Reviewerly/exHarmony huggingface dataset:
Development and Application of Experiential Learning Program for High School STEAM Convergence Class

The purpose of this study is to improve the quality of a year-long STEAM convergence experiential learning program that connects different subject matters during weekend activities and vacation camp programs. The participating teachers cooperated with each other to explore convergence topics to develop STEAM educational programs in terms of the relationship, values, and attitudes of people and the environment. The 4 teachers’ cooperation enabled their H high school students engage in the science-themed experiential learning. Almost 40 students visited Sinan Jeungdo Tidal Flat Ecology and Yangpyeong Jungmi Mountain Observatory in summer and autumn 2012 under the guidance of STEAM convergence teachers. After collecting and analyzing reports and photographs on students’ activities, they revealed that their activities were geared toward cultivating sensitivity and values in environmental conservation. Participants also showed their interests by actively participating in STEAM convergence classes. Finally, we put several suggestions for further improvement of the proposed STEAM program.
The Study on Features of STEAM Teachers’ Research Group in Korea: Focused on the report of the Outstanding Teachers’ Research Group

STEAM education, which represents the integrated education in Korea, has a goal to cultivate problem-solving ability and integrated thinking ability based on science and technology. Among the various policies for the spread of STEAM education, supporting the teachers’ research groups is very effective because it is based on the spontaneity of the teachers. In this study, we investigated the features of the STEAM teachers’ research groups in Korea by analyzing the reports of the STEAM Teachers’ research groups selected as excellence. The success of the STEAM teachers’ research groups depended on the capability of the research directing teacher. The research group activities turned out to be more effective when they were organized with the same grade level of students in the same school. In addition, it was noted that coding education has been utilized in many STEAM educational programs as part of science-technology-related contents. The STEAM teachers’ research groups intended to share their experiences and knowledge actively, and offered various training and consulting programs in order to enhance the participating teachers’ professional development. For their quality enhancement of the upcoming events, we suggest that they need to expand the number of leader teachers, reduce the constraints related with the teachers’ research group activities and secure enough time and space for the participating teachers.
A Case Study of On-line Academic Achievement Prediction Model by Analyzing LMS Log Data

The purpose of the present study was to analyze the online learning activities of students through the data log of LMS through a learning analytical approach and to find out which variables predict academic achievement. After transforming the data left by the learner into a form that can be properly analyzed, the behavior data was set as an independent variable, and the learner’s academic achievement was used as a dependent variable to develop a model for predicting academic achievement. The study subjects were conducted for 55 students who took a course in Educational Technology, a department of education at S Women’s University in Seoul, independent variable data was collected through LMS’s learner log analysis, and the dependent variable, academic achievement, is the final exam score. The research results are as follows. First, a technical statistical analysis was conducted to examine the trend of online activity data of college students. Based on result, the type, frequency, and average of learners’ learning activities and learning behaviors were found. Second, as a result of analysis related to the development of the academic performance prediction model, the number of course accesses, the number of clicks on learning materials, and the reading of posts significantly predicted academic achievement. Third, as a result of verifying the effect of online behavior in the LMS environment on academic achievement through a multiple regression analysis, the model showed 13.0% of the academic achievement. It was within the scope of predictive explanatory power. Based on these results, several suggestions for the follow-up studies were added at the end.
A Study on Teachers’ Perceptions of Career Education in Free Semester System - Focusing on Busan Region

This study was conducted to understand the overall perceptions of teachers in the free semester career system based on survey data.&#x0D; First, although most of the teachers responded to understand the purpose of the free semester system at a very high level, there was a difference between the purpose of operation and the specific perception of the basic direction. Second, it was recognized that career education was needed at a macro level that looked at career path from a lifetime design perspective rather than a micro-level career education focused on experience in terms of career management, and there was a difference in the perception of operational content depending on the teaching experience. In terms of operation, teachers with less than 20 years of experience recognized that the ‘experience for experience’ was increased, which did not match the goals and purpose of free semester system. And they also recognized that the learning gap between students was widened. Third, there was no difference in perception about improvement plan according to teaching career. Teachers recognized the need for administrative and financial support to reduce job burdens and the provision of external instructor support and quality management measures were urgent.&#x0D; It is necessary to integrate teachers’ perceptions about the identity of career education in the future, and it is necessary to systematically manage teacher training for free semester system in accordance with teaching career, subject etc. Finally, sufficient discussion and improvement plans are needed to ensure that teachers’ perceptions and practice on career education in free semester system.
DIGITALIZATION OF NURSING ENGLISH LEARNING MEDIA BASED ON E-BOOK IN IMPROVING STUDENT LEARNING OUTCOMES AT STIKES YAPIKA MAKASSAR

The development of technology is one of the factors that participate in influencing the behavior of students in learning. In this study, the author tries to create an interesting learning media for students and can be used not only during class hours but wherever and whenever they can access it. One of the media that can be used is E-book. This study uses Research and Development using the ADDIE Model. The population and sample of this study were Nursing Students STIKES YAPIKA Makassar in the 2020/2021 academic year. The development procedure is divided into five stages, namely 1) Analysis stage in the form of literature study and field study and needs analysis, 2) Design stage, 3) Development stage, 4) Implementation stage and 5) Evaluation stage. Research data were collected using documentation and questionnaires which were analyzed descriptively and inferential statistics through SPSS version 22 for Windows. It is hoped that from the results of this study, lecturers can apply or use learning media that are in accordance with the times, namely E-books that can be accessed by students not only in class but anywhere and anytime. With the E-book-based learning media, it is hoped that student literacy culture can also increase
A deep learning anomaly detection framework with explainability and robustness

The prevalence of encrypted Internet traffic has resulted in a pressing need for advanced analysis techniques for traffic analysis and classification. Traditional rule-based and signature-based approaches have been hindered by the introduction of network encryption methods. With the emergence of machine learning (ML) and deep learning (DL), several preliminary works have been developed for anomaly detection in encrypted network traffic. However, complex Artificial Intelligence (AI) models like neural networks lack explainability, limiting the understanding of their predictions. To address this limitation, eXplainable Artificial Intelligence (XAI) has emerged, aiming to provide users with a rationale for understanding AI system outputs and fostering trust. However, existing explainable frameworks still lack comprehensive support for adversarial attacks and defenses.
Agile Course Planning in Educational Programs

An approach to course planning in educational programs was presented in Madan and Gnanendran (2020).There, each student's journey through a degree program was viewed as a "project" requiring completion of a set of tasks (courses) with each task having a specific duration (semester) and, possibly, precedence requirements (prerequisites).The institution is expected to offer courses in an appropriate sequence and timing so that students may progress toward graduation efficiently.Given the large number of students that could matriculate every semester, the institution needs to manage myriad individual degree pathways.To make this problem tractable, Madan and Gnanendran (2020) considered cohorts of students, rather than individuals, according to when each entered the program.The institution then only needs to manage a limited number of simultaneous projects with outcomes that are measured on the typical criteria of time and cost.Recently, researchers (e.g., Rigby et.al., 2016) have espoused the "agile" approach over the traditional "waterfall" approach to managing projects in environments where changes to requirements are to be expected, the work can be modularized, and there are avenues to collaborate with end-users.Since degree programs possess all of these characteristics, we propose heuristics based on agile methodology to address course planning and demonstrate their application via numerical examples.
Systematic Review of the Literature on Data Governance in healthcare institutions

In recent years, data governance has become a topic of great interest for organizations since it defines power relationships between the actors involved in management and the socio-technical guidelines that are established in order to generate value for the company.organization by understanding the data that reflects evidence of performance.For this reason, data governance refers more than to data management, to the decisions that are made with respect to them, establishing those responsible for decisions, the methodology for processing, the guidelines for access, distribution, storage and control of data in the organization.Although there is no evidence that data governance adds value to organizations, data governance provides guidelines for exploiting the potential value of data in pursuit of organizational objectives, under the premise that good administration of data produces well-informed decisions, optimizes healthcare processes, and reduces the risk of privacy breaches This article presents a systematic review of recent literature on the subject in healthcare institutions in order to identify the main trends which are; disease diagnosis and prediction, patient relationship management, and resource management.The main challenges in data management in the sector were also identified, which are; technological, legal and business.
A Method for Implementing a SHA256 Hardware Accelerator Inside an Quantum True Random Number Generator (QTRNG)

Availability of streams of random numbers is critical in a number of significant applications. e.g. computer security and cryptography, privacy preservation procedures, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{IoT}$</tex> secure communication, numerical simulation of complex phenomena, gaming and gambling. Hardware generation of random numbers, especially when based on quantum phenomena, is made unbreakable by the same laws of nature. Random Power [1] [2] is focusing on the development of a Quantum-True Random Number generation (QTRNG) platform, producing unpredictable bit streams analyzing the time series of self-amplified endogenous pulses due to stochastically generated charge carriers in a dedicated silicon device. As per NIST recommendations [3], a SHA256 conditioning function was used in order to reduce potential biases and guarantee the required level of entropy. This paper reports the firmware development and implementation of a real-time SHA256 accelerator, which is capable of generating bits in bursts at a maximum rate of 330 Mbps.
IC COMPETENCE’ AND ATTITUDE’ TEACHERS TO INTRODUCTION OF ICT IN FUTURE PSYCHOLOGISTS’ DISTANCE LEARNING DURING RETRACTIONS PERIODS IN UKRAINIAN UNIVERSITY

In the developed countries of the world, IC-competence of higher education teachers has reached a high level. However, the level of IC-competence of teachers to teach higher education applicants for less developed countries, including Ukraine, is not high enough. There are many obstacles to the realization of distance learning. Therefore, the purpose of the study was the experimental determination of the levels of IC-competence and attitude of teachers of Ukrainian higher education to the introduction of ICT in the process of distance learning of future psychologists of during the COVID-19, as well as the main obstacles to the implementation of this process from the standpoint of teachers. The study, which was conducted online during the COVID-19 pandemic, was attended by 147 teachers of higher education institutions from different regions of Ukraine (84 – at the beginning of the pandemic and 63 – 2 years from its beginning). The data obtained were analyzed with SPSS 20. It was found that within two years of distance learning, the number of teachers with high and medium levels of IC-competence increased and decreased with low ones. The number of teachers with a positive and negative and ambivalent attitude towards the use of ICT in training future psychologists increased statistically significantly. A strong and direct link between IC-competence levels and teachers’ attitude to the use of ICT in the process of remote training of future psychologists is determined. From the standpoint of teachers, the main obstacles to the realization of distance learning of future psychologists are: lack of proper technical support, low level of IC-competence of teachers and students, unwillingness of teachers to use ICT, low level of cognitive activity of students. Teachers believe that to overcome these obstacles requires proper technical support, a high level of teachers’ motivation and a separate quality online platform for higher education. Therefore, we can conclude that strategies and incentives to increase the level of IC-competence and motivation of teachers, as well as providing ICT institutions of higher education institutions.
Lightweight multi-level feature difference fusion network for RGB-D-T salient object detection

In recent years, bimodal salient object detection has developed rapidly. In view of the advanced performance of their robustness to extreme situations such as background similarity and illumination variation, researchers began to focus on RGB-Depth-Thermal salient object detection (RGB-D-T SOD). However, most existing bimodal methods usually need expensive computational costs to complete accurate prediction, and this situation is even more serious for three-modal methods, which undoubtedly limits their applicability. To solve this problem, we are the first to propose a lightweight multi-level feature difference fusion network (MFDF) for real-time RGB-D-T SOD. In view of the depth modality contains less useful information, we design an asymmetric three-stream encoder based on MobileNetV2. Due to the differences in semantics and details between high and low level features, using the same module without discrimination will lead to a large number of redundant parameters. On the contrary, in the coding stage, we introduce a cross-modal enhancement module (CME) and a cross-modal fusion module (CMF) to fuse low-level and high-level features respectively. In order to reduce redundant parameters, we design a low-level feature decoding module (LFD) and a multi-scale high-level feature fusion module (MHFF). A great deal of experiments proves that the proposed MFDF has more advantages than the 17 state-of-the-art methods. On the efficiency side, MFDF has a faster speed (124 FPS when the image size is 320 × 320) and much fewer parameters (8.9 M).
General cross-modality registration framework for visible and infrared UAV target image registration

In all-day-all-weather tasks, well-aligned multi-modality images pairs can provide extensive complementary information for image-guided UAV target detection. However, multi-modality images in real scenarios are often misaligned, and images registration is extremely difficult due to spatial deformation and the difficulty narrowing cross-modality discrepancy. To better overcome the obstacle, in this paper, we construct a General Cross-Modality Registration (GCMR) Framework, which explores generation registration pattern to simplify the cross-modality image registration into a easier mono-modality image registration with an Image Cross-Modality Translation Network (ICMTN) module and a Multi-level Residual Dense Registration Network (MRDRN). Specifically, ICMTN module is used to generate a pseudo infrared image taking a visible image as input and correct the distortion of structural information during the translation of image modalities. Benefiting from the favorable geometry correct ability of the ICMTN, we further employs MRDRN module which can fully extract and exploit the mutual information of misaligned images to better registered Visible and Infrared image in a mono-modality setting. We evaluate five variants of our approach on the public Anti-UAV datasets. The extensive experimental results demonstrate that the proposed architecture achieves state-of-the-art performance.
Detection of fraud card and data breaches in credit card transactions

Data breaches and credit card fraud are now among the biggest problems affecting financial organizations and customers globally. The purpose of this study is to develop an effective fraud detection system that can detect fraudulent credit card transactions and prevent data breaches. The strategy proposed in this paper makes use of machine learning techniques like decision trees and logistic regression, to analyze large datasets of credit card transactions and identify suspicious patterns. The proposed system also includes a real-time monitoring mechanism that alerts the relevant authorities in case of any suspicious activity. The results of the experiments show that the suggested system achieves great accuracy and efficiency in detecting fraudulent transactions and data breaches. It can provide a powerful tool for financial institutions to prevent financial losses and maintain their customers' trust.
Measuring laser-speckle statistics in scaled-laboratory experiments

Generating fully developed speckle in a repeatable way is of interest to ongoing scaled-laboratory experiments. Such experiments often look to validate theoretical and numerical predictions for numerous laser-based applications. Unfortunately, experimental constraints such as camera-pixel sampling, residual-sensor noise, and cover-glass etaloning limit one’s ability to match the statistics of fully formed speckle in a straightforward way. In this paper, we develop expressions for the speckle probability density function (PDF) and speckle contrast, which account for the effects of camera-pixel sampling (relative to the size of the speckles), as well as Gaussian-distributed additive noise. We validate these expressions using wave-optics simulations, which also account for the separate effects of cover-glass etaloning. Next, we set up an experiment that limits the effects of the cover-glass etaloning (as much as possible). The results show excellent agreement with the expressions we develop for the speckle PDF and speckle contrast. This agreement will enable future scaled-laboratory experiments to match the statistics of fully developed speckle in a straightforward way.
Low-frequency regular pulse and intermittent oscillation in a mid-infrared interband cascade laser with optoelectronic feedback

In this work, we experimentally investigate the nonlinear dynamics of a mid-infrared interband cascade laser (ICL) subject to optoelectronic feedback (OEF) through inspecting the time series and power spectrum of the laser output. The results show that, within the range of feedback strength limited by the experiment condition, the ICL sequentially presents stable state, continuously periodical oscillation (CPO), low-frequency regular pulse (LF-RP) and intermittent oscillation state with the increase of feedback strength. For the LF-RP state, the peak-to-peak value and the oscillation period increase with the increase of feedback strength. For the intermittent oscillation state, the time series is composed of the laminar region and burst region appeared alternately, and the average value and standard deviation for the duration of burst region gradually decrease with the increase of feedback strength.
Aasna: Kinematic Yoga Posture Detection And Correction System Using CNN

Yoga is a very popular form of exercise that originated in India that has numerous benefits for the mind and body. According to recent statistics, there are over 300 million yoga practitioners worldwide, with the number of yoga instructors increasing annually. However, incorrect yoga postures can lead to injuries and health complications. This highlights the importance of correct yoga posture and the need for a system that can detect and correct improper poses. This abstract presents a yoga posture detection and correction system designed using OpenCV for computer vision, and kinematic representation of the human body considering 17 points mapped on the human body, utilizing the tf-pose estimation algorithm for precise pose estimation. The system also includes a convolutional neural network (CNN) model developed using the Keras API and trained on the TensorFlow platform's MoveNet architecture for handling training of the model. The MoveNet pose estimation module has been used to detect keypoints of the human body which achieved an accuracy of 99.88%. The system works by live capturing of the yoga practitioner using a camera, extracting the key features of the pose, and comparing them with a trained data model of known yoga poses. If the pose is incorrect, the system provides real-time feedback to correct the pose.
A smart resume screening tool for customized shortlisting

Hundreds of resumes are received, processed, and managed by large companies and recruitment agencies. Furthermore, many people post their resumes on the internet. Organizations all across the world, on the other hand, are battling to locate the greatest resource. To complete this work, these organisations rely on industry expertise. Manual interaction is required in the resume screening process. Most of the current technologies search for keywords but do not consider semantics, resulting in many superfluous resumes being shortlisted. The goal of the proposed study is to create a smart resume screening algorithm that can automatically retrieve and process resumes. Name, phone / cell numbers, e-mail addresses, qualification, experience, skill sets, and other fields are mapped to the retrieved data. The proposed model uses AI and ML techniques to do so. The gathered data can be utilised to develop applicant profiles that meet the organization’s recruitment needs. By applying multiple filters to the data, an efficient man-less screening process can be achieved. The model is applied to the resumes that the company receives. The model has performed with an average accuracy of more than 90%. The model can be enhanced to apply on the resumes written in languages other than English.