Reviewerly/exHarmony huggingface dataset:
Chatbots: A New Digital Teaching Tool Paradigm in Artificial Intelligence (AI) Technology Among Secondary School Teachers

Teachers around the world are committed to improving student-centered learning, intending to enhance the digital teaching tool in the classroom, in tandem with the rapid evolution of technology. In addition, as a teacher, education is one of the sectors that play an essential role in preparing students for the changes, as students begin to recognize what they are learning on the first day of formal education. Consequently, exploring teachers’ experience and reviewing the challenges in teaching, is in line with the current digital era. The implementation has significance important in the scenario of Industrial Revolution 4.0 (IR4.0). One of the pillars, Artificial Intelligence (AI) Technology, which is defined as the ability of a computer or a computer-controlled robot to perform tasks that are normally performed by humans has given a dynamic viewpoint in various fields, especially in teaching. Nowadays, decision-making is simplified by artificial decision-making, which does not require the presence of a human. As a result, Chatbots as in AI Technology provide a new paradigm for teachers at school, and the mushrooming of digital mediums in education enhances the capability of teaching by using chatbot applications in the lessons. The purpose of this study is to explore Chat Bots in AI Technology as a digital teaching tool among Secondary School Teachers. The researcher used a qualitative case study guide with the purposive sampling method with three research questions to gather data through semi-structured interview questions and document review. Next, the researcher used manual coding to create themes for the findings. All in all, the informants are sharing the concept, the use, and the challenges based on content representation, the students’ interactions in previous experiences, assessments, and feedback. How teachers respond in depth led to the researcher discovering the need for training, professional development, and future classrooms for secondary education.
An Investigation about Education as Envisioned by Confucius and Its Implications for Contemporary Moral Education

This study investigates Confucius' views on education and their rel-evance in the 4.0 era. Confucius is known as "the great model teach-er for centuries" for his dedication to education and his thoughts regarding its role, purpose, substance, and teaching techniques. Con-fucius founded Confucianism. Confucius felt education should be a right for everyone, not just the affluent. "No class difference in school" quickly transforms people's educational views. It makes formal schooling possible for most individuals. The idea of Confu-cius is innovative because people from all backgrounds desire jus-tice and equality in education. However, a synthesis of Confucius' education concepts based on past research adds to the literature on educational philosophy. Confucius's thoughts help people world-wide understand the incredible education ideas that have ever de-veloped in moral education. The results of this research can be guid-ed and affect modern educational authorities and policymakers.
Formas de tratamento no português europeu: algumas considerações sobre formas neutras e não neutras

Forms of address in the Portuguese language are complex and seem to be a fairly rapidly changing area of the language. These forms, due to their gradation, complexity and subtlety, generate confusion and even conflict among speakers. In this short paper, guided in the beginning by Cintra (1972) and Allen (2019), to whom we later added other contributions, we explore the va-riety of forms in contemporary European Portuguese and, at the same time, try to understand the evolution of this multifaceted system since before the foundation of the Portuguese language. We focus in particular on a form of address that is problematic in some cases: “você”, relating it to the alternative form of the 3rd person without an explicit subject. Finally, we carry out a small explora-tory study with data collection, which, although limited, allowed us to obtain some results regarding the potential of the 3rd person, without subject, and the way the pronoun "você" is used by certain social classes.
Examining The Role Of Unethical Behavior In Moderating The Influence Of Governance, Risk Management, And Compliance On Financial Fraud Prevention In Indonesian Local Governments

This study examined the moderation effect of unethical behavior on the relationship of governance, risk management, and compliance on financial fraud prevention in the context of Indonesian Local Governments.Focusing on Regional Apparatus Organizations and local government auditor offices, the research comprised of a robust sample of 120 respondents.Employing the rigorous Structural Equation Modeling-Partial Least Square (SEM-PLS) with WarpPLS software for analysis, the findings revealed a significant weakening effect of unethical behavior on the relationship between governance, risk management, and compliance and financial fraud prevention in the local government setting.Notably, this research contributes to the limited literature on governance, risk management and compliance in the realm of local government studies.Moreover, by incorporating a moderating variable, this study enhances its academic significance, as it addresses the pervasive issue of unethical conduct that permeates the local government landscape and further engages with prior debates in the field.
Deep Learning-Based Image Stitching Technique Using Transformer

Image stitching is an image processing technique that combines multiple overlapping images into one. Classical image stitching extracts common feature keypoints from input images and use them as reference points for aligning the images. Convolutional neural network-based image stitching network combines latent feature vectors to produce stitched output from input overlapping images. This paper proposes a transformer-based image stitching network along with its implementation and training strategies. Unlike previous methods, the proposed transformerbased image stitching network explicitly learns to produce stitched output images from connection between patches of input images. It achieves 3.7dB higher PSNR and 0.12 higher SSIM than classical image stitching technique.
Challenges in Implementing Indonesian Language Teaching Materials in Elementary Schools

The success of the learning process greatly depends on the smooth functioning of its components. This study aims to shed light on the challenges faced by elementary school teachers when implementing teaching materials in the Indonesian language learning process. The research employs a descriptive design by utilizing quantitative methods, questionnaires as the research instruments. The questionnaires used are teacher challenges (14 items) and proficiencies questionnaires (13 items) on a Likert scale, which was presented using Google Forms and distributed through WhatsApp groups. The sample comprised 94 respondents, elementary school teachers from many regions in Sumatra, Indonesia. According to the research findings, it can be deduced that elementary school teachers encounter various difficulties when teaching Indonesian language. These challenges encompass the impact of the local or mother tongue on the learning process and the complexities associated with dynamic language development. Moreover, the study employed factor analysis to pinpoint four specific areas that teachers can enhance, as they significantly improve the obstacles they face. These factors consist of ensuring adequate learning resources, enabling teachers to develop teaching materials that align with the existing curriculum, enabling teachers to design teaching materials based on students' characteristics, and fostering the ability of teachers to establish a positive and captivating learning environment.
Task Management System Untuk Osis

Students are one of the important factors for the continuity of education in schools. Without students, school programs cannot be realized. Therefore, students need to be facilitated well so that school programs can move and successfully achieve educational goals. One of the facilities for developing student potential is the existence of an OSIS (Intra-School Student Organization). Writing articles uses the library research method. This article will reveal how the task management system can be utilized by the OSIS to manage work programs that will be carried out by joining in a system and then collaborating, coordinating, distributing tasks, and so on so that the work program can be carried out effectively and efficiently.
Strategi Pemetaan Genangan Banjir Sungai di Kota Pekalongan Menggunakan Model SCS-CN

Pekalongan City consistently experiences annual floods. In this research, we aimed to map the distribution of potential flood inundation in Pekalongan City under two scenarios: the existing land cover and urban planning (Rencana Tata Ruang Wilayah/RTRW). We utilized hydrological modeling to assess the suitability of the RTRW for future implementation. The Soil Conservation Service – Curve Number (SCS-CN) method was employed, incorporating Digital Elevation Model (DEM) data, soil data, precipitation data, and two sets of land cover data (existing and urban planning). The findings indicate that under the existing land cover, approximately 13.97% of the area is susceptible to flooding, while the remaining 86.03% is not at risk. Conversely, the model using the urban planning (RTRW) scenario suggests that 14.06% of the area has the potential to flood, while 85.94% is not susceptible to flooding. The increase in potential flooding in the urban planning scenario may be attributed to the expansion of residential areas and the reduction of plantation and dry land cover. Furthermore, in assessing monthly modeling results, we observed that the area with flood potential is highest in December 2022 and lowest in June 2022. Considering topographical conditions (DEM), we found that the potential for flood inundation is fairly and evenly distributed in flat areas, mostly with elevations of less than 11 meters. Areas at higher elevations are predominantly free from flood potential, but it is noteworthy that certain areas in the Pekalongan Selatan District, with elevations higher than 11 meters, still have the potential for flooding. Keywords: Flood, hydrology model, SCS-CN, existing land cover, urban planning
EKSPLORASI KREATIF MELALUI PENGINTEGRASIAN KEBHINEKAAN SENI VISUAL DALAM PEMBENTUKAN KARAKTER SISWA SMAN 1 TERARA

This research aims to investigate how visual arts can influence students' creativity and develop more positive characters. A qualitative study combined with a case study research style was the methodology used. PPKn professors and class Data collection methods include documentation, interviews, and cross-referencing observations with information from related magazines. The inductive analysis model is used as a data analysis method in this research. Research findings show that learning media has a significant impact on the way students develop their personalities and creative thinking, teacher's creativity in choosing the right media also very important, as is the case with teachers at SMA Negeri 1 Terara who have succeeded in using learning media in the form of visual arts to create student creativity in visual art form and develop a more positive student character.
Kebijakan, Sumber Daya, Program dan Kegiatan Perpustakaan

The library is the most appropriate place to provide knowledge for studying, reading, conducting research, and also seeking answers to every problem that arises, whether it is a problem that concerns the human individual or problems that exist in society, nation and state. Libraries must be managed with good management so that the library can run well and be enjoyed by visitors who come to the library. Library management ensures that all efforts to achieve library goals utilize human and non-human resources while still paying attention to functions, roles and expertise. Human resources play an important role as organizers and the key to the success of a library. Libraries as information units will have good performance if they are managed with quality human resource management. With good human resource management, library activities will achieve the stated goals.
A Domain Adaptive Feature Relevance Analysis for Human Activity Recognition using Transfer Learning

Wearable electronics are electronic gadgets designed to be incorporated into clothing, worn on the body, or integrated into other items. These devices collect data, provide information, and perform specific tasks. Human Activity Recognition (HAR) utilizes Transfer Learning algorithms to track movements using sensor data from devices like accelerometers and gyroscopes. HAR finds applications in security, fitness tracking, and health monitoring systems. Utilizing pre-trained models and engineered features from these models aids in creating accurate HAR systems. Transfer learning reduces data requirements and simplifies feature engineering, making it a valuable approach for HAR, especially in diverse real-world scenarios.
Evolution and Advancements of the World Wide Web: From Concept to Global Phenomenon

This paper examines how the World Wide Web evolved through its numerous stages, including Web 1.0, Web 2.0, Web 3.0, Web 4.0, and the hypothetical Web 5.0. Each stage represents a shift in how users interact with the internet and how technologies shape online experiences. Web 3.0 aspires for intelligence and decentralisation, while Web 2.0 introduces user-generated content and social interaction. Web 4.0 remains a concept, expected to integrate AI, IoT, and more personalized experiences. Web 5.0 speculates about highly intelligent and interconnected machines. The paper also examines the potential applications of these web generations in agriculture, highlighting the prominence of Web 2.0 in information sharing, precision agriculture in Web 3.0, and potential developments in Web 4.0 and 5.0 for the agricultural sector.
Cultivating a Green Future: Augmented Reality Veggie and Fruit Explorer App Empowering Indian Curriculum with Agricultural Knowledge and Healthy Habits

Indian education holds that agriculture is essential for a stable and healthy society. To teach Indian schoolchildren about fruits and vegetables, the AR Veggie and Fruit Explorer app was developed. With the help of this revolutionary app, users can learn about fruits and vegetables in a fun and engaging way. With the help of the AR Veggie and Fruit Explorer app, agricultural education is integrated into the curriculum to better help students understand agriculture and the value of a healthy diet. By scanning QR codes Marker Based Augmented Reality, students can gain knowledge about produce’s growing conditions, nutritional facts, and culinary applications in Audio form.Indian education is benefited by AR Veggie and Fruit Explorer. It first aids students in understanding the significance of agriculture in sustainable food production. It also instructs students in nutrition and promotes nutritious eating. The app fosters curiosity and environmental awareness by showcasing India’s wide variety of fruits and vegetables.Student engagement is increased by AR technology. The application uses machine learning algorithms to tailor learning and offer dietary suggestions, this aids students in choosing what to eat. Students are taught about vegetables and fruits through the AR Veggie and Fruit Explorer app and agricultural education in the Indian curriculum. Healthy eating, environmentally friendly farming, and India’s agricultural heritage are all encouraged. This study emphasizes the significance of including agriculture in the educational curriculum to help the next generation become knowledgeable and accountable land administrators.
An Experiment to Develop an Enhanced Medical Image Security by using Deep Learning Assisted Crypto Policy

Ensuring a high level of safety and security is imperative when transmitting medical images via open access channels. Medical images hold immense importance in various applications, particularly in real-time contexts like telemedicine. Due to the unique characteristics of medical imaging data, determining the appropriate techniques for safeguarding confidential images against unauthorized access becomes challenging. Existing encryption methods primarily cater to textual data, leaving a gap in effectively securing multimedia data such as images. Using DNN leaves these systems open to attack from adversarial samples, which are pictures with slight changes that can trick the model into producing inaccurate predictions. Utilizing the MNIST database, this research demonstrates that Deep Learning algorithms may be used to encrypted data without compromising accuracy. This finding holds significance as it offers a potential solution for preserving data security while benefiting from advanced machine learning capabilities. In addition, the research goes farther by training a model using encrypted information to categories X-ray coronary angiography images, making it a more difficult scenario. The suggested DNN model's remarkable accuracy score of 0.983 highlights its ability to make predictions with an exceptional level of correctness.
Lung Tumor Volumetric Estimation and Segmentation using Adaptive Multiple Resolution Contour Model

The need to identify pathology is strongly felt in image guided analysis. Tumor localization is essential for targeting the tumor. Tumor boundaries are often hard to delineate due to high variation in shape and size of the tumor. The tumor boundary is usually defined using the thresholding technique which may have high FP rate due to partial volume effects. Hence, this AMRC technique has to be repeated several times in order to get a more accurate result. The multiple thresholding technique often leads to an undesirable situation where the segmentation boundary contains holes which are hard to fill. The AMRC is a dynamic contour model that is able to adaptively adjust the contour to better fit the contour of the tumor. Segmentation of SVT involves two steps viz. identification of peak intensity pixels with the HECS model and definition of an adaptive contour with AMRC.
Facial Expression-Based Emotion Recognition: Analysing Human Affect through Facial Cues

This study presents a CNN-RF (Convolutional Neural Network-Random Forest) model for emotion identification using facial expressions. The goal is to create a model that can accurately and efficiently discern emotions from face photographs. For feature extraction and downsampling, the proposed CNN-RF model consists of several convolutional layers followed by max pooling layers. To transform the 2D feature maps to a 1D vector, a flattened layer is employed. The RF layer connects the CNN and RF components and accepts flattened features as input. The completely linked layer connects the RF output to the emotion categorization layer at the end. The experimental findings demonstrate the CNN-RF model's efficacy in emotion recognition. The accuracy values range from 91.69% to 96.44%, indicating a low percentage of false positive predictions, while the recall values range from 92.84% to 96.13%, demonstrating the model's capacity to catch a large number of real positive cases. The F1 scores, which take precision and recall into account, vary from 93.39% to 95.21%, further supporting the model's overall ability to reliably categorize emotions. With an accuracy of 98%, the CNN-RF model demonstrates its capacity to discern emotions based on facial expressions. This study's findings emphasize the CNN-RF model's ability to effectively recognize emotions from facial expressions. The presented model is a viable method for real-world applications where precise emotion identification is critical.
OCR-Based Multi-class Classification of Hate Speech in Images

The depersonalization and anonymity afforded by ubiquitous social media platforms facilitate open discourse, yet also create a potential avenue for hate speech dissemination. The rising incidence of hate speech on these platforms necessitates vigilant monitoring and intervention. However, the sheer volume of user-generated content renders manual oversight infeasible. Technological solutions must be developed to efficiently identify and mitigate hate speech, striking a balance between maintaining open expression and safeguarding against harmful content. Additionally, when utilizing traditional machine learning methodologies as prediction methods, the language being utilized and the length of the messages provide a barrier. In this study, a Genetic Programming (GP) model for identifying hate speech is presented, where each chromosome acts as a classifier with a universal sentence encoder feature. The performance of the GP model was enhanced by enriching the offspring pool with alternative solutions using a unique mutation strategy that only modifies the feature values in addition to the conventional one-point mutation technique. For the six categories of hate-type hate speech text datasets, the suggested GP model beat all cutting-edge solutions. In contrast to the machine learning models such as Random Forest, Decision Tree, and Naive Bayes which gave the following accuracy of 79.25%, 77.88% and 77.33% whereas GP model outperformed with an accuracy of 97%. The following evaluation matrices are considered as precision, recall training, and testing accuracy.
Enhancing Public Transportation Detection using YOLOv5

In the current context of urbanization and transportation expansion, the need for accurate and efficient detection systems for public transportation has become of the utmost importance. The paper presents a novel strategy to establish new standards in transportation detection systems. Using the power of the YOLOv5 deep learning algorithm, the dataset is divided into training, testing, and validation segments to ensure a thorough evaluation. With a training dataset size of 75% and a test-validation split of 25%, our methodology showcases a compelling mean Average Precision (mAP) value of 0.973. Our findings highlight a precision of 0.971, pointing to accurate predictions in approximately 97.1% of cases, and a recall of 0.953, underscoring the model's efficiency in capturing around 95.3% of relevant objects. Such results, particularly the distinguishable taxi class among similar objects, represent significant improvements over previous benchmarks. The model's prowess is evident in its ability to distinguish even in situations involving entities that closely resemble one another, such as taxis and police cars. Our proposed system excels with increased accuracy, precision, and F1 scores compared to a standard study. This paper concludes that with the strategic application of YOLOv5, the future of public transportation detection systems is bright and on the cusp of a new era of efficiency and precision.
Implementation of Intuitive 3-Dimensional Manipulation for 3DCG Objects Using Monocular Camera

The widespread use of smartphones has led to an increase in opportunities for manipulating 3-Dimensional-Computer-Graphics (3DCG) objects in Augmented Reality (AR) and Virtual Reality (VR) applications. However, these interactions usually require touch panel input, which can be difficult for users who lack prior experience. To address this issue, one possible solution is to use depth sensors to infer hand shapes from skeletal data and control 3DCG objects using gesture movements. Nevertheless, these sensors can be challenging to implement due to cost and other limitations. Thus, we will investigate the possibility of manipulating 3DCG objects using only a monocular camera based on gesture movements.