Search results for: students with learning disabilities

Authors: Bilel Chalghaf, Mathieu Varin

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Forest characterization in Quebec, Canada, is usually assessed based on photo-interpretation at the stand level. For species identification, this often results in a lack of precision. Very high spatial resolution imagery, such as DigitalGlobe, and Light Detection and Ranging (LiDAR), have the potential to overcome the limitations of aerial imagery. To date, few studies have used that data to map a large number of species at the tree level using machine learning techniques. The main objective of this study is to map 11 individual high tree species ( > 17m) at the tree level using an object-based approach in the broadleaf forest of Kenauk Nature, Quebec. For the individual tree crown segmentation, three canopy-height models (CHMs) from LiDAR data were assessed: 1) the original, 2) a filtered, and 3) a corrected model. The corrected CHM gave the best accuracy and was then coupled with imagery to refine tree species crown identification. When compared with photo-interpretation, 90% of the objects represented a single species. For modeling, 313 variables were derived from 16-band WorldView-3 imagery and LiDAR data, using radiance, reflectance, pixel, and object-based calculation techniques. Variable selection procedures were employed to reduce their number from 313 to 16, using only 11 bands to aid reproducibility. For classification, a global approach using all 11 species was compared to a semi-hierarchical hybrid classification approach at two levels: (1) tree type (broadleaf/conifer) and (2) individual broadleaf (five) and conifer (six) species. Five different model techniques were used: (1) support vector machine (SVM), (2) classification and regression tree (CART), (3) random forest (RF), (4) k-nearest neighbors (k-NN), and (5) linear discriminant analysis (LDA). Each model was tuned separately for all approaches and levels. For the global approach, the best model was the SVM using eight variables (overall accuracy (OA): 80%, Kappa: 0.77). With the semi-hierarchical hybrid approach, at the tree type level, the best model was the k-NN using six variables (OA: 100% and Kappa: 1.00). At the level of identifying broadleaf and conifer species, the best model was the SVM, with OA of 80% and 97% and Kappa values of 0.74 and 0.97, respectively, using seven variables for both models. This paper demonstrates that a hybrid classification approach gives better results and that using 16-band WorldView-3 with LiDAR data leads to more precise predictions for tree segmentation and classification, especially when the number of tree species is large.

Keywords: tree species, object-based, classification, multispectral, machine learning, WorldView-3, LiDAR

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Authors: Marcio Freire Cruz, Naoaki Ono, Shigehiko Kanaya, Carlos Arthur Mattos Teixeira Cavalcante

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The systems that record patient care information, known as Electronic Medical Record (EMR) and those that monitor vital signs of patients, such as heart rate, body temperature, and blood pressure have been extremely valuable for the effectiveness of the patient’s treatment. Several kinds of research have been using data from EMRs and vital signs of patients to predict illnesses. Among them, we highlight those that intend to predict, classify, or, at least identify patterns, of sepsis illness in patients under vital signs monitoring. Sepsis is an organic dysfunction caused by a dysregulated patient's response to an infection that affects millions of people worldwide. Early detection of sepsis is expected to provide a significant improvement in its treatment. Preceding works usually combined medical, statistical, mathematical and computational models to develop detection methods for early prediction, getting higher accuracies, and using the smallest number of variables. Among other techniques, we could find researches using survival analysis, specialist systems, machine learning and deep learning that reached great results. In our research, patients are modeled as points moving each hour in an n-dimensional space where n is the number of vital signs (variables). These points can reach a sepsis target point after some time. For now, the sepsis target point was calculated using the median of all patients’ variables on the sepsis onset. From these points, we calculate for each hour the position vector, the first derivative (velocity vector) and the second derivative (acceleration vector) of the variables to evaluate their behavior. And we construct a prediction model based on a Long Short-Term Memory (LSTM) Network, including these derivatives as explanatory variables. The accuracy of the prediction 6 hours before the time of sepsis, considering only the vital signs reached 83.24% and by including the vectors position, speed, and acceleration, we obtained 94.96%. The data are being collected from Medical Information Mart for Intensive Care (MIMIC) Database, a public database that contains vital signs, laboratory test results, observations, notes, and so on, from more than 60.000 patients.

Keywords: dynamic analysis, long short-term memory, prediction, sepsis

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Authors: Zahide Tepeli Temiz, Itir Tari Comert

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Attachment patterns based on early emotional interactions between infant and primary caregiver continue to be influential in adult life, in terms of mental health and behaviors of individuals. Several studies reveal that infant-caregiver relationships have impressed the affect regulation, coping with stressful and negative situations, general satisfaction of life, and self image in adulthood, besides the attachment styles. The present study aims to examine the relationships between university students’ attachment style and their self-esteem, alexithymic features, satisfaction of life, and level of resilience. In line with this aim, the hypothesis of the prediction of attachment styles (anxious and avoidant) over life satisfaction, self-esteem, alexithymia, and psychological resilience was tested. Additionally, in this study Structural Equational Modeling was conducted to investigate the mediational role of self-esteem in the relationship between attachment styles and alexithymia, life satisfaction, and resilience. This model was examined with path analysis. The sample of the research consists of 425 university students who take education from several region of Turkey. The participants who sign the informed consent completed the Demographic Information Form, Experiences in Close Relationships-Revised, Rosenberg Self-Esteem Scale, The Satisfaction with Life Scale, Toronto Alexithymia Scale, and Resilience Scale for Adults. According to results, anxious, and avoidant dimensions of insecure attachment predicted the self-esteem score and alexithymia in positive direction. On the other hand, these dimensions of attachment predicted life satisfaction in negative direction. The results of linear regression analysis indicated that anxious and avoidant attachment styles didn’t predict the resilience. This result doesn’t support the theory and research indicating the relationship between attachment style and psychological resilience. The results of path analysis revealed the mediational role self esteem in the relation between anxious, and avoidant attachment styles and life satisfaction. In addition, SEM analysis indicated the indirect effect of attachment styles over alexithymia and resilience besides their direct effect. These findings support the hypothesis of this research relation to mediating role of self-esteem. Attachment theorists suggest that early attachment experiences, including supportive and responsive family interactions, have an effect on resilience to harmful situations in adult life, ability to identify, describe, and regulate emotions and also general satisfaction with life. Several studies examining the relationship between attachment styles and life satisfaction, alexithymia, and psychological resilience draw attention to mediational role of self-esteem. Results of this study support the theory of attachment patterns with the mediation of self-image influence the emotional, cognitive, and behavioral regulation of person throughout the adulthood. Therefore, it is thought that any intervention intended for recovery in attachment relationship will increase the self-esteem, life satisfaction, and resilience level, on the one side, decrease the alexithymic features, on the other side.

Keywords: alexithymia, anxious attachment, avoidant attachment, life satisfaction, path analysis, resilience, self-esteem, structural equation

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Authors: Jeremy Ritchey

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Undocumented immigrants in the U.S. face many challenges when looking to progress in society, especially when pursuing post-secondary education. The majority of research done on state-level policy adoption pertaining to undocumented higher-education pursuits, specifically in-state resident tuition and financial aid eligibility policies, have framed the discussion on the potential and actual impacts which implementation can and has achieved. What is missing is a model to view the social, political and demographic landscapes upon which such policies (in their various forms) find a route to legislative enactment. This research looks to address this gap in the field by investigating the correlations and significant state-level variables which can be operationalized to construct a framework for adoption of these specific policies. In the process, analysis will show that past unexamined conceptualizations of how such policies come to fruition may be limited or contradictory when compared to available data. Circling on the principles of Policy Innovation and Policy Diffusion theory, this study looks to use variables collected via Michigan State University’s Correlates of State Policy Project, a collectively and ongoing compiled database project centered around annual variables (1900-2016) collected from all 50 states relevant to policy research. Using established variable groupings (demographic, political, social capital measurements, and educational system measurements) from the time period of 2000 to 2014 (2001 being when such policies began), one can see how this data correlates with the adoption of policies related to undocumented students and in-state college tuition. After regression analysis, the results will illuminate which variables appears significant and to what effect, as to help formulate a model upon which to explain when adoption appears to occur and when it does not. Early results have shown that traditionally held conceptions on conservative and liberal identities of the state, as they relate to the likelihood of such policies being adopted, did not fall in line with the collected data. Democratic and liberally identified states were, overall, less likely to adopt pro-undocumented higher education policies than Republican and conservatively identified states and vis versa. While further analysis is needed as to improve the model’s explanatory power, preliminary findings are showing promise in widening our understanding of policy adoption factors in this realm of policies compared to the gap of such knowledge in the publications of the field as it currently exists. The model also looks to serve as an important tool for policymakers in framing such potential policies in a way that is congruent with the relevant state-level determining factors while being sensitive to the most apparent sources of potential friction. While additional variable groups and individual variables will ultimately need to be added and controlled for, this research has already begun to demonstrate how shallow or unexamined reasoning behind policy adoption in the realm of this topic needs to be addressed or else the risk is erroneous conceptions leaking into the foundation of this growing and ever important field.

Keywords: policy adoption, in-state tuition, higher education, undocumented immigrants

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Authors: Thassanant Unnanantn, Suttipong Boonphadung

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This research study aimed to survey and analyze the attitudes of pre-service teachers’ the analytical thinking development based on Miller’s Model. The informants of this study were 22 third year teacher students majoring in Thai. The course where the instruction was conducted was English for Academic Purposes in Thai Language 2. The instrument of this research was an open-ended questionnaire with two dimensions of questions: academic and satisfaction dimensions. The investigation revealed the positive attitudes. In the academic dimension, the majority of 12 (54.54%), the highest percentage, reflected that the method of teaching analytical thinking and language simultaneously was their new knowledge and the similar percentage also belonged to text cohesion in writing. For the satisfaction, the highest frequency count was from 17 of them (77.27%) and this majority favored the openness or friendliness of the teacher.

Keywords: analytical thinking development, Miller’s Model, attitudes, pre-service teachers

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Authors: Zan Azma Nasruddin, Irwan Mazlin, Nor Aziah Daud, Fauziah Redzuan, Fariza Hanis Abdul Razak

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This paper focuses on a machine learning that learn the correct pronunciation of Makhraj Huroofs. Usually, people need to find an expert to pronounce the Huroof accurately. In this study, the researchers have developed a system that is able to learn the selected Huroofs which are ha, tsa, zho, and dza using the Convolutional Neural Network. The researchers present the chosen type of the CNN architecture to make the system that is able to learn the data (Huroofs) as quick as possible and produces high accuracy during the prediction. The researchers have experimented the system to measure the accuracy and the cross entropy in the training process.

Keywords: convolutional neural network, Makhraj recognition, speech recognition, signal processing, tensorflow

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Authors: Juanita Wallace, Mark Jackson, Bethany Jurs

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Introduction: Exergames, VR games used as a form of exercise, are being used to reduce sedentary lifestyles in a vast number of populations. However, there is a distinct lack of research comparing the physiological response during VR exergaming to that of traditional exercises. The purpose of this study was to create a foundationary investigation establishing changes in physiological responses resulting from VR exergaming in a college aged population. Methods: In this IRB approved study, college aged students were recruited to play a virtual reality exergame (Beat Saber) on the Oculus Quest 2 (Facebook, 2021) in either a control group (CG) or training group (TG). Both groups consisted of subjects who were not habitual users of virtual reality. The CG played VR one time per week for three weeks and the TG played 150 min/week three weeks. Each group played the same nine Beat Saber songs, in a randomized order, during 30 minute sessions. Song difficulty was increased during play based on song performance. Subjects completed a pre- and posttests at which the following was collected: • Beat Saber Game Metrics: song level played, song score, number of beats completed per song and accuracy (beats completed/total beats) • Physiological Data: heart rate (max and avg.), active calories • Demographics Results: A total of 20 subjects completed the study; nine in the CG (3 males, 6 females) and 11 (5 males, 6 females) in the TG. • Beat Saber Song Metrics: The TG improved performance from a normal/hard difficulty to hard/expert. The CG stayed at the normal/hard difficulty. At the pretest there was no difference in game accuracy between groups. However, at the posttest the CG had a higher accuracy. • Physiological Data (Table 1): Average heart rates were similar between the TG and CG at both the pre- and posttest. However, the TG expended more total calories. Discussion: Due to the lack of peer reviewed literature on c exergaming using Beat Saber, the results of this study cannot be directly compared. However, the results of this study can be compared with the previously established trends for traditional exercise. In traditional exercise, an increase in training volume equates to increased efficiency at the activity. The TG should naturally increase in difficulty at a faster rate than the CG because they played 150 hours per week. Heart rate and caloric responses also increase during traditional exercise as load increases (i.e. speed or resistance). The TG reported an increase in total calories due to a higher difficulty of play. The song accuracy decreases in the TG can be explained by the increased difficulty of play. Conclusion: VR exergaming is comparable to traditional exercise for loads within the 50-70% of maximum heart rate. The ability to use VR for health could motivate individuals who do not engage in traditional exercise. In addition, individuals in health professions can and should promote VR exergaming as a viable way to increase physical activity and improve health in their clients/patients.

Keywords: virtual reality, exergaming, health, heart rate, wellness

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Authors: Osifeko Olalekan Remigious, Osifeko Christiana Osikorede, Folarin Bolanle Eunice, Olugbenga Adebola Shodiya

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In this period of economic problem, nations are looking for avenues to improve their economy, preserve their environment and socio-political environment, educational institutions are not left out as there is the need for them to increase their economy and preserve their socio political environment. Sports is one of the ways through which sustainable development can be achieved. The purpose of this study was to examine the barriers to sports participation. A total of 1025 students were purposively selected from all the five schools in the College. A questionnaire which has a reliability coefficient of 0.71 was used for data collection. Data collected were subjected to the descriptive survey research design. Findings showed that facilities, funds and lectures schedule were significant barriers to sports participation. It was recommended that sports facilities should be provided by the Lagos State government.

Keywords: MOCPED sports, sustainable development, sports participation, state government

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Authors: Hend Al-Ma'seb, Anwar Alkhurinej

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In the field of social work, social workers understand that it is very difficult for individuals to ask for help from therapists. Therefore, it is important to study the variables associated with seeking professional help. A total of 478 undergraduate students from Kuwait University participated voluntarily in the study. The findings for this study showed that the participants of the study have a slightly high degree of public stigma, low self–stigma, and positive attitude toward seeking professional help. In addition, the findings of the study reveal that there are significant relationships between gender, taking social work classes, thinking about receiving counseling and having social problems and participants' attitude towards seeking professional help. Furthermore, the findings of the study showed that there were significant relationships between gender, and thinking about receiving counseling, and self-stigma. The findings of the current study have implications for the field of social work in Kuwait that would help to improve the knowledge in this area.

Keywords: attitude towards help, social work, social workers, stigma

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Authors: Wang, Kuei-Ing, Jou-Fan Shih

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This study adopts deterrence theory as well as social identities as moderators, and explores their moderating affects on online toxic disinhibition. Survey and Experimental methodologies are applied to test the research model and four hypotheses are developed in this study. The controllability of identity positively influenced the behavior of toxic disinhibition both in experimental and control groups while the fluidity of the identity did not have significant influences on online disinhibition. Punishment certainty, punishment severity as well as social identity negatively moderated the relation between the controllability of the identity and the toxic disinhibition. The result of this study shows that internet users hide their real identities when they behave inappropriately on internet, but once they acknowledge that the inappropriate behavior will be found and punished severely, the inappropriate behavior then will be weakened.

Keywords: seductive properties of internet, online disinhibition, punishment certainty, punishment severity, social identity

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Authors: M. Imran

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This research explores the intersections of applied linguistics, corpus linguistics, translation, and technology, aiming to present innovative cross-disciplinary tools and frameworks. It highlights significant contributions to language, corpora, and technology within applied linguistics, which deepen our understanding of these domains and provide practical resources for scholars, educators, and translators. By showcasing these advancements, the study seeks to enhance collaboration and application in language-related fields. The significance of applied linguistics is emphasized by some of the research that has been emphasized, which presents pedagogical perspectives that could enhance instruction and the learning results of student’s at all academic levels as well as translation trainees. Researchers provided useful data from language studies with classroom applications from an instructional standpoint.

Keywords: linguistics, language, corpora, technology

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Authors: Doris C. C. K. Kowaltowski, Marcella S. Deliberador

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The architectural design process, in general, is becoming more complex, as new technical, social, environmental, and economical requirements are imposed. For school buildings, this scenario is also valid. The quality of a school building depends on known design criteria and professional knowledge, as well as feedback from building performance assessments. To attain high-performance school buildings, a design process should add a multidisciplinary team, through an integrated process, to ensure that the various specialists contribute at an early stage to design solutions. The participation of stakeholders is of special importance at the programming phase when the search for the most appropriate design solutions is underway. The composition of a multidisciplinary team should comprise specialists in education, design professionals, and consultants in various fields such as environmental comfort and psychology, sustainability, safety and security, as well as administrators, public officials and neighbourhood representatives. Users, or potential users (teachers, parents, students, school officials, and staff), should be involved. User expectations must be guided, however, toward a proper understanding of a response of design to needs to avoid disappointment. In this context, appropriate tools should be introduced to organize such diverse participants and ensure a rich and focused response to needs and a productive outcome of programming sessions. In this paper, different stakeholder in a school design process are discussed in relation to their specific contributions and a tool in the form of a card game is described to structure the design debates and ensure a comprehensive decision-making process. The game is based on design patterns for school architecture as found in the literature and is adapted to a specific reality: State-run public schools in São Paulo, Brazil. In this State, school buildings are managed by a foundation called Fundação para o Desenvolvimento da Educação (FDE). FDE supervises new designs and is responsible for the maintenance of ~ 5000 schools. The design process of this context was characterised with a recommendation to improve the programming phase. Card games can create a common environment, to which all participants can relate and, therefore, can contribute to briefing debates on an equal footing. The cards of the game described here represent essential school design themes as found in the literature. The tool was tested with stakeholder groups and with architecture students. In both situations, the game proved to be an efficient tool to stimulate school design discussions and to aid in the elaboration of a rich, focused and thoughtful architectural program for a given demand. The game organizes the debates and all participants are shown to spontaneously contribute each in his own field of expertise to the decision-making process. Although the game was specifically based on a local school design process it shows potential for other contexts because the content is based on known facts, needs and concepts of school design, which are global. A structured briefing phase with diverse stakeholder participation can enrich the design process and consequently improve the quality of school buildings.

Keywords: architectural program, design process, school building design, stakeholder

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Authors: Ezgi Şendil

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Information extracted from social media has received great attention since it has become an effective alternative for collecting people’s opinions and emotions based on specific experiences in a faster and easier way. The paper aims to put data in a meaningful way to analyze users’ posts and get a result in terms of the experiences and opinions of the users during and after natural disasters. The posts collected from Reddit are classified into nine different categories, including injured/dead people, infrastructure and utility damage, missing/found people, donation needs/offers, caution/advice, and emotional support, identified by using labelled Twitter data and four different machine learning (ML) classifiers.

Keywords: disaster, NLP, postdisaster management, sentiment analysis

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Authors: A. Bortkiewcz, A. Dudarewicz, P. Małecki, M. Kłaczyński, T. Wszołek, Małgorzata Pawlaczyk-Łuszczyńska

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Low-frequency noise (LFN) has been recognized as a special environmental pollutant. It is usually considered a broadband noise with the dominant content of low frequencies from 10 Hz to 250 Hz. A growing body of data shows that LFN differs in nature from other environmental noises, which are at comparable levels but not dominated by low-frequency components. The primary and most frequent adverse effect of LFN exposure is annoyance. Moreover, some recent investigations showed that LFN at relatively low A-weighted sound pressure levels (40−45 dB) occurring in office-like areas could adversely affect the mental performance, especially of high-sensitive subjects. It is well documented that high-frequency noise disturbs various types of human functions; however, there is very little data on the impact of LFN on well-being and health, including the cardiovascular system. Heart rate variability (HRV) is a sensitive marker of autonomic regulation of the circulatory system. Walker and co-workers found that LFN has a significantly more negative impact on cardiovascular response than exposure to high-frequency noise and that changes in HRV parameters resulting from LFN exposure tend to persist over time. The negative reactions of the cardiovascular system in response to LFN generated by wind turbines (20-200 Hz) were confirmed by Chiu. The scientific aim of the study is to assess the relationship between the spectral-temporal characteristics of LFN and the activity of the autonomic nervous system, considering the subjective assessment of annoyance, sensitivity to this type of noise, and cognitive and general health status. The study will be conducted in 20 male students in a special, acoustically prepared, constantly supervised room. Each person will be tested 4 times (4 sessions), under conditions of non-exposure (sham) and exposure to noise of wind turbines recorded at a distance of 250 meters from the turbine with different frequencies and frequency ranges: acoustic band 20 Hz-20 kHz, infrasound band 5-20 Hz, acoustic band + infrasound band. The order of sessions of the experiment will be randomly selected. Each session will last 1 h. There will be a 2-3 days break between sessions to exclude the possibility of the earlier session influencing the results of the next one. Before the first exposure, a questionnaire will be conducted on noise sensitivity, general health status using the GHQ questionnaire, hearing organ status and sociodemographic data. Before each of the 4 exposures, subjects will complete a brief questionnaire on their mood and sleep quality the night before the test. After the test, the subjects will be asked about any discomfort and subjective symptoms during the exposure. Before the test begins, Holter ECG monitoring equipment will be installed. HRV will be analyzed from the ECG recordings, including time and frequency domain parameters. The tests will always be performed in the morning (9-12) to avoid the influence of diurnal rhythm on HRV results. Students will perform psychological tests 15 minutes before the end of the test (Vienna Test System).

Keywords: neurovegetative control, heart rate variability (HRV), cognitive processes, low frequency noise

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Authors: Guezouli Larbi, Belkacem Soundes

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The automation of the text detection process can help the human in his driving task. Its application can be very useful to help drivers to have more information about their environment by facilitating the reading of road signs such as directional signs, events, stores, etc. In this paper, a system consisting of two stages has been proposed. In the first one, we used pseudo-Zernike moments to pinpoint areas of the image that may contain text. The architecture of this part is based on three main steps, region of interest (ROI) detection, text localization, and non-text region filtering. Then, in the second step, we present a convolutional neural network architecture (On-Road Text Detection Network - ORTDN) which is considered a classification phase. The results show that the proposed framework achieved ≈ 35 fps and an mAP of ≈ 90%, thus a low computational time with competitive accuracy.

Keywords: text detection, CNN, PZM, deep learning

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Authors: Mimoun Melliti

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This research paper investigates the promises, challenges, and perspectives associated with teaching STEM subjects in English within Tunisian higher education institutions. The study explores the potential benefits of English-medium instruction in Science, Technology, Engineering, and Mathematics (henceforth STEM) education, with a special focus on enhanced global competitiveness, improved English language proficiency, and increased access to international resources and collaborations. Additionally, it examines the various challenges faced by educators and students, such as language shift/barriers, curriculum adaptation, faculty training, and student support. Through a comprehensive analysis of promises and challenges, this paper aims to provide insights and recommendations for effectively implementing English medium instruction (henceforth EMI) of STEM in Tunisian higher education institutions. The paper concludes with a recommended action plan for the proper introduction of EMI of STEM in Tunisia.

Keywords: EMI;, STEM education, EFL, language reforms

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Authors: Maria Korman, Carmit Gal, Ella Gabitov, Avi Karni

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The main question addressed in this study was whether the time-of-day wherein training is afforded is a significant factor for motor skill ('how-to', procedural knowledge) acquisition and consolidation into long term memory in the healthy elderly population. Twenty-nine older adults (60-75 years) practiced an explicitly instructed 5-element key-press sequence by repeatedly generating the sequence ‘as fast and accurately as possible’. Contribution of three parameters to acquisition, 24h post-training consolidation, and 1-week retention gains in motor sequence speed was assessed: (a) time of training (morning vs. evening group) (b) sleep quality (actigraphy) and (c) chronotype. All study participants were moderately morning type, according to the Morningness-Eveningness Questionnaire score. All participants had sleep patterns typical of age, with average sleep efficiency of ~ 82%, and approximately 6 hours of sleep. Speed of motor sequence performance in both groups improved to a similar extent during training session. Nevertheless, evening group expressed small but significant overnight consolidation phase gains, while morning group showed only maintenance of performance level attained at the end of training. By 1-week retention test, both groups showed similar performance levels with no significant gains or losses with respect to 24h test. Changes in the tapping patterns at 24h and 1-week post-training were assessed based on normalized Pearson correlation coefficients using the Fisher’s z-transformation in reference to the tapping pattern attained at the end of the training. Significant differences between the groups were found: the evening group showed larger changes in tapping patterns across the consolidation and retention windows. Our results show that morning-oriented older adults effectively acquired, consolidated, and maintained a new sequence of finger movements, following both morning and evening practice sessions. However, time-of-training affected the time-course of skill evolution in terms of performance speed, as well as the re-organization of tapping patterns during the consolidation period. These results are in line with the notion that motor training preceding a sleep interval may be beneficial for the long-term memory in the elderly. Evening training should be considered an appropriate time window for motor skill learning in older adults, even in individuals with morning chronotype.

Keywords: time-of-day, elderly, motor learning, memory consolidation, chronotype

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Authors: Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Kensuke Nakamura, Dongeun Choi, Byung-Woo Hong

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Artefacts are commonly encountered in the imaging process of clinical computed tomography (CT) where the artefact refers to any systematic discrepancy between the reconstructed observation and the true attenuation coefficient of the object. It is known that CT images are inherently more prone to artefacts due to its image formation process where a large number of independent detectors are involved, and they are assumed to yield consistent measurements. There are a number of different artefact types including noise, beam hardening, scatter, pseudo-enhancement, motion, helical, ring, and metal artefacts, which cause serious difficulties in reading images. Thus, it is desired to remove nuisance factors from the degraded image leaving the fundamental intrinsic information that can provide better interpretation of the anatomical and pathological characteristics. However, it is considered as a difficult task due to the high dimensionality and variability of data to be recovered, which naturally motivates the use of machine learning techniques. We propose an image restoration algorithm based on the deep neural network framework where the denoising auto-encoders are stacked building multiple layers. The denoising auto-encoder is a variant of a classical auto-encoder that takes an input data and maps it to a hidden representation through a deterministic mapping using a non-linear activation function. The latent representation is then mapped back into a reconstruction the size of which is the same as the size of the input data. The reconstruction error can be measured by the traditional squared error assuming the residual follows a normal distribution. In addition to the designed loss function, an effective regularization scheme using residual-driven dropout determined based on the gradient at each layer. The optimal weights are computed by the classical stochastic gradient descent algorithm combined with the back-propagation algorithm. In our algorithm, we initially decompose an input image into its intrinsic representation and the nuisance factors including artefacts based on the classical Total Variation problem that can be efficiently optimized by the convex optimization algorithm such as primal-dual method. The intrinsic forms of the input images are provided to the deep denosing auto-encoders with their original forms in the training phase. In the testing phase, a given image is first decomposed into the intrinsic form and then provided to the trained network to obtain its reconstruction. We apply our algorithm to the restoration of the corrupted CT images by the artefacts. It is shown that our algorithm improves the readability and enhances the anatomical and pathological properties of the object. The quantitative evaluation is performed in terms of the PSNR, and the qualitative evaluation provides significant improvement in reading images despite degrading artefacts. The experimental results indicate the potential of our algorithm as a prior solution to the image interpretation tasks in a variety of medical imaging applications. This work was supported by the MISP(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by the IITP(Institute for Information and Communications Technology Promotion).

Keywords: auto-encoder neural network, CT image artefact, deep learning, intrinsic image representation, noise reduction, total variation

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Authors: Simon Espigolé, Igor Shalyminov, Helen Hastie

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Deep machine learning techniques including deep neural networks (DNN) have been used to model language and dialogue for conversational agents to perform tasks, such as giving technical support and also for general chit-chat. They have been shown to be capable of generating long, diverse and coherent sentences in end-to-end dialogue systems and natural language generation. However, these systems tend to imitate the training data and will only generate the concepts and language within the scope of what they have been trained on. This work explores how deep neural networks can be used in a task that would normally require human creativity, whereby the human would read the movie description and/or watch the movie and come up with a compelling, interesting movie title. This task differs from simple summarization in that the movie title may not necessarily be derivable from the content or semantics of the movie description. Here, we train a type of DNN called a sequence-to-sequence model (seq2seq) that takes as input a short textual movie description and some information on e.g. genre of the movie. It then learns to output a movie title. The idea is that the DNN will learn certain techniques and approaches that the human movie titler may deploy that may not be immediately obvious to the human-eye. To give an example of a generated movie title, for the movie synopsis: ‘A hitman concludes his legacy with one more job, only to discover he may be the one getting hit.’; the original, true title is ‘The Driver’ and the one generated by the model is ‘The Masquerade’. A human evaluation was conducted where the DNN output was compared to the true human-generated title, as well as a number of baselines, on three 5-point Likert scales: ‘creativity’, ‘naturalness’ and ‘suitability’. Subjects were also asked which of the two systems they preferred. The scores of the DNN model were comparable to the scores of the human-generated movie title, with means m=3.11, m=3.12, respectively. There is room for improvement in these models as they were rated significantly less ‘natural’ and ‘suitable’ when compared to the human title. In addition, the human-generated title was preferred overall 58% of the time when pitted against the DNN model. These results, however, are encouraging given the comparison with a highly-considered, well-crafted human-generated movie title. Movie titles go through a rigorous process of assessment by experts and focus groups, who have watched the movie. This process is in place due to the large amount of money at stake and the importance of creating an effective title that captures the audiences’ attention. Our work shows progress towards automating this process, which in turn may lead to a better understanding of creativity itself.

Keywords: creativity, deep machine learning, natural language generation, movies

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Authors: Vedant Vardhan Rathour, Anant Agrawal

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The project aims to provide a touchless, intuitive interface for human-computer interaction, enabling users to control their computer using hand gestures and voice commands. The system leverages advanced computer vision techniques using the MediaPipe framework and OpenCV to detect and interpret real time hand gestures, transforming them into mouse actions such as clicking, dragging, and scrolling. Additionally, the integration of a voice assistant powered by the Speech Recognition library allows for seamless execution of tasks like web searches, location navigation and gesture control on the system through voice commands.

Keywords: gesture recognition, hand tracking, machine learning, convolutional neural networks

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Authors: Smita Mathur

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Play is an innate, player-centric, joyful, fundamental activity of early childhood development that significantly contributes to social, emotional, and academic learning. Leveraging the power of play can enhance these domains by creating engaging, interactive, and developmentally appropriate learning experiences for young children. This research aimed to systematically examine young children’s play behaviors with a focus on four primary objectives: (1) the frequency and duration of on-task behaviors, (2) social interactions and emotional expressions during play, (3) the correlation between academic skills and play, and (4) identifying best practices for integrating play-based curricula. To achieve these objectives, a mixed-method study was conducted among young preschool-aged children in low socio-economic populations in the United States. The children were identified using purposive sampling. The children were observed during structured play in classrooms and unstructured play during outdoor playtime and in their home environments. The study sampled 97 preschool-aged children. A total of 3970 minutes of observations were coded to address the research questions. Thirty-seven percent of children lived in linguistically isolated families, and 76% lived in basic budget poverty. Children lived in overcrowded housing situations (67%), and most families had mixed citizenship status (66%). The observational study was conducted using the observation protocol during the Oxford Study Project. On-task behaviors were measured by tracking the frequency and duration of activities where children maintained focus and engagement. In examining social interactions and emotional expressions, the study recorded social interactions, emotional responses, and teacher involvement during play. The study aimed to identify best practices for integrating play-based curricula into early childhood education. By analyzing the effectiveness of different play-based strategies and their impact on on-task behaviors, social-emotional development, and academic skills, the research sought to provide actionable recommendations for educators and caregivers. The findings from study 1. Highlight play behaviors that increase on-task behaviors and academic, & social skills in young children. 2. Offers insights into teacher preparation and designing play-based curriculum 3. Research critiques observation as a data collection technique.

Keywords: play, early childhood education, social-emotional development, academic development

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Authors: Jihee Jang, Seowon Yoon, Gaeun Son, Minjung Kang, Joon Yeon Choeh, Kee-Hong Choi

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Background: Self-report multiple choice questionnaires have been widely utilized to quantitatively measure one’s personality and psychological constructs. Despite several strengths (e.g., brevity and utility), self-report multiple-choice questionnaires have considerable limitations in nature. With the rise of machine learning (ML) and Natural language processing (NLP), researchers in the field of psychology are widely adopting NLP to assess psychological constructs to predict human behaviors. However, there is a lack of connections between the work being performed in computer science and that psychology due to small data sets and unvalidated modeling practices. Aims: The current article introduces the study method and procedure of phase II, which includes the interview questions for the five-factor model (FFM) of personality developed in phase I. This study aims to develop the interview (semi-structured) and open-ended questions for the FFM-based personality assessments, specifically designed with experts in the field of clinical and personality psychology (phase 1), and to collect the personality-related text data using the interview questions and self-report measures on personality and psychological distress (phase 2). The purpose of the study includes examining the relationship between natural language data obtained from the interview questions, measuring the FFM personality constructs, and psychological distress to demonstrate the validity of the natural language-based personality prediction. Methods: The phase I (pilot) study was conducted on fifty-nine native Korean adults to acquire the personality-related text data from the interview (semi-structured) and open-ended questions based on the FFM of personality. The interview questions were revised and finalized with the feedback from the external expert committee, consisting of personality and clinical psychologists. Based on the established interview questions, a total of 425 Korean adults were recruited using a convenience sampling method via an online survey. The text data collected from interviews were analyzed using natural language processing. The results of the online survey, including demographic data, depression, anxiety, and personality inventories, were analyzed together in the model to predict individuals’ FFM of personality and the level of psychological distress (phase 2).

Keywords: personality prediction, psychological distress prediction, natural language processing, machine learning, the five-factor model of personality

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Authors: Valencia Fernandes, Dharmendra Kumar Khatri, Shashi Bala Singh

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Background and Aim: Epigenetics are the inaudible signatures of several pathological processes in the brain. This study understands the influence of DNA methylation, a major epigenetic modification, in the prefrontal cortex and hippocampus of the diabetic brain and its notable effect on the cellular chaperones and synaptic proteins. Method: Chronic high fat diet and STZ-induced diabetic mice were studied for cognitive dysfunction, and global DNA methylation, as well as DNA methyltransferase (DNMT) activity, were assessed. Further, the cellular chaperones and synaptic proteins were examined using DNMT inhibitor, 5-aza-2′-deoxycytidine (5-aza-dC)-via intracerebroventricular injection. Moreover, % methylation of these synaptic proteins were also studied so as to correlate its epigenetic involvement. Computationally, its interaction with the DNMT enzyme were also studied using bioinformatic tools. Histological studies for morphological alterations and neuronal degeneration were also studied. Neurogenesis, a characteristic marker for new learning and memory formation, was also assessed via the BrdU staining. Finally, the most important behavioral studies, including the Morris water maze, Y maze, passive avoidance, and Novel object recognition test, were performed to study its cognitive functions. Results: Altered global DNA methylation and increased levels of DNMTs within the nucleus were confirmed in the cortex and hippocampus of the diseased mice, suggesting hypermethylation at a genetic level. Treatment with AzadC, a global DNA demethylating agent, ameliorated the protein and gene expression of the cellular chaperones and synaptic fidelity. Furthermore, the methylation analysis profile showed hypermethylation of the hsf1 protein, a master regulator for chaperones and thus, confirmed the epigenetic involvement in the diseased brain. Morphological improvements and decreased neurodegeneration, along with enhanced neurogenesis in the treatment group, suggest that epigenetic modulations do participate in learning and memory. This is supported by the improved behavioral test battery seen in the treatment group. Conclusion: DNA methylation could possibly accord in dysregulating the memory-associated proteins at chronic stages in type 2 diabetes. This could suggest a substantial contribution to the underlying pathophysiology of several metabolic syndromes like insulin resistance, obesity and also participate in transitioning this damage centrally, such as cognitive dysfunction.

Keywords: epigenetics, cognition, chaperones, DNA methylation

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Authors: Johanna Sagner, María José Sandoval

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At the early stages of adolescence, young people, regardless of a disability or not, start to establish closer friendship ties. Unlike previous developmental phases, these ties are rather reciprocal, more committed, and require more time. Friendship ties during adolescence allow the development of social and personal skills, specifically the skills to start constructing identity. In an inclusive context that incorporates young people with a disability, friendship among peers also takes place. Nonetheless, the relation is shaped, among others, by the alterity construction about the other with disability. Research about peers’ relation between young people with and without disability in an inclusive context has shown that the relation tends to become a helper-helpee relation, where those with a disability are seen as people in need. Prejudices about the others’ condition or distancing from the other because of his/hers disability are common. In this sense, the helper-helpee relation, as a non-reciprocal and protective relation, will not promote friendship between classmates, but a rather asymmetric alterity. Our research is an explorative case study that wants to know how the relation between peers is shaped within a different inclusive program, were also the integrated group has special educational needs. Therefore, we analyze from a qualitative and quantitative approach the data of an inverted inclusive program. This is a unique case of a special public school for visual disability in Germany that includes young people from a mainstream school who had learning difficulties. For the research, we analyze data from interviews, focal interviews and open-ended questions with an interpretative phenomenological analysis approach. The questionnaires include a five point Likert scale, for which we calculate the acceptance rate. The findings show that the alterity relation between pupils is less asymmetrical and represents a rather horizontal alterity. The helper-helpee relation is marked by exchange, since both groups have special educational needs and therefore, those with visual disability and those with learning difficulties help each other indistinctly. Friendship is more present among classmates. The horizontal alterity peers’ relation is influenced by a sort of tie, where none of the groups need more or less help than other groups. Both groups identify that they themselves and the other have special needs. The axiological axe of alterity is not of superiority or inferiority, recognizing each other’s differences and otherness. Another influential factor relates with the amount of time they spend together, since the program does not have a resource room or a teacher who teaches parallel lessons. Two probable causes for that rather equal peer relation might be the constellation of fewer pupils per classroom and the differentiated lessons taught by teachers with a special educational formation.

Keywords: alterity, disability, inverted inclusion, peers’ relation

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Authors: Adel Edwar Waheeb Louka

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deeplearning, image processing, machine learningSarapin, intraarticular, chronic knee pain, osteoarthritisFNS, trauma, hip, neck femur fracture, minimally invasive surgery

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Authors: Carlos Monge López, Patricia Gómez Hernández

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Educational innovation is a quality factor of teaching-learning processes and institutional accreditation. There is an increasing of these change processes, especially after 2000. However, the publications about this topic are more associated with ICTs in currently century. The main aim of the study was to determine the tendency of educational innovations around ICTs. The used method was mixed research design (content analysis, review of scientific literature and descriptive, comparative and correlation study) with 649 papers. In summary, the results indicated that, progressively, the educational innovation is associated with ICTs, in comparison with this type of change processes without ICTs. In conclusion, although this tendency, scientific literature must divulgate more kinds of pedagogical innovation with the aim of deepening in other new resources.

Keywords: descriptive study, knowledge society, pedagogical innovation, technologies

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Authors: Mladen Dragičević, Mirjana Pejić Bach, Vanja Šimičević

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The purpose of this paper is to develop models that would enable predicting student success. These models could improve allocation of students among colleges and optimize the newly introduced model of government subsidies for higher education. For the purpose of collecting data, an anonymous survey was carried out in the last year of undergraduate degree student population using random sampling method. Decision trees were created of which two have been chosen that were most successful in predicting student success based on two criteria: Grade Point Average (GPA) and time that a student needs to finish the undergraduate program (time-to-degree). Decision trees have been shown as a good method of classification student success and they could be even more improved by increasing survey sample and developing specialized decision trees for each type of college. These types of methods have a big potential for use in decision support systems.

Keywords: data mining, knowledge discovery in databases, prediction models, student success

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Authors: Andréanne Gagné, Jo Anni Joncas, Éric Tendon

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Teaching in vocational training requires an excellent mastery of the trade being taught, but also solid professional skills in pedagogy. Teachers are typically recruited on the basis of their trade expertise, and they do not necessarily have training or experience in pedagogy. In order to counter this lack, the Ministry of Education (Québec, Canada) requires them to complete a 120-credit university program to obtain their teaching certificate. They must complete this training in addition to their teaching duties. This training was rarely planned in the teacher’s life course, and each teacher approaches it differently: some are enthusiastic, but many feel reluctant discouragement and even frustration at the idea of committing to a training program lasting an average of 10 years to completion. However, Quebec is experiencing an unprecedented shortage of teachers, and the perseverance of vocational teachers in their careers requires special attention because of the conditions of their specific integration conditions. Our research examines the perceptions that vocational teachers in training have of their academic experience in pre-service teaching. It differs from previous research in that it focuses on the influence of the academic experience on the teaching employment experience. The goal is that by better understanding the university experience of teachers in vocational education, we can identify support strategies to support their school experience and their teaching. To do this, the research is based on the theoretical framework of the sociology of experience, which allows us to study the way in which these “teachers-students” give meaning to their university program in articulation with their jobs according to three logics of action. The logic of integration is based on the process of socialization, where the action is preceded by the internalization of values, norms, and cultural models associated with the training context. The logic of strategy refers to the usefulness of this experience where the individual constructs a form of rationality according to his objectives, resources, social position, and situational constraints. The logic of subjectivation refers to reflexivity activities aimed at solving problems and making choices. These logics served as a framework for the development of an online questionnaire. Three hundred respondents, newly enrolled in an undergraduate teaching program (bachelor's degree in vocational education), expressed themselves about their academic experience. This paper relates qualitative data (open-ended questions) subjected to an interpretive repertory analysis approach to descriptive data (closed-ended questions) that emerged. The results shed light on how the respondents perceive themselves as teachers and students, their perceptions of university training and the support offered, and the place that training occupies in their professional path. Indeed, their professional and academic paths are inextricably linked, and it seems essential to take them into account simultaneously to better meet their needs and foster the development of their expertise in pedagogy. The discussion focuses on the strengths and limitations of university training from the perspective of the logic of action. The results also suggest support strategies that can be implemented to better support the integration and retention of student teachers in professional education.

Keywords: teacher, vocational training, pre-service training, academic experience

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Authors: Martin Camps

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The two-thousand-mile border that divides the United States and Mexico is a “contact zone” of cultural friction and unbalanced power relations as defined by Mary Louise Pratt. The interest of this paper is to analyze digital platforms created to address the study and comprehension of the borderlands with pedagogical and research reasons. The paper explores ways to engage students in archival and analytical practices to build a repository of resources, links, and digital tools and consider how to adapt them to the study of the borderlands. Sites such as “Torn Apart / Separados,” “Digital Borderlands,” “Borderlands Archives Cartography,” and “Juaritos Literario” show visualizations, mapping, and access to materials and marginal literature on the border phenomenon. Analyzing these projects contributes to highlighting digital projects and the study of the border and how to engage in activism via the study of literature and the representation of a human tragedy that underscores the divisions and biopolitics imposed on the Global South and imagine the digital border futures.

Keywords: borderlands, digital humanities, activism, border literature

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Authors: Farhana Alam Bhuiyan, Saad Khan, Tanveer Hassan, Jhalok Ranjon Talukder, Syeda Farjana Ahmed, Rahil Roodsaz, Els Rommes, Sabina Faiz Rashid

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Breaking the shame’ is a 3 year (2015-2018) qualitative implementation research project which investigates several aspects of sexual and reproductive health and rights (SRHR) issues for adolescents living in Bangladesh. Scope of learning SRHR issues for adolescents is limited here due to cultural and religious taboos. This study adds to the ongoing discussions around adolescent’s SRHR needs and aims to, 1) understand the overall SRHR needs of urban and rural unmarried female and male adolescents and the challenges they face, 2) explore existing gaps in the content of SRHR curriculum and 3) finally, addresses some critical knowledge gaps by developing and implementing innovative SRHR educational materials. 18 in-depth interviews (IDIs) and 10 focus-group discussions (FGDs) with boys and 21 IDIs and 14 FGDs with girls of ages 13-19, from both urban and rural setting took place. Curriculum materials from two leading organizations, Unite for Body Rights (UBR) Alliance Bangladesh and BRAC Adolescent Development Program (ADP) were also reviewed, with discussions with 12 key program staff. This paper critically analyses the relevance of some of the SRHR topics that are covered, the challenges with existing pedagogic approaches and key sexuality issues that are not covered in the content, but are important for adolescents. Adolescents asked for content and guidance on a number of topics which remain missing from the core curriculum, such as emotional coping mechanisms particularly in relationships, bullying, impact of exposure to porn, and sexual performance anxiety. Other core areas of concern were effects of masturbation, condom use, sexual desire and orientation, which are mentioned in the content, but never discussed properly, resulting in confusion. Due to lack of open discussion around sexuality, porn becomes a source of information for the adolescents. For these reasons, several myths and misconceptions regarding SRHR issues like body, sexuality, agency, and gender roles still persist. The pedagogical approach is very didactic, and teachers felt uncomfortable to have discussions on certain SRHR topics due to cultural taboos or shame and stigma. Certain topics are favored- such as family planning, menstruation- and presented with an emphasis on biology and risk. Rigid formal teaching style, hierarchical power relations between students and most teachers discourage questions and frank conversations. Pedagogy approaches within classrooms play a critical role in the sharing of knowledge. The paper also describes the pilot approaches to implementing new content in SRHR curriculum. After a review of findings, three areas were selected as critically important, 1) myths and misconceptions 2) emotional management challenges, and 3) how to use condom, that have come up from adolescents. Technology centric educational materials such as web page based information platform and you tube videos are opted for which allow adolescents to bypass gatekeepers and learn facts and information from a legitimate educational site. In the era of social media, when information is always a click away, adolescents need sources that are reliable and not overwhelming. The research aims to ensure that adolescents learn and apply knowledge effectively, through creating the new materials and making it accessible to adolescents.

Keywords: adolescents, Bangladesh, media, sexuality education, unmet needs

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