Reviewerly/exHarmony huggingface dataset:
Impact of Information Technology Training on Staff Engagement and Productivity: A Study on Some Selected Commercial Banks in Chattogram City

This study aims to examine the effect of Information Technology (IT) training on the staff engagement and productivity of bank employees in Chattogram, Bangladesh.Extensive literature related to technological advancement, training, and employee performance were reviewed from the database and website to develop a conceptual model to check the effect.The relationship between the performance of the employee and IT training was measured by collecting and analyzing primary data.They have been processed using SPSS.Hypotheses are tested to determine the impacts.A total of 130 questionnaires were delivered to various banks where 100 of them were completed and returned.It is revealed from this study that IT Training does have a major impact on staff engagement and productivity after thoroughly evaluating the data.Furthermore, there is a considerable association between IT Training and employee effectiveness.According to the findings of this study, IT adoption through training has resulted in employee skill gains as well as a pleasant, supportive work environment, which motivates employees to support the vision and goal of their organizations.
Embedded System for Wheelchair Using IoT

Those who have permanent disabilities due to accidents, paralysis, or old age are frequently dependent on others assistance when it comes to movement.This Embedded system is created to assist individuals, giving them access to remote health services via a health monitoring system and increases their independence because their health is regularly recorded and monitored by the sensors without any effort.Because disabled patients cannot afford to travel, smart healthcare systems assist them in gaining access to healthcare.A feasible solution to monitor the patient's health is by developing a health monitoring system with additional features since it is adequate for a wider range of audiences and it doesn't require tons of maintenance unlike the wearable systems.This project aims to develop a smart sensing embedded system for wheelchair by integrating sensors into its structure and developing an app that provides data visualization of all the monitored data along with automatic alerts when an anomaly is detected.With Internet of Things, sensors detect heart rate and sp02 levels, and embedded systems process them before sending them to the cloud that kicks off a trigger in case of any abnormalities.Depending on the user's preference, the trigger can be sent via SMS or e-mail.
Creator Of the Universe Adjusted the Dark Energy/Dark Matter Ratio and Baryons to Be Function In Π And Eπ: Predicted and Deciphered by Sayeed’s Theorem

The fine tuning of the universe was adjusted by Allah – the Creator. This article deciphered the dark energy (DE), dark matter (DM) and baryonic matter (BM) pattern ambiguity as a one of the physics mysteries. In this article and for the first time, the ratio of the dark energy/dark matter and baryons are predicted by Sayed`s theorem to be function in π and eπ. The previous published measurements and estimations have been showing different values and variations. It is predicted that the ideal and optimum (DE/DM) ratio is equal to the constant π; called Sayed El-mongy (DE/DM) ratio; symbolized as (sAM). While, the ratio (DE/DM+BM) is to the constant e. The value of (DE/DM) multiplied by (DE/DM+BM) was found to be eπ. The typical predicted values of dark energy, dark matter and baryons are 71.314093%, 22.7% and 3.538% respectively. The remainder 2.450907% could be due to undiscovered and any other form of matter (OM) including neutrinos and antimatter. The comparison of the predicted values with the world range and published data was also carried out and given with the % discrepancy. It was observed that the closest values to the predicted (DE/DM) Sayed ratio (π) are with the difference of 0.35508198% for SWIN and 1.02083609% for WAMP respectively.
Penerapan Algoritma K-Means Dalam Pengelompokan Kepadatan Penduduk Menurut Kecamatan di Kabupaten Simalungun

One of the problems related to population that still has to be faced by Simalungun is the problem of the imbalance in the distribution of the population. Incomplete division of the population brings problems to population density and population pressure in an area. This study uses data sources from the Central Statistics Agency (BPS) Simalungun. The data used in this study is data from 2015-2019 which consists of 32 Districts. Therefore, the researchers used the K-Means algorithm in clustering 32 sub-districts in Simalungun Regency. The data will be processed by clustering in 3 clusters, namely clusters with high population levels, clusters with moderate population levels and clusters with low population levels. The iteration process takes 5 times so that the results obtained are 4 sub-districts with high population level clusters (C1), 11 cluster sub-districts with moderate population level (C2) and 17 cluster sub-districts with low population level (C3)
Implementation of Android Application In React-Native: Daily Dairy

Abstract: This paper describes the whole process of android application development using the react-native framework and firebase of google and developed the application “DailyDairy''. The Application is developed for medium-sized and digitally illiterate dairy farmers. The software is divided into milk distribution management and payment transactions, statistics and analysis can be a variety of technical data to analyze, synthesize, organize, and the User Interface is practical, economic, and user-friendly. This application also solves the differences between Vendor and his Customers. This paper also describes the integration of firebase firestore (database provided by google) with android application development with javascript framework react-native and code editor used in is visual studio code by Microsoft. Keywords: Dairy Management, Android application, Vendor, Customer, Milk, Dairy Farmer, Digital Literacy
Development of students’ scientific knowledge on computer modeling while teaching inverse problems for differential equations

Problem statement. Currently, the higher school provides students of physical and mathematical training areas with fundamental subject knowledge, forms professional competencies, develops creative abilities and creativity, teaches them to use modern computer technologies to solve applied problems. One of such academic disciplines, in which students are taught to use computer technologies in solving applied mathematical problems, is a training course called Inverse problems for differential equations. Such an academic discipline has been taught in some Russian universities in the form of elective courses since the 70s of the 20th century. The educational material of this training course includes advanced research results on inverse problems belonging to specialists from different countries, such as Germany, Italy, China, Sweden, Netherlands, Russia, Japan and other foreign countries. During the practical classes, much attention is paid to the use of computer simulation for the study of such applied problems. This circumstance implies the development of educational materials taking into account the professional training of university students, in which attention should be paid to the use of computer modeling in solving mathematical models of inverse problems. Methodology. The implementation of training of university students in physical and mathematical areas of training involves taking into account modern scientific achievements of world science in the field of inverse problems using computer simulation and implementing advanced pedagogical technologies in the classroom. Results. Students acquire scientific knowledge of computer modeling and master the wide possibilities of computer modeling in the study of inverse problems. Conclusion. The presence of scientific knowledge in the field of computer simulation and practical experience of its application for solving inverse problems gives students great advantages and opportunities to be successful specialists in the field of applied mathematics and to be in demand on the labor market in various spheres of human activity.
A Data-Driven Real-Time Analytical Framework with Improved Granularity using Machine Learning and Big Data Analysis

During daily studying and working, people have to research massive amounts of information on the internet and download numerous files. Some of these files can be easily categorized to relevant files. However, there are always some files left unorganized due to their difficulty in categorization [1]. Such files pile up in the download folder as time passes, making the folder extremely messy. Many people do not have the motive to clean up the folder as it requires a lot of energy and time. Based on this common problem, my group developed an app that can clean the messy folder up. After applying our program which is based on machine learning, the files will firstly be divided into five general parts – document, video, music, photos and package. Then the files will be further categorized based on the contents they present. For example, photos are divided into animals, families and so on. In order to achieve the content-categorizing function, several powerful apis were introduced in our program, and they helped us to achieve the optimal results.
Penerapan Algoritma Apriori dalam Menentukan Pola Penjualan Produk Pertanian

Agricultural products are an important component in agriculture. Agricultural products cover various types of needs in agriculture such as fertilizers, pesticides, and plant seeds. Good layout arrangement of goods in a store can increase time efficiency in terms of service to consumers and increase consumer buying interest in the store. Transaction data on sales of agricultural products owned by UD. Muara Tani has not been used properly by shop owners. The a priori algorithm is one of the methods found in data mining that is often used to find the occurrence/frequency of interconnected data patterns. The a priori algorithm can be used to find patterns of association relationships that exist in sales patterns. Based on calculations using the a priori algorithm, 5 association rules were formed with amount minimum of support 30% and minimum confidence 80%. That is, if you buy UREA agricultural products, you may also buy KCL, ZA, SP-36, NPK and LANNATE agricultural products
Cybersecurity as illuminator for the future of computing research

Considering the shifting fundamentals of cybersecurity research.
A Deterministic and Probabilistic Prediction Method for Short- Term Photovoltaic Power Considering Spatial Correlation

With the application of distributed photovoltaics in recent years, spatial correlation is necessary to improve forecasting accuracy. Therefore, a deterministic and probabilistic forecasting method of short-term photovoltaic power considering spatial correlation is proposed in this paper. This method firstly analyzes the spatial correlation of multiple photovoltaic sequences of similar power stations and selects the reference photovoltaic sequence with a strong correlation. Then, an approach is proposed for selecting similarity days based on grey correlation degree theory. The selected similarity days are used as a training model, and Extreme Gradient Boosting (XGBoost) is utilized to construct a prediction model to obtain single-value forecasting results. Finally, by obtaining the corresponding error probability density function and error distribution interval, and adding up to the single-value forecasting result, the photovoltaic power probability prediction can be realized. The effectiveness of the proposed method is verified by the simulation.
Analisis Kesulitan Belajar pada Mata Pelajaran Matematika selama Pembelajaran Tatap Muka (PTM)

This study aims to describe the results of research regarding: (1) The process of learning mathematics during Face-to-face Learning (PTM); The difficulties faced by students in learning mathematics during Face-to-face Learning (PTM); (3) Solutions to overcome students’ mathematics learning difficulties during Face-to-face Learning (PTM). This study uses a descriptive qualitative research type by directly examining the field. The subjects of this research are teachers, and students of lass VI. Data collection techniques by observation, interviews, and documentation. Based on the data analysis that has been carried out, the results of the research show the following: first, the process of implementing PTM learning in mathematics subjects, it can be seen that the teacher when entering the classroom to start learning does not use any media except the teaching material book and learning methods explain on the blackboard so that it is not effective and makes students bored. Second, the learning difficulties faced by students during PTM in mathematics are difficulty understanding concepts, difficulty in counting, and difficulty solving problems. Third, efforts to overcome difficulties in learning mathematics during PTM are by using concrete learning media, increasing practice questions, and cooperating with parents.
CycleGAN and SRGAN to Enrich the Dataset

When developments in the field of computer science are growing rapidly. For example, the development of image or video predictions for various fields has been widely applied to assist further processes. The field of computer vision has created many ideas about processing using deep learning algorithms. Sometimes the problem with using deep learning or machine learning is in the availability of the dataset or the unavailability of the dataset. Various methods are used to add to or enrich the dataset. One way is to add an image dataset by creating a synthetic image. One of the well-known algorithms is Generative Adversarial Networks as an algorithm for generating synthetic images. Currently, there are many variations of the GAN to around 500 variants. This research is to utilize the Cycle GAN architecture in order to enrich the dataset. By doing GAN as a synthetic image generator. This is very important in procuring image datasets, for training and testing models of Deep Learning algorithms such as Convolutional Neural Networks. In addition, the use of synthetic images produces a deep learning model to avoid overfitting. One of the causes of the overfitting problem is the lack of datasets. There are many ways to add image datasets, by cropping, continuously rotating 90 degrees, 180 degrees. The reason for using Cycle Generative Adversarial Networks is because this method is not as complicated as other GANs, but also not as simple. Cycle GAN synthetic images are processed with Super Resolution GAN, which aims to clarify image quality. So that it produces a different image and good image quality.
Exploring Data Science Initiatives Through an International Lens

Application of data science tools, techniques, and principles have increased within the field of library and information science. This trend is especially noticeable in academic libraries where strategic priorities around data science initiatives have been created to further support and add value to the research enterprises at their institutions. This chapter seeks to highlight a global outlook on how data science has been addressed in library and information studies via case studies in areas including digital humanities, machine learning, and visual analytics. Increasing awareness of how partners across the globe are addressing data science needs at their institutions can help raise visibility of how data science can be infused and utilized within a variety of contexts.
Peningkatan Kompetensi Menerapkan Microsoft Office 365 Dalam Pembelajaran Abad 21 Dimasa Pandemi Melalui IHT

The purpose of this study was to find out whether the implementation of IHT which was carried out programmatically by researchers could improve the competence of teachers to apply Microsoft Office 365 in 21st century learning at SMP Negeri 3 Rembang in implementing online learning during the COVID-19 pandemic. The research subjects were all teachers of SMP Negeri 3 Rembang. The research time is for six months, starting from January to June 2021. Each cycle consists of 4 stages of activities, namely planning, implementing actions, observing and reflecting. In the implementation of IHT actions in each cycle, there was an increase in the average competence of teachers in implementing Microsoft Office 365 in 21st century learning during the pandemic, namely from 2.30 with sufficient category (C+) in the pre-cycle, increasing 0.59 points to 2.89 with good category (B) in the first cycle, and then increased again by 0.31 points to 3.20 with category B+ in the second cycle. Implementation of the IHT action in each cycle, there was an increase in the activity of researchers in conducting IHT for teachers of SMP Negeri 3 Rembang, from 2.74 with a good category (B-) in the first cycle, and then increased by 0.82 points to 3.56 with very good category (A-) in cycle II. In the implementation of the IHT action in each cycle, there was an increase in teacher activity at SMP Negeri 3 Rembang, from 66.67% in the active category or 2.67 in the good category (B-) in the first cycle, then increased by 25.92% to 92 .59% in the very active category or an increase of 1.03 points to 3.70 in the very good category (A-) in the second cycle.
Psychosocial Risk Factors Affecting the Nurses Working at An Educational Research Hospital in the Province of Istanbul

The main purpose of the present study is to prioritize the risk factors affecting the nurses working at a public educational research hospital located at the European Side of the province of Istanbul. By this means, it was intended to provide concrete information to health executives on the subjects of motivating the employees, affiliating them to the institution, and ensuring them in being more peaceful and happier at the workplace. In this study, psychosocial risk factors, affecting the nurses working at an educational research hospital affiliated to Ministry of Health, were prioritized by the Analytic Hierarchy Process (AHP) which is one of the Multi-Criteria Decision Making (MCDM) Techniques. In the research, data collection tool was designed as Saaty’s 1-9 scale conforming to the format of AHP. The collected data was analyzed by the researchers according to Analytic Hierarchy Process (AHP) with the help of Microsoft Office Excel 2013 analysis. The study was conducted in 2019 and 25 nurses in total participated. According to the results of the study, the most significant psychosocial risk factor affecting the nurses was found as Interpersonal Relations.
On the Computational Complexity of Model Reconciliations

Model-reconciliation explanation is a popular framework for generating explanations for planning problems. While the framework has been extended to multiple settings since its introduction for classical planning problems, there is little agreement on the computational complexity of generating minimal model reconciliation explanations in the basic setting. In this paper, we address this lacuna by introducing a decision-version of the model-reconciliation explanation generation problem and we show that it is Sigma-2-P Complete.
Modulating quantum evolution of moving-qubit by using classical driving

In this work, we study quantum evolution of an open moving-qubit modulated by a classical driving field. We obtain the density operator of qubit at zero temperature and analyze its quantum evolution dynamics by using quantum speed limit time (QSLT) and a non-Markovianity measure introduced recently. The results show that both the non-Markovian environment and the classical driving can speed up the evolution process, this quantum speedup process is induced by the non-Markovianity and the critical points only depend on the qubit velocity. Moreover, the qubit motion will delay the evolution process, but this negative effect of the qubit velocity on the quantum speedup can be suppressed by the classical driving. Finally, we give the corresponding physical explanation by using the decoherence rates.
Data Mining Menggunakan Metode Asosiasi Apriori untuk Merekomendasi Pola Obat Pada Puskesmas

Drugs are one of the most important components in terms of health, both to cure and reduce pain due to illness suffered by everyone, besides that the use of drugs also gives us information about what diseases everyone suffers so that the information is very helpful for health workers. For this reason, drugs need to be managed properly, effectively and efficiently. This study aims to analyze the a priori algorithm on drug output data at the Parsoburan Health Center Pematangsiantar to find out what types of drugs are most needed by patients at the same time. The data used is in the form of drug output data in April 2021. Based on the a priori algorithm calculations, 70 association rules were formed with a number minimum of support 90% and a minimum confidence of 90%. It is hoped that the results of the research can help the Parsoburan Health Center Pematangsiantar optimize quality health services for planning future drug needs and produce useful information for decision making.
NEEDS ANALYSIS OF ENGLISH FOR MIDWIFERY PURPOSES AT MIDWIFERY ACADEMY OF HARAPAN MULYA PONOROGO

This study aimed at describing the need analysis of English for Midwifery Purposes. The study was conducted at Midwifery Academy of Harapan Mulya Ponorogo in February 2018. The respondents were 34 students and 7 lecturers. Data collected through a questionnaire that was given to the students. The interview was conducted with 7 midwife lecturers. Data analyzed descriptively. The results of the study revealed that most students (61.8% believe that English was very useful. Almost half of the respondents (38,2%) said that English was useful to communicate with foreigner patients. 41.2 % of respondents wanted to study English in order to succeed in getting a job in the future. 23% of the respondents suggested that English teaching should be emphasized on speaking skill. 18.8% of respondents wanted the English instructional materials consisted of Antenatal care, 15.3 % about therapeutic communication. Meanwhile, Need analysis based on teachers’ perception in many things have similarity with students’ perception. They were the usefulness of English, Emphasizing English materials and teaching process. Researchers suggested to the lecturer to design English teaching material that suits the needs of midwifery students.
REQUEST STRATEGIES USED BY INTERNATIONAL STUDENTS IN ENGLISH AS A LINGUA FRANCA CONTEXT

This study explored the permeating strategies used by the international student in request acts and the common features appearing during the language use as international students in Indonesia, where English is used as a Lingua Franca. The study employed a set of Oral Discourse Completion Test (ODCT) to collect the data from eight international students. Ten ODCTs were composed based on the relative power, social distance, and imposition and were analysed based on the Cross-Cultural Study of Speech Act Realization Patterns (CCSARP). The result was presented in descriptive qualitative where the most frequently used strategies were conventionally indirect – suggestory formulae. Meanwhile, the content analysis indicated that address terms and lengthy reasonings were the most common features appearing in the request acts made. The results offer that interlanguage pragmatic in lingua franca context occurs smoothly as the international students can combine the local culture in their language use.