Search results for: machine language

Authors: M. Govindarajan

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One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed with homogeneous ensemble classifier using bagging and heterogeneous ensemble classifier using arcing and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function (RBF) and Support Vector Machine (SVM) as base classifiers. The feasibility and the benefits of the proposed approaches are demonstrated by the means of standard datasets of intrusion detection. The main originality of the proposed approach is based on three main parts: preprocessing phase, classification phase, and combining phase. A wide range of comparative experiments is conducted for standard datasets of intrusion detection. The performance of the proposed homogeneous and heterogeneous ensemble classifiers are compared to the performance of other standard homogeneous and heterogeneous ensemble methods. The standard homogeneous ensemble methods include Error correcting output codes, Dagging and heterogeneous ensemble methods include majority voting, stacking. The proposed ensemble methods provide significant improvement of accuracy compared to individual classifiers and the proposed bagged RBF and SVM performs significantly better than ECOC and Dagging and the proposed hybrid RBF-SVM performs significantly better than voting and stacking. Also heterogeneous models exhibit better results than homogeneous models for standard datasets of intrusion detection. 

Keywords: data mining, ensemble, radial basis function, support vector machine, accuracy

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Authors: Djeddi Khaled, Houssou Hind, Miloudi Abdellatif, Rabah Siham

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In the field of veterinary medicine, there is a growing application of artificial intelligence (AI) for diagnosing bovine mastitis, a prevalent inflammatory disease in dairy cattle. AI technologies, such as automated milking systems, have streamlined the assessment of key metrics crucial for managing cow health during milking and identifying prevalent diseases, including mastitis. These automated milking systems empower farmers to implement automatic mastitis detection by analyzing indicators like milk yield, electrical conductivity, fat, protein, lactose, blood content in the milk, and milk flow rate. Furthermore, reports highlight the integration of somatic cell count (SCC), thermal infrared thermography, and diverse systems utilizing statistical models and machine learning techniques, including artificial neural networks, to enhance the overall efficiency and accuracy of mastitis detection. According to a review of 15 publications, machine learning technology can predict the risk and detect mastitis in cattle with an accuracy ranging from 87.62% to 98.10% and sensitivity and specificity ranging from 84.62% to 99.4% and 81.25% to 98.8%, respectively. Additionally, machine learning algorithms and microarray meta-analysis are utilized to identify mastitis genes in dairy cattle, providing insights into the underlying functional modules of mastitis disease. Moreover, AI applications can assist in developing predictive models that anticipate the likelihood of mastitis outbreaks based on factors such as environmental conditions, herd management practices, and animal health history. This proactive approach supports farmers in implementing preventive measures and optimizing herd health. By harnessing the power of artificial intelligence, the diagnosis of bovine mastitis can be significantly improved, enabling more effective management strategies and ultimately enhancing the health and productivity of dairy cattle. The integration of artificial intelligence presents valuable opportunities for the precise and early detection of mastitis, providing substantial benefits to the dairy industry.

Keywords: artificial insemination, automatic milking system, cattle, machine learning, mastitis

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Authors: Ibrahim Beldjilali, Adel Ghenaiet

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The contra-rotating axial machine is a promising solution for several applications, where high pressure and efficiencies are needed. Also, they allow reducing the speed of rotation, the radial spacing and a better flexibility of use. However, this requires a better understanding of their operation, including the influence of second rotor on the overall aerodynamic performances. This work consisted of both experimental and numerical studies to characterize this counter-rotating fan, especially the analysis of the effects of the blades stagger angle and the inter-distance between the rotors. The experimental study served to validate the computational fluid dynamics model (CFD) used in the simulations. The numerical study permitted to cover a wider range of parameter and deeper investigation on flow structures details, including the effects of blade stagger angle and inter-distance, associated with the interaction between the rotors. As a result, there is a clear improvement in aerodynamic performance compared with a conventional machine.

Keywords: aerodynamic performance, axial fan, counter rotating rotors, CFD, experimental study

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Authors: Sakae Suzuki

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Although Japanese students study English for six years in secondary schools, they demonstrate little success with it when they enter higher education. Learners’ beliefs can predict the future behavior of students, so it may be effective to investigate how learners’ beliefs limit their success and how beliefs might be nudged in a positive direction. While many researchers still depend on a questionnaire called BALLI to reveal explicit beliefs, alternative approaches, especially those designed to reveal implicit beliefs, might be helpful for promoting learning. The present study seeks to identify beliefs with a discursive approach using visual metaphors and narratives. Employing a sociocultural framework, this study investigates how students’ beliefs are revealed by drawings of themselves and their surrounding environments and artifacts while they are engaged in language learning. Research questions are: (1) Can we identify beliefs through an analysis of students’ visual narratives? (2) What environments and artifacts can be found in students’ drawings, and what do they mean? (3) To what extent do students see language learning as a solitary, rather than a social, activity? Participants are university students majoring in science and technology in Japan. The questionnaire was administered to 70 entering students in April, 2014. Data included students drawings of themselves as learners of English as well as written descriptions of students’ backgrounds, English-learning experiences, and analogies and metaphors that they used in written descriptions of themselves as learners. Data will be analyzed qualitatively and quantitatively. Anticipated results include students’ perceptions of themselves as language learners, including their sense of agency, awareness of artifacts, and social contexts of language learning. Comments will be made on implications for teaching, as well as the use of visual narratives as research tools, and recommended further research.

Keywords: drawings, learners' beliefs, metaphors, BALLI

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Authors: Arezoo Seifollahi

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One of the topics that is receiving a lot of attention in artistic circles and among Iran today and has been the subject of many conversations is the issue of Iranian graphics. In this research, the functional and practical aspects of Iranian painting as a visual-identity language in Iranian graphics have been investigated by relying on Iranian cultural and social posters in order to gain an understanding of the trend of contemporary graphic art in Iran and to help us reach the identity of graphics. In order to arrive at Iranian graphics, first, the issue of identity and what it is has been examined, and then this category has been addressed in Iran and throughout the history of this country in order to reveal the characteristics of the identity that has come to us today under the name of Iranian identity cognition. In the following, the search for Iranian identity in the art of this land, especially the art of painting, and then the art of contemporary painting and the search for identity in it have been discussed. After that, Iranian identity has been investigated in Iranian graphics. To understand Iranian graphics, after a brief description of its contemporary history, this art is examined at the considered time point. By using the inductive method of examining the posters of each course and taking into account the related cultural and social conditions, we tried to get a general and comprehensive understanding of the graphic features of each course.

Keywords: Iranian painting, graphic visual language, Iranian identity, social cultural poster

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Authors: Noor Azwa Azreen Binti Abd. Aziz, Muhamad Zaim Bin Mohd Rozi

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Moving towards the 4th Industrial Revolution, artificial intelligence (AI) is now more popular than ever. This subject is gaining significance every day and is continually expanding, often merging with other fields. Instead of merely being passive observers, there are benefits to understanding modern technology by delving into its inner workings. However, in a world teeming with digital information, the impact of AI on the spread of disinformation has garnered significant attention. The dissemination of inaccurate or misleading information is referred to as misinformation, posing a serious threat to democratic society, public debate, and individual decision-making. This article delves deep into the connection between AI and the dissemination of false information, exploring its potential, risks, and ethical issues as AI technology advances. The rise of AI has ushered in a new era in the dissemination of misinformation as AI-driven technologies are increasingly responsible for curating, recommending, and amplifying information on online platforms. While AI holds the potential to enhance the detection and mitigation of misinformation through natural language processing and machine learning, it also raises concerns about the amplification and propagation of false information. AI-powered deepfake technology, for instance, can generate hyper-realistic videos and audio recordings, making it increasingly challenging to discern fact from fiction.

Keywords: artificial intelligence, digital information, disinformation, ethical issues, misinformation

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Authors: Abeer Heider

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This paper explores and classifies the different kinds of avoidances that students commonly make in the acquisition of Arabic as a second/foreign language, and suggests specific strategies to help students lessen their avoidance trends in hopes of streamlining the learning process. Students most commonly use avoidance strategies in grammar, and word choice. These different types of strategies have different implications and naturally require different approaches. Thus the question remains as to the most effective way to help students improve their Arabic, and how teachers can efficiently utilize these techniques. It is hoped that this research will contribute to understand the role of avoidance in the field of the second language acquisition in general, and as a type of input. Yet some researchers also note that similarity between L1 and L2 may be problematic as well since the learner may doubt that such similarity indeed exists and consequently avoid the identical constructions or elements (Jordens, 1977; Kellermann, 1977, 1978, 1986). In an effort to resolve this issue, a case study is being conducted. The present case study attempts to provide a broader analysis of what is acquired than is usually the case, analyzing the learners ‘accomplishments in terms of three –part framework of the components of communicative competence suggested by Michele Canale: grammatical competence, sociolinguistic competence and discourse competence. The subjects of this study are 15 students’ 22th year who came to study Arabic at Qatar University of Cairo. The 15 students are in the advanced level. They were complete intermediate level in Arabic when they arrive in Qatar for the first time. The study used discourse analytic method to examine how the first language affects students’ production and output in the second language, and how and when students use avoidance methods in their learning. The study will be conducted through Fall 2015 through analyzing audio recordings that are recorded throughout the entire semester. The recordings will be around 30 clips. The students are using supplementary listening and speaking materials. The group will be tested at the end of the term to assess any measurable difference between the techniques. Questionnaires will be administered to teachers and students before and after the semester to assess any change in attitude toward avoidance and selectivity methods. Responses to these questionnaires are analyzed and discussed to assess the relative merits of the aforementioned strategies to avoidance and selectivity to further support on. Implications and recommendations for teacher training are proposed.

Keywords: the second language acquisition, learning languages, selectivity, avoidance

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Authors: Chinlun Lai, Lunjyh Jiang

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Due to the heavy burden and pressure of caring for infants, an integrated automatic baby watching system based on IoT smart sensing and deep learning machine vision techniques is proposed in this paper. By monitoring infant body conditions such as heartbeat, breathing, body temperature, sleeping posture, as well as the surrounding conditions such as dangerous/sharp objects, light, noise, humidity and temperature, the proposed system can analyze and predict the obvious/potential dangerous conditions according to observed data and then adopt suitable actions in real time to protect the infant from harm. Thus, reducing the burden of the caregiver and improving safety efficiency of the caring work. The experimental results show that the proposed system works successfully for the infant care work and thus can be implemented in various life fields practically.

Keywords: baby care system, Internet of Things, deep learning, machine vision

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Authors: Anum Ali, Kashaf ad Dooja, Asif Saleem

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The future smart cities trend will be towards Internet of Things (IoT); IoT creates dynamic connections in a ubiquitous manner. Smart cities offer ease and flexibility for daily life matters. By using small devices that are connected to cloud servers based on IoT, network traffic between these devices is growing exponentially, whose security is a concerned issue, since ratio of cyber attack may make the network traffic vulnerable. This paper discusses the latest machine learning approaches in related work further to tackle the increasing rate of cyber attacks, machine learning algorithm is applied to IoT-based network traffic data. The proposed algorithm train itself on data and identify different sections of devices interaction by using supervised learning which is considered as a classifier related to a specific IoT device class. The simulation results clearly identify the attacks and produce fewer false detections.

Keywords: IoT, traffic security, deep learning, classification

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Authors: Mahmoud Nabilu

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The present study explored the effect of using Mobile listening applications on developing listening skills by Iranian intermediate EFL learners. Fifty male intermediate English learners whose age range was between 15 and 20, participated in the study. The participants were placed in two groups on the basis of their scores on a placement test. Therefore, the participants of the study were homogenized in terms of general proficiency, and groups were assigned as one experimental group and one control group. The experimental group was instructed by the treatment which was using mobile applications to develop their listening skills while the control group received traditional methods. The research data were obtained from the 40-item multiple-choice tests as a pre-test and a post-test. The results of the t-test clearly revealed that the learners in the experimental group performed better in the post-test than the pre-test. This implies that using a mobile application for developing listening skills as a treatment was effective in helping the language learners perform better on post-test. However, a statistically significant difference was found between the post-tests scores of the two groups. The mean of the experimental group was greater compared to the control group. The participants were Iranian and from an Iranian Language Institute, so care should be taken while generalizing the results to the learners of other nationalities. However, in the researcher's view, the findings of this study have valuable implications for teachers and learners, methodologists and syllabus designers, linguists and MALL/CALL (mobile/computer-assisted language learning) experts. Using the result of the present paper is an aim of raising the consciousness of a better technique of developing listening skills in order to make language learning more efficient for the learners.

Keywords: Mobile listening applications, intermediate EFL learners, MALL, CALL

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Authors: Mette Pedersen

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This paper outlines the criteria that assessment specialists use when they design the 'Persuasive Essay' task for the four Advanced Placement World Language and Culture Exams (AP French, German, Italian, and Spanish). The 'Persuasive Essay' is a free-response, source-based, standardized measure of presentational writing. Each 'Persuasive Essay' item consists of three sources (an article, a chart, and an audio) and a prompt, which is a statement of the topic phrased as an interrogative sentence. Due to its richness of source materials and due to the amount of time that test takers are given to prepare for and write their responses (a total of 55 minutes), the 'Persuasive Essay' is the free-response task on the AP World Language and Culture Exams that goes to the greatest lengths to unleash the test takers' proficiency potential. The author focuses on the work that goes into designing the 'Persuasive Essay' task, outlining best practices for the selection of topics and sources, the interplay that needs to be present among the sources and the thinking behind the articulation of prompts for the 'Persuasive Essay' task. Using released 'Persuasive Essay' items from the AP World Language and Culture Exams and accompanying data on test taker performance, the author shows how different passages, and features of passages, have succeeded (and sometimes not succeeded) in eliciting writing proficiency among test takers over time. Data from approximately 215.000 test takers per year from 2014 to 2017 and approximately 35.000 test takers per year from 2012 to 2013 form the basis of this analysis. The conclusion of the study is that test taker performance improves significantly when the sources that test takers are presented with express directly opposing viewpoints. Test taker performance also improves when the interrogative prompt that the test takers respond to is phrased as a yes/no question. Finally, an analysis of linguistic difficulty and complexity levels of the printed sources reveals that test taker performance does not decrease when the complexity level of the article of the 'Persuasive Essay' increases. This last text complexity analysis is performed with the help of the 'ETS TextEvaluator' tool and the 'Complexity Scale for Information Texts (Scale)', two tools, which, in combination, provide a rubric and a fully-automated technology for evaluating nonfiction and informational texts in English translation.

Keywords: advanced placement world language and culture exams, designing presentational writing assessments, large-scale standardized assessments of written language proficiency, source-based language testing

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Authors: Abhigyan Hedau, Priya Shelke, Riddhi Mirajkar, Shreyash Chaple, Mrunali Gadekar, Himanshu Akula

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The heart is the coordinating centre of the major endocrine glandular structure of the body, which produces hormones that profoundly affect the operations of the body, and diagnosing cardiovascular disease is a difficult but critical task. By extracting knowledge and information about the disease from patient data, data mining is a more practical technique to help doctors detect disorders. We use a variety of machine learning methods here, including logistic regression and support vector classifiers (SVC), K-nearest neighbours Classifiers (KNN), Decision Tree Classifiers, Random Forest classifiers and Gradient Boosting classifiers. These algorithms are applied to patient data containing 13 different factors to build a system that predicts heart disease in less time with more accuracy.

Keywords: logistic regression, support vector classifier, k-nearest neighbour, decision tree, random forest and gradient boosting

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Authors: Alexandros Pournaras, Anastasia Papadopoulou, Serafim Kontos, Anastasios Karakostas

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The air quality of modern cities is an important concern, as poor air quality contributes to human health and environmental issues. Reliable air quality forecasting has, thus, gained scientific and governmental attention as an essential tool that enables authorities to take proactive measures for public safety. In this study, the potential of Machine Learning (ML) models to forecast PM2.5 at local scale is investigated in the city of Thessaloniki, the second largest city in Greece, which has been struggling with the persistent issue of air pollution. ML models, with proven ability to address timeseries forecasting, are employed to predict the PM2.5 concentrations and the respective Air Quality Index 5-days ahead by learning from daily historical air quality and meteorological data from 2014 to 2016 and gathered from two stations with different land use characteristics in the urban fabric of Thessaloniki. The performance of the ML models on PM2.5 concentrations is evaluated with common statistical methods, such as R squared (r²) and Root Mean Squared Error (RMSE), utilizing a portion of the stations’ measurements as test set. A multi-categorical evaluation is utilized for the assessment of their performance on respective AQIs. Several conclusions were made from the experiments conducted. Experimenting on MLs’ configuration revealed a moderate effect of various parameters and training schemas on the model’s predictions. Their performance of all these models were found to produce satisfactory results on PM2.5 concentrations. In addition, their application on untrained stations showed that these models can perform well, indicating a generalized behavior. Moreover, their performance on AQI was even better, showing that the MLs can be used as predictors for AQI, which is the direct information provided to the general public.

Keywords: Air Quality, AQ Forecasting, AQI, Machine Learning, PM2.5

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Authors: Huda Al Shuaily, Karen Renaud

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Databases comprise the foundation of most software systems. System developers inevitably write code to query these databases. The de facto language for querying is SQL and this, consequently, is the default language taught by higher education institutions. There is evidence that learners find it hard to master SQL, harder than mastering other programming languages such as Java. Educators do not agree about explanations for this seeming anomaly. Further investigation may well reveal the reasons. In this paper, we report on our investigations into how novices learn SQL, the actual problems they experience when writing SQL, as well as the differences between expert and novice SQL query writers. We conclude by presenting a model of SQL learning that should inform the instructional material design process better to support the SQL learning process.

Keywords: pattern, SQL, learning, model

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Authors: Min Chen, Rubi Gupta

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Understanding and predicting stock market movements is a challenging problem. It is believed stock markets are partially driven by public sentiments, which leads to numerous research efforts to predict stock market trend using public sentiments expressed on social media such as Twitter but with limited success. Recently a microblogging website StockTwits is becoming increasingly popular for users to share their discussions and sentiments about stocks and financial market. In this project, we analyze the text content of StockTwits tweets and extract financial sentiment using text featurization and machine learning algorithms. StockTwits tweets are first pre-processed using techniques including stopword removal, special character removal, and case normalization to remove noise. Features are extracted from these preprocessed tweets through text featurization process using bags of words, N-gram models, TF-IDF (term frequency-inverse document frequency), and latent semantic analysis. Machine learning models are then trained to classify the tweets' sentiment as positive (bullish) or negative (bearish). The correlation between the aggregated daily sentiment and daily stock price movement is then investigated using Pearson’s correlation coefficient. Finally, the sentiment information is applied together with time series stock data to predict stock price movement. The experiments on five companies (Apple, Amazon, General Electric, Microsoft, and Target) in a duration of nine months demonstrate the effectiveness of our study in improving the prediction accuracy.

Keywords: machine learning, sentiment analysis, stock price prediction, tweet processing

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Authors: A. Noll, J. Roth, M. Scholz

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The importance of written language in a democratic society is non-controversial. Students with physical, learning, cognitive or developmental disabilities often have difficulties in understanding information which is presented in written language only. These students suffer from obstacles in diverse domains. In order to reduce such barriers in educational as well as in out-of-school areas, access to written information must be facilitated. Readability can be enhanced by linguistic simplifications like the application of easy-to-read language. Easy-to-read language shall help people with disabilities to participate socially and politically in society. The authors state, for example, that only short simple words should be used, whereas the occurrence of complex sentences should be avoided. So far, these guidelines were not empirically proved. Another way to reduce reading barriers is the use of visual support, for example, symbols. A symbol conveys, in contrast to a photo, a single idea or concept. Little empirical data about the use of symbols to foster the readability of texts exist. Nevertheless, a positive influence can be assumed, e.g., because of the multimedia principle. It indicates that people learn better from words and pictures than from words alone. A qualitative Interview and Eye-Tracking-Study, which was conducted by the authors, gives cause for the assumption that besides the illustration of single words, the visualization of complete sentences may be helpful. Thus, the effect of photos, which illustrate the content of complete sentences, is also investigated in this study. This leads us to the main research question which was focused on: Does the use of easy-to-read language and/or enriching text with symbols or photos facilitate pupils’ comprehension of learning tasks? The sample consisted of students with learning difficulties (N = 144) and students without SEN (N = 159). The students worked on the tasks, which dealt with introducing fractions, individually. While experimental group 1 received a linguistically simplified version of the tasks, experimental group 2 worked with a variation which was linguistically simplified and furthermore, the keywords of the tasks were visualized by symbols. Experimental group 3 worked on exercises which were simplified by easy-to-read-language and the content of the whole sentences was illustrated by photos. Experimental group 4 received a not simplified version. The participants’ reading ability and their IQ was elevated beforehand to build four comparable groups. There is a significant effect of the different setting on the students’ results F(3,140) = 2,932; p = 0,036*. A post-hoc-analyses with multiple comparisons shows that this significance results from the difference between experimental group 3 and 4. The students in the group easy-to-read language plus photos worked on the exercises significantly more successfully than the students who worked in the group with no simplifications. Further results which refer, among others, to the influence of the students reading ability will be presented at the ICERI 2018.

Keywords: inclusive education, mathematics education, easy-to-read language, photos, symbols, special educational needs

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Authors: Sheetal R. Jadhwani, Daisy Sang, Chang-Shyh Peng

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Web applications are an integral part of modem life. They are mostly based upon the HyperText Markup Language (HTML). While HTML meets the basic needs, there are some shortcomings. For example, applications can cease to work once user goes offline, real-time updates may be lagging, and user interface can freeze on computationally intensive tasks. The latest language specification HTML5 attempts to rectify the situation with new tools and protocols. This paper studies the new Web Storage, Geolocation, Web Worker, Canvas, and Web Socket APIs, and presents applications to test their features and efficiencies.

Keywords: HTML5, web worker, canvas, web socket

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Authors: Selim M. Khan, Dustin D. Pearson, Tryggve Rönnqvist, Markus E. Nielsen, Joshua M. Taron, Aaron A. Goodarzi

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Accumulation of radioactive radon gas in indoor air poses a serious risk to human health by increasing the lifetime risk of lung cancer and is classified by IARC as a category one carcinogen. Radon exposure risks are a function of geologic, geographic, design, and human behavioural variables and can change over time. Using time series and deep machine learning modelling, we analyzed long-term radon test outcomes as a function of building metrics from 25,489 Canadian and 38,596 Swedish residential properties constructed between 1945 to 2020. While Canadian and Swedish properties built between 1970 and 1980 are comparable (96–103 Bq/m³), innate radon risks subsequently diverge, rising in Canada and falling in Sweden such that 21st Century Canadian houses show 467% greater average radon (131 Bq/m³) relative to Swedish equivalents (28 Bq/m³). These trends are consistent across housing types and regions within each country. The introduction of energy efficiency measures within Canadian and Swedish building codes coincided with opposing radon level trajectories in each nation. Deep machine learning modelling predicts that, without intervention, average Canadian residential radon levels will increase to 176 Bq/m³ by 2050, emphasizing the importance and urgency of future building code intervention to achieve systemic radon reduction in Canada.

Keywords: radon health risk, time-series, deep machine learning, lung cancer, Canada, Sweden

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Authors: A. Biran, P. Sobhe Bidari, A. Almazroe, V. Lakshminarayanan, K. Raahemifar

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Diabetic Retinopathy (DR) is a severe retinal disease which is caused by diabetes mellitus. It leads to blindness when it progress to proliferative level. Early indications of DR are the appearance of microaneurysms, hemorrhages and hard exudates. In this paper, an automatic algorithm for detection of DR has been proposed. The algorithm is based on combination of several image processing techniques including Circular Hough Transform (CHT), Contrast Limited Adaptive Histogram Equalization (CLAHE), Gabor filter and thresholding. Also, Support Vector Machine (SVM) Classifier is used to classify retinal images to normal or abnormal cases including non-proliferative or proliferative DR. The proposed method has been tested on images selected from Structured Analysis of the Retinal (STARE) database using MATLAB code. The method is perfectly able to detect DR. The sensitivity specificity and accuracy of this approach are 90%, 87.5%, and 91.4% respectively.

Keywords: diabetic retinopathy, fundus images, STARE, Gabor filter, support vector machine

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Authors: Amic G. Ho

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Language usage and typography in Hong Kong are unique, as can be seen clearly on the streets of the city. In contrast to many other parts of the world, where there is only one language, in Hong Kong many signs and billboards display two languages: Chinese and English. The language usage on signage, fonts and types used, and the designs in magazines and advertisements all demonstrate the unique features of Hong Kong typographic design, which reflect the multicultural nature of Hong Kong society. This study is the first step in investigating the nature and development of Hong Kong typography. The preliminary research explored how the historical development of Hong Kong is reflected in its unique typography. Following a review of historical development, a quantitative study was designed: Local Hong Kong participants were invited to provide input on what makes the Hong Kong typographic style unique. Their input was collected and analyzed. This provided us with information about the characteristic criteria and features of Hong Kong typography, as recognized by the local people. The most significant typographic designs in Hong Kong were then investigated and the influence of Chinese and other cultures on Hong Kong typography was assessed. The research results provide an indication to local designers on how they can strengthen local design outcomes and promote the values and culture of their mother town.

Keywords: typography, Hong Kong, historical developments, multiple cultures

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Authors: Wen Xu

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The expansion of Confucius institutes and Chinese as a Foreign Language (CFL) education is often considered as cultural invasion and part of much bigger, if not ambitious, Chinese central government agenda among Western public opinion. The CFL knowledge and teaching practice inherent in textbooks are also harshly critiqued as failing to align with Western educational principles. This paper takes up these concerns and attempts to articulate that Confucius’s idea of ‘education without discrimination’ appears to have become synonymous with social justice touted in contemporary Australian education and policy discourses. To do so, it capitalises on Bernstein's conceptualization of classification and pedagogic rights to articulate CFL curriculum's potential of drawing in and drawing out curriculum boundaries to achieve educational justice. In this way, the potential useful knowledge of CFL constitutes a worthwhile tool to engage in a peripheral Western country’s education issues, as well as to include disenfranchised students in the multicultural Australian society. It opens spaces for critically theorising CFL curricular justice in Australian educational contexts, and makes an original contribution to scholarly argumentation that CFL curriculum has the potential of including socially and economically disenfranchised students in schooling.

Keywords: curriculum justice, Chinese as a Foreign Language curriculum, Bernstein, equity

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Authors: Esther Mafunda, Simbarashe Muparangi

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The present paper tests the applicability of the Regression Hypothesis on the pathological language dissolution of a Shona male adult with Broca’s aphasia. It particularly assesses the prediction of the Regression Hypothesis, which states that the process according to which language is forgotten will be the reversal of the process according to which it will be acquired. The main aim of the paper is to find out whether mirror symmetries between L1 acquisition and L1 dissolution of tense in Shona and, if so, what might cause these regression patterns. The paper also sought to highlight the practical contributions that Linguistic theory can make to solving language-related problems. Data was collected from a 46-year-old male adult with Broca’s aphasia who was receiving speech therapy at St Giles Rehabilitation Centre in Harare, Zimbabwe. The primary data elicitation method was experimental, using the probe technique. The TART (Test for Assessing Reference Time) Shona version in the form of sequencing pictures was used to access tense by Broca’s aphasic and 3.5-year-old child. Using the SPSS (Statistical Package for Social Studies) and Excel analysis, it was established that the use of the future tense was impaired in Shona Broca’s aphasic whilst the present and past tense was intact. However, though the past tense was intact in the male adult with Broca’s aphasic, a reference to the remote past was made. The use of the future tense was also found to be difficult for the 3,5-year-old speaking child. No difficulties were encountered in using the present and past tenses. This means that mirror symmetries were found between L1 acquisition and L1 dissolution of tense in Shona. On the basis of the results of this research, it can be concluded that the use of tense in a Shona adult with Broca’s aphasia supports the Regression Hypothesis. The findings of this study are important in terms of speech therapy in the context of Zimbabwe. The study also contributes to Bantu linguistics in general and to Shona linguistics in particular. Further studies could also be done focusing on the rest of the Bantu language varieties in terms of aphasia.

Keywords: Broca’s Aphasia, regression hypothesis, Shona, language dissolution

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Authors: Krismonika Khoirunnisa

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This research article aims to describe the variation of the Javanese lexicon "Sleep " and its distribution in the Jombang area, East Java. The objectives of this study were (1) to classify the variation of the "Sleep" lexicon in the Jombang area and (2) to design the fish rips for the variation of the "Sleep" lexicon according to their distribution. This type of research is a qualitative descriptive study using the method of leading proficiency, namely conducting interviews with speakers without directly meeting the speakers (interviews via WhatsApp and email as the media). This research article uses techniques record as support and tools for mapping and classifying data, collecting data in this study conducted at four points, namely the Kaliwungu village (Jombang City), Banjardowo village (District of Jombang), Mayangan Village (Subdistrict Jogoroto), and Karobelah village (Subdistrict Mojoagung) as a target investigators to conduct the interview. This study uses the dialectology theory as a basis for analyzing the data obtained. The results of this study found that the Javanese language variation "Sleep" has many different linguals, meanings, and forms even though they are in the same area (Jombang).

Keywords: geographical dialectology, lexicon variations, jombangan dialect, sssavanese language

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Authors: Alireza Karimi

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Artificial Intelligence (AI) is reshaping various industries, and accounting is no exception. With the ability to process vast amounts of data quickly and accurately, AI is revolutionizing how financial professionals manage, analyze, and report financial information. In this article, we will explore the diverse applications of AI in accounting and its profound impact on the field. Automation of Repetitive Tasks: One of the most significant contributions of AI in accounting is automating repetitive tasks. AI-powered software can handle data entry, invoice processing, and reconciliation with minimal human intervention. This not only saves time but also reduces the risk of errors, leading to more accurate financial records. Pattern Recognition and Anomaly Detection: AI algorithms excel at pattern recognition. In accounting, this capability is leveraged to identify unusual patterns in financial data that might indicate fraud or errors. AI can swiftly detect discrepancies, enabling auditors and accountants to focus on resolving issues rather than hunting for them. Real-Time Financial Insights: AI-driven tools, using natural language processing and computer vision, can process documents faster than ever. This enables organizations to have real-time insights into their financial status, empowering decision-makers with up-to-date information for strategic planning. Fraud Detection and Prevention: AI is a powerful tool in the fight against financial fraud. It can analyze vast transaction datasets, flagging suspicious activities and reducing the likelihood of financial misconduct going unnoticed. This proactive approach safeguards a company's financial integrity. Enhanced Data Analysis and Forecasting: Machine learning, a subset of AI, is used for data analysis and forecasting. By examining historical financial data, AI models can provide forecasts and insights, aiding businesses in making informed financial decisions and optimizing their financial strategies. Artificial Intelligence is fundamentally transforming the accounting profession. From automating mundane tasks to enhancing data analysis and fraud detection, AI is making financial processes more efficient, accurate, and insightful. As AI continues to evolve, its role in accounting will only become more significant, offering accountants and finance professionals powerful tools to navigate the complexities of modern finance. Embracing AI in accounting is not just a trend; it's a necessity for staying competitive in the evolving financial landscape.

Keywords: artificial intelligence, accounting automation, financial analysis, fraud detection, machine learning in finance

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Authors: Fadila Hadjeris

Abstract:

The study aims at investigating the functions of English-Arabic code switching in English as a foreign language classroom oral discourse and the extent to which they can contribute to the flow of classroom interaction. It also seeks to understand the views, beliefs, and perceptions of teachers and learners towards this practice. We hypothesized that code switching is a communicative strategy which facilitates classroom interaction. Due to this fact, both teachers and learners support its use. The study draws on a key body of literature in bilingualism, second language acquisition, and classroom discourse in an attempt to provide a framework for considering the research questions. It employs a combination of qualitative and quantitative research methods which include classroom observations and questionnaires. The analysis of the recordings shows that teachers’ code switching to Arabic is not only used for academic and classroom management reasons. Rather, the data display instances in which code switching is used for social reasons. The analysis of the questionnaires indicates that teachers and pupils have different attitudes towards this phenomenon. Teachers reported their deliberate switching during EFL teaching, yet the majority was against this practice. According to them, the use of the mother has detrimental effects on the acquisition and the practice of the target language. In contrast, pupils showed their preference to their teachers’ code switching because it enhances and facilitates their understanding. These findings support the fact that the shift to pupils’ mother tongue is a strategy which aids and facilitates the teaching and the learning of the target language. This, in turn, necessitates recommendations which are suggested to teachers and course designers.

Keywords: bilingualism, codeswitching, classroom interaction, classroom discourse, EFL learning/ teaching, SLA

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Authors: Ajibade Adeyemo

Abstract:

The paper examined local terms used for spaces, forms and building practices in southwest Nigeria as cultural semiotics. Housing has more cultural meaning than mere shelter as shown in building terms such as ‘roof over my head’. The study is significant in the study area because its people were traditionally orally centered until ‘culture contact’ led to graphical presentation and appreciation in the form of drawings which is a modern language of architecture. This semiotic study will facilitate the understanding of the wholesomeness of traditional building practices and thoughts. This is in the culture of the traditional multi-sensory appreciation of architecture, urban design and the arts. It will analyze traditional aphoristic words and terms which are like proverbs which are significant in language because of their metaphorical essence. Many of such terms in the dominant Yoruba language of the study area are oftentimes phenomenal reducing universal terms like the earth and heaven to the simple module of housing. These words could be worth investigating because they are symbolic serve as codes which are cultural tool of regional ethnic significance. Sassure’s and Pierce’s concepts of Semiotics in line with Eco’s concept of semiotics of metaphor shall be deployed.

Keywords: traditional terms, spaces, forms, artifacts, cultural semiotics, southwest

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Authors: George Kurian, Hongmei Chi

Abstract:

Digital evidence plays a critical role in most legal investigations. In many cases, thumbnail databases show important information in that investigation. The probability of having digital evidence retrieved from a computer or smart device has increased, even though the previous user removed data and deleted apps on those devices. Due to the increase in digital forensics, the ability to store residual information from various thumbnail applications has improved. This paper will focus on investigating thumbnail information from Windows 10. Thumbnail images of interest in forensic investigations may be intact even when the original pictures have been deleted. It is our research goal to recover useful information from thumbnails. In this research project, we use various forensics tools to collect left thumbnail information from deleted videos or pictures. We examine and describe the various thumbnail sources in Windows and propose a methodology for thumbnail collection and analysis from laptops or desktops. A machine learning algorithm is adopted to help speed up content from thumbnail pictures.

Keywords: digital forensic, forensic tools, soundness, thumbnail, machine learning, OCR

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Authors: Ahmed Masrai

Abstract:

Listening comprehension constitutes a considerable challenge for the second language (L2) learners, but a little is known about the explanatory power of different variables in explaining variance in listening comprehension. Since research in this area, to the researcher's knowledge, is relatively small in comparison to that focusing on the relationship between reading comprehension and factors such as vocabulary and working memory, there is a need for studies that are seeking to fill the gap in our knowledge about the specific contribution of working memory capacity (WMC), aural vocabulary knowledge and written vocabulary knowledge to explaining listening comprehension. Among 130 English as foreign language learners, the present study examines what proportion of the variance in listening comprehension is explained by aural vocabulary knowledge, written vocabulary knowledge, and WMC. Four measures were used to collect the required data for the study: (1) A-Lex, a measure of aural vocabulary knowledge; (2) XK-Lex, a measure of written vocabulary knowledge; (3) Listening Span Task, a measure of WMC and; (4) IELTS Listening Test, a measure of listening comprehension. The results show that aural vocabulary knowledge is the strongest predictor of listening comprehension, followed by WMC, while written vocabulary knowledge is the weakest predictor. The study discusses implications for the explanatory power of aural vocabulary knowledge and WMC to listening comprehension and pedagogical practice in L2 classrooms.

Keywords: listening comprehension, second language, vocabulary knowledge, working memory

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Authors: Arun Prasad Jaganathan

Abstract:

In today's dynamic industrial world, traditional task allocation methods often fall short in adapting to evolving operational conditions. This paper introduces an AI-enabled task assistance and management system designed to overcome the limitations of conventional approaches. By using artificial intelligence (AI) and machine learning (ML), the system intelligently interprets user instructions, analyzes tasks, and allocates resources based on real-time data and environmental factors. Additionally, geolocation tracking enables proactive identification of potential delays, ensuring timely interventions. With its transparent reporting mechanisms, the system provides stakeholders with clear insights into task progress, fostering accountability and informed decision-making. The paper presents a comprehensive overview of the system architecture, algorithm, and implementation, highlighting its potential to revolutionize task management across diverse industries.

Keywords: artificial intelligence, machine learning, task allocation, operational efficiency, resource optimization

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Authors: B. Bricklin Zeff

Abstract:

This research explores the development and application of a specialized chatbot tailored for a nursing English course, with a primary objective of augmenting student engagement through situational content and responsiveness to key expressions and vocabulary. Introducing the chatbot, elucidating its purpose, and outlining its functionality are crucial initial steps in the research study, as they provide a comprehensive foundation for understanding the design and objectives of the specialized chatbot developed for the nursing English course. These elements establish the context for subsequent evaluations and analyses, enabling a nuanced exploration of the chatbot's impact on student engagement and language learning within the nursing education domain. The subsequent exploration of the intricate language model development process underscores the fusion of scientific methodologies and artistic considerations in this application of artificial intelligence (AI). Tailored for educators and curriculum developers in nursing, practical principles extending beyond AI and education are considered. Some insights into leveraging technology for enhanced language learning in specialized fields are addressed, with potential applications of similar chatbots in other professional English courses. The overarching vision is to illuminate how AI can transform language learning, rendering it more interactive and contextually relevant. The presented chatbot is a tangible example, equipping educators with a practical tool to enhance their teaching practices. Methodologies employed in this research encompass surveys and discussions to gather feedback on the chatbot's usability, effectiveness, and potential improvements. The chatbot system was integrated into a nursing English course, facilitating the collection of valuable feedback from participants. Significant findings from the study underscore the chatbot's effectiveness in encouraging more verbal practice of target expressions and vocabulary necessary for performance in role-play assessment strategies. This outcome emphasizes the practical implications of integrating AI into language education in specialized fields. This research holds significance for educators and curriculum developers in the nursing field, offering insights into integrating technology for enhanced English language learning. The study's major findings contribute valuable perspectives on the practical impact of the chatbot on student interaction and verbal practice. Ultimately, the research sheds light on the transformative potential of AI in making language learning more interactive and contextually relevant, particularly within specialized domains like nursing.

Keywords: chatbot, nursing, pragmatics, role-play, AI

Procedia PDF Downloads 58