Reviewerly/exHarmony huggingface dataset:
MANAJEMEN KEUANGAN BERBASIS E-RKAM DALAM MENINGKATKAN MUTU PENDIDIKAN MADRASAH DI KOTA BALIKPAPAN

The aim of the research is to describe e-RKAM-based financial management in improving the quality of Madrasah education in Balikpapan City. To analyze the inhibiting and supporting factors for E-RKAM-based financial management in improving the quality of Madrasah education in Balikpapan City. This type of research is descriptive qualitative with a phenomenological approach. Research location at MTs Negeri 1 and MTs Negeri 2 and MTs Ibnu Khaldun Balikpapan. Data collection techniques are observation, interviews and documentation. The analysis technique uses the Milles Huberman and Saldana model which includes data collection, data presentation, condensation and conclusions. The data validity test was carried out using triangulation. The result of this research is to find that e-RKAM-based financial management in improving the quality of Madrasah education in Balikpapan City has been implemented according to the stages and elements of the financial management function which includes planning, implementation, organizing and monitoring or evaluating. According to the instrument, the quality of madrasah education can be improved by running a budget according to the needs of the madrasah, the quality of education is carried out in accordance with the vision and mission of the madrasah and to achieve the goals of the madrasah, realizing improvements in madrasah facilities and infrastructure by creating transparency in madrasah financial management. Meanwhile, the findings of the obstacles and supporting factors for E-RKAM-based financial management in improving the quality of Madrasah education in Balikpapan City, are that the obstacle factors include operators who do not properly understand the E-RKAM application and must take part in training or outreach activities, limited infrastructure and facilities. which needs to be fought for in budgeting, the quality of teaching staff and educational staff. Meanwhile, the supporting factors for financial transparency and accountability are between kamad, head of TU, treasurer, operators and other staff. Kamad and related staff take part in upgrading, there are improvements to support all financial transparency and accountability between kamad, head of TU, treasurer, operators and other staff.
Peningkatan Hasil Belajar Siswa Melalui Kooperatif Picture And Picture: Suatu Studi di SDN 5 Menteng

The picture and picture type cooperative learning models is an appropriate learning model in developing student creativity, generating is a sense of fun during learning by arranging pictures in the correct order. This research uses quantitative approach with experimental research data analysis with descriptive statistics and inferential statistics and hypothesis testing to see the influence of two variables with the t-test. The aims of this research are 1). To determine the effect of the picture and picture type cooperative learning model on the learning outcomes of class III-B students 2). To compare picture and picture type cooperative learning model with lecture model on the learning outcomes of class III-B students at Junior High School 5 Menteng Palangka Raya. The results of this research in the paired sample t-test show a 2-tailed sig result of 0.00 &lt; 0.05, so There is an influence of the picture and picture model on student learning outcomes and the independent sample t-test obtained a result of 0.01 &lt; 0.05. So it can be concluded that there is an influence of the picture and picture cooperative learning model on student learning outcomes compared to the lecture model.
SUBJECTIVE SIGNS OF CYBER CRIME

In this article, small and medium-sized enterprises (SMEs) are very easy victims of cybercrime, they primarily related the growth of cybercrime to SMEs, not large enterprises. Such enterprises, because of their small budget, lack of qualified personnel andgaps in the knowledge of employees, cannot provide high-quality information security to the required level, and they should approach subjectively this.
ENHANCING FACIAL RECOGNITION THROUGH CONTRASTIVE CONVOLUTION: A COMPREHENSIVE METHODOLOGY

This study presents an innovative approach to enhance facial recognition technology using contrastive convolutional neural networks (CNNs). The primary focus is on improving the accuracy and efficiency of face recognition systems under varying conditions. Key elements of this approach include meticulous data preparation and preprocessing, where images undergo normalization and diverse augmentation techniques to ensure quality inputs. The network architecture is designed to process pairs of face images, utilizing a common feature extractor and cascaded convolution layers for detailed feature representation. A specialized kernel generator further refines the process, emphasizing unique facial characteristics. The core of the training regimen is a contrastive loss function, optimized through gradient descent to enhance the network's discriminatory capabilities. Results from the study demonstrate a significant improvement in recognition accuracy, particularly highlighted by the superior performance of the proposed model in comparison to standard facial recognition algorithms. This research provides a comprehensive methodology that could revolutionize face recognition technology, offering more reliable and efficient solutions for various applications.
Serial Murder: Power, Control and Social Domains

This paper presents new explanatory perspectives on the behaviour of serial murders via the theory of social domains platform.
NKU Applied Software Engineering Program: A Novel Approach to Software Engineering Education

The educational focus in Software Engineering and Computer Science has traditionally been on the theoretical aspects of computation, problem-solving algorithms, and coding skills. Accreditation standards and the Association of Computing Machinery (ACM) curriculum are designed to prepare students for careers and graduate school. Nevertheless, the majority of graduates engage in technical occupations where they become integral members of teams developing software products or services, requiring a diverse skillset including software de-sign, human-computer interaction, problem-solving methodolo-gies, and software testing, to deliver customer-oriented solutions within specified time and budget constraints. The gap between the skills acquired in academia and those demanded by the industry has given rise to a need for a different software engineering education. To address this need, in 2020, the Computer Science (CS) de-partment at Northern Kentucky University (NKU) launched the Applied Software Engineering (ASE) program, curated to meet industry demands and diversify the appeal of computer science to traditionally underrepresented demographics. The program was designed by faculty members with industry backgrounds and co-designed with the help of alumni and local industry leaders. Since 2020, the program has attracted an array of new and returning students with degrees in disciplines beyond software engineering, leading to a significant increase in enrollment from 6 to 53 students. In this work-in-progress paper, we detail the structure of the program and the elements that incorporate novel and high-impact practices in computing education and, specifically, software engineering to share our approach and invite feedback and comments that can help us measure and improve its success.
Promoting Students' Cognitive Ability to Identify Human Error-Prone Scenarios in Programs

This Research to Practice Full Paper presents a framework designed to train students in identifying human error-prone scenarios in source code. Human errors are a leading cause of defective programs written by students, and learning how to identify error-prone contexts in code can help students detect defects in their own code (debugging), identify defects in other people's code (code review), and prevent defects in similar situations in the future (defect prevention), thereby improving their skills of producing reliable programs. Despite the overwhelming diversity of defects in different programming contexts, recent scientific research has confirmed that they share limited forms of cognitive mechanisms. To identify diverse defects using a limited forms of human errors, a framework to bridge the limited cognitive forms (Human Error Modes) to diverse specific contexts (defects) is essential. The framework is known as Error-Prone Scenario Analysis, which is a method recently proposed and used for forecasting software defects that may be later introduced in code based on analysis of requirements. The purpose of this paper is to explore the extent to which the human Error-Prone Scenarios framework can be used to enhance students' abilities to identify defects in source code. The author incorporated the training framework into a graduate course called “Software Verification and Validation” as two lectures, following the ethical and approval guidelines of the University of Coimbra. The training process and materials used are reported in detail in this paper. Forty graduate students in Computer Science, spread across two classes, received this training. The results show that the training improved their performances in identifying error-prone scenarios by approximately 400%. The study demonstrates that the Error-Prone Scenario framework is effective in helping students recognize the underlying patterns of software defects that appear diverse in different programming contexts, thus enabling them to identify more defects accurately. The students who received the training highly regard the EPS training as interesting, important, and useful. The key successful experience gained from this study is using a pool of Human Error Modes, the Error-Prone Scenario for each Human Error Mode, and a graphic notation to display the mechanisms of how a Human Error Mode manifests as a defect. The research provides valuable insights that will benefit learners, educators, and professionals involved in programming, code review, and software quality assurance.
Robotics Innovative Technologies and Education (RITE) Lab: A Multi-Disciplinary Human-Robot Interaction (HRI) Lab

The Robotics Innovative Technologies and Education (RITE) Lab provides an interdisciplinary environment for designing and constructing intelligent social robots leveraging artificial intelligence (AI) and Human-Computer Interaction (HCI). This paper is divided into two parts. In the first part, we delineate five distinct generations of robotics technology identified through a comprehensive literature review. The second part showcases our lab's successful strategy in covering these five generations by offering a comprehensive multi-disciplinary theoretical and hands-on experience using nine individual modules. This is achieved by using a unique ‘black box/white box’ approach and continuous improvement since 2012, ensuring a 100% success rate, with students capable of building and programming their robots from scratch. Our program's broad scope spans imminent robotics innovations (2023-2028) and envisions long-term future developments. This paper serves as a blueprint for similar educational endeavors, encapsulating the RITE Lab's successful ten-year journey.
Scrum in the Classroom: An Implementation Guide

Over the years, Agile approaches have been proven successful in industry settings, as documented in the literature. In response to this success, education professionals have developed ways to introduce Agile practices into engineering classrooms with similar success. These practices have been most popular in project-based courses because they enhance student learning and prepare students for using Agile practices in industry after graduation. The Scrum approach is one of the most popular Agile methods in industry and classroom adoption. Modified versions of Scrum are utilized within the classroom to align with student needs, familiarity with Scrum, and the materials presented within the class. As a result of this adaptation, many different Scrum-based implementations are found in classrooms. The popularity of this approach has led to numerous publications detailing individual experiments using Scrum in the classroom, with most of these adoptions occurring in engineering classrooms. This paper presents a literature review of Scrum applied in the classroom. This work explores the advantages of using Scrum in the classroom, providing details on the type and level of university classroom used for implementation. Information on methods of implementation, appropriate class subjects, and student educational levels are provided within this paper. This guide can be used by those looking to utilize Scrum within their classroom as a stand-alone practice. The findings of this paper demonstrate that Scrum can be used in correlation with a wide variety of classroom structures and topics. This paper is intended to guide future educators who wish to implement Scrum into classes and educational programs.
AR-Classroom: Usability of AR Educational Technology for Learning Rotations Using Three-Dimensional Matrix Algebra

The AR-Classroom application utilizes augmented reality technology (AR) to make the three-dimensional (3D) rotations underlying matrix algebra visible and interactive. The AR-Classroom has physical and virtual versions, where users can perform rotations using a physical LEGO model or by manipulating the application's x, y, and z axes sliders to rotate a virtual model. Both versions provide 3D matrices, color-coded axes lines, and a green wireframe superimposed onto a LEGO model to represent transformations. To ensure that the AR-Classroom makes learning 3D matrix algebra more engaging and accessible, two usability tests were used to evaluate the discoverability and usability of the app. The benchmark test assessed usability in the AR-Classroom's original format, and recommendations were made to improve the app., such as adding additional instructions on model set-up, restructuring, and updating the instructions, and turning the 'visualization type 'function into a button to make it easier to find. After the improvements, the updated usability test assessed usability again so that the impact of the modifications could be evaluated. Participants followed similar procedures in both the benchmark <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathrm{N}=12)$</tex> and updated usability <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathrm{N}=12)$</tex> tests. Participants completed a pre-test assessing their math abilities and confidence, watched a video on geometric transformations, and then were randomly assigned to interact with either the physical or virtual version of the app. While interacting with the app, participants were given tasks to complete while thinking out loud and provided an ease-of-use rating from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1=\text{very}$</tex> easy to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$7=\text{very}$</tex> difficult (i.e., SEQ score). Once done interacting with the app, participants completed a post-test assessing their math abilities and confidence and provided feedback on their overall experience with the app (i.e., SUS). A thematic analysis was conducted after each test to identify and code themes in interaction and compare findings from the benchmark and updated tests. Results indicated that after changes were made to the app, the usability of both versions significantly improved: users were better able to set up the space shuttle model, effectively utilize the in-app instructions, and quickly access all of the app's features. Findings from the updated usability test contribute to enhancing the AR-Classroom app and further its use in higher education classrooms for learning matrix algebra.
A Systematic Mapping of Affective Intelligent Tutor Systems in Engineering Education

This full research paper presents a systematic mapping of applications of affective intelligent tutor systems in engineering education. These systems employ techniques such as self-questionnaires, voice or facial recognition, system usage tracking, or combinations of these approaches. The data collection occurs through the sensors that measure input from devices, such as microphones, cameras, and body sensors. This paper presents a systematic mapping of affective intelligent tutoring systems in engineering courses and seeks to answer the following questions: Q1. Which engineering courses employ affective intelligent systems?; Q2. What secondary or auxiliary technologies, if any, do the authors use in combination with affective tutoring systems?; Q3. How do these studies perform emotion recognition?; Q4. What emotions do these studies recognize?; Q5. Which evaluation methods do the studies use? The research concentrated on primary sources, such as articles and conference proceedings published between 2010 and 2022 on established scientific databases, including ACM Digital Library, IEEE Xplore, Science Direct, and Springer. We identified 413 potential articles and narrowed the options down to 23 papers that fully matched the inclusion criteria. We contribute to the literature by answering the proposed questions and categorizing the existing papers to find directions for future research.
Investigating Cognitive Engagement in Collaborative Desktop Virtual Reality (VR) Statics Activities Based on ICAP Framework

This work-in-progress research paper investigates students' cognitive engagement in collaborative virtual reality (VR) statics activities. The use of VR in engineering education is increasing with the advance in technology. To maximize the affordance of the VR for engineering education, it is important to understand how active learning enfolds in the VR environment since the level of student engagement during active learning is a vital indicator for the effectiveness of the learning environment. Using content analysis, this study investigates the modes of cognitive engagement of students working collaboratively to solve engineering problems in a VR learning environment. The video-recorded collaboration and the transcribed verbal interaction of the students in the VR learning environment were analyzed based on the Interactive-Constructive-Active-Passive (ICAP) framework. The analysis coded the patterns of the observed cognitive engagement based on the video recording and transcripts of the groups' conversation at each phase of the task. The analysis revealed that the active and constructive modes dominated the students' conversation while the interactive mode was the least observed mode of cognitive engagement during the task. The implications of the findings for further research and practice are discussed.
A Peer Review Approach to Grading Projects in Computer Courses

This research to practice full paper presented a peer review approach to grading projects in computer courses. Educators commonly adopt project-based learning activities in computer courses to achieve the goal. Implementations of these projects typically involve hardly quantifiable dimensions, such as novelty, functionality, user-friendly, coding style, and document. Automated tools are not competent for project assessment. However, assessing projects by instructors can be labor intensive and generally involves a high degree of subjectivity. Peer review is widely used in high education as effective active learning in computer courses, especially in the past decade. Despite its many advantages, peer review still faces some challenges. Students need to be more expert in assessment and might make mistakes when grading, and instructors still need to make great efforts to supervise the process and ensure that students give fair scores to their peers. In addition, the social relationship between students could lead to subjective assessments and affect the fairness of grading. We present the peer review approach to grading projects in computer courses. First, we design a grading specification to improve the accuracy of scoring. Second, we propose a score calculation method based on Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to calculate the assignment's final score, which is a multi-objective decision-making problem. Third, we give an anomaly detection algorithm based on Dixon's Q test to filter peer reviewers' unreasonable scores. Based on the approach, we designed a peer review workflow and developed an online peer review tool called Peer Review Studio (PRS). We carry out the method in the undergraduate computer course of 2022. We collect and analyze the learning data between 2020 and 2022. When measured by Krippendorff's alpha, the inter-rater reliability between instructor and peer grading is in good agreement. When measured by Kruskal-Wallis, students' project performance is significantly improved in the first year of peer review. Although learning engagement increases in 2022, there is no statistically significant difference with 2021. The course questionnaire of 2022 shows that most students approve of the peer review approach.
Web Scraping Data Collection from Leather Craft E-Commerce

In the field of information research leather crafts are an important part. Especially about leather crafts, about the most dominant materials used and to assist process making of other products. With the emerge of e-commerce and digital computing, gathering information from digital sources and structuring it in a convenient way can provide meaningful information. Data sources are obtained by using web scraping in extracting information from websites based on text by presenting data statistics, the results of this study show the dominant use of synthetic leather materials, which is Polyurethane polymers can be applied to all types of leather handicraft products.
Fall incidence prediction system for elderly people based on IoT and classification techniques

Health monitoring systems based on the internet of things (IoT) improve patient well-being and reduce mortality risks. Machine learning techniques are most helpful in early fall prediction and detection. In this paper, fall prediction analysis and decision-making are done with existing benchmark clinical records. Classification techniques are incorporated to track the consistency and precision of data acquired by the IoT-based remote health monitoring for elderly people, especially those who are living alone. This work undertakes two approaches to early predicting a patient’s acute illness. The first approach has analyzed the existing benchmark patient activity data with different features. This approach builds the classification model for fall incidence with the help of machine learning models. In second approach, we collect real-time sensor data such as blood pressure and heart rate from IoT sensor gadgets which are transmitted to the prediction model for early prediction. Experimental results prove that the random forest (RF) classifiers and XGBoost provides the maximum accuracy.