Reviewerly/exHarmony huggingface dataset:
Breaking Textbook Barriers: PowToon Videos in Engaging ICT Learning for Young EFL Learners

The significant interest in the development and integration of information and communication technologies (ICT) in the field of education is still marked with an unequal level of performance in many areas. This research explores the researcher-teacher collaboration of creating and using PowToon video in teaching English to achieve the mission of elevating the ICT competence of teachers and students in schools, especially in areas that are underdeveloped or far from the central government. The participants of the study include the sole English teacher of SD Kartika VI-7 Malang and twenty students in the fifth grade. Through four steps (analysing, designing, implementing, evaluating), this descriptive qualitative study collected data from classroom observation, teacher interview, expert analysis of the developed media, and short evaluation questionnaire. Results from the analysis stage confirmed the preliminary results of the English class’s minimal ICT usage. The design stage is where the researchers and the teacher worked together to adapt the textbook materials into interesting PowToon video for the students. The implementation stage is where the teacher used the video validated by three experts of material, media, and language. Finally, the media’s effectiveness is evaluated based on the questionnaire responses given by the students. The research concludes that the validation process of the PowToon ascertained its quality for effective English instruction to motivate student engagement. Although neither PowToon video nor its implementation is particularly new, they are novel, effective, and—arguably most importantly—friendly teaching media for classrooms that have yet to adapt with the rapid technological world.
An Arrow-Based Dynamic Logic of Normative Systems and Its Decidability

Normative arrow update logic (NAUL) is a logic that combines normative temporal logic (NTL) and arrow update logic (AUL). In NAUL, norms are interpreted as arrow updates on labeled transition systems with a CTL-like logic. We show that the satisfiability problem of NAUL is decidable with a tableau method and it is in EXPSPACE.
EDUCAÇÃO EM SAÚDE: PRÁTICAS DE TELECONSULTA EM PACIENTES COM DIABETES MELLITUS NO SÉCULO XXI

INTRODUCTION: Teleconsultation practices are a strategy that helps promote self-care for patients with diabetes mellitus.OBJECTIVE: to reflect on the importance of teleconsultation practices in patients with diabetes mellitus from the perspective of health education in the 21st century.METHODOLOGY: The present text consists of a literature review of the Narrative type, and we used scientific databases to include proposed authors.The aim was to bring relevant content to the theme with a focus on various theories.An analytical and bibliographical research was carried out with a qualitative approach on the subject through books, articles and video lessons from databases such as Pepsic, Scielo and Google Scholar.Then, the most relevant materials were included, excluding content that did not relate to the theme.FINAL CONSIDERATIONS: The teleconsultation for health education in diabetic patients helps with behavioral interventions and improvement of glycemic control, through video consultations with previous upload of glucose measurements taken on a tablet, complementing the standard clinical treatment of diabetes mellitus and increases the care that results in treatment success, physiological and psychological benefits.It is thus a valuable aid, both for future teachers and for all those who want to update their knowledge and deepen their training.It is hoped that from this research, professors from higher education academic institutions and related areas will have a better understanding of the subject, a more comprehensive technical and scientific vision.
ViT Spatio-Temporal Feature Fusion for Aerial Object Tracking

The object tracking technology for aerial remote sensing images has made significant development, but it is still a very challenging work. The related difficulties of object tracking include the accumulation of long-term tracking errors, similar object interference, partial or full occlusion, scale change, etc, which can lead to object tracking failure. In this paper, an aerial object tracker with ViT Spatio-Temporal Feature Fusion (STFF) for the aerial remote sensing images is proposed, which can achieve accurate tracking of aviation objects. Firstly, we propose a spatial-temporal feature fusion strategy based on the characteristics of object tracking timing. In this strategy, the object information of the previous frames is applied to enhance both the real-time responsiveness of the model and the performance of the tracker. Secondly, the dynamic change information of objects in space and time context is used for spatio-temporal feature information fusion, which can further enhance the appropriate correlation and promote the feature aggregation and information transmission of visual tracking. Finally, a dataset with real and virtual scenarios is collected and constructed to address training data requirements for aviation object tracking. According to our experiments, STFF can achieve accurate tracking of aerial objects and has achieved excellent performance on UAV123, DTB70 and our benchmarks.
ExTreeM: Scalable Augmented Merge Tree Computation via Extremum Graphs

Over the last decade merge trees have been proven to support a plethora of visualization and analysis tasks since they effectively abstract complex datasets. This paper describes the ExTreeM-Algorithm: A scalable algorithm for the computation of merge trees via extremum graphs. The core idea of ExTreeM is to first derive the extremum graph G of an input scalar field f defined on a cell complex K, and subsequently compute the unaugmented merge tree of f on G instead of K; which are equivalent. Any merge tree algorithm can be carried out significantly faster on G, since K in general contains substantially more cells than G. To further speed up computation, ExTreeM includes a tailored procedure to derive merge trees of extremum graphs. The computation of the fully augmented merge tree, i.e., a merge tree domain segmentation of K, can then be performed in an optional post-processing step. All steps of ExTreeM consist of procedures with high parallel efficiency, and we provide a formal proof of its correctness. Our experiments, performed on publicly available datasets, report a speedup of up to one order of magnitude over the state-of-the-art algorithms included in the TTK and VTK-m software libraries, while also requiring significantly less memory and exhibiting excellent scaling behavior.
Construction of a linguistically interactive thesaurus for English second language acquisition based on an eigenvalue-fitting superiority algorithm

Abstract This paper completes the overall design of a linguistic interactive terminology database based on the characteristics of second language acquisition and terminology and completes the construction of the terminology database by combining a goodness-of-fit detection algorithm based on terminology eigenvalue extraction. The efficiency of terminology information recognition is analyzed and compared with the terminology conversion rate of the eigenvalue goodness-offit algorithm using a neural network learning model of long and short-term memory to optimize the performance of the terminology database. The metric approach's classifier performance evaluation metrics are used to compare the accuracy and recall of the two algorithms accurately. The results show that the accuracy of the fitted superiority classifier with the application of word eigenvalue embedding compared to the LSTM classifier for the classification of electric power terms is improved by about 11% in all categories, and the average accuracy of the classifier exceeds 76.5%.
A Survey of Indonesian Islamic Senior High School Teachers’ Vocabulary Teaching Strategies /Activities

The current study is designed to investigate vocabulary teaching strategies/activities employed by teachers in Islamic senior high schools by referring to the principle of the Four Strands proposed by Nation (2007). A survey research design was used in line with the study's goal. English teachers who teach in Islamic senior high schools or Madrasah Aliyah, both public and private schools in West Sumatera, were the study's target demographic. The questionnaire and interviews collected the information required to respond to the survey's question. The finding reveals that teachers employ four kinds of strategies/activities following the Four Strands by Nation (2007) in teaching vocabulary to students. The findings of the study suggest that this study might give an insight that more strategies/activities need to be done by teachers to enhance vocabulary learning. The findings of the study provide some implications for future practice in teaching-learning process of teaching vocabulary. In reference to the findings, the recommendations are addressed to EFL teachers and future researchers. Some implications of the study are also discussed.
The application of virtual reality: Authenticity and interactivity

In recent years, with the continuous advancement of science and technology, virtual reality technology has also been further developed and gradually applied to various fields. In the field of games, virtual reality technology has been fully applied, and virtual reality games provide the public with a more playable virtual space. At present, the research and analysis on virtual reality games is not sufficient enough to fully analyze and summarize the uniqueness of virtual reality games. Therefore, this paper makes the following research. First analyzes the characteristics of traditional computer games, then focuses on the application of virtual reality technology in games, elaborates from three aspects of game quality, authenticity and interactivity, and finally summarizes some shortcomings of the game at current virtual reality. Game quality refers to the improvement in game design, and the entire virtual reality game provides players with more realistic pictures and different interaction methods. At the end of the article, this article summarizes the full text and looks forward to the future research direction.
Analysis based on object detection algorithms

With the increasing demand for intelligent systems capable of comprehending visual information, the discipline of image object detection has experienced rapid expansion. Despite the fact that numerous methods have been proposed, the existing literature lacks exhaustive analyses and summaries of these methods. This paper seeks to address this deficiency by providing a thorough overview and analysis of image object detection techniques. This paper analyzes and discusses traditional methods and deep learning-based methods, with a focus on analyzing the current state and shortcomings of traditional methods. Further discussion is given to deep network-based object detection methods, mainly through a comparative analysis of two-stage and one-stage methods. The basic performance of the You Look Only Once (YOLO) series methods is highlighted. The contribution of large-scale datasets and evaluation metrics to the advancement of the state of the art is also examined. This comprehensive analysis is a useful reference for researchers who aim to contribute to the continual progress of image object detection.
The application of virtual reality in art

Virtual reality (VR) is an important technology applied in the field of art in recent years. VR has revolutionized the field of art with its unique immersion and interactivity. At present, there are very few articles that focuses on and discusses the development and limitations of the use of VR in the field of art. This paper will give readers a full understanding of the application of VR in art. Paper summarizes the application of VR in art, including VR exhibitions, VR scenic spots and historical sites, and 3D movie. We have cited the most representative examples in each field, discussed in detail how they are combined with VR, and demonstrated the advantages of VR in these fields. Then, this paper concludes some limitations of the use of VR in art and ends up with a discussion on the future development direction of VR in the art field.
Deep learning methods used in movie recommendation systems

As the amount of internet movie data grows rapidly, traditional movie recommendation systems face increasing challenges. They typically rely on statistical algorithms such as item-based or user-based collaborative filtering. However, these algorithms struggle to handle large-scale data and often fail to capture the complexity and contextual information of user behavior. Therefore, deep learning techniques have been widely applied to movie recommendation systems. This paper reviews movie recommendation algorithms based on traditional statistical models and introduces three main deep learning techniques: Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). ANN can extract features at different levels of users and movies; CNN can capture features of movie posters and movie data to recommend similar movies; RNN can consider user historical behavior and contextual information to better understand user interests and demands. The application of these deep learning techniques can enhance the accuracy and user experience of movie recommendation systems. This paper also demonstrates the advantages and disadvantages of these models and their specific application methods in movie recommendation systems, and points out the direction for further development and improvement of deep learning models in this field.
A case study on computer-vision based automatic meter reading recognition

There is a lot of economic pressure in the conversion of traditional meters to smart meters, so there is a need to develop smart meter reading technology based on computer vision. There are a large number of computer-vision-related meter reading technologies on the market, but they may be applicable to different scenarios and have their own advantages and disadvantages. After comparative research, it was found that many simple algorithms can solve meter-reading weaknesses, but they all have some weaknesses. Our analysis of common meter reading technologies based on computer vision can allow us to better understand this field, understand the use of various algorithms, and at the same time have positive guiding significance for the pursuit of better algorithms.
Performance comparison of different convolutional neural network models and optimizers for dog bread classification

This study explores the performance of various deep learning models including ResNet152, VGG16, VGG19 and ResNet256 on the dog breed classification task. During training, observe the loss and accuracy trends. The loss gradually decreases, showing that the model is fitting the training data better. With the improvements of the capacity of the model, the accuracy trend shows a steady increase. These models converge after about 20 epochs and fluctuate little after that. The initial learning rate, adjustment factor and patience parameters play key roles in the convergence process. However, the achieved accuracy is below 90%, suggesting that further optimization or more complex architectures may be beneficial. Among all models, ResNet512 has the highest overall accuracy (83%), followed by ResNet256 (83%), VGG19 256 (79%) and VGG16 256 (78%). The ResNet model outperforms the VGG model in most cases, probably because its network structure reduces computational complexity while maintaining accuracy. Increasing the input size can improve the accuracy of the same network structure, such as ResNet 256 and ResNet 512, while modifying the network structure by adding more layers. Learning rate decay scheduling methods, such as ReduceLROnPlateau and CosineAnnealingLR, and optimizers such as SGD, Adam, and Adagrad, are explored as well.
A content-based collaborative filtering algorithm for movies and TVS recommendation

With the rapid development of multimedia technology and the constant upgrading of film and television libraries, users' demand for movies and television is increasing. How to accurately and timely find favorite movies from massive movie and television resources according to user's preferences and needs has become a great challenge. In recent years, the recommendation of movies and TVs has attracted a lot of research interest from academia and industry. The existing recommendation algorithms mainly include content based and collaborative filtering. The former recommends projects through collaborative learning of others' interests, while the content-based method examines the rich context of the project. In this paper, to further improve the performance of recommendations, a content based collaborative filtering method is proposed to provide recommendations for movies and television. Specifically, we extract and vectorize feature and category information from movies based on TF-IDF and apply truncated SVD to reduce the dimensions of the rating and TF-IDF matrix to retain the most representative information. We calculate the cosine similarity between the vectors from these two matrices. The final recommendation is to list 10 movies based on the average similarity of content and ratings. Extensive experiments on Amazon review data have proven the effectiveness of this method.
A Study on the Improvement of the Operation Performance of Local Governments in Kore

In order to improve more reasonably and productively in a series of administrative operations and policy processes in Korean local governments, systematic operational performance evaluation of administrative work execution results was recognized as an important management function. It will be possible to promote the development of local administration and enhance administrative autonomy and accountability by checking the productivity of local governments' administrative operations and conducting a rigorous and accurate evaluation of leadership and administrative performance. In this context, this study will review the evaluation areas and measurement indicators of local government operation performance for local government evaluation, extract the determinants of local government evaluation through a review of previous studies, present a performance evaluation model and seek policy directions to improve operational performance. This study on efficient local government operation plans and the role and leadership direction of local government heads can recognize their importance from a political, social, economic, and customer (resident)-oriented perspective. Only when the evaluation system for local government's operational performance is established and utilized under mutual connection with local residents will the local government increase its efforts to improve the quality of life and maximize residents' satisfaction, and regional development policies can also be implemented from a long-term perspective.
IMPROVEMENT OF ENGLISH PRAGMATIC COMPETENCE OF AL-GHOZALI HIGH SCHOOL STUDENTS

The aim of this community service was to improve students’ English pragmatic competence. The problem of the students found in SMA Al-ghozali was that they often misunderstood the implicit meaning of words. Hence, the students needed to be given a training related to learning English pragmatics. This PkM activity involved 31 students of Al-Ghozali High School located at Jalan Permata No. 19 RT. 06 RW 05, Curug, Gudung Sindur, Bogor, West Java. The stages of training in PkM activities consisted of (1) planning, (2) implementation, and (3) evaluation. At the planning stage, three lecturers and five students of Pamulang University involved prepared a learning method and pre-test. At the implementation stage, the students of Pamulang University provided a training by presenting materials and conducting a discussion on a topic of Implicature. The students collaborated to complete the exercises given by the students of UNPAM team. They carried out correction activity accompanied by the students of UNPAM. The students then competed to answer questions by using games in order to create a fun learning atmosphere. At the evaluation stage, the students carried out post-test. The students of UNPAM concluded the lesson and closed the training session at the end of the training. The average scores of the pre-test and post-test increased from 48.1 to 72.9 or 51.5 %. It can be said that the students’ English Pragmatics competency increases.
Encryption of digital images and voices using the Arneodo-Coullet-Tresser chaotic system

Abstract In this study, we investigate the application of the Arneodo-Coullet-Tresser chaotic system in both image and voice encryption, considering the increasing importance of data security in the digital age. The analysis of the chaotic system involves exploring phase diagrams, time series, bifurcation diagrams, Lyapunov exponent analysis, and Poincaré maps to understand its dynamics. For image encryption, we evaluate the effectiveness of the system through various analyses, including histogram analysis, correlation analysis, entropy analysis, NPCR and UACI analysis, and noise attack analysis. Similarly, for voice encryption, we assess it through various analyses, including waveform plots, FFT, spectrograms, correlation coefficients, entropy analysis, and RMSE. The research findings demonstrate the suitability of the Arneodo-Coullet-Tresser chaotic system for both image and voice encryption, suggesting its potential as a data transmission masking technique. The research includes numerical simulations conducted using Python to support the proposed approach.
Accurate calibration for fringe projection profilometry based on an improved subpixel mapping with local gray distribution

Fringe projection profilometry is an efficient and accurate technique for three-dimensional (3D) measurement to calibrate a camera and projector setup. The feature centers of circles on a calibration board are extracted on the camera image plane and mapped to the projector image plane during the calibration procedure. The accuracy of the mapping between camera pixels and projector pixels is crucial to the calibration accuracy, which directly affects the measurement precision of the system. In this paper, we investigate an improved subpixel mapping with local gray distribution from the camera to the projector. The mapped pixels and their gray values are regarded as a set of 3D grayscale space points. The subpixel coordinates of the feature centers on the projector image plane are obtained by directly processing the 3D points. The entire procedure retains the subpixel precision. Calibration experiments were designed to verify the feasibility of our calibration method, which was compared to three existing methods. The reprojection errors and object-space errors were used to evaluate the calibration accuracy of the methods. Additionally, measurement experiments of displacement and in-plane distance were employed to verify the calibration results of the methods. Compared to the three existing methods, we believe our method can improve the calibration accuracy for fringe projection profilometry.