Search results for: adult learning.

Authors: Xian Xie, Qinghua Fang

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This study used quantitative and qualitative methods to explore the characteristics of flipped classroom’s influence on classroom environment of college English reading, writing, and translating, and to summarize and reflect on the teaching characteristics of college English Reading, writing, and translating. The results of the study indicated that after the flipped classroom applied to reading, writing, and translating, students’ performance was improved to a certain extent, the classroom environment was improved to some extent, students of the flipped classroom are generally satisfied with the classroom environment; students showed a certain degree of individual differences to the degree of cooperation, participation, self-responsibility, task-orientation, and the teacher leadership and innovation. The study indicated that the implementation of flipped classroom teaching mode can optimize College English reading, writing, and translating classroom environment and realize target－learner as the center in foreign language teaching and learning, but bring a greater challenge to teachers.

Keywords: classroom environment, college English reading, writing and translating, individual differences, flipped classroom

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Authors: Hossein Aghababaei, Sergio Vitale, Giampaolo Ferraioli

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In the context of Synthetic Aperture Radar (SAR) data, polarization is an important source of information for Earth's surface monitoring. SAR Systems are often considered to transmit only one polarization. This constraint leads to either single or dual polarimetric SAR imaging modalities. Single polarimetric systems operate with a fixed single polarization of both transmitted and received electromagnetic (EM) waves, resulting in a single acquisition channel. Dual polarimetric systems, on the other hand, transmit in one fixed polarization and receive in two orthogonal polarizations, resulting in two acquisition channels. Dual polarimetric systems are obviously more informative than single polarimetric systems and are increasingly being used for a variety of remote sensing applications. In dual polarimetric systems, the choice of polarizations for the transmitter and the receiver is open. The choice of circular transmit polarization and coherent dual linear receive polarizations forms a special dual polarimetric system called hybrid polarimetry, which brings the properties of rotational invariance to geometrical orientations of features in the scene and optimizes the design of the radar in terms of reliability, mass, and power constraints. The complete characterization of target scattering, however, requires fully polarimetric data, which can be acquired with systems that transmit two orthogonal polarizations. This adds further complexity to data acquisition and shortens the coverage area or swath of fully polarimetric images compared to the swath of dual or hybrid polarimetric images. The search for solutions to augment dual polarimetric data to full polarimetric data will therefore take advantage of full characterization and exploitation of the backscattered field over a wider coverage with less system complexity. Several methods for reconstructing fully polarimetric images using hybrid polarimetric data can be found in the literature. Although the improvements achieved by the newly investigated and experimented reconstruction techniques are undeniable, the existing methods are, however, mostly based upon model assumptions (especially the assumption of reflectance symmetry), which may limit their reliability and applicability to vegetation and forest scenarios. To overcome the problems of these techniques, this paper proposes a new framework for reconstructing fully polarimetric information from hybrid polarimetric data. The framework uses Deep Learning solutions to augment hybrid polarimetric data without relying on model assumptions. A convolutional neural network (CNN) with a specific architecture and loss function is defined for this augmentation problem by focusing on different scattering properties of the polarimetric data. In particular, the method controls the CNN training process with respect to several characteristic features of polarimetric images defined by the combination of different terms in the cost or loss function. The proposed method is experimentally validated with real data sets and compared with a well-known and standard approach from the literature. From the experiments, the reconstruction performance of the proposed framework is superior to conventional reconstruction methods. The pseudo fully polarimetric data reconstructed by the proposed method also agree well with the actual fully polarimetric images acquired by radar systems, confirming the reliability and efficiency of the proposed method.

Keywords: SAR image, polarimetric SAR image, convolutional neural network, deep learnig, deep neural network

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Authors: Khadijat T. Bamigbade, Olufade F. W. Onifade

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The field of automatic facial expression analysis has been an active research area in the last two decades. Its vast applicability in various domains has drawn so much attention into developing techniques and dataset that mirror real life scenarios. Many techniques such as Local Binary Patterns and its variants (CLBP, LBP-TOP) and lately, deep learning techniques, have been used for facial expression recognition. However, the problem of occlusion has not been sufficiently handled, making their results not applicable in real life situations. This paper develops a simple, yet highly efficient method tagged Local Binary Pattern-Histogram of Gradient (LBP-HOG) with occlusion detection in face image, using a multi-class SVM for Action Unit and in turn expression recognition. Our method was evaluated on three publicly available datasets which are JAFFE, CK, SFEW. Experimental results showed that our approach performed considerably well when compared with state-of-the-art algorithms and gave insight to occlusion detection as a key step to handling expression in wild.

Keywords: automatic facial expression analysis, local binary pattern, LBP-HOG, occlusion detection

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Authors: Dinesh Khanduja, Pardeep Kumar Sharma

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Education as the most important resource in any country has multiplying effects on all facets of development in a society. The new social realities, particularly, the interplay between democratization of education; unprecedented developments in the IT sector; emergence of knowledge society, liberalization of economy, and globalization have greatly influenced the educational process of all nations. This turbulence entails upon education to undergo dramatic changes to keep up with the new expectations. Growth of entrepreneurship among Indian women is highly important for empowering them and this is highly essential for the socio-economic development of a society. Unfortunately, in India, there is poor acceptance of entrepreneurship among women as unfounded myths and fears restrain them to be enterprising. To remove these inhibitions, the education system needs to be re-engineered to make entrepreneurship more acceptable. This paper empirically analyses the results of a survey done on around 500 female graduates in North India to measure and evaluate various entrepreneurial traits present in them. A formative model has been devised in this context, which should improve the teaching-learning process in our education system, which can lead to a sustainable growth of women entrepreneurship in India.

Keywords: women empowerment, entrepreneurship, education system, women entrepreneurship, sustainable development

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Authors: Jiazhi Zeng, Leiyu Shi, Xia Zou, Wen Chen, Li Ling

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Background: China is facing the unprecedented challenge of rapidly increasing rural-to-urban migration. Due to the household registration system, migrants are in a vulnerable state when they attempt to access to primary care services. A strong primary care system can reduce health inequities and mitigate socioeconomic disparities in healthcare utilization. Literature indicated that migrants were more reliant on the primary care system than local residents. Although the Chinese government has attached great importance to creating an efficient health system, primary care services are still underutilized. The referral system between primary care institutions and hospitals has not yet been completely established in China. The general populations often go directly to hospitals instead of primary care institutions for their primary care. Primary care institutions generally consist of community health centers (CHCs) and community health stations (CHSs) in urban areas, and township health centers (THCs) and rural health stations (THSs) in rural areas. In addition, primary care services are also provided by the outpatient department of municipal hospitals and tertiary hospitals. A better understanding of migrants’ experiences with primary care in the above-mentioned medical institutions is critical for improving the performance of primary care institutions and providing indications of the attributes that require further attention. The purpose of this pioneering study is to explore rural-to-urban migrants’ experiences in primary care, compare their primary care experiences in four types of medical institutions in Guangzhou, China, and suggest implications for targeted interventions to improve primary care for the migrants. Methods: This was a cross-sectional study conducted with 736 rural-to-urban migrants in Guangzhou, China, in 2014. A multistage sampling method was employed. A validated Chinese version of Primary Care Assessment Tool - Adult Short Version (PCAT-AS) was used to collect information on migrants’ primary care experiences. The PCAT-AS consists of 10 domains. Analysis of covariance was conducted for comparison on PCAT domain scores and total scores among migrants accessing four types of medical institutions. Multiple linear regression models were used to explore factors associated with PCAT total scores. Results: After controlling for socio-demographic characteristics, migrant characteristics, health status and health insurance status, migrants accessing primary care in tertiary hospitals had the highest PCAT total scores when compared with those accessing primary care THCs/ RHSs (25.49 vs. 24.18, P=0.007) and CHCs/ CHSs(25.49 vs. 24.24, P=0.006). There was no statistical significant difference for PCAT total scores between migrants accessing primary care in CHCs/CHSs and those in municipal hospitals (24.24 vs. 25.02, P=0.436). Factors positively associated with higher PCAT total scores also included insurance covering parts of healthcare payment (P < 0.001). Conclusions: This study highlights the need for improvement in primary care provided by primary care institutions for rural-to-urban migrants. Migrants receiving primary care from THCs, RHSs, CHSs and CHSs reported worse primary care experiences than those receiving primary care from tertiary hospitals. Relevant policies related to medical insurance should be implemented for providing affordable healthcare services for migrants accessing primary care. Further research exploring the specific reasons for poorer PCAT scores of primary care institutions users will be needed.

Keywords: China, PCAT, primary care, rural-to-urban migrants

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Authors: Mikkel Snorre Wilms Boysen

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From a theoretical perspective, expertise is generally considered a precondition for creativity. The assumption is that an individual needs to master the common and accepted rules and techniques within a certain knowledge-domain in order to create something new and valuable. However, real life cases, and a limited amount of empirical studies, demonstrate that this assumption may be overly simple. In this article, this question is explored through a number of semi-experimental case studies conducted within the fields of music, technology, and youth culture. The studies indicate that, in various ways, expertise plays an important part in creative processes. However, the case studies also indicate that expertise sometimes leads to an entrenched perspective, in the sense that knowledge and experience may work as a path into the well-known rather than into the unknown. In this article, these issues are explored with reference to different theoretical approaches to creativity and learning, including actor-network theory, the theory of blind variation and selective retention, and Csikszentmihalyi’s system model. Finally, some educational aspects and implications of this are discussed.

Keywords: creativity, expertise , education, technology

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Authors: Rachael Andrews, Viktoria McMillan, Shuaib Nasser, Christopher M. Roberts

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Background and objective: The National Review of Asthma Deaths (NRAD) found that asthma management and care was inadequate in 26% of cases reviewed. Major shortfalls identified were adherence to national guidelines and standards and, particularly, the organisation of care, including supervision and monitoring in primary care, with 70% of cases reviewed having at least one avoidable factor in this area. 5.4 million people in the UK are diagnosed with and actively treated for asthma, and approximately 60,000 are admitted to hospital with acute exacerbations each year. The majority of people with asthma receive management and treatment solely in primary care. This has therefore created concern that many people within the UK are receiving sub-optimal asthma care resulting in unnecessary morbidity and risk of adverse outcome. NRAD concluded that a national asthma audit programme should be established to measure and improve processes, organisation, and outcomes of asthma care. Objective: To develop a primary care dataset enabling extraction of information from GP practices in Wales and providing robust data by which results and lessons could be drawn and drive service development and improvement. Methods: A multidisciplinary group of experts, including general practitioners, primary care organisation representatives, and asthma patients was formed and used as a source of governance and guidance. A review of asthma literature, guidance, and standards took place and was used to identify areas of asthma care which, if improved, would lead to better patient outcomes. Modified Delphi methodology was used to gain consensus from the expert group on which of the areas identified were to be prioritised, and an asthma patient and carer focus group held to seek views and feedback on areas of asthma care that were important to them. Areas of asthma care identified by both groups were mapped to asthma guidelines and standards to inform and develop primary and secondary care datasets covering both adult and pediatric care. Dataset development consisted of expert review and a targeted consultation process in order to seek broad stakeholder views and feedback. Results: Areas of asthma care identified as requiring prioritisation by the National Asthma Audit were: (i) Prescribing, (ii) Asthma diagnosis (iii) Asthma Reviews (iv) Personalised Asthma Action Plans (PAAPs) (v) Primary care follow-up after discharge from hospital (vi) Methodologies and primary care queries were developed to cover each of the areas of poor and variable asthma care identified and the queries designed to extract information directly from electronic patients’ records. Conclusion: This paper describes the methodological approach followed to develop primary care datasets for a National Asthma Audit. It sets out the principles behind the establishment of a National Asthma Audit programme in response to a national asthma mortality review and describes the development activities undertaken. Key process elements included: (i) mapping identified areas of poor and variable asthma care to national guidelines and standards, (ii) early engagement of experts, including clinicians and patients in the process, and (iii) targeted consultation of the queries to provide further insight into measures that were collectable, reproducible and relevant.

Keywords: asthma, primary care, general practice, dataset development

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Authors: Proud Arunrangsiwed, Sarinya Kongtieng

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Teachers and university lecturers face an unsolved problem, which is students’ multitasking behaviors during class time, such as texting or playing a game. It is important to examine the most powerful predictor that can result in students’ educational performances. Meta-analysis was used to analyze the research articles, which were published with the keywords, multitasking, class performance, and texting. We selected 14 research articles published during 2008-2013 from online databases, and four articles met the predetermined inclusion criteria. Effect size of each pair of variables was used as the dependent variable. The findings revealed that the students’ expectancy and value on SNSs usages is the best significant predictor of their educational performances, followed by their motivation and ability in using SNSs, prior educational performances, usage behaviors of SNSs in class, and their personal characteristics, respectively. Future study should conduct a longitudinal design to better understand the effect of multitasking in the classroom.

Keywords: meta-regression analysis, social networking sites, academic Performances, multitasking, motivation

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Authors: Aisulu Tileukulova, Zhexebay Dauren

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Radar plays an important role because it is widely used in civil and military fields. Target detection is one of the most important radar applications. The accuracy of detecting inconspicuous aerial objects in radar facilities is lower against the background of noise. Convolutional neural networks can be used to improve the recognition of this type of aerial object. The purpose of this work is to develop an algorithm for recognizing aerial objects using convolutional neural networks, as well as training a neural network. In this paper, the structure of a convolutional neural network (CNN) consists of different types of layers: 8 convolutional layers and 3 layers of a fully connected perceptron. ReLU is used as an activation function in convolutional layers, while the last layer uses softmax. It is necessary to form a data set for training a neural network in order to detect a target. We built a Confusion Matrix of the CNN model to measure the effectiveness of our model. The results showed that the accuracy when testing the model was 95.7%. Classification of echo signals using CNN shows high accuracy and significantly speeds up the process of predicting the target.

Keywords: radar, neural network, convolutional neural network, echo signals

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Authors: Edward Guillén, Jeisson Sánchez, Carlos Omar Ramos

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In recent years, a wide variety of applications are developed with Support Vector Machines -SVM- methods and Artificial Neural Networks -ANN-. In general, these methods depend on intrusion knowledge databases such as KDD99, ISCX, and CAIDA among others. New classes of detectors are generated by machine learning techniques, trained and tested over network databases. Thereafter, detectors are employed to detect anomalies in network communication scenarios according to user’s connections behavior. The first detector based on training dataset is deployed in different real-world networks with mobile and non-mobile devices to analyze the performance and accuracy over static detection. The vulnerabilities are based on previous work in telemedicine apps that were developed on the research group. This paper presents the differences on detections results between some network scenarios by applying traditional detectors deployed with artificial neural networks and support vector machines.

Keywords: anomaly detection, back-propagation neural networks, network intrusion detection systems, support vector machines

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Authors: M. Rodionov, N. Sharapova, Z. Dedovets

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The article describes methods of preparation of future teachers that includes the entire diversity of traditional and computer-oriented methodological approaches. The authors reveal how, in the specific educational environment, a teacher can choose the most effective combination of educational technologies based on the nature of the learning task. The key conditions that determine such a choice are that the methodological approach corresponds to the specificity of the problem being solved and that it is also responsive to the individual characteristics of the students. The article refers to the training of students in the proper use of mathematical electronic tools for educational purposes. The preparation of future mathematics teachers should be a step-by-step process, building on specific examples. At the first stage, students optimally solve problems aided by electronic means of teaching. At the second stage, the main emphasis is on modeling lessons. At the third stage, students develop and implement strategies in the study of one of the topics within a school mathematics curriculum. The article also recommended the implementation of this strategy in preparation of future teachers and stated the possible benefits.

Keywords: education, methodological approaches, teacher, secondary school

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Authors: Carmen Axisa, Louise Nash, Patrick Kelly, Simon Willcock

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Introduction: Doctors experience high levels of burnout, stress and psychiatric morbidity. This can affect the health of the doctor and impact patient care. Study Aims: To evaluate the effectiveness of a workshop intervention to promote wellbeing for Australian Physician Trainees. Methods: A workshop was developed in consultation with specialist clinicians to promote health and wellbeing for physician trainees. The workshop objectives were to improve participant understanding about factors affecting their health and wellbeing, to outline strategies on how to improve health and wellbeing and to encourage participants to apply these strategies in their own lives. There was a focus on building resilience and developing long term healthy behaviours as part of the physician trainee daily lifestyle. Trainees had the opportunity to learn practical strategies for stress management, gain insight into their behaviour and take steps to improve their health and wellbeing. The workshop also identified resources and support systems available to trainees. The workshop duration was four and a half hours including a thirty- minute meal break where a catered meal was provided for the trainees. Workshop evaluations were conducted at the end of the workshop. Sixty-seven physician trainees from Adult Medicine and Paediatric training programs in Sydney Australia were randomised into intervention and control groups. The intervention group attended a workshop facilitated by specialist clinicians and the control group did not. Baseline and post intervention measurements were taken for both groups to evaluate the impact and effectiveness of the workshop. Forty-six participants completed all three measurements (69%). Demographic, personal and self-reported data regarding work/life patterns was collected. Outcome measures include Depression Anxiety Stress Scale (DASS), Professional Quality of Life Scale (ProQOL) and Alcohol Use Disorders Identification Test (AUDIT). Results: The workshop was well received by the physician trainees and workshop evaluations showed that the majority of trainees strongly agree or agree that the training was relevant to their needs (96%) and met their expectations (92%). All trainees strongly agree or agree that they would recommend the workshop to their medical colleagues. In comparison to the control group we observed a reduction in alcohol use, depression and burnout but an increase in stress, anxiety and secondary traumatic stress in the intervention group, at the primary endpoint measured at 6 months. However, none of these differences reached statistical significance (p > 0.05). Discussion: Although the study did not reach statistical significance, the workshop may be beneficial to physician trainees. Trainees had the opportunity to share ideas, gain insight into their own behaviour, learn practical strategies for stress management and discuss approach to work, life and self-care. The workshop discussions enabled trainees to share their experiences in a supported environment where they learned that other trainees experienced stress and burnout and they were not alone in needing to acquire successful coping mechanisms and stress management strategies. Conclusion: These findings suggest that physician trainees are a vulnerable group who may benefit from initiatives that promote wellbeing and from a more supportive work environment.

Keywords: doctors' health, physician burnout, physician resilience, wellbeing workshop

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Authors: Greeshma Sharma, Sushil Chandra, Vijander Singh, Alok Prakash Mittal

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Navigational ability requires spatial representation, planning, and memory. It covers three interdependent domains, i.e. cognitive and perceptual factors, neural information processing, and variability in brain microstructure. Many attempts have been made to see the role of spatial representation in the navigational ability, and the individual differences have been identified in the neural substrate. But, there is also a need to address the influence of planning, memory on navigational ability. The present study aims to evaluate relations of aforementioned factors in the navigational ability. Total 30 participants volunteered in the study of a virtual shopping complex and subsequently were classified into good and bad navigators based on their performances. The result showed that planning ability was the most correlated factor for the navigational ability and also the discriminating factor between the good and bad navigators. There was also found the correlations between spatial memory recall and navigational ability. However, non-verbal episodic memory and spatial memory recall were also found to be correlated with the learning variable. This study attempts to identify differences between people with more and less navigational ability on the basis of planning and memory.

Keywords: memory, planning navigational ability, virtual reality

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Authors: Juan Bojórquez, Henry E. Reyes, Edén Bojórquez, Alfredo Reyes-Salazar

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This paper addressed the use of Artificial Intelligence to obtain the structural reliability of reinforced concrete buildings. For this purpose, artificial neuronal networks (ANN) are developed to predict seismic demand hazard curves. In order to have enough input-output data to train the ANN, a set of reinforced concrete buildings (low, mid, and high rise) are designed, then a probabilistic seismic hazard analysis is made to obtain the seismic demand hazard curves. The results are then used as input-output data to train the ANN in a feedforward backpropagation model. The predicted values of the seismic demand hazard curves found by the ANN are then compared. Finally, it is concluded that the computer time analysis is significantly lower and the predictions obtained from the ANN were accurate in comparison to the values obtained from the conventional methods.

Keywords: structural reliability, seismic design, machine learning, artificial neural network, probabilistic seismic hazard analysis, seismic demand hazard curves

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Authors: Wongwarit Nipitwittaya

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The objectives of this research is bringing folk song with the teaching of Western music were to examine to investigate, to compare, develop the skill, technique, knowledge of Thai folk song and to preserve folk song of Thailand to be known more widely also learn Thai culture from Thai folk song. Study by bringing Thailand folk song is widely known for learning with Western music in course brass performance. Bringing the melody of Thai folk music and changing patterns to western music notes for appropriate on brass performance. A sample was selected from brass students, using research by assessment of knowledge from test after used Thai folk song lesson. The lesson focus for scales and key signature in western music by divided into two groups, the one study by used research tools and another one used simple lesson and a collection of research until testing. The results of the study were as follows: 1. There are good development skill form research method 2. Sound recognition can be even better. The study was a qualitative research and data collection by observation.

Keywords: Thai folk song, brass instrument, key signature, western music

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Authors: Ebere Ellison Obisike, Justina N. Adalikwu-Obisike

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Artificial intelligence (AI) is progressively transforming health and social care. With the rapid invention of various electronic devices, machine learning, and computing systems, the use of AI istraversing many health and social care practices. In this systematic review of journal and grey literature, this study explores how the applications of AI might promote the prevention of micro and macrovascular complications in type 1 diabetic patients. This review focuses on the use of a digitized blood glucose meter and the application of insulin pumps for the effective management of type 1 diabetes in low and middle-income countries. It is projected that the applications of AI may assist individuals with type 1 diabetes to monitor and control their blood glucose level and prevent the early onset of micro and macrovascular complications.

Keywords: artificial intelligence, blood glucose meter, insulin pump, low and middle-income countries, micro and macrovascular complications, type 1 diabetes

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Authors: Sanjay Adhikesaven

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Workplace accidents are a critical problem that causes many deaths, injuries, and financial losses. Climate change has a severe impact on industrial workers, partially caused by global warming. To reduce such casualties, it is important to proactively find unsafe environments where injuries could occur by detecting the use of personal protective equipment (PPE) and identifying unsafe activities. Thus, we propose an industrial workplace alerting and monitoring platform to detect PPE use and classify unsafe activity in group settings involving multiple humans and objects over a long period of time. Our proposed method is the first to analyze prolonged actions involving multiple people or objects. It benefits from combining pose estimation with PPE detection in one platform. Additionally, we propose the first open-source annotated data set with video data from industrial workplaces annotated with the action classifications and detected PPE. The proposed system can be implemented within the surveillance cameras already present in industrial settings, making it a practical and effective solution.

Keywords: computer vision, deep learning, workplace safety, automation

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Authors: Sean McCarthy, Patrice M. Ludwig, Will Watson

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This exploratory case study explores the deployment of Social Network Analysis (SNA) in mapping community assets in an interdisciplinary, undergraduate, team-taught course focused on income insecure populations in a rural area in the US. Specifically, it analyzes how students were taught to collect data on community assets and to visualize the connections between those assets using Kumu, an SNA data visualization tool. Further, the case study shows how social network data was also collected about student teams via their written communications in Slack, an enterprise messaging tool, which enabled instructors to manage and guide student research activity throughout the semester. The discussion presents how SNA methods can simultaneously inform both community-based research and social innovation pedagogy through the use of data visualization and collaboration-focused communication technologies.

Keywords: social innovation, social network analysis, pedagogy, problem-based learning, data visualization, information communication technologies

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Authors: Rashini Maduka, C. R. Wijesinghe, A. R. Weerasinghe

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Drug-drug interactions (DDIs) can happen when two or more drugs are taken together. Today DDIs have become a serious health issue due to adverse drug effects. In vivo and in vitro methods for identifying DDIs are time-consuming and costly. Therefore, in-silico-based approaches are preferred in DDI identification. Most machine learning models for DDI prediction are used chemical and biological drug properties as features. However, some drug features are not available and costly to extract. Therefore, it is better to make automatic feature engineering. Furthermore, people who have diabetes already suffer from other diseases and take more than one medicine together. Then adverse drug effects may happen to diabetic patients and cause unpleasant reactions in the body. In this study, we present a model with a graph convolutional autoencoder and a graph decoder using a dataset from DrugBank version 5.1.3. The main objective of the model is to identify unknown interactions between antidiabetic drugs and the drugs taken by diabetic patients for other diseases. We considered automatic feature engineering and used Known DDIs only as the input for the model. Our model has achieved 0.86 in AUC and 0.86 in AP.

Keywords: drug-drug interaction prediction, graph embedding, graph convolutional networks, adverse drug effects

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Authors: Erion Luga, Aksel Seitllari, Kemal Pervanqe

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This study investigates the relation between physical and mechanical properties of concrete paving stones using neuro-fuzzy approach. For this purpose 200 samples of concrete paving stones were selected randomly from different sources. The first phase included the determination of physical properties of the samples such as water absorption capacity, porosity and unit weight. After that the indirect tensile strength test and compressive strength test of the samples were performed. İn the second phase, adaptive neuro-fuzzy approach was employed to simulate nonlinear mapping between the above mentioned physical properties and mechanical properties of paving stones. The neuro-fuzzy models uses Sugeno type fuzzy inference system. The models parameters were adapted using hybrid learning algorithm and input space was fuzzyfied by considering grid partitioning. It is concluded based on the observed data and the estimated data through ANFIS models that neuro-fuzzy system exhibits a satisfactory performance.

Keywords: paving stones, physical properties, mechanical properties, ANFIS

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Authors: Sandra Figueiredo

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To guarantee the well-being and the academic achievement it is crucial into the global society to develop techniques to assess language competence and control psychological aspects on the second language learning context. The current scenario of the war conflicts that are emerging mostly in Europe and Middle East have been resulting in forced immigration and refugees’ maladjustment. The inclusion is the priority for United Nations concerning the sustainability of societies. For inclusion, psychological screening tests and educational tools are urgent. Method: Approximately 100 refugees from Ukraine were assessed, in Portugal, under the administration of the PCL-5. This 20-item instrument evaluates the Post-Traumatic Disorder. Expected results: The statistical analysis will be performed with the International Database Analyzer and SPSS (v. 28). The results expected are the relationship between traumatic events caused by war and post-traumatic symptomatology (anxiety, hypervigilance, stress). Implications: The data will be discussed concerning the problems of belonging, the psychological constraints and educational attainment (language needs included) experienced by the individuals more recently arrived to the hosting societies. The refugees’ acculturation process and the emotional regulation will be addressed.

Keywords: refugees, immigration, educational needs, trauma, inclusion, second language.

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Authors: Wanghongshu Zhou

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This study aims to explore the relationship between the L2MSS and WTC among Chinese non-English major students in order to provide pedagogical implications for English as a Foreign Language (EFL) classrooms in Chinese universities. By employing a mixed methods approach, we involved 103 Chinese non-English major students from a typical university in China, conducted questionnaire survey to measure their levels of L2WTC and L2MSS level, and then analyzed the correlation between the two above mentioned variables. Semi-structured interviews were conducted with eight participants to provide a deeper understanding and explanation of the questionnaire data. Findings show that 1) Chinese non-English major students’ ideal L2 self and L2 learning experience could positively predict their L2 WTC in EFL class; 2) Chinese non-English major students’ ought-to L2 self might have no significant impact on their L2 WTC in EFL class; and 3) self-confidence might be another main factor that will influence Chinese non-English major students’ L2 WTC in EFL class. These findings might shed light on the second language acquisition field and provide pedagogical recommendations for pre-service as well as in-service EFL teachers.

Keywords: L2 willingness to communicate, L2 motivation, self-confidence, Chinese non-English major students

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Authors: Selaphares Jatico Tajale

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This research aims to understand the notions of quality education by conducting case studies among internationally-accredited Christian schools in Southeast Asia. Five internationally-accredited Christian schools from Cambodia, Indonesia, Malaysia, The Philippines, and Singapore will be chosen as cases for this study. This study will utilize the processes of interviews, filling up of questionnaires, and writing of reflections in order to obtain data and relevant information. These processes will be conducted through multi-sectoral respondents such as administrators, academic heads, and faculty. This study employs five aspects within the realm of education as guides in the formulation of questionnaire and guide questions in the interview, namely: a) school context, b) classroom, c) quality assurance, d) stakeholders, e) faculty and staff. Guide interview questions and questions in the questionnaires are formulated to uncover information on how those five aspects were managed to achieve desired student learning outcomes and uncover other information useful for the study.

Keywords: internationally-accredited, notions of quality education, quality education, quality education in Southeast Asia

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Authors: Samira Taleb, Sakina Aoun, Slimane Ziani, Zoheir Mentouri, Adel Boudiaf

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Rolling is a pressure treatment technique that modifies the shape of steel ingots or billets between rotating rollers. During this process, defects may form on the surface of the rolled sheets and are likely to affect the performance and quality of the finished product. In our study, we developed a method for detecting surface hole defects using a pixel classification approach. This work includes several steps. First, we performed image preprocessing to delimit areas with and without hole defects on the sheet image. Then, we developed the histograms of each area to generate the gray level membership intervals of the pixels that characterize each area. As we noticed an intersection between the characteristics of the gray level intervals of the images of the two areas, we finally performed a learning step based on a series of detection tests to refine the membership intervals of each area, and to choose the defect detection criterion in order to optimize the recognition of the surface hole.

Keywords: classification, defect, surface, detection, hole

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Authors: Bilal Liaqat, Muhammad Umar, Zara Bashir, Hassan Rafique, Mohsin Abbasi, Zarak Khan

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Transformational leadership is one of the most studied topics in the organization sciences. However, the impact of transformational leadership on employee’s individual attributes have not yet been studied. Purpose: This research aims to discover the relationship between transformational leadership and employee motivation, performance and creativity. Moreover, the study will also investigate the influence of transformational leadership on employee performance through employee motivation and employee creativity. Design-Methodology-Approach: The data was collected from employees in different organization. This cross-sectional study collected data from employees and the methodology used includes survey data that were collected from employees in organizations. Structured interviews were also conducted to explain the outcomes from the survey. Findings: The results of this study reveal that transformational leadership has a positive impact on employee’s individual attributes. Research Implications: Although this study expands our knowledge about the role of learning orientation between transformational leadership and employee motivation, performance and creativity, the prospects for further research are still present.

Keywords: employee creativity, employee motivation, employee performance, transformational leadership

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Authors: Bakhtiar Amen, Abduladim Ali, Joan Lu

Abstract:

Recently, wireless technologies have made tremendous influences in advanced technology era, precisely on the learning environment through PADs and smart phones to engage learners to collaborate effectively. In fact, the wireless communication technologies are widely adopted in the education sectors within most of the countries to deliver education support electronically. Today, Introducing multilingual Wireless Response System (WRS) application is an enormous challenge and complex. The purpose of this paper is to implementing internationalization testing strategy through WRS application case study and proposed a questionnaire in multilingual speakers like (Arabic, Kurdish, Chines, Malaysian, Turkish, Dutch, Polish, Russian) to measure the internationalization testing results which includes localization and cultural testing results. This paper identifies issues with each language’s specification attributes for instance right to left (RTL) screen direction related languages, Linguistic test or word spaces in Chines and Dutch languages. Finally, this paper attempt to emphasizes many challenges and solutions that associated with globalization testing model.

Keywords: mobile WRS, internationalization, globalization testing

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Authors: Harry Einhorn

Abstract:

This paper aims to explore the integration of Buddhism into educational settings with the goal of fostering the holistic development of children. By incorporating Buddhist principles and practices, educators can create a nurturing environment that cultivates wisdom, compassion, and ethical values in children. The teachings of Buddhism provide valuable insights into mindfulness, compassion, and critical thinking, which can be adapted and applied to educational curricula to enhance children's intellectual, emotional, and moral growth. One of the fundamental aspects of Buddhist philosophy that is particularly relevant to education is the concept of mindfulness. By introducing mindfulness practices, such as meditation and breathing exercises, children can learn to cultivate present-moment awareness, develop emotional resilience, and enhance their ability to concentrate and focus. These skills are essential for effective learning and can contribute to reducing stress and promoting overall well-being in children. Mindfulness practices can also teach children how to manage their emotions and thoughts, promoting self-regulation and creating a positive classroom environment. In addition to mindfulness, Buddhism emphasizes the cultivation of compassion and empathy toward all living beings. Integrating teachings on kindness, empathy, and ethical behavior into the educational framework can help children develop a deep sense of interconnectedness and social responsibility. By engaging children in activities that promote empathy and encourage acts of kindness, such as community service projects and cooperative learning, educators can foster the development of compassionate individuals who are actively engaged in creating a more harmonious and compassionate society. Moreover, Buddhist teachings encourage critical thinking and inquiry, which are crucial skills for intellectual development. By introducing children to fundamental Buddhist concepts such as impermanence, interdependence, and the nature of suffering, educators can engage them in philosophical reflections and broaden their perspectives on life. These teachings promote open-mindedness, curiosity, and a deeper understanding of the interconnectedness of all things. Through the exploration of these concepts, children can develop critical thinking skills and gain insights into the complexities of the world, enabling them to navigate challenges with wisdom and discernment. While integrating Buddhism into education requires sensitivity, cultural awareness, and respect for diverse beliefs and backgrounds, it holds great potential for nurturing the holistic development of children. By incorporating mindfulness practices, fostering compassion and empathy, and promoting critical thinking, Buddhism can contribute to the creation of a more compassionate, inclusive, and harmonious educational environment. This integration can shape well-rounded individuals who are equipped with the necessary skills and qualities to navigate the complexities of the modern world with wisdom, compassion, and resilience. In conclusion, the integration of Buddhism into education offers a valuable framework for cultivating wisdom, compassion, and ethical values in children. By incorporating mindfulness, compassion, and critical thinking into educational practices, educators can create a supportive environment that promotes children's holistic development. By nurturing these qualities, Buddhism can help shape individuals who are not only academically proficient but also morally and ethically responsible, contributing to a more compassionate and harmonious society.

Keywords: Buddhism, education, children, mindfulness

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Authors: Zahra Mohagheghian, Fatema Agharebparast

Abstract:

Professional ethic is a subject that has been an issue today, so most of the businesses, including the teaching profession, understand the need and importance of it. So they need to develop a code of professional ethics for their own. In this regard, this study seeks to answer the question, with respect to the integrity of the Qur'an (Nahl / 89), is it possible to contemplate the divine teachers conduct to extract the divine pattern for teaching and training? In the code of conduct for divine teachers what are the most important moral obligations and duties of the teaching professionals? The results of this study show that the teaching of Khidr, according to the Quran’s verses, Abundant and subtle hints emphasized that it can be as comprehensive and divine pattern used in teaching and in the drafting of the charter of professional ethics of teachers used it. Also, the results show that in there have been many ethical principles in prophet Khidr’s teaching pattern.The most important ethical principles include: Student assessment, using objective and not subjective examples, assessment during teaching, flexibility, and others. According to each of these principles can help teachers achieve their educational goals and lead human being in their path toward spiritual evaluation.

Keywords: professional ethics, teaching-learning process, teacher, student, Quran

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Authors: Elham Serkani, Hossein Gharaee Garakani, Naser Mohammadzadeh, Elaheh Vaezpour

Abstract:

Intrusion detection systems (IDS) are the main components of network security. These systems analyze the network events for intrusion detection. The design of an IDS is through the training of normal traffic data or attack. The methods of machine learning are the best ways to design IDSs. In the method presented in this article, the pruning algorithm of C5.0 decision tree is being used to reduce the features of traffic data used and training IDS by the least square vector algorithm (LS-SVM). Then, the remaining features are arranged according to the predictor importance criterion. The least important features are eliminated in the order. The remaining features of this stage, which have created the highest level of accuracy in LS-SVM, are selected as the final features. The features obtained, compared to other similar articles which have examined the selected features in the least squared support vector machine model, are better in the accuracy, true positive rate, and false positive. The results are tested by the UNSW-NB15 dataset.

Keywords: decision tree, feature selection, intrusion detection system, support vector machine

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Authors: Payam Mousavi, Andrew L. Stewart, Huiwen You, Aryeh F. G. Fayerman

Abstract:

We present a decision-support tool to assist an operator in the detection and tracking of a suspect vehicle traveling to an unknown target destination. Multiple data sources, such as traffic cameras, traffic information, weather, etc., are integrated and processed in real-time to infer a suspect’s intended destination chosen from a list of pre-determined high-value targets. Previously, we presented our work in the detection and tracking of vehicles using traffic and airborne cameras. Here, we focus on the fusion and processing of that information to predict a suspect’s behavior. The network of cameras is represented by a directional graph, where the edges correspond to direct road connections between the nodes and the edge weights are proportional to the average time it takes to travel from one node to another. For our experiments, we construct our graph based on the greater Los Angeles subset of the Caltrans’s “Performance Measurement System” (PeMS) dataset. We propose a Bayesian approach where a posterior probability for each target is continuously updated based on detections of the suspect in the live video feeds. Additionally, we introduce the concept of ‘soft interventions’, inspired by the field of Causal Inference. Soft interventions are herein defined as interventions that do not immediately interfere with the suspect’s movements; rather, a soft intervention may induce the suspect into making a new decision, ultimately making their intent more transparent. For example, a soft intervention could be temporarily closing a road a few blocks from the suspect’s current location, which may require the suspect to change their current course. The objective of these interventions is to gain the maximum amount of information about the suspect’s intent in the shortest possible time. Our system currently operates in a human-on-the-loop mode where at each step, a set of recommendations are presented to the operator to aid in decision-making. In principle, the system could operate autonomously, only prompting the operator for critical decisions, allowing the system to significantly scale up to larger areas and multiple suspects. Once the intended target is identified with sufficient confidence, the vehicle is reported to the authorities to take further action. Other recommendations include a selection of road closures, i.e., soft interventions, or to continue monitoring. We evaluate the performance of the proposed system using simulated scenarios where the suspect, starting at random locations, takes a noisy shortest path to their intended target. In all scenarios, the suspect’s intended target is unknown to our system. The decision thresholds are selected to maximize the chances of determining the suspect’s intended target in the minimum amount of time and with the smallest number of interventions. We conclude by discussing the limitations of our current approach to motivate a machine learning approach, based on reinforcement learning in order to relax some of the current limiting assumptions.

Keywords: autonomous surveillance, Bayesian reasoning, decision support, interventions, patterns of life, predictive analytics, predictive insights

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