Search results for: word learning

Authors: Ali Gheshmi

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The highest rate of death, after World War II, is due to the Coronavirus epidemic and more than 2 million people have died since the epidemic outbreak in December 2019, so the word “death” is one of the highest frequency words in social media; moreover, the use of social media has grown due to quarantine and successive restrictions and lockdowns. The most important aspects of the approach used by this study include the analysis of Iranian social media users’ reactions to the images of those who died due to Coronavirus, investigating if seeing such images via social media is effective on the users’ perception of the closeness of death, and evaluating the extent to which the fear of Coronavirus death is instrumental in persuading users to observe health protocols or causing mental problems in social media users. Since the goal of this study is to discover how social media users perceive and react to the images of people who died of Coronavirus, the cultural studies approach is used Receipt analysis method and in-depth interviews will be used for collecting data from Iranian users; also, snowball sampling is used in this study. The probable results would show that cyberspace users experience the closeness of “death” more than any time else and to cope with these annoying images, avoid viewing them or if they view, it will lead them to suffer from mental problems.

Keywords: death, receipt analysis method, mental health, social media, Covid-19

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Authors: Muzaffar Hussain

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Requests strategies are considered as a part of the speech acts, which are frequently used in everyday communication. Each language provides speech acts to the speakers; therefore, the selection of appropriate form seems more culture-specific rather than language. The present paper investigates the gender-based difference in the use of request strategies by native speakers of Urdu/Punjabi male and female who are learning English as a second language. The data for the present study were collected from 68 graduate students, who are learning English as an L2 in Pakistan. They were given an online close-ended questionnaire, based on Discourse Completion Test (DCT). After analyzing the data, it was found that the L1 male Urdu/Punjabi speakers were inclined to use more direct request strategies while the female Urdu/Punjabi speakers used indirect request strategies. This paper also found that in some situations female participants used more direct strategies than male participants. The present study concludes that the use of request strategies is influenced by culture, social status, and power distribution in a society.

Keywords: gender variation, request strategies, face-threatening, second language pragmatics, language competence

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Authors: Kamaludeen Samaila, Khashyaullah Abdulfattah, Fahimi Ahmad Bin Amir

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The study was conducted to examine the relationship between selected factors (performance expectancy, effort expectancy, social influence and facilitating condition) and students’ intention to use the learning management system (LMS), as well as investigating the factors predicting students’ intention to use the LMS. The study was specifically conducted at the Faculty of Educational Study of University Putra Malaysia. Questionnaires were distributed to 277 respondents using a random sampling technique. SPSS Version 22 was employed in analyzing the data; the findings of this study indicated that performance expectancy (r = .69, p < .01), effort expectancy (r=.60, p < .01), social influence (r = .61, p < .01), and facilitating condition (r=.42, p < .01), were significantly related to students’ intention to use the LMS. In addition, the result also revealed that performance expectancy (β = .436, p < .05), social influence (β=.232, p < .05), and effort expectancy (β = .193, p < .05) were strong predictors of students’ intention to use the LMS. The analysis further indicated that (R2) is 0.054 which means that 54% of variation in the dependent variable is explained by the entire predictor variables entered into the regression model. Understanding the factors that affect students’ intention to use the LMS could help the lecturers, LMS managers and university management to develop the policies that may attract students to use the LMS.

Keywords: LMS, postgraduate students, PutraBlas, students’ intention, UPM, UTAUT model

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Authors: Swatantra

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Hinglish, a blended version of Hindi and English, emerged due to the lack of the competence and command of the speakers over the foreign language, i., e., English. But, amazingly, the trend has gained wide acceptance. In India, this acceptance has gone up to the extent that popular news anchors at the prime time shows are frequently using it. At the moment, instead of being considered a flaw of their presentation Hinglish is emerging as a trendy genre. Its pervasive usage and extensive acceptance is motivating youngsters to opt for the similar kind of patterns. The current study is an endeavour to assess the impact of this trend on the new language learners. With the help of semi-structured interviews, the researcher has tried to gauge the level of comfort and desire to be at par with the other fluent English speakers. The results clearly depict a substantiated boost in the confidence level of learners because they are able to use the vocabulary and sentence patterns of their own choice and convenience. The prevalence and acceptance of the trend in the main stream media have really served as a catalyst and the desire to be at par with the other fluent speakers is also fading away. The users of Hinglish find this trend to be closer to their heart as in the earlier times in the absence of exact translation they had to compromise with the meaning or spirit of the word/phrase / sentence. But now enhanced flexibility is leaving them more comfortable and confident.

Keywords: Hinglish, language learners, linguistic trends, media

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Authors: Joshua van Staden, Dane Brown, Karen Bradshaw

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Gun violence is a significant problem in modern-day society. Early detection of carried handguns through closed-circuit television (CCTV) can aid in preventing potential gun violence. However, CCTV operators have a limited attention span. Machine learning approaches to automating the detection of dangerous gun carriers provide a way to aid CCTV operators in identifying these individuals. This study provides insight into the relationship between human key points extracted using human pose estimation (HPE) and their intention to fire a weapon. We examine the feature importance of each keypoint and their correlations. We use principal component analysis (PCA) to reduce the feature space and optimize detection. Finally, we run a set of classifiers to determine what form of classifier performs well on this data. We find that hips, shoulders, and knees tend to be crucial aspects of the human pose when making these predictions. Furthermore, the horizontal position plays a larger role than the vertical position. Of the 66 key points, nine principal components could be used to make nonlinear classifications with 86% accuracy. Furthermore, linear classifications could be done with 85% accuracy, showing that there is a degree of linearity in the data.

Keywords: feature engineering, human pose, machine learning, security

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Authors: Zhi Liu, Xian Peng, Monika Domanska, Lingyun Kang, Sannyuya Liu

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Massive Open education has become increasingly popular among worldwide learners. An increasing number of course reviews are being generated in Massive Open Online Course (MOOC) platform, which offers an interactive feedback channel for learners to express opinions and feelings in learning. These reviews typically contain subjective emotion and topic information towards the courses. However, it is time-consuming to artificially detect these opinions. In this paper, we propose an emotion-oriented topic detection model to automatically detect the students’ opinioned aspects in course reviews. The known overall emotion orientation and emotional words in each review are used to guide the joint probabilistic modeling of emotion and aspects in reviews. Through the experiment on real-life review data, it is verified that the distribution of course-emotion-aspect can be calculated to capture the most significant opinioned topics in each course unit. This proposed technique helps in conducting intelligent learning analytics for teachers to improve pedagogies and for developers to promote user experiences.

Keywords: Massive Open Online Course (MOOC), course reviews, topic model, emotion recognition, topical aspects

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Authors: Elham A. Alsadoon, Hamadah B. Alsadoon

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Educators try to design learning environments that are preferred by their students. With the wide-spread adoption of cell phones surpassing any other technology, educators should not fail to invest in the power of such technology. This study aimed to explore the current use of cell phones in education among Saudi students in Saudi universities and how students perceive such use. Data was collected from 237 students at King Saud University. Descriptive analysis was used to analyze the data. A T-test for independent groups was used to examine whether there was a significant difference between males and females in their perception of using cell phones in education. Findings suggested that students have a positive attitude toward the use of cell phones in education. The most accepted use was for sending notification to students, which has already been experienced through the Twasel system provided by King Saud University. This electronic system allows instructors to easily send any SMS or email to their students. The use of cell phone applications came in the second rank of using cell phones in education. Students have already experienced the benefits of having these applications handy wherever they go. On the other hand, they did not perceive using cell phones for assessment as practical educational usage. No gender difference was detected in terms of students’ perceptions toward using cell phones in education.

Keywords: cell phone, mobile learning, educational sciences, education

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Authors: Khairani Binti Supyan, Fatimah Khalid, Mas Rina Mustaffa, Azreen Bin Azman, Amirul Azuani Romle

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Perennial plant classification plays a significant role in various agricultural and environmental applications, assisting in plant identification, disease detection, and biodiversity monitoring. Nevertheless, attaining high accuracy in perennial plant image classification remains challenging due to the complex variations in plant appearance, the diverse range of environmental conditions under which images are captured, and the inherent variability in image quality stemming from various factors such as lighting conditions, camera settings, and focus. This paper proposes an adaptation approach to optimize perennial plant image classification by fine-tuning the pre-trained DNNs model. This paper explores the efficacy of fine-tuning prevalent architectures, namely VGG16, ResNet50, and InceptionV3, leveraging transfer learning to tailor the models to the specific characteristics of perennial plant datasets. A subset of the MYLPHerbs dataset consisted of 6 perennial plant species of 13481 images under various environmental conditions that were used in the experiments. Different strategies for fine-tuning, including adjusting learning rates, training set sizes, data augmentation, and architectural modifications, were investigated. The experimental outcomes underscore the effectiveness of fine-tuning deep neural networks for perennial plant image classification, with ResNet50 showcasing the highest accuracy of 99.78%. Despite ResNet50's superior performance, both VGG16 and InceptionV3 achieved commendable accuracy of 99.67% and 99.37%, respectively. The overall outcomes reaffirm the robustness of the fine-tuning approach across different deep neural network architectures, offering insights into strategies for optimizing model performance in the domain of perennial plant image classification.

Keywords: perennial plants, image classification, deep neural networks, fine-tuning, transfer learning, VGG16, ResNet50, InceptionV3

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Authors: Maurice Ntahobari, Levin Kuhlmann, Mario Boley, Zhinoos Razavi Hesabi

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For machine learning based epileptic seizure prediction, it is important for the model to be implemented in small implantable or wearable devices that can be used to monitor epilepsy patients; however, current state-of-the-art methods are complex and computationally intensive. We use Shapley Additive Explanation (SHAP) to find relevant intracranial electroencephalogram (iEEG) features and improve the computational efficiency of a state-of-the-art seizure prediction method based on the extra trees classifier while maintaining prediction performance. Results for a small contest dataset and a much larger dataset with continuous recordings of up to 3 years per patient from 15 patients yield better than chance prediction performance (p < 0.004). Moreover, while the performance of the SHAP-based model is comparable to that of the benchmark, the overall training and prediction time of the model has been reduced by a factor of 1.83. It can also be noted that the feature called zero crossing value is the best EEG feature for seizure prediction. These results suggest state-of-the-art seizure prediction performance can be achieved using efficient methods based on optimal feature selection.

Keywords: machine learning, seizure prediction, extra tree classifier, SHAP, epilepsy

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Authors: N. D'Amico, E. Grossi, B. Colombo, F. Rigiroli, M. Buscema, D. Fazzini, G. Cornalba, S. Papa

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Purpose: To characterize, through a radiomic approach, the nature of nodules considered non-specific by expert radiologists, recognized in magnetic resonance mammography (MRm) with T1-weighted (T1w) sequences with paramagnetic contrast. Material and Methods: 47 cases out of 1200 undergoing MRm, in which the MRm assessment gave uncertain classification (non-specific nodules), were admitted to the study. The clinical outcome of the non-specific nodules was later found through follow-up or further exams (biopsy), finding 35 benign and 12 malignant. All MR Images were acquired at 1.5T, a first basal T1w sequence and then four T1w acquisitions after the paramagnetic contrast injection. After a manual segmentation of the lesions, done by a radiologist, and the extraction of 150 radiomic features (30 features per 5 subsequent times) a machine learning (ML) approach was used. An evolutionary algorithm (TWIST system based on KNN algorithm) was used to subdivide the dataset into training and validation test and to select features yielding the maximal amount of information. After this pre-processing, different machine learning systems were applied to develop a predictive model based on a training-testing crossover procedure. 10 cases with a benign nodule (follow-up older than 5 years) and 18 with an evident malignant tumor (clear malignant histological exam) were added to the dataset in order to allow the ML system to better learn from data. Results: NaiveBayes algorithm working on 79 features selected by a TWIST system, resulted to be the best performing ML system with a sensitivity of 96% and a specificity of 78% and a global accuracy of 87% (average values of two training-testing procedures ab-ba). The results showed that in the subset of 47 non-specific nodules, the algorithm predicted the outcome of 45 nodules which an expert radiologist could not identify. Conclusion: In this pilot study we identified a radiomic approach allowing ML systems to perform well in the diagnosis of a non-specific nodule at MR mammography. This algorithm could be a great support for the early diagnosis of malignant breast tumor, in the event the radiologist is not able to identify the kind of lesion and reduces the necessity for long follow-up. Clinical Relevance: This machine learning algorithm could be essential to support the radiologist in early diagnosis of non-specific nodules, in order to avoid strenuous follow-up and painful biopsy for the patient.

Keywords: breast, machine learning, MRI, radiomics

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Authors: Guru Prakash, Revanth Dugalam

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A road’s condition often deteriorates due to harsh loading such as overload due to trucks, and severe environmental conditions such as heavy rain, snow load, and cyclic loading. In absence of proper maintenance planning, this results in potholes, wide cracks, bumps, and increased roughness of roads. In this paper, a data-driven model will be developed to detect these damages using vibration and image signals. The key idea of the proposed methodology is that the road anomaly manifests in these signals, which can be detected by training a machine learning algorithm. The use of various machine learning techniques such as the support vector machine and Radom Forest method will be investigated. The proposed model will first be trained and tested with artificially simulated data, and the model architecture will be finalized by comparing the accuracies of various models. Once a model is fixed, the field study will be performed, and data will be collected. The field data will be used to validate the proposed model and to predict the future road’s health condition. The proposed will help to automate the road condition monitoring process, repair cost estimation, and maintenance planning process.

Keywords: SVM, data-driven, road health monitoring, pot-hole

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Authors: Lisa Graham, Kathleen Grant

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South Africa is a multicultural country with eleven official languages, yet most of the formal education at institutions of higher education in the country is in English. It is well known that many students, irrespective of their home language, struggle to grasp difficult scientific concepts and the same is true for students enrolled in the Extended Curriculum Programme at the Cape Peninsula University of Technology (CPUT), studying biomedical sciences. Today we are fortunate in that there is a plethora of resources available to students to research and better understand subject matter online. For example, the students often use YouTube videos to supplement the formal education provided in our course. Unfortunately, most of this material is presented in English. The rationale behind this project lies in that it is well documented that students think and grasp concepts easier in their home language and addresses the fact that the lingua franca of instruction in the field of biomedical science is English. A project aimed at addressing the lack of available resources in most of the South African languages is planned, where students studying Bachelor of Health Science in Medical Laboratory Science will collaborate with those studying Film and Video Technology to create educational videos, explaining scientific concepts in their home languages. These videos will then be published on our own YouTube channel, thereby making them accessible to fellow students, future students and anybody with interest in the subject. Research will be conducted to determine the benefit of the project as well as the published videos to the student community. It is suspected that the students engaged in making the videos will benefit in such a way as to gain further understanding of their course content, a broader appreciation of the discipline, an enhanced sense of civic responsibility, as well as greater respect for the different languages and cultures in our classes. Indeed, an increase in student engagement has been shown to play a central role in student success, and it is well noted that deeper learning and more innovative solutions take place in collaborative groups. We aim to make a meaningful contribution towards the production and repository of knowledge in multilingual teaching and learning for the benefit of the diverse student population and staff. This would strengthen language development, multilingualism, and multiculturalism at CPUT and empower and promote African languages as languages of science and education at CPUT, in other institutions of higher learning, and in South Africa as a whole.

Keywords: educational videos, multiculturalism, multilingualism, student engagement

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Authors: Raheleh Saifiabolhassan

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Gardens symbolize human effort to bring Eden to earth and are defined as the purest pleasures and the greatest inspiration for men. According to Persian mythology, a garden called "Paris" is a magical, perfumed place populated by beautiful and angelic creatures. "Pardis" comes from the word "paridaiza," which means "walled garden." Gardening has always been a worldwide attraction due to the abundance of green space, and desert gardens are no exception. Because most historical garden designs use a similar pattern, such as Chahar-Bagh, climate effects have not been considered. The purpose of studying these general designs was to determine whether location and weather conditions are affecting them. So, two gardens were chosen for comparison: a desert (Shahzadeh-Mahan) and a humid garden (Shah) and compared their geometry, irrigation system, entrances, and pavilions. The findings of the study revealed that there are several notable differences among their architectural principles. For example, the desert garden design is introverted with transparent surfaces and a single focal point, while the moderate garden is extraverted with high complexity and multiple perspectives. In conclusion, the study recognizes the richness and significance of the Persian garden concept, which can be applied in many different contexts.

Keywords: Pardis, Chahar-bagh, Persian garden, temperate, humid climate, geometry, pavilion, irrigations, culture

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Authors: Mehrnaz Mostafavi, Mahtab Shabani, Alireza Azani, Fatemeh Ghafari

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Artificial intelligence (AI) can transform breast cancer diagnosis and therapy by providing sophisticated solutions for screening, imaging interpretation, histopathological analysis, and treatment planning. This literature review digs into the many uses of AI in breast cancer treatment, highlighting the need for collaboration between AI scientists and healthcare practitioners. It emphasizes advances in AI-driven breast imaging interpretation, such as computer-aided detection and diagnosis (CADe/CADx) systems and deep learning algorithms. These have shown significant potential for improving diagnostic accuracy and lowering radiologists' workloads. Furthermore, AI approaches such as deep learning have been used in histopathological research to accurately predict hormone receptor status and categorize tumor-associated stroma from regular H&E stains. These AI-powered approaches simplify diagnostic procedures while providing insights into tumor biology and prognosis. As AI becomes more embedded in breast cancer care, it is crucial to ensure its ethical, efficient, and patient-focused implementation to improve outcomes for breast cancer patients ultimately.

Keywords: breast cancer, artificial intelligence, cancer diagnosis, clinical practice

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Authors: Mirza Waseem Abbas, Syed Danish Raza

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For many countries agriculture is still the major force driving the economy and a critically important socioeconomic sector, despite exceptional industrial development across the globe. In countries like Pakistan, this sector is considered the backbone of the economy, and most of the economic decision making revolves around agricultural outputs and data. Timely and accurate facts and figures about this vital sector hold immense significance and have serious implications for the long-term development of the economy. Therefore, any significant improvements in the statistics and other forms of data regarding agriculture sector are considered important by all policymakers. This is especially true for decision making for the betterment of crops and the agriculture sector in general. Provincial and federal agricultural departments collect data for all cash and non-cash crops and the sector, in general, every year. Traditional data collection for such a large sector i.e. agriculture, being time-consuming, prone to human error and labor-intensive, is slowly but gradually being replaced by remote sensing techniques. For this study, remotely sensed data were used for change detection (machine learning, supervised & unsupervised classification) to assess the increase or decrease in area under agriculture over the last fifteen years due to urbanization. Detailed Landsat Images for the selected agricultural districts were acquired for the year 2000 and compared to images of the same area acquired for the year 2016. Observed differences validated through detailed analysis of the areas show that there was a considerable decrease in vegetation during the last fifteen years in four major agricultural districts of the Punjab province due to urbanization (housing societies).

Keywords: change detection, area estimation, machine learning, urbanization, remote sensing

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Authors: Andreas Johansson

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Conceptual understanding needs to be grounded on observation of physical phenomena, experiences or metaphors. Observation of physical phenomena using demonstration experiments has a long tradition within physics education and students need to develop mental models to relate the observations to concepts from scientific theories. This study investigates how live and video experiments involving an acoustic trap to visualize particle-field interaction, field properties and particle properties can help develop students' mental models and how they can be used differently to realize their potential as teaching tools. Initially, they were treated as analogs and the lesson designs were kept identical. With a design-based approach, the experimental and video designs, as well as best practices for a respective teaching tool, were then developed in iterations. Variation theory was used as a theoretical framework to analyze the planned respective realized pattern of variation and invariance in order to explain learning outcomes as measured by a pre-posttest consisting of conceptual multiple-choice questions inspired by the Force Concept Inventory and the Force and Motion Conceptual Evaluation. Interviews with students and teachers were used to inform the design of experiments and videos in each iteration. The lesson designs and the live and video experiments has been developed to help teachers improve student learning and make school physics more interesting by involving experimental setups that usually are out of reach and to bridge the gap between what happens in classrooms and in science research. As students’ conceptual knowledge also rises their interest in physics the aim is to increase their chances of pursuing careers within science, technology, engineering or mathematics.

Keywords: acoustic trap, design-based research, experiments, variation theory

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Authors: William Whiteley, Jens Gregor

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In recent developments of direct neural network based positron emission tomography (PET) reconstruction, two prominent architectures have emerged for converting measurement data into images: 1) networks that contain fully-connected layers; and 2) networks that primarily use a convolutional encoder-decoder architecture. In this paper, we present a multi-modal direct PET reconstruction method called MDPET, which is a hybrid approach that combines the advantages of both types of networks. MDPET processes raw data in the form of sinograms and histo-images in concert with attenuation maps to produce high quality multi-slice PET images (e.g., 8x440x440). MDPET is trained on a large whole-body patient data set and evaluated both quantitatively and qualitatively against target images reconstructed with the standard PET reconstruction benchmark of iterative ordered subsets expectation maximization. The results show that MDPET outperforms the best previously published direct neural network methods in measures of bias, signal-to-noise ratio, mean absolute error, and structural similarity.

Keywords: deep learning, image reconstruction, machine learning, neural network, positron emission tomography

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Authors: Franklin Uakhemen Ajogbor

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The issue of the English sentence has remained controversial from Traditional Grammar to modern linguistics. The English sentence occupies the highest rank in the hierarchy of grammatical units. Its consideration is therefore very necessary in learning English as a second language. Unfortunately, divergent views by grammarians on the concept of the English sentence have generated much controversy. There seems not to be a unanimous agreement on what actually constitute a sentence. Some schools of thought believe that a sentence must have a subject and a predicate while some believe that it should not. The types of sentence according to structure are also not devoid of controversy as the views of several linguists have not been properly harmonized. Findings have shown that serious effort and attention have not been paid by previous linguists to clear these ambiguities as it has a negative implication in the learning and teaching of English language. The variations on the concept of the English sentence have become particularly worrisome as a result of the widening patronage of English as a global language. The paper is therefore interested in the investigation of this controversy and suggesting a solution to the problem. In doing this, data was collected from students and scholars that show lack of uniformity in what a sentence is. Using the Systemic Functional Model as theoretical framework, the paper launches into the views held by these various schools of thought with the aim of reconciling these divergent views and also an attempt to open up further research on what actually constitute a sentence.

Keywords: traditional grammar, linguistics, controversy, sentence, grammatical units

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Authors: Azzah Alghamdi

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The Interactive White Boards (IWBs) as one of the innovative educational technologies have been extensively investigated in advanced countries such as the UK, US, and Australia. However, there is a significant lack of research studies, which mainly examine the use of IWBs in Saudi Arabia. Therefore, this study aims to investigate the attitudes of primary teachers towards using IWBs in both the teaching and learning processes. Moreover, it aims to investigate if there is any significant difference between male teachers and females regarding their attitudes towards using this technology. This study concentrated on teachers in primary schools, which participated in Tatweer project in the city of Jeddah, in Saudi Arabia. Mixed methods approach was employed in this study using a designed questionnaire, classroom observations, and a semi-structured interview. 587 teachers (286 men and 301 women) from Tatweer primary schools were completed the questionnaire as well as twenty teachers were interviewed including seven female teachers were observed in their classrooms. The findings of this study indicated that approximately 11% of the teachers within the sample (n=587) had negative attitudes towards the use of IWBs in the teaching and learning processes. However, the majority of them nearly 89% agreed about the benefits of using IWBs in their classrooms. Additionally, all the twenty teachers who were interviewed (including the seven observed female teachers) had positive attitudes towards the use of these technologies. Moreover, 87% of male teachers and 91% of female teachers who completed the questionnaire accepted the usefulness of using IWBs in improving their teaching and students' learning. Thus, this indicates that there was no significant difference between male and female teachers in Tatweer primary schools in terms of their views about using these innovative technologies in their lessons. The findings of the current study will help the Ministry of Education to improve the policies of using IWBs in Saudi Arabia. Indeed, examining teachers’ attitudes towards IWBs is a very important issue because they are the main users in classrooms. Hence, their views should be considered to addressing the powers and boundaries of using IWBs. Moreover, students will feel comfortable to use IWBs if their teachers accept and use them well.

Keywords: IWBs, Saudi teachers’ views, Tatweer schools, teachers' gender

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Authors: G. Arockiasamy, B. Sujeevan Kumar, Surendheran

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Human progress and development are highly influenced by the power of information access and technology. A global and multi-national transformation all over the word is possible due to digitalization. In the process of exchanging information, experience, and resources, there is a radical shift in who controls them. Mass media has turned the world into a global village by strengthening communication network. As a result, a new digital culture has emerged as a social network commonly known as new media. Today the advancement of technology is at the doorstep of everyone linking to anywhere. The traditional social restrictions are broken down by the new type of virtual communication modality that transcends people beyond boundaries At the same time media empire has invaded every nook and corner of the world through great expansion. Media activism is growing stronger and stronger but the truth and true meaning lost in the process. This paper explores the peoples’ attitude to media activism and tracing its direction. The methodology employed is random sampling survey and content analysis method. Both qualitatively and quantitatively measured. The findings tend to show 60 percent indicate media activism as positive and others indicate as negative. As a conclusion, media activism has danger within but depends on nature of the development of human orientation.

Keywords: media activism, media industry, program, truth information, orientation and nature

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Authors: Kerrin Burnell

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In a world of increasingly technology-dependent students, the English language classroom should ideally keep up with developments to keep students engaged as much as possible. Unfortunately, as is the case in Oman, funding is not always adequate to ensure students have the most up to date technology, and most institutions are still reliant on paper-based textbooks. In order to try and bridge the gap between the technology available (smartphones) and textbooks, augmented reality (AR) technology can be utilized to enhance classroom, homework, and extracurricular activities. AR involves overlaying media (videos, images etc) over the top of physical objects (posters, book pages etc) and then sharing the media. This case study involved introducing students to a freely available entry level AR app called Aurasma. Students were asked to augment their English textbooks, word walls, research project posters, and extracurricular posters. Through surveys, interviews and an analysis of time spent accessing the different media, a determination of the appropriateness of the technology for the classroom was determined. Results indicate that the use of AR has positive effects on many aspects of the English classroom. Increased student engagement, total time spent on task, interaction, and motivation were evident, along with a decrease in technology-related anxiety. As it is proving very difficult to get tablets or even laptops in classrooms in Oman, these preliminary results indicate that many positive outcomes will come from introducing students to this innovative technology.

Keywords: augmented reality, classroom technology, classroom innovation, engagement

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Authors: Aishatu Ibrahim Birma, Aminu Tukur, Abdulkarim Abbass Gora

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Machine translation plays a vital role in the Field of Natural Language Processing (NLP), breaking down language barriers and enabling communication across diverse communities. In the context of Hausa, a widely spoken language in West Africa, mainly in Nigeria, effective translation systems are essential for enabling seamless communication and promoting cultural exchange. However, due to the unique linguistic characteristics of Hausa, accurate translation remains a challenging task. The research proposes an approach to improving the machine learning translation of Hausa by integrating Named Entity Recognition (NER) techniques. Named entities, such as person names, locations, organizations, and dates, are critical components of a language's structure and meaning. Incorporating NER into the translation process can enhance the quality and accuracy of translations by preserving the integrity of named entities and also maintaining consistency in translating entities (e.g., proper names), and addressing the cultural references specific to Hausa. The NER will be incorporated into Neural Machine Translation (NMT) for the Hausa to English Translation.

Keywords: machine translation, natural language processing (NLP), named entity recognition (NER), neural machine translation (NMT)

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Authors: Chia-Hao Chang, Geng-Gui Wang, Jee-Cheng Wu

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The prediction of a landslide is a difficult task because it requires a detailed study of past activities using a complete range of investigative methods to determine the changing condition. In this research, first step, LiDAR 1-meter by 1-meter resolution of digital elevation model (DEM) was used to generate six environmental factors of landslide. Then, back-propagation neural networks (BPNN) was adopted to identify scarp, landslide areas and non-landslide areas. The BPNN uses 6 environmental factors in input layer and 1 output layer. Moreover, 6 landslide areas are used as training areas and 4 landslide areas as test areas in the BPNN. The hidden layer is set to be 1 and 2; the hidden layer neurons are set to be 4, 5, 6, 7 and 8; the learning rates are set to be 0.01, 0.1 and 0.5. When using 1 hidden layer with 7 neurons and the learning rate sets to be 0.5, the result of Network training root mean square error is 0.001388. Finally, evaluation of BPNN classification accuracy by the confusion matrix shows that the overall accuracy can reach 94.4%, and the Kappa value is 0.7464.

Keywords: digital elevation model, DEM, environmental factors, back-propagation neural network, BPNN, LiDAR

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Authors: Christina Van Wingerden

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This paper focuses on a sense of belonging, isolation, and the use of a learning management system as a “third space” for connection and community. Given student use of learning management systems (LMS) for courses on campuses, moderate to high use of social media and hand-held devices, the author explores the possibilities of LMS as a third space. The COVID-19 pandemic has exacerbated student experiences of isolation, and research indicates that students who experience a sense of belonging have a greater likelihood for academic retention and success. The impacts on students of an LMS designed for student employee orientation and training were examined through a mixed methods approach, including a survey, individual interviews, and focus groups. The sample involved 250-450 undergraduate student employees at a US northwestern university. The goal of the study was to find out the efficiency and effectiveness of the orientation information for a wide range of student employees from multiple student affairs departments. And unexpected finding emerged within the study in 2015 and was noted again as a finding in the 2017 study. Students reported feeling like they individually connected to the department, and further to the university because of the LMS orientation. They stated they could see themselves as part of the university community and like they belonged. The orientation, through the LMS, was designed for and occurred online (asynchronous), prior to students traveling and beginning university life for the academic year. The students indicated connection and belonging resulting from some of the design features. With the onset of COVID-19 and prolonged sheltering in place in North America, as well as other parts of the world, students have been precluded from physically gathering to educate and learn. COVID-19 essentially paused face-to-face education in 2020. Media, governments, and higher education outlets have been reporting on widespread college student stress, isolation, loneliness, and sadness. In this context, the author conducted a current mixed methods study (online survey, online interviews) of students in advanced degree programs, like Ph.D. and Ed.D. specifically investigating isolation and sense of belonging. As a part of the study a prototype of a Canvas site was experienced by student interviewees for their reaction of this Canvas site prototype as a “third” space. Some preliminary findings of this study are presented. Doctoral students in the study affirmed the potential of LMS as a third space for community and social academic connection.

Keywords: COVID-19, isolation, learning management system, sense of belonging

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Authors: Pascal Alberti, Mustapha Mouloua

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In today’s global market economy, various companies are often confronted with the dynamic and complex nature of current competitive markets. The dynamics of these markets are becoming more and more fluid, often requiring companies to provide competitive, definite advantages, and technological responses within increasingly shorte time frames. To meet these demands, companies must rely on the cognitive abilities of actors of creativity to provide tangible answers to the current contextual problems. Thus, it is important to provide a variety of instruments and design tools to support this particular stage of innovation, and to meet their demand expectations. For a number of years now, we have been extensively conducting experiments on the use of mind maps in the context of innovative projects with collaborative research teams from various nationalities. Our research findings reported a significant difference between a “Word” Mind Map and “Photographic” Mind Map, a correlation between the different uses of iconic tools and certain types of innovation, and a relationship between the different cognitive logics. In this paper, we will present our new results related to the effect of symmetrical presentation of a Photographic Mind Map" on the production of design solutions. Finally, we will conclude by highlighting the importance of our experimental method, and discussing both the theoretical and practical implications of our research.

Keywords: creativity, innovation, management, mind mapping, design product

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Authors: Samir Roy

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This paper investigates the relationship among knowledge, technology, and empowerment. In Physics power is defined as rate of doing work. In everyday use, the meaning of the word power is related to the capacity to bring change of value in the world. It appears that the popular aphorism “Knowledge is power” should be revisited in the context of contemporary states of affairs. For instance, classical mechanics is a system of knowledge, so also thermodynamics. But neither of them, per se, is sufficient to produce automobilin es. Boolean algebra, the logical foundation of digital electronic computers, was introduced by George Boole in 1847. But that knowledge was practically useless for almost one hundred years until digital electronics was developed in early twentieth century, which eventually led to invention of digital electronic computers. Empowerment of women is a burning issue in the arena of social justice. However, if we carefully analyze the functional elements of women’s empowerment, we find them to be highly technology driven as well as technology dependent in real life. On the other hand, technology has empowered modern states to maintain social order and promote democracy in an effective manner. This paper includes a few case studies to establish the close correspondence between knowledge, especially scientific knowledge, technology, and empowerment. It appears that in contemporary scenario, “Technology is power” is a more appropriate statement than the traditional aphorism “Knowledge is power”.

Keywords: knowledge, science, technology, empowerment, change, social justice

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Authors: Uswah Ahmad Khan, Muhammad Moazam Fraz, Humayoon Shafique Satti, Qasim Aziz

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Approximately 10-14% of the global population experiences a functional disorder known as irritable bowel syndrome (IBS). The disorder is defined by persistent abdominal pain and an irregular bowel pattern. IBS significantly impairs work productivity and disrupts patients' daily lives and activities. Although IBS is widespread, there is still an incomplete understanding of its underlying pathophysiology. This study aims to help characterize the phenotype of IBS patients by differentiating the comorbidities found in IBS patients from those in non-IBS patients using machine learning algorithms. In this study, we extracted samples coding for IBS from the UK BioBank cohort and randomly selected patients without a code for IBS to create a total sample size of 18,000. We selected the codes for comorbidities of these cases from 2 years before and after their IBS diagnosis and compared them to the comorbidities in the non-IBS cohort. Machine learning models, including Decision Trees, Gradient Boosting, Support Vector Machine (SVM), AdaBoost, Logistic Regression, and XGBoost, were employed to assess their accuracy in predicting IBS. The most accurate model was then chosen to identify the features associated with IBS. In our case, we used XGBoost feature importance as a feature selection method. We applied different models to the top 10% of features, which numbered 50. Gradient Boosting, Logistic Regression and XGBoost algorithms yielded a diagnosis of IBS with an optimal accuracy of 71.08%, 71.427%, and 71.53%, respectively. Among the comorbidities most closely associated with IBS included gut diseases (Haemorrhoids, diverticular diseases), atopic conditions(asthma), and psychiatric comorbidities (depressive episodes or disorder, anxiety). This finding emphasizes the need for a comprehensive approach when evaluating the phenotype of IBS, suggesting the possibility of identifying new subsets of IBS rather than relying solely on the conventional classification based on stool type. Additionally, our study demonstrates the potential of machine learning algorithms in predicting the development of IBS based on comorbidities, which may enhance diagnosis and facilitate better management of modifiable risk factors for IBS. Further research is necessary to confirm our findings and establish cause and effect. Alternative feature selection methods and even larger and more diverse datasets may lead to more accurate classification models. Despite these limitations, our findings highlight the effectiveness of Logistic Regression and XGBoost in predicting IBS diagnosis.

Keywords: comorbidities, disease association, irritable bowel syndrome (IBS), predictive analytics

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Authors: Sumalee Tientongdee

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In this study, it was aimed to investigate Physics achievement of the pre-service science teacher studying in general science program at Suan Sunandha Rajabhat University, Bangkok, Thailand. The program has provided the new curriculum that focuses on 21st-century skills development. Active learning approaches are used to teach in all subjects. One of the active learning approaches Peer Instruction, or PI was used in this study to teach physics of waves as a compulsory course. It was conducted in the second semester from January to May of 2017. The concept test was given to evaluate pre-service science teachers’ understanding in concept of waves. Problem-solving assessment form was used to evaluate their problem-solving skill. The results indicated that after they had learned through Peer Instruction in physics of waves course, their concepts in physics of waves was significantly higher at 0.05 confident levels. Their problem-solving skill from the whole class was at the highest level. Based on the group interview on the opinions of using Peer Instruction in Physics class, they mostly felt that it was very useful and helping them understand more about physics, especially for female students.

Keywords: peer instruction, physics of waves, pre-service science teacher, Suan Sunandha Rajabhat university

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Authors: Yong Chen

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To understand the detailed molecular mechanisms of memory formation in engram cells is one of the most fundamental questions in neuroscience. Recent single-cell RNA-seq (scRNA-seq) and single-nucleus RNA-seq (snRNA-seq) techniques have allowed us to explore the sparsely activated engram ensembles, enabling access to the molecular mechanisms that underlie experience-dependent memory formation and consolidation. However, the absence of specific and powerful computational methods to detect memory-related genes (modules) and their regulatory relationships in the sc/snRNA-seq datasets has strictly limited the analysis of underlying mechanisms and memory coding principles in mammalian brains. Here, we present a deep-learning method named SCENTBOX, to detect memory-related gene modules and causal regulatory relationships among themfromsc/snRNA-seq datasets. SCENTBOX first constructs codifferential expression gene network (CEGN) from case versus control sc/snRNA-seq datasets. It then detects the highly correlated modules of differential expression genes (DEGs) in CEGN. The deep network embedding and attention-based convolutional neural network strategies are employed to precisely detect regulatory relationships among DEG genes in a module. We applied them on scRNA-seq datasets of TRAP; Ai14 mouse neurons with fear memory and detected not only known memory-related genes, but also the modules and potential causal regulations. Our results provided novel regulations within an interesting module, including Arc, Bdnf, Creb, Dusp1, Rgs4, and Btg2. Overall, our methods provide a general computational tool for processing sc/snRNA-seq data from case versus control studie and a systematic investigation of fear-memory-related gene modules.

Keywords: sc/snRNA-seq, memory formation, deep learning, gene module, causal inference

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Authors: Siyao Zhu, Yifang Xu

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After natural and man-made disasters, robots can bypass the danger, expedite the search, and acquire unprecedented situational awareness to design rescue plans. The hands-free requirement from the first responders excludes the use of tedious manual control and operation. In unknown, unstructured, and obstructed environments, natural-language-based supervision is not amenable for first responders to formulate, and is difficult for robots to understand. Brain-computer interface is a promising option to overcome the limitations. This study aims to test the feasibility of using electroencephalography (EEG) signals to decode human intentions and detect the level of consensus on robot-provided information. EEG signals were classified using machine-learning and deep-learning methods to discriminate search intentions and agreement perceptions. The results show that the average classification accuracy for intention recognition and consensus assessment is 67% and 72%, respectively, proving the potential of incorporating recognizable users’ bioelectrical responses into advanced robot-assisted systems for emergency response.

Keywords: consensus assessment, electroencephalogram, emergency response, human-robot collaboration, intention recognition, search and rescue

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