Reviewerly/exHarmony huggingface dataset:
Arabic Sentiment Analysis of Mobile Banking Services Reviews

Online customer reviews have developed into a significant source of information about a business's performance. Due to shifting consumer expectations and growing internet penetration, the Middle East, especially Jordan, is seeing an increase in the usage of online banking. This propensity toward digital transition has been accelerated by the COVID-19 pandemic. Mobile banking service companies aim to effectively use customers' feedback to identify areas for improvement in customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on mobile banking reviews in Arabic, exploiting machine learning, natural language processing, and resampling methods to obtain subjective feedback, determine polarity, and identify customers' feelings in the banking domain. This work walks the reader through detailed steps of cleaning and preprocessing a manually annotated dataset of online banking reviews in Arabic, followed by features extraction and modeling, dataset splitting, and balancing training data. Finally, we examined classification algorithms including Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), K-Nearest Neighbors (KNN), and Random Forest (RF). Ensemble hard and soft voting ML methods were also evaluated. We achieved a maximum accuracy of about 94% using the LR classifier and a maximum precision of 94% using the LR and hard voting methods. Our methodology will enable banks to acquire insights on how to develop their online presence and meet the demands of customers and stakeholders.
Augmented Reality-Based Drone Pilot Assistance Using Consumer-Grade Hardware

Unmanned Aerial Systems (UASs), also known as drones, are experiencing a surge in usage across industries and among consumers of diverse backgrounds. This rise in adoption introduces challenges for novice pilots. This paper explores how Augmented Reality (AR) can help address these challenges by providing an intuitive visualization of the drone's pose given the pilot's third-person perspective of the aircraft. We (1) experimentally evaluate if commercially available AR and UAS technology provides the necessary foundation in terms of data and capabilities, (2) propose a digital twin-based assistance system, incorporating a simulation environment, a Microsoft HoloLens 2, and a DJI Mavic Mini drone to enhance the piloting experience and increase safety by visualizing flight information in real time, and (3) implement the system and perform experiments for a first evaluation of the proposed approach. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> A commented mixed reality capture video of the final system setup in action is available online at https://youtu.be/R3mcoBQ4NZc.
Transformadores de visão para previsão densa

In this review, we delve into the advancements and applications of monocular depth prediction, focusing on the intricacies of the protocol experiments, dataset transfer, and fine-tuning. We highlight the incorporation of gradient matching loss and the significance of employing a depth inverse representation. Furthermore, our study underscores the expansive utility of such predictive models, not only in surveillance within smart city paradigms and public security but also in diverse domains such as agribusiness, education, and biomedical engineering. Leveraging datasets like MIX 5, MIX 6, and ADE20K, we elucidate the performance enhancement achieved by Transformer-based Neural Networks in dense prediction tasks. Through these findings, the potential for Transformer-based models in practical applications, respecting data protection norms like LGPD, becomes evident. The collaborative efforts of academic and domestic partners in realizing this review are also acknowledged.
Implicit Neural Representation with Learnable Fourier Feature Encoding for View Synthesis

The implicit neural representation uses a Multilayer Perceptron (MLP) to parameterize a 3D scene as a continuous function. Synthetic images are inaccurate due to the inherent "spectral bias" of MLPs, which preferentially learn low-frequency information during network training. With learnable Fourier Features, we encode position in the network in a shift-invariant manner, capturing position relationships in multidimensional space effectively. A periodic activation function is also introduced to learn the information contained in higher-order derivatives, allowing the MLP to extract high-frequency information from input images efficiently. Simulation experiments are conducted on two types of datasets, and the quality of view synthesis is superior to previous work. Ablation experiments demonstrate that trainable encoding based on learnable Fourier Feature mapping combined with periodic activation functions can significantly improve the quality of novel view synthesis.
The preventive and remedial potential of studies regarding the victim- aggressor relationship

This article is a pleading for the valorization of victimological studies, especially studies on the aggressor-victim relationship. Almost all research in this field has highlighted 2 diametrically opposed forms of behavioral deviance: the deviance of the aggressor, based on offensive behavior, and the deviance of the victim, based on receiving of the offence. Mendelsohn used an interesting notion, namely potential of victimal receptivity. As soon as this notion was introduced in the field of victimological science, the aggressor was no longer seen as the only key element in the causal system of the crime. The victim was viewed in this way as well. In the meantime there have been other disco-veries related to the behavioral mechanisms of the victim and the aggressor that are relevant for the elucidation of all the essential aspects of the criminal act which is also an act of victimization. Their preventive and reparative role has not been fully explored for this reason, we will argue the need to implement the results of scientific research in the prevention of crimes and the reduction of the consequences that result after a crime has been committed. At the same time, we will describe the ways in which, this fact can be achieved.
Machine learning-enhanced hybrid cryptography and image steganography algorithm for securing cloud data

Ensuring data security in cloud computing is crucial due to the growing reliance on cloud-based services. Hybrid cryptography and image steganography have emerged as robust techniques to enhance data confidentiality in the cloud. In this research paper, we propose a novel algorithm, “Machine Learning-Enhanced Hybrid Cryptography and Image Steganography,” integrating these methods to provide comprehensive data protection. The algorithm employs key generation, encryption, steganography, cloud storage, data retrieval, and machine learning-based attack detection to defend against advanced cyber threats. Our experimentation demonstrates the algorithm’s effectiveness in detecting DoS attacks, data breaches, and data leakage attempts using SVM, Neural Network, Isolation Forest, and Random Forest models. The proposed approach offers broad applicability, fortifying data security and fostering further advancements in cloud security research.
De paucalis in het Nederlands en de structuur van de grammatica

In general linguistics, a paucal is a specific construction that only allows for insertion of the lowest numerals (num), e.g. the Russian num + N.gen.sg, which only allows the numerals 2-4 (Pesetsky 2009). In this paper it is argued that also Dutch shows paucal effects in at least three constructions with numerals: (i) the alle + num + de + N construction, (ii) the num + diminutive construction, and (iii) the historical num + genitive construction. The third paucal construction, which is related to the modern quantitative er construction, which lacks the paucal effect, takes us to the origins and nature of the paucal. Following traditional grammarians who assume a connection of quantitative er with the genitive ending -er (Bech 1952; van der Horst 2009), we are led to the conclusion that extraction of the genitive morpheme to the verbal domain cancels the paucal effect. This brings us to an interpretive rule of paucality that an inflected num at the surface triggers the paucal restriction.
A Deep Learning Model for Identical National Flag Recognition in Selected African Countries

The national flags are among the symbolic representations of a country. They make us understand the country of interest in a particular issue. Therefore, they are commonly used in both private and government organizations. It has been discovered in recent times that the younger generation mostly and idly spend its time online; hence, knowing little about national flags. Additionally, some national flags (particularly in West Africa) are identical in nature. The likeness is in terms of layout, colours, shapes and objects on the national flags. Hence, there is a need to have a model for flag recognition. In this paper, national flag images of some West African countries were gathered to form a dataset. After this, the images were preprocessed by cropping out the irrelevant parts of the images. VGG-16 was used to extract necessary features and to develop the deep learning model. This contrasted with the existing handcrafted feature extraction and traditional machine learning techniques used on this subject matter. It was observed from this study that the proposed approach performed excellently well in predicting national flags; with an Accuracy of 98.20%, and an F1 score of 98.16%. In the future, it would be interesting to incorporate the national flag recognition into Human-Computer Interaction System. For instance, it could be used as flag recognition in some mobile and web applications for individuals with colour blindness. This research work presents a robust model because of nature of the dataset used in this work compared to previous works.
Study on Teachers’ Perception of Edtech Utilized Classes and its Application

This study investigated teachers' perceptions in the process of implementing edtech in school classrooms. The subjects comprised teachers participating in a demonstration project aimed at applying products from edtech companies to public education. The study examined aspects such as satisfaction with the demonstration process, appropriateness, the stage of applying edtech in class, important influencing factors, the role of the teacher, observed changes, student participation, and areas needing improvement. Teachers' responses to related questions were systematically organized and analyzed. The research findings are as follows.&#x0D; First, the responses regarding satisfaction and appropriateness of the process for selecting the subjects and the class stages for applying the Edutech products and conducting the verification process were found to be significantly positive.&#x0D; Second, the abundance of engaging interactions emerged as the primary motivation behind incorporating edtech materials or apps in classrooms.&#x0D; Third, teachers preferred to integrate Edutech during the class process itself rather than before or after the class. Moreover, they indicated a future preference for using it for learning evaluation and feedback, post-class review, and providing the learning materials.&#x0D; Lately, there has been much anticipation around digital transformation and the implementation of efficient edtech-powered classes in educational settings. To this end, continued research is crucial, exploring how teachers and education experts can actively participate in edtech development and identify effective approaches and verification methods for implementing various edtech solutions in schools.
REGRESSION ANALYSIS OF ROBUST ESTIMATION-S WITH TUKEY BISQUARE WEIGHTING ON POVERTY LEVEL ON SULAWESI ISLAND

Poverty is a situation where a person experiences difficulty in meeting basic needs. There are several factors that influence poverty, including population, unemployment, gross regional domestic product, human development index, average years of schooling and labor force participation rate. Therefore, it is necessary to carry out regression analysis to determine the relationship between one variable and other variables. One method for estimating regression parameters is the least squares method. Some classic assumptions are not met because there are outlier data. Outliers are data that do not follow the overall distribution pattern, so a method is used that can overcome outliers, namely the S-estimation robust regression method with the Tukey bisquare weighting function. The results of the research show that the best model was obtained from robust S-estimation regression with Tukey bisquare weighting, namely factors that influence the level of poverty on the island of Sulawesi, namely Population Number ), Human Development Index ( ), Average Years of Schooling ( ) and, Force Participation Level. Work .
SENTIMENT ANALYSIS OF REVIEW DATA OF THE RUANGGURU ONLINE LEARNING APPLICATION USING THE C5.0 ALGORITHM

Sentiment analysis is process to determine the sentiment of a person that is manifested in the form of text. Internet users write their opinions and everything that concerns them in the google play store review column. Moreover, when the world of education could not carry out face-to-face learning due to the covid-19 pandemic, learning turned to e-learning applications. Through this innovation, many pros and cons flow from the community with the existence of Ruangguru online learning application in the world of education. This research was conducted with the aim of determining word cloud visualization and the accuracy of the results of sentiment analysis of review data on the Ruangguru application using the C5.0 algorithm. The word cloud visualization results are dominated by word such as “paham”, “bagus”, “mudah”, “suka”, “langganan”, “seru”, “nyaman”, “senang”, “menarik”, “keren”, “lancar”, “sukses”. This shows that Ruangguru Application is a good application because it is dominated by positive sentiment words which means that users find it helpful and easy to understand the learning material in Ruangguru. The results of the Confusion Matrix show that the model successfully classifies 0.8557 or 85.57% of the data correctly from all test data
MODELING PNEUMONIA CASES IN TODDLERS IN INDONESIA USING GENERALIZED ADDITIVE MODEL FOR LOCATION, SCALE, AND SHAPE (GAMLSS) WITH LOESS SMOOTHING

Pneumonia is an acute respiratory infectious disease that is the primary death cause due to infection in children worldwide, including Indonesia. Pneumonia case modeling is necessary to predict the incidence, especially pneumonia in toddlers. In this study, case modeling using the Generalized Additive Model for Location, Scale, and Shape (GAMLSS) method with LOESS smoothing to determine the model form and the factors influencing pneumonia cases in toddlers in Indonesia in 2021. The research results obtained indicate that with the Inverse Gaussian distribution, the model form for the location parameter is 9,719 + 0,013 + 0,001 + 0,031 and for the scale parameter is -10,897 + 0,125. The resulting model is accurate and suitable for use because the model residuals follow a normal distribution. Along with factors that influence pneumonia cases in toddlers in Indonesia are the percentage of babies receiving exclusive breastfeeding (), population density (), and the percentage of toddlers receiving measles immunization ().
Twitter Please Do Your Magic: The Vulnerability of Fraud Victims On the Social Media

For Twitter users, the term “Twitter Please Do Your Magic” is familiar. This means that with good delivery and communication, even Twitter accounts have the same potentials in solving problems raised. In reality, crime always finds its own loophole. The existence of this trend is used by irresponsible parties as part of the fraud mode by forming a narrative that is able to bring disadvantages to humanity. Therefore, as an effort to understand this, an assessment of the characteristics of the victims was carried out using the perspective of lifestyle-exposure criminology theory. Using a qualitative research method by taking a fraud case study on twitter accounts, the researchers utilized data from interviews with four victims, a witness resource person and an expert. The result was that the victims of the case study have some characteristics in common related to each other, which has the potential to endanger their lives in explaining the cause of the victim's position. Among them, first, making twitter their favorite social media among all other social media they have. Second, the victims like content topics related to social issues supported by their involvement in previous donation activities. Third, the victims have the right and access to their own finances. Fourth, the victims have a "common thread" with perpetrators such as a sense of closeness and similarity. Finally, the fifth, the victims tend to trust their online friends on twitter. Although these results cannot be generalized to all cases of "Twitter Please Do Your Magic" fraud that have occurred, at least this research is expected help understand problems surrounding the issue, especially from the perspective of the victims.
College Students’ Perceptions of Virtual Reality-Based STEAM Education

This study aims to examine college students’ perceptions of virtual reality (VR)-based STEAM education. Fifty-one college students who enrolled in the VR-based STEAM program of an engineering course at a science and technology-oriented university participated in this study. They were asked to complete an online survey asking about the usability of virtual reality, learning engagement, and affective learning experiences during the VR-based STEAM program. The results of the descriptive statistics and ANOVA show: First, overall, students have positive perceptions of VR usability in STEAM education. There were significant differences in the perceived usefulness of VR between academic levels. Second, students revealed high levels of behavioral, emotional, and cognitive engagement while participating in the VR-based STEAM program. Lastly, during the STEAM program students experienced various affective learning experiences, such as positive and negative emotion, perceived challenges and skills, and performance self-efficacy, expectations, and goals. Notably, the means of perceived challenges and performance goals significantly differed among different majors. This study provides significant insight into the effective integration of VR in post-secondary STEAM education and the pursuit of substantial pathways to achieve STEAM education goals.
Study of the need for project-based learning citizenship e-books to expand national literacy of students pgri university palembang

This study raises the question of what resources are best suited to help students develop their civic literacy and acquire optimal knowledge about their nationality through citizenship education. The research's goal is to gather the information required for citizenship education so that students can learn as much as possible about their nationality. This study used an exploratory survey strategy in conjunction with a quantitative research method. The sample size was 60 individuals, comprising 10 lecturers and 50 students. The inclusion criteria for students are those who have completed the Citizenship Education course so that students can provide responses based on their experience of attending lectures in nine different study programs at the Faculty of Teacher Training and Education, PGRI University Palembang. Based on research findings, 92% of teachers and students believe that the analysis of national e-book creation demands meets the Very Good standard. The findings of this research support the assumption that to increase student citizenship literacy, lecturers and students at PGRI Palembang University really need Nationality E-Books.
Basic research on elementary career education program development Using generative artificial intelligence

This study aimed to establish a basic needs analysis and set program development directions for an elementary career education program using generative artificial intelligence. To that end, a needs analysis was conducted involving an expert panel comprising 13 elementary school teachers with master's degrees in career education. This analysis informed the development of an elementary career education employing generative artificial intelligence. The program’s development direction was then set.&#x0D; The findings revealed a strong consensus among participants on the necessity of integrating generative artificial intelligence into elementary school career education. Participants also acknowledged the high potential of using it for specific goals. Moreover, the validity of most sample topics for elementary career education topics using generative artificial intelligence was found to be high.&#x0D; Drawing on these findings, the direction for elementary career education employing generative artificial intelligence was established. This includes a proposed 8-session program using generative artificial intelligence, including examples of teaching and learning guidance as well as corresponding worksheets.
Linguistic Interplay on Social Media: Unraveling Indonesian-English Code Mixing on Twitter

This qualitative descriptive study delves into the intricate realm of code mixing between Indonesian and English on the Twitter account known as Convomf. With a substantial following of over 1.3 million users, Convomf serves as a dynamic autobase forum where individuals express their concerns and experiences anonymously. This research is driven by two primary objectives: firstly, to categorize the distinct types of code mixing evident in the Convomf tweets, and secondly, to uncover the underlying reasons users engage in this linguistic practice. Employing a purposive sampling method, the study meticulously analyzed tweets collected between February and March 2023. The tweets selected for analysis were those that prominently featured code mixing, providing a rich dataset for examination. The methodology centered around the documentation of these tweets, followed by an in-depth analysis of the language patterns and structures used by the account holders. The findings of the study indicate a diverse array of code-mixing types, with six primary categories identified: insertion of a word (16%), phrase (22%), word reduplication (1%), clause (42%), hybrid constructions (9.5%), and idioms (9.5%). These variations demonstrate the complexity and versatility of code mixing as a linguistic strategy. Furthermore, the study explored the motivations behind code mixing, aligning with Hoffman's theoretical framework. The reasons range from discussing particular topics (65%) and clarifying speech (18.5%) to emphasizing certain points (15%), among others. Notably, interjections and repetition for clarification were also observed, albeit less frequently. Overall, this study contributes to the understanding of code mixing in digital communication on social media platforms. It reveals that code mixing is not only prevalent but also serves various communicative functions, reflecting the nuanced ways in which language is used in the context of social media. The insights gained from this research offer valuable implications for linguistics, particularly in the realm of bilingualism and language usage online.