Search results for: machine language

Authors: Julian Wise

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Newcrest Mining is one of the world’s top five gold and rare earth mining organizations by production, reserves and market capitalization in the world. This paper elaborates on the data acquisition processes employed by Newcrest in collaboration with Fortune 500 listed organization, Insight Enterprises, to standardize machine learning solutions which process data from over a hundred thousand distributed Internet of Things (IoT) devices located at mine sites globally. Through the utilization of software architecture cloud technologies and edge computing, the technological developments enable for standardized processes of machine learning applications to influence the strategic optimization of mineral processing. Target objectives of the machine learning optimizations include time savings on mineral processing, production efficiencies, risk identification, and increased production throughput. The data acquired and utilized for predictive modelling is processed through edge computing by resources collectively stored within a data lake. Being involved in the digital transformation has necessitated the standardization software architecture to manage the machine learning models submitted by vendors, to ensure effective automation and continuous improvements to the mineral process models. Operating at scale, the system processes hundreds of gigabytes of data per day from distributed mine sites across the globe, for the purposes of increased improved worker safety, and production efficiency through big data applications.

Keywords: mineral technology, big data, machine learning operations, data lake

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Authors: Rendong Cai, Bei Peng, Yanping Dong

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In risk decision making, individuals are found to be susceptible to 'frames': people tend to be risk averse when the choice is described in terms of potential 'gains' (gain frame), whereas they tend to be risk seeking when the same choice is described in terms of potential 'losses' (loss frame); this effect is termed the framing effect. The framing effect has been well documented and some studies even find a developmental reversal in the framing effect: The more experience an individual has in a certain field, the easier for him to be influenced by the frame relevant to the field, resulting in greater decision inconsistency. Recent studies reported that using a foreign language can reduce the framing effect. However, it is not clear whether foreign language use can overcome the developmental reversal in the framing effect. The present study investigated three potential factors that may influence the developmental reversal in the framing effect: specialized knowledge of the participants, the language in which the problem is presented, and the types of problems. The present study examined the decision making behavior of 188 Chinese-English bilinguals who majored in Finance, with a group of 277 English majors as the control group. They were asked to solve a financial problem (experimental condition) and a life problem (control condition). Each problem was presented in one of the following four versions: native language-gain frame, foreign language-gain frame, native language-loss frame, and foreign language-loss frame. Results revealed that for the life problem, under the native condition, both groups were affected by the frame; but under the foreign condition, this framing effect disappeared for the financial majors. This confirmed that foreign language use modulates framing effects in general decision making, which served as an effective baseline. For the financial problem, under the native condition, only the financial major was observed to be influenced by the frame, which was a developmental reversal; under the foreign condition, however, this framing effect disappeared. The results provide further empirical evidence for the universal of the developmental reversal in risky decision making. More importantly, the results suggest that using a foreign language can overcome such reversal, which has implications for the reduction of decision biases in professionals. The findings also shed new light on the complex interaction between general decision-making and bilingualism.

Keywords: the foreign language effect, developmental reversals, the framing effect, bilingualism

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Authors: Zhengrong Wu, Haibo Yang

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In the context of escalating global climate change and environmental degradation, the complexity and frequency of natural disasters are continually increasing. Confronted with an abundance of information regarding natural disasters, traditional knowledge graph construction methods, which heavily rely on grammatical rules and prior knowledge, demonstrate suboptimal performance in processing complex, multi-source disaster information. This study, drawing upon past natural disaster reports, disaster-related literature in both English and Chinese, and data from various disaster monitoring stations, constructs question-answer templates based on large language models. Utilizing the P-Tune method, the ChatGLM2-6B model is fine-tuned, leading to the development of a disaster knowledge graph based on large language models. This serves as a knowledge database support for disaster emergency response.

Keywords: large language model, knowledge graph, disaster, deep learning

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Authors: A. Shannaq

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English is the accepted lingua franca of the academic world, and English medium higher education institutions host many second-language speakers of English (L2) who wish to pursue their studies through the medium of English. Assessment in higher education institutions is largely done in writing, which makes the mastery of academic writing essential. While such mastery can be, and often is, difficult for students who speak English as a first language, it is undoubtedly more so for L2 students attempting to adopt Anglophone academic written norms. There does not appear to be a great deal of research with regard to L2 students’ perceptions of their academic writing practices. This research investigates the writing practices of international L2 students in their first year of undergraduate study at NZ universities. Qualitative longitudinal data in the form of semi-structured interviews and documentation (assignments’ written instructions, students’ written assignments, tutors’ feedback on the students’ assignments) were collected from 4 undergraduate international L2 students at the beginning, middle, and end of the academic year 2017. Findings reveal that motivation, agency, and self-efficacy impact students’ perceptions of their academic writing practices and define the course of actions learners take under the time constraints which are set for their assignments.

Keywords: academic writing, English as a second language, international second language students, undergraduate writing practices

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Authors: Hamzat Shittu Atunnise

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This study on Assessment of the Implementation of Recommended Teaching and Evaluation Methods of the Nigeria Certificate in Education (NCE) Arabic Language Curriculum in Colleges of Education in North Western Nigeria was conducted with four objectives, four research questions and four null hypotheses. Descriptive survey design was used and the multistage sampling procedure adopted. Frequency count and percentage were used to answer research questions and chi-square was used to test all the null hypotheses at an Alpha 0.05 level of significance. Two hundred and ninety one subjects were drawn as sample. Questionnaires were used for data collection. The Context, Input, Process and Product (CIPP) model of evaluation was employed. The study findings indicated that: there were no significant difference in the perceptions of lecturers and students from Federal and State Colleges of Education on the following: extent of which lecturers employ appropriate methods in teaching the language and extent of which recommended evaluation methods are utilized for the implementation of Arabic Curriculum. Based on these findings, it was recommended among other things that: lecturers should adopt teaching methodologies that promote interactive learning; Governments should ensure that information and communication technology facilities are made available and usable in all Colleges of Education; Lecturers should vary their evaluation methods because other methods of evaluation can meet and surpass the level of learning and understanding which essay type questions are believed to create and that language labs should be used in teaching Arabic in Colleges of Education because comprehensive language learning is possible through both classroom and language lab teaching.

Keywords: assessment, arabic language, curriculum, methods of teaching, evaluation methods, NCE

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Authors: Bitewulign Mekonnen

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Context: This scientific paper focuses on the use of near-infrared (NIR) spectroscopy to determine glucose concentration in aqueous solutions accurately and rapidly. The study compares six different machine learning methods for predicting glucose concentration and also explores the development of a deep learning model for classifying NIR spectra. The objective is to optimize the detection model and improve the accuracy of glucose prediction. This research is important because it provides a comprehensive analysis of various machine-learning techniques for estimating aqueous glucose concentrations. Research Aim: The aim of this study is to compare and evaluate different machine-learning methods for predicting glucose concentration from NIR spectra. Additionally, the study aims to develop and assess a deep-learning model for classifying NIR spectra. Methodology: The research methodology involves the use of machine learning and deep learning techniques. Six machine learning regression models, including support vector machine regression, partial least squares regression, extra tree regression, random forest regression, extreme gradient boosting, and principal component analysis-neural network, are employed to predict glucose concentration. The NIR spectra data is randomly divided into train and test sets, and the process is repeated ten times to increase generalization ability. In addition, a convolutional neural network is developed for classifying NIR spectra. Findings: The study reveals that the SVMR, ETR, and PCA-NN models exhibit excellent performance in predicting glucose concentration, with correlation coefficients (R) > 0.99 and determination coefficients (R²)> 0.985. The deep learning model achieves high macro-averaging scores for precision, recall, and F1-measure. These findings demonstrate the effectiveness of machine learning and deep learning methods in optimizing the detection model and improving glucose prediction accuracy. Theoretical Importance: This research contributes to the field by providing a comprehensive analysis of various machine-learning techniques for estimating glucose concentrations from NIR spectra. It also explores the use of deep learning for the classification of indistinguishable NIR spectra. The findings highlight the potential of machine learning and deep learning in enhancing the prediction accuracy of glucose-relevant features. Data Collection and Analysis Procedures: The NIR spectra and corresponding references for glucose concentration are measured in increments of 20 mg/dl. The data is randomly divided into train and test sets, and the models are evaluated using regression analysis and classification metrics. The performance of each model is assessed based on correlation coefficients, determination coefficients, precision, recall, and F1-measure. Question Addressed: The study addresses the question of whether machine learning and deep learning methods can optimize the detection model and improve the accuracy of glucose prediction from NIR spectra. Conclusion: The research demonstrates that machine learning and deep learning methods can effectively predict glucose concentration from NIR spectra. The SVMR, ETR, and PCA-NN models exhibit superior performance, while the deep learning model achieves high classification scores. These findings suggest that machine learning and deep learning techniques can be used to improve the prediction accuracy of glucose-relevant features. Further research is needed to explore their clinical utility in analyzing complex matrices, such as blood glucose levels.

Keywords: machine learning, signal processing, near-infrared spectroscopy, support vector machine, neural network

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Authors: Xiaogang Li, Jieqiong Miao

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As for the problem of the grey forecasting model prediction accuracy is low, an improved grey prediction model is put forward. Firstly, use trigonometric function transform the original data sequence in order to improve the smoothness of data , this model called SGM( smoothness of grey prediction model), then combine the improved grey model with support vector machine , and put forward the grey support vector machine model (SGM - SVM).Before the establishment of the model, we use trigonometric functions and accumulation generation operation preprocessing data in order to enhance the smoothness of the data and weaken the randomness of the data, then use support vector machine (SVM) to establish a prediction model for pre-processed data and select model parameters using genetic algorithms to obtain the optimum value of the global search. Finally, restore data through the "regressive generate" operation to get forecasting data. In order to prove that the SGM-SVM model is superior to other models, we select the battery life data from calce. The presented model is used to predict life of battery and the predicted result was compared with that of grey model and support vector machines．For a more intuitive comparison of the three models, this paper presents root mean square error of this three different models .The results show that the effect of grey support vector machine (SGM-SVM) to predict life is optimal, and the root mean square error is only 3.18%. Keywords: grey forecasting model, trigonometric function, support vector machine, genetic algorithms, root mean square error

Keywords: Grey prediction model, trigonometric functions, support vector machines, genetic algorithms, root mean square error

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Authors: Wanatchapong Kongkaew

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This paper proposes an application of probabilistic technique, namely Gaussian process regression, for estimating an optimal sequence of the single machine with total weighted tardiness (SMTWT) scheduling problem. In this work, the Gaussian process regression (GPR) model is utilized to predict an optimal sequence of the SMTWT problem, and its solution is improved by using an iterated local search based on simulated annealing scheme, called GPRISA algorithm. The results show that the proposed GPRISA method achieves a very good performance and a reasonable trade-off between solution quality and time consumption. Moreover, in the comparison of deviation from the best-known solution, the proposed mechanism noticeably outperforms the recently existing approaches.

Keywords: Gaussian process regression, iterated local search, simulated annealing, single machine total weighted tardiness

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Authors: Lokes Parvatha Kumaran S., Sakthi Jay Mahenthar C., Sathyaprakash P., Jayakumar V., Shobanadevi A.

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With the evolution of technology, the need to solve complex computational problems like machine learning and deep learning has shot up. But even the most powerful classical supercomputers find it difficult to execute these tasks. With the recent development of quantum computing, researchers and tech-giants strive for new quantum circuits for machine learning tasks, as present works on Quantum Machine Learning (QML) ensure less memory consumption and reduced model parameters. But it is strenuous to simulate classical deep learning models on existing quantum computing platforms due to the inflexibility of deep quantum circuits. As a consequence, it is essential to design viable quantum algorithms for QML for noisy intermediate-scale quantum (NISQ) devices. The proposed work aims to explore Variational Quantum Circuits (VQC) for Deep Reinforcement Learning by remodeling the experience replay and target network into a representation of VQC. In addition, to reduce the number of model parameters, quantum information encoding schemes are used to achieve better results than the classical neural networks. VQCs are employed to approximate the deep Q-value function for decision-making and policy-selection reinforcement learning with experience replay and the target network.

Keywords: quantum computing, quantum machine learning, variational quantum circuit, deep reinforcement learning, quantum information encoding scheme

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Authors: Yumi Okano

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The low level of English proficiency among Japanese people has been a problem for a long time. Japanese non-native English language teachers, under social or ideological constraints, feel a gap between government policy and their language proficiency and cannot maintain high self-esteem. This paper focuses on current Japanese policies and the social context in which teachers are placed and examines the measures necessary for their positive identity formation from a macro-meso-micro perspective. Some suggestions for achieving this are: 1) Teachers should free themselves from the idea of native speakers and embrace local needs and accents, 2) Teachers should be involved in student discussions as facilitators and individuals so that they can be good role models for their students, and 3) Teachers should invest in their classrooms. 4) Guidelines and training should be provided to help teachers gain confidence. In addition to reducing the workload to make more time available, 5) expanding opportunities for investment outside the classroom into the real world is necessary.

Keywords: language teacher identity, native speakers, government policy, critical pedagogy, investment

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Authors: Mamiko Orii

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This study reports on primary school second language teachers’ sources of language insecurity. Furthermore, it aims to develop an in-service training course to reduce anxiety and build sufficient English communication skills. Language/Linguistic insecurity refers to a lack of confidence experienced by language speakers. In particular, second language/non-native learners often experience insecurity, influencing their learning efficacy. While language learner insecurity has been well-documented, research on the insecurity of language teaching professionals is limited. Teachers’ language insecurity or anxiety in target language use may adversely affect language instruction. For example, they may avoid classroom activities requiring intensive language use. Therefore, understanding teachers’ language insecurity and providing continuing education to help teachers to improve their proficiency is vital to improve teaching quality. This study investigated Japanese primary school teachers’ language insecurity. In Japan, teachers are responsible for teaching most subjects, including English, which was recently added as compulsory. Most teachers have never been professionally trained in second language instruction during college teacher certificate preparation, leading to low confidence in English teaching. Primary source of language insecurity is a lack of confidence regarding English communication skills. Their actual use of English in classrooms remains unclear. Teachers’ classroom speech remains a neglected area requiring improvement. A more refined programme for second language teachers could be constructed if we can identify areas of need. Two questionnaires were administered to primary school teachers in Tokyo: (1) Questionnaire A: 396 teachers answered questions (using a 5-point scale) concerning classroom teaching anxiety and general English use and needs for in-service training (Summer 2021); (2) Questionnaire B: 20 teachers answered detailed questions concerning their English use (Autumn 2022). Questionnaire A’s responses showed that over 80% of teachers have significant language insecurity and anxiety, mainly when speaking English in class or teaching independently. Most teachers relied on a team-teaching partner (e.g., ALT) and avoided speaking English. Over 70% of the teachers said they would like to participate in training courses in classroom English. Questionnaire B’s results showed that teachers could use simple classroom English, such as greetings and basic instructions (e.g., stand up, repeat after me), and initiate conversation (e.g., asking questions). In contrast, teachers reported that conversations were mainly carried on in a simple question-answer style. They had difficulty continuing conversations. Responding to learners’ ‘on-the-spot’ utterances was particularly difficult. Instruction in turn-taking patterns suitable in the classroom communication context is needed. Most teachers received grammar-based instruction during their entire English education. They were predominantly exposed to displayed questions and form-focused corrective feedback. Therefore, strategies such as encouraging teachers to ask genuine questions (i.e., referential questions) and responding to students with content feedback are crucial. When learners’ utterances are incorrect or unsatisfactory, teachers should rephrase or extend (recast) them instead of offering explicit corrections. These strategies support a continuous conversational flow. These results offer benefits beyond Japan’s English as a second Language context. They will be valuable in any context where primary school teachers are underprepared but must provide English-language instruction.

Keywords: english as a second/non-native language, in-service training, primary school, teachers’ language insecurity

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Authors: Xueyan Hu, Liwen Chen, Weilin He, Sujie Peng

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Writing is an important but challenging skill For English language learners. Due to the small amount of time allocated for writing classes at schools, students have relatively few opportunities to practice writing in the classroom. While the practice of integrating listening and speaking activates with writing instruction has been used for adult English language learners, its application for young English learners has seldom been examined due to the challenge of listening and speaking activities for young English language learners. The study attempted to integrating integrating listening and speaking activities with writing instruction for middle school English language learners so as to improving their writing achievements and writing abilities in terms of the word use, coherence, and complexity in their writings. Guided by Gagne's information processing learning theory and memetics, this study conducted a 8-week writing instruction with an experimental class (n=44) and a control class (n=48) . Students in the experimental class participated in a series of listening and retelling activities about a writing sample the teacher used for writing instruction during each period of writing class. Students in the control class were taught traditionally with teachers’ direction instruction using the writing sample. Using the ANCOVA analysis of the scores of students’ writing, word-use, Chinese-English translation and the text structure, this study showed that the experimental writing instruction can significantly improve students’ writing performance. Compared with the students in the control class, the students in experimental class had significant better performance in word use and complexity in their essays. This study provides useful enlightenment for the teaching of English writing for middle school English language learners. Teachers can skillfully use information technology to integrate listening, speaking, and writing teaching, considering students’ language input and output. Teachers need to select suitable and excellent composition templates for students to ensure their high-quality language input.

Keywords: wring instruction, retelling, English language learners, listening and speaking

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Authors: Giovanni Cicceri, Roberta Maisano, Nathalie Morey, Salvatore Distefano

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The main goal of this paper is to present a solution for a water purification system based on an Environmental Internet of Things (EIoT) platform to monitor and control water quality and machine learning (ML) models to support decision making and speed up the processes of purification of water. A real case study has been implemented by deploying an EIoT platform and a network of devices, called Gramb meters and belonging to the Gramb project, on wastewater purification systems located in Calabria, south of Italy. The data thus collected are used to control the wastewater quality, detect anomalies and predict the behaviour of the purification system. To this extent, three different statistical and machine learning models have been adopted and thus compared: Autoregressive Integrated Moving Average (ARIMA), Long Short Term Memory (LSTM) autoencoder, and Facebook Prophet (FP). The results demonstrated that the ML solution (LSTM) out-perform classical statistical approaches (ARIMA, FP), in terms of both accuracy, efficiency and effectiveness in monitoring and controlling the wastewater purification processes.

Keywords: environmental internet of things, EIoT, machine learning, anomaly detection, environment monitoring

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Authors: Serge Lacroix

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The proposed study looks at the psychoeducational assessment of bilingual students, in English and French in this case. It will be the opportunity to look at language of assessment and specifically how certain tests can be administered in one language and others in another language. It is also a look into the questioning of the validity of the test scores that are obtained as well as the quality and generalizability of the conclusions that can be drawn. Bilingualism and multiculturalism, although in constant expansion, is not considered in norms development and remains a poorly understood factor when it is at play in the context of a psychoeducational assessment. Student placement, diagnoses, accurate measures of intelligence and achievement are all impacted by the quality of the assessment procedure. The same is true for questionnaires administered to parents and self-reports completed by bilingual students who, more often than not, are assessed in a language that is not their primary one or are compared to monolinguals not dealing with the same challenges or the same skills. Results show that students, when offered to work in a bilingual fashion, chooses to do so in a significant proportion. Recommendations will be offered to support educators aiming at expanding their skills when confronted with multilingual students in an assessment context.

Keywords: psychoeducational assessment, bilingualism, multiculturalism, intelligence, achievement

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Authors: Khaled Hamdan, Abid Amorri, Fatima Hamdan

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Involving students in English language learning process and achieving an adequate English language proficiency in the target language can be a great challenge for both teachers and students. This can prove even a far greater challenge to engage students with special needs (Dyslexia) if they have physical impairment and inadequate mastery of basic communicative language competence/proficiency in the target language. From this perspective, technology like robots can probably be used to enhance learning process for the special needs students who have extensive communication needs, who face continuous struggle to interact with their peers and teachers and meet academic requirements. Robots, precisely NAO, can probably provide them with the perfect opportunity to practice social and communication skills, and meet their English academic requirements. This research paper aims to identify to what extent robots can be used to improve students’ social interaction and communication skills and to understand the potential for robotics-based education in motivating and engaging UAEU dyslexic students to meet university requirements. To reach this end, the paper will explore several factors that come into play – Motion Level-involving cognitive activities, Interaction Level-involving language processing, Behavior Level -establishing a close relationship with the robot and Appraisal Level- focusing on dyslexia students’ achievement in the target language.

Keywords: dyslexia, robot technology, motion, interaction, behavior and appraisal levels, social and communication skills

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Authors: Amit Kumar, Amanpreet Chander, Ashish Sahani

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This paper presents our recent work on real-time human activity detection based on the media pipe pipeline and machine learning algorithms. The proposed system can detect human activities, including running, jumping, squatting, bending to the left or right, and standing still. This is a robust solution for developing a yoga, dance, metaverse, and fitness application that checks for the correction of the pose without having any additional monitor like a personal trainer. MediaPipe solution offers an open-source cross-platform which utilizes a two-step detector-tracker ML pipeline for live detection of key landmarks on our body which can be used for motion data collection. The prediction of real-time poses uses a variety of machine learning techniques and different types of analysis. Without primarily relying on powerful desktop environments for inference, our method achieves real-time performance on the majority of contemporary mobile phones, desktops/laptops, Python, or even the web. Experimental results show that our method outperforms the existing method in terms of accuracy and real-time capability, achieving an accuracy of 99.92% on testing datasets.

Keywords: human activity detection, media pipe, machine learning, metaverse applications

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Authors: Oleg Vlasovets, Fabian Schaipp, Christian L. Mueller

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we conduct a network analysis and predict the sex of 1000 participants based on ”connectome” - pairwise Pearson’s correlation across 436 brain parcels. We solve the non-smooth convex optimization problem, known under the name of Graphical Lasso, where the solution includes a low-rank component. With this solution and machine learning model for a sex prediction, we explain the brain parcels-sex connectivity patterns.

Keywords: network analysis, neuroscience, machine learning, optimization

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Authors: Sorayya Malek, Mogeeb Mosleh, Sharifah M. Syed

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In this study, Support Vector Machine (SVM) technique was applied to predict the dichotomized value of Dissolved oxygen (DO) from two freshwater lakes namely Chini and Bera Lake (Malaysia). Data sample contained 11 parameters for water quality features from year 2005 until 2009. All data parameters were used to predicate the dissolved oxygen concentration which was dichotomized into 3 different levels (High, Medium, and Low). The input parameters were ranked, and forward selection method was applied to determine the optimum parameters that yield the lowest errors, and highest accuracy. Initial results showed that pH, water temperature, and conductivity are the most important parameters that significantly affect the predication of DO. Then, SVM model was applied using the Anova kernel with those parameters yielded 74% accuracy rate. We concluded that using SVM models to predicate the DO is feasible, and using dichotomized value of DO yields higher prediction accuracy than using precise DO value.

Keywords: dissolved oxygen, water quality, predication DO, support vector machine

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Authors: Subodh Chandra Shakya, Rajendra Sapkota, Aakash Tamang, Shushant Pudasaini, Sujan Adhikari, Sajjan Adhikari

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Lots of efforts have been made in order to measure the semantic similarity between the text corpora in the documents. Techniques have been evolved to measure the similarity of two documents. One such state-of-art technique in the field of Natural Language Processing (NLP) is word to vector models, which converts the words into their word-embedding and measures the similarity between the vectors. We found this to be quite useful for the task of resume ranking. So, this research paper is the implementation of the word2vec model along with other Natural Language Processing techniques in order to rank the resumes for the particular job description so as to automate the process of hiring. The research paper proposes the system and the findings that were made during the process of building the system.

Keywords: chunking, document similarity, information extraction, natural language processing, word2vec, word embedding

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Authors: Yufen Qin

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Topic-to-Essay generation is a challenging task in Natural language processing, which aims to generate novel, diverse, and topic-related text based on user input. Previous research has overlooked the generation of articles under the constraints of event elements, resulting in issues such as incomplete event elements and logical inconsistencies in the generated results. To fill this gap, this paper proposes an event-constrained approach for a topic-to-essay generation that enforces the completeness of event elements during the generation process. Additionally, a language model is employed to verify the logical consistency of the generated results. Experimental results demonstrate that the proposed model achieves a better BLEU-2 score and performs better than the baseline in terms of subjective evaluation on a real dataset, indicating its capability to generate higher-quality topic-related text.

Keywords: event element, language model, natural language processing, topic-to-essay generation.

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Authors: Mehul Srivastava, Sharma Tushar Ravikant, Mridul Krishn Mishra

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In structural design, the evaluation of safety and probability failure of structure is of significant importance, mainly when the variables are random. On real structures, structural reliability can be evaluated obtaining an implicit limit state function. The structural reliability limit state function is obtained depending upon the statistically independent variables. In the analysis of reliability, we considered the statistically independent random variables to be the load intensity applied and the depth or height of the beam member considered. There are many approaches for structural reliability problems. In this paper Extreme Learning Machine technique and First Order Second Moment Method is used to determine the reliability indices for the same set of variables. The reliability index obtained using ELM is compared with the reliability index obtained using FOSM. Higher the reliability index, more feasible is the method to determine the reliability.

Keywords: reliability, reliability index, statistically independent, extreme learning machine

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Authors: Abdolghani Ebrahimi, Diego Klabjan, Chenxi Ge, Daniela Ladner, Parker Stride

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In this work, we use machine learning and novel data analysis techniques to predict the one-year mortality of cirrhotic patients. Data from 2,322 patients with liver cirrhosis are collected at a single medical center. Different machine learning models are applied to predict one-year mortality. A comprehensive feature space including demographic information, comorbidity, clinical procedure and laboratory tests is being analyzed. A temporal pattern mining technic called Frequent Subgraph Mining (FSM) is being used. Model for End-stage liver disease (MELD) prediction of mortality is used as a comparator. All of our models statistically significantly outperform the MELD-score model and show an average 10% improvement of the area under the curve (AUC). The FSM technic itself does not improve the model significantly, but FSM, together with a machine learning technique called an ensemble, further improves the model performance. With the abundance of data available in healthcare through electronic health records (EHR), existing predictive models can be refined to identify and treat patients at risk for higher mortality. However, due to the sparsity of the temporal information needed by FSM, the FSM model does not yield significant improvements. To the best of our knowledge, this is the first work to apply modern machine learning algorithms and data analysis methods on predicting one-year mortality of cirrhotic patients and builds a model that predicts one-year mortality significantly more accurate than the MELD score. We have also tested the potential of FSM and provided a new perspective of the importance of clinical features.

Keywords: machine learning, liver cirrhosis, subgraph mining, supervised learning

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Authors: Mohamed F. Othman

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Manchester, Britain has become the destination of immigrants from different parts of the world. As a result, it is currently home to over 150 different ethnic languages. The present study investigates minority language policy and planning at the micro-level of the city. In order to get an in-depth investigation of such a policy, it was decided to cover it from two angles: the first is the policy making process. This was aimed at getting insights on how decisions regarding the provision of government services in minority languages are taken and what criteria are employed. The second angle is the service provider; i.e. the different departments in Manchester City Council (MCC), the NHS, the courts, and police, etc., to obtain information on the actual provisions of services. Data was collected through semi-structured interviews with different personnel representing different departments in MCC, solicitors, interpreters, etc.; through the internet, e.g. the websites of MCC, NHS, courts, and police, etc.; and via personal observation of provisions of community languages in government services. The results show that Manchester’s language policy is formulated around two concepts that work simultaneously: one is concerned with providing services in community languages in order to help minorities manage their life until they acquire English, and the other with helping the integration of minorities through encouraging them to learn English. In this regard, different government services are provided in community languages, though to varying degrees, depending on the numerical strength of each individual language. Thus, it is concluded that there is awareness in MCC and other government agencies working in Manchester of the linguistic diversity of the city and there are serious attempts to meet this diversity in their services. It is worth mentioning here that providing such services in minority languages are not meant to support linguistic diversity, but rather to maintain the legal right to equal opportunities among the residents of Manchester and to avoid any misunderstanding that may result due to the language barrier, especially in such areas as hospitals, courts, and police. There is actually no explicitly-mentioned language policy regarding minorities in Manchester; rather, there is an implied or covert policy resulting from factors that are not explicitly documented. That is, there are guidelines from the central government, which emphasize the principle of equal opportunities; then the implementation of such guidelines requires providing services in the different ethnic languages.

Keywords: community language, covert language policy, micro-language policy and planning, minority language

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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: Mehdi Damaliamiri, Firouzeh Akbari

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Teaching literature to children at an early age is of great importance, and there have been different methods to facilitate learning literature. Based on the law, all children going to school in Iran should learn the Persian language and literature. This has been concomitant with two different levels of learning related to urban or rural bilingualism. For bilingual children living in the villages, learning literature and a new language (Persian) turns into a big challenge as it is done based on the translation the teacher does while in the city, it is easier as the confrontation of children with the Persian language is more. Over recent years, to change the trend of learning Persian by children speaking another language, the TV and radio programs have been considered to be effective, but the scores of the students in Persian language national exams show that these programs have not been so effective for the bilingual students living in the villages. To identify the determinants of weak learning of Persian by bilingual children, two different regions were chosen, Turkish-speaking and Kurdish-speaking communities, to compare their learning of Persian at the first and second levels of elementary school. The criteria of learning was based on the syllabification of Persian words, word order in the sentence, and compound sentences. Students were taught in Persian how to recognize syllabification without letting them translate the words in their own languages and were asked to produce simple sentences in Persian in response to situational questions. Teaching methods, language relatedness with Persian, and exposure to social media programs, especially TV and radio, were the factors that were considered to affect the potential of children in learning Persian.

Keywords: bilingualism, persian, education, Literature

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Authors: Zsuzsanna Schnell, Francesca Ervas

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Background: Our study aims to clarify how language fosters further cognitive development and how we eventually arrive at the complex human specific skill of pragmatic competence and reveal what levels of mentalization and theory of mind are in place before language. Method: Our experimental pragmatic investigation maps the interaction of mentalization and pragmatic competence. We map the different levels of mentalization that empower different levels of pragmatic meaning construction and evaluate the results with statistical analysis (MannWhitney and ANOVA). Analyzing the comprehension of literal and non-compositional (figurative) utterances, we apply linguistic trials, among them metaphor-, irony-, irony with surface cue-, humor- and the recognition of maxim infringements trial in neurotypical (NT) preschoolers with a coherent and comparative methodology. Results: The findings reveal the relationship and direction of interaction between Language and theory of mind. On the one hand social-cognitive skills enhance, facilitate and provide a basis for language acquisition, and in return linguistic structures (DeVilliers 2000, 2007) provide a framework for further development of mentalizing skills. Conclusions: Findings confirm that this scaffolding becomes a mutually supportive system where language and social cognition develops in interaction. Certain stages in ToM development serve as a precursor of understanding grammatically complex sentences, like embedded phrases which mirror embedded mental states; which, in turn, facilitates the development of pragmatic competence, thus, the social use of language, integrating social, cognitive, linguistic and psychological factors in discourse. Future implications: Our investigation functions as a differential-diagnostic measure, with typically developing results thus serve as a baseline in further empirical research for atypical cases. This enables the study of populations where language and ToM development is disturbed, reveals how language and ToM are acquired and interact, and gives an insight into what this has to do with clinical symptoms. This in turn can reveal the causal link to the syndrome at hand, which can set directions for therapeutic development and training.

Keywords: theory of mind, language development, mentalization, language philosophy, experimental pragmatics

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Authors: Syrine Bassi

Abstract:

Sound symbolism has been able to give us an exciting new tool to target consumers’ brand perception. It acts on a subconscious level making them less likely to reject the implicit message delivered by the sound of the brand name. Most of the research conducted in the field was focused on the English language as it is the language used for international branding campaigns and global companies. However, more research is examining the sound symbolism in other languages and comparing it to the English language findings. Besides, researchers have been able to study luxury brand names and spot out the patterns used in them to provoke luxury and sophistication. It stands to a reason to connect the luxury brand names and the local language’s sound effects since a considerable number of these brands are promoting the origin of the Maison, therefore, have names in foreign languages. This study was established around the Hungarian luxury brand names. It aims to spot out the patterns used in these names that connect to the previous findings of luxury sound effects and also the differences. We worked with a non-Hungarian speaking sample who had some basic knowledge of the language just to make sure they were able to correctly pronounce the names. The results have shown both similarities and differences when it comes to perceiving luxury based on the brand name. As the Hungarian language can be qualified as a saturated language, consonant wise, it was easy to feed the luxury feeling only by using designers' names, however, some complicated names were too difficult and repulsive to consider as luxurious. On the other hand, oversimplifying some names did not convey the desired image as it was too simple and easy. Overall, some sounds have been proved to be linked to luxury as the literature suggests, the difficulty of pronunciation has also proved effective since it highlights the distant feeling consumers crave when looking for luxury. These results suggest that sound symbolism can set up an aura of luxury when used properly, leveraging each languages’ convenient assets.

Keywords: hungarian language, linguistics, luxury brands, sound symbolism

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Authors: Somchai Buaroong

Abstract:

This research aims to investigating strong points and challenges in application of an online learning management system to an English course. Data were collected from observation, learners’ oral and written reports, and the teacher’s journals. A questionnaire was utilized as a tool to collect data. Statistics utilized in this research included frequency, percentage, mean, standard deviation, and multiple regression analysis. The findings show that the system was an additional channel to enhance English language learning through written class assignments that were digitally accessible by any group members, and through communication between the teacher and learners and among learners themselves. Thus, the learning management system could be a promising tool for foreign language teachers. Also revealed in the study were difficulties in its use. The article ends with discussions of findings of the system for foreign language classes in association to pedagogy are also included and in the level of signification.

Keywords: english course, foreign language system, online learning management system, teacher’s journals

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Authors: Samina Khalid, Shamila Nasreen

Abstract:

Dimensionality reduction as a preprocessing step to machine learning is effective in removing irrelevant and redundant data, increasing learning accuracy, and improving result comprehensibility. However, the recent increase of dimensionality of data poses a severe challenge to many existing feature selection and feature extraction methods with respect to efficiency and effectiveness. In the field of machine learning and pattern recognition, dimensionality reduction is important area, where many approaches have been proposed. In this paper, some widely used feature selection and feature extraction techniques have analyzed with the purpose of how effectively these techniques can be used to achieve high performance of learning algorithms that ultimately improves predictive accuracy of classifier. An endeavor to analyze dimensionality reduction techniques briefly with the purpose to investigate strengths and weaknesses of some widely used dimensionality reduction methods is presented.

Keywords: age related macular degeneration, feature selection feature subset selection feature extraction/transformation, FSA’s, relief, correlation based method, PCA, ICA

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Authors: Alanoud Moraya Aldalan, Abdulaziz Almaleh

Abstract:

A crucial component of maintaining a customer-oriented business as in the telecom industry is understanding the reasons and factors that lead to customer churn. Competition between telecom companies has greatly increased in recent years. It has become more important to understand customers’ needs in this strong market of telecom industries, especially for those who are looking to turn over their service providers. So, predictive churn is now a mandatory requirement for retaining those customers. Machine learning can be utilized to accomplish this. Churn Prediction has become a very important topic in terms of machine learning classification in the telecommunications industry. Understanding the factors of customer churn and how they behave is very important to building an effective churn prediction model. This paper aims to predict churn and identify factors of customers’ churn based on their past service usage history. Aiming at this objective, the study makes use of feature selection, normalization, and feature engineering. Then, this study compared the performance of four different machine learning algorithms on the Orange dataset: Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting. Evaluation of the performance was conducted by using the F1 score and ROC-AUC. Comparing the results of this study with existing models has proven to produce better results. The results showed the Gradients Boosting with feature selection technique outperformed in this study by achieving a 99% F1-score and 99% AUC, and all other experiments achieved good results as well.

Keywords: machine learning, gradient boosting, logistic regression, churn, random forest, decision tree, ROC, AUC, F1-score

Procedia PDF Downloads 131