Reviewerly/exHarmony huggingface dataset:
Profiling ESP Students’ Needs as Recommendation for Designing Hospitality Syllabus in Indonesian Vocational Schools

This descriptive qualitative study examines the needs of ESP students’ as a fundamental basis for designing an English for hotel program in the context of vocational secondary school in Indonesia. English teachers and students from hospitality program collaborated to investigate learners’ specific needs, which were then used to make recommendations for classroom implementation and instruction. The study aimed to understand how students in the hospitality program perceive their English needs and assess the adequacy of the current syllabus in meeting those needs. The findings revealed that students identified a range of language skills they need to acquire, including speaking, listening, reading and writing skills with particular emphasis on communication in hotel costumer service situations. Students expressed their expectations for practical, interactive and industry-relevant language learning experiences. The study emphasizes the importance of incorporating materials and real-world scenarios into syllabus to enhance students’ language proficiency and job readiness. The research findings serve as a valuable contribution to reorganizing the syllabus to better align with the students’ needs. Further research is recommended to explore the significance of addressing students’ specific needs as language learners and in relation to their career goals in the hotel industry.
EFEKTIVITAS PERMAINAN PUZZLE TERHADAP UPAYA PENINGKATAN KOGNITIF PADA LANSIA

Cognitive therapy needs to be given to the elderly whose function is to increase brain power, improve memory, prevent the risk of Alzheimer's disease, and decrease memory processes in the elderly. The research aims to analyze the effectiveness of puzzle games on the cognitive improvement of the elderly. Pre-experimental research design with a pretest-posttest approach. The target population is the elderly who are in the Karangwerda association with a total of 25 elderly people. The researcher chose a total sampling technique with inclusion criteria, namely elderly people aged 60 years and over, have a good visual function or wear glasses, have a full level of awareness or composition, and are willing to be respondents. The instrument used was the Mini-Mental State Examination (MMSE) questionnaire. Analysis of the results of the study using the Wilcoxon test found that there was an average difference in cognitive function before and after being given puzzle-playing therapy, namely an increase of 1.19 points. The parametric test obtained a p-value of 0.000 (p ≤ 0.05) meaning that puzzle games are effective in improving cognitive function in the elderly. It can be concluded that puzzle games are effectively used in improving the cognitive abilities of the elderly. So that they can be used as an alternative therapy that supports that all the elderly have a good cognitive function and can ultimately maintain the mental health of the elderly.
Exploring Students’ Attribution: Why Did Students Fail in Their Online Basic Grammar Class?

Grammar is one of the crucial components that must be mastered by English as a Foreign Language (EFL) students when they want to learn English. Undoubtedly, many students think grammar is quite challenging to understand and master. In an English Language Education Program at a private university in Central Java, Indonesia, many EFL students failed their Basic Grammar classes. Therefore, this study aims to explore the EFL students’ attributions or reasons for their failure in their Basic Grammar class. The data were gathered through a semi-structured interview with five EFL students who failed their Basic Grammar class. My preliminary results indicate some factors that cause them to fail in their Basic Grammar classes, such as the lack of knowledge, internet connection, and students’ factors. Recommendations for future research are discussed.
ЦИФРОВІ МИСТЕЦЬКІ ТЕХНОЛОГІЇ ЯК ОСНОВА ЕЛЕКТРОННОГО МУЗИЧНОГО ІНСТРУМЕНТАРІЮ

The proposed article examines the interaction of culture and art technologies, as well as the undeniable influence of digital electronic musical instruments on art forms. As a result of the effect of modern technological achievements on art forms, the phenomenon of digital arts, or “art media”, was formed, which are characterized by interactivity, elitism, new means of artistic expression, forms and genres. On the basis of the concept of “technical aesthetics”, the article reveals the dualistic nature of the concepts of art and technology – sound technologies, sound and direction – sound direction. Taking into account the opinion about “technically equipped types of art”, the expressed idea is confirmed that “sound technologies” are an integral part of art, a direct participant in the creative process, an irreplaceable sequence of step-by-step actions of the artist (using technical means), the result of which is a work of art. The historical milestones of the scientific and technical revolution are highlighted step by step and its direct influence on the development of musical art from the first steps of its development to modern times is depicted. The theoretical-methodological approaches used in the research of foreign and Ukrainian scientists and practitioners of art technologies, as well as sound engineering, have been analyzed. In view of the rapid development of science, the work states that the issue of the invention and analysis of artistic technologies still remains open for further self-development in cultural and artistic areas. It was also announced that despite the very high level of mastering of processors and computer software, the last word still remains with the specialist, with his professionalism, with the ability to use the acquired knowledge and use it to the maximum. Without all this, it is simply impossible to become a truly practicing mastering engineer. Creative work requires from him a proper meticulous and critical attitude towards it at all stages of the mastering project, and he, in turn, assumes the basis of practical work and technical knowledge, understanding of music and the presence of artistic and aesthetic taste.
Characterization of darknet traffic detection using time domain features

Darknet traffic is a type of network traffic that travels via several server&apos;s while being encrypted to conceal its origin and destination. Darknet communication is difficult to detect and analyses as a result, and criminals take advantage of it to carry out illegal conduct. One technique for identifying darknet traffic is the extraction of time-related data from the traffic. These features can be used to distinguish darknet traffic from harmless traffic, or traffic that is not encrypted nor spread over several servers. Utilizing an N-gram strategy is one way to extract time-related characteristics. This strategy uses an N-gram technique to organize the potential subnets. The potential subnets, for instance, would be categorized into subnets that begin with the same two characters if the N-gram was two. It is possible to build a machine learning model to categorize darknet data using the time-related characteristics that have been retrieved. It is possible to classify darknet traffic with high accuracy using straightforward algorithms like decision trees and random forests.
A ship detection method in SAR images based on YOLOv5

Achieving accurate ship detection in SAR (Synthetic Aperture Radar) images holds great significance in maritime operations. Various researchers have proposed customized strategies to enhance object detection frameworks specifically for SAR ship images. Building upon YOLOv5s, this study introduces several improvement methods and identifies the most effective approach by incorporating: (1) a coordinate attention mechanism in the network&apos;s feature extraction section, and (2) a bi-directional feature pyramid network in the feature fusion section. The paper explains the fundamental principles of the proposed method, theoretically demonstrates its enhanced detection performance, and performs comparative experiments to validate its effectiveness. Results indicate that when applied to the open SSDD ship dataset, the method yields a 3.0% increase in accuracy and a 2.4% improvement to 98.2% in average accuracy (IOU&gt;0.5) compared to the YOLOv5s model. The improved model demonstrates superior detection results and effectively reduces instances of leakage and misdetection on the SAR ship image test set.
Interactive learning environment for networking topics based on augmented reality

Augmented Reality (AR) implemented on mobile devices has emerged as a prominent subject of study within the field of mobile applications and human-machine interaction. The mobile augmented reality (AR) technique integrates intelligent display, registration tracking, virtual and reality convergence, and human-computer interactions using portable devices or intelligent terminals. This enables the seamless integration of the 3D virtual object with the customer&apos;s real-life environment, thereby enhancing the perceived depth and breadth of the experience. Augmented reality has been extensively utilized across diverse domains, including the field of education. In the domain of education, augmented reality is employed to enhance the level of engagement and appeal in the learning process. Beginning with the issue of acquiring knowledge about computer networks, it is observed that the conventional approach involves the introduction of network devices, TCP/IP models, and OSI models. This research addresses the problem by creating a learning media solution that utilizes augmented reality (AR) technology. AR technology integrates two-dimensional or three-dimensional virtual objects into a real environment and displays these virtual objects in real-time. The novelty of this work implies on the design of augmented reality (AR) applications for teaching the OSI model in the field of computer networks. The aim is to enhance comprehension, stimulate motivation, and generate student interest in learning.
A Study on the Improvement of Hardware Design of Multimedia System for Kindergarten Education

Currently, education for multimedia systems stimulates children"s various senses and brings them a different learning experience. Recently, in China, following this trend, conversion to smart classrooms and learning using multimedia contents are becoming more common. However, the multimedia system for kindergarten education currently used in kindergartens in China is lagging in terms of hardware and has many other problems. In this paper, we tried to identify the problems of the multimedia system for kindergarten education through prior research, fact-finding surveys, and expert interviews, and based on this, we wanted to present an improved design of hardware optimized for kindergarten education.
Dual Branch based Super Resolution Method for the Quality Enhancement of Facial Areas

Because high-quality facial images have an effect on face detection and recognition areas,the quality improvements of the poor facial areas are essential to raise the accuracy of those areas from the low-resolution(LR) images. Recently, convolutional neural network(CNN) based Super Resolution(SR) methods have shown the visual quality improvements to generate high-resolution(HR) images from LR images. In this paper, we propose dual branch based super resolution method for the quality enhancement of facial areas. The proposed method consists of two parallel CNN networks to focus on the facial areas. Experimental results shows the significant quality enhancement, compared to the existing SR networks.
Automated Video Events Detection and Classification using CNN-GRU Model

In the era of vast and continuous video content creation, manually identifying crucial events becomes a tedious and inefficient task. To address this challenge, we propose a CNN-GRU model that automatically detects and classifies significant events in videos. This model employs ResNet50 Convolutional Neural Networks (CNNs) to extract visual features from video frames, followed by Gated Recurrent Units (GRUs) for temporal modelling and event recognition. By leveraging the sequential data handling capabilities of GRUs, our model captures temporal patterns across frames. We evaluate the model's performance using accuracy and F1-score metrics on the VIRAT dataset, containing 1,555 events across 12 event classes. Our approach achieves promising results, with an event classification accuracy of 75.22%.
Applying AR Technology Integrating Unity3D with the Vuforia SDK for Oral English Teaching

With the rapid progress of information technology, its application to teaching has gradually become a hot topic in the education field. Augmented reality (AR) combines virtual and real characteristics that can improve comprehension in a virtual environment, bringing new development opportunities to oral English teaching. Based on integration of the Vuforia SDK in the U-nity3D augmented reality engine, this research applies AR technology to spoken English teaching, improves a convolutional neural network (CNN), and proposes an English speech recognition system based on a connectionist temporal classification (CTC)-CNN (maxout). The results from experiments varying the number of iterations and the loss value, the proposed model converges after 80 iterations with strong performance. In recognition of spoken English with or without noise, the accuracy of this method was highest at 0.957 and 0.894, respectively, which is better than the CTC-CNN (sigmoid) model. In recognizing six kinds of spoken English, the accuracy of the CTC-CNN (maxout) model stabilizes at about 95%, with the highest accuracy at 97%. The accuracy rate shows that the method can be effectively applied to oral English teaching, and can provide a new reference method for innovations in oral English teaching and the improvement of teaching efficiency.
A Case Study on the Establishment of Whole Cycle Real-time Monitoring System for Biopharmaceutical Logistics: Focusing on Biologic Agent: Immuncell LC

The cold chain monitoring market is drawing attention as regulations on drug transport are strengthened to maintain biopharmaceutical quality. The comprehensive temperature excursion management for biopharmaceutical distribution is inevitable in order to maintain the effectiveness and efficacy of them. For this reason, it is emphasized to establish the cold chain monitoring over the entire period, which is delivered from the place of origin to the customer. Immucell LC was selected as the subject of this research on the real-time monitoring system. The delivery processes of Immuncell LC can be divided into the process of manufacturing biologic agent from the blood culture of patient and the process of delivering it to the patient. This study aims to analyze the real case which is applied real-time monitoring system through the entire processes. In conclusion, it suggested the implications for improving work productivity, product stability and logistics management by analyzing transportation resources(packing materials, vehicles, carriers, temperature logger), logistics information systems, transportation procedures.
Meaning of Democratic Citizenship Education and Possibility of Utilizing Democratic History in the 2022 Revised Curriculum: Focusing on the Case of Gwangju Metropolitan City

The 2022 revised curriculum pursues self-directed students with inclusivity and creativity in an ideal way. This is because, amid the advent of the 4th Industrial Revolution artificial intelligence era due to the COVID-19 pandemic and the climate crisis, a new change to a practical educational paradigm such as on-site acceptance, diversity, and student-customization of school education was required. However, the importance of democratic citizenship education, which intends to promote community self-discovery, awareness of social participation, and practice solidarity, has increased. This paper examines the contents of the 2022 revised curriculum from the perspective of democratic citizenship education and compares the history of student democracy, a characteristic of the region's historical culture, with the contents of the new curriculum as the subject of democratic citizenship education that can be conducted by the Gwangju Metropolitan Office of Education. To this end, I introduce the purpose of the revision and future talent appearance in the general overview of the 2022 revised curriculum announced by the Ministry of Education (2021), and analyzes the relationship between the four tasks and democratic citizenship education. In addition, I explore whether the Gwangju Metropolitan Office of Education can educate students in the history of participating in social issues in the Japanese colonial era independence movement and the post-liberation democratization movement in light of the 2022 revised curriculum. As a result, it is argued that the unique culture of the regional level can be the subject of democratic citizenship education necessary for the formation of students' community self.
Efektivitas Media Teka-Teki Silang Terhadap Penguasaan Kosakata Bahasa Jepang Siswa SMA

This study was inspired by the researcher's observation that pupils struggle to learn and remember Japanese terminology. The goal of this research was to examine the impact of crossword media on high school students' ability to learn Japanese vocabulary. This study employed a quantitative approach and a Pre Experimental Design for its investigation. The study employed a pre- and post-test design with a single group. Twenty-five students from IPS 1 in Grade 12 made up the study's sample. This research employed a systematic, unbiased sampling strategy. The information in this study comes from before and after examinations. The method of information gathering is a multiple-choice test with as many as 30 questions. The results of the Paired Samples T-Test show that the crossword game medium is significantly effective on the Japanese vocabulary mastery of students in class XII at IPS 1 SMAN 13 Padang, with a sig (2-tailed) value of 0.000 &lt; 0.05. Additionally, pupils improved their Japanese vocabulary knowledge after utilising crossword puzzles as a learning tool. The average score before the test was 70.76, thus this boost from that to 83.32 is clear.
The control of greenhouse gas emissions generated by the use of cryptocurrencies

This research outlines how the use of cryptocurrencies like blockchain can be detrimental to the environment, primarily due to the emission of greenhouse gases. The research is justified in light of the current relevance of the topic, as carbon emission control is a global and governmental concern for the preservation of the environment for future generations. The investigation began by addressing the issue of greenhouse gas emissions due to the use of cryptocurrencies such as bitcoin through blockchain. Subsequently, the importance of controlling greenhouse gas emissions and the associated indices due to the use of cryptocurrencies were discussed. Following that, the policies for controlling greenhouse gas emissions in Brazil were examined. Through theoretical-literature review, documentary research, and deductive methods, it was determined that the use of clean energy sources is necessary for the maintenance of this technology to reduce the environmental impacts it currently poses. The study highlights the need for using clean energy or alternative methods in the utilization of cryptocurrencies to avoid the high release of greenhouse gases as observed in current practices.
Blockchain for Science, from Covid-19 towards DeSci: the Implementation of Decentralized Technologies in Young Academia

Objective: This paper explores the evolving landscape of blockchain technology and its applications in the field of science, with a specific focus on the period from 2021 to 2023. It examines the challenges and opportunities posed by blockchain, particularly in the context of decentralized autonomous organizations (DAOs) and the concept of decentralized science (DeSci). Methods: The research draws on a comprehensive review of existing literature, including prior works by the author related to blockchain in science, as well as an analysis of recent developments in the blockchain ecosystem. It discusses the principles of blockchain technology and its potential in academia, addressing both the technical and socio-organizational aspects of its implementation. Results: The paper reveals the shift in focus from blockchain as a tool for transparency and automation in science to its decentralization aspects, such as DAOs. It highlights the application of DAOs in research crowdfunding, decentralized decision-making, and cross-border collaboration, with a special emphasis on the Covid-19 interlude in 2020-2021. The discussion also delves into the role of non-fungible tokens (NFTs) in science, which provide new avenues for monetizing research and democratizing funding and organization in the field. Scientific Novelty: The novelty of this paper lies in its examination of the latest wave of blockchain projects in science, particularly the emergence of DAOs and DeSci in the years 2021-2023, an analysis that is still largely absent from scholarly literature. It underscores the evolving blockchain ideologies and controversies within the scientific community, as well as the tensions between transparency and decentralization. Practical Significance: The paper highlights the practical significance of blockchain technology in academia, particularly in facilitating research funding, transparent decision-making, and cross-border collaboration. It discusses how blockchain and NFTs offer innovative ways to monetize research and democratize funding, reducing dependence on traditional funding sources.
Improving Orange and Lemon Object Detection Using YOLOv5 and Image Augmentation

One of the crucial techniques that can be used to augment the dataset at the pre-processing stage is augmentation techniques. Widely used augmentation techniques are the transformation of the original image, change of scale variation, rotation, setting the brightness level of the image, and many more. Image augmentation aims to increase the robustness and capability of the built model in generalizing object detection using the YOLOv5 algorithm. In this study, we evaluate several image augmentation techniques with a combination of epoch configurations in the YOLOv5 algorithm. Our results show that the image inversion technique can consistently improve the performance of YOLOv5 compared to raw images. On the other hand, based on the analysis of execution time with larger epoch configurations, more time is needed for model building. Thus, the trade-off between the time needed to execute and the accuracy of the model's performance in detecting objects allows decision-making according to the needs.
Reinforcement Learning for Turn-Based Strategy Game

Predictability and lack of adaptability have long been recognized as significant challenges in strategy turn-based games. This is due to the prevalent use of heuristic-based approaches in their agent's decision-making. In this paper, we attempt to solve this issue by replacing heuristics with an intelligent agent trained by Reinforcement Learning (RL). We compare the performance of our trained agent against two systemic approaches, Monte Carlo Tree Search (MCTS) and Online Evolutionary Planning (OEP). Experiments show that while RL is vulnerable to play styles that differ sharply from its training data, it is competitive against MCTS but falls short to OEP. This suggests that RL can be a promising approach after further improvement and optimization in future work.