Reviewerly/exHarmony huggingface dataset:
Pengaruh Penggunaan Media Pembelajaran terhadap Hasil Belajar Matematika

Learning media is one means that can increase effectiveness and efficiency teaching and learning activities. The purpose of this study was to examine the influence of media use learning on student learning outcomes in mathematics. Data collected through related journals for review. The method used in this research is SLR (Systematic Literature Review) method. Researchers collect accredited national journal articles sourced from Google Scholar. Articles that 18 national journal articles were used in this study Obtained from google scholar. Articles that are collected are only articles that published in the period 2015-2023. The results of the research show that the use of media learning in learning mathematics is very influential to arouse interest and motivation of students and have an impact on student learning outcomes.
The Use of English Greetings and Terms of Address by Employees and Homestay Owners in Kintamani Tourism Area, Bali

How to communicate properly and correctly is needed by the people of Bali, especially those who live in tourism areas. The increasing arrivals of foreign tourists to the Island of the Gods has made English an important means of communication, which is therefore, badly needed by people in tourist areas, such as the Kintamani. Those who are directly involved in tourism industry activities are expected to provide good service to meet customer satisfaction. This qualitative research, which is based on a phenomenological philosophy approach, aims to determine the understanding of employees and homestay owners in Kintamani on the use of formal English greetings and terms to address customers in their daily tasks. 30 respondents were selected as samples by accidental sampling technique. Meanwhile, data were collected from respondents through interviews and observations, with recording techniques when respondents served guests in eight representative homestays. Furthermore, the data were descriptively analyzed, referring to the basic English grammatical theory. The results of the study show that in general, employees or homestay owners in Kintamani have already understood the formal use of greetings and terms to address the guests in English well. But in daily conversation, they do not use greetings and terms to address guests in formal English which is theoretically appropriate, and prioritize the communicative aspect instead.
Emergency immersive stimulation: an approach to educational artifacts.

The case study describes the effects of immersive stimulation and its factors that affect the attitude of students in pandemic times.The experiment used an educational plan and immersive artifacts with a situated approach.The case study used a survey, their reliability calculation had 9 items that were rated with a Likert scale.The experiment had 17 participants and two questions as axis."How is your information about immersion?" was the first question, "Which factors affect the students' attitude in an immersive space?" was the second question.Last question was a disaggregated survey with 3 variables.As a result of the research, there were an average of 9.3 in male participation with 12.5 years old; average of 4.5 in female participation with 12.3 years old.Cronbach's Alpha coefficient was 0.969, as a positive influence of the immersive artifacts and a situated learning perspective.
Classification of types of documents containing the results of intellectual and creative activity

The development of information and communication technologies encourages the improvement of processes, methods and tools for archiving and depositing the results of intellectual activity. In order to deposit the results of intellectual activity, it is important to have the appropriate infrastructure, technologies and classification that will ensure proper preservation, quick access and retrieval. In the proposed study, an analysis of the most common results of intellectual activity was carried out, and it was proposed to classify them according to four types of documents: scientific, technical, educational and artistic. Scientific documents can be classified by topic, discipline, type of research, type of publication, industry, and other criteria. The classification of objects for depositing educational documents can be carried out according to the level of education and depends on their purpose and storage terms. The classification of objects of deposit of artistic documents can be carried out depending on their purpose. The classification of the objects of deposit according to the proposed types of documents makes it possible to ensure reliable storage, access and simplifies the search in electronic scientific and information systems of various objects deposited in the fields of education, science, culture, etc.
Noise-Free Digital Interfaces From the Near Future

This doctoral project explores how unconventional design approaches can mitigate noise within digital interfaces and enhance user experience for consumers through the exploration of three interaction elements: visual attention, mindfulness, and tactility.I specifically focus on the issues of noisy digital interfaces such as 'cold' generic interfaces, mindless and touch-poor interactions, and provide practical solutions.Therefore, this practice-based research project adapts the Research through Design approach and the use of Design Fiction methods such as 'What if' questions, diegetic prototypes, and narrative planning.My design practice also aims to raise ethical awareness of designing better IT tools within designers and software developers.The development of noisefree digital interfaces includes creating three fictional prototypes, which communicate the practical application of hand-drawn interfaces, mindful and touch-rich interactions.These diegetic prototypes and near-future scenarios are used to explore to what extent noise can be mitigated within digital interfaces, and how this can enhance user experience for consumers.I predict and recommend that noisy digital experiences can be mitigated by implementing mindful elements, purposefully using colour and shapes, hand-drawn interfaces, and touch-rich interactions, when designing noise-free digital interfaces.The next step of this project is to use the Expert Review method to collect and analyse insights from the designers and software developers working in IT companies on how handdrawn interfaces, mindful and touch-rich interactions can be adapted to benefit large IT companies.Noise-free digital interfaces.Hand-drawn interfaces.Mindful interactions.
Volumetric Music Composition in a VR Context

The development of XR technologies creates new conditions of the art perception, allowing increased user interaction.New technological possibilities cause the shift in the art boundaries, aiming towards expansion of the art practices and leading to the synaesthetic perception.The role of the listener expands to the role of a performer and co-creator of a piece of art.In these conditions, the definition of a music composition must be reconsidered and in this paper, we propose a new concept of a volumetric music composition, which reflects the changes that the modern technological context induces.The practical realisation of a volumetric music composition concept is shown on the example of two projects -"Omega" and "Atmos", which substantially differ in terms of music style and visual representation, however, share the very essence of a new concept, reposing on interactivity and co-creation.VR.
Social Environment of a Modern University: Theory and Methodology of Diagnostics

The social environment ensures the quality of education, its attractiveness for applicants, partners, and reputation capital. Diagnostics of the social environment allow one to identify its qualitative growth or decline, stress points, and development factors and risks. Diagnostic procedures require continuous improvement of methods and approaches. The paper is part of a research paper on the topic “Development of a methodology for monitoring the satisfaction of employees, students, and partners with the quality of the social environment and its implementation.” The goal of the mentioned research paper is to develop a monitoring methodology to identify the level of satisfaction of employees, students, and partners of a university with the quality of the social environment. The paper utilizes (1) analysis of scientific data on the research problem, (2) methods of comparative and system analysis, and (3) interpretation and personal work experience of the authors in the higher school system. The research results include justification of theoretical approaches to the diagnosis of the quality of the social environment of a university. They also include the authorly theoretical interpretation of the category “satisfaction with the quality of the social environment of a university.” The results include the development of indicators and criteria for assessing the satisfaction of employees, students, and partners with the quality of the social environment. The paper also comprises justification of the methods of the primary stages of the implementation of the diagnosis of the quality of the social environment, namely, monitoring and the method of expert assessments in the technique of in-depth interviews. The authors consider the possibilities of further improvement of the system of diagnostics of the quality of the social environment of a university. There are arguments on the need for further improvement of diagnostic tools.
Assessing the Impact of Automated Document Classification Decisions on Human Decision-Making

As machine learning (ML) algorithms are incorporated into more high-consequence domains, it is important to understand their impact on human decision-making. This need becomes particularly apparent when the goal is to augment performance rather than replace a human analyst. The derivative classification (DC) document review process is an area that is ripe for the application of such ML algorithms. In this process, derivative classifiers (DCs), who are technical experts in specialized topic areas, make decisions about a document’s classification level and category by comparing the document with a classification guide. As the volume of documents to be reviewed continues to increase, and text analytics and other types of models become more accessible, it may be possible to incorporate automated classification suggestions to increase DC efficiency and accuracy. However, care must be taken to ensure that tool-generated suggestions do not introduce errors into the process, which could lead to disastrous impacts for national security. In the current study, we assess the impact of model-generated classification decisions on DC accuracy, response time, and confidence while reviewing document snippets in a controlled environment and compare them to DC performance in the absence of the tool (baseline). Across two assessments, we found that correct tool suggestions improved human accuracy relative to baseline, and decreased response times relative to baseline in one of these assessments. Incorrect tool suggestions produced a higher human error rate but did not impact response times. Interestingly, incorrect tool suggestions also resulted in higher confidence ratings when DCs made errors that aligned with the incorrect suggestion relative to cases in which they correctly disregarded its suggestion. These results highlight that while ML tools can enhance performance when the output is accurate, they also have the potential for impairing analyst decision-making performance if inaccurate. This has the potential for negative impacts on national security. Findings have implications for the incorporation of ML or other automated suggestions not only in the derivative classification domain, but also in other high-consequence domains that incorporate automated tools into a human decision-making process. The effects of factors such as tool accuracy, transparency, and DC expertise should all be taken into account when designing such systems to ensure the automated suggestions improve performance without introducing additional errors. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525
Shemza Digital: Across Generations

This paper focuses on the shemza.digital
Q-GENius: A GPT Based Modified MCQ Generator for Identifying Learner Deficiency

This research proposes a novel methodology to address the challenge of identifying learner deficiencies from multiple choice questions (MCQs). The proposed approach involves manipulating the standard MCQ into a modified MCQ by generating distractors based on three different aspects: ‘Fact’, ‘Process’, and ‘Accuracy’. ‘Fact’ refers to the fundamental information related to the answer of the question, ‘Process’ refers to the concepts and application methods required to solve the problem, and ‘Accuracy’ refers to the degree of closeness of the answer to its true value. To achieve this, a Generative Pre-trained Transformer (GPT) model was used to develop a system called ‘Q-GENius’, that can take a question stem and some other optional additional information as input, and generate a modified MCQ with key and three distractors, each having a deficiency either in the ‘Fact,’ ‘Process,’ or ‘Accuracy’ aspect of the answer. The study was validated using inter-annotator agreement of subject matter experts on the accuracy and reproducibility of the model. The validation was performed on four different subjects. The results demonstrated that the proposed approach of modified MCQ was effective in identifying learner deficiencies when compared to standard MCQs. Overall, the proposed methodology has the potential to improve the accuracy of identifying learner deficiencies in MCQs.
TRAINING OF NEURAL NETWORKS TO DECIPHER THE ROAD NETWORK ACCORDING TO SPACE IMAGERY RECEIVED BY THE ”RESURS-P”

Abstract. Our team has developed a neural network for road recognition on our digital twin, aimed at enhancing transportation-related applications. The neural network is trained on large datasets of road images and utilizes various deep learning architectures and techniques to improve its accuracy and reliability. The embedded neural network can recognize different road features, such as lane markings, road signs, and obstacles, and can identify the location and direction of the road. The integration of this neural network in our digital twin can help optimize transportation-related operations, reduce accidents, and improve overall traffic flow. The developed neural network architecture and training methodology, as well as its performance evaluation on various datasets, are presented in this paper. Additionally, the paper discusses the future directions for research in this area and the potential of the developed neural network for other applications in the digital twin domain.
Implementation of Generative Adversarial Network to Generate Fake Face Image

In recent years, many crimes use technology to generate someone's face which has a bad effect on that person. Generative adversarial network is a method to generate fake images using discriminators and generators. Conventional GAN involved binary cross entropy loss for discriminator training to classify original image from dataset and fake image that generated from generator. However, use of binary cross entropy loss cannot provided gradient information to generator in creating a good fake image. When generator creates a fake image, discriminator only gives a little feedback (gradient information) to generator update its model. It causes generator take a long time to update the model. To solve this problem, there is an LSGAN that used a loss function (least squared loss). Discriminator can provide astrong gradient signal to generator update the model even though image was far from decision boundary. In making fake images, researchers used Least Squares GAN (LSGAN) with discriminator-1 loss value is 0.0061, discriminator-2 loss value is 0.0036, and generator loss value is 0.575. With the small loss value of the three important components, discriminator accuracy value in terms of classification reaches 95% for original image and 99% for fake image. In classified original image and fake image in this studyusing a supervised contrastive loss classification model with an accuracy value of 99.93%.
Run Length Encoding Compresion on Virtual Tour Campus to Enhance Load Access Performance

Virtual tour is one of the rapidly growing applications of multimedia technology which is used for various purposes, including the dissemination of information in an interesting way. The education sector is also not spared from using virtual tour media for promotional purposes, and campuses are no exception to this rule. Large virtual tour content causes high access speed, ultimately reducing the level of comfort experienced by users. This study aims to compress panoramic images displayed on a campus virtual tour using a lossless compression method and the Run Length Encoding (RLE) algorithm. First, panoramic images are combined into one, then individual images are compressed. When recreating a virtual campus tour, compressed images are used so that the amount of data transferred is smaller. The load access speed index increases from 7,233 seconds to 3,789 seconds when images are compressed from 64 bits to 8 bits, with a compression percentage of 27%. The findings from this research are that the RLE algorithm has not been able to compress large files effectively even though it is quite successful in increasing the load access of the virtual tour website.
The Implementation of Restricted Boltzmann Machine in Choosing a Specialization for Informatics Students

Choosing a specialization was not an easy task for some students, especially for those who lacked confidence in their skill and ability. Specialization in tertiary education became the benchmark and key to success for students’ future careers. This study was conducted to provide the learning outcomes record, which showed the specialization classification for the Informatics students by using the data from the students of 2013-2015 who had graduated. The total data was 319 students. The classification method used for this study was the Restricted Boltzmann Machine (RBM). However, the data showed imbalanced class distribution because the number of each field differed greatly. Therefore, SMOTE was added to classify the imbalanced class. The accuracy obtained from the combination of RBM and SMOTE was 70% with a 0.4 mean squared error.
Data Mining for Heart Disease Prediction Based on Echocardiogram and Electrocardiogram Data

Traditional methods of detecting cardiac illness are often problematic in the medical field. The doctor must next study and interpret the findings of the patient's medical record received from the electrocardiogram and echocardiogram. These tasks often take a long time and require patience. The use of computational technology in medicine, especially the study of cardiac disease, is not new. Scientists are continuously striving for the most reliable method of diagnosing a patient's cardiac illness, particularly when an integrated system is constructed. The study attempted to propose an alternative for identifying cardiac illness using a supervised learning technique, namely the multi-layer perceptron (MLP). The study started with the collection of patient medical record data, which yielded up to 534 data points, followed by pre-processing and transformation to provide up to 324 data points suitable to be employed by learning algorithms. The last step is to create a heart disease classification model with distinct activation functions using MLP. The degree of classification accuracy, k-fold cross-validation, and bootstrap are all used to test the model. According to the findings of the study, MLP with the Tanh activation function is a more accurate prediction model than logistics and Relu. The classification accuracy level (CA) for MLP with Tanh and k-fold cross-validation is 0.788 in a data-sharing situation, while it is 0.672 with Bootstrap. MLP using the Tanh activation function is the best model based on the CA level and the AUC value, with values of 0.832 (k-fold cross-validation) and 0.857 (bootstrap).
Regression Analysis for Crop Production Using CLARANS Algorithm

Crop production rate relies on rainfall over Rejang Lebong district. Data showed a discrepancy between increased crop production and rainfall in Rejang Lebong District. However, the spatiotemporal distribution of the crop variable's dependencies remains unclear. This study analyses the relationship between rainfall and crop production rate in the Rejang Lebong district based on the performance of the machine learning method. In addition, this research also performed regression analysis to carry out rainfall clusters and crop production. This order provides information in the form of cluster results to determine how much the rainfall variable influences the crop production rate in each cluster. Harnessing the Elbow, CLARANS, Simple Linear Regression, and Silhouette Coefficient methods, this study used 231 rainfall data sourced from the Bengkulu BMKG and 110 data for plant production obtained from BPS Bengkulu Province from 2000-2022. This research found that the optimal clusters were 3 clusters. C1 contains 106 data with the largest regression value for chili = 0.127, C2 contains 15 data with the largest regression value for mustard greens = 0.135, and C3 contains 110 data with the largest regression value for cabbage = 0.408, eggplant = 0.197, and carrots = 0.201. Furthermore, this research also found that the biggest correlation of crops with highly significant improvement would be cabbage commodity (Y=0.4114X+0.2013) and chili plantation with high RSME (0.9897).
THE IMPACT OF LANGUAGE CHANGES CAUSED BY TECHNOLOGY AND SOCIAL MEDIA

This research discusses language change as a result of the influence of social media. In an increasingly advanced digital era, social media has become one of the primary communication tools for individuals worldwide. This study utilizes descriptive and comparative analysis methods to explore the influence of social media on language change. Firstly, the research identifies grammar, syntax, and vocabulary changes due to social media usage. Then, the study compares the language used in traditional communication with that used in social media communication. The findings of this research indicate significant language changes due to the use of social media. There is an increase in the use of abbreviations, emoticons, and distinctive terms specific to social media that affect the way humans communicate in a digital context. Additionally, casual writing styles, non-formal language use, and the adaptation of foreign words have become characteristics of social media communication. These language changes can have both positive and negative impacts. On the positive side, social media has enabled faster and more efficient communication between individuals across the globe. Using a more casual and non-formal language can also strengthen social bonds among social media users. However, on the other hand, these language changes can also pose challenges to understanding and communication between different generations or in formal contexts.