Search results for: learning Maltese as a second language

Authors: Lana Burmistrova

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Introduction: The influence of visual impairments on several cognitive processes used in the music learning process is an increasingly important area in special education and cognitive musicology. Many children have several visual impairments due to the refractive errors and irreversible inhibitors. However, based on the compensatory neuroplasticity and functional reorganization, congenitally blind (CB) and early blind (EB) individuals use several areas of the occipital lobe to perceive and process auditory and tactile information. CB individuals have greater memory capacity, memory reliability, and less false memory mechanisms are used while executing several tasks, they have better working memory (WM) and short-term memory (STM). Blind individuals use several strategies while executing tactile and working memory n-back tasks: verbalization strategy (mental recall), tactile strategy (tactile recall) and combined strategies. Methods and design: The aim of the pilot study was to substantiate similar tendencies while executing attention, memory and combined auditory tasks in blind and sighted individuals constructed for this study, and to investigate attention, memory and combined mechanisms used in the music learning process. For this study eight (n=8) blind and eight (n=8) sighted individuals aged 13-20 were chosen. All respondents had more than five years music performance and music learning experience. In the attention task, all respondents had to identify pitch changes in tonal and randomized melodic pairs. The memory task was based on the mismatch negativity (MMN) proportion theory: 80 percent standard (not changed) and 20 percent deviant (changed) stimuli (sequences). Every sequence was named (na-na, ra-ra, za-za) and several items (pencil, spoon, tealight) were assigned for each sequence. Respondents had to recall the sequences, to associate them with the item and to detect possible changes. While executing the combined task, all respondents had to focus attention on the pitch changes and had to detect and describe these during the recall. Results and conclusion: The results support specific features in CB and EB, and similarities between late blind (LB) and sighted individuals. While executing attention and memory tasks, it was possible to observe the tendency in CB and EB by using more precise execution tactics and usage of more advanced periodic memory, while focusing on auditory and tactile stimuli. While executing memory and combined tasks, CB and EB individuals used passive working memory to recall standard sequences, active working memory to recall deviant sequences and combined strategies. Based on the observation results, assessment of blind respondents and recording specifics, following attention and memory correlations were identified: reflective attention and STM, reflective attention and periodic memory, auditory attention and WM, tactile attention and WM, auditory tactile attention and STM. The results and the summary of findings highlight the attention and memory features used in the music learning process in the context of blindness, and the tendency of the several attention and memory types correlated based on the task, strategy and individual features.

Keywords: attention, blindness, memory, music learning, strategy

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Authors: Aaditya U. Jhamb

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Due to the current energy crisis that many countries are battling, energy-efficient buildings are the subject of extensive research in the modern technological era because of growing worries about energy consumption and its effects on the environment. The paper explores 8 factors that help determine energy efficiency for a building: (relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution), with Tsanas and Xifara providing a dataset. The data set employed 768 different residential building models to anticipate heating and cooling loads with a low mean squared error. By optimizing these characteristics, machine learning algorithms may assess and properly forecast a building's heating and cooling loads, lowering energy usage while increasing the quality of people's lives. As a result, the paper studied the magnitude of the correlation between these input factors and the two output variables using various statistical methods of analysis after determining which input variable was most closely associated with the output loads. The most conclusive model was the Decision Tree Regressor, which had a mean squared error of 0.258, whilst the least definitive model was the Isotonic Regressor, which had a mean squared error of 21.68. This paper also investigated the KNN Regressor and the Linear Regression, which had to mean squared errors of 3.349 and 18.141, respectively. In conclusion, the model, given the 8 input variables, was able to predict the heating and cooling loads of a residential building accurately and precisely.

Keywords: energy efficient buildings, heating load, cooling load, machine learning models

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Authors: Smail Hadj Mahammed

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

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

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Authors: Mohamed Elmogbail

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The present study aimed at investigating difficulties those Translators encounter in understanding idiomatic expressions between Arabic and English languages. To achieve this goal, the researcher raised the three questions are:(1) What are the major difficulties that translators encounter in translating idiomatic expressions? (2) What factors cause such difficulties that translators encountered in translating idiomatic expressions? (3) What are the possible techniques that should be followed to overcome these difficulties? To answer these questions, the researcher designed questionnaire Table (2) and mentioned tables related to Test Show the second question in the study is about the factors that stand behind the challenges. Translators encounter while translating idiomatic expressions. The translators asked Provided the following factors:1- Because of lack of exposure to the source culture, they do not know the connotations of the cultural words that are related to the environment, food, folklore 2- Misusing dictionaries made the participants unable to find a proper target language idiomatic expression. 3-Lack of using idiomatic expressions in daily life. Table (3): (Questionnaire) Results to the table (3) Questions Of the study are About suggestions that can be inferred to handle these challenges. The questioned translators provided the following solutions:1- translators must be exposed to source language culture, including religion, habits, and traditions.2- translators should also be exposed to source language idiomatic expressions by introducing English culture in textbooks and through participating in extensive English culture courses.3- translators should be familiar with the differences between source and target language cultures.4- translators should avoid literal translation that results in most cases in wrong or poor translation.5- Schools, universities, and institutions should introduce translators to English culture.6- translators should participate in cultural workshops at universities.7- translators should try to use idiomatic expressions in everyday situations.8- translators should read more idiomatic expressions books. And researcher also designed a translation test consisted of 40 excerpts given to a random sample of 100 Translators in Khartoum capital of Sudan to translate them. After Collected data for the study, the researcher proceeded to a more detailed analysis, the methodology used in the analysis of idiomatic expressions Is empirical and descriptive. This study is qualitative by nature, but the quantitative method used the analysis of the data. Some figure and statistics are used, such as (statistical package for the social sciences). The researcher calculated the percentage proportion of each translation expressions. And compared them to each other. The finding of the study showed that most translations are inadequate as the translators faced difficulties while communication, these difficulties were mostly due to their unfamiliarity with idiomatic expressions producing improper equivalence in the communication, and not being able to use translation techniques as required, and resorted to literal translation, furthermore, the study recommended that more comprehensive studies to executed on translating idiomatic expressions to enrich the translation field.

Keywords: translation, translators, idioms., expressions

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Authors: Ahmed I. Alutabi, Naghmeh Dezhabad, Sudhakar Ganti

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Data centers have been growing in size and dema nd continuously in the last two decades. Planning for the deployment of resources has been shallow and always resorted to over-provisioning. Data center operators try to maximize the availability of their services by allocating multiple of the needed resources. One resource that has been wasted, with little thought, has been energy. In recent years, programmable resource allocation has paved the way to allow for more efficient and robust data centers. In this work, we examine the predictability of resource usage in a data center environment. We use a number of models that cover a wide spectrum of machine learning categories. Then we establish a framework to guarantee the client service level agreement (SLA). Our results show that using prediction can cut energy loss by up to 55%.

Keywords: machine learning, artificial intelligence, prediction, data center, resource allocation, green computing

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Authors: Todd Zhou, Mikhail Yurochkin

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Out-of-distribution (OOD) detection is receiving increasing amounts of attention in the machine learning research community, boosted by recent technologies, such as autonomous driving and image processing. This newly-burgeoning field has called for the need for more effective and efficient methods for out-of-distribution generalization methods. Without accessing the label information, deploying machine learning models to out-of-distribution domains becomes extremely challenging since it is impossible to evaluate model performance on unseen domains. To tackle this out-of-distribution detection difficulty, we designed a model selection pipeline algorithm and developed a model selection framework with different impurity and randomness measurements to evaluate and choose the best-performing models for out-of-distribution data. By exploring different randomness scores based on predicted probabilities, we adopted the out-of-distribution entropy and developed a custom-designed score, ”CombinedScore,” as the evaluation criterion. This proposed score was created by adding labeled source information into the judging space of the uncertainty entropy score using harmonic mean. Furthermore, the prediction bias was explored through the equality of opportunity violation measurement. We also improved machine learning model performance through model calibration. The effectiveness of the framework with the proposed evaluation criteria was validated on the Folktables American Community Survey (ACS) datasets.

Keywords: model selection, domain generalization, model fairness, randomness measurements, bias index

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Authors: Sadeq Al Yaari, Muhammad Alkhunayn, Ayman Al Yaari, Montaha Al Yaari, Aayah Al Yaari, Adham Al Yaari, Sajedah Al Yaari, Fatehi Eissa

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Stuttering is a multidimensional disorder that is influenced by different factors. As a result of their un-understanding of the genuine reasons behind stuttering, psychiatrists and Speech and Language Pathologists/Therapists (SLP/Ts) are often unfamiliar with the psychoneurolinguistic characteristics, support needs, and the disability measurement impacting requested rehabilitation of the stuttering population. PubMed, PsycInfo, Web of Science, Scopus, and Google scholar searches, in addition to some unpublished literature, were conducted in this Systematic Literature Review and Meta-analysis (SLR and Meta-analysis) to identify whether stuttering is caused by psychological or neurological reasons. The study concluded that psychological, not neurolinguistic factors were identified as most significant for the causality of childhood stuttering. Stutterers have intact language skills, but impaired ability more to communicate with others than to form letters in the brain or to articulate them. The study recommends research in the future that sheds light on the adult stuttering population often left out of the focus of diagnosis and in need of further exploration vis-a-vis issues they encounter, as well as the possible ways to deal with them psychoneurolinguistically.

Keywords: causality, childhood stuttering, psychology, neurolinguistics, systematic literature review, meta-analysis

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Authors: Seyedeh Khadijeh. Mirbazel, Masoumeh Arjmandi

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Semantics is a linguistic approach and a scientific stream, and like all scientific streams, it is dynamic. The study of meaning is carried out in the broad semantic collections of words that form the discourse. In other words, meaning is not something that can be found in a word; rather, the formation of meaning is a process that takes place in a discourse as a whole. One of the contemporary semantic theories is Izutsu's Semantic Field Theory. According to this theory, the discovery of meaning depends on the function of words and takes place within the context of language. The purpose of this research is to identify the meaning of the word "Nas" in the discourse of verse 24 of Surah Al-Baqarah, which introduces "Nas" as the firewood of hell, but the translators have translated it as "people". The present research has investigated the semantic structure of the word "Nas" using the aforementioned theory through the descriptive-analytical method. In the process of investigation, by matching the semantic fields of the Quranic word "Nas", this research came to the conclusion that "Nas" implies those persons who have forgotten God and His covenant in believing in His Oneness. For this reason, God called them "Nas (the forgetful)" - the imperfect participle of the noun /næsiwoɔn/ in single trinity of Arabic language, which means “to forget”. Therefore, the intended meaning of "Nas" in the verses that have the word "Nas" is not equivalent to "People" which is a general noun.

Keywords: Nas, people, semantics, semantic field theory.

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Authors: Sandhya Baskaran, Hari Kumar Nagabushanam

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Sarcasm sentiment recognition is an area of natural language processing that is being probed into in the recent times. Even with the advancements in NLP, typical translations of words, sentences in its context fail to provide the exact information on a sentiment or emotion of a user. For example, if something bad happens, the statement ‘That's just what I need, great! Terrific!’ is expressed in a sarcastic tone which could be misread as a positive sign by any text-based analyzer. In this paper, we are presenting a unique real time ‘word with its tone’ spotting technique which would provide the sentiment analysis for a tone or pitch of a voice in combination with the words being expressed. This hybrid approach increases the probability for identification of special sentiment like sarcasm much closer to the real world than by mining text or speech individually. The system uses a tone analyzer such as YIN-FFT which extracts pitch segment-wise that would be used in parallel with a speech recognition system. The clustered data is classified for sentiments and sarcasm score for each of it determined. Our Simulations demonstrates the improvement in f-measure of around 12% compared to existing detection techniques with increased precision and recall.

Keywords: sarcasm recognition, tone-word spotting, natural language processing, pitch analyzer

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Authors: Jami Wang, Brian Kao, Davin Agustines

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COVID-19 highlighted the active discrimination against the Asian American population easily seen through media, social tension, and increased crimes against the specific population. It is well known that long-term racism can also have a large impact on both emotional and psychological well-being. However, the healthcare disparity during this time also revealed how the Asian American community lacked the research data, political support, and medical infrastructure for this particular population. During a time when Asian American fear for safety with decreasing mental health, telepsychiatry is particularly promising. COVID-19 demonstrated how well psychiatry could integrate with telemedicine, with psychiatry being the second most utilized telemedicine visits. However, the Asian American community did not utilize the telepsychiatry resources as much as other groups. Because of this, we wanted to understand why the patient population who was affected the most by COVID-19 mentally did not seek out care. To do this, we decided to study the top top telepsychiatry platforms. The current top telepsychiatry companies in the United States include Teladoc and BetterHelp. In the Teladoc mental health sector, they only had 4 available languages (English, Spanish, French, and Danis,) with none of them being an Asian language. In a similar manner, Teladoc’s top competitor in the telepsychiatry space, BetterHelp, only listed a total of 3 Asian languages, including Mandarin, Japanese, and Malaysian. However, this is still a short list considering they have over 20 languages available. The shortage of available physicians that speak multiple languages is concerning, as it could be difficult for the Asian American community to relate with. There are limited mental health resources that cater to their likely cultural needs, further exacerbating the structural racism and institutional barriers to appropriate care. It is important to note that these companies do provide interpreters to comply with the nondiscrimination and language assistance federal law. However, interactions with an interpreter are not only more time-consuming but also less personal than talking directly with a physician. Psychiatry is the field that emphasizes interpersonal relationships. The trust between a physician and the patient is critical in developing patient rapport to guide in better understanding the clinical picture and treating the patient appropriately. The language barrier creates an additional barrier between the physician and patient. Because Asian Americans are one of the largest growing patient population bases, these telehealth companies have much to gain by catering to the Asian American market. Without providing adequate access to bilingual and bicultural physicians, the current system will only further exacerbate the growing disparity. The healthcare community and telehealth companies need to recognize that the Asian American population is a severely underserved population in mental health and has much to gain from telepsychiatry. The lack of language is one of many reasons why there is a disparity for Asian Americans in the mental health space.

Keywords: telemedicine, psychiatry, Asian American, disparity

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Authors: Vesile Evrim, Aliyu Awwal

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Today as in the other domains, there are an enormous number of political transcripts available in the Web which is waiting to be mined and used for various purposes such as statistics and recommendations. Therefore, automatically determining the political orientation on these transcripts becomes crucial. The methodologies used by machine learning algorithms to do the automatic classification are based on different features such as Linguistic. Considering the ideology differences between Liberals and Conservatives, in this paper, the effect of Personality Traits on political orientation classification is studied. This is done by considering the correlation between LIWC features and the BIG Five Personality Traits. Several experiments are conducted on Convote U.S. Congressional-Speech dataset with seven benchmark classification algorithms. The different methodologies are applied on selecting different feature sets that constituted by 8 to 64 varying number of features. While Neuroticism is obtained to be the most differentiating personality trait on classification of political polarity, when its top 10 representative features are combined with several classification algorithms, it outperformed the results presented in previous research.

Keywords: politics, personality traits, LIWC, machine learning

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Authors: Ekaterina Lukianchenko

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The present article addresses the nuances of teaching English vocabulary to Russian-speaking students. The experiment involving 39 participants aged 17 to 21 proves that the key to understanding spoken coinages is not only the knowledge of their constituents, but rather the understanding of the context and co-text. The volunteers who took part knew the constituents, but did not know the meaning of the words. The assumption of the authors consists in the fact that the structure of the concept has a direct relation with the form of the particular vocabulary unit, but its form is secondary to its meaning, if the word is a spoken coinage, which is partly proved by the fact that in modern slang words have multiple meanings, as well as one notion can have various embodiments that have virtually nothing in common. The choice of vocabulary items that youngsters use is not exactly arbitrary, but, even if complex nominals are taken into consideration, whose meaning seems clear, as it looks like a sum of their constituents’ meanings, they are still impossible to understand without any context or co-text, as a lot of them are idiomatic, non-transparent. It is further explained what methods might be effective in teaching students how to deal with new words they encounter in real-life situations and how student’s knowledge of vocabulary might be enhanced.

Keywords: spoken language, cognitive linguistics, complex nominals, nominals with the incorporated object, concept, EFL, communicative language teaching

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Authors: Camille Charlebois-Plante, Marie-Ève Bourassa, Gaelle Dumel, Meriem Sabir, Louis De Beaumont

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To the extent of our knowledge, there has been little interest in the chronic effects of mild traumatic brain injury (mTBI) on cognition during normal aging. This is rather surprising considering the impacts on daily and social functioning. In addition, sustaining a mTBI during late adulthood may increase the effect of normal biological aging in individuals who consider themselves normal and healthy. The objective of this study was to characterize the persistent neuropsychological repercussions of mTBI sustained during late adulthood, on average 12 months prior to testing. To this end, 35 mTBI patients and 42 controls between the ages of 50 and 69 completed an exhaustive neuropsychological assessment lasting three hours. All mTBI patients were asymptomatic and all participants had a score ≥ 27 at the MoCA. The evaluation consisted of 20 standardized neuropsychological tests measuring memory, attention, executive and language functions, as well as information processing speed. Performance on tests of visual (Brief Visuospatial Memory Test Revised) and verbal memory (Rey Auditory Verbal Learning Test and WMS-IV Logical Memory subtest), lexical access (Boston Naming Test) and response inhibition (Stroop) revealed to be significantly lower in the mTBI group. These findings suggest that a mTBI sustained during late adulthood induces lasting effects on cognitive function. Episodic memory and executive functions seem to be particularly vulnerable to enduring mTBI effects.

Keywords: cognitive function, late adulthood, mild traumatic brain injury, neuropsychology

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Authors: Dasom Kim, William Xiu Shun Wong, Yoonjin Hyun, Donghoon Lee, Minji Paek, Sungho Byun, Namgyu Kim

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There have been many researches on document classification for classifying voluminous documents automatically. Through document classification, we can assign a specific category to each unlabeled document on the basis of various machine learning algorithms. However, providing labeled documents manually requires considerable time and effort. To overcome the limitations, the semi-supervised learning which uses unlabeled document as well as labeled documents has been invented. However, traditional document classifiers, regardless of supervised or semi-supervised ones, cannot sufficiently explain the reason or the process of the classification. Thus, in this paper, we proposed a methodology to visualize major topics and class components of each document. We believe that our methodology for visualizing topics and classes of each document can enhance the reliability and explanatory power of document classifiers.

Keywords: data mining, document classifier, text mining, topic modeling

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Authors: Mary Titilayo Olowe

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The controversies that trailed the political campaign and eventual choice of Donald Trump as the American president is so great that expectations are high as to what the content of his inaugural speech will portray. Given the fact that language is a dynamic vehicle of expressing intentions, the speech needs to be objectively assessed so as to access its content in the manner intended through the three strands of meaning postulated by the Systemic Functional Grammar (SFG): the ideational, the interpersonal and the textual. The focus of this paper, however, is on the interpersonal meaning which deals with how language exhibits social roles and relationship. This paper, therefore, attempts to analyse President Donald Trump’s inaugural speech to elicit interpersonal meaning in it. The analysis is done from the perspective of mood and modality which are housed in SFG. Results of the mood choice which is basically declarative, reveal an information-centered speech while the high option for the modal verb operator ‘will’ shows president Donald Trump’s ability to establish an equal and reliant relationship with his audience, i.e., the Americans. In conclusion, the appeal of the speech to different levels of Interpersonal meaning is largely responsible for its overall effectiveness. One can, therefore, understand the reason for the massive reaction it generates at the center of global discourse.

Keywords: interpersonal, modality, mood, systemic functional grammar

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Authors: Chirine Dannaoui, Maya Antoun

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This paper explores the intricate dynamics of play-based early childhood education (ECE) and the impact of professional development on teachers implementing play-based pedagogy, particularly in the context of vulnerable Syrian refugee children in Lebanon. By utilizing qualitative methodologies, including classroom observations and in-depth interviews with five early childhood educators and a field manager, this study delves into the challenges and transformations experienced by teachers in adopting play-based learning strategies. The research unveils the critical role of continuous and context-specific professional development in empowering teachers to implement play-based pedagogies effectively. When appropriately supported, it emphasizes how such educational approaches significantly enhance children's cognitive, social, and emotional development in crisis-affected environments. Key findings indicate that despite diverse educational backgrounds, teachers show considerable growth in their pedagogical skills through targeted professional development. This growth is vital for fostering a learning environment where vulnerable children can thrive, particularly in humanitarian settings. The paper also addresses educators' challenges, including adapting to play-based methodologies, resource limitations, and balancing curricular requirements with the need for holistic child development. This study contributes to the discourse on early childhood education in crisis contexts, emphasizing the need for sustainable, well-structured professional development programs. It underscores the potential of play-based learning to bridge educational gaps and contribute to the healing process of children facing calamity. The study highlights significant implications for policymakers, educators, schools, and not-for-profit organizations engaged in early childhood education in humanitarian contexts, stressing the importance of investing in teacher capacity and curriculum reform to enhance the quality of education for children in general and vulnerable ones in particular.

Keywords: play-based learning, professional development, vulnerable children, early childhood education

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Authors: Kwangmin Joo

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Automatic detection of lumbar vertebrae and classification method is proposed for evaluating the degree of disc narrowing. Prior to classification, deep learning based segmentation is applied to detect individual lumbar vertebra. M-net is applied to segment five lumbar vertebrae and fine-tuning segmentation is employed to improve the accuracy of segmentation. Using the features extracted from previous step, clustering technique, k-means clustering, is applied to estimate the degree of disc space narrowing under four grade scoring system. As preliminary study, techniques proposed in this research could help building an automatic scoring system to diagnose the severity of disc narrowing from X-ray images.

Keywords: Disc space narrowing, Degenerative disc disorders, Deep learning based segmentation, Clustering technique

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Authors: Ghazala Hcini, Imen Jdey, Hela Ltifi

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Malaria disease should be considered and handled as a potential restorative catastrophe. One of the most challenging tasks in the field of microscopy image processing is due to differences in test design and vulnerability of cell classifications. In this article, we focused on applying deep learning to classify patients by identifying images of infected and uninfected cells. We performed multiple forms, counting a classification approach using the Hue, Saturation, Value (HSV) color space. HSV is used since of its superior ability to speak to image brightness; at long last, for classification, a convolutional neural network (CNN) architecture is created. Clusters of focus were used to deliver the classification. The highlights got to be forbidden, and a few more clamor sorts are included in the information. The suggested method has a precision of 99.79%, a recall value of 99.55%, and provides 99.96% accuracy.

Keywords: deep learning, convolutional neural network, image classification, color transformation, HSV color, malaria diagnosis, malaria cells images

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Authors: Ayyaz Hussain, Tariq Sadad

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Breast cancer (BC) is one of the widespread ailments among females globally. The early prognosis of BC can decrease the mortality rate. Exact findings of benign tumors can avoid unnecessary biopsies and further treatments of patients under investigation. However, due to variations in images, it is a tough job to isolate cancerous cases from normal and benign ones. The machine learning technique is widely employed in the classification of BC pattern and prognosis. In this research, a deep convolution neural network (DCNN) called AlexNet architecture is employed to get more discriminative features from breast tissues. To achieve higher accuracy, K-nearest neighbor (KNN) classifiers are employed as a substitute for the softmax layer in deep learning. The proposed model is tested on a widely used breast image database called MIAS dataset for experimental purposes and achieved 99% accuracy.

Keywords: breast cancer, DCNN, KNN, mammography

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Authors: Fiona Jensen, Barb Goodwin, Nancy Kleiman, Rhonda Usunier

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Background: Clinical simulation is now a common component in many health profession curricula in preparation for clinical practice. In the Rady Faculty of Health Sciences (RFHS) college leads in simulation and interprofessional (IP) education, planned an eight hour simulated hospital day shift, where seventy students from six health professions across two campuses, learned with each other in a safe, realistic environment. Learning about interprofessional collaboration, an expected competency for many health professions upon graduation, was a primary focus of the simulation event. Method: Faculty representatives from the Colleges of Nursing, Medicine, Pharmacy and Rehabilitation Sciences (Physical Therapy, Occupation Therapy, Respiratory Therapy) and Pharmacy worked together to plan the IP event in a simulation facility in the College of Nursing. Each college provided a faculty mentor to guide the same profession students. Students were placed in interprofessional teams consisting of a nurse, physician, pharmacist, and then sharing respiratory, occupational, and physical therapists across the team depending on the needs of the patients. Eight patient scenarios were role played by health profession students, who had been provided with their patient’s story shortly before the event. Each team was guided by a facilitator. Results and Outcomes: On the morning of the event, all students gathered in a large group to meet mentors and facilitators and have a brief overview of the six competencies for effective collaboration and the session objectives. The students assuming their same profession roles were provided with their patient’s chart at the beginning of the shift, met with their team, and then completed professional specific assessments. Shortly into the shift, IP team rounds began, facilitated by the team facilitator. During the shift, each patient role-played a spontaneous health incident, which required collaboration between the IP team members for assessment and management. The afternoon concluded with team rounds, a collaborative management plan, and a facilitated de-brief. Conclusions: During the de-brief sessions, students responded to set questions related to the session learning objectives and expressed many positive learning moments. We believe that we have a sustainable simulation IP collaborative learning opportunity, which can be embedded into curricula, and has the capacity to grow to include more health profession faculties and students. Opportunities are being explored in the RFHS at the administrative level, to offer this event more frequently in the academic year to reach more students. In addition, a formally structured event evaluation tool would provide important feedback and inform the qualitative feedback to event organizers and the colleges about the significance of the simulation event to student learning.

Keywords: simulation, collaboration, teams, interprofessional

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Authors: Nina Dzamashvili Fogelström

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This paper examines concepts of Game-Based Learning and Gamification. Conducted literature survey found an increased interest in the academia in these concepts, limited evidence of a positive effect on student motivation and academic performance, but also certain scepticism for adding games to traditional educational activities. A small-scale empirical study presented in this paper aims to evaluate student experience and usefulness of GameBased Learning and Gamification for a better understanding of the threshold concepts in software engineering project courses. The participants of the study were 22 second year students from bachelor’s program in software engineering at Blekinge Institute of Technology. As a part of the course instruction, the students were introduced to a digital game specifically designed to simulate agile software project. The game mechanics were designed as to allow manipulation of the agile concept of team velocity. After the application of the game, the students were surveyed to measure the degree of a perceived increase in understanding of the studied threshold concept. The students were also asked whether they would like to have games included in their education. The results show that majority of the students found the game helpful in increasing their understanding of the threshold concept. Most of the students have indicated that they would like to see games included in their education. These results are encouraging. Since the study was of small scale and based on convenience sampling, more studies in the area are recommended.

Keywords: agile development, gamification, game based learning, digital games, software engineering, threshold concepts

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Authors: J. Fox, J. Randhawa, M. Chan, L. Campbell, A. Weakely, D. J. Harvey, S. Tomaszewski Farias

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Early identification of individuals at risk for conversion to dementia provides an opportunity for preventative treatment. Many older adults (30-60%) report specific subjective cognitive decline (SCD); however, previous research is inconsistent in terms of what types of complaints predict future cognitive decline. The purpose of this study is to identify which specific complaints from the Everyday Cognition Scales (ECog) scales, a measure of self-reported concerns for everyday abilities across six cognitive domains, are associated with: 1) conversion from a clinical diagnosis of normal to either MCI or dementia (categorical variable) and 2) progressive cognitive decline in memory and executive function (continuous variables). 415 cognitively normal older adults were monitored annually for an average of 5 years. Cox proportional hazards models were used to assess associations between self-reported ECog items and progression to impairment (MCI or dementia). A total of 114 individuals progressed to impairment; the mean time to progression was 4.9 years (SD=3.4 years, range=0.8-13.8). Follow-up models were run controlling for depression. A subset of individuals (n=352) underwent repeat cognitive assessments for an average of 5.3 years. For those individuals, mixed effects models with random intercepts and slopes were used to assess associations between ECog items and change in neuropsychological measures of episodic memory or executive function. Prior to controlling for depression, subjective concerns on five of the eight Everyday Memory items, three of the nine Everyday Language items, one of the seven Everyday Visuospatial items, two of the five Everyday Planning items, and one of the six Everyday Organization items were associated with subsequent diagnostic conversion (HR=1.25 to 1.59, p=0.003 to 0.03). However, after controlling for depression, only two specific complaints of remembering appointments, meetings, and engagements and understanding spoken directions and instructions were associated with subsequent diagnostic conversion. Episodic memory in individuals reporting no concern on ECog items did not significantly change over time (p>0.4). More complaints on seven of the eight Everyday Memory items, three of the nine Everyday Language items, and three of the seven Everyday Visuospatial items were associated with a decline in episodic memory (Interaction estimate=-0.055 to 0.001, p=0.003 to 0.04). Executive function in those reporting no concern on ECog items declined slightly (p <0.001 to 0.06). More complaints on three of the eight Everyday Memory items and three of the nine Everyday Language items were associated with a decline in executive function (Interaction estimate=-0.021 to -0.012, p=0.002 to 0.04). These findings suggest that specific complaints across several cognitive domains are associated with diagnostic conversion. Specific complaints in the domains of Everyday Memory and Language are associated with a decline in both episodic memory and executive function. Increased monitoring and treatment of individuals with these specific SCD may be warranted.

Keywords: alzheimer’s disease, dementia, memory complaints, mild cognitive impairment, risk factors, subjective cognitive decline

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Authors: Penelope Paliadelis, Asheley Jones, Glenn Campbell

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This paper discusses the development of co-designed capability frameworks for two large multinational organizations led by a university department. The aim was to create evidence-based, integrated capability frameworks that could define, identify, and measure human skill capabilities independent of specific work roles. The frameworks capture and cluster human skills required in the workplace and capture their application at various levels of mastery. Identified capability gaps inform targeted learning opportunities for workers to enhance their employability skills. The paper highlights the value of this evidence-based framework development process in capturing, defining, and assessing desired human-focused capabilities for organizational growth and success.

Keywords: capability framework, human skills, work-integrated learning, credentialing, digital badging

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Authors: Shivani Singh, Shantanu Nakhare, Kalyani Nair, Rohan Shetty

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As social networking is growing at a rapid pace today it is vital that we work on improving its management. Research has shown that the content present in online social networks may have significant influence on impressionable minds. If such platforms are misused, it will lead to negative consequences. Detecting insults or inappropriate messages continues to be one of the most challenging aspects of Online Social Networks (OSNs) today. We address this problem through a Machine Learning Based Soft Text Classifier approach using Support Vector Machine algorithm. The proposed system acts as a screening mechanism the alerts the user about such messages. The messages are classified according to their subject matter and each comment is labeled for the presence of profanity and insults.

Keywords: machine learning, online social networks, soft text classifier, support vector machine

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Authors: Brian Tung, Yam Wing Siu, Tsun Se Cheong

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Purpose: This research aims to explore how personal and environmental factors, especially the socio-economic changes and behavioral changes fostered by the COVID-19 outbreak pandemic, affect the financial vulnerability of a specific segment of people in financial distress. Innovative research methodology of machine learning will be applied to data collected from over 300 local individuals in Hong Kong seeking counseling or similar services in recent years. Results: First, machine learning has found that too much exposure to digital services and information on digitized services may lead to adverse effects on respondents’ financial vulnerability. Second, the improvement in financial literacy level provides benefits to the financially vulnerable group, especially those respondents who have started with a lower level. Third, serious addiction to digital technology can lead to worsened debt servicing ability. Machine learning also has found a strong correlation between debt servicing situations and income-seeking behavior as well as spending behavior. In addition, if the vulnerable groups are able to make appropriate investments, they can reduce the probability of incurring financial distress. Finally, being too active in borrowing and repayment can result in a higher likelihood of over-indebtedness. Conclusion: Findings can be employed in formulating a better counseling strategy for professionals. Debt counseling services can be more preventive in nature. For example, according to the findings, with a low level of financial literacy, the respondents are prone to overspending and unable to react properly to the e-marketing promotion messages pop-up from digital services or even falling into financial/investment scams. In addition, people with low levels of financial knowledge will benefit from financial education. Therefore, financial education programs could include tech-savvy matters as special features.

Keywords: personal finance, digitization of the economy, COVID-19 pandemic, addiction to digital technology, financial vulnerability

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Authors: Shyamani Hettiarachchi, Thilini Lokubalasuriya, Shakeela Saleem, Dinusha Nonis, Isuru Dharmaratne, Lakshika Udugama

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Introduction: Late identification of language difficulties in children could result in long-term negative consequences for communication, literacy and self-esteem. This highlights the need for early identification and intervention for speech, language and communication difficulties. Speech and language therapy is a relatively new profession in Sri Lanka and at present, there are no formal standardized screening tools to assess language skills in Sinhala-speaking children. The development and validation of a short, accurate screening tool to enable the identification of children with syntactic difficulties in Sinhala is a current need. Aims: 1) To develop test items for a Sinhala Syntactic Structures (S3 Short Form) test on children aged between 3;0 to 5;0 years 2) To validate the test of Sinhala Syntactic Structures (S3 Short Form) on children aged between 3; 0 to 5; 0 years Methods: The Sinhala Syntactic Structures (S3 Short Form) was devised based on the Renfrew Action Picture Test. As Sinhala contains post-positions in contrast to English, the principles of the Renfrew Action Picture Test were followed to gain an information score and a grammar score but the test devised reflected the linguistic-specificity and complexity of Sinhala and the pictures were in keeping with the culture of the country. This included the dative case marker ‘to give something to her’ (/ejɑ:ʈə/ meaning ‘to her’), the instrumental case marker ‘to get something from’ (/ejɑ:gən/ meaning ‘from him’ or /gɑhən/ meaning ‘from the tree’), possessive noun (/ɑmmɑge:/ meaning ‘mother’s’ or /gɑhe:/ meaning ‘of the tree’ or /male:/ meaning ‘of the flower’) and plural markers (/bɑllɑ:/ bɑllo:/ meaning ‘dog/dogs’, /mɑlə/mɑl/ meaning ‘flower/flowers’, /gɑsə/gɑs/ meaning ‘tree/trees’ and /wɑlɑ:kulə/wɑlɑ:kulu/ meaning ‘cloud/clouds’). The picture targets included socio-culturally appropriate scenes of the Sri Lankan New Year celebration, elephant procession and the Buddhist ‘Wesak’ ceremony. The test was piloted with a group of 60 participants and necessary changes made. In phase 1, the test was administered to 100 Sinhala-speaking children aged between 3; 0 and 5; 0 years in one district. In this presentation on phase 2, the test was administered to another 100 Sinhala-speaking children aged between 3; 0 to 5; 0 in three districts. In phase 2, the selection of the test items was assessed via measures of content validity, test-retest reliability and inter-rater reliability. The age of acquisition of each syntactic structure was determined using content and grammar scores which were statistically analysed using t-tests and one-way ANOVAs. Results: High percentage agreement was found on test-retest reliability on content validity and Pearson correlation measures and on inter-rater reliability. As predicted, there was a statistically significant influence of age on the production of syntactic structures at p<0.05. Conclusions: As the target test items included generated the information and the syntactic structures expected, the test could be used as a quick syntactic screening tool with preschool children.

Keywords: Sinhala, screening, syntax, language

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Authors: Ujjal Chattaraj, P. C. Dasbebartta, S. Bhuyan

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This paper aims to propose a method to detect the action of the driver’s eyes, using the concept of face detection. There are three major key contributing methods which can rapidly process the framework of the facial image and hence produce results which further can program the reactions of the vehicles as pre-programmed for the traffic safety. This paper compares and analyses the methods on the basis of their reaction time and their ability to deal with fluctuating images of the driver. The program used in this study is simple and efficient, built using the AdaBoost learning algorithm. Through this program, the system would be able to discard background regions and focus on the face-like regions. The results are analyzed on a common computer which makes it feasible for the end users. The application domain of this experiment is quite wide, such as detection of drowsiness or influence of alcohols in drivers or detection for the case of identification.

Keywords: AdaBoost learning algorithm, face detection, framework, traffic safety

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Authors: Kai Chen, Shuguang Cui, Feng Yin

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Gaussian process (GP) with spectral mixture (SM) kernel demonstrates flexible non-parametric Bayesian learning ability in modeling unknown function. In this work a novel time-varying and non-stationary convolution spectral mixture (TN-CSM) kernel with a significant enhancing of interpretability by using process convolution is introduced. A way decomposing the SM component into an auto-convolution of base SM component and parameterizing it to be input dependent is outlined. Smoothly, performing a convolution between two base SM component yields a novel structure of non-stationary SM component with much better generalized expression and interpretation. The TN-CSM perfectly allows compatibility with the stationary SM kernel in terms of kernel form and spectral base ignored and confused by previous non-stationary kernels. On synthetic and real-world datatsets, experiments show the time-varying characteristics of hyper-parameters in TN-CSM and compare the learning performance of TN-CSM with popular and representative non-stationary GP.

Keywords: Gaussian process, spectral mixture, non-stationary, convolution

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Authors: Amir Zand-Moghadam, Mahnaz Alizadeh

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The aim of the present study was to investigate the effect of teacher-scaffolding versus peer-scaffolding on EFL learners’ metapragmatic awareness in the paradigm of task-based language teaching (TBLT). To this end, a number of dialogic information-gap tasks requiring two-way interactant relationship were designed for the five speech acts of request, refusal, apology, suggestion, and compliment following Ellis’s (2003) model. Then, 48 intermediate EFL learners were randomly selected, homogenized, and assigned to two groups: 26 participants in the teacher-scaffolding group (Group One) and 22 in the peer-scaffolding group (Group Two). While going through the three phases of pre-task, while-task, and post-task, the participants in the first group completed the designed tasks by the teacher’s interaction, scaffolding, and feedback. On the other hand, the participants in the second group were required to complete the tasks in expert-novice pairs through peer scaffolding in all the three phases of a task-based syllabus. The findings revealed that the participants in the teacher-scaffolding group developed their L2 metapragmatic awareness more than the peer-scaffolding group. Thus, it can be concluded that teacher-scaffolding is more effective than peer scaffolding in developing metapragmatic awareness among EFL learners. It can also be claimed that the use of tasks can be more influential when they are accompanied by teacher-scaffolding. The findings of the present study have implications for language teachers and researchers.

Keywords: ILP, metapragmatic awareness, scaffolding, task-based instruction

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Authors: Onur Kaya

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Languages date back thousands of years of formation and journeys through the world. In these journeys and formations, they travel, reach and mixes to the very far corners and languages of the world. In this perspective, in order to analyze such language examples, the analysis of the formation, affection, travel, thus immigration of Anatolian Turkish and Inuit of Greenland is significant. Firstly, it is significant to analyze the historical connections between the Turks in Anatolia and the Inuit people in Greenland. So, the intersection of Turks and Inuit's immigrations in history and all these connections to Greenland and Anatolia will be revealed. Then, it is necessary to analyze the linguistic qualities of Turkish and Inuit languages. For this aim, the linguistic theories and linguistic features of the two languages and their common points will be emphasized. After all these explanations and analyses, the effects of the two languages two each other, common words, and the existence of all these in written and literary works of the two languages will be analyzed and exemplified. Finally, the lecture will focus on two different geographies as, Anatolia and Greenland. The two societies’ historical commonness will be revealed. The immigration and the intersecting locations of the two societies will be analyzed. By means of all this information and within the light of the linguistic theories, the commonness of the two languages, the affections caused by each other, the result of these affections, and their examples in written works will be revealed.

Keywords: greenland, anatolia, turk, inuit, immigration

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