Reviewerly/exHarmony huggingface dataset:
Cross-Cultural Understanding Of Bipa International Students At Andalas University

This research is motivated by understanding the language and culture of other people in a society is very important, especially for international students who do not understand other languages and cultures that they encounter in other countries, for example how to act and behave. The position of international students is that they are individuals who enter and meet a new culture and situation. International students entering a new environment is not an easy thing, of course they need adjustments to face language and cultural differences. During this adjustment, international students usually experience culture shock or culture shock. Culture shock is a normal phenomenon for international students due to differences in language, culture, social environment and daily habits. This type of research is qualitative with descriptive methods. The respondent data in this research consisted of 3 BIPA international students. Data collection using observation, interview and documentation techniques. Data analysis uses the Miles and Huberman analysis model. Based on data analysis and discussion, the results obtained: Forms of cross-cultural understanding of BIPA international students at Andalas University 1) Patterns of perception in the results of interviews with BIPA international students, namely (a) recognizing Indonesian, (b) recognizing Indonesian culture, and (c) interpreting information about Indonesian language and culture 2) Conceptualization in the results of BIPA international student interviews, namely (a) interpreting differences in Indonesian language and culture, and (b) resolving an Indonesian language and culture problem 3) Communication in the results of BIPA international student interviews, namely (a) being able to speak Indonesian, and (b) can interact, international students interact well using two languages, namely Indonesian and English.
Cultural Riches: An Analysis of BIPA Materials at Andalas University

This research is motivated by the cultural elements contained in the BIPA teaching materials at Andalas University. The method used in this research is content analysis. The data used is data obtained from cultural material contained in BIPA teaching materials at Andalas University. Data collection techniques use reading and note-taking techniques. The data sources in this research are six BIPA teaching materials including Indonesian language textbooks for foreign speakers (BIPA) at the basic level, Indonesian language textbooks for foreign speakers (BIPA) at the advanced basic level, Indonesian grammar for foreign speakers at the basic level, writing. Indonesian for basic level foreign speakers, listening Indonesian for basic level foreign speakers, speaking Indonesian for basic level foreign speakers from Andalas University. According to the results of the analysis, 2 vocal and language symbol systems were found, a knowledge system, a social organization system, a living equipment and technology system, a livelihood system, a religious and artistic system.
Online Course: Factors Influencing Students’ Decision

Purpose – This research aims to analyze the factors that influence students' decision-making in order to continue choosing online learning methods after the COVID-19 pandemic. Methodology – The research method was quantitative research using survey research techniques. The research sample included 375 students who were currently enrolled in university and had attended online courses. The collected data will be analyzed using SEM Amos. Result – The results of data processing and analysis revealed that the factor influencing students' decisions to continue choosing online learning methods was students' attitudes towards online learning methods. Because there was a strong incentive to choose online lectures if the student's attitude was positive. In addition, students' decisions to choose online learning methods in the post-COVID-19 pandemic will be heavily influenced by their experience taking online lectures during the COVID-19 pandemic. Therefore, experience influences student attitudes, perceptions, and motivation. Originality – This research used Attitude, Perception, Motivation, and Experience as mediators to analyze students' decisions to continue choosing online learning methods. In addition to perception and motivation, attitude and experience factors as mediators can help to understand the factors that most influence student decisions.
Analysis of the Impact of Digital Currency on Traditional Banking and Financial Stability

Digital currency, as a representative product of blockchain technology, such as Bitcoin and Ethereum, has begun to have a far-reaching impact on traditional banking business, but also brought new challenges to financial stability. This article studies the financial risk assessment and pre-alarm model based on support vector machine (SVM) algorithm, aiming at accurately assessing the risks in digital currency market and providing timely pre-alarm. Through comparative experiments, it is found that the algorithm has obvious advantages in dealing with high-dimensional and nonlinear problems, and can accurately classify and warn financial risks. The accuracy of the model is as high as 96.88%, and the average absolute error is reduced by 40.22%, which proves the effectiveness and superiority of the model. Generally speaking, the financial risk assessment and pre-alarm model studied in this article provides a scientific and effective method for digital currency market risk management, which has important application value. Regulators need to invest more resources and energy to monitor and analyze the dynamic changes in the digital currency market and reduce financial risks.
Evaluation Research on Burden Reduction for Digitization-enabled Grassroots from Power Grid Enterprises

The evaluation of digitally empowered grassroots level to reduce burden and increase efficiency is an important theoretical and practical issue to promote the digital transformation of enterprises. Due to the differences in assessment and evaluation subjects and the tendency of evaluation selection indexes, the evaluation indexes with their respective geographical attributes are adopted, and the digitally empowered grassroots are divided into three levels through the corresponding "digital empowerment grassroots triangular model", based on the concepts of "implementation empowerment, decision-making empowerment, thinking empowerment" and "digital empowerment". Based on the concept of "implementation empowerment, decision-making empowerment, thinking empowerment", the "three-dimensional burden reduction digital empowerment grassroots burden reduction and efficiency evaluation system" has been constructed to cover three secondary indicators, six tertiary indicators and 16 quaternary indicators, including ideological transformation to reduce the burden and increase efficiency, decision-making optimization to reduce the burden and increase efficiency, as well as the implementation of convenient and easy to reduce the burden and increase efficiency. Explore the construction of grid enterprise digital empowerment grassroots load reduction and efficiency evaluation index system, for enterprises to carry out further good digital empowerment grassroots load reduction and efficiency work to provide orientation guide.
Effective Quaternion Rotation in An Ellipsoid through Sphere Transformation: A Linear Approach

The application of quaternion rotation in ellipsoids and spheres is an intriguing field with significant implications in computer graphics, robotics, and physics simulations. The necessity to decipher quaternion rotation in ellipsoid is crucial. This research aims to utilize the principles of quaternion rotation in spheres to compute the equivalent in the ellipsoid. This involves the application of a linear transformation to convert quaternion rotation in ellipsoids into spheres. By controlling the full rotation of quaternions, the spheres can be transformed back into ellipsoids, achieving the ultimate goal of controlling quaternion rotation within an ellipsoid. The results demonstrate that this approach effectively addresses the quaternion rotation on the ellipsoid and aligns with the fundamental properties of quaternions. Furthermore, it serves as a significant aid in implementing quaternion rotation on the ellipse.
Research on Named Entity Recognition Method of Chinese Classics Under the Supervision of Domain Knowledge

The current dominant named entity recognition methods of Chinese classics are classified as data-driven methods, which are limited by the data quality. The domain knowledge is introduced in this paper to supervise the process of the named entity recognition, so as to solve the poor performance problem because of the low-quality data. The experiments on the Historical Records corpus show that compared with the domain knowledge unsupervised case, the average accuracy, recall rate, and F1 value have respectively improved by 2.76%, 2.70%, and 2.75% under the supervision of domain knowledge. Domain knowledge plays an important role in improving the performance of the named entity recognition methods of Chinese classics.
Development of Augmented Reality Learning Media for Plantae Material in Class X Biology Subjects Using Arttoolkit

&lt;p&gt;A lot of material discusses Plantae, so interesting learning is needed so that students can easily accept and understand the material provided. AR technology can facilitate learning Plantae in the classroom. This research develops AR-based "Plantar" media. The application was developed based on Android to make it easier for students to learn plantae material in biology subjects. Plantar was developed using the Ionic platform and ARToolkit as the AR feature development library. Plantar uses a PWA (Progressive Web App) type to open its use via a browser. Field tests were conducted on 36 Kebakkramat SMA students to assess presence, affordance, and usability. The results showed that the Plantar media received a very good rating and did not require repair. So that the general public can use the Plantar application, especially high school students, to study Plantae material in an exciting way using the AR feature. In the future, it is hoped that the Plantar application will be useful in helping the learning process of Plantae material. &lt;/p&gt;&lt;p&gt;Keywords: Learning Media, Augmented Reality, ARToolkit&lt;/p&gt;
Permalasahan Media Pembelajaran IPS Yang Kurang Variatif Dan Strategi Pemecahannya

Learning media has a very important role in the educational process. The problem of learning media in Social Sciences (IPS) subjects is that it is less varied. The reason is due to a lack of learning facilities, educators find it difficult to offer a variety of learning resources. Additionally, there is a scarcity of reading materials and teachers find it difficult to innovate effective teaching solutions. The solution that is considered appropriate to overcome this problem is to utilize varied media. Utilization of varied media in making progress through students searching for and finding ideas with or with help. This article can help with the problem of less varied learning media because you can see the strategies and their implementation. The purpose of writing this article is to describe (1) the causes of less varied social studies learning media (2) solution strategies (3) application of learning strategies (4) variations in social studies teaching materials.
Application of Problem-based Learning Assisted by Decision Maker Application on Computational Thinking Materials to Improve Student Learning Outcomes at SMP Negeri 2 Gemolong Sragen Regency

&lt;p&gt;This research is a classroom action research, which aims to improve student learning outcomes in the informatics subject matter of computational thinking. This study applies Problem-based learning assisted by decision-makers on smartphones. Students are directed to download and install the decision-maker application on their smartphones. Then students use the information in the application as a reference for information as material for discussion with their respective groups. From the results of the summative assessment, the data showed an increase in the average value of learning outcomes in each cycle.&lt;/p&gt;
Web-Based Mail Management Information System at SMA Negeri 1 Sidareja

&lt;p&gt;Every institution including schools has the responsibility to manage archives, especially letters. But the conventional methods of mail management take up a lot of time and space with the risk of losing documents. With a mail management information system, it can simplify and speed up mail management and minimize the risk of losing documents. This application use Yii2 framework and MySQL for the database. Rapid Application Development used in this research and 5 of 8 aspect ISO 25010 for the evaluation. With good results on all evaluation aspects this application can be used in a school environment.&lt;/p&gt;
Penerapan Algoritma Genetika Untuk Penjadwalan Mata Pelajaran Di SMK Negeri 1 Kota Tambolaka

consider other things such as avoiding the same or repeated schedules in one day, the number of teaching staff involved, and practicum subjects which in their implementation require a laboratory. The subject scheduling process at SMK Negeri 1 Tambolaka City is still carried out manually by Deputy Head 1 for the curriculum field, who currently uses Microsoft Excel software, which has the potential for errors and often requires correction before the schedule is implemented. Because the subject scheduling process is very important, a solution is needed in making subject schedules. Therefore, this research aims to optimize course schedules by applying a genetic algorithm to the course scheduling application. In this research, the process of preparing rosters for applied subjects was carried out at school using a web-based genetic algorithm method so that schedule collisions did not occur and the process was more optimal. In the experiment, the results of this subject scheduling application system consisted of several forms and menus, including a login form, department data menu, subject data, class data, teachers, hour ranges, special schedules, and the scheduling menu. Creating a teacher's teaching schedule using a genetic algorithm can produce a schedule automatically and systematically, thereby displaying the schedule for the day and time of each teacher's teaching schedule. The application of genetic algorithms in scheduling teacher teaching at SMK Negeri 1 Tambolaka City is a very optimal solution for scheduling, so that the possibility of similarities in the same teacher's teaching time and hours is very small.
특성화고등학교 산림 교육과정 운영에 관한 연구

This study examined the vocational forestry curriculum implementation in specialized high schools with a focus on the perspectives of teachers, administrators, and students. The research aimed to identify the obstacles faced in the curriculum implementation and the strategies used to overcome them. Data were collected through document analysis, in-depth interviews, and participant observation. The results showed that the curriculum implementation faced challenges, such as lack of resources and inadequate infrastructure, as well as difficulties in motivating and engaging students. The findings suggest that the forestry curriculum needs to be reorganized with a sustainability focus rather than solely relying on a vocational education framework.
Study on Translation Principles and Strategies of Naval Terminology

Based on the analysis of the structure, semantic and usage features of naval terminology, this paper discusses the common problems of translating naval terms and explores the translation principles and strategies in order to provide reference and assistance for improving the translation quality of military terminology and effectively reducing translation errors.
Teaching Reform of Digital Signal Processing Driven by Probabilistic Neural Network

In this paper, teaching reform of Digital Signal Processing (DSP) is conducted based on the data analysis by machine learning methodologies. First, the Spearman correlation coefficients between different process assessments and the total scores are computed to show their relevance. Then, a probabilistic neural network is trained based on real data, and the test result proves that one student’s final score level can be roughly inferred based on his/her process assessment. Hence, several reform schemes are proposed centering at the process assessment to help strengthen learning of important knowledge points. Finally, the assessment results show that the proposed teaching reform plans are reasonable and effective in improving the achievability of DSP teaching.
Multi-granularity adaptive extractive document summarization with heterogeneous graph neural networks

The crucial aspect of extractive document summarization lies in understanding the interrelations between sentences. Documents inherently comprise a multitude of sentences, and sentence-level models frequently fail to consider the relationships between distantly-placed sentences, resulting in the omission of significant information in the summary. Moreover, information within documents tends to be distributed sparsely, challenging the efficacy of sentence-level models. In the realm of heterogeneous graph neural networks, it has been observed that semantic nodes with varying levels of granularity encapsulate distinct semantic connections. Initially, the incorporation of edge features into the computation of dynamic graph attention networks is performed to account for node relationships. Subsequently, given the multiplicity of topics in a document or a set of documents, a topic model is employed to extract topic-specific features and the probability distribution linking these topics with sentence nodes. Last but not least, the model defines nodes with different levels of granularity—ranging from documents and topics to sentences—and these various nodes necessitate different propagation widths and depths for capturing intricate relationships in the information being disseminated. Adaptive measures are taken to learn the importance and correlation between nodes of different granularities in terms of both width and depth. Experimental evidence from two benchmark datasets highlights the superior performance of the proposed model, as assessed by ROUGE metrics, in comparison to existing approaches, even in the absence of pre-trained language models. Additionally, an ablation study confirms the positive impact of each individual module on the model's ROUGE scores.
Enhancement of Position Control and Joint Motion Range by FEM and Fuzzy PID Control

The voice coil motor (VCM) has been a pivotal component in high-precision positioning products, including hard disk drives, camera lens modules, and the semiconductor industry since its inception in 1820. VCMs offer advantages such as compact and flexible structures with rapid response times. However, certain aspects need improvement, one of which is the loss of joint motion range due to the uneven magnetic distribution resulting from the built-in magnetic arc angle. To address this issue, we designed and fabricated a prototype rotary VCM. Subsequently, we developed and implemented the fuzzy PID controller, demonstrating a significant 20-30% extension in the rotational motion range.
Improving the Performance of DPT-based Monocular Depth Estimation Models Using Bidirectional LSTMs Initialized With CLS Tokens

Monocular depth estimation (MDE) is a computer vision task that estimates the depth value for each pixel in a single monocular RGB image. Given its inherently ill-posed nature, recent studies have focused on data-driven learning-based methods, particularly deep learning techniques based on convolutional neural networks. Building on the success of vision transformers (ViTs), ViT-based models have gained widespread attention for MDE applications. To enhance the performance of MDE, we introduce an intermediate bi-directional long-short term memory (BiLSTM) module, a common component in natural language processing, to learn correlations between each token in a sequence both in the forward and backward directions. Given that transformers naturally encode sequential data, leveraging BiLSTMs to learn the correlations between each token in both directions is a reasonable approach. In addition, we enhance the weight initialization method for BiLSTMs by employing the CLS tokens of transformers, which was originally developed for natural language processing, to make it applicable for processing two-dimensional image data. Subsequently, we developed an MDE model by inserting a BiLSTM module initialized using our method between the encoder and decoder of a DPTbased architecture. Our experiments reveal notable results, with our model achieving 0.913 in 𝛿₁ and 0.097 in AbsRel for the NYU Depth V2 dataset. These results demonstrate a significant improvement over the previous models.
GENETIC ALGORITHM AND RANDOM FOREST CLASSIFIER FUSION: A CUTTING-EDGE APPROACH FOR BREAST CANCER DIAGNOSIS

Breast cancer was a significant cause of mortality in women worldwide, highlighting the importance of early detection in improving patient survival rates. Although machine learning algorithms had shown effectiveness in diagnosing breast cancer, there was still room for improvement. This paper introduced a ground-breaking method that combined Genetic Algorithms (GAs) with Random Forest Classifiers (RFCs) for breast cancer diagnosis. GA’s were used to select the most informative features from the breast cancer dataset, while RFCs were employed to classify the data into cancerous and non-cancerous cases. The proposed approach was evaluated on a publicly available breast cancer dataset, and the results demonstrated a remarkable accuracy of 79.31%, surpassing the accuracy of RFCs without GA-based feature selection (77.58%). This innovative approach held great promise in improving the accuracy of early diagnosis and potentially saving lives. By leveraging the strengths of GAs and RFCs, this novel approach offered an effective means of diagnosing breast cancer and had the potential to revolutionize early detection practices.
Enhancing data security of cardiac patients in IoMT with Twin-Shield Encryption

&lt;p&gt;Cardiac disease kills most people worldwide. Predicting and monitoring cardiac problems early improves disease treatment and patient outcomes. The Internet of Medical Things (IoMT) can monitor and analyze physiological data in real-time, changing healthcare. Many researchers find data generation problematic. Encryption is needed to secure a massive amount of data. This paper presents a Twin-Shield Encryption (TSE) that combines Elliptic Curve Cryptography (HECC) and Rivest-Shamir-Adleman (RSA) IoMT assistance for heart illness patient monitoring. Cleveland cardiac dataset from the University of California Irvine (UCI) research repository is collected. It has 12 qualities and 303 occurrences. The data is pre-processed using normalization; feature extracted using Principal Component Analysis (PCA), and securely transmitted to the cloud infrastructure for further processing and analysis. TSE encrypts patient data to prevent unauthorized access and maintain data integrity during transmission and storage. The framework could enhance cardiac ailment diagnosis, treatment, and management by giving clinicians and patients individualized care based on physiological profiles.&lt;/p&gt;