Reviewerly/exHarmony huggingface dataset:
Exploring The Forecasting Inflation in Kota Palu: An Application of the ARIMA Model

Inflation is an economic phenomenon that significantly impacts financial stability and the welfare of society. The movement of inflation in Kota Palu is challenging to explain using economic theories, as there are factors that are difficult to incorporate into models, such as the COVID-19 pandemic throughout 2020 to 2022, which impacted transportation, housing, and other services components. However, accurately predicting inflation rates remains a complex challenge. The objective of this study is to forecast the inflation movement for the year 2023. The data used in this analysis is the Consumer Price Index (CPI) of Kota Palu from January 2017 to March 2023, with 74 observations. ARIMA model with an Autoregressive (AR) model with lags six and nine was employed. The results of this study show that for the forecasted CPI or inflation from April 2023 to December 2023, the predicted CPI and inflation values closely approximate the actual CPI and inflation values. Therefore, the analysis can be considered accurate in predicting inflation movements.
Scalable Automated Design and Development of Deep Neural Networks for Scientific and Engineering Applications

Deep learning is becoming crucial for tackling the increasing modeling complexity of scientific and engineering applications. However, designing high-performing deep neural network (DNN) models can be a challenging and time-consuming task that requires expertise. To address this challenge, we have developed DeepHyper [1], a software package that automates the design and development of DNN models for scientific and engineering applications through scalable neural architecture and hyperparameter search. Our approach emphasizes deep learning over parallel and distributed infrastructures, enabling us to efficiently design and train DNNs for a wide range of scientific applications. In this talk, we will present our recent work on using DeepHyper to automatically generate an ensemble of DNNs at scale and using them to estimate data (aleatoric) and model (epistemic) uncertainties. Our approach enables us to leverage the power of parallel and distributed infrastructures to scale the training of DNNs and improve their performance, while reducing the time and expertise required for manual architecture design and hyperparameter tuning.
Management of Blended Learning and Digital Skills and Their Influence on the Learning Independence of Vocational Students

Changes in the implementation of learning due to the Covid 19 pandemic impact learning methods, so teachers and students must be able to adapt to online learning policies. This study analyzed the effect of blended learning management and digital skills on student learning independence. This type of research is ex post facto research, namely comparative causal research. The population in this study were all vocational school teachers, with a total sample of 103 respondents. Data collection was carried out by distributing instruments in the form of questionnaires. Data analysis techniques are correlation and regression. The results of the data analysis show that the model, namely the teacher's blended learning management and digital skills, can explain variations in student learning independence. The R square value of 0.589 indicates that the model, namely X1 and X2, can explain the variation in Y of 58.9%. Based on these results, teachers must implement blended learning management optimally and be supported by developing students' digital skills to increase learning independence. The implications of this research can be used as input, reference, and discourse on students' independent learning about blended learning management and digital skills.
Profile of Calistung (Reading, Writing and Arithmetic Skills) in Early Childhood

The importance of reading, writing and arithmetic for early childhood has become a major concern for educators and parents. The calistung abilities (reading, writing and arithmetic) that children have from an early age will make it easier for them to take further education. The purpose of this study was to describe the ability to read, write, and arithmetic in early childhood. This research is a qualitative descriptive study that describes the ability to read, write and arithmetic in Keleton Senye Kindergarten, Central Aceh Regency . This research was conducted on 4 early childhood students, consisting of two boys and two girls. Data collection techniques in this study used a task sheet and semi-structured interviews. The data analysis technique used the Miles and Huberman technique; data reduction, data presentation and conclusions. The results showed that of the 4 children studied, AF subjects have not met the indicators of reading and arithmetic skills, NF subjects have not met the indicators of reading and arithmetic skills, NR subjects have not met the indicators of reading and writing skills and AM subjects have not met the indicators of reading skills. In principle, learning in early childhood is learning carried out by children through games that are given in stages that are fun and interesting for children.
METHODOLOGICAL IMPLEMENTATION TOOLS PRACTICE-ORIENTED APPROACH IN TEACHING PHYSICS

ПЕДАГОГИЧЕСКИЕ НАУКИ (5.8.1, 5.8.2, 5.8.3, 5.8.7) УДК 378.1
Ensemble Deep Learning (EDL) for Cyber-bullying on Social Media

Cyber-bullying is a growing problem in the digital age, affecting millions of people worldwide.Deep learning algorithms have the potential to assist in identifying and combating Cyber-bullying by detecting and classifying harmful messages.This paper uses two Ensemble deep learning (EDL) models to detect Cyber-bullying on text data, images and videos-and an overview of Cyber-bullying and its harmful effects on individuals and society.The advantages of using deep learning algorithms in the fight against Cyber-bullying include their ability to process large amounts of data and learn and adapt to new patterns of Cyber-bullying behaviour.For text data, firstly, a pre-trained model BERT (Bidirectional Encoder Representations from Transformers) is used to train on cyberbullying text data.The next step describes the data preprocessing and feature extraction techniques required to prepare data for deep learning algorithms.We also discuss the different types of deep learning algorithms that can be used for Cyberbullying detection, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep belief networks (DBNs).This paper combines the sentiment analysis model, such as Aspect-based Sentiment Analysis (ABSA), for classifying bullying messages.Deep Neural network (DNN) used the classification of Cyber-bullying images and videos.Experiments were conducted on three datasets such as Twitter (Kaggle), Images (Online), and Videos (Online).Datasets are collected from various online sources.The results demonstrate the effectiveness of EDL and DNN in detecting Cyber-bullying in terms of detecting bullying data from relevant datasets.The EDL and DNN obtained an accuracy of 0.987, precision of 0.976, F1score of 0.975, and recall of 0.971 for the Twitter dataset.The performance of Ensemble CNN brought an accuracy of 0.887, precision of 0.88, F1-score of 0.88, and recall of 0.887 for the Image dataset.For the video dataset, the performance of Ensemble CNN is an accuracy of 0.807, precision of 0.81, F1score of 0.82, and recall of 0.81.Future research should focus on developing more accurate and efficient deep learning algorithms for Cyber-bullying detection and investigating the ethical implications of using such algorithms in practice.
Performance Evaluation of Face Mask Detection for Real-Time Implementation on an Rpi

Mask-wearing remains to be one of the primary protective measures against COVID-19.To address the difficulty of manual compliance monitoring, face mask detection models considerate of both frontal and angled faces were developed.This study aimed to test the performance of the said models in classifying multi-face images and upon running on a Raspberry Pi device.The accuracies and inference speeds were measured and compared when inferencing images with one, two, and three faces and on the desktop and the Raspberry Pi.With an increasing number of faces in an image, the models' accuracies were observed to decline, while their speeds were not significantly affected.Moreover, the YOLOv5 Small model was regarded to be potentially the best model for use on lower resource platforms, as it experienced a 3.33% increase in accuracy and recorded the least inference time of two seconds per image among the models.
O USO DA INTELIGÊNCIA EMOCIONAL NOS PROCESSOS DE GESTÃO E ASSISTÊNCIA EM ENFERMAGEM

This article addresses women's health, taking into account that it is the result of biological, social, economic, cultural and historical factors.In this way, we seek to contribute to the debate about women's access to health.The research subject is the social worker who works in the health area, in this sense, he seeks to know the strategies used by professionals to promote women's health.The research will include a theoretical framework based on a critical perspective in order to seek to understand the object, within the social reality.The general objective of the research was to analyze the importance of the role of the social worker in promoting women's health, and the specific objectives, to address public policies aimed at women's health, the socioeconomic profile of SUS users, and to learn about the programs and projects developed by the Social Service aimed at women's health.It is a bibliographic, documentary, descriptive, qualitative research.The survey results demonstrate the importance of the professional's work in actions and public policies aimed at women's health.Therefore, the professional who works in women's health care aims to develop the autonomy of users, working to strengthen and guarantee social rights, and encourage the promotion of self-care.
CONTRIBUIÇÕES DA MONITORIA ACADÊMICA NO PROCESSO DE ENSINO-APRENDIZAGEM DA ÁREA DA SAÚDE NO PERÍODO DA PANDEMIA PELA COVID-19: UM RELATO DE EXPERIÊNCIA

This article aims to discuss some elements such as culture, communication practices and daily life, in the context of the school, since we understand that these and their various nuances are part of the school organization.For that, we used interlocutors such as Barroso (2012), Chervel (1998), Tragtenberg (2012) and Santos (2006) to talk about school culture.Christino (2012), Habermas (2012), Cruz (2021) to address communication practices.Certeau (2014), Giard (2013), Certeau, Giard, Mayol (2013), when we approach everyday life, interlocutors who support the discussion presented.We conclude that there is no way to think about school without the human and social dimensions.There are also multiple forms and angles of analysis to understand how culture and educational and everyday communication practices are (re)elaborated and configure each educational organizational space.
Tahukah Anda? Komik Biologi Berbasis Weblog dapat Meningkatkan Motivasi Belajar Siswa SMA

The research is the development of research or Research and Development (R &amp; D). Development models used in this research is the development model of ASSURE development model. This model consists of 6 stages of activities, namely: 1) learner characteristic analysis, 2) state objectives, 3) select, modify or design materials, 4 ) utilize technology, media, and materials, 5) require learner response, and 6) evaluate. The purpose of this research is to produce a valid, practical and effective weblog based learning media for biology comics and increase the motivation of high school students to learn. The assessment instruments consist of three, namely: a) Evaluation of the validity of weblog-based comics biology learning media, b) Practicality in the form of teacher response instruments and student responses to weblog-based biology comics learning media and c) Effectiveness in the form of student response instruments to learning motivation. Based on data analysis, the average validity of the media obtained by three validators is 4.25 (valid). The practicality of weblog-based biology comics learning media obtained teacher responses of 4.4 (high) and student responses of 4.2 (high). The effectiveness value of high school students' learning motivation using weblog-based biology comics as measured by normalized n-gain has increased by 0.61 (medium category). Based on the results of the study it can be concluded that the biology-based learning media for weblogs that are developed are valid, practical and effective. Weblog-based comic media is expected to be one of the media used by teachers, students and schools in the world of learning biology
SISTEM INFORMASI KEARSIPAN PADA NOTARIS &amp; PPAT SULUH SEDYANTOMO, SH DENGAN METODE ALFRED

In the current era of globalization, science and technology are developing so rapidly, especially information technology. The human need for everything is demanded to be more efficient. For example, jobs that initially still used a lot of manual systems have now begun to switch to computerized systems. With a computerized process, any work will be easier to do. Every company, regardless of the type of business, definitely needs computer assistance. One of them is the type of notary service business.In the city of Semarang there are many notary service offices. Notary &amp; PPAT SULUH SEDYANTOMO, SH is a PPAT notary having his address at Jalan Erlangga Barat VI No.13 RT 007/RW 004, Pleburan Village, South Semarang District, Semarang City which was established in 2002 until now. The duties and responsibilities of employees at the Notary &amp; PPAT office of Suluh Sedyantomo, SH, include customer data, deed data collection, and finances, so that the admin and accounting departments are quite overwhelmed to handle it. With that many tasks, various problems arise, namely: 1) recording notarial data is less effective and efficient because it is still written by hand, 2) data collection on the cost of making the deed still uses a ledger so it is difficult to know the amount of costs up to date and there are often errors in calculations.For this reason, in order to overcome the existing problems, the author builds a document archive information system using the ALFRED method. This system is designed so that the registration process, dealing with relationships, and applying for a deed can be carried out swiftly without having to register them manually. The system developed is expected to serve faster deed making, namely with an automatic, efficient, and effective system.
Credit Card Fraud Detection Using Autoencoder &amp; Decoder ML

Abstract: In recent years credit card fraud has become one of the growing problems. It is vital that credit card companies can identify fraudulent credit card transactions, so that customers are not charged for the items that they did not purchase. The reputation of companies will heavily damage and endangered among the customers due to fraud in financial transactions The fraud detection techniques were increasing to improve accuracy to identify the fraudulent transactions. This project intends to build an unsupervised fraud detection method using autoencoder. An Autoencoder with four hidden layers which has been trained and tested with a dataset containing a European cardholder transaction that occurred in two days with 284,807 transactions from September 2013
The influence of cyber security on financial fraud in the Nigerian banking industry

The study looked at Financial Institution in Nigeria for how cyber security affects financial fraud in Nigerian banks. The study focused on the following topics: the types of electronic frauds committed in the banking industry, the reasons why cyber fraud occurs in banks, the difficulties in preventing cyber fraud in banks, the impact of cyber fraud on Nigerian banks, and potential solutions for preventing cyber fraud in banks. The two hypotheses that the study is based on. Six Nigeria Banks made up the study's population, which used a survey research design. 557 bank employees were sampled using multistage sampling techniques. The bank staff's responses were gathered using a structured questionnaire, which was then, analyzed using the SPSS statistical package for social science version 27. Descriptive analysis was used to analyze the objectives, and multiple regression and Kendal Tau B were used to test the hypotheses. The study's findings indicated that computer viruses, hacking/cracking, phishing, pharming, and accounting fraud by bank employees were the main types of cyber fraud in the banking system. The causes of cybercrime in banks included a lack of line manager or senior manager oversight on deviations from existing electronic processes or controls, current business pressure to meet set targets, collusion between employees and outside parties, insufficient data encryption, the use of third-party services, and parodying. Similarly, the study's findings showed that the challenges impeding the effort to stop cyber fraud in Nigeria's banking system included a lack of standards and national central control, a lack of infrastructure, the internet's vulnerability, a lack of national functional databases, and inadequate awareness by bank customers. A security audit, antivirus and antimalware software, the use of multi-factor authentication, the use of biometrics, and automatic logout were found to be solutions to cybercrime in banks. The findings also showed that financial loss, decreased productivity, and vulnerability of banks' Information and Communication Technology (ICT) systems and networks were effects of cybercrime on banks in Nigeria. According to the study's findings, banks' cyberspace security cannot be overextended in Nigeria, so necessary precautions should be taken to reduce cybercrime in Nigeria Banks. The study recommended that Nigeria Banks implement stringent measures to monitor staff activities, particularly in the confidentiality of customer information, cyber security audit be performed by financial institution regularly, sensitization of bank customers, and Multi-factor authentication, biometrics and automatic log out, and strong firewall should be adopted by Nigeria Financial Institutions.
A Study of the Effects of Learning and Practicing Motor Skills on Cognitive Abilities and Psychological Well-Being among Older Adults with Mild Cognitive Impairment

Background: Older adults' mild cognitive impairment (MCI) can affect their daily activities.Therefore, researchers have developed a variety of rehabilitation strategies for this impairment.Aim: This study examines the impact of learning and practicing motor skills on the cognitive functioning and psychological well-being of patients suspected of being affected by MCI. Method and results:For this purpose, 32 patients aged 55-75 year-old men were selected among people suspected of having MCI syndrome by interviewing and psychological evaluation by a psychologist and randomly divided into two experimental and control groups.Nejati cognitive ability tests (2013) (which evaluates seven cognitive abilities; memory, inhibitory control and selective attention, decision making, planning, sustained attention, social cognition, and cognitive flexibility) and the Oxford Happiness Questionnaire (1989) (which evaluates five psychological well-being factors; life satisfaction, happiness, self-esteem, self-control, and selfefficacy) was implemented on the groups.Three months and three weekly sessions were organized for the experimental group, in which movement skills (adapted from authentic martial arts) were taught and practiced.The multivariate covariance analysis (MANCOVA) statistical method analyzed the pre-and post-test data.The results revealed a substantial rise in the average scores of five categories of cognitive skills (memory, decision-making, inhibitory control, sustained attention, cognitive flexibility) and all five psychological well-being scales in the experimental group. Conclusion:The results showed that learning and practicing movement skills impacted brain functioning, improving several cognitive abilities and aspects of psychological wellbeing in older adults who were believed to suffer from MCI syndrome.Furthermore, from a theoretical standpoint, these findings are consistent with the results of neuroscience and specific theories that see cognition as a type of movementdependent brain functioning.
DyTSCL: Dynamic graph representation via tempo-structural contrastive learning

With the massive growth of graph-structured data, extensive research has focused on graph representation learning. Recently, graph representation learning frameworks have made great efforts toward dynamic graph learning. Although dynamic graph methods have achieved impressive results, they require labeled data for model training. The contrastive learning does not require human annotation to complete model training and has been shown to be extremely competitive in visual representation learning and natural language processing. In this paper, we propose a novel Dynamic graph representation framework via Tempo-Structural Contrastive Learning, DyTSCL, which trains the model by identifying three different subgraphs as a task, named Tempo-Structural subgraph, Non-Temporal subgraph and Non-Structural subgraph. Moreover, we propose a Tempo-Structural encoder, which aggregates the temporal and structural information. Finally, a Tempo-Structural contrastive learning module is proposed to maximize the consistency between node and subgraph in temporal and structural perspectives, respectively. To demonstrate the effectiveness of DyTSCL, we validate DyTSCL by applying it on the Wikipedia, Reddit and Mooc datasets, which show that DyTSCL can significantly outperform the existing approaches.
The Effectiveness of Flash Card Media in Improving Students' English Vocabulary at SMP Negeri 2 Galesong Utara

The researcher aims to determine the effectiveness of using flashcards in improving students' English vocabulary by conducting pre-experimental research and making 7th grade students at SMP Negeri 2 Galesong Utara as the research location and taking samples with a total population of 214 7th grade students while the sample used was 25 students. The process of data collection using pre-test and post-test used multiple choice by analyzing data using SPSS. The results of this study found that the use of flashcard media in the learning process to improve students' English vocabulary that considered effective based on the data obtained where the mean score in the pre-test 44,28 and the post-test 71,60. It can be seen that the mean score of the post-test is higher than the mean score of the pre-test. And it is proven again from the results of the analysis of the "paired sample test" using SPSS with a significance value of 0.000 &lt;0.05. Based on the decision making is if the significance value is &lt; 0.05, then H0 is rejected and H1 is accepted. Based on these results, it can be concluded that flashcard media is effective in improving students' English vocabulary.
Teachers' Challenges in Implementing Vocabulary Games in English Class for the Tenth Grade of

This research aims to determine the challenges faced by teachers in implementing vocabulary games in the tenth grade at SMAN 7 Selayar. This study uses a qualitative descriptive method conducted with an English teacher at SMAN 7 Selayar to find out the challenges faced by teachers in implementing vocabulary games in classroom learning. This study uses checklist observations and interviews as instruments in data collection.The results of this research indicate that there are two factors that become a challenge for teachers in learning English in vocabulary games applied at SMAN 7 Selayar, namely internal and external factors, in which internal factors come from the teacher himself while external factors come from students. The teacher's internal challenges are the limited vocabulary of students and lazy students, while the external challenges of teachers are creative learning/teachers, and also limited learning time when using games.