Reviewerly/exHarmony huggingface dataset:
Naive Bayes Method and C4.5 in Classification of Birth Data

Data on the birth and productive age of a mother to get pregnant in Lampung is still high. to find out the comparison of the productive age of pregnant women and whether they have met the minimum and maximum requirements for a mother to become pregnant, and the criteria for babies born. Where the results of data processing will be used as a source of data for counseling mothers, especially for residents of Banjar Kertahayu village. The data processing requires a special method so that the results become a benchmark for a decision later, such as Data Mining. The method used for data processing used is Naive Bayes and C4.5 Algorithm. The data used is birth data in 2017-2021, the source of data from the Banjar Village Midwife-Central Lampung Regency. Research Results Method C 4.5 Middle age has a dominant age category value of 0.3324138. where the highest value is in 2017, and accuracy is 100 percent from the 2017-2021 data. The baby weight criterion using the Naïve Bayes Class Method has a dominant Middle-aged category value of 0.09675, the highest value in 2017, The results of accuracy for 5 years have accuracy of 92.84% based on 2017-2021 birth data
Risk Assessment for Logistics Applications in Cloud Migration

The increase in the number of cloud data centers is due to an increase in the number of companies migrating to cloud computing. There are many advantages that companies get when migrating to the cloud, but there are also many disadvantages. Multitenancy security and privacy are important challenges for cloud migration users. This study proposes a way to assess the risks that may arise in the cloud migration process for logistics business applications. The research method used is semi-quantitative with a 3-phase approach, namely before migration, during migration, and after migration by considering the criteria for risk aspects and environmental aspects that will have an impact on the company, so that companies can make risk mitigation plans. The results of this study identified 11 (eleven) threats in the cloud that occupy the top ranking and identify as many as 17 (seventeen) indicators obtained from the identification of indicators in the previous model or framework used to assess risks in logistics business applications that will be implemented. migrated to the cloud. Based on the experimental results in this study, the application risk value during migration and after migration has a higher value than before migration, and the risk value during migration are higher than the risk value after migration.
Neural Network Pruning in Unsupervised Aspect Detection based on Aspect Embedding

Aspect detection systems for online reviews, especially based on unsupervised models, are considered better strategically to process online reviews, generally a very large collection of unstructured data. Aspect embedding-based deep learning models are designed for this problem however they still rely on redundant word embedding and they are sensitive to initialization which may have a significant impact on model performance. In this research, a pruning approach is used to reduce the redundancy of deep learning model connections and is expected to produce a model with similar or better performance. This research includes several experiments and comparisons of the results of pruning the model network weights based on the general neural network pruning strategy and the lottery ticket hypothesis. The result of this research is that pruning of the unsupervised aspect detection model, in general, can produce smaller submodels with similar performance even with a significant amount of weights pruned. Our sparse model with 80% of its total weight pruned has a similar performance to the original model. Our current pruning implementation, however, has not been able to produce sparse models with better performance.
Quantizing Rare Random Maps: Application to Flooding Visualization

Visualization is an essential operation when assessing the risk of rare events such as coastal or river floodings. The goal is to display a few prototype events that best represent the probability law of the observed phenomenon, a task known as quantization. It becomes a challenge when data is expensive to generate and critical events are scarce, like extreme natural hazard. In the case of floodings, each event relies on an expensive-to-evaluate hydraulic simulator which takes as inputs offshore meteo-oceanic conditions and dyke breach parameters to compute the water level map. In this article, Lloyd's algorithm, which classically serves to quantize data, is adapted to the context of rare and costly-to-observe events. Low probability is treated through importance sampling, while Functional Principal Component Analysis combined with a Gaussian process deal with the costly hydraulic simulations. The calculated prototype maps represent the probability distribution of the flooding events in a minimal expected distance sense, and each is associated to a probability mass. The method is first validated using a 2D analytical model and then applied to a real coastal flooding scenario. The two sources of error, the metamodel and the importance sampling, are evaluated to quantify the precision of the method.
User Characteristics Analysis Based on Web Log Mining

In order to solve the contradiction between massive network information and limited learning needs, personalized recommendation service becomes the hotspot in research area.User characteristics analysis is the key point in personalized recommendation service.Based on the massive web access log in the web server, this research gradually puts forward the steps of user characteristics analysis which including data pretreatment, user feature extraction and user clustering.This paper focuses on the rules of user recognition, definition of user feature, user feature extraction algorithm and user group clustering.Finally, take access log files of a web server as sample, simulation experiments have been made to prove the thought put forward by this context.Improvement has been taken to the push service repository on the basis of the experimental results, which achieved good practical results.Behavior characteristics and laws of visitors access based on log analysis may lay the foundation of recommender service for resources platform.
English Language Anxiety

English as lingua franca has aroused the demand to include English in early childhood education curriculum, while most of the teachers experienced English Language Anxiety symptoms. In PAUD J, English material had been delivered to the students but the practice itself needs a lot of improvements. One of the factors hindering the improvements is the teachers English Language Anxiety. This study aimed to explore deeper into the teacher’s English language anxiety that hinder their performance to deliver English to their class proficiently. Case study method with observation and interview was conducted to collect data from three early childhood education (ECE) teachers. The result of this study was that the ECE teachers participating in this study experience an anxiety regarding English language that they must deliver to their students. There are three themes discussed in this study, namely (1) the manifestation of their English Language Anxiety., (2) confidence in their English proficiency, and (3) prior exposure to English
Sentiment Analysis Using Backpropagation Method to Recognize the Public Opinion

Improve the service quality of tourism actors by conducting sentiment analysis on digital platforms owned by tourism businesses and collecting negative sentiments to improve the quality of services from companies owned by tourism businesses. The growth of the hospitality industry in Indonesia is experiencing rapid growth every year. The tourism industry, part of the hospitality industry, also does not escape the influence of positive and negative sentiments. One method to perform accurate sentiment analysis is Backpropagation Neural Network. Based on the results of tests on the neural network, the best accuracy is obtained when using one hidden layer with the first layer of 10 neurons. The learning rate is 0.000002, where the accuracy is 71.630%. More epochs do not guarantee better accuracy. Based on the results of the research that has been done, suggestions for further researchers are to analyze the review dataset processing method so that it gets a cleaner dataset and is expected to improve better accuracy.
Supplemental Material for Historical time machines: Experimentally investigating potentials and impacts of immersion in historical VR on history education and morality.

Virtual reality (VR) is known for its immersive characteristics that enable users the experience of presence in virtual environments.However, the experience of presence in emotionally charged historical VR is controversially discussed as it might decrease the critical, cognitive reflection of the historical content and lead to strong emotional reactions.Therefore, we examined the effects of the VR content's emotionality on presence and emotional response.Following the transportation-imagery model and the model of intuitive morality and exemplars, we investigated the effects of presence and emotional response on the recipients' enjoyment, historical interest, beliefs, awareness, and moral reasoning.Moreover, we tested whether additional information after watching historical VR can support the critical reflection of the historical content.Taking different modalities into account, we conducted two experiments following a 2 (emotionality of the content) × 2 (reception of additional information)-between-subjects design in which participants were exposed to historical VR via a media player (Experiment 1, N = 493) or VR glasses (Experiment 2, N = 99).In line with the assumptions, the recipients' experience of presence and emotional response supported historical interest, enjoyment, and affected moral reasoning.Moreover, findings indicated that additional material promotes the critical reflection of historical VR.
Improving the Speaking Skills of Junior High School Students Through the Role Play Method

This study aims to help teachers improve students' speaking skills by using learning methods that are expected to increase students' interest in learning and students' speaking skills. English is the language most widely mastered and used by people around the world in various sectors such as politics, economics, education and so on. English is also a globally recognized language as an international language. The ability to master English is mostly judged by how proficient a person can communicate using English, therefore many people practice to be able to master this skill. However, English speaking skills in education in Indonesia are still under-appreciated, when compared to other aspects of English skills such as reading and writing. To overcome this, it is recommended to use role-play because it can increase the fun in learning and make it easier for students to learn to communicate with someone or a group using English. The method I use in this research is Role Play, with the subject of class VIII junior high school students. Collecting data using observation and tests. This type of research is Classroom Action Research. The results showed that the average score of students before the use of role-play was 57.3 which then increased in the first cycle with an average score of 66.7 with an increase of 14%. Then the second cycle was carried out which showed the average score of students to be 77 with an increase of 12.7% from the first cycle.
Promuovere l’apprendimento delle STEM con le tecnologie educative nella scuola secondaria di secondo grado: opportunità e criticità. X-Reality e STEM: opportunità e criticità

Nowadays, scientific literacy is a key aspect for active citizenship and for making informed decisions regarding scientific issues of everyday life. However, the PISA 2018 data highlight the low scientific competence of the upper secondary school students, underlining the need to foster STEM teaching. In particular, the use of the so-called X-Reality for acquiring scientific skills and to increase interest in the discipline seem promising. During the first year of my PhD, I carried out a scoping review on STEM teaching with X-Reality in the context of secondary school. The main purpose was to draw a state-of-the-art regarding X-Reality use, focusing on potential, critical aspects and challenges. After the search and selection process, 10 articles were included in the analysis. The results showed the effectiveness of X-Reality in promoting the achievement of learning objectives, the interest in the discipline and the long-term retention of knowledge. These results will be the starting point for setting up the empirical part of the PhD project.
Mining Parallel Sentences from Internet with Multi-view Knowledge Distillation for Low-resource Language Pairs

Abstract The neural machine translation (NMT), which relies on a large training data (bilingual parallel sentences, for NMT) to obtain the state-of-the-art performance, is similar with deep learning. In order to construct NMT systems, the number of parallel sentences is very important. However, these bilingual resources are scare to many low-resource language pairs. Although several works attempt to obtain bilingual parallel data from Internet, the quality and quantity of mined bilingual corpus are limited for low-resource language pairs. To address this problem, we propose the multi-view knowledge distillation model (MvKD) that use the knowledge of high-resource language pairs transfer into low-resource languages by leveraging internal language-invariant cross different languages. In particular , we treat the mining bilingual parallel sentence pair task as classifying task and use the multi-view classifier to detect bilingual parallel sentence pair. For multi-view classifier, we use two views to recognize the semantic difference of two sentences:(i) word-level representations (ii) sentence-level representations. We encode sentence-level representations to capture semantically similar of two sentences. Moreover, we encode word-level representations to capture word translations in a pair of parallel sentences to avoid the problem that semantically similar but non-parallel sentences. Experimental results demonstrate that our proposed method can significantly mine amount of bilingual corpus and improve the quality of parallel sentences. In particular, we carry out the experiments on several real-world low-resource situations and achieve excellent results.
Analisis Unjuk Kerja K-Nearest Neighbour untuk Klasifikasi Citra Aksara Bali Tulis Tangan

A lack of philologists and the vulnerability of palm leaf material have become triggers for the scripting automation or transliteration of Balinese script images on computer-assisted palm leaves. One possibility to solve this problem is to create a transliteration machine. We proposed a machine learning technique using the k-NN algorithm to create a transliteration of Balinese script images. The benefit of the kNN algorithm is simply working by matching the similarity of new data to the nearest test data. Instead of focusing on the classification technique, the study approaches also analyze the two previous processes: the first process is an image preparation process consisting of binarization, cutting the blanks, equalizing size, and thinning. The second is a feature extraction process using the character intensity algorithm. Our experiment employed 18 classes representing 18 Balinese characters. The optimal accuracy using a 3-fold cross-validation method to 1001 image data yields an average of accuracy is 84.746%. Although the image data used is handwritten, however, kNN algorithm performed classification well using an extensive training dataset. For that reason, the kNN algorithm could be potential for Balinese script images transliteration.
Highlights in the Development of Tense-Logic

In 1954, on the 27 th August, Arthur Norman Prior presented his ideas of tense-logic 1 for the first time.He developed the field further in many publications until his death in 1969.His books Time and Modality (1957a), Past, Present and Future (1967), and Papers on Time and Tense (1968) were clearly vital milestones.Much of Prior's personal motivation had to do with his struggle with the logical tension between the theological doctrines of divine foreknowledge and human freedom.It turned out that tense-logic gave rise to a powerful tool for dealing with this and similar problems.Furthermore, important highlights in Prior's tense-logic were the development of branching time and the introduction of instant propositions (leading to what has later been called 'hybrid logic').Since Prior's death, many further developments of formal tenselogic and its semantics have been presented and carefully investigated.In philosophical logic, many researchers have focused on discussions regarding 'the true future' and the notion of 'the thin red line'.
Controlling and Monitoring of Temperature and Humidity of Oyster Mushrooms in Tropical Climates

Controlling the temperature and humidity of oyster mushroom cultivation is done manually by spraying air on the mushroom container so it takes a lot of time and effort. This is done to meet the requirements for growing oyster mushrooms which are strongly influenced by temperature and humidity conditions so that they can grow well. In this study, a device for controlling and monitoring the temperature and humidity of oyster mushroom cultivation was made automatically based on Arduino UNO. This tool can regulate and monitor the temperature and humidity in oyster mushroom cultivation automatically so that the temperature and humidity can be maintained without having to spend a lot of time and effort. The components used in building the automatic temperature and humidity controller for mushroom cultivation based on the Arduino UNO are the dht11 sensors, Arduino UNO, L298N driver, relay, and 16x2 I2C LCD. From the results of the tests that have been carried out, it can be concluded that the temperature and humidity control and monitoring device for automatic oyster mushroom cultivation based on Arduino UNO has been able to work well in regulating and monitoring temperature and humidity as expected.
SVEAuAdIR model of COVID-19 Transmission

The COVID-19 pandemic that has occurred has received worldwide attention due to the rapid rate of transmission of the outbreak and the large number of deaths that occurred. The aim of this study is to build the SVEAuAdIR model , determine the transmission of COVID-19 in Indonesia by forecast the spread of the disease, and determine the effect of vaccination by looking at the basic reproduction number of SVEAuAdIR model. The results obtained from MAPE on the model are 12%. So it can be said that the SVEAuAdIR model is good for prediction models for the spread of COVID-19. The situation where there are no more individuals infected with COVID-19 is called COVID-19 disease free, thus it is predicted that Indonesia will be free of COVID-19 on October 7, 2021. The target of the Indonesian Ministry of Health is that by the end of 2021 the spread of COVID-19 can be stopped . However, on October 7, 2021, judging from the actual data during this research, there were still new cases of COVID-19. On that day there were 1393 new cases infected with COVID-19. Thus, showing that Indonesia's target of being free of COVID-19 disease by the end of 2021 has not been achieved. The number of the SVEAuAdIR model is in the range of values , which means that the spread of disease is close to disease-free. Based on the results of the value of the SVEAuAdIR model, this study concluded that vaccination could reduce the spread of COVID-19 compared to those who did not vaccinate
PERANCANGAN STANDAR OPERASIONAL PROSEDUR LABORATORIUM KOMPUTER DELL POLITEKNIK INDUSTRI FURNITUR DAN PENGOLAHAN KAYU

Dell Computer Laboratory is a laboratory under the auspices of the Furniture Design study program, Polytechnic of Furniture Industry and Wood Processing located in Kendal, Central Java. This laboratory supports the Furniture Design practicum course. There are currently no guidelines for laboratory use. This study aims to compile a guide in the form of Standard Operating Procedures (SOP) in the use of a computer laboratory for the Furniture Design study program at the Polytechnic of Furniture Industry and Wood Processing. The results of this study are in the form of SOPs related to the main activities of the laboratory, namely student practicum SOPs, SOPs for use for final projects, research, and community service, SOPs for laboratory facility maintenance, SOPs for software system maintenance, and SOPs for software reinstallation. SOP testing is carried out with a Focus Group Discussion (FGD) with the top management to ensure the correctness of the information contained in the SOP and through simulations to users in the laboratory. &#x0D; Keywords: Computer Laboratory, Management, SOP
APPROACHES TO BUILD AND DEVELOP DATA WAREHOUSES ACCORDING TO THE NOSQL MODEL

Traditional data warehouses support atomic, consistent, isolated, and durable (abbreviated, ACID) transactions. The ACID property of transactions guarantees the correction of concurrent access to a data warehouse. However, as the CAP1theorem explains, this property is always costly in terms of performance. For this reason, traditional data warehousesshow their limits when used in a very demanding context, such as the Cloud. The work presented in this article aims to propose approaches to build and develop data warehouses according to the column-oriented NoSQL model.
5. Sentiment Analysis in Twitter: Felix Meimberg's Twitterature (2011)

Open Access5. Sentiment Analysis in Twitter: Felix Meimberg's Twitterature (2011)https://doi.org/10.14220/9783737015707.99SectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInRedditEmail About Previous chapter Next chapter FiguresReferencesRelatedDetails Download book coverInterfacing Science, Literature, and the Humanities / ACUME 2Volume 15 1st editionISBN: 978-3-8471-1570-0 eISBN: 978-3-7370-1570-7HistoryPublished online:April 2023 Information© 2023 V&R unipress, Theaterstraße 13, D-37073 Göttingen, ein Imprint der Brill-GruppeUnless otherwise stated, this publication is licensed under the Creative Commons License Attribution-Non Commercial-No Derivatives 4.0 (see https://creativecommons.org/licenses/by-nc-nd/4.0/) and can be accessed under DOI 10.14220/9783737015707. Any use in cases other than those permitted by this license requires the prior written permission from the publisher.PDF download
Developing a Class Scheduling Mobile Application for Private Campus in Tangerang with the Extreme Programming (XP) Model

Class schedule is one of the important essences in lecture activities. The class scheduling process at STMIK Dharma Putra, a private campus in Tangerang, has several obstacles. The obstacle is class schedules that still need to be distributed to students and lecturers via short messaging applications such as WhatsApp (WhatsApp Group Chat) and email services, resulting in duplicate schedule files. Another obstacle was when revisions to class schedules and re-submissions caused the class schedule files to accumulate (data redundancy). Moreover, the campus reminder activities are carried out conventionally by sending paper photos of the daily lecture schedule via the WhatsApp group. Based on literature studies conducted in previous research, one way to disseminate information that is quite effective and successful in dealing with similar problems is through mobile-based applications. A mobile-based class scheduling application is developed using Flutter tools with an Extreme Programming model to overcome the problem. The final stage of this research is application testing using the User Acceptance Test (UAT) method with the Black-box testing model. From the results of the UAT test, the results obtained were 92.82% with excellent indicators and following user needs.