Search results for: neural machine translation (NMT)

Authors: Youru Zhou

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This paper seeks to unveil the quality assurance practices in the Chinese translation industry. Since China’s reform and opening up, the Chinese language service industry has enjoyed impressively rapid growth. However, while still in its early stage of professionalization, the Chinese translation industry is also facing many challenges, such as the lack of clear admission requirements, a powerful regulation authority and a great number of qualified professionals. ‘How quality is assured’ means a great deal to translation in China at this stage. In order to examine the mechanisms in which quality is assured, this paper studied four international and national standards that have gained widespread adoption by Chinese translation companies and examined the content that is relevant to translation quality assurance. Case studies with six selected Chinese translation companies of different sizes were conducted to confirm and exemplify the descriptions on the standards. It has been found that quality in the industry is a relative concept which is mainly determined by the demand of clients. Furthermore, the procedures of translation can vary from task to task dependent on the agreement made between the service provider and clients. Finally, there are companies relying on expert-oriented mechanisms to assure the quality of translation, while other companies and standards insist on process-oriented ones.

Keywords: case study, Chinese translation industry, professional practice, translation quality assurance, translation standards

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Authors: Xiangyu Yang

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Xiao Qian (1910-1999) was a prolific literary translator between Chinese and English in both directions and an influential commentator on Chinese translation practices for nearly 70 years (1931-1998). During his stay in Britain from 1939 to 1946, Xiao self-translated and published a series of short stories, essays, and feature articles. With Pedersen's theoretical framework, the paper finds that Xiao flexibly adopted seven translation strategies (i.e. phonemic retention, specification, direct translation, generalization, substitution, omission, and official equivalent) to deal with the expressions specific to Chinese culture, struggling to seek a balance between adequate translation and acceptable translation in a historical condition of the huge gap between China and the west in the early twentieth century. Besides, the study also discovers that Xiao's translation strategies were greatly influenced by his own translational purpose as well as the literary systems, ideologies, and patronage in China and Britain in the 1940s.

Keywords: self-translation, extralinguistic cultural reference, Xiao Qian, Pedersen

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Authors: Zhi Huang

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This study follows on from previous survey and focus group research exploring the effective teaching process in a translation classroom in Australian universities through case study method. The data analysis draws on social constructivist theory in translation teaching and focuses on teaching process aiming to discover how effective translation teachers conduct teaching in the classroom. The results suggest that effective teaching requires the teacher to have ability in four aspects: classroom management, classroom pedagogy, classroom communication, and teacher roles. Effective translation teachers are able to control the whole learning process, facilitate students in independent learning, guide students to be more critical about translation, giving both positive and negative feedback for students to reflect on their own, and being supportive, patient and encouraging to students for better classroom communication and learning outcomes. This study can be applied to other teachers in translation so that they can reflect on their own teaching in their education contexts and strive for being a more qualified translation teacher and achieving teaching effectiveness.

Keywords: case study, classroom observation, classroom teaching, effective translation teaching, teacher effectiveness

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Authors: Saad Chahba, Rabia Sehab, Ahmad Akrad, Cristina Morel

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Nowadays, the electric machines used in urban electric vehicles are, in most cases, three-phase electric machines with or without a magnet in the rotor. Permanent Magnet Synchronous Machine (PMSM) and Induction Machine (IM) are the main components of drive trains of electric and hybrid vehicles. These machines have very good performance in healthy operation mode, but they are not redundant to ensure safety in faulty operation mode. Faced with the continued growth in the demand for electric vehicles in the automotive market, improving the reliability of electric vehicles is necessary over the lifecycle of the electric vehicle. Multiphase electric machines respond well to this constraint because, on the one hand, they have better robustness in the event of a breakdown (opening of a phase, opening of an arm of the power stage, intern-turn short circuit) and, on the other hand, better power density. In this work, a diagnosis approach using a neural network for an open circuit fault or more of a five-phase induction machine is developed. Validation on the simulator of the vehicle drivetrain, at reduced power, is carried out, creating one and more open circuit stator phases showing the efficiency and the reliability of the new approach to detect and to locate on-line one or more open phases of a five-induction machine.

Keywords: electric vehicle drivetrain, multiphase drives, induction machine, control, open circuit (OC) fault diagnosis, artificial neural network

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Authors: Prachi Desai, Ankita Gandhi, Mitali Acharya

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Rainfall forecast is mainly used for predictions of rainfall in a specified area and determining their future rainfall conditions. Rainfall is always a global issue as it affects all major aspects of one's life. Agricultural, fisheries, forestry, tourism industry and other industries are widely affected by these conditions. The studies have resulted in insufficient availability of water resources and an increase in water demand in the near future. We already have a new forecast system that uses the deep Convolutional Neural Network (CNN) to forecast monthly rainfall and climate changes. We have also compared CNN against Artificial Neural Networks (ANN). Machine Learning techniques that are used in rainfall predictions include ARIMA Model, ANN, LR, SVM etc. The dataset on which we are experimenting is gathered online over the year 1901 to 20118. Test results have suggested more realistic improvements than conventional rainfall forecasts.

Keywords: ANN, CNN, supervised learning, machine learning, deep learning

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Authors: Jianhua Wang, Ying Zhou, Han Guo

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Translation underwent culture turn for more than half a century, which brought translation and its studies beyond intra-texts. Different cultures in recent years have developed towards a translation turn, which made a great contribution to relocate national or local cultures being localized to become regional or global cultures. As China grows quickly economically integrating into the world, it becomes urgent to relate China’s story and disseminate the Chinese culture. Due to the weaknesses and drawbacks of different existing cultural translation theories for Chinese culture to go out, a new perspective on translation turn for the go-out of Chinese culture should be drawn to spread better and disseminate Chinese culture to other countries. Based on the existing cultural translation theories, the equivalence of ideology, style of the translator and agency of the support are proposed to draw a new perspective: an innovative poly-system theory for Chinese culture translation.

Keywords: cultural translation theory, Chinese culture, innovative poly system, global cultures

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Authors: Min Gao

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In the past year, the advent of large language models (LLMs) has brought about a revolution in the language service industry, making it possible to efficiently produce more satisfactory and higher-quality translations. This is groundbreaking news for commercial companies involved in language services since much of a translator's work can now be completed by machines. However, it may be bad news for universities that provide translation training programs. They need to confront the challenges posed by AI in education by reconsidering issues such as the reform of traditional teaching methods, the translation ethics of students, and the new demands of the job market for their graduates. This article is an exploratory study of these issues based on the author's experiences in translation teaching. The research combines methods in the form of questionnaires and interviews. The findings include: (1) students may lose their motivation to learn in the AI era, but this can be compensated for by encouragement from the lecturer; (2) Translation ethics are not a serious problem in schools, considering the strict policies and regulations in place; (3) The role of translators has evolved in the new era, necessitating a reform of the traditional teaching methods.

Keywords: job market of translation, large language model, translation ethics, translation training

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Authors: Mohammed Affanuddin H. Siddique, Jayesh S. Shukla, Chetan B. Meshram

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The neural networks are one of the power tools of machine learning. After the invention of perceptron in early 1980's, the neural networks and its application have grown rapidly. Neural networks are a technique originally developed for pattern investigation. The structure of a neural network consists of neurons connected through synapse. Here, we have investigated the different algorithms and cost function reduction techniques for optimization of vertical axis wind turbine (VAWT) rotor blades. The aerodynamic force coefficients corresponding to the airfoils are stored in a database along with the airfoil coordinates. A forward propagation neural network is created with the input as aerodynamic coefficients and output as the airfoil co-ordinates. In the proposed algorithm, the hidden layer is incorporated into cost function having linear and non-linear error terms. In this article, it is observed that the ANNs (Artificial Neural Network) can be used for the VAWT’s optimization.

Keywords: VAWT, ANN, optimization, inverse design

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Authors: Parisa Golbayani, Dan Wang, Ionut¸ Florescu

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In this work we implement machine learning techniques to financial statement reports in order to asses company’s credit rating. Specifically, the work analyzes the performance of four neural network architectures (MLP, CNN, CNN2D, LSTM) in predicting corporate credit rating as issued by Standard and Poor’s. The paper focuses on companies from the energy, financial, and healthcare sectors in the US. The goal of this analysis is to improve application of machine learning algorithms to credit assessment. To accomplish this, the study investigates three questions. First, we investigate if the algorithms perform better when using a selected subset of important features or whether better performance is obtained by allowing the algorithms to select features themselves. Second, we address the temporal aspect inherent in financial data and study whether it is important for the results obtained by a machine learning algorithm. Third, we aim to answer if one of the four particular neural network architectures considered consistently outperforms the others, and if so under which conditions. This work frames the problem as several case studies to answer these questions and analyze the results using ANOVA and multiple comparison testing procedures.

Keywords: convolutional neural network, long short term memory, multilayer perceptron, credit rating

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Authors: Yasser F. Hassan

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Deep learning structure is a branch of machine learning science and greet achievement in research and applications. Cellular neural networks are regarded as array of nonlinear analog processors called cells connected in a way allowing parallel computations. The paper discusses how to use deep learning structure for representing neural cellular automata model. The proposed learning technique in cellular automata model will be examined from structure of deep learning. A deep automata neural cellular system modifies each neuron based on the behavior of the individual and its decision as a result of multi-level deep structure learning. The paper will present the architecture of the model and the results of simulation of approach are given. Results from the implementation enrich deep neural cellular automata system and shed a light on concept formulation of the model and the learning in it.

Keywords: cellular automata, neural cellular automata, deep learning, classification

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Authors: Almagul Assainova , Dariya Abykenova, Liudmila Goncharenko, Sergey Sybachin, Saule Rakhimova, Abay Aman

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The recognition of a handwritten Kazakh text is a relevant objective today for the digitization of materials. The study presents a model of a hybrid neural network for handwriting recognition, which includes a convolutional neural network and a multi-layer perceptron. Each network includes 1024 input neurons and 42 output neurons. The model is implemented in the program, written in the Python programming language using the EMNIST database, NumPy, Keras, and Tensorflow modules. The neural network training of such specific letters of the Kazakh alphabet as ә, ғ, қ, ң, ө, ұ, ү, h, і was conducted. The neural network model and the program created on its basis can be used in electronic document management systems to digitize the Kazakh text.

Keywords: handwriting recognition system, image recognition, Kazakh font, machine learning, neural networks

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Authors: Ya-Mei Chen

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Online crowdsourced translation, an innovative translation practice brought by Web 2.0 technologies and the democratization of information, has become increasingly popular in the Internet era. Carried out by grass-root internet users, crowdsourced translation contains fundamentally different features from its off-line traditional counterpart, such as voluntary participation and parallel collaboration. To better understand such a participatory and collaborative nature, this paper will use the online Chinese translation project of Global Voices as a case study to investigate the following issues: (1) the changes in volunteer translators’ and reviewers’ motivations for participation, (2) translators’ and reviewers’ use of translation strategies and (3) the correlations of translators’ and reviewers’ motivations and strategies with the organizational mission, the translation style guide, the translator-reviewer interaction, the mediation of the translation platform and various types of capital within the translation field. With an aim to systematically explore the above three issues, this paper will collect both quantitative and qualitative data and then draw upon Engestrom’s activity theory and Bourdieu’s field theory as a theoretical framework to analyze the data in question. An online anonymous questionnaire will be conducted to obtain the quantitative data. The questionnaire will contain questions related to volunteer translators’ and reviewers’ backgrounds, participation motivations, translation strategies and mutual relations as well as the operation of the translation platform. Concerning the qualitative data, they will come from (1) a comparative study between some English news texts published on Global Voices and their Chinese translations, (2) an analysis of the online discussion forum associated with Global Voices’ Chinese translation project and (3) the information about the project’s translation mission and guidelines. It is hoped that this research, through a detailed sociological analysis of a cause-driven crowdsourced translation project, can enable translation researchers and practitioners to adequately meet the translation challenges appearing in the digital age.

Keywords: crowdsourced translation, global voices, motivation, translation strategies

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Authors: Sultan Samah A Almjlad

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The translation industry in Saudi Arabia is experiencing significant shifts driven by Vision 2030, which aims to diversify the economy and enhance international engagement. This change highlights the need for translators who are skilled in various languages and cultures, playing a crucial role in the nation's global integration efforts. However, there's a notable gap between the skills taught in academic institutions and what the job market demands. Many translation programs in Saudi universities don't align well with industry needs, resulting in graduates who may not meet employer expectations. To tackle this challenge, it's essential to thoroughly analyze the market to identify the key skills required, especially in sectors like legal, medical, technical, and audiovisual translation. At the same time, existing translation programs need to be evaluated to see if they cover necessary topics and provide practical training. Involving stakeholders such as translation agencies, professionals, and students is crucial to gather diverse perspectives. Identifying discrepancies between academic offerings and market demands will guide the development of targeted strategies. These strategies may include enriching curricula with industry-specific content, integrating emerging technologies like machine translation and CAT tools, and establishing partnerships with industry players to offer practical training opportunities and internships. Industry-led workshops and seminars can provide students with valuable insights, and certification programs can validate their skills. By aligning academic programs with industry needs, Saudi Arabia can build a skilled workforce of translators, supporting its economic diversification goals under Vision 2030. This alignment benefits both students and the industry, contributing to the growth of the translation sector and the overall development of the country.

Keywords: translation industry, briging gap, labor market, requirements

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Authors: Go Woon Kwak, Siyoung Jun, Soyun Maeng, Haeyoung Lee

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Entry is an education platform used in South Korea, created to help students learn to program, in which they can learn to code while playing. Using the online version of the entry, teachers can easily assign programming homework to the student and the students can make programs simply by linking programming blocks. However, the programs may be made by others, so that the authors of the programs should be identified. In this paper, as the first step toward author identification of entry programs, we present an artificial neural network based classification approach to identify genders of authors of a program written in an entry. A neural network has been trained from labeled training data that we have collected. Our result in progress, although preliminary, shows that the proposed approach could be feasible to be applied to the online version of entry for gender identification of authors. As future work, we will first use a machine learning technique for age identification of entry programs, which would be the second step toward the author identification.

Keywords: artificial intelligence, author identification, deep neural network, gender identification, machine learning

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Authors: Milind Patil

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Present paper is a discussion on Hindi-Marathi Morphological Analysis and generating rules for Machine Translation on the basis of Machine Readable Dictionary (MRD). This used Transformative Generative Grammar (TGG) rules to design the MRD. As per TGG rules, the suffix of a particular root word is based on its Tense, Aspect, Modality and Voice. That's why the suffix is very important for the word meanings (or root meanings). The Hindi and Marathi Language both have relation with Indo-Aryan language family. Both have been derived from Sanskrit language and their script is 'Devnagari'. But there are lots of differences in terms of semantics and grammatical level too. In Marathi, there are three genders, but in Hindi only two (Masculine and Feminine), the Natural gender is absent in Hindi. Likewise other grammatical categories also differ in their level of use. For MRD the suffixes (or Morpheme) are of particular root word for GNP (Gender, Number and Person) are based on its natural phenomena. A particular Suffix and Morphine change as per the need of person, number and gender. The design of MRD also based on this format. In first, Person, Number, Gender and Tense are key points than root words and suffix of particular Person, Number Gender (PNG). After that the inferences are drawn on the basis of rules that is (V.stem) (Pre.T/Past.T) (x) + (Aux-Pre.T) (x) → (V.Stem.) + (SP.TM) (X).

Keywords: MRD, TGG, stem, morph, morpheme, suffix, PNG, TAM&V, root

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Authors: Natalia Espinosa, Arthur Amorim, Rudolf Huebner

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Epilepsy is a chronic neural disease and around 50 million people in the world suffer from this disease, however, in many cases, the individual acquires resistance to the medication, which is known as drug-resistant epilepsy, where a detection system is necessary. This paper showed the development of an automatic system for seizure detection based on artificial neural networks (ANN), which are common techniques of machine learning. Discrete Wavelet Transform (DWT) is used for decomposing electroencephalogram (EEG) signal into main brain waves, with these frequency bands is extracted features for training a feedforward neural network with backpropagation, finally made a pattern classification, seizure or non-seizure. Obtaining 95% accuracy in epileptic EEG and 100% in normal EEG.

Keywords: Artificial Neural Network (ANN), Discrete Wavelet Transform (DWT), Epilepsy Detection , Seizure.

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Authors: Guessabi Fatiha

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Nowadays, there is no sphere of human life that does not use Information and Communication Technologies (ICTs) in practice. This type of development grew widely in the last years of the 20th century and impacted many fields such as education, health, financing, job markets, communication, governments, industrial productivity, etc. Recently, in higher education, the use of ICTs has been essential and significant during the Covid19 pandemic. Thanks to technology, although the universities in Algeria were locked down during the period of covid19, learning was easily continued, and students were collaborating, communicating, socializing, and learning at a distance. Therefore, ICT tools are required in translation courses to enhance and improve translation teaching. This research explores the use of ICT in teaching and learning translation. The research comes along with a theoretical framework; the literature review is produced to highlight some essential ICT concepts and translation teaching. In order to achieve the study objective, a questionnaire is distributed to the third-year English LMD students at Tahri Mohamed University, and an interview is addressed to the translation teacher. The results and discussion obtained from this investigation confirmed the hypothesis and revealed that the use of ICT is essential in translation courses and it improves translation teaching. Hence, by using ICT in the classroom, the students become more active, and the teachers of translation become knowledge facilitators and leaders.

Keywords: COVID19, ICT, learning, students, teaching, TMU, translation

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Authors: Mehwish Asghar

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Breast Cancer (BC) is a common type of cancer among women. Its screening is usually performed using different imaging modalities such as magnetic resonance imaging, mammogram, X-ray, CT, etc. Among these modalities’ mammogram is considered a powerful tool for diagnosis and screening of breast cancer. Sophisticated machine learning approaches have shown promising results in complementing human diagnosis. Generally, machine learning methods can be divided into two major classes: one is Radiomics analysis (RA), where image features are extracted manually; and the other one is the concept of convolutional neural networks (CNN), in which the computer learns to recognize image features on its own. This research aims to improve the incidence of early detection, thus reducing the mortality rate caused by breast cancer through the latest advancements in computer science, in general, and machine learning, in particular. It has also been aimed to ease the burden of doctors by improving and automating the process of breast cancer detection. This research is related to a relative analysis of different techniques for the implementation of different models for detecting and classifying breast cancer. The main goal of this research is to provide a detailed view of results and performances between different techniques. The purpose of this paper is to explore the potential of a convolutional neural network (CNN) w.r.t feature extractor and as a classifier. Also, in this research, it has been aimed to add the module of Radiomics for comparison of its results with deep learning techniques.

Keywords: breast cancer (BC), machine learning (ML), convolutional neural network (CNN), radionics, magnetic resonance imaging, artificial intelligence

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Authors: Elahe Kamari

Abstract:

Research on translation process has been conducted for more than 20 years, investigating various issues and using different research methodologies. Most recently, researchers have started to use eye tracking to study translation processes. They believed that the observable, measurable data that can be gained from eye tracking are indicators of unobservable cognitive processes happening in the translators’ mind during translation tasks. The aim of this study was to investigate directionality in translation processes through using eye tracking. The following hypotheses were tested: 1) processing the target text requires more cognitive effort than processing the source text, in both directions of translation; 2) L2 translation tasks on the whole require more cognitive effort than L1 tasks; 3) cognitive resources allocated to the processing of the source text is higher in L1 translation than in L2 translation; 4) cognitive resources allocated to the processing of the target text is higher in L2 translation than in L1 translation; and 5) in both directions non-professional translators invest more cognitive effort in translation tasks than do professional translators. The performance of a group of 30 male professional translators was compared with that of a group of 30 male non-professional translators. All the participants translated two comparable texts one into their L1 (Persian) and the other into their L2 (English). The eye tracker measured gaze time, average fixation duration, total task length and pupil dilation. These variables are assumed to measure the cognitive effort allocated to the translation task. The data derived from eye tracking only confirmed the first hypothesis. This hypothesis was confirmed by all the relevant indicators: gaze time, average fixation duration and pupil dilation. The second hypothesis that L2 translation tasks requires allocation of more cognitive resources than L1 translation tasks has not been confirmed by all four indicators. The third hypothesis that source text processing requires more cognitive resources in L1 translation than in L2 translation and the fourth hypothesis that target text processing requires more cognitive effort in L2 translation than L1 translation were not confirmed. It seems that source text processing in L2 translation can be just as demanding as in L1 translation. The final hypothesis that non-professional translators allocate more cognitive resources for the same translation tasks than do the professionals was partially confirmed. One of the indicators, average fixation duration, indicated higher cognitive effort-related values for professionals.

Keywords: translation processes, eye tracking, cognitive resources, directionality

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Authors: Hervice Roméo Fogno Fotsoa, Germaine Djuidje Kenmoe, Claude Vidal Aloyem Kazé

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One of the main applications of machine learning is the prediction of time series. But a more accurate prediction requires a more optimal model of machine learning. Several optimization techniques have been developed, but without considering the input variables disposition of the system. Thus, this work aims to present a new machine learning architecture optimization technique based on their optimal input variables disposition. The validations are done on the prediction of wind time series, using data collected in Cameroon. The number of possible dispositions with four input variables is determined, i.e., twenty-four. Each of the dispositions is used to perform the prediction, with the main criteria being the training and prediction performances. The results obtained from a static architecture and a dynamic architecture of neural networks have shown that these performances are a function of the input variable's disposition, and this is in a different way from the architectures. This analysis revealed that it is necessary to take into account the input variable's disposition for the development of a more optimal neural network model. Thus, a new neural network training algorithm is proposed by introducing the search for the optimal input variables disposition in the traditional back-propagation algorithm. The results of the application of this new optimization approach on the two single neural network architectures are compared with the previously obtained results step by step. Moreover, this proposed approach is validated in a collaborative optimization method with a single objective optimization technique, i.e., genetic algorithm back-propagation neural networks. From these comparisons, it is concluded that each proposed model outperforms its traditional model in terms of training and prediction performance of time series. Thus the proposed optimization approach can be useful in improving the accuracy of time series forecasts. This proves that the proposed optimization approach can be useful in improving the accuracy of time series prediction based on machine learning.

Keywords: input variable disposition, machine learning, optimization, performance, time series prediction

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Authors: Sadixa Sanjel, Ahmed Sadek, Naseef Mansoor, Zelalem Denekew

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Collegiate ice hockey (NCAA) sports analytics is different from the national level hockey (NHL). We apply and compare multiple machine learning models such as Linear Regression, Random Forest, and Neural Networks to predict the win ratio for a team based on their statistics. Data exploration helps determine which statistics are most useful in increasing the win ratio, which would be beneficial to coaches and team managers. We ran experiments to select the best model and chose Random Forest as the best performing. We conclude with how to bridge the gap between the college and national levels of sports analytics and the use of machine learning to enhance team performance despite not having a lot of metrics or budget for automatic tracking.

Keywords: NCAA, NHL, sports analytics, random forest, regression, neural networks, game predictions

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Authors: Sheela Tiwari, R. Naresh, R. Jha

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The paper presents an investigation into the effect of neural network predictive control of UPFC on the transient stability performance of a multi-machine power system. The proposed controller consists of a neural network model of the test system. This model is used to predict the future control inputs using the damped Gauss-Newton method which employs ‘backtracking’ as the line search method for step selection. The benchmark 2 area, 4 machine system that mimics the behavior of large power systems is taken as the test system for the study and is subjected to three phase short circuit faults at different locations over a wide range of operating conditions. The simulation results clearly establish the robustness of the proposed controller to the fault location, an increase in the critical clearing time for the circuit breakers and an improved damping of the power oscillations as compared to the conventional PI controller.

Keywords: identification, neural networks, predictive control, transient stability, UPFC

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Authors: Peng Wang, Tigang Jiang

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The computer based lip reading method of different languages cannot be universal. At present, for the research of Chinese lip reading, whether the work on data sets or recognition algorithms, is far from mature. In this paper, we study the Chinese lipreading method based on machine learning, and propose a Chinese Sentence-level lip-reading network (CNLipNet) model which consists of spatio-temporal convolutional neural network(CNN), recurrent neural network(RNN) and Connectionist Temporal Classification (CTC) loss function. This model can map variable-length sequence of video frames to Chinese Pinyin sequence and is trained end-to-end. More over, We create CNLRS, a Chinese Lipreading Dataset, which contains 5948 samples and can be shared through github. The evaluation of CNLipNet on this dataset yielded a 41% word correct rate and a 70.6% character correct rate. This evaluation result is far superior to the professional human lip readers, indicating that CNLipNet performs well in lipreading.

Keywords: lipreading, machine learning, spatio-temporal, convolutional neural network, recurrent neural network

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Authors: Yingying Ding

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This study presents a methodology of specialized translation with the objective of helping teachers to improve the strategies in teaching translation. In order to allow students to acquire skills to translate specialized texts, they need to become familiar with the semantic and syntactic features of source texts and target texts. The aim of our study is to use a corpus-based approach in the teaching of specialized translation between Chinese and Italian. This study proposes to construct a specialized Chinese - Italian comparable corpus that consists of 50 economic contracts from the domain of food. With the help of AntConc, we propose to compile a comparable corpus in for translation teaching purposes. This paper attempts to provide insight into how teachers could benefit from comparable corpus in the teaching of specialized translation from Italian into Chinese and through some examples of passive sentences how students could learn to apply different strategies for translating appropriately the voice.

Keywords: contrastive studies, specialised translation, corpus-based approach, teaching

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Authors: Mitat Uysal, Aynur Uysal

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Artificial Intelligence (AI) is fundamentally driven by mathematics, with many of its core algorithms rooted in mathematical principles such as linear algebra, probability theory, calculus, and optimization techniques. This paper explores the deep connection between AI and mathematics, highlighting the role of mathematical concepts in key AI techniques like machine learning, neural networks, and optimization. To demonstrate this connection, a case study involving the implementation of a neural network using Python is presented. This practical example illustrates the essential role that mathematics plays in training a model and solving real-world problems.

Keywords: AI, mathematics, machine learning, optimization techniques, image processing

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Authors: Zachary Pan

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Our general mission is to solve the problem of classifying images into different body part types and deciding if each of them is healthy or not. However, for now, we will determine healthiness for only one-sixth of the body parts, specifically the chest. We will detect pneumonia in X-ray scans of those chest images. With this type of AI, doctors can use it as a second opinion when they are taking CT or X-ray scans of their patients. Another ad-vantage of using this machine learning classifier is that it has no human weaknesses like fatigue. The overall ap-proach to this problem is to split the problem into two parts: first, classify the image, then determine if it is healthy. In order to classify the image into a specific body part class, the body parts dataset must be split into test and training sets. We can then use many models, like neural networks or logistic regression models, and fit them using the training set. Now, using the test set, we can obtain a realistic accuracy the models will have on images in the real world since these testing images have never been seen by the models before. In order to increase this testing accuracy, we can also apply many complex algorithms to the models, like multiplicative weight update. For the second part of the problem, to determine if the body part is healthy, we can have another dataset consisting of healthy and non-healthy images of the specific body part and once again split that into the test and training sets. We then use another neural network to train on those training set images and use the testing set to figure out its accuracy. We will do this process only for the chest images. A major conclusion reached is that convolutional neural networks are the most reliable and accurate at image classification. In classifying the images, the logistic regression model, the neural network, neural networks with multiplicative weight update, neural networks with the black box algorithm, and the convolutional neural network achieved 96.83 percent accuracy, 97.33 percent accuracy, 97.83 percent accuracy, 96.67 percent accuracy, and 98.83 percent accuracy, respectively. On the other hand, the overall accuracy of the model that de-termines if the images are healthy or not is around 78.37 percent accuracy.

Keywords: body part, healthcare, machine learning, neural networks

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Authors: Smail Hadj Mahammed

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Translation is not merely a process of conveying the meaning from one particular language into another to overcome language barriers and ensure a good understanding; it is also a work of civilization and progress. Without the translation of Greek, Indian and Persian works, Arabic and Islamic Civilization would not have taken off, and without the translations of Arabic works into Latin, and then into European languages, the scientific and technological revolution of the modern world would not have taken place. In this context, the present paper seeks to investigate how the translation movement contributed to the Arabic and Islamic Civilizations during the Golden Age. The research paper consists of three major parts: the first part provides a brief historical overview of the translation movement during the golden age, which witnessed two important eras: the Umayyad and Abbasid eras. The second part shows the main reasons why translation was a prominent cultural activity during the Golden Age and why it gained great interest from the Arabs. The last part highlights the constructive contribution of translation to the Arabic and Islamic Civilization during the period (661–1258). The results demonstrate that Arabic translation movement was unprecedented in the transmission of knowledge in the whole history of humankind and that translation during the Golden Age had significantly assisted in enriching the Arabic and Islamic civilizations, which had absorbed major and important scientific works of old Greek, Indian and Persian civilizations.

Keywords: Arabic and Islamic civilization, contribution, golden age, translation

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Authors: JaeHwan Yang, Da-Woon Jeong, Seung-Young Kho, Dong-Kyu Kim

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In terms of ITS, information on link characteristic is an essential factor for plan or operation. But in practical cases, not every link has installed sensors on it. The link that does not have data on it is called “Missing Link”. The purpose of this study is to impute data of these missing links. To get these data, this study applies the machine learning method. With the machine learning process, especially for the deep learning process, missing link data can be estimated from present link data. For deep learning process, this study uses “Recurrent Neural Network” to take time-series data of road. As input data, Dedicated Short-range Communications (DSRC) data of Dalgubul-daero of Daegu Metropolitan Area had been fed into the learning process. Neural Network structure has 17 links with present data as input, 2 hidden layers, for 1 missing link data. As a result, forecasted data of target link show about 94% of accuracy compared with actual data.

Keywords: data estimation, link data, machine learning, road network

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Authors: Ahmed Alaoui

Abstract:

This paper contributes to the ongoing debate as to the relevance of translation studies to professional practitioners. It exposes the various misconceptions permeating the links between theory and practice in the translation landscape in the Arab World. It is a thesis of this paper that specialization in translation should be redefined; taking account of the fact, that specialized knowledge alone is neither crucial nor sufficient in technical translation. It should be tested against the readability of the translated text, the appropriateness of its style and the usability of its content by end-users to carry out their intended tasks. The paper also proposes a preliminary model to establish a working link between theory and practice from the perspective of professional trainers and practitioners, calling for the latter to participate in the production of knowledge in a systematic fashion. While this proposal is driven by a rather intuitive conviction, a research line is needed to specify the methodological moves to establish the mediation strategies that would relate the components in the model of knowledge transfer proposed in this paper.

Keywords: knowledge transfer, misconceptions, specialized texts, translation theory, translation practice

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Authors: Ge Song

Abstract:

Hong Kong’s bilingual street signs declare a kind of correspondence, equivalence and thus translation between the English and Chinese languages. This study finds four translation phenomena among the street signs: domestication with positive connotation, foreignization with negative connotation, bilingual incompatibilities, and cross-street complexities. The interplay of, and the tension between, the four features open up a space where the local and the foreign, the vulgar and the elegant, alternate and experiment with each other, creating a kaleidoscope of methods for expressing and domesticating foreign otherness by virtue of translation. An analysis of the phenomena from the functional perspective reveals how translation has been emancipated to inform a variety of dimensions. This study also renews our understanding of translation as both a concept and a practice.

Keywords: street signs, linguistic landscape, cultural hybridity, Hong Kong

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