Reviewerly/exHarmony huggingface dataset:
The Effects of Situational Role Playing-Centered Safety Education through Fairy Tales on Children's Safety Knowledge and Safety Problem Solving Ability

This study investigated the effect of situational role playing-centered safety education through fairy tales on children's safety knowledge and safety problem-solving ability.&#x0D; The study selected 50 five-year-old children in Y Kindergarten in I Metropolitan City as the study subjects; the children were divided into experimental and control groups of 25 children each. Both groups carried out each assigned program with the same safety topics based on the seven standards for school safety, but the experiment group conducted the safety education program based on situational role playing through fairy tales planned by the researcher, and the control group conducted the annual safety education programs that were planned in kindergarten. Both pre-and post-tests were conducted across two weeks, and the experimental treatment was conducted twice a week for 12 weeks, resulting in a total of 24 sessions. The mean and standard deviation were calculated from the collected data, and covariance analysis was performed to determine the difference between the groups.&#x0D; The results of this study showed that first, the experimental group improved significantly compared to the control group in terms of safety knowledge and its sub-factors, such as life safety, traffic safety, violence prevention and personal protection, prevention of drug and cyber addiction, disaster safety, occupational safety, and first aid. Second, the experimental group showed a significant improvement over the control group in terms of the ability to solve safety problems and their sub-factors including playing, complying with traffic safety rules, and emergency response.&#x0D; This suggests that it is necessary to make active efforts to provide concrete and practical child safety education activities in order to derive sustainable effectiveness in each area of the seven standards for school safety education in the field of early childhood education by utilizing situational role playing-centered safety education programs through fairy tales.
Mediational Effect of Teacher Efficacy of the Early Childhood Teachers on the Relationship between Resilience and Professional Development Level

본 연구는 유아교사의 회복탄력성이 전문성 발달수준에 미치는 영향을 알아보고 이들 사이에서 교사효능감의 매개효과를 검증하여 유아교사의 전문성 발달수준을 높이는 방안을 제시하기 위하여 수행되었다. 이를 위하여 대전과 충남의 유치원과 어린이집에 근무하고 있는 교사를 대상으로 2020년 2월 1일～2월 10일에 걸쳐 MMS를 통해 설문조사를 진행하였다. 연구목적 달성을 위해 SPSS 프로그램으로 기초 통계와 신뢰도를 구하고, 탐색적 요인분석을 실시하여 문항의 타당성을 검증하였다. 상관관계를 알아보기 위하여 Pearson 상관분석을 실시하고 위계적 회귀분석으로 영향력을 검증하였다. 회복탄력성과 전문성 발달수준 사이에서 교사효능감의 매개효과를 알아보기 위하여 Baron과 Kenny의 3단계 회귀분석을 실시하고 Sobel Test로 매개효과의 유의성을 검증하였다. 연구결과 유아교사의 회복탄력성, 교사효능감, 전문성 발달수준은 모두 정적인 상관관계가 있는 것으로 나타났으며, 회복탄력성의 하위요인 긍정성, 대인관계능력, 자기조절능력의 순으로 전문성 발달수준에 영향을 미치는 것으로 나타났고 교사효능감의 하위요인 일반적 교사효능감, 개인적 교사효능감의 순으로 전문성 발달수준에 영향을 미치는 것으로 나타났다. 또한, 회복탄력성은 교사효능감을 통하여 전문성 발달수준에 유의한 영향을 미치는 것으로 나타나 교사효능감이 부분매개역할을 하는 것으로 나타났다. 이러한 연구결과는 유아교사의 전문성 발달수준을 높이기 위해서는 회복탄력성 뿐만 아니라 교사효능감을 증진시키기 위한 노력이 필요함을 시사한다고 볼 수 있다. This study was conducted to investigate the effect of the resilience of early childhood teachers on the level of professional development, and to verify the mediating effect of teacher efficacy among them, to suggest a way to increase the level of professional development of early childhood teachers. A survey was conducted through MMS from February 1, 2020 to February 10, 2020, targeting teachers working in kindergartens and daycare centers in Daejeon and Chungcheongnam-do. To achieve the research purpose, SPSS program was used to obtain basic statistics and reliability, and exploratory factor analysis was conducted to verify the validity of the items. To investigate the correlation, Pearson correlation analysis was performed and the influence was verified by hierarchical regression analysis. To investigate the mediating effect of teacher efficacy between recovery resilience and professional development level, Baron and Kenny’s three-step regression analysis method was conducted and the significance of the mediating effect was verified by Sobel Test. As a result of the study, it was found that the resilience, teacher efficacy, and professional development level of early childhood teachers all have a positive relationship. It was found that in the order of positiveness, interpersonal ability and self-regulation ability, which are sub-elements of resilience, it affects the level of professional development. It was found that in order of general teacher efficacy and individual teacher efficacy, which are sub-elements of teacher efficacy, professional development level was affected. In addition, the resilience was found to have a significant effect on the level of professional development through teacher efficacy, indicating that teacher efficacy plays a partial mediating role. These findings suggest that in order to increase the level of professional development of early childhood teachers, efforts not only to improve resilience but also to increase teacher efficacy are necessary.
Educating educators on social engineering Experiences developing and implementing a social engineering workshop for all education levels

There has been a noticeable increase in social engineering (SE) attack across the world, especially those that use phishing, vishing, and smshing. Despite the prevalence of cyberattacks that rely on these tactics and techniques, education about SE and how to defend against it is lacking. Instead, the focus of cybersecurity education has been heavily concentrated on technical skills. Recognizing that gap, this paper describes a case study of an attempt to improve SE education by hosting workshops for educators from diverse backgrounds. The workshop that was developed included education on the basics of SE and the psychology behind these attacks, the ethics around SE, and methods for implementing SE exercises in the classroom. Details are also provided on the experiences of those who attended the workshop. Lastly, challenges related to the implementation of the workshop are discussed as well as rationale for the continued use of workshops of this kind.
Postgraduate Teaching Reform Based on Machine Learning and Improved SVM Algorithm

Since 1999, colleges and universities have begun to expand enrollment on a large scale, and correspondingly, postgraduate education has also expanded.With the continuous increase in the number of graduate students, the quality of their education has attracted increasing attention.For graduate students, curriculum is an important carrier of their training and one of the core contents of their education quality assurance.The teaching of ideological and political theory courses for postgraduates is an effective way for postgraduates to impart theoretical knowledge, cultivate moral quality, and stimulate innovation ability.Constructing a scientific and effective new teaching model of ideological and political theory courses for postgraduates in colleges and universities will help to increase the interest and enthusiasm of postgraduates in studying ideological and political theory courses, and help to cultivate innovative talents who are suitable for the needs of the times and develop in an all-round way.The main goal of this paper is to train graduate students with comprehensive development, and aims to study the reform of postgraduate ideological and political education based on machine learning and improved SVM algorithm.This paper proposes machine learning and SVM algorithms, and proposes an improved algorithm based on the previous classic algorithm.The support vector machine model and experiments prove that the improved algorithm is more accurate and faster.The experimental results in this paper show that the improved support vector machine algorithm has the best learning curve, and the improved SVM learning algorithm is found to significantly improve the classification accuracy in the 758 samples taken; when K=16, the correct rate of the picture is as high as it is more than 90%.
Research Information Security Technology Based on Machine Learning

With the rapid development and widespread popularity of the Internet, the types and quantities of abnormal traffic are also increasing day by day.Network intrusion detection has become an important fortress to ensure network information security.People put forward more and more urgent demands for the robustness and development of network security technology.Therefore, this paper studies information security technology based on machine learning.In this paper, the concepts of network security situation assessment and network intrusion detection are described in detail at first, then the machine based learning data preprocessing module, network vulnerability level and coarse and fine granularity hybrid detection system are designed, and finally the detection performance and defense performance of fine granularity hybrid detection system are analyzed in detail, and a conclusion is drawn.
Quality Control Method of Exploration and Development Data Based on Machine Learning

Because of increased exploration depth and increasingly complex geological environment survey area, makes the actual acquired seismic exploration data contains a lot of noise, the noise composition is serious interference signal effectively, affect the signal to noise ratio and resolution of the seismic data, reduce the quality of the seismic exploration data, to the subsequent inversion and interpretation, and finally brought difficulties such as oil and gas exploration work.This paper mainly studies the quality control method of exploration and development data based on machine learning.In this paper, the classification and source of desert noise are analyzed first, and a convolutional neural network with branch structure (BCDNet) is proposed to enhance the ability of extracting effective signal features from desert seismic exploration data, so as to better recover the seismic in-phase axis polluted by desert seismic random noise.
Guidelines for Scholarly Blogs

These guidelines are recommendations for authors of scholarly blogs to help with long-term archiving, discoverability, and citation of blog content.They are modeled after the publication A Data Citation Roadmap for Scholarly Data Repositories, where many of the same guidelines apply, and where I was the first author and co-chair of the corresponding Force11 working group.These guidelines focus on the required or recommended work for scholarly blog authors. For scholarly blog archives such as...
Analysing the SENSOTRA project

This chapter reviews the processes of analysis that took place within the SENSOTRA project. The project, being across three cities in three countries, produced an enormous amount of data, including textual, visual, and aural. Although much of this data was analysed by researchers working within ‘their own’ data sets, from the countries in which they are based, a key part of the project was to come together in our approach to the analysis. This chapter explicates this process, from developing coding frames and protocols to undertaking the coding on ATLAS.ti. It demonstrates the need for researchers, especially those working on large interdisciplinary projects, to proceed with care, sensitivity and vigilance.
Change Management as an Approach to the Development of the Administrative Performance of Academic Leaders in Saudi Universities in the Light of Prince Mohammed Bin Salman's Directives: A Theoretical Study

Vision 2030 referred to the Kingdomâs efforts in all its educational departments and institutions to bridge the gap between education outputs and the needs of the labor market. The vision aimed at making at least five Saudi universities among the best (200) international universities by 2030. The purpose is to present the change management as an approach to the development of the administrative performance of academic leaders in Saudi universities in the light of Prince Mohammed Bin Salman's Directives This paper provided the foundations of the proposed model, the premises of the proposed model, the objectives of the proposed model, the dimensions of the proposed model, requirements for implementing the proposed model, and finally the obstacles to implementing the proposed vision and how to overcome them.
Structural Study of the Precambrian Basement of Kaele (Far North Cameroon): Contribution of Remote Sensing (Application of Landsat 8 OLI/TIRS Images) and Field Data

The Kaele is located in the northern domain of the Pan-African fold belt of Central Africa in Cameroon, north of Mayo- Kébbi domain. The structural study carried out in this area is based on Landsat 8 OLI/ TIRS image processing techniques and traditional geological prospection methods. The objective is to evaluate the contribution of landsat 8 OLI/TIRS image processing and field data in the structural mapping of the Kaélé region. The application of the 7×7 directional filters of the Sobel type in the N-S, E-W, NE-SW and NW-SE directions, to the ETM+3 channels gives better results. The rose diagram of the lineament map (obtained from image processing) shows a major E-W direction (N100E) and a secondary direction N-S (N10E). The studied area is affected by a polyphase deformation D1 to D4. The first phase of deformation (D1) is tangential and has set up the sub-horizontal dipping S1 schistosity which bears a composite L1 lineation (stretching, mineral). The D2 phase is constrictional and responsible for the establishment of the subvertical S2 axial plane foliation of the P2 folds, carrying the composite L2 lineation (stretching, mineral) and the B2 boudinage. The dextral-moving C2 shear cuts the S1 foliation, the P2 folds are asymmetrical isopach and anisopach. The third phase D3 is a constriction and set up the S3 schistosity which appears in syn -tectonic granite and highlight the C3 shear planes. The C3 shear is dextral and the P3 folds are isopach, anisopach, intrafolial and sheath fold. The last phase of deformation D4 is brittle and has set up faults and joints. There exist similarities between orientation of lineament from image processing and direction of D2, D3 and D4 deformational phases structures.
Unsupervised Learning of Temporal Abstractions With Slot-Based Transformers

Abstract The discovery of reusable subroutines simplifies decision making and planning in complex reinforcement learning problems. Previous approaches propose to learn such temporal abstractions in an unsupervised fashion through observing state-action trajectories gathered from executing a policy. However, a current limitation is that they process each trajectory in an entirely sequential manner, which prevents them from revising earlier decisions about subroutine boundary points in light of new incoming information. In this work, we propose slot-based transformer for temporal abstraction (SloTTAr), a fully parallel approach that integrates sequence processing transformers with a slot attention module to discover subroutines in an unsupervised fashion while leveraging adaptive computation for learning about the number of such subroutines solely based on their empirical distribution. We demonstrate how SloTTAr is capable of outperforming strong baselines in terms of boundary point discovery, even for sequences containing variable amounts of subroutines, while being up to seven times faster to train on existing benchmarks.
Transliteration from English to Telugu Using Phrase-Based Machine Translation for General Domain English Words

Transliteration techniques are used to preserve the phonetic information associated with a word written in one language called the source language and write the word in another language called the target language with the pronunciation constrained of the target language. In this paper, a comparative study of English and Telugu language in terms of transliteration problem is presented, and a simple phrase-based machine translation (PBMT) system is trained using Moses and Giza++ for English to Telugu transliteration of general English words used in the English language. To evaluate the results, a modified edit distance-based method is used.
Spectral Clustering to Detect Malignant Prostate Using Multimodal Images

Globally, the prostate is a second most frequent cancer in men and is the fifth leading cause of death. At early state, prostates are asymptomatic and do not require any care. However, if screened at early state, it can be removed immediately. The screening is done either with most widely used transrectal TRUS imaging or with magnetic imaging. The paper introduces a novel approach to demark the prostate boundary using spectral clustering approach with Gaussian similarity. The multimodal image database is obtained with the ethically approved collaboration. The algorithm is tested on 217 MRI images and 402 TRUS images. The competency of the approach was evaluated with respect to five quality metrics in both the modalities. The results showed that, the proposed approach is robust in demarking the prostate region with segmentation accuracy of 92.02 and $$93.78\%$$ for MRI and TRUS database. The highest average sensitivity and specificity of $$95.7\%$$ in segmenting MRI and TRUS images shows the robustness of the proposed SC approach in accurate delineation of prostate boundaries.
Facial Recognition-Based Attendance and Smart COVID-19 Norms Monitor

Attendance is an important part of the academic environment. The manual method of marking student attendance is time-consuming and also not accurate. So, the use of biometric attendance is a better alternative to the manual method. There are many biometric techniques that can be considered to design an automated system to mark attendance. Facial recognition is one such biometric technique that can be used. In this paper, we propose the implementation of facial recognition where the attendance is marked by recognizing the faces detected in the video feed from the classroom. We are in the midst of the once in a century crisis, ever since the COVID-19 pandemic broke out it has become imperative to accommodate certain behavioral changes in our day to day lives, one such major change which is essential to curb the spread of COVID-19 is to wear a face mask, and thus, the facial recognition-based attendance adds another advantage by recognizing the faces even though students would be wearing the masks. Another important measure that needs to be followed to contain the spread of COVID-19 is to ensure social distancing in all public spaces; hence, there is a need to ensure that social distancing norms are followed by the students. So, we propose implementation of a system to monitor the social distancing among the students. Further, we propose to implement a COVID-19 vaccination status monitoring system using which we can monitor the vaccination status of the individuals through the video feed from the classroom.
ENTRY-LEVEL STUDENTâ€™S READINESS TO USE E-LEARNING IN A SOUTH AFRICAN RURAL UNIVERSITY

Ponte Academic JournalFeb 2023, Volume 79, Issue 2 ENTRY-LEVEL STUDENT’S READINESS TO USE E-LEARNING IN A SOUTH AFRICAN RURAL UNIVERSITYAuthor(s): Jose Manappattukunnel Lukose ,Adelin Kantore, Agyei FosuJ. Ponte - Feb 2023 - Volume 79 - Issue 2 doi: 10.21506/j.ponte.2023.2.3 Abstract:E-learning receives great attention among educational practitioners in recent times. Many institutions of higher learning are using E-learning as a supplementary tool to the traditional ways of teaching and learning. Though many African universities, like their counterparts globally, have adopted E-learning as a response to the Covid-19 pandemic, the success of it, among the institutions located in rural settings depends heavily on the e-readiness of the students these institutions enroll at undergraduate levels. Walter Sisulu University (WSU), situated in one of the most disadvantaged areas in South Africa, predominantly enrolls students who are hailing from the rural and townships population. This study investigated the readiness of entry-level students enrolled at (WSU) undergraduate programmes to use E-learning as a tool to enhance their learning. A quantitative approach in a case study design was used in this study. Data were collected from a sample of 240 students using a questionnaire. There is a growing concern about the readiness of matriculant’s from previously disadvantaged black high schools for higher education learning environment. This study makes its contribution by assessing how ready in terms of technological skills of first entry university students predominately from previously disadvantaged former black high schools to engage in e-learning. The study found that only 32% of the students owned a computer device but most of them owned a smartphone. Their major means of access to the Internet were Wi-Fi/Internet ports. The study further found that the students entering university seriously lacked in essential computer skills. Download full text:Check if you have access through your login credentials or your institution Username Password
Underwater Image Enhancement Using Color Balance and Image Fusion via Gamma Correction

The images that are captured underwater contain various artifacts and also degraded due to the inherent properties of the location parameters such as dispersion and refraction. In general, most of the fusion algorithms in image processing applications are mostly based on fusion of two images, but in this work, it is only one image approach, where the same image is converted into varied forms of two images based on color balance and white balance procedures. The proposed method indicates that images are enhanced by means of global contrast and also preserving the edges of the images with little modifications. Basic filtering operations are also utilized in this work to enhance the images in order to retain the integrity of the images to identify the underwater objects vividly.
An Anchor-Based Fuzzy Rough Feature Selection for Text Categorization

Big datasets are characterized by large dimension consisting of hundreds of thousands features with uncertainties and imprecisions. It becomes a challenging task to represent these datasets in memory short environments. Feature selection techniques enable dimensionality reduction of such datasets by finding subsets of relevant features from the original feature space. To produce optimal feature subset, an ideal feature selection technique should be capable of handling the interdependencies and uncertainties in the features. In this paper, we propose a new hybrid feature selection technique called anchor-based fuzzy rough feature selection (ABFRFS) based on anchor graph-based learning and fuzzy rough feature selection for text categorization. Although anchor graph-based feature selection and fuzzy rough feature selection have been proposed independently earlier, yet the hybrid of anchor graph and fuzzy rough feature selection called ABFRFS is proposed with the intuition to overcome the inherent problem of representing big datasets in memory short environments and at the same time retain the interdependency uncertainty among its features while maintaining the clustering accuracy. It is observed that with the proposed ABFRFS technique the feature space is reduced to an extent of 98% on an average on the considered benchmark datasets with acceptable degree of clustering accuracy.