Reviewerly/exHarmony huggingface dataset:
Kesulitan Belajar Akademik dalam Proses Pembelajaran

Learning difficulties are an important problem in the world of education that requires deep understanding. This article aims to provide a comprehensive overview of understanding and identifying learning difficulties. Learning difficulties refer to the difficulties a person experiences in understanding and processing information taught in a formal educational environment. This article presents various aspects related to this phenomenon, including its causative factors, identifying marks, and their impact on the development of each individual. The causes of learning difficulties include internal factors and external factors. Some signs that can be used to identify learning difficulties include difficulty reading, writing, arithmetic. It is important for educators, parents, and health professionals to recognize these signs early so that appropriate interventions can be provided. This article also discusses the impact of learning difficulties, namely inhibition of children's growth and development, disruption of children's interaction and environment. In addition, various identification and intervention strategies that can help those with learning difficulties are presented, such as psychological assessments, special education support, and adaptive learning approaches. This article provides a solid foundation to encourage greater understanding and interest in these educational issues.
The online language of work-personal conflict

Abstract With the blurring of boundaries in this digital age, there is increasing concern around work-personal conflict. Assessing and tracking work-personal conflict is critical as it not only affects individual workers but is also a vital measure among broader well-being and economic indices. This inductive study examines the extent to which work-personal conflict corresponds to individuals’ language use on social media. We apply an open-vocabulary analysis to the posts of 2810 Facebook users who also completed a survey for an established work-personal conflict scale. It was found that the language-based model can predict personal-to-work conflict ( r = 0.23) and work-to-personal conflict ( r = 0.15) and provide important insights into such conflicts. Specifically, we found that high personal-to-work conflict was associated with netspeak and swearing, while low personal-to-work conflict was associated with language about work and positivity. We found that high work-to-personal conflict was associated with negative emotion and negative tone, while low work-to-personal conflict was associated with positive emotion and language about birthdays.
Fine-Grained Classification of Remote Sensing Ship Images Based on Improved VAN

The remote sensing ships’ fine-grained classification technology makes it possible to identify certain ship types in remote sensing images, and it has broad application prospects in civil and military fields. However, the current model does not examine the properties of ship targets in remote sensing images with mixed multi-granularity features and a complicated backdrop. There is still an opportunity for future enhancement of the classification impact. To solve the challenges brought by the above characteristics, this paper proposes a Metaformer and Residual fusion network based on Visual Attention Network (VAN-MR) for fine-grained classification tasks. For the complex background of remote sensing images, the VAN-MR model adopts the parallel structure of large kernel attention and spatial attention to enhance the model’s feature extraction ability of interest targets and improve the classification performance of remote sensing ship targets. For the problem of multi-grained feature mixing in remote sensing images, the VAN-MR model uses a Metaformer structure and a parallel network of residual modules to extract ship features. The parallel network has different depths, considering both high-level and low-level semantic information. The model achieves better classification performance in remote sensing ship images with multi-granularity mixing. Finally, the model achieves 88.73% and 94.56% accuracy on the public fine-grained ship collection-23 (FGSC-23) and FGSCR-42 datasets, respectively, while the parameter size is only 53.47 M, the floating point operations is 9.9 G. The experimental results show that the classification effect of VAN-MR is superior to that of traditional CNNs model and visual model with Transformer structure under the same parameter quantity.
Digital marketing program design based on abnormal consumer behavior data classification and improved homomorphic encryption algorithm

This article endeavors to delve into the conceptualization of a digital marketing framework grounded in consumer data and homomorphic encryption. The methodology entails employing GridSearch to harmonize and store the leaf nodes acquired post-training of the CatBoost model. These leaf node data subsequently serve as inputs for the radial basis function (RBF) layer, facilitating the mapping of leaf nodes into the hidden layer space. This sequential process culminates in the classification of user online consumption data within the output layer. Furthermore, an enhancement is introduced to the conventional homomorphic encryption algorithm, bolstering privacy preservation throughout the processing of consumption data. This augmentation broadens the applicability of homomorphic encryption to encompass rational numbers. The integration of the Chinese Remainder Theorem is instrumental in the decryption of consumption-related information. Empirical findings unveil the exceptional generalization performance of the amalgamated model, exemplifying an AUC (area under the curve) value of 0.66, a classification accuracy of 98.56% for online consumption data, and an F1-score of 98.41. The enhanced homomorphic encryption algorithm boasts attributes of stability, security, and efficiency, thus fortifying our proposed solution in facilitating companies’ access to precise, real-time market insights. Consequently, this aids in the optimization of digital marketing strategies and enables pinpoint positioning within the target market.
Between corpora, tools and authority files

In this article, we discuss the ways in which Digital Humanities projects organise their resources, and argue for the need to collaborate with specialised services from other institutions for specific tasks, just as research publishers do. More specifically, we argue that libraries have a new central role to play in the publication of research data in repositories. Using the TextGrid Repository (TGR) as an example, we present its main components, its history, its main features, and discuss its advantages and disadvantages in comparison with similar repositories and technologies. We then present its current development within the Text+ consortium, both in the form of new features but also in the integration of existing corpora, making it interesting not only for German literature (as has been the case until now), but also for other traditions, especially Spanish literature.
The application of fractal theory in real-life

As a relatively new and mathematics-related discipline, fractal has had a certain influence on the development of many aspects of today's society. At the same time, the science of fractal geometry to explore and study the practical use of fractal properties and fractal geometry is called a fractal theory. What's more, the emergence of fractal theory has promoted the development of various fields, provided new solutions and methods for some problems, provided a both logical and rigorous reference basis for some decisions, and also facilitated people's lives. By observing actual life and consulting some relevant literature, this article discusses the application of fractal theory in real life mainly from the aesthetics, health, and safety, respectively from three angles which are aesthetic art, medical and health, and highway traffic, such as pattern, numerical reference, material selection and so on, to understand the value of fractal theory in academic development and life application.
Transformer and Natural language processing; A recent development.

The convergence of transformers and natural language processing (NLP) represents a watershed moment in the realm of artificial intelligence. Recent NLP advancements have been profoundly shaped by the emergence of transformers, a class of deep learning models renowned for their remarkable proficiency in comprehending, generating, and manipulating human language. This abstract offers a succinct exploration of the symbiotic relationship between transformers and NLP, emphasizing their central role in propelling recent process.&#x0D; The introduction of transformers heralded a paradigm shift in the realm of NLP, primarily owing to their innovative self-attention mechanism, which empowers them to adeptly capture intricate contextual associations within textual data. Distinguished by their multi-head attention layers and feed-forward networks, the architecture of transformers has ushered in a new era in NLP. Models such as BERT,&#x0D; GPT-3, and their offshoots have not only redefined but also set the gold standard for a wide array of NLP tasks.
Peningkatan Kesantunan Berbahasa Indonesia: Tulis dan Lisan dalam Literasi di SMPN 43 Padang

The activity aimed to improve the language skills of SMPN 43 Padang students, both written and verbal, in writing letters. It was considered necessary due to problems arising from the impoliteness of some students of junior high school in both oral and written communication for both formal and non-formal situations. The method used in this community service was participatory, planned by the Study Program of Indonesian Literature lecturer of Universitas Andalas. Furthermore, the team visited the service centre through observation and communication with SMPN 43 Padang teachers to look for problems and solutions related to language politeness in the school. After that, the team and teachers collaborated by compiling an agenda for implementing the engagement service. During the implementation stage, the material was presented, followed by the active participation of students through oral and written communication practices. In the final stage, an evaluation was conducted to formulate the main problems and assess subsequent developments after the community service activities. Based on the review carried out through an analysis of student work results, in the form of a comparison before and after receiving the material and engaging in writing practice, this service increased students' Indonesian politeness.
Pengembangan E-Modul Serli Berbasis Android Materi Magnet bagi Siswa Sekolah Dasar

This research is based on the lack of learning media that can help students during Distance Learning. Learning Media developed in this study can be used and accessed anywhere and anytime to help students understand the material being taught. The Media that will be developed in the research will be in the form of applications that help teachers in delivering material and help students to understand more about the material being taught. This study focused on science subjects, especially magnetic materials for Grade VI students. E-module is a learning module that is packaged into electronic form to facilitate the learning process that is made systemically, and is an example of the benefits of technology in the field of Education. The development of this e-module uses discovery learning approach. In developing e-module Seroid Learning media based on Android on magnetic material for Grade VI elementary school students . Achievement in this development to test apalikasi developed is included in the category of valid and practical, with research development methods RnD (Research and Development) using Thiagarajan 4D model that Define, Design, Develop, and Disseminate. The development of the serli e-module application will be carried out in the sixth grade of Inpres 12 Baiya, elementary school by conducting a small trial of 6 students.
UNDERSTANDING BAYES RULE: BAYESIAN NETWORKS IN ARTIFICIAL INTELLIGENCE

Bayes' Rule and Bayesian Networks are foundational elements of AI, enabling probabilistic reasoning and informed decision-making in uncertain domains. This article introduces the core concepts and practical applications of these tools. We explore the historical origins, step-by-step construction of Bayesian Networks, and real-world AI applications. By understanding Bayes' Rule and Bayesian Networks, readers can unlock their potential to tackle complex AI challenges and uncertainties. And this article underscores the undeniable importance of Bayes' Rule and Bayesian Networks in AI. We hope to inspire a deeper understanding of these foundational concepts and encourage the exploration of their vast potential in artificial intelligence. Keywords: Bayesian Networks, AI, probabilistic reasoning, informed decision-making
NOVEL STUDY ON NATURAL LANGUAGE PROCESSING

Natural Language Processing (NLP) is the tech wiz working tirelessly to break down language barriers between us and our devices. It's the reason our smart phone, tablet or laptop understands our voice commands and translates our languages in a second. NLP is like giving machines the ability to comprehend and respond to language nuances, turning our interactions into seamless conversations. Think of it as the digital polyglot that not only reads but truly understands the messages we convey, from the simplest text to the most intricate emotions. From predictive text to chatbots, NLP is the digital linguist enhancing the way we communicate with our devices, making technology feel more like a conversation with a helpful virtual friend. Keywords: seamless conversations, digital polyglot, predictive text, chatbots, digital linguist, technology communication, virtual friend.
FLOWCHART FOR CHOOSING INFERENTIAL STATISTICAL TEST

Appears in: ICERI2023 Proceedings Publication year: 2023Pages: 9329-9335ISBN: 978-84-09-55942-8ISSN: 2340-1095doi: 10.21125/iceri.2023.2393Conference name: 16th annual International Conference of Education, Research and InnovationDates: 13-15 November, 2023Location: Seville, Spain
EARLY YEARS TEACHER'S CONCEPTIONS AND PRACTICES TO ASSESS AND DOCUMENT CHILDREN'S CONCEPTIONS

1 Research Center in Basic Education, Instituto Politécnico de Bragança (PORTUGAL)2 Research Center in Digitalization and Intelligent Robotics, Instituto Politécnico de Bragança (PORTUGAL)
Correlation of Whole Blood Cell Morphology on Electrical Impedance Spectroscopic Characteristics on the Severity Level of Ischemic Stroke Patients

This paper discusses the influence of peripheral blood smear morphology methods on the electrical impedance spectroscopic characteristics of blood samples from ischemic stroke patients. In this study, there were 26 blood samples, consisting of a control group with 5 blood samples and a group of ischemic stroke patients with 21 samples with varying degrees of severity. The methods used are electrical impedance spectroscopy (EIS) and whole blood cell morphology methods. The EIS method was carried out using the BISDAQ tool and application, and results were obtained in the form of Bode Plot, Bode Phase Plot, Nyquist Plot, and measurement data values in the form of Ms. Excel. Meanwhile, the Morphological method is carried out by making a peripheral blood smear, then viewing and obtaining an image using a digital microscope, and then counting the number of cells using the Image Raster application. Based on the results of the method carried out, the correlation can be seen to conclude that whole blood cell morphology greatly influences the characteristics of EIS and determines the severity of ischemic stroke patients.
Electrical Impedance Spectroscopic Analysis on Whole Blood Cells to Correlate Severity Level of Ischemic Stroke Patients

Measuring the impedance value of biological materials in the form of blood samples has been widely used to determine a person's health status. Ischemic stroke patients in terms of impedance values and correlates with morphological tests on changes in red blood cells. The Electrical Impedance Spectroscopy (EIS) method is applied by providing a blood sample which is detected by electrodes and read using a BIA tool set so that the results are obtained on a laptop. The results obtained from the EIS test are in the form of Bode graphs, Bode phase graphs, Nyquist graphs, and impedance values. The EIS method uses a frequency of 100 Hz to 100 kHz by injecting a current of 10 μA. In this study, the impedance value obtained for normal people was 1058 Ω to 709 Ω, while for ischemic stroke patients with various levels of condition, it was from 517 Ω to 761 Ω. When counting the number of morphological cells in the patient's blood, changes were found ranging from the most cell changes to the least, namely with a value of 44% to 19%. The severity level of ischemic stroke patients obtained based on impedance tests and morphological tests includes the order P4, P5, P3, P2, P10, P21, P15, P19, P1, P6, P13, P18, P14, P17, P16, P20, P12, P11, P8, P7, P9.
LEARNING LOSS: WHY IS COLLABORATION BETWEEN EDUCATORS AND PARENTS IMPORTANT?

The condition of learning loss has already occurred or been experienced by students in suburban or rural areas. While it cannot be denied that this phenomenon also occurs in urban areas, its frequency is not as high as in suburban and rural regions. This research is conducted in the form of an ex-post facto study, which means data collection is carried out after the event being studied has occurred. The subjects of this research involve 15 teachers, comprising 2 civil servants (PNS) and 13 contract teachers (honorer). Grade 5 has a total of 20 students with diverse abilities. The data collection methods used in this research include observation, interviews, questionnaires, and tests. The analytical technique applied in this study is based on the assumption of linearity using regression linearity testing. The research results indicate a significant influence between the roles of educators and parents in addressing learning loss, as indicated by significance values smaller than 0.05. Furthermore, the calculation results show that the combined influence of the variables of educators' and parents' roles is 0.879. The inactivity of educators and parents in the learning process can lead to learning loss, which in turn negatively impacts the quality of education. Therefore, effective communication, collaboration, cooperation, and coordination are necessary to ensure that the education process remains of high quality.
The Effect of Model Discovery Learning on Students’ Self-Efficacy and Student Learning Outcomes

One of the factors that influences a person's level of success in the learning process is self-efficacy. Low self-efficacy and student learning outcomes in the learning process are the background for this research. In order for student self-efficacy and learning outcomes to increase, there needs to be a learning process that makes students learn meaningfully, one of which is the model discovery learning. The purpose of writing the article is to see whether there is an increase in student self-efficacy after implementing the discovery learning model and whether there are differences in learning outcomes between the application of the model discovery learning and student learning outcomes using the scientific approach. The method in this research uses experimental research methods with research instruments in the form of questionnaires given to students before and after the learning process using discovery learning models and learning outcomes tests. Based on the calculations, it was found that there is an increase in students' self-efficacy after applying the model discovery learning, with a value of 0.43 categorized as a "moderate" improvement. This indicates that the discovery learning model positively contributes to enhancing students' self-confidence. This means that there is an increase in student self-efficacy by implementing the model discovery learning. For student learning outcomes, the calculation results obtained are 𝑡ℎ𝑖𝑡𝑢𝑛𝑔 = 2.38 and 𝑡𝑡𝑎𝑏𝑒𝑙 = 1.67. This means that student learning outcomes by applying the model discovery learning are better than by applying learning using the scientific approach.
Prediction Of Bogor City Rainfall Parameters Using Long Short Term Memory (LSTM)

Bogor is a city that has high rainfall and has erratic rainfall. So it is necessary to predict Bogor's rainfall. Rainfall prediction can be done using the LSTM algorith. In the LSTM algorithm, there are hidden neuron parameters and epochs to produce good results, so it is necessary to predict the best parameters in Bogor rainfall. The prediction parameters results obtained by LSTM have worked well using optimal hidden neuron values of 256, optimal epoch of 150, mape of 1,64, and the comparison of actual data patterns and prediction data already has the same data patterns.
Comparison of Error Prediction Methods in Claassification Modeling with CHAID Methods for Balanced Data

Chi-Squared Automatic Interaction Detection (CHAID) is an exploratory method for classifying data by building classification trees. The classification result are displayed in the form of a tree diagram model. After the model is formed, it is necessary to calculate the accuracy of the model. The goal is to see the performance of the model. The accuracy of this model can be determined by calculating the level of prediction error in the model. The error rate prediction method works by dividing data into training data and testing data. There are three methods in the error rate prediction method, such as Leave one out cross validation (LOOCV), Hold out, and k-fold cross validation. These methods have different performance in dividing data into training data and test data, so that each method has advantages and disadvantages. Therefore, a comparison of the three error rate prediction methods was carried out with the aim of determining the appropriate method for the CHAID. This research is included in experimental research and uses simulation data from data generation results in RStudio. This comparison is carried out by considering several factors, namely the marginal probability matrix and different correlations. The comparison results will be observed using a boxplot by looking at the median error rate and lowest variance. This research found that k-fold cross validation is the most suitable error rate prediction method applied to the CHAID method for balanced data.
THE DIFFICULTIES OF TEACHING SPEAKING FOR YOUNG LEARNERS

Speaking is one of four skills that should be mastered by EFL students. However, there are several problems that teachers found while teaching speaking, especially for teaching young learners. The problems usually come from the students itself, but the teacher's method in teaching may cause a problem too. This paper is intended to share the teacher's difficulties in teaching speaking for young learners and the way to solve those problems. The methods that the writers use in this paper are descriptive research and literature research. The data was collected through a survey for elementary school teachers around Purwokerto, which are formal and non-formal schools. The finding shows that the teachers facing the same problems in teaching speaking, such as students can't remember the pronunciation and can't understand the materials well, the method that commonly used to teaching speaking, and teacher's suggestions on how to teach speaking better. This paper also provides some solutions that teachers can use to achieve the higher quality of teaching speaking especially for young learners and make students improve their skills in speaking. There are audio lingual method (ALM) and Communicative Language Teaching (CLT) in teaching speaking for young learners that expected to help solve those problems.