Reviewerly/exHarmony huggingface dataset:
An Evaluation of the English Textbook Grow with English for Fourth-grade Elementary School Students

Textbooks are the major instructional resource that has a crucial role in the teaching and learning process. To obtain an appropriate textbook, a teacher must evaluate or analyze it before applying it in the classroom. A textbook is expected to cover student needs, support national education goals and implement curriculum values. However, in reality, many textbooks still need to meet the criteria of a good textbook based on previous studies, and most of the research is done on high school English textbooks. Therefore, this research examines the suitability and quality of the four-grade primary school English textbook (Grow with English for grade four). This study used descriptive qualitative research and adapted adopted the BSNP (Badan Standar Nasional Pendidikan) framework and theory of Developing Criteria for Textbook Evaluation by David Williams (1983) in analyzing the textbook. The findings show that this textbook follows the 2013 ELT (English Language Teaching) curriculum of Indonesian Education goals and Indonesia's National Education goals. Furthermore, this textbook meets William's (1983) criteria, such as being designed with meaningful drills and learning activities, connecting to the student's age, interests, culture, and environment, having guidance for teachers and students, and graphically having good quality. Then, for further research, observing and analyzing other EFL (English as a Foreign Language) textbooks is recommended to see if they meet the criteria for a good textbook.
Virtuality+: The physical body in virtual reality and the path toward augmented virtuality

While many scholars have decried the erasure of the body in virtual reality (VR), this paper focuses on the body – and the physical reality for which it stands – as a critical component of any experience of virtual reality. Specifically, studying VR from the perspective of the physical body allows for a more nuanced appreciation of the unique reality of this «virtual» reality. Moreover, this paper argues that the body should not be seen as a distraction from the immersive potential of VR, but rather as a potential tool for augmenting what virtual reality can currently offer.
Computational framework for turbid water single-pixel imaging by polynomial regression and feature enhancement

The quality of underwater imaging is greatly impacted by the scattering and absorption of light in turbid water environments. Single pixel imaging (SPI) has emerged as a promising solution for turbid underwater imaging, as it effectively suppresses the effects of scattering and is cost-effective due to the use of a single photodetector. However, the quality of SPI in highly turbid water is still unsatisfactory. To address this issue, we propose a novel computational framework for turbid water single-pixel imaging. The framework involves a machine learning-based polynomial regression fitting method, followed by data feature enhancement in the spectrum domain to obtain the rectified data, and ultimately, high-contrast image recovery. Furthermore, we propose a new metric, Edge Detection-based Enhancement Measure Evaluation (EDEME), to quantitatively evaluate the contrast of the recovered images. Our experimental results demonstrate that our proposed method can recover images in low turbidity water to a level comparable to clear water, and even in highly turbid water (turbidity greater than 50 NTU), the recovered images are legible with significantly improved EDEME values. Additionally, our method exhibits wide adaptability, requires minimal data operations, and outperforms some post-image processing methods. This work has significant implications for imaging, inspections, search and rescue, resource exploitation, and other applications in underwater environments.
On the ergodicity of a three-factor CIR model

This work illustrates a tri-factor model referred to as the CIR3 model, where both the trend and the volatility are stochastic and correlated. For the said model we prove that a pathwise unique global strong solution exists. We present a generalization of the Feller condition to ensure that each factor remains positive up to a Markov time determined by the preceding factors. The main results of the paper concern the Wasserstein ergodicity of the model, which cannot be obtained by means of the standard Dobrushin theorem, but requires some more sophisticated arguments involving topological aspects of Wasserstein spaces and Kolmogorov equations for measures. The same strategy can be then used to prove the Wasserstein ergodicity of the well-known tri-factor Chen model.
Relationship between perceived threat of artificial intelligence and turnover intention in luxury hotels

When artificial intelligence technology erodes employees' professional knowledge, they tend to feel highly anxious, and turnover intention is created. This study aimed to test the impact of the perceived threat of artificial intelligence on turnover intention through perceived organizational support and the perceived value of artificial intelligence. The method and procedure were as follow: construct a theoretical framework and propose hypotheses - collect questionnaires through voluntary sampling - use a two-step approach to test the model. This study has some findings. Theoretically, this study proposes a conceptual model of artificial intelligence perception. The combination of technology threat avoidance, organizational support, and perceived value theories applies to the research background of this study. Methodologically, the relationship between the perceived threat of artificial intelligence, perceived organizational support, perceived value of artificial intelligence, and turnover intention variables was studied together for the first time, and the perceived value of artificial intelligence as a new significant mediator between perceived organizational support and turnover intention is discovered. Managementarily, when facing the threats of artificial intelligence to employees, hotel managers should emphasize organizational support, especially in finance, career, and adjustment. This study has important implications for luxury hotel management. First, hotel employees' perceptions of artificial intelligence are dual. Second, luxury hotel managers should consider perceived organizational support to be a key variable.
Decomposition of measure from symmetry for analyzing collapsed ordinal square contingency tables

In some situations, square contingency tables with ordered categories are analyzed by considering collapsed tables where adjacent categories are combined. This study proposes measures to represent the degree of departure from symmetry using collapsed tables. The proposed measures are defined as the arithmetic mean of submeasures of each collapsed 3 × 3 table. Additionally, a theorem affirms that the value of the measure for symmetry is equal to the sum of the value of the proposed measures. Finally, examples are given.
Students’ Perception of Google Translate in Improving Writing Skill

The aim of this study is to determine the perception on using Google Translate in the third semester of Engineering Students of Akademi Maritim Nusantara Cilacap to improve their writing skill. Ten students were selected as the research participants for this study. The data for this study is obtained by experimental study and questionnaire. The questions consist of the students’ perception in using Google Translate to help them improve their writing skill in EFL and their perception of Google Translate for improving their writing. By using descriptive-qualitative analysis, it is identified that the respondents produce better writing by utilizing Google Translate to help them with their writing task. The study also shows that although there are some disadvantages in using Google Translate, most respondents agree that there are more advantages to it in helping them improve their writing skill. In conclusion, this study reveals that there is positive perception in using Google Translate to improve writing skill and it is useful to improve the students’ writing skill. &#x0D;
Strategi Guru Dalam Mengelola Manajemen Pendidikan Berbasis Seni

Arts-based education has been recognized as a valuable approach in enriching students' learning experiences and developing their creativity. Teachers have a key role in managing effective arts-based education management. However, there is still limited research that specifically focuses on the strategies used by teachers in this context. Therefore, this study aims to identify and analyze the strategies used by teachers in managing arts-based education management. This study uses a qualitative method with a case study approach. Data was collected through classroom observations and interviews with art teachers who have experience in managing arts-based education management. Data analysis was carried out through coding and thematic processes. The results of the study show that there are several strategies used by teachers in managing art-based education management. First, the teacher uses a collaborative approach by actively involving students in the art learning process. Second, the teacher applies a differentiation approach to meet the different learning needs of each student. Third, teachers integrate the arts with the core curriculum to increase the relevance of learning. Fourth, teachers utilize technology in art learning to expand access and increase interaction between teachers and students. In conclusion, teachers have an important role in managing arts-based education management.
SISTEM INFORMASI PERSEDIAAN BARANG SEDERHANA PT. WISEMAN MULIA SEJAHTERA DENGAN APLIKASI BARANG DAN PERSEDIAAN BERBASIS ANDROID

PT. Wiseman Mulia Sejahtera previously used the manual method by collecting all data in and out of goods by recording them in the WhatsApp group "Exit and Exit Goods". This makes admins, supervisors, and directors of PT. Wiseman Mulia Sejahtera could not find relevant inventory information. The expected result of this research is that warehouse operations are computerized. The flow in completing this research system consists of needs analysis, development, implementation, and application testing. In this development using the Samsung Galaxy A51 Android Smartphone Android 12 One UI 4.1 version and Goods and Inventory Software from Chester SW Def. The result of this system is that admins, supervisors and directors can manage the availability of goods easily, carry out complete and computerized transactions. Based on the assessment interviews, the respondents who had been asked to informants stated that the results were very good with the Goods and Inventory application because it made PT.'s warehouse operations easier. Wiseman Mulia Sejahtera.
Real Time Snatch Theft Detection using Deep Learning Networks

Snatch theft is a common crime in urban areas that poses a serious threat to public safety. It involves forcefully grabbing a victim's personal belongings, such as purses or mobile phones, before quickly fleeing the scene. Detecting snatch theft incidents in real-time is a challenging task due to the speed at which they occur. The current methods used to detect snatch theft incidents rely heavily on human intervention, which can lead to significant delays and potential errors. Therefore, there is a need for an automated technique that can accurately and efficiently detect these incidents in real-time. Hence, the study aims to detect snatch theft using a transfer learning approach based on eight pre-trained convolutional neural networks (CNNs) as classifiers: AlexNet, VGG16, VGG19, GoogleNet, InceptionV3, ResNet-18, ResNet-50, and ResNet-101. The modified pre-trained CNN models are evaluated in both offline and real-time modes. Based on the offline mode, VGG19 achieved 100% training accuracy, and ResNet50 had the highest testing accuracy of 98.9%. In the offline mode, all models accurately classified normal scenes, with ResNet-10 having the lowest false negative rate and ResNet-50 achieving the lowest false positive rate with only 44 misclassified anomaly frames related to snatch theft. The study further evaluated and validated the eight models in real-time mode, and the results showed that AlexNet and ResNet-18 were the only models capable of categorizing snatch theft scenarios with promising findings.
Design of Angklung Music Scoring System Based on Remote Training

Angklung is a traditional musical instrument from West Java, Indonesia. To play a song using this instrument can only be played in groups. The size of the musical instrument is quite large and prone to damage if carried anywhere, making it challenging to practice except in schools or music clubs. Since the pandemic, it has become increasingly difficult for students to practice angklung due to the limitations of face-to-face learning. This study aims to make it easier for students to practice and play angklung. The system is based on online multiplayer games so that students can learn and play angklung with more fun, and players can play anywhere. Players can use the mobile version of the angklung application as a substitute for the angklung instrument. Players can perform a duet with other players in playing the same song. The results of the player's performance will be displayed as scores and stored in the database. Besides that, a real-time feedback function is always displayed for each note played. To distinguish individual players and/or notes, we use vibration sensor technology installed in each mobile application or signal processing, and the system will recognise them properly. We propose a Short-time Fourier Transform (STFT) algorithm as a scoring system and realtime-feedback performance. We use class diagrams to describe the whole application system and describe the functionality of the system in detail. We collect some research related to angklung and technology. Currently, there is a little study that could answer all the problems of learning and training angklung. The results of this study in the form of a system design are the initial research stage and are used for the following research phase. In conclusion, the design of this study can solve the existing problems so that the system will make it easier for students to practice and play angklung.
Selective Layered Blockchain Framework for Privacy-preserving Data Management in Low-latency Mobile Networks

&lt;p&gt;With the gradual development of Fourth Industrial Revolution technologies, such as artificial intelligence, the Internet of Things, and big data, and the considerable amount of data in mobile networks, low-latency communication and security management are becoming crucial. Blockchain is a data-distributed processing technology that tracks data records to support secure electronic money transactions and data security management in a peer-to-peer environment without the need of a central trusted authority. The data uploaded to the blockchain-shared ledger are immutable, making tracking integrity preservation facile. However, blockchain technology is limited because it is challenging to utilize in the industry owing to its inability to correct data, even when inaccurate data are uploaded. Accordingly, research on blockchain mechanisms that consider privacy-preserving data management is required to commercialize blockchain technology. Previously, off-chain, blacklist, and hard-fork methods have been proposed; however, their application is challenging or impractical. Therefore, to protect privacy, we propose a layered blockchain mechanism that can correct data by adding a buffer blockchain. We evaluated the latency, security, and space complexity of layered blockchains. The security and security-to-latency ratio for data management of the selective layered blockchain is 2.2 and 11.3 times higher than the conventional blockchains, respectively. The proposed selective layered blockchain is expected to promote the commercialization of blockchain technologies in various industries by protecting user privacy.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Prompt-Guided Level Generation

Automated generation of complex and diverse environments can be achieved through the use of Procedural Content Generation (PCG) algorithms. However, generating content that is both meaningful and reflective of specific intentions and constraints remains a challenge. Recent advances in Large Language Models (LLMs) have demonstrated their effectiveness in various domains. These models can be fine-tuned and information can be reused to accelerate training for new tasks. Our study presents MarioGPT, a fine-tuned GPT2 model that has been trained to generate tile-based game levels for Super Mario Bros. The results demonstrate that MarioGPT can generate diverse levels and can be text-prompted for controllable level generation, addressing a critical challenge in current PCG techniques.
Product Aspect Ranking Framework Based Feature Selection Using Sentimental Classification Algorithm For Consumer Feedbacks

A product-based aspect ranking methodology to find essential product features from consumer feedback. The consumer has been using these websites and frequently adds their comments and feedback that will be useful for other users for product evaluation. In existing system refers to only partial comments based on the opinion and consumers simultaneously exploiting the aspect frequencies using the probabilistic aspect ranking algorithm. However, it is impractical for consumers to manually pick the main characteristics of items from among the numerous reviews that are available. The proposed system uses Sentimental Analysis along with SentiwordNet methods. The data was collected from various websites and extracted the user's comments or opinions of good comments, bad comments, or unbiassed commentaries and marked in the manuscript. Feature founded product removal is one of the important levels of sentimental classification algorithms that will explain the feature of the assumed commentaries and categorize the comments for individually feature of the product. The proposed system of SentiWordNet Scores used as Features found 69.4% accuracy compared to SentiwordNet term counting. The aspect-based sentimental classification algorithm is proposed with 67% accuracy to classify the optimal comments, pessimistic comments, and impartial comments for each product feature.
Categorical Distinctiveness, Category Spanning, &amp; Attention Endurance: A Configurational Perspective

The categories literature generally assumed that categories are quick decision toolkits to simplify evaluation when audiences utilize cognitive resources sparingly. However, from both theoretical and practical perspectives, categories are present to inform decision-making even when audiences engage in effortful evaluation. Under such evaluation, categories would undergo systematic processing rather than serving as mental shortcuts. As effortful evaluations are likely to occur when audiences make important decisions, not knowing how categories play a role in such a situation limits our understanding of how categories and their associating properties affect attention. We propose that audiences are likely to engage in effortful evaluation when they examine older products for a critical decision. Products that continuously attract audiences’ attention are termed as having high “attention endurance.” Drawing from the category-spanning literature, we identified three conceptual fragments that could determine attention endurance: categorical distinctiveness, capability signals, and ambiguity signals. We differentiate between within-category distinctiveness (i.e., contrast) and between-category-combination distinctive (i.e., category-combination uniqueness) and theorize their effects, respectively. By adopting a configurational approach, we study the context of online US feature film reviews using fuzzy-set qualitative analysis (fsQCA). We propose multiple context-dependent paths that link categorical distinctiveness to attention endurance.
Ranking Pull Requests in Open Source Software

Issue tracking is an important part of the open source software (OSS) development process. Users submit bugs, new features, and other requests to OSS maintainers as issues, and new code to address these issues can be submitted by the OSS community as pull requests. It is up to a maintainer to prioritize these pull requests for review and inclusion into the code base. Estimating the importance of a pull request before reviewing it, however, is a difficult task. We propose and evaluate a new framework for classifying pull requests that incorporates community, contributor, and content features. These features take into account three important aspects of the OSS ecosystem: (1) Community consent as measured by the sentiment analysis of issue comments, (2) Contributor competence as determined by the frequency a contributor commits to the OSS code base, and (3) Content importance as determined by the number of times a particular file has been changed. Our evaluation on over 4000 closed issues across 54 popular GitHub repositories shows that the proposed features significantly contribute to the prediction of an issue’s time to close. With Area Under the ROC Curve (AUC) scores up to 0.75, the results further show that these features are suitable predictors for classifying pull requests by importance and that repository-specific classifiers perform well with only a few hundred training examples.
Sign Language Recognition

Deaf and hard-of-hearing persons communicate with each other and their communities by using sign language. As this is a natural method for people to interact with computers, many academics are researching it in order to make it simpler and more natural without the use of additional gadgets. So, the main objective of gesture recognition research is to develop systems that can recognise certain human gestures and use them, for instance, to communicate information. Quick, extremely accurate hand detection and real-time gesture identification must be possible with vision-based hand gesture interfaces. Learning sign movements is the first step in creating words and sentences for computer-assisted sign language interpretation. Both static and dynamic sign actions are available. Both types of gesture recognition are crucial to human culture, even if static gesture recognition is easier than dynamic gesture recognition. This paper outlines the processes needed to recognise sign language. The method of gathering data, as well as its preprocessing, transformation, feature extraction, categorization, and outcomes, are all examined. For this area of study, there are also some suggested directions for further research. The visual-manual modality used by sign languages is a collection of established languages. When a hand motion is inputted, the system instantly displays the appropriate recognized character on the monitor screen. In the following, research projects that led to a system that uses convolutional neural networks to identify handwriting on the basis of depth pictures the camera collects.
Experimental Demonstration of Probabilistic-Bit (p-bit) Utilizing Stochastic Oscillation of Threshold Switch Device

To realize p-bits that fluctuate between 0 and 1 with precisely controlled probabilities, stochastic oscillation behavior of threshold switch (TS) device was induced by applying supply voltage ($\mathrm{V}_{\mathrm{D}\mathrm{D}}$) near-threshold voltage ($\mathrm{V}_{\mathrm{T}\mathrm{H}}$). Insulator-metal transition (IMT) device and ovonic threshold switch (OTS) device were investigated as a source of p-bit generation. It was confirmed that a small voltage change of mV-scale in the near-threshold region can give a high degree of stochasticity to the oscillation frequency. Also, the probability was controlled by a comparator and shift of a compare-voltage ($\mathrm{V}_{\mathrm{comp}}$) with excellent controllability. Finally, a simple probabilistic Boolean logic operation was tested by 100 p-bits generated from IMT devices.