an acrylic network as the main body, aluminum (III) and bayberry tannin as the crosslinking agent, water/ethylene glycol as the solvent system, and a polyvinyl alcohol network to optimize the network performance. Using this e-skin, a smart glove was further built, which could carry out the large-scale data collection of common gestures and sign languages. With the help of the deep learning method, specific recognition and translation for various gestures and sign languages could be achieved. The accuracy was 93.5%, showing the ultra-high classification accuracy of a sign language interpreter. In short, by integrating multiple characteristics and combining deep learning technology with hydrogel materials, the e-skin achieved an important breakthrough in human-computer interaction and artificial intelligence, and provided a feasible strategy for solving the dilemma of mutual exclusion between flexible electronic devices and human bodies.

Partial spontaneous intersubunit rotations in pretranslocation ribosomes

The two main steps of translation, peptidyl transfer, and translocation are accompanied by counterclockwise and clockwise rotations of the large and small ribosomal subunits with respect to each other. Upon peptidyl transfer, the small ribosomal subunit rotates counterclockwise relative to the large subunit, placing the ribosome into the rotated conformation. Simultaneously, tRNAs move into the hybrid conformation, and the L1 stalk moves inward toward the P-site tRNA. The conformational dynamics of pretranslocation ribosomes were extensively studied by ensemble and single-molecule methods. Different experimental modalities tracking ribosomal subunits, tRNAs, and the L1 stalk showed that pretranslocation ribosomes undergo spontaneous conformational transitions. Thus, peptidyl transfer unlocks the ribosome and decreases an energy barrier for the reverse ribosome rotation during translocation. However, the tracking of translation with ribosomes labeled at rRNA helices h44 and H101 showed a lack of spontaneous rotations in pretranslocation complexes. Therefore, reverse intersubunit rotations occur during EF-G catalyzed translocation. To reconcile these views, we used high-speed single-molecule microscopy to follow translation in real time. We showed spontaneous rotations in puromycin-released h44-H101 dye-labeled ribosomes. During elongation, the h44-H101 ribosomes undergo partial spontaneous rotations. Spontaneous rotations in h44-H101-labeled ribosomes are restricted prior to aminoacyl-tRNA binding. The pretranslocation h44-H101 ribosomes spontaneously exchanged between three different rotational states. This demonstrates that peptidyl transfer unlocks spontaneous rotations and pretranslocation ribosomes can adopt several thermally accessible conformations, thus supporting the Brownian model of translocation.

Self-correction of automatic speech recognition

The rapid development of modern technology and society has raised the need for a large number of real-time translations, such as Automatic Speech Recognition (ASR) technology. One of the important factors determining the fluency of cooperation is that ASR translates the input speech into readable text so that both parties can understand each other's meaning. There is also a huge demand for and reliance on automatic speech recognition technology for modern applications. This article focuses on the word matching algorithm for backward search (WMA-BS), which requires two separate steps, one for the word matching algorithm and the other for the backward search step, for the first step, which can also be done by applying the word-level N-Gram language model. n-Gram model is a language model in which when n words appear in a text, we can use these n words to predict the structure of the text. we can use these n words to predict the structure of the text. In order to make the word-level N-gram language model suitable for the method, it is more useful for us to integrate it into shallow fusion. One of the most important parts to be addressed in the word matching algorithm is the boundary, or distance, between two words in English.

Translation machinery captured in motion.

Translation accuracy is one of the most critical factors for protein synthesis. It is regulated by the ribosome and its dynamic behavior, along with translation factors that direct ribosome rearrangements to make translation a uniform process. Earlier structural studies of the ribosome complex with arrested translation factors laid the foundation for an understanding of ribosome dynamics and the translation process as such. Recent technological advances in time-resolved and ensemble cryo-EM have made it possible to study translation in real time at high resolution. These methods provided a detailed view of translation in bacteria for all three phases: initiation, elongation, and termination. In this review, we focus on translation factors (in some cases GTP activation) and their ability to monitor and respond to ribosome organization to enable efficient and accurate translation. This article is categorized under: Translation > Ribosome Structure/Function Translation > Mechanisms.

REAL TIME MALAYSIAN SIGN LANGUAGE TRANSLATOR – TUTURJOM

Malaysian Sign Language (MSL) is a vital language that is used as the primary means of communication between the deaf and mute and hearing communities in Malaysia. Malaysians are currently very unfamiliar with MSL, and the present platform for learning the language is scarce, not to mention the lack of efficacy of the available mobile and web learning tools. Therefore, the aim of this study is to propose software for real-time translation of MSL through a Long Short-Term Memory (LSTM) model. The following paper explains six phases of CRISP-DM in creating the application while looking through various findings and important algorithms. This article also intends to provide a clear understanding of hand gesture and pose recognition, LSTM networks and its uses in understanding sign languages.Keywords: LSTM, Malaysian sign language, CRISP-DM, gesture recognition, sign language recognition

Application of Machine Learning Combined with Wireless Network in Design of Online Translation System

The importance of translation services has become increasingly prominent with the acceleration of economic globalization. Compared with human translation, machine translation is cheaper and faster, and therefore more suitable for the current era. The current mainstream machine translation method is neural machine translation, which employs machine methods to train on parallel corpora and create translation models. Research into neural machine translation has yielded a wealth of information. Learning and generalization abilities of neural networks have substantially enhanced the effectiveness of neural machine translation. This work applies machine learning and wireless network technology to build an online translation system for real-time translation. First, this work proposes a multigranularity feature fusion method based on a directed acyclic graph, which uses a directed acyclic graph to fuse different granularities as input and obtain a position representation. Secondly, this paper improves the Transformer model and proposes multigranularity position encoding and multigranularity self-attention. Then, on the basis of multigranularity features as input, this work introduces dynamic word vectors to improve the word embedding module, and uses the ELMo model to obtain dynamic word vector embeddings. Finally, this work builds a multigranularity feature-dynamic word vector machine translation model with above strategy, deploys it on server. Users can upload the content to be translated and download the translated content through the wireless network and realize an online translation system based on machine learning and wireless network.

Acquisition of English Corpus Machine Translation Based on Speech Recognition Technology

In the present information age, with the rapid development of computer software and hardware technology and the mature manufacturing system of English-speaking enterprises, it is no longer impossible to use statistics for machine translation. The level and quality of machine translation are expected to meet human expectations. This explains the meaning and realization value of the acquisition of English corpus machine translation, introduces the basic principles of speech recognition technology, and combines the characteristics of the English language on the basis of the original Chinese speech recognition system, adopts the technical means of speech recognition, and leads to in-depth research. In the new era of machine translation acquisition of English corpus, we use the combination of LabVIEW and MATLAB to complete the collection, editing, feature extraction, and speech recognition of speech signals and use VQ pattern matching technology to realize the English recognition of a large number of short vocabulary and individuals. In the application part, we applied the classic LabVIEW technology to the speech recognition technology, which actually realized the idea of “software instead of hardware” and achieved better translation results. Experiments show that the accuracy rate of English machine translation can be as high as 94% when using speech recognition technology. According to the results, it takes about 0.2 seconds to complete machine translation for a 30-second speech, which is basically okay to achieve the effect of real-time translation.