Reviewerly/exHarmony huggingface dataset:
Using Appsheet and Apps Script to Develop Management and Implementation Applications for University Education Programs

A smart university management system is developed in a synchronized manner based on various interconnected modules. Managing and implementing the training program is a rigorous and highly synchronized process of school management. In this article, we introduce the use of Appsheet and Apps Script to build an application for managing the implementation of a university's training program. The result of this research, which effectively supports the university's operations and is expandable, integrates with other applications to form a smart educational ecosystem.
Abstract WP73: Temporal Effects on Mobile Stroke “Hot” Zones Using a Geo-Spatial Software Analysis

Background: We sought to identify variations of mobile stroke unit (MSU) hotspot activity based on time of day by using a geospatial analysis software. Methods: The Mobile Stroke Treatment Unit of metropolitan Columbus, Ohio, provides emergency stroke care coverage for 325 square miles which spans 47 zip codes. Our MSU is operational from 0700-1900 and is dispatched for all 911 calls designated as probable or possible strokes. We retrospectively reviewed all MSU calls from May 2019 to June 2022. The calls were organized into zip codes and 3 time epochs (0700-1100, 1101-1500, 1501-1900). Our endpoint was to determine hotspots of MSU activity during these epochs using the ESRI/ArcGIS Online geospatial analysis software. Statistically significant hotspots are qualitatively defined as clustered regions with high activity and quantitatively defined as having a Gi Bin score &gt; 2, which correlates to statistical confidence &gt; 95%. Results: We received 5198 calls during our study period; 1535 from 0700-1100; 2002 from 1101-1500; 1661 from 1501-1900. All hotspots had an average separation of 6.2 miles. From 0700-1100, 5 zip codes (12.9% of calls) were considered hotpots (Figure-Top). From 1101-1500, 15 zip codes (44.7% of calls) were considered hotspots (Figure-Middle). From 1501-1900, 9 zip codes (34.1% of calls) were considered hotspots (Figure-Bottom). Five zip codes were statistical hotspots during the entire coverage period. Four additional zip codes also showed hotspot activity and overlapped 1101-1900 time epochs. Conclusions: Our study showed that hotspot activity can vary throughout the day and must be considered for strategic MSU deployment. Prospective validation is needed to determine if such strategic deployment is feasible and can improve clinical outcomes.
Non-Invasive Real-Time Multimodal Deception Detection Using Machine Learning and Parallel Computing Techniques

Abstract Detecting deception reliably has been a sought-after goal for researchers since the early 20th century. This is due to its high stakes nature, especially when considering the setting under which a deception detection system would be utilized, such as law enforcement sectors, judicial bodies, and criminal investigation bodies. Therefore, recent literature has greatly focused on developing systems with ever increasing accuracies to minimize false positives. Attempts to diversify the sources of data being analyzed and classified as well as the reliance on artificial intelligence technologies have shown great success. But little attention was paid to the applicability of these systems in real-life scenarios. an idle deception detection system needs to exhibit accuracy but also perform in real-time, a feature that is lacking in the current state-of-the-art. In this work, a non-invasive real-time multimodal deception detection system is developed using advanced machine learning techniques. It combines data sources from video and audio streams to extract visual, acoustic, and linguistic features. It also utilizes parallel computing techniques to ensure high performance adequate for real-time usage. Furthermore, a user-friendly graphical user interface was built to facilitate the use of the system. Multiple experiments were conducted to determine its accuracy under various circumstances and combinations of features, the system had a detection accuracy of 99.83% under real-life, high-stakes scenarios using visual and acoustic features, and 91.03% accuracy under controlled environments, while an 89.54% accuracy was achieved using mixed environments and using visual, acoustic, and linguistic features.
Balancing Green and Gold: The Link Between Sustainability Reporting and Corporate Reputation in Malaysia

Corporate reputation has gained significant attention within academic circles and emerged as a critical concern in practical business contexts. However, prior studies on this topic have been somewhat fragmented, creating fertile ground for exploring new research avenues, particularly in assessing reputation. Sustainability reporting, corporate governance, and company attributes have been associated with enhancing corporate reputation. Nevertheless, there remains a dearth of research exploring these variables' impact on the novel reputation measurement proxy, especially in emerging economies like Malaysia. Therefore, this study investigates the intricate relationship between sustainability reporting and corporate reputation using the new proxy of reputation in the context of Malaysia, a dynamic and rapidly growing economy. Balancing the 'Green' aspects of sustainability with the 'Gold' aspects of company performance and reputation is a pivotal concern for organizations in the region. The results showed sustainability reporting significantly impacts corporate reputation to sustain a harmonious equilibrium characterized by "Green" and "Gold" elements. The findings encourage Malaysian PLCs to navigate this duality successfully and can reap long-term benefits regarding reputation, stakeholder trust, and sustainable profitability.&#x0D; Keywords: sustainability reporting, reputation, transparency, greenwashing, Malaysian public listed companies
PENERAPAN PSO UNTUK SENTIMEN ANALISIS PADA REVIEW MATA UANG KRIPTO MENGGUNAKAN METODE NAÏVE BAYES

In the digital age emerging currencies using digital technology called currency crypto money. Many people use cryptocurrencies to invest. This triggered the sentiment in society on social media twitter, there are positive opinions and there are negative opinions. The purpose of this study is to determine the public sentiment regarding the review of crypto currency and then classify it into two sentiments, namely positive and negative sentiments. The classifier method used is Naïve Bayes, Naïve Bayes is a good classifier method but has shortcomings in the selection of features therefore Particle Swarm Optimization (PSO) is applied as a feature selection in order to improve the accuracy value. After conducted experiments using Naïve Bayes method, obtain accuracy value of 66% with AUC 0.482 and after Applied Particle Swarm Optimization (PSO) as feature selection in Naïve Bayes obtain accuracy value of 85% with AUC 0.716 has increased accuracy .
PENERAPAN ALGORITMA CNN MENGGUNAKAN FRAMEWORK YOLO UNTUK DETEKSI OBJEK PRODUK DI PERUSAHAAN MANUFAKTUR

Component products used for manufacturing a machine in manufacturing companies have two types of products, type A and B. The problem that often occurs in the industry is product sorting errors due to the traditional sorting process, using human labor. The disadvantages are limited human labor so that fatigue can occur, causing errors in sorting products and losses for the company. Many studies discuss object detection, Industrial problems in the checking process can be approached with the help of this technology. Object detection works to analyze frames with the method of finding objects. There are methods in digital image processing, CNN algorithms which include methods in computer vision. The growing framework makes the CNN algorithm more powerful. YOLO includes a framework based on the CNN algorithm. YOLOv5 detects objects by taking into account the object's confidence value, the output of the detected object is a bounding box on the object. The problem in the industry in the checking process can be approached with the help of this technology. For this reason, this research aims to create a model for product object detection in manufacturing companies. The process carried out is data collection, image annotation, training, testing, evaluation. The images collected were 137 for training data and 34 for validation data totaling 171 image data. The results of the model using YOLOv5 with epoch 1000 get a precision value of 100%, recall 100% and mAP 99%, the product detection results get an average value of 100%.
CLUSTERING DATA METEOROLOGI WILAYAH INDONESIA TIMUR DENGAN METODE K-MEANS DAN FUZZY C-MEANS

Climate change is a global issue that affect human life and the environment. Signs of climate change can be observed from long-term meteorological data. This research uses clustering techniques with the K-Means and Fuzzy C-Means methods to group cities in the Eastern Indonesia region based on numerical daily time series meteorological data from 1 January 2010 to 31 August 2023. The variables are minimum temperature, maximum temperature, temperature average, humidity, rainfall, duration of sunlight, maximum wind speed, and average wind speed. The dataset was collected from 28 meteorological stations. The K-Means and Fuzzy C-Means methods obtained the same results, namely the highest silhouette value of 0.218 with the number of clusters k = 2. In general, the annual trend shows an increase in temperature and a decrease in wind speed which are signs of climate change. This research is an early study of climate change in East Indonesia. The results of this research are expected to contribute to the study of climate change in Indonesia.
Increasing Vocabulary of Grade VII Students at SMPS Miftaahul Khairaat Palu through Crossword Puzzle Game

The main obstacles encountered by students to communicate in English is the lack of their vocabulary, Therefore, there should be strategic efforts made by teachers to increase students’ vocabulary mastery, like using Crossword Puzzle Game. The objective of this research was to examine whether the application of crossword puzzle game can increase vocabulary of grade VII students at SMPS Miftaahul Khairaat Palu or not. This study utilized the Class Action Research design, implemented in a cyclical process encompassing planning, action, observation, and reflection. The research involved 17 seventh-grade students during the academic year 2023-2024.This research was conducted in two cycles; the first cycle consisted of four meetings and the second one consisted of two meetings. Thus, there were totally six meetings in two cycles (cycle 1 and cycle 2). In collecting the data, there were three types of instruments used in this research: tests, observation sheets, and field notes. The results of quantities data analysis indicate that the mean score of posttests in cycle 2, 81.05 is higher than that of posttest in cycle 1, 69.05, and that of pretest in cycle 1, 60.58, Thus, based on the comparison of the students’ vocabulary achievement in pretest and in posttest in cycle 1 and in cycle 2, it can be concluded that the use crossword puzzle game can increase vocabulary of the grade VII students. While the results of qualitative data indicate that the students’ vocabulary increases from cycle 1 to cycle 2.
Precision camera calibration using known target motions along three perpendicular axes

We present a method for precision intrinsic calibration of pinhole-model cameras. We refer to this method as “3-Axis.” The algorithm employs a target moving with known displacements along three perpendicular axes to decrease the number of unknown terms that must be determined. This method along with new evaluation strategies for comparing the accuracy of pinhole camera calibration algorithms is described in detail. The 3-Axis approach is then compared with the current state-of-the art algorithms in simulated and physical settings and is shown to exceed them in accuracy and computational performance assuming an equal number of samples acquired.
Implementasi Program Literasi terhadap Eskstrakkulikuler di MTsN 2 Medan

&#x0D; This study aims to explain the condition of the extracurricular literacy program at MTSN 2 Medan. In addition, it also explains the effectiveness and constraints that occur in the development of this literacy program at MTSN 2 Medan. This research uses descriptive qualitative research. This study provides an overall picture of the conditions, effectiveness, and obstacles that occur in literacy programs. The results of the research show that the literacy program has not run optimally. Problems with the literacy program in learning Indonesian that occurred at MTSN 2 Medan included irrelevant learning strategies, the availability of inadequate and incomplete facilities and infrastructure, and students' low interest in reading. Sources of data in this study are documents and school implementation processes literacy program. The data in this study were collected using documentation, interviews and observation methods. The results of this study indicate that the governance of extracurricular literacy at MTSN 2 Medan is as follows (1) First, the school planning for the literacy program has been carried out by setting literacy goals for the school movement program. However, this planning has not been based on a needs analysis (2) extracurricular activities about Indonesian that have not been implemented at this school (4) the control that has been carried out is still limited to ensuring that the Thursday reading program is carried out or not. Controls to improve program quality have not been carried out. The management of the school literacy movement program needs to be managed properly, starting from the strategic planning process, organizing by giving job descriptions, implementing programs in accordance with strategic planning, and continuous evaluation&#x0D; Keywords Indonesian, Ekstrakulikuler, Program Literacy
Effectiveness of social robots as a tutoring and learning companion: a bibliometric analysis

A long-term perspective on how technology will mature is needed whereby robotics and artificial intelligence (AI) have accomplished a consequential and remarkable impact by finding their way into mainstream higher education. Robots have already become an indispensable factor in society and possess high potency as a part of educational technology. Social robot education is limited to complementing the digital aptitude of students in the world of information, and the role of social robots is crucial in polishing students ‘cognitive and social abilities. This study reviews the effectiveness of social robots in education, where we highlight the application of educational robots, surrounded by a blend of social robots and enactive didactics, which could lead to promising ideas for tutoring activities in education. It is empirically proven that social robots can assist with literature, science, or technology education. We synthesize the role of social robots in education and weigh their pros and cons by examining the impact of their appearance on robots’ performance as tutors, tools, or peers in learning exercises. The current study is the first bibliometric analysis that reflects robots’ impact in the education field as tutors and learning companions. A total of 288 articles were reviewed, and the data were extracted to construct an overview through bibliometrics. The outcome of this study paves the way for educational institutes to make informed and fruitful decisions on the applicability of robots, which can help them comprehend the learning styles of students and create knowledgeable and well-adjusted learners.
Analysis of Student Perceptions of Teaching Campus Participants

&#x0D; &#x0D; &#x0D; &#x0D; The Independent Campus Learning (MBKM) policy provides students with the opportunity to take part directly in society to solve the country's problems. The Teaching Campus is one of the MBKM programs with the aim of solving educational literacy and numeracy problems in Indonesia. The research in this article aims to describe the impact of implementing the MBKM program, namely the 6th Teaching Campus organized by the Ministry of Education and Culture, which comes from students from HKBP Nommensen Pematangsiantar University. The research used mixed methods. Quantitative data in this research includes questionnaires from teaching campus school partners and students participating in teaching campuses. The research data is in the form of perception interviews from students participating in the teaching campus class 6. Based on the research results, it is known that students' perceptions after attending the teaching campus are very good, namely on average 90%. This is in line with the partner's high perception of the quality and abilities of students, namely 93%. Based on student perceptions, campus teaching programs not only provide classroom teaching experience and literacy and numeracy skills but can also help improve students' collaboration abilities and soft skills.&#x0D; &#x0D; &#x0D; &#x0D;
CPT: Competence-progressive Training Strategy for Few-shot Node  Classification

Graph Neural Networks (GNNs) have made significant advancements in node classification, but their success relies on sufficient labeled nodes per class in the training data. Real-world graph data often exhibits a long-tail distribution with sparse labels, emphasizing the importance of GNNs' ability in few-shot node classification, which entails categorizing nodes with limited data. Traditional episodic meta-learning approaches have shown promise in this domain, but they face an inherent limitation: it might lead the model to converge to suboptimal solutions because of random and uniform task assignment, ignoring task difficulty levels. This could lead the meta-learner to face complex tasks too soon, hindering proper learning. Ideally, the meta-learner should start with simple concepts and advance to more complex ones, like human learning. So, we introduce CPT, a novel two-stage curriculum learning method that aligns task difficulty with the meta-learner's progressive competence, enhancing overall performance. Specifically, in CPT's initial stage, the focus is on simpler tasks, fostering foundational skills for engaging with complex tasks later. Importantly, the second stage dynamically adjusts task difficulty based on the meta-learner's growing competence, aiming for optimal knowledge acquisition. Extensive experiments on popular node classification datasets demonstrate significant improvements of our strategy over existing methods.
Overview of the Student Learning Environment of the Universitas Padjadjaran Nursing Profession Study Program

The learning environment is an important component in determining the quality and success of the nursing education curriculum. A learning environment that is not supportive can result in decreased student learning performance, including decreased interest and motivation, anxiety and stress, and reduced self-confidence. Evaluation of the learning environment helps improve the quality of the learning environment which consists of the learning process, teaching staff, academic achievement, learning atmosphere, and social environment. This research aims to identify a description of the learning environment for students in the Padjadjaran University nursing professional study program. This research design will use a quantitative descriptive research design. The sampling technique that will be used is proportionate stratified random sampling with 142 nursing professional students as samples. The instrument used is the Dundee Ready Education Environment Measure (DREEM) with 50 statement items. The data analysis that will be carried out is univariate analysis. The results of research on the learning environment dimension show an average score of 148.96/200 in the good category. The average is obtained for each sub-variable; learning process (35.44/48), teaching staff (33.74/44), academic achievement (25.44/32), learning atmosphere (34.49/48), and social environment (19.82/28). Academic achievement with the highest score and social environment with the lowest score. The assessment of the learning environment for nursing professional students shows the Good category, which means that the learning environment for the nursing profession is running well, although there are things that need to be improved and corrected to improve the quality of learning.
Utilization of Chat GPT Artificial Intelligence (AI) in Student’s Learning Experience Gen-Z Class

Technology presents data very quickly, if students do not get enough assistance from lecturers in analytical thinking, the students will present data without studying further or it will convince them that what is presented is justifiable. Lecturers need to understand the students' learning habit nowadays. The focus in this study is the usage of Chat GPT AI in Student Learning Experience in Gen-Z classroom. This study provides in-depth attention to educators, especially lecturers, so that they can adapt to Gen Z's learning experience tendencies and the latest developments in learning media to create the material. The method used in this study is descriptive quantitative with questionnaires distributed to students. The respondent in this study is 392 Gen Z students chosen with purposive random sampling. The use of Chat GPT AI is familiar to Gen-Z students. Therefore, they should double-check the results of Chat GPT so as not to make misinformation and they provide a complete understanding. Lecturers as facilitators in the classroom must be more careful in reviewing the assignments collected by students, so that lecturers can measure precisely that the assignments are the same as the level of student understanding. In addition, the lecturers should notice that the information presented is correct.
The Effect of Emergency Response Training on Flood Disaster Preparedness on Student Council Administrators at SMPN 1 Tibawa

The effect of emergency response training on flood disaster preparedness on student council administrators at SMPN 1 TIBAWA. Guided by PIPIN YUNUS as chairman and HASLINDA DAMANSYAH as member Disaster emergency response is a series of activities immediately at the time of a disaster to deal with the adverse effects caused. These activities include pre-disaster, during-disaster, and post-disaster. This study aims to determine the effect of emergency response training on flood disaster preparedness in student council administrators at SMPN 1 TIBAWA. This research method uses a quantitative approach with a type of quasi-experimental research with one group. Samples were taken using total sampling techniques, the number of samples was 36 respondents. Data collection using a questionnaire sheet with data analysis using a Paired T-test with meaning (p-value = 0.05). The results showed that there was an average value of preparedness before 46.61 and preparedness after 73.72 there was an increase of 27.11 preparedness values after being given treatment, then the statistical value was obtained p-value = 0.00 smaller than 0.05 (p&lt;0.05). In conclusion, there is an effect of emergency response training on flood disaster preparedness.