Reviewerly/exHarmony huggingface dataset:
EFEKTIVITAS MEDIA POWER POINT UNTUK MENINGKATKAN HASIL BELAJAR MEMBACA PEMAHAMAN SISWA KELAS III SD INPRES PARANG MAKASSAR

This study aims to (1) examine the learning outcomes of students' reading comprehension in class III SD Inpres Parang Makassar using power point media; (2) assessing the learning outcomes of students' reading comprehension in grade III SD Inpres Parang Makassar without using power point media; (3) examining the effectiveness of power point media to improve learning outcomes for reading comprehension of third grade students of SD Inpres Parang Makassar. This study used an experimental research design with a posttest only control design. The population of this research is the third grade students of SD Inpres Parang Makassar. The data that had been collected were analyzed using descriptive statistical techniques and parametric inferential statistics type T Test that were processed using the SPSS version 20 windows computer program. The results of hypothesis testing show that (1) the value of t-count shows the number -9.708 with sig. (2 tailed) = 0.000. At the 95% significance level with 46 degrees of freedom. Because the significance value or p-value &lt;0.005, then the null hypothesis (H0) is rejected or in other words the alternative hypothesis (H1) is accepted. Thus, it can be concluded that there is a significant difference between the pretest and posttest scores in the experimental class in the learning outcomes of students in class IIIB SD Inpres Parang Makassar. The use of power point media to improve students' reading comprehension learning outcomes of SD Inpres Parang Makassar is more effective using power point media. This can be seen from the t-test calculation of -9.708 with db = 46 at the 5% significance level. These results indicate that the price p = 0.000. The p value is less than 0.05.&#x0D;
Pengaruh Fasilitas, Motivasi dan Kedisiplinan Terhadap Prestasi Belajar MIPA di SMA Negeri 3 Klaten

The objectives to be achieved in this study are to find out, (1) To find out the influence of learning facilities at home on the learning achievement, learning motivation, learning discipline of Mathematics and Natural Sciences students of class XI MIPA of SMA Negeri 3 Klaten, Klaten Regency. The results showed that learning facilities at home had a positive and significant effect on the learning achievement, so that hypothesis 1 was accepted. The effective contribution of variable learning facilities at home to the learning achievement of Mathematics and Natural Sciences was 15.1% and the relative contribution was 23,%. The effective contribution of MIPA learning motivation to the MIPA learning achievement of students of Class XI MIPA in SMA Negeri 3 Klaten was 15.3% and the relative contribution was 23.7%. The effective contribution of learning discipline to the learning achievement of Mathematics and Natural Sciences students of class XI MIPA in SMA Negeri 3 Klaten was 34.2% and the relative contribution was 52.9%. MIPA learning motivation and joint learning discipline toward MIPA learning achievement 64.6% and the remaining 35.4% is influenced by other variables that are not included in this study.
Multi Categories Analytic Method Using Continuous Bernoulli Distribution and Conditional Distribution

This book provides four model designs to discuss how continuous Bernoulli distribution extends to the analysis of K categories. By contrast to the discrete polynomial distribution which is extended from Bernoulli distribution depending on the additive property, the random variable of continuous Bernoulli should be tested the pdf, cdf, distribution, and checked if maintain the characteristics of CB distribution or not. Model 1 is from random variable method(variable-added), Model 2 and 3 are from the probability model-building and suitable for the parameter-added or the conditional relationship of variables, respectively. Model 4 is from the continuous trinomial distribution and suitable for the joint relationship of variables.
A Retrospective Analysis of 118 Stroke Cases Admitted to Aseer Hospital in Abha: Classification and Associated Factors

Background: A stroke is a medical disorder in which inadequate flow of blood through the brain contributes to the death of cells.There are two kinds of common strokes, ischemic and hemorrhage.Hypertension, diabetes mellitus and cerebrovascular injury are the risk factors for strokes.This research aimed to classify the most common type of stroke as well as the common risk factors associated with it in one of Saudi Arabia's major hospitals, located in the city of Abha.Methods and materials: From 1/1/2018 to 1/10/2019, data was collected from 118 patients with stroke.Results: The most prevalent type of event was ischemic stroke (91%), with hemorrhagic stroke comprising the remainder (9%).In addition, hypertension was the most common risk factor for stroke.In addition, we found that stroke events associated with hypertension and diabetes in patients over the age of 70 were more likely to occur relative to younger patients, p>0.05.Further study showed no correlation between stroke type and patient age, p>0.05.We also found no connection between the type of stroke and the specific imaging examinations performed, p>0.05 and the type of examination was independent of the stroke stage.Conclusion: We conclude that ischemic stroke was the most common type in the Abha city region of the Kingdom of Saudi Arabia, particularly among aging populations, and the common risk factor associated with stroke was hypertension.
Software TLB Management Method Based on Balanced Binary Tree

Abstract Purpose: with the development of the computer system, there is a higher request to reduce the failure times of Translation Look—aside Buffer and relieve the failure influence, normal solution is deal with TLB failure by software or hardware, it find the page table and then implement the index operation to locate the page want. Method: In order to satisfy the needs of software management method for mapping speed from virtual address to physical address and to expand the size of TLB, our team design a software management method based on balanced binary search tree. Page management is implemented in software management, TLB is managed by operating system based on abstract model, and MMU (memory management unit) is no longer used Unit, our team build a balanced binary tree to search TLB. Result: Binary search tree has the advantage of pruning in the interval search problem under the balanced state. Therefore, searching TLB by this method is conducive to the expansion of TLB capacity, making space for cache and other performance improvement designs on chip and reducing costs. However, the search speed is reduced and some time is sacrificed to free up CPU space. Conclusion: This method can free up CPU design space, also can expand more size of TLB and reduce the cost, its optimization algorithm is worthy of further research.
Financial Innovation and Demand for Money in Nigeria

This study examined the impact of financial innovation on money demand in Nigeria, using quarterly time series for the period 2009-2019. The dependent variable was money demand, represented by broad money, while the independent variable was financial innovation represented by modern payment channels such as volume of Automated Teller Machines (ATMs) transactions, volume of Point of Sales (POS) transactions, volume of Internet banking transactions, and volume of Mobile banking transactions. The study employed the ordinary least squares (OLS) regression technique as the estimation method within the cointegration, granger causality, and error correction modeling. The result obtained showed that financial innovation has mixed impact on money demand in Nigeria during the period of analysis. For instance, financial innovation has positive impact on money demand through volume of ATM transactions in the current period, two periods lagged of volume of mobile banking transactions, current period and one period lagged of volume of internet banking transactions, and current period’s volume of Point of Sales (POS) transactions in Nigeria. On the other hand, financial innovation has negative impact on money demand through one period lagged of volume of point of sales in Nigeria. On the stability of the demand for money function, the result of the stability tests based on the CUSUM test and CUSUM of squares test showed that the demand for money function was stable during the evaluation period. The study recommended that monetary policy strategy of the central bank of Nigeria (CBN) should be fine-tuned to ensure it is well suited to deal with the challenges posed by financial innovation by way of proliferation of sophisticated payment channels.
Review: Gambaran Aktivitas Enzim Alkaline Phosphatase (ALP) pada Pemain Game Online di Kecamatan Jekan Raya Kota Palangka Raya

An online game is a game that is played using an internet network. Online games can cause addictions, which result in changes in sleep patterns that can disrupt circadian rhythms. The body produces more free radicals and causes oxidative stress, which can attack hepatocyte cells. To assess liver function abnormalities, one of them can be known by checking the enzyme Alkaline Phosphatase (ALP). This study aimed to determine the activity of the Alkaline Phosphatase (ALP) enzyme in Online game players in the city of Palangka Raya. This research was conducted with a descriptive method using the Snowball Sampling technique, and the sample in this study amounted to 50 people. From the results of the examination of the activity of the enzyme Alkaline Phosphatase (ALP), ALP values ​​were obtained above normal 10 people (20%).
The Application of Multidimensional Interactive Teaching Model in College English Teaching

A good language environment is an important bridge to help students quickly master a language, so in order to enable students to better complete the learning of knowledge, teachers can effectively guarantee the teaching through the introduction of multi-dimensional interactive classroom teaching methods when teaching college English Increased effectiveness. This article starts with the positive meaning of establishing a multi-dimensional interactive classroom of college English, and actively explores the use of it, hoping to create a high-quality English classroom while ensuring the better completion of teaching work.
The TEI as a modeling infrastructure: TEI beyond the TEI realms

Whereas the Text Encoding Initiative (TEI) has become the reference standard for encoding textual material of all kinds in the humanities, the power of the underlying TEI modelling infrastructure to deal with professional document management scenarios or even non-TEI based vocabularies could deserve more attention. The aim of my presentation will be to show concrete projects where I have contributed to use the ODD (One document does it all) specification language of the TEI to deal with such applications as the management of patent documents, the modelling of lexical resources or the integration of heterogeneous archival descriptions in the EAD (Encoded Archival Description) standard. Starting from an introduction of the TEI as a standard, I will try to conclude on its potential bright future as a real infrastructure for the humanities.
Artificial Intelligence-Based Textual Cyberbullying Detection for Twitter Data Analysis in Cloud-Based Internet of Things

Detection of cyberbullying that often occurs on social networks, for instance, twitter, is one of the targeted areas of research nowadays. Due to the easy accessibility and popularity of social media, cyberbullying has become a major challenge in electronic communication, while the behavior of cyberbullying has received increasing attention recently. A set of input data is sent to the data processing unit that is incorporated to improve the quality of input data. Data preprocessing also involves the removal of keywords and special characters. After processing the data, the output data is sent to feature extraction phase. This research work leveraged feature ranking method to select the options from tweets. The advanced deep learning model of oppositional grasshopper optimization with convolutional neural network (OGHOCNN) was used in the next step to detect lucky messages for cybercrime. Performance measurements, for instance, classification accuracy, were selected to assess the effectiveness of OGHOCNN classifiers. The model was implemented in MATLAB tool and the results exhibited promising outcomes.
Fundamentals Of Entreprenuerships ENT300 Business Plan : Online Clothing Store (Shibusa) / Nur Aiman Rafiqah Abd Rahman

Shibusa.Co are offering hoodies that focusing on customer comfort. Our hoodies will be come out in various colours. We also provide embroideries and patches to put at the hoodies as well we also offering the customers to custom made their design to the hoodies
Multi-Dimensional Rajan Transform

In this paper, we describe the formulation of a novel transform called Multi-Dimensional Rajan Transform, which is an extension of Rajan Transform. Basically, Rajan Transform operates on a number sequence, whose length is a power of two. It transforms any sequence of arbitrary numbers into a sequence of interrelated numbers. As regards 2D Rajan Transform, there are two methods to implement it: (i) Row- Column method and (ii) Column-Row method. The 2D Rajan Transform obtained using the first method need not be the same as that obtained using second method. Similarly, one can implement 3-D Rajan Transform using the following approaches: (i) Row-Column-Depth approach, (ii) Row-Depth- Column approach, (iii) Column-Row-Depth approach, (iv) Column-Depth-Row approach, (v) Depth-Row-Column approach and (vi) Depth-Column-Row approach. This paper explains these approaches to implement two and three dimensional Rajan Transforms.
On Interfacing the Brain with Quantum Computers: An Approach to Listen to the Logic of the Mind.

This chapter presents a quantum computing-based approach to study and harness neuronal correlates of mental activity for the development of Brain-Computer Interface (BCI) systems. It introduces the notion of a logic of the mind, where neurophysiological data are encoded as logical expressions representing mental activity. Effective logical expressions are likely to be extensive, involving dozens of variables. Large expressions require considerable computational power to be processed. This is problematic for BCI applications because they require fast reaction times to execute sequences of commands. Quantum computers hold much promise in terms of processing speed for some problems, including those involving logical expressions. Hence, we propose to use quantum computers to process the logic of the mind. The chapter begins with an introduction to BCI and the electroencephalogram, which is the neurophysiological signal that is normally used in BCI. Then, it briefly discusses how the EEG corresponds to mental states, followed by an introduction to the logic of the mind. After that, there is an overview of quantum computing, focusing on the basics deemed necessary to understand how it processes logical expressions. An example of a BCI system is presented. In a nutshell, the system reads the EEG and builds logical expressions, which are sent to a quantum computer to solve them. In turn, the system converts the results into sounds by means of a bespoke synthesiser. Essentially, the BCI here is a musical instrument controlled by the mind of the player. Our BCI is a proof-of-concept aimed at demonstrating how quantum computing may support the development of sophisticated BCI systems. The remaining of the chapter is devoted to technical and practical considerations on the limitations of current quantum computing hardware technology and scalability of the system.
Experience in electronic support of academic subjects at the Department of Russian history of the Belarusian state University

The article analyzes the experience of using electronic learning tools for academic disciplines at the Department of Russian history of BSU.
Identifying Local Health Issues Using Data Analytics and Data Mining Method from a Large-Combined Datasets of Supermarket and Health Centre

A large amount of the data was stored from people’s activities in buying products. The real-time analysis method was able to be used to provide information about health and wellbeing of people who particularly lived in a countries, cities, or suburb area of the world. This health and wellbeing information was submitted to the decision makers (e.g., policy makers, government officials, and health professionals) to change policies, created innovative solution, and improved the daily life of people. With current power of the data mining and the data analytic methods, it was currently possible to collect the data about human activities (e.g., the data of buying smartwatches, smartphones, and health consultation) so that the human activities and wellbeing of people were identified. This data analysis was able to improve the understandings of physical and mental wellbeing of people; to detect the unknown health issues and behaviours; and, to provide the solutions to support people. The result of this data analysis was able to provide the solutions for all purposes (e.g., current health programs, health policies in terms of sugar and oil selling in supermarket, and bicycle, footpaths, parks and recreational place designs), to improve better health facilities for neighbourhoods and cities, and to allocate funds to combat obesity. The available data about the supermarket product purchases, physical activities, health problems, and social interactions and communication provided the information of the profile of health people. The data of supermarket purchases, smartwatch purchases, and smartphone purchases were able to provide the information about the life and activities of people. The formulations of the problems were: how were the collected data able to be utilized to improve the life of people? Was it sufficient to analyse the data of food buying and online activities of people to make the decision making and improve the life of people? Were the collected data of the supermarket and online activities sufficient to be used for determining the profile of health people? Were the collected data from supermarket product purchases, smartwatches, and smartphones with or against you? This discussion gave the understandings of the data mining in identifying the health problems based on the collected data of human activities and product purchases. Keywords:       Data Analytics, Data Mining, Supermarket, Health Data
An Analysis of the Employee Behavior on Sales in L'Oreal: A Case Study of Pakistan

This research is carried out to investigate the most important traits of employee behavior. A quantitative approach was employed for this study. Data were collected from 120 participants using an online questionnaire. These participants are active users of Facebook who are residents of Pakistan and currently working on L'Oreal Pakistan, who are at a different position in L'Oreal Pakistan. Statistical analysis, such as binomial regression which is used for qualitative analysis used together with descriptive analysis, was used to 'assess factors affecting employee behavior in L'Oreal Pakistan. The findings of the research show that the independent variable is indicators of leadership and reward has changed the behavioral motivation of company employees and this motivational, behavioral change has a positive impact on sales of L'Oreal Pakistan. This research investigated the factors which industry can use as a tool to change their employee behavior and sales. This will help to gain increasing generous offers and valuable insights to marketing practitioners and academics.
Performance Evaluation of Next Generation Video Compression Approaches Using Objective Quality Measurement Methods

With the expansion of services based on television and video transmission over the Internet, the need for more effective compression of video has become important. Since all of the video compression approaches in use are based on lossy compression, some degree of quality loss is inevitable in the compression process. Instead of subjective methods for managing this quality loss, video quality should be measured automatically and objectively. The recently developed VP9 and AV1 approaches, which are currently in the expansion and experimental phase, offer better compression levels than the currently widely used H.264 (AVC) approach. On the other hand, the hardware requirements of these new approaches are higher than those of AVC. In this study, results of the laboratory tests comparing AVC, VP9 and AV1 approaches for different video types in terms of `compression ratio', `quality level' and `compression time' where quality loss is automatically determined based on VMAF, PSNR and SSIM values.
Application of Knowledge Graphs to Provide Side Information for Improved Recommendation Accuracy

Personalized recommendations are popular in these days of Internet driven activities, specifically shopping. Recommendation methods can be grouped into three major categories, content based filtering, collaborative filtering and machine learning enhanced. Information about products and preferences of different users are primarily used to infer preferences for a specific user. Inadequate information can obviously cause these methods to fail or perform poorly. The more information we provide to these methods, the more likely it is that the methods perform better. Knowledge graphs represent the current trend in recording information in the form of relations between entities, and can provide additional (side) information about products and users. Such information can be used to improve nearest neighbour search, clustering users and products, or train the neural network, when one is used. In this work, we present a new generic recommendation systems framework, that integrates knowledge graphs into the recommendation pipeline. We describe its software design and implementation, and then show through experiments, how such a framework can be specialized for a domain, say movie recommendations, and the improvements in recommendation results possible due to side information obtained from knowledge graphs representation of such information. Our framework supports different knowledge graph representation formats, and facilitates format conversion, merging and information extraction needed for training recommendation methods.
A Preliminary Intergenerational Photo Conversation Support System based on Fine-tuning VGG16 Model

China has the largest number of elderly people in the world, young volunteers have become the main force in caring for the elderly.It is urgent to establish a photo conversation support system to build a bridge of communication between young volunteers and the elderly.Previous research generally used perceptual analysis or machine learning methods to find photos suitable for intergenerational conversation, this paper uses deep learning models to further learn the potential features of two datasets suitable for and not suitable for intergenerational conversations.However, the original datasets are too small, it was first proposed to use TF-IDF in conversation recording and data augmentation technology in images to expand the datasets.Then on the basis of the VGG16 model combined with transfer learning and fine-tuning technologies, five models were designed.The accuracy of the best model on the validation set and test set reached 96% and 94.5%.In particular, the recall rate of the not suitable dataset reached 100%, all not suitable photos were identified.At the same time, the recall rate of other datasets reached 71% for not suitable photos.It shows that the system is also applicable to other datasets and can effectively eliminate photos that are not suitable for intergenerational conversations.