Search results for: English language learning experiences

Authors: Esther Chiramal, Kelvin Soh Boon Kai

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Explainable AI is a strong strategy implemented to understand complex black-box model predictions in a human-interpretable language. It provides the evidence required to execute the use of trustworthy and reliable AI systems. On the other hand, however, it also opens the door to locating possible vulnerabilities in an AI model. Traditional adversarial text attack uses word substitution, data augmentation techniques, and gradient-based attacks on powerful pre-trained Bidirectional Encoder Representations from Transformers (BERT) variants to generate adversarial sentences. These attacks are generally white-box in nature and not practical as they can be easily detected by humans e.g., Changing the word from “Poor” to “Rich”. We proposed a simple yet effective Grey-box cum Black-box approach that does not require the knowledge of the model while using a set of surrogate Transformer/BERT models to perform the attack using Explainable AI techniques. As Transformers are the current state-of-the-art models for almost all Natural Language Processing (NLP) tasks, an attack generated from BERT1 is transferable to BERT2. This transferability is made possible due to the attention mechanism in the transformer that allows the model to capture long-range dependencies in a sequence. Using the power of BERT generalisation via attention, we attempt to exploit how transformers learn by attacking a few surrogate transformer variants which are all based on a different architecture. We demonstrate that this approach is highly effective to generate semantically good sentences by changing as little as one word that is not detectable by humans while still fooling other BERT models.

Keywords: BERT, explainable AI, Grey-box text attack, transformer

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Authors: Elnaz Lashgari, Emel Demircan

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Electromyography (EMG) is one of the most important interfaces between humans and robots for rehabilitation. Decoding this signal helps to recognize muscle activation and converts it into smooth motion for the robots. Detecting each muscle’s pattern during walking and running is vital for improving the quality of a patient’s life. In this study, EMG data from 10 muscles in 10 subjects at 4 different speeds were analyzed. EMG signals are nonlinear with high dimensionality. To deal with this challenge, we extracted some features in time-frequency domain and used manifold learning and Laplacian Eigenmaps algorithm to find the intrinsic features that represent data in low-dimensional space. We then used the Bayesian classifier to identify various patterns of EMG signals for different muscles across a range of running speeds. The best result for vastus medialis muscle corresponds to 97.87±0.69 for sensitivity and 88.37±0.79 for specificity with 97.07±0.29 accuracy using Bayesian classifier. The results of this study provide important insight into human movement and its application for robotics research.

Keywords: electromyography, manifold learning, ISOMAP, Laplacian Eigenmaps, locally linear embedding

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Authors: Kunto Nurcahyoko

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The fundamental perception used in this study is that teaching and learning activities in Indonesian classroom have potentially generated individual’s sensitivity on cross-cultural understanding. This study aims at investigating Indonesian university students’ perception on cross-cultural understanding after doing Cultural Snapshot Project. The data was critically analyzed through multicultural ideology and diversity theories. The subjects were 30 EFL college students in one of colleges in Indonesia. Each student was assigned to capture a photo which depicted the existence of any cultural manifestation in their surrounding such as discrimination, prejudice and stereotype. Students were then requested asked to reflect on the picture by writing a short description on the picture and make an exhibition using their pictures. In the end of the project, students were instructed to fill in questionnaires to show their perception before and after the project. The result reveals that Cultural Snapshot Project has given the opportunity for the students to better realize cross-cultural understanding in their environment. In conclusion, the study shows that Cultural Snapshot Project has specifically enhanced students’ perception of multiculturalism in three major areas: cultural sensitivity and empathy, social tolerance, and understanding of diversity.

Keywords: cultural snapshot, cross-cultural understanding, students’ perception, multiculturalism

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Authors: Yangzhi Li

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Network transfer of information and performance customization is now a viable method of digital industrial production in the era of Industry 4.0. Robot platforms and network platforms have grown more important in digital design and construction. The pressing need for novel building techniques is driven by the growing labor scarcity problem and increased awareness of construction safety. Robotic approaches in construction research are regarded as an extension of operational and production tools. Several technological theories related to robot autonomous recognition, which include high-performance computing, physical system modeling, extensive sensor coordination, and dataset deep learning, have not been explored using intelligent construction. Relevant transdisciplinary theory and practice research still has specific gaps. Optimizing high-performance computing and autonomous recognition visual guidance technologies improves the robot's grasp of the scene and capacity for autonomous operation. Intelligent vision guidance technology for industrial robots has a serious issue with camera calibration, and the use of intelligent visual guiding and identification technologies for industrial robots in industrial production has strict accuracy requirements. It can be considered that visual recognition systems have challenges with precision issues. In such a situation, it will directly impact the effectiveness and standard of industrial production, necessitating a strengthening of the visual guiding study on positioning precision in recognition technology. To best facilitate the handling of complicated components, an approach for the visual recognition of parts utilizing machine learning algorithms is proposed. This study will identify the position of target components by detecting the information at the boundary and corner of a dense point cloud and determining the aspect ratio in accordance with the guidelines for the modularization of building components. To collect and use components, operational processing systems assign them to the same coordinate system based on their locations and postures. The RGB image's inclination detection and the depth image's verification will be used to determine the component's present posture. Finally, a virtual environment model for the robot's obstacle-avoidance route will be constructed using the point cloud information.

Keywords: robotic construction, robotic assembly, visual guidance, machine learning

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Authors: Kemal Polat

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In this paper, three feature weighting methods have been used to improve the classification performance of diabetic retinopathy (DR). To classify the diabetic retinopathy, features extracted from the output of several retinal image processing algorithms, such as image-level, lesion-specific and anatomical components, have been used and fed them into the classifier algorithms. The dataset used in this study has been taken from University of California, Irvine (UCI) machine learning repository. Feature weighting methods including the fuzzy c-means clustering based feature weighting, subtractive clustering based feature weighting, and Gaussian mixture clustering based feature weighting, have been used and compered with each other in the classification of DR. After feature weighting, five different classifier algorithms comprising multi-layer perceptron (MLP), k- nearest neighbor (k-NN), decision tree, support vector machine (SVM), and Naïve Bayes have been used. The hybrid method based on combination of subtractive clustering based feature weighting and decision tree classifier has been obtained the classification accuracy of 100% in the screening of DR. These results have demonstrated that the proposed hybrid scheme is very promising in the medical data set classification.

Keywords: machine learning, data weighting, classification, data mining

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Authors: Helene Jacobson Pettersson, Linda Lill

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Demographic changes and globalization in society provide new opportunities for social work and social work education in Sweden. There has been an ambition to include these aspects into the Swedish social work education. However, the Swedish welfare state standard continued to be as affectionate as invisible starting point in discussions about people’s way of life and social problems. The aim of this study is to explore content given to ethnic relations in social work in the social work education in Sweden. Our standpoint is that the subject can be understood both from individual and structural levels, it changes over time, varies in different steering documents and differs from the perspectives of teachers and students. Our question is what content is given to ethnic relations in social work by the teachers in their strategies and teaching material. The study brings together research in the interface between education science, social work and research of international migration and ethnic relations. The presented narratives are from longer interviews with a total of 17 university teachers who teach in social work program at four different universities in Sweden. The universities have in different ways a curriculum that involves the theme of ethnic relations in social work, and the interviewed teachers are teaching and grading social workers on specific courses related to ethnic relations at undergraduate and graduate levels. Overall assesses these 17 teachers a large number of students during a semester. The questions were concerned on how the teachers handle ethnic relations in education in social work. The particular focus during the interviews has been the teacher's understanding of the documented learning objectives and content of literature and how this has implications for their teaching. What emerges is the teachers' own stories about the educational work and how they relate to the content of teaching, as well as the teaching strategies they use to promote the theme of ethnic relations in social work education. The analysis of this kind of pedagogy is that the teaching ends up at an individual level with a particular focus on the professional encounter with individuals. We can see the shortage of a critical analysis of the construction of social problems. The conclusion is that individual circumstance precedes theoretical perspective on social problems related to migration, transnational relations, globalization and social. This result has problematic implications from the perspective of sustainability in terms of ethnic diversity and integration in society. Thus these aspects have most relevance for social workers’ professional acting in social support and empowerment related activities, in supporting the social status and human rights and equality for immigrants.

Keywords: ethnic relations in Swedish social work education, teaching content, teaching strategies, educating for change, human rights and equality

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Authors: Dawood Al Chanti, Alice Caplier

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Sparse representations of signals have received a great deal of attention in recent years. In this paper, the interest of using sparse representation as a mean for performing sparse discriminative analysis between spontaneous facial expressions is demonstrated. An automatic facial expressions recognition system is presented. It uses a KSVD-SVM approach which is made of three main stages: A pre-processing and feature extraction stage, which solves the problem of shared subspace distribution based on the random projection theory, to obtain low dimensional discriminative and reconstructive features; A dictionary learning and sparse coding stage, which uses the KSVD model to learn discriminative under or over dictionaries for sparse coding; Finally a classification stage, which uses a SVM classifier for facial expressions recognition. Our main concern is to be able to recognize non-basic affective states and non-acted expressions. Extensive experiments on the JAFFE static acted facial expressions database but also on the DynEmo dynamic spontaneous facial expressions database exhibit very good recognition rates.

Keywords: dictionary learning, random projection, pose and spontaneous facial expression, sparse representation

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Authors: Yu-Chen Wei

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The relationship between organizational human capital and organizational effectiveness have been a common topic of interest to organization researchers. Much of this research has concluded that higher human capital can predict greater organizational outcomes. Whereas human capital research has traditionally focused on organizations, the current study turns to the team level human capital. In addition, there are no known empirical studies assessing the effect of human capital divergence on team performance. Team human capital refers to the sum of knowledge, ability, and experience embedded in team members. Team human capital divergence is defined as the variation of human capital within a team. This study is among the first to assess the role of human capital divergence as a moderator of the effect of team human capital on team performance. From the traditional perspective, team human capital represents the collective ability to solve problems and reducing operational risk of all team members. Hence, the higher team human capital, the higher the team performance. This study further employs social learning theory to explain the relationship between team human capital and team performance. According to this theory, the individuals will look for progress by way of learning from teammates in their teams. They expect to have upper human capital, in turn, to achieve high productivity, obtain great rewards and career success eventually. Therefore, the individual can have more chances to improve his or her capability by learning from peers of the team if the team members have higher average human capital. As a consequence, all team members can develop a quick and effective learning path in their work environment, and in turn enhance their knowledge, skill, and experience, leads to higher team performance. This is the first argument of this study. Furthermore, the current study argues that human capital divergence is negative to a team development. For the individuals with lower human capital in the team, they always feel the pressure from their outstanding colleagues. Under the pressure, they cannot give full play to their own jobs and lose more and more confidence. For the smart guys in the team, they are reluctant to be colleagues with the teammates who are not as intelligent as them. Besides, they may have lower motivation to move forward because they are prominent enough compared with their teammates. Therefore, human capital divergence will moderate the relationship between team human capital and team performance. These two arguments were tested in 510 team-seasons drawn from major league baseball (1998–2014). Results demonstrate that there is a positive relationship between team human capital and team performance which is consistent with previous research. In addition, the variation of human capital within a team weakens the above relationships. That is to say, an individual working with teammates who are comparable to them can produce better performance than working with people who are either too smart or too stupid to them.

Keywords: human capital divergence, team human capital, team performance, team level research

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Authors: Pei Yi Lin

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Objective: The objective of this study is to use machine learning methods to build an early prediction classifier model for acute delirium to improve the quality of medical care for intensive care patients. Background: Delirium is a common acute and sudden disturbance of consciousness in critically ill patients. After the occurrence, it is easy to prolong the length of hospital stay and increase medical costs and mortality. In 2021, the incidence of delirium in the intensive care unit of internal medicine was as high as 59.78%, which indirectly prolonged the average length of hospital stay by 8.28 days, and the mortality rate is about 2.22% in the past three years. Therefore, it is expected to build a delirium prediction classifier through big data analysis and machine learning methods to detect delirium early. Method: This study is a retrospective study, using the artificial intelligence big data database to extract the characteristic factors related to delirium in intensive care unit patients and let the machine learn. The study included patients aged over 20 years old who were admitted to the intensive care unit between May 1, 2022, and December 31, 2022, excluding GCS assessment <4 points, admission to ICU for less than 24 hours, and CAM-ICU evaluation. The CAMICU delirium assessment results every 8 hours within 30 days of hospitalization are regarded as an event, and the cumulative data from ICU admission to the prediction time point are extracted to predict the possibility of delirium occurring in the next 8 hours, and collect a total of 63,754 research case data, extract 12 feature selections to train the model, including age, sex, average ICU stay hours, visual and auditory abnormalities, RASS assessment score, APACHE-II Score score, number of invasive catheters indwelling, restraint and sedative and hypnotic drugs. Through feature data cleaning, processing and KNN interpolation method supplementation, a total of 54595 research case events were extracted to provide machine learning model analysis, using the research events from May 01 to November 30, 2022, as the model training data, 80% of which is the training set for model training, and 20% for the internal verification of the verification set, and then from December 01 to December 2022 The CU research event on the 31st is an external verification set data, and finally the model inference and performance evaluation are performed, and then the model has trained again by adjusting the model parameters. Results: In this study, XG Boost, Random Forest, Logistic Regression, and Decision Tree were used to analyze and compare four machine learning models. The average accuracy rate of internal verification was highest in Random Forest (AUC=0.86), and the average accuracy rate of external verification was in Random Forest and XG Boost was the highest, AUC was 0.86, and the average accuracy of cross-validation was the highest in Random Forest (ACC=0.77). Conclusion: Clinically, medical staff usually conduct CAM-ICU assessments at the bedside of critically ill patients in clinical practice, but there is a lack of machine learning classification methods to assist ICU patients in real-time assessment, resulting in the inability to provide more objective and continuous monitoring data to assist Clinical staff can more accurately identify and predict the occurrence of delirium in patients. It is hoped that the development and construction of predictive models through machine learning can predict delirium early and immediately, make clinical decisions at the best time, and cooperate with PADIS delirium care measures to provide individualized non-drug interventional care measures to maintain patient safety, and then Improve the quality of care.

Keywords: critically ill patients, machine learning methods, delirium prediction, classifier model

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Authors: Maria L. Swanson

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Language is a system which constantly changes and reflects social and cultural transformations of a speech community. If it is phonetic system, morphological patterns and syntactic arrangements undergo little charge and are not easily transferable from one language to another, the lexicon has a high degree of flexibility. Borrowings in Arabic have always been an interesting and important subject of study to various fields of linguistics, history and culturology, and there is quite number of works devoted to this subject (al-Khalīl, Sībawīḥ, Jeffery, Belkin, al-Maghribii, Holes, Stetkevich, el-Mawlūdī, between many others). At the same time, the history of borrowing has never been described as a process starting from its originating and up to the present time. Most of the researches study lexical and morphological adaptation of borrowed words for specific or several historical periods or delineate this process on the whole. Meanwhile, we have described the whole history of borrowings in Arabic with the brief depicting of lexical and morphological specifics for each historical period using quantitative method through dividing Arabic borrowings into several groups, basing on the specific of their adaptation of new vocabulary which is tightly related to the global transformations in the Arabic history. We explain reasons for borrowings of specific lexical layers for each historical period together with the description of its morphological specifics. We also use qualitative approach through performing statistics about the share of loan vocabulary in Arabic during different periods and the percentage of borrowings from donor languages. The history of a character and amount of borrowings is a good resource for theoretical and practical lexicography and morphology studies. It is also beneficial for researchers in the field of global and specific national, political and social developments, and different types of contacts.

Keywords: anthropological linguistics, borrowings, historical linguistics, sociolinguistics

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Authors: Nur Hidayah Iwani Mohd Kamal

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The higher education institutions in Malaysia is challenged with a more socially and culturally diverse student population than ever before, especially with the increasing number of international students studying in Malaysia in the recent years. First year university is a critical time in students’ lives. Students are not only developing intelectually, they are also establishing and maintaining personal relationships, developing an identity, deciding about career and lifestyle, maintaining personal health and wellness, and developing an integrated philosohy of life. The higher education institutions work as a diverse community of learners to provide a supportive environment for these first year students in assisting them in their transition from high school to university. Although many universities are taking steps to improve the first year experience for their new local and international students, efforts must be taken to ensure organized and coordinated manner in order for the initiatives to be successful. The objectives of the study are to examine the international students’ perceptions and interpretation of their first year experiences in shaping and determining their attitudes toward study and the quality of their entire undergraduate academic career; and identify an appropriate mechanism to encounter the international students’ adjustment in the new environment in order to facilitate cross-functional communication and create a coherent and meaningful first year experience. A key construct in this study is that if universities wish to recruiting and retaining international students, it is their ethical responsibility to determine how they can best meet their needs at the academic and social level, create a supportive ‘learning community’ as a foundation of their educational experience, hence facilitate cross-cultural communication and create a coherent and meaningful first year experience. This study is simultaneously frames in relation to focus on the factors that influence a successful and satisfying transition to university life by the first year international students. The study employs a mixed-method data collection involving semi-structured interviews, questionnaire, classroom observation and document analysis. This study provides valuable insight into the struggles that many international students face as they attempt to make the adjustment not only to a new educational system but factors such as psychosocial and cultural problems. It would discuss some of the factors that impact the international students during their first year in university in their quest to be academically successful. It concludes with some recommendations on how Malaysian universities provide these students with a good first year experience based on some the best practices of universities around the world.

Keywords: first year experience, Malaysian universities, international students, education

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Authors: D. Zhigulskaya, V. Anisimov, A. Pikunov, K. Babanova, S. Zuev, A. Latyshkova, K. Сhernozatonskiy, A. Revazov

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Electrophysiology of information processing in reading is certainly a popular research topic. Natural reading, however, has been relatively poorly studied, despite having broad potential applications for learning and education. In the current study, we explore the relationship between text categories and spontaneous electroencephalogram (EEG) while reading. Thirty healthy volunteers (mean age 26,68 ± 1,84) participated in this study. 15 Russian-language texts were used as stimuli. The first text was used for practice and was excluded from the final analysis. The remaining 14 were opposite pairs of texts in one of 7 categories, the most important of which were: interesting/boring, fiction/non-fiction, free reading/reading with an instruction, reading a text/reading a pseudo text (consisting of strings of letters that formed meaningless words). Participants had to read the texts sequentially on an Apple iPad Pro. EEG was recorded from 12 electrodes simultaneously with eye movement data via ARKit Technology by Apple. EEG spectral amplitude was analyzed in Fz for theta-band (4-8 Hz) and in C3, C4, P3, and P4 for alpha-band (8-14 Hz) using the Friedman test. We found that reading an interesting text was accompanied by an increase in theta spectral amplitude in Fz compared to reading a boring text (3,87 µV ± 0,12 and 3,67 µV ± 0,11, respectively). When instructions are given for reading, we see less alpha activity than during free reading of the same text (3,34 µV ± 0,20 and 3,73 µV ± 0,28, respectively, for C4 as the most representative channel). The non-fiction text elicited less activity in the alpha band (C4: 3,60 µV ± 0,25) than the fiction text (C4: 3,66 µV ± 0,26). A significant difference in alpha spectral amplitude was also observed between the regular text (C4: 3,64 µV ± 0,29) and the pseudo text (C4: 3,38 µV ± 0,22). These results suggest that some brain activity we see on EEG is sensitive to particular features of the text. We propose that changes in theta and alpha bands during reading may serve as electrophysiological tools for assessing the reader’s cognitive state as well as his or her attitude to the text and the perceived information. These physiological markers have prospective practical value for developing technological solutions and biofeedback systems for reading in particular and for education in general.

Keywords: EEG, natural reading, reader's cognitive state, theta-rhythm, alpha-rhythm

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Authors: Chandu Rathnayake, Isuri Anuradha

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Early detection and accurate diagnosis of lung diseases play a crucial role in improving patient prognosis. However, conventional diagnostic methods heavily rely on subjective symptom assessments and medical imaging, often causing delays in diagnosis and treatment. To overcome this challenge, we propose a novel lung disease prediction system that integrates patient symptoms and X-ray images to provide a comprehensive and reliable diagnosis.In this project, develop a mobile application specifically designed for detecting lung diseases. Our application leverages both patient symptoms and X-ray images to facilitate diagnosis. By combining these two sources of information, our application delivers a more accurate and comprehensive assessment of the patient's condition, minimizing the risk of misdiagnosis. Our primary aim is to create a user-friendly and accessible tool, particularly important given the current circumstances where many patients face limitations in visiting healthcare facilities. To achieve this, we employ several state-of-the-art algorithms. Firstly, the Decision Tree algorithm is utilized for efficient symptom-based classification. It analyzes patient symptoms and creates a tree-like model to predict the presence of specific lung diseases. Secondly, we employ the Random Forest algorithm, which enhances predictive power by aggregating multiple decision trees. This ensemble technique improves the accuracy and robustness of the diagnosis. Furthermore, we incorporate a deep learning model using Convolutional Neural Network (CNN) with the RestNet50 pre-trained model. CNNs are well-suited for image analysis and feature extraction. By training CNN on a large dataset of X-ray images, it learns to identify patterns and features indicative of lung diseases. The RestNet50 architecture, known for its excellent performance in image recognition tasks, enhances the efficiency and accuracy of our deep learning model. By combining the outputs of the decision tree-based algorithms and the deep learning model, our mobile application generates a comprehensive lung disease prediction. The application provides users with an intuitive interface to input their symptoms and upload X-ray images for analysis. The prediction generated by the system offers valuable insights into the likelihood of various lung diseases, enabling individuals to take appropriate actions and seek timely medical attention. Our proposed mobile application has significant potential to address the rising prevalence of lung diseases, particularly among young individuals with smoking addictions. By providing a quick and user-friendly approach to assessing lung health, our application empowers individuals to monitor their well-being conveniently. This solution also offers immense value in the context of limited access to healthcare facilities, enabling timely detection and intervention. In conclusion, our research presents a comprehensive lung disease prediction system that combines patient symptoms and X-ray images using advanced algorithms. By developing a mobile application, we provide an accessible tool for individuals to assess their lung health conveniently. This solution has the potential to make a significant impact on the early detection and management of lung diseases, benefiting both patients and healthcare providers.

Keywords: CNN, random forest, decision tree, machine learning, deep learning

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Authors: Prima Gusti Yanti, Fairul Zabadi

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The purpose of the study is to find out relation of moral massage with the authority ang globalization in proverb. Proverb is one of the many forms of cultural identity of the Indonesian/Malay people fulled with moral values. The values contained within those proverbs are beneficial not only to the society, but also to those who held power amidst on this era of globalization. The method being used is qualitative research by using content analysis which is done by describing and uncovering the forms and meanings of proverbs used within Indonesia Minangkabau society. Sources for this study’s data were extracted from a Minangkabau native speaker in the subdistrict of Tanah Abang, Jakarta. Said sources were retrieved through a series of interviews with the Minangkabau native speaker, whose speech is still adorned with idiomatic expressions. The research findings show that there existed 30 proverbs or idiomatic expressions in the Minangkabau language that are often used by its indigenous people. The thirty data contain moral values that are closely interwoven with the matter of power and globalization. Analytical results show that there are fourteen moral values contained within proverbs reflect a firm connection between rule and power in globalization; such as: responsible, brave, togetherness and consensus,tolerance, politeness, thorough and meticulous,honest and keeping promise, ingenious and learning, care, self-correction, be fair, alert, arbitrary, self-awareness. Structurally, proverbs possess an unchangeably formal construction; symbolically, proverbs possess meanings that are clearly decided through ethnographic communicative factors along with situational and cultural contexts. Values contained within proverbs may be used as a guide in social management, be it between fellow men, men between nature, or even men between their Creator. Therefore, the meanings and values contained within the morals of proverbs could also be utilized as a counsel for those who rule and in charge of power in order to stem the tides of globalization that had already spread into sectoral, territorial and educational continuums.

Keywords: continuum, globalization, identity, proverb, rule-power

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Authors: Genevieve Jorolan-Quintero

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The recording as well as the preservation of indigenous literature is an important task as it deals with a significant heritage of pre-colonial culture. The beliefs and traditions of a people are reflected in their oral narratives, such as the folk epic, which must be written down to insure their preservation. The epic poem for instance, known as dawot among the Mandaya, one of the indigenous communities in the southern region of the Philippines, narrates the customs, the ways of life, and the adventures of an ancient people. Nabayra, an expert on Philippine folkloric studies, stresses that still extant after centuries of unknown origin, the dawot was handed down to the magdadawot (bard) by word of mouth, forming the greatest bulk of Mandaya oral tradition. Unhampered by modern means of communication to distract her/him, the magdadawot has a sharp memory of the intricacies of the ancient art of chanting the panayday (verses) of the epic poem. The dawot has several hullubaton (episodes), each of which takes several nights to chant . The language used in these oral traditions is archaic Mandaya, no longer spoken or clearly understood by the present generation. There is urgency to the task of recording and writing down what remain of the epic poem since the singers and storytellers who have retained the memory and the skill of chanting and narrating the dawot and other forms of oral tradition in their original forms are getting fewer. The few who are gifted and skilled to transmit these ancient arts and wisdom are old and dying. Unlike the other Philippine epics (i.e. the Darangen, the Ulahingan, the Hinilawod, etc.), the Mandaya epic is yet to be recognized and given its rightful place among the recorded epics in Philippine Folk Literature. The general aim of this study was to put together and preserve an intangible heritage, the Mandaya hullubaton (episodes of the dawot), in order to preserve and promote appreciation for the oral traditions and cultural legacy of the Mandaya. It was able to record, transcribe, and translate four hullubaton of the folk epic into two languages, Visayan and English to insure understanding of their contents and significance among non-Mandaya audiences. Evident in the contents of the episodes are the cultural practices, ideals, life values, and traditions of the ancient Mandaya. While the conquests and adventures of the Mandaya heroes Lumungtad, Dilam, and Gambong highlight heroic virtues, the role of the Mandaya matriarch in family affairs is likewise stressed. The recording and the translation of the hullubaton and the dawot into commonly spoken languages will not only promote knowledge and understanding about their culture, but will also stimulate in the members of this cultural community a sense of pride for their literature and culture. Knowledge about indigenous cultural system and philosophy derived from their oral literature will serve as a springboard to further comparative researches dealing with indigenous mores and belief systems among the different tribes in the Philippines, in Asia, in Africa, and other countries in the world.

Keywords: Dawot, epic poem, Mandaya, Philippine folk literature

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Authors: Paramvir Singh

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The locomotion of aquatic organisms has long fascinated biologists and engineers alike, with fish fins serving as a prime example of nature's remarkable adaptations for efficient underwater propulsion. This paper presents a comprehensive study focused on the hydrodynamic analysis of fish fin kinematics, employing an innovative approach that combines machine learning and image processing techniques. Through high-speed videography and advanced computational tools, we gain insights into the complex and dynamic motion of the fins of a Tilapia (Oreochromis Niloticus) fish. This study was initially done by experimentally capturing videos of the various motions of a Tilapia in a custom-made setup. Using deep learning and image processing on the videos, the motion of the Caudal and Pectoral fin was extracted. This motion included the fin configuration (i.e., the angle of deviation from the mean position) with respect to time. Numerical investigations for the flapping fins are then performed using a Computational Fluid Dynamics (CFD) solver. 3D models of the fins were created, mimicking the real-life geometry of the fins. Thrust Characteristics of separate fins (i.e., Caudal and Pectoral separately) and when the fins are together were studied. The relationship and the phase between caudal and pectoral fin motion were also discussed. The key objectives include mathematical modeling of the motion of a flapping fin at different naturally occurring frequencies and amplitudes. The interactions between both fins (caudal and pectoral) were also an area of keen interest. This work aims to improve on research that has been done in the past on similar topics. Also, these results can help in the better and more efficient design of the propulsion systems for biomimetic underwater vehicles that are used to study aquatic ecosystems, explore uncharted or challenging underwater regions, do ocean bed modeling, etc.

Keywords: biomimetics, fish fin kinematics, image processing, fish tracking, underwater vehicles

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Authors: Mahmoud B. Rokaya

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The study of opinion mining and sentiment analysis includes analysis of opinions, sentiments, evaluations, attitudes, and emotions. The rapid growth of social media, social networks, reviews, forum discussions, microblogs, and Twitter, leads to a parallel growth in the field of sentiment analysis. The field of sentiment analysis tries to develop effective tools to make it possible to capture the trends of people. There are two approaches in the field, lexicon-based and corpus-based methods. A lexicon-based method uses a sentiment lexicon which includes sentiment words and phrases with assigned numeric scores. These scores reveal if sentiment phrases are positive or negative, their intensity, and/or their emotional orientations. Creation of manual lexicons is hard. This brings the need for adaptive automated methods for generating a lexicon. The proposed method generates dynamic lexicons based on the corpus and then classifies text using these lexicons. In the proposed method, different approaches are combined to generate lexicons from text. The proposed method classifies the tweets into 5 classes instead of +ve or –ve classes. The sentiment classification problem is written as an optimization problem, finding optimum sentiment lexicons are the goal of the optimization process. The solution was produced based on mathematical programming approaches to find the best lexicon to classify texts. A genetic algorithm was written to find the optimal lexicon. Then, extraction of a meta-level feature was done based on the optimal lexicon. The experiments were conducted on several datasets. Results, in terms of accuracy, recall and F measure, outperformed the state-of-the-art methods proposed in the literature in some of the datasets. A better understanding of the Arabic language and culture of Arab Twitter users and sentiment orientation of words in different contexts can be achieved based on the sentiment lexicons proposed by the algorithm.

Keywords: social media, Twitter sentiment, sentiment analysis, lexicon, genetic algorithm, evolutionary computation

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Authors: Yongjiang Zhang, Kallol Barai, Umesh R. Hodeghatta, Trang Tran, Vikas Dhiman

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Detecting water stress on crops early and accurately is crucial to minimize its impact. This study aims to measure water stress in wild blueberry crops non-destructively by analyzing proximal hyperspectral data. The data collection took place in the summer growing season of 2022. A drought experiment was conducted on wild blueberries in the randomized block design in the greenhouse, incorporating various genotypes and irrigation treatments. Hyperspectral data ( spectral range: 400-1000 nm) using a handheld spectroradiometer and leaf water potential data using a pressure chamber were collected from wild blueberry plants. Machine learning techniques, including multiple regression analysis and random forest models, were employed to predict leaf water potential (MPa). We explored the optimal wavelength bands for simple differences (RY1-R Y2), simple ratios (RY1/RY2), and normalized differences (|RY1-R Y2|/ (RY1-R Y2)). NDWI ((R857 - R1241)/(R857 + R1241)), SD (R2188 – R2245), and SR (R1752 / R1756) emerged as top predictors for predicting leaf water potential, significantly contributing to the highest model performance. The base learner models achieved an R-squared value of approximately 0.81, indicating their capacity to explain 81% of the variance. Research is underway to develop a neural vegetation index (NVI) that automates the process of index development by searching for specific wavelengths in the space ratio of linear functions of reflectance. The NVI framework could work across species and predict different physiological parameters.

Keywords: hyperspectral reflectance, water potential, spectral indices, machine learning, wild blueberries, optimal bands

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Authors: Zerrifi Meryem

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Discourse on Muslim women has been criticized in scientific research as being racist, sexist, and ideological. Convert women who come from various backgrounds are cases that form a rich area of investigation that have been covered from a narrative perspective in which experiences of convert women are represented through stories telling journey to conversion to Islam. They have not been problematized in relation to the Algerian media discourse; yet, their omnipresence cannot be denied as a Muslim community. This research aims at demonstrating ideologies that are perpetuated along newspapers’ discourse. The present study investigates the discursive portrayal of female Muslim converts in this type of discourse.

Keywords: media discourse, muslim women converts, algerian newspapers, ideologies

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Authors: Hongmei Chi

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The Blockchain has become a necessity for many different societal industries and ordinary lives including cryptocurrency technology, supply chain, health care, public safety, education, etc. Therefore, training our future blockchain developers to know blockchain programming vulnerability and I.T. students' cyber security is in high demand. In this work, we propose a framework including learning modules and hands-on labs to guide future I.T. professionals towards developing secure blockchain programming habits and mitigating source code vulnerabilities at the early stages of the software development lifecycle following the concept of Secure Software Development Life Cycle (SSDLC). In this research, our goal is to make blockchain programmers and I.T. students aware of the vulnerabilities of blockchains. In summary, we develop a framework that will (1) improve students' skills and awareness of blockchain source code vulnerabilities, detection tools, and mitigation techniques (2) integrate concepts of blockchain vulnerabilities for IT students, (3) improve future IT workers’ ability to master the concepts of blockchain attacks.

Keywords: software vulnerability detection, hands-on lab, static analysis tools, vulnerabilities, blockchain, active learning

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Authors: Bhekisisa Nyoni, Nomakhosi Ndiweni, Annatoria Chinyama

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The aim of this paper was to explore ways to enhance the quality of higher education with a bias towards engineering education in Zimbabwe universities. A search through relevant literature was conducted looking at both international and local scholars. It also involved reviewing the Dakar Framework for Action and Incheon Declaration and Framework for Action plans for education for sustainable development. Goals were set for 2030 as a standard for quality to be adopted by all countries in improving access as well as the quality of education from early childhood and through to adult learning. Despite the definition of quality being difficult to express due to diverse expectations from different stakeholders, the view of quality adopted is based on the World Education Forum’s propositions on quality education going beyond the classroom experience. It considers factors such as learning environment, governance and management, and teacher caliber. The study concludes by illustrating that the quality of engineering education in Zimbabwe has come a long way. It has made strides in increasing access and variety to education though at the expense of quality in its totality. To improve the quality of engineering education, programs have been introduced to promote the professionalism of lecturers, such as industrial secondment and professional development courses.

Keywords: engineering education, quality of education, professional development, industrial secondment

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Authors: Ioanna Skoura

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Past research shows that personal space has been investigated since the 1950s. Also, personality traits have been found to have a significant relationship with personal space. However, some of these studies have been criticized for being ethically inappropriate. In an attempt to avoid ethical issues, a new scale measuring desire for personal space has been created. The purpose of the present study is to investigate the impact of ethnicity on desire for personal space. Additionally, extraversion and neuroticism are expected to predict significantly desire for personal space. Furthermore, the study is looking for any impact of religiosity on desire for personal space. In order to test the previous hypotheses, 115 participants from three cultural groups (English, Greeks in Greece and Greeks in the UK) are recruited online. Results indicate that only extraversion and religiosity are significant predictors of desire for personal space. Implications of the findings are discussed and suggestions for future research are made.

Keywords: ethnicity, religiosity, personality, personal space

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Authors: Rui Yin, Haojie Wang, Xun Li

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Recently, mainstream dense retrieval methods have obtained state-of-the-art results on some datasets and tasks. However, they require large amounts of training data, which is not available in most domains. The severe performance degradation of dense retrievers on new data domains has limited the use of dense retrieval methods to only a few domains with large training datasets. In this paper, we propose an unsupervised domain-adaptive approach based on query generation. First, a generative model is used to generate relevant queries for each passage in the target corpus, and then, the generated queries are used for mining negative passages. Finally, the query-passage pairs are labeled with a cross-encoder and used to train a domain-adapted dense retriever. We also explore contrastive learning as a method for training domain-adapted dense retrievers and show that it leads to strong performance in various retrieval settings. Experiments show that our approach is more robust than previous methods in target domains that require less unlabeled data.

Keywords: dense retrieval, query generation, contrastive learning, unsupervised training

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Authors: L. Bruckert, A. Mansourzadeh

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The voice has a major influence on interpersonal communication in everyday life via the perception of pleasantness. The evolutionary perspective postulates that the mechanisms underlying the pleasantness judgments are universal adaptations that have evolved in the service of choosing a mate (through the process of sexual selection). From this point of view, the favorite voices would be those with more marked sexually dimorphic characteristics; for example, in men with lower voice pitch, pitch is the main criterion. On the other hand, one can postulate that the mechanisms involved are gradually established since childhood through exposure to the environment, and thus the prosodic elements could take precedence in everyday life communication as it conveys information about the speaker's attitude (willingness to communicate, interest toward the interlocutors). Our study focuses on voice pleasantness and its relationship with social impression formation, exploring both the spectral aspects (pitch, timbre) and the prosodic ones. In our study, we recorded the voices through two vocal corpus (five vowels and a reading text) of 25 French males speaking French and 25 Iranian males speaking Farsi. French listeners (40 male/40 female) listened to the French voices and made a judgment either on the voice's pleasantness or on the speaker (judgment about his intelligence, honesty, sociability). The regression analyses from our acoustic measures showed that the prosodic elements (for example, the intonation and the speech rate) are the most important criteria concerning pleasantness, whatever the corpus or the listener's gender. Moreover, the correlation analyses showed that the speakers with the voices judged as the most pleasant are considered the most intelligent, sociable, and honest. The voices in Farsi have been judged by 80 other French listeners (40 male/40 female), and we found the same effect of intonation concerning the judgment of pleasantness with the corpus «vowel» whereas with the corpus «text» the pitch is more important than the prosody. It may suggest that voice perception contains some elements invariant across culture/language, whereas others are influenced by the cultural/linguistic background of the listener. Shortly in the future, Iranian people will be asked to listen either to the French voices for half of them or to the Farsi voices for the other half and produce the same judgments as the French listeners. This experimental design could potentially make it possible to distinguish what is linked to culture and what is linked to language in the case of differences in voice perception.

Keywords: cross-cultural psychology, impression formation, pleasantness, voice perception

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Authors: Güzi̇n Ti̇rkeş, Özge Teki̇n, Kerem Kurtuluş, Y. Yekta Yurtseven, Murat Baran

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A scanning electron microscope (SEM) is a type of electron microscope mainly used in nanoscience and nanotechnology areas. Automatic image recognition and classification are among the general areas of application concerning SEM. In line with these usages, the present paper proposes a deep learning algorithm that classifies SEM images into nine categories by means of an online application to simplify the process. The NFFA-EUROPE - 100% SEM data set, containing approximately 21,000 images, was used to train and test the algorithm at 80% and 20%, respectively. Validation was carried out using a separate data set obtained from the Middle East Technical University (METU) in Turkey. To increase the accuracy in the results, the Inception ResNet-V2 model was used in view of the Fine-Tuning approach. By using a confusion matrix, it was observed that the coated-surface category has a negative effect on the accuracy of the results since it contains other categories in the data set, thereby confusing the model when detecting category-specific patterns. For this reason, the coated-surface category was removed from the train data set, hence increasing accuracy by up to 96.5%.

Keywords: convolutional neural networks, deep learning, image classification, scanning electron microscope

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Authors: Zihuan (Dylan) Wang

Abstract:

This paper analyzes the formation of human food aversion from a biological perspective. It points out that this biased behavior is formed through the accumulation of long-term survival and life experiences. By introducing the "Food Chain Energy Pyramid" model and the analogous deduction of the "Human Food Aversion Pyramid," with energy conversion efficiency as the primary reason, it analyzes the underlying reasons for the formation of food preferences. Food industry professionals can gain inspiration from this article to combine the theory presented with their expertise in order to leverage product quality and promote environmentally conscious practices.

Keywords: food aversion, food preference, energy conversion efficiency, food and culture, nutrition, research and development

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Authors: Maria Poulou

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The study investigated how teachers’ self-rated Emotional Intelligence (EI), competence in implementing Social and Emotional Learning (SEL) skills and teaching efficacy relate to teacher-student relationships and students’ emotional and behavioral difficulties. Participants were 98 elementary teachers from public schools in central Greece. They completed the Self-Rated Emotional Intelligence Scale (SREIS), the Teacher SEL Beliefs Scale, the Teachers’ Sense of Efficacy Scale (TSES), the Student-Teacher Relationships Scale-Short Form (STRS-SF) and the Strengths and Difficulties Questionnaire (SDQ) for 617 of their students, aged 6-11 years old. Structural equation modeling was used to examine an exploratory model of the variables. It was demonstrated that teachers’ emotional intelligence, SEL beliefs and teaching efficacy were significantly related to teacher-student relationships, but they were not related to students’ emotional and behavioral difficulties. Rather, teachers’ perceptions of teacher-students relationships were significantly related to these difficulties. These findings and their implications for research and practice are discussed.

Keywords: emotional intelligence, social and emotional learning, teacher-student relationships, teaching efficacy

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Authors: Rupinder Kaler, Faith Ndebele, Nadia Saleem, Hafsa Sheikh

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Background: Workplace related violence and aggression seems to be considered an acceptable occupational hazard for staff in mental health services. There is literature evidence that healthcare workers in mental health settings are at higher risk from aggression from patients. Aggressive behaviours pose a physical and psychological threat to the psychiatric staff and can result in stress, burnout, sickness, and exhaustion. Further evidence informs that health professionals are the most exposed to psychological disorders such as anxiety, depression and post-traumatic stress disorder. Fear that results from working in a dangerous environment and exhaustion can have a damaging impact on patient care and healthcare relationship. Aim: The aim of this study is to investigate the prevalence and impact of aggressive behaviour on staff working at Coventry and Warwickshire Partnership Trust. Methodology: The study methodology included carrying out a manual, anonymised, multi-disciplinary cross-sectional survey questionnaire across all clinical and non-clinical staff at CWPT from both inpatient and community settings. Findings: The unsurprising finding was that of higher prevalence of aggressive behaviours in in-patients in comparison to community staff. Conclusion: There is a high rate of verbal and physical aggression at work and this has a negative impact on the staff emotional and physical well- being. There is also a higher reliance on colleagues for support on an informal basis than formal organisational support systems. Recommendations: A workforce that is well and functioning is the biggest resource for an organisation. Staff safety during working hours is everyone's responsibility and sits with both individual staff members and the organisation. Post-incident organisational support needs to be consolidated, and hands-on, timely support offered to help maintain emotionally well staff on CWPT. The authors recommend development of preventative and practical protocols for aggression with patient and carer involvement. Post-incident organisational support needs to be consolidated, and hands-on, timely support offered to help maintain emotionally well staff on CWPT.

Keywords: safer staff, survey of staff experiences, violence and aggression, mental health

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Authors: Duygu Özdemir, Bilal Özçakır

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Usage of augmented reality in education helps students to make sense of the three-dimensional world of mathematics. In this study, it was aimed to develop activities about fractions for 5th-grade students by augmented reality and also aimed to assess these activities in terms of students’ understanding and views. Data obtained from 60 students in a private school in Marmaris, Turkey was obtained through classroom observations, students’ worksheets and semi-structured interviews during two weeks. Data analysis was conducted by using constant-comparative analysis which leads to meaningful categories of findings. Findings of this study indicated that usage of augmented reality is a facilitator to make concretize and provide real-life application for fractions. Moreover, students’ opinions about its usage were lead to categories as benefit for learning, enjoyment and creating awareness of usage of augmented reality in mathematics education. In general, this study could be a bridge to show the contributions of augmented reality applications to mathematics education and also highlights that augmented reality could be used with subjects like fractions rather than subjects only in geometry learning domain.

Keywords: augmented reality, mathematics, fractions, students

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Authors: Blythe Maltby

Abstract:

Homeless populations are one of the sections of society most vulnerable to the effects of natural disasters. Channels of communication to these populations are limited as they lack access to mainstream modes of emergency notification, often being the last to know about state emergencies. This study aims to answer if there is a way that cities and policies be designed to help reduce casualty rates to the homeless during state emergencies, such as earthquake and tsunami preparations. The study used a qualitative research approach, namely by speaking to levels of government, homelessness charities and workers and others about preparations and their experiences with the response of state emergencies. The proposed paper may help countries identify the gaps in their preparations to help facilitate better resources to look after these vulnerable populations.

Keywords: accessibility, disaster mitigation, homeless, Vancouver

Procedia PDF Downloads 212