Search results for: learning attitude

Authors: Wafa' Slaibi Alsharafat

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Cloud computing is a modern approach in network environment. According to increased number of network users and online systems, there is a need to help these systems to be away from unauthorized resource access and detect any attempts for privacy contravention. For that purpose, Intrusion Detection System is an effective security mechanism to detect any attempts of attacks for cloud resources and their information. In this paper, Cloud Intrusion Detection System has been proposed in term of reducing or eliminating any attacks. This model concerns about achieving high detection rate after conducting a set of experiments using benchmarks dataset called KDD'99.

Keywords: IDS, cloud computing, anticipating classifier system, intrusion detection

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Authors: Muuz Abraha Meshesha

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Illegal Migration, specifically Trafficking in person is one of the primary issues of the day affecting all states of the world with variation on the extent of the root causes and consequences that led people to migrate irregularly and the consequences it is costing on humanity. This paper intends to investigate the root causes and consequences of illegal migration in Ethiopia’s Tigray Regional state and come up with alternative intervening strategy. To come up with pertinent and robust research finding, this study employed mixed research approach involving qualitative and quantitative data in line with purposive and snow ball sampling selection technique. The study revealed that, though poverty is the most commonly sensed pushing factor for people to illegally migrate, the issue of psycho-social orientation and attitudinal immersion of the local community for illegal migration, both in thinking and action is the most pressing problem that urges serious intervention. Trafficking in persons and Illegal migration in general, is becoming the norm of the day in the study area that overtly reveal illegal migration is an issue beyond livelihood securing demand in practice. Basically, parties engaged in illegal migration and the accomplice with human traffickers these days in the study area are found to be more than urgency for food security and a need to escape from livelihood impoverishment. Therefore, this study come up with a new paradigm insight indicating that illegal migration is believed by the local community members as an optional path way of doing business in illegal way while the attitude of the community and officials authorized to regulate is being part of the channel or to the least tolerant of this grave global danger. The study also found that the effect of illegal migration is significantly manifested in long run than in short term periods. Therefore, a need for critical consideration on attitudinal based intervention and youth oriented and enforceable legal and policy framework accountability framework is required to face and control illegal migration by international, national, local stakeholders. Besides this, economy based development interventions that could engage and reorient the youth, as primary victims of trafficking, and expansion of large scale projects that can employ large number of youths at a time.

Keywords: human traficking, illegal migration, migration, tigray region

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Authors: Nizar Mansour, Chiha Gaha, Emna Gara

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The present research investigates the relationship among HRM practices, knowledge sharing behavior and a certain number of intermediary variables in the context of Tunisian knowledge-intensive firms. Results suggest that five HR practices influence either directly or indirectly the knowledge sharing behavior through enhancing the value of human capital and fostering a learning-oriented organizational climate. Results have strong theoretical implications for both the fields of knowledge management and strategic human resource management. Managerial implications are also derived.

Keywords: human capital, knowledge intensive firms, knowledge sharing, organizational climate, Tunisia

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Authors: Jacqueline M. Dias, Amina A Khowaja

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Clinical education is one of the most important components of a nursing curriculum as it develops the students’ cognitive, psychomotor and affective skills. Clinical teaching ensures the integration of knowledge into practice. As the numbers of students increase in the field of nursing coupled with the faculty shortage, clinical preceptors are the best choice to ensure student learning in the clinical settings. The clinical preceptor role has been introduced in the undergraduate nursing programme. In Pakistan, this role emerged due to a faculty shortage. Initially, two clinical preceptors were hired. This study will explore clinical preceptors views and experiences of precepting Bachelor of Science in Nursing (BScN) students in an undergraduate program. A case study design was used. As case studies explore a single unit of study such as a person or very small number of subjects; the two clinical preceptors were fundamental to the study and served as a single case. Qualitative data were obtained through an iterative process using in depth interviews and written accounts from reflective journals that were kept by the clinical preceptors. The findings revealed that the clinical preceptors were dedicated to their roles and responsibilities. Another, key finding was that clinical preceptors’ prior knowledge and clinical experience were valuable assets to perform their role effectively. The clinical preceptors found their new role innovative and challenging; it was stressful at the same time. Findings also revealed that in the clinical agencies there were unclear expectations and role ambiguity. Furthermore, clinical preceptors had difficulty integrating theory into practice in the clinical area and they had difficulty in giving feedback to the students. Although this study is localized to one university, generalizations can be drawn from the results. The key findings indicate that the role of a clinical preceptor is demanding and stressful. Clinical preceptors need preparation prior to precepting students on clinicals. Also, institutional support is fundamental for their acceptance. This paper focuses on the views and experiences of clinical preceptors undertaking a newly established role and resonates with the literature. The following recommendations are drawn to strengthen the role of the clinical preceptors: A structured program for clinical preceptors is needed along with mentorship. Clinical preceptors should be provided with formal training in teaching and learning with emphasis on clinical teaching and giving feedback to students. Additionally, for improving integration of theory into practice, clinical modules should be provided ahead of the clinical. In spite of all the challenges, ten more clinical preceptors have been hired as the faculty shortage continues to persist.

Keywords: baccalaureate nursing education, clinical education, clinical preceptors, nursing curriculum

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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: 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: Liping Li, Wenan Tan, Na Wang

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With the rapid development of information technology, project management has gained more and more attention recently. Based on CDIO, this paper proposes some teaching reform ideas for software project management curriculum. We first change from Teacher-centered classroom to Student-centered and adopt project-driven, scenario animation show, teaching rhythms, case study and team work practice to improve students' learning enthusiasm. Results showed these attempts have been well received and very effective; as well, students prefer to learn with this curriculum more than before the reform.

Keywords: CDIO, teaching reform, engineering education, project-driven, scenario animation simulation

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Authors: Yei Mian Adrian Yap

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This study aims to assess the Japanese and Singaporean mainstream teachers’ attitudes and teaching-efficacy towards the inclusion of students with special needs in the regular classrooms by investigating what kind of key variables influence their attitudes and teaching-efficacy. It also further investigates how they strategize to address their challenges to include their students with special needs in their regular classrooms. In order to understand the nature of teachers’ attitudes and teaching-efficacy towards the inclusive education, a mixed-method research methodology was carried out in Japan and Singapore; it involved an explanatory sequential method of employing quantitative research first before qualitative research. In the quantitative research, 189 Japanese and 183 Singaporean teachers were invited to participate in the questionnaires and out of these participants, 38 Japanese and 15 Singaporean teachers shared their views during their semi-structured interviews. Based on the empirical findings, Japanese teachers’ attitudes and teaching-efficacy were more likely to be influenced by their experiences in teaching students with special needs, knowledge about disability legislation, presence of their disabled family members and level of confidence to teach students with special needs. On the other hand, Singaporean teachers’ attitudes and teaching-efficacy were affected by gender, educational level, received trainings in special needs education, knowledge about disability legislation and level of confidence to teach students with special needs. Both country results also demonstrated that there was a positive correlation between their teaching-efficacy and attitude. Narrative findings further expanded the reasons behind these quantitative factors that shaped teachers’ attitudes and teaching-efficacy. Also, it discussed the various problems faced by Japanese and Singaporean teachers and how they identified their coping strategies to circumvent their challenges in including their students with special needs in their regular classrooms. The significance of this research manifests in necessary educational reforms in both countries especially in the context of inclusive education. These findings may not be as definitive as expected but it is believed that it could provide useful information on the current situation about teachers’ concerns towards the inclusive education. In conclusion, this research could potentially make its positive contribution to the body of literature on teachers’ attitudes and teaching-efficacy in the context of Asian developed countries. Further, these findings could posit that regular teachers’ positive attitudes and strong sense of teaching self-efficacy could directly improve the success rate of inclusion of students with special needs in the regular classrooms.

Keywords: attitudes, inclusive education, special education, teaching-efficacy

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Authors: Deepa Idnani

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Attendance is an important factor in school success among children. Studies show that better attendance is related to higher academic achievement for students of all backgrounds, but particularly for children with lower socio-economic status. Beginning from the early years, students who attend school regularly score higher on tests than their peers who are frequently absent. The present study in different types of School In Delhi tries to highlight the impact of student absenteeism and the challenges it poses for the students. The study relies on Lewin ‘Model of Exclusion’ and tries to focus on the analysis of children with special needs and the inclusion and exclusion of students in the school.

Keywords: student absenteeism, pedagogy, learning, right to education act, exclusion

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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: 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: Kazuhisa Takagi

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This paper is about movies and dynamic objects for mobile phones. Dynamic objects are the software programmed by JavaScript. They consist of geometric figures and work on HTML5-compliant browsers. Mobile phones are very popular among teenagers. They like watching movies and playing games on them. So, mathematics movies and dynamic objects would enhance teaching and learning processes. In the movies, manga characters speak with artificially synchronized voices. They teach trigonometry together with dynamic mathematical objects. Many movies are created. They are Windows Media files or MP4 movies. These movies and dynamic objects are not only used in the classroom but also distributed to students. By watching movies, students can study trigonometry before or after class.

Keywords: dynamic mathematical object, javascript, google drive, transfer jet

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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: Belyihun Muchie

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This study investigates the low English oral communication skills of 5 second-year English major students at Debark University. It aims to identify the key factors contributing to their weaknesses and propose effective interventions to improve their spoken English proficiency. Mixed-methods research will be employed, utilizing observations, questionnaires, and semi-structured interviews to gather data from the participants. To clearly identify these factors, structured and informal observations will be employed; the former will be used to identify their fluency, pronunciation, vocabulary use, and grammar accuracy, and the later will be suited to observe the natural interactions and communication patterns of learners in the classroom setting. The questionnaires will assess their self-perceptions of their skills, perceived barriers to fluency, and preferred learning styles. Interviews will also delve deeper into their experiences and explore specific obstacles faced in oral communication. Data analysis will involve both quantitative and qualitative responses. The structured observation and questionnaire will be analyzed quantitatively, whereas the informal observation and interview transcripts will be analyzed thematically. Findings will be used to identify the major causes of low oral communication skills, such as limited vocabulary, grammatical errors, pronunciation difficulties, or lack of confidence. They are also helpful to develop targeted solutions addressing these causes, such as intensive pronunciation practice, conversation simulations, personalized feedback, or anxiety-reduction techniques. Finally, the findings will guide designing an intervention plan for implementation during the action research phase. The study's outcomes are expected to provide valuable insights into the challenges faced by English major students in developing oral communication skills, contribute to the development of evidence-based interventions for improving spoken English proficiency in similar contexts, and offer practical recommendations for English language instructors and curriculum developers to enhance student learning outcomes. By addressing the specific needs of these students and implementing tailored interventions, this research aims to bridge the gap between theoretical knowledge and practical speaking ability, equipping them with the confidence and skills to flourish in English communication settings.

Keywords: oral communication skills, mixed-methods, evidence-based interventions, spoken English proficiency

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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: Fuad Hamsyah

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Since last two decades, major disasters have happened in the Philippines and Indonesia as two countries that are located in the pacific ring of fire territory. While in Southeast Asian countries, the process of psychosocial support provision is facing various constraints such as limited number of mental health professionals and the limited knowledge about the provision of psychosocial support for disaster survivors. Yet after the tsunami disaster in 2004, many Asian countries begin to develop policies about the provision of psychosocial interventions as an effort for future disasters preparedness. In addition, mental health professionals have to consider the local cultural values and beliefs in order to provide people with effective psychosocial support since cultural values and beliefs play a significant role in the diversity of psychological distress that forms symptoms formation, and people’s way to seek for psychological assistance. This study is a critical literature review on 130 relevant selected documents and literatures. IASC MHPSS guideline is used as the research framework in doing critical analysis. The purpose of this study is to conduct a critical analysis on the mental health and psychosocial support provision in the Philippines and Indonesia with three main objectives: 1) To describe strengths, weaknesses, and challenges in the process of psychosocial supports given by public and private organizations in emergency settings of disaster in the Philippines and Indonesia, 2) To compare psychosocial support practices between the Philippines and Indonesia, and to identify the good practices among these countries, 3) To learn how cultural values influence the implementation of psychosocial supports in emergency settings of disaster. This research indicated that almost every function from IASC MHPSS guidelines has been implemented effectively in the Philippines and Indonesia, yet not in every detail of IASC MHPSS guidelines. Several similarities and differences are indicated in this study also based on the IASC MHPSS guidelines as the analysis framework. Further, both countries have some good practices that can be useful as an example of a comprehensive psychosocial support implementation. Apart from the IASC MHPSS guideline, cultural values and beliefs in the Philippines such as kanya-kanya syndrome, pakikipakapwa, utang na loob, bahala na, pagkaya are indicated as several cultural values that have strong influences towards people’s attitude and behavior in disaster situations. While in Indonesia, several cultural values such as sabar and nrimo become two important attitudes to cope disaster situations.

Keywords: disaster, Indonesia, psychosocial support, Philippines

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Authors: George Bocean

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In the mainstream academic spheres of thought, there is a tendency to associate the writings of Russian Marxists as being constantly against the practice of religion itself. Such arguments mainly stem from how the attitude of the Russian Marxists, specifically the Bolsheviks, towards the concept of religion supposedly originates from its own Marxist ideology. Although Marxism is critical of religion as an institution, the approach that Marxism would have on the question of religion is not as clear. Such aspect is specifically observed in the use of language of major leading Russian Marxist figures, such as Lenin and Trotsky, throughout the early 20th century, where the use of religious metaphors was widely used in their philosophical writings and speeches, as well as in propaganda posters of general left-wing movements in Russia as a whole. The methodology of the research will consist of a sociolinguistic and sociology of language approach within a sociohistorical framework of late Tsarist and early Soviet Russia, 1905-1926. The purpose of such approaches are not simply to point out the religious metaphors used in the writings and speeches of Marxists in Russia, but rather in order to analyse how the use of such metaphors represent an important socio-political connection with the context of Russia at the time. In other words, the use of religious metaphors was not only more akin to Russian culture at the time, but this also resonated and was more familiar with the conditions of the working class and peasantry. An example in this study can be observed in the writings of Lenin, where the theme of chudo (miracle) is often mentioned in his writings, and such a word is commonly associated with an idealist philosophy rather than a materialist one, which represents a common theme in Russian culture in regards to the principle of hope for a better life. A further and even more obvious example is Trotsky’s writings about how the revolution of 1905 “would be revived”, which not only resonates with the theme of resurrection, but also prophesises the “second coming” of a future revolution. Such metaphors are important in the writings of such authors, as they simultaneously contain Marxist ideas, as well as religious themes. In doing this research, this paper will demonstrate two aspects. Firstly, the paper will analyse the use of the metaphors by Russian Marxists as a whole in regards to a socio-political and ideological perspectives akin to those of Marxism. Secondly, it will also demonstrate the role that such metaphors have in regards to their impact on the left-wing movements within Russia itself, as well as their relation to the working class and peasantry of Russia within the historical context.

Keywords: language and politics, Marxism, Russian history, social history, sociology of language

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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: Mohammad Khatib, Salam Hadid

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Introduction: Developing nurses' cultural competence begins in their basic training and requires them to participate in an array of activities which raise their awareness and stimulate their interest, desire and curiosity about different cultures, by creating opportunities for intercultural meetings promoting the concept of 'culture' and its components, including recognition of cultural diversity and the legitimacy of the other. Importantly, professionals need to acquire specific cultural knowledge and thorough understanding of the values, norms, customs, beliefs and symbols of different cultures. Similarly, they need to be given opportunities to practice the verbal and non-verbal communication skills of other cultures according to their cultural codes. Such a system is being implemented as part of nursing studies at Zefat Academic College in two study frameworks; firstly, a course integrating nursing theory and practice in multicultural nursing; secondly, a course in learning the languages spoken in Israel focusing on medical and nursing terminology. Methods: Students participating in the 'Transcultural Nursing' course come from a variety of backgrounds: Jews, or Arabs, religious, or secular; Muslim, Christian, new immigrants, Ethiopians or from other cultural affiliations. They are required to present and discuss cultural practices that affect health. In addition, as part of the language course, students learn and teach their friends 5 spoken languages (Arabic, Russian, Amharian, Yidish, and Sign language) focusing on therapeutic interaction and communication using the vocabulary and concepts necessary for the therapeutic encounter. An evaluation of the process and the results was done using a structured questionnaire which includes series of questions relating to the contributions of the courses to their cultural knowledge, awareness and skills. 155 students completed the questionnaire. Results: A preliminary assessment of this educational system points an increase in cultural awareness and knowledge among the students as well as in their willingness to recognize the other's difference. A positive atmosphere of multiculturalism is reflected in students' mutual interest and respect was created. Students showed a deep understanding of cultural issues relating to health and care (consanguinity and genetics, food customs; cultural events, reincarnation, traditional treatments etc.). Most of the students were willing to recommend the courses to others and suggest some changes relating learning methods (more simulations, role playing and activities).

Keywords: cultural competence, nursing education, culture, language

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Authors: Ivana Scidà, Francesco Alotto, Anna Osello

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Due to the importance given today to innovation, the education sector is evolving thanks digital technologies. Virtual Reality (VR) can be a potential teaching tool offering many advantages in the field of training and education, as it allows to acquire theoretical knowledge and practical skills using an immersive experience in less time than the traditional educational process. These assumptions allow to lay the foundations for a new educational environment, involving and stimulating for students. Starting from the objective of strengthening the innovative teaching offer and the learning processes, the case study of the research concerns the digitalization of MastrLAB, High Quality Laboratory (HQL) belonging to the Department of Structural, Building and Geotechnical Engineering (DISEG) of the Polytechnic of Turin, a center specialized in experimental mechanical tests on traditional and innovative building materials and on the structures made with them. The MastrLAB-VR has been developed, a revolutionary innovative training tool designed with the aim of educating the class in total safety on the techniques of use of machinery, thus reducing the dangers arising from the performance of potentially dangerous activities. The virtual laboratory, dedicated to the students of the Building and Civil Engineering Courses of the Polytechnic of Turin, has been projected to simulate in an absolutely realistic way the experimental approach to the structural tests foreseen in their courses of study: from the tensile tests to the relaxation tests, from the steel qualification tests to the resilience tests on elements at environmental conditions or at characterizing temperatures. The research work proposes a methodology for the virtualization of technical laboratories through the application of Building Information Modelling (BIM), starting from the creation of a digital model. The process includes the creation of an independent application, which with Oculus Rift technology will allow the user to explore the environment and interact with objects through the use of joypads. The application has been tested in prototype way on volunteers, obtaining results related to the acquisition of the educational notions exposed in the experience through a virtual quiz with multiple answers, achieving an overall evaluation report. The results have shown that MastrLAB-VR is suitable for both beginners and experts and will be adopted experimentally for other laboratories of the University departments.

Keywords: building information modelling, digital learning, education, virtual laboratory, virtual reality

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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: Hiba Naccache, Rima Hleiss

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Additive manufacturing AM or 3D printing is one of the pillars of Industry 4.0. Compared to traditional manufacturing, AM provides a prototype before production in order to optimize the design and avoid the stock market and uses strictly necessary material which can be recyclable, for the benefit of leaning towards local production, saving money, time and resources. Different types of AM exist and it has a broad range of applications across several industries like aerospace, automotive, medicine, education and else. The Laser Powder Bed Fusion (LPBF) is a metal AM technique that uses a laser to liquefy metal powder, layer by layer, to build a three-dimensional (3D) object. In industry 4.0 and aligned with the numbers 9 (Industry, Innovation and Infrastructure) and 12 (Responsible Production and Consumption) of the Sustainable Development Goals of the UNESCO 2030 Agenda, the AM’s manufacturers committed to minimizing the environmental impact by being sustainable in every production. The LPBF has several environmental advantages, like reduced waste production, lower energy consumption, and greater flexibility in creating components with lightweight and complex geometries. However, LPBF also have environmental drawbacks, like energy consumption, gas consumption and emissions. It is critical to recognize the environmental impacts of LPBF in order to mitigate them. To increase awareness and promote sustainable practices regarding LPBF, the researchers use the Elaboration Likelihood Model (ELM) theory where people from multiple universities in France and Qatar process information in two ways: peripherally and centrally. The peripheral campaigns use superficial cues to get attention, and the central campaigns provide clear and concise information. The authors created a seminar including a video showing LPBF production and a website with educational resources. The data is collected using questionnaire to test attitude about the public awareness before and after the seminar. The results reflected a great shift on the awareness toward LPBF and its impact on the environment. With no presence of similar research, to our best knowledge, this study will add to the literature on the sustainability of the LPBF production technique.

Keywords: additive manufacturing, laser powder bed fusion, elaboration likelihood model theory, sustainable development goals, education-awareness, France, Qatar, specific energy consumption, environmental impact, lightweight components

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Authors: Lusi Susanti, Prima Fithri

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The rapid growth of Indonesia population is directly proportional to the energy needs of the country, but not all of Indonesian population can relish the electricity. Indonesia's electrification ratio is still around 80.1%, which means that approximately 19.9% of households in Indonesia have not been getting the flow of electrical energy. Household electricity consumptions in Indonesia are generally still dominated by the public urban. In the city of Padang, West Sumatera, Indonesia, about 94.10% are power users of government services (PLN). The most important thing of the issue is human resources efficient energy. User behavior in utilizing electricity becomes significant. However repair solution will impact the user's habits sustainable energy issues. This study attempts to identify the user behavior and lifestyle that affect household electricity consumption and to evaluate the potential for energy saving. The behavior component is frequently underestimated or ignored in analyses of household electrical energy end use, partly because of its complexity. It is influenced by socio-demographic factors, culture, attitudes, aesthetic norms and comfort, as well as social and economic variables. Intensive questioner survey, in-depth interview and statistical analysis are carried out to collect scientific evidences of the behavioral based changes instruments to reduce electricity consumption in household sector. The questioner was developed to include five factors assuming affect the electricity consumption pattern in household sector. They are: attitude, energy price, household income, knowledge and other determinants. The survey was carried out in Padang, West Sumatra Province Indonesia. About 210 questioner papers were proportionally distributed to households in 11 districts in Padang. Stratified sampling was used as a method to select respondents. The results show that the household size, income, payment methods and size of house are factors affecting electricity saving behavior in residential sector. Household expenses on electricity are strongly influenced by gender, type of job, level of education, size of house, income, payment method and level of installed power. These results provide a scientific evidence for stakeholders on the potential of controlling electricity consumption and designing energy policy by government in residential sector.

Keywords: electricity, energy saving, household, behavior, policy

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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: Inger Eriksson, Lisbeth Ohlsson, Jeremias Rosenqvist

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Ingress: Focus in the study is directed towards phenomena and concepts of segregation, integration, and inclusion of students attending a special school form in Sweden, namely compulsory school for pupils with learning disabilities (in Swedish 'särskola') as an alternative to mainstream compulsory school. Aim: The aim of the study is to examine the school situation for students attending särskola from a historical perspective focussing the 1980s, 1990s and the 21st century, from an integration perspective, and from a perspective of power. Procedure: Five sub-studies are reported, where integration and inclusion are looked into by observation studies and interviews with school leaders, teachers, special and remedial teachers, psychologists, coordinators, and parents in the special schools/särskola. In brief, the study about special school students attending mainstream classes from 1998 takes its point of departure in the idea that all knowledge development takes place in a social context. A special interest is taken in the school’s role for integration generally, and the role of special education particularly and on whose conditions the integration is taking place – the special school students' or the other students,' or may be equally, in the class. Pedagogical and social conditions for so called individually integrated special school students in elementary school classes were studied in eleven classes. Results: The findings are interpreted in a power perspective supported by Foucault and relationally by Vygotsky. The main part of the data consists of extensive descriptions of the eleven cases, here called integration situations. Conclusions: In summary, this study suggests that the possibilities for a special school student to get into the class community and fellowship and thereby be integrated with the class are to a high degree dependant on to what extent the student can take part in the pedagogical processes. The pedagogical situation for the special school student is affected not only by the class teacher and the support and measures undertaken but also by the other students in the class as they, in turn, are affected by how the special school student is acting. This mutual impact, which constitutes the integration process in itself, might result in a true integration if the special school student attains the status of being accepted on his/her own terms not only being cared for or cherished by some classmates. A special school student who is not accepted even on the terms of the class will often experience severe problems in the contacts with classmates and the school situation might thus be a mere placement.

Keywords: integration/inclusion, mainstream school, power, special school students

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Authors: Lucian Vasile Bagiu, Paraschiva Bagiu

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The novel written in Italian and published in Italy in 1992 by the Romanian scholar Marin Mincu is meant for the foreign reader, aiming apparently at a better knowledge of the historical character of Vlad the Empalor (Vlad Dracul), within the European cultural, political and historical context of 1463. Throughout the very well written tome, one comes to realize that one of the underlining levels of the fiction is the exposing of various fundamental features of the Romanian culture and civilization. The author of the diary, Dracula, makes mention of Corpus Hermeticum no less than fifteen times, suggesting his own diary is some sort of a philosophical counterpart. The essay focuses on several ‘truths’ and ‘wisdom’ revealed in the fictional teachings of Dracula. The boycott of History by the Romanians is identified as an echo of the philosophical approach of the famous Romanian scholar and writer Lucian Blaga. The orality of the Romanian culture is a landmark opposed to written culture of the Western Europe. The religion of the ancient Dacian God Zalmoxis is seen as the basis for the Romanian existential and/or metaphysical ethnic philosophy (a feature tackled by the famous Romanian historian of religion Mircea Eliade), with a suggestion that Hermes Trismegistus may have written his Corpus Hermeticum being influenced by Zalmoxis. The historical figure of the last Dacian king Decebalus (death 106 AD) is a good pretext for a tantalizing Indo-European suggestion that the prehistoric Thraco-Dacian people may have been the ancestors of the first Romans settled in Latium. The lost diary of the Emperor Trajan The Bello Dacico may have proved that the unknown language of the Dacians was very much alike Latin language (a secret well hidden by the Vatican). The attitude towards death of the Dacians, as described by Herodotus, may have later inspired Pitagora, Socrates, the Eleusinian and Orphic Mysteries, etc. All of these within the Humanistic and Renascentist European context of the epoch, Dracula having a close relationship with scholars such as Nicolaus Cusanus, Cosimo de Medici, Marsilio Ficino, Pope Pius II, etc. Thus The Diary of Dracula turns out as exciting and stupefying as Corpus Hermeticum, a book impossible to assimilate entirely, yet a reference not wise to be ignored.

Keywords: Corpus Hermeticum, Dacians, Dracula, Zalmoxis

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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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