Reviewerly/exHarmony huggingface dataset:
ASSESSING THE TECHNOLOGICAL PROGRESS OF THE INTERNET OF THINGS (IOT) IN POST-PANDEMIC PERIOD

IoT-based smart products such as smartphones and smartwatches, smart homes and cars, smart TVs and entertainment devices, beside healthcare devices, have generated a great deal of interest among customers. The global threat COVID-19, with the recent and future opportunities in IoT have made the companies even more exited and eager to exploit the technology. Although, there are some challenges regarding the security issues, returns of IoT mostly outweigh the pitfalls. The objective of this study is to analyze the literature, sta-tistics and predictions for IoT in different industries and sectors, in post-pandemic era. Sev-eral topics involving use and implementation of IoT during and after pandemic, main IoT opportunities for companies, main challenges related to adoption and further development of IoT are examined and presented with figures. Keywords: Internet of Things (IoT), digital technology, smart devices, post-pandemic, in-dustrial 4.0
Simple, Fast, and Accurate Cybercrime Detection on E-Government with Elastic Stack SIEM

Increased public activity in cyberspace (Internet) during the Covid-19 pandemic has also increased cybercrime cases with various attack targets, including E-Government services. Cybercrime is hidden and occurs unnoticed in E-Government, so handling it is challenging for all government agencies. The characteristics of E-Government are unique and different from other service systems in general, requiring extra anticipation for the prevention and handling of cybercrime attack threats. This research proposes log and event data analysis to detect cybercrime in e-Government using System Information and Event Management (SIEM). The main contribution of this research is a simple, fast, and accurate cybercrime detection process in the e-Government environment by increasing the level of log and event data analysis with the SIEM approach. SIEM technology based on machine learning and big data is implemented with Elastic Stack. The implemented technique can be used as a mitigation program against cybercrime threats that often attack and target e-Government. With simple, accurate, and fast cybercrime detection, it is expected to improve e-Government security and increase public confidence in public services organized by government agencies.
APPLICATION OF DATA MINING TO PREDICT STOCK INVENTORY IN RAGIL MART SHOP USING MULTIPLE LINEAR REGRESSION METHODS

Along with the development of information technology, the need for fast, accurate and relevant information is increasing. The need for accurate information is needed in everyday life, so that information will be an important element in the development of society today and in the future. . Often this information still has to be extracted from data with a very large population. The Ragil Mart store provides goods for the basic needs of the community, namely goods that concern the lives of many people with a high scale of meeting needs and are a supporting factor for people's welfare. the concept of implementing data mining that is used to assist the store in determining stock availability which is expected to be able to provide the best results so that the risk of errors in providing stock is small. In the grouping process, a method is used, namely the method.
The Impact of Exposure on Second Language Acquisition

Exposure to English is something that learners experience in their daily lives. When they are doing their everyday activities, they will encounter English words. When they watch television, listen to a song, play games, or scroll through social media, they are exposed to English and this exposure can affect their second language acquisition. Many research articles have been published on the relationship between exposure and second language acquisition. However, there is no consensus on the effect exposure has on second language acquisition. By reviewing available research publications, this study sought to describe the impact of exposure on second language acquisition. The researcher employed a systematic literature review method to discover the link between all of the publications. The objective of this research is to determine the impact of exposure on second language acquisition. The publications reviewed demonstrated that exposure had a positive impact on second language acquisition. The majority of the publications reviewed stated that exposure helps with second language learning. Language exposure is necessary for learning and mastering a second language. Finally, the majority of the publications reviewed agree that exposure aids in the acquisition of a second language. Learners with extensive exposure to the language have a greater probability of speaking and comprehending it.
The Effect of Using Mind Mapping to Students’ Reading Comprehension

This research was intended to analyze the effect of mind mapping towards students reading comprehension at twelfth grade in MAN 2 Probolinggo. A lot of students still struggle with reading. To overcome students' challenges, teachers should have tactics when teaching reading comprehension. Using two groups pre- and post-test at an experiment and a control group, this study used the quasi-experimental methodology. The sample classes, which included 17 students in each, were twelfth social 2 and twelfth social 3. Technique for data analysis utilizing the Paired Sample T Test. The alternative hypothesis is rejected if the significance value, or Sig (2-tailed), is more than 0.05. The hypothesis is accepted if the significance value, or Sig (2-tailed), is less than 0.05. According to the findings, students who learnt by using mind map were able to develop at a higher level. If the results of hypothesis testing indicate a Sig value (2-tailed) of 0.000 &lt; 0.05, then the test decision-making methodology is appropriate. It can be inferred from paired Sample T Test that the mind map method affects students' capacity for reading comprehension
The Utilizing of Tongue Twister Media for English Speaking Activity of Management Students in School of Management

The ability of using English is important and is needed nowadays in many aspects. Everyone is supposed to be able to speak English for communication. The problem happens in non-English students who learn English for specific purpose. They do not feel confident to perform speaking due to they are very rare practicing English previously that causes some problems in pronunciation and mastery vocabulary. Therefore, this study is aimed to know the management students’ pronunciation of English words using tongue twister media and to probe the management students’ perception in speaking English activity. This research applies a qualitative approach with 24 Management major students from one class who mostly work and enroll online and offline English class. The instrument used are field note observation, questionnaires, and recorder. The findings show that most management students found speaking problems in pronouncing the sounds of /θ/, /ð/, /ʃ/ and /ai/. Although they were not confident in speaking English for the improper pronunciation, they were still trying to learn English well. They expressed that the utilize of tongue twister media in speaking activity is helpful, fun and challenging to pronounce and listen to the good pronunciations.
Investigating the Use of First Language in English Language Teaching at secondary School

Papuan Malay is the second language considered by Papuans after the regional language and not a few people use Papuan Malay in various aspects of life, one of which is in the field of education. At present, there are many phenomena of using Papuan Malay as the language used in mastering English. This study applied a qualitative descriptive method with six English teachers in secondary schools as research subjects. The data collected through classroom observation and depth interviews. The finding obtained that Papuan Malay is used in presenting instructions, providing motivation and explanations in teaching materials. The finding also revealed that the teacher used the first language to show anger, sadness, and happiness, its use is carried out with the aim of providing a sense of comfort when communicating and interacting properly between the teacher and students.
The Effect of Using Controlled Writing Technique and Clustering Technique on Students’ Writing Skills at Junior High School

This study deals with the effect of using controlled writing technique and clustering technique on students’ writing skill. This study used an experimental quantitative research that incriminating three classes were experimental 1 class, experimental 2 class and control class, in the experimental class the writer applied controlled writing technique and Clustering technique while in controlled class applied conventional technique. The populations of this study were the students’ class of VII of SMP St. Ignasius Medan. The researcher used 75 students as the sample. The sample of this research was taken by using random sampling technique, 25 from VII-1 and 25 VII-2 as experimental class and 25 from VII-3 as controlled class. To obtain the data the researcher used writing test, as the instrument for collecting the data. These test consisted of two types, namely pre-test and post-test. As the result of the ANOVA test, P= 0.000 &lt; 0.05 or F count = 23.40 &gt; F table = 2.28 significant affect. It means that hypothesis alternative (Ha) was accepted while the null hypothesis (H0) was rejected. The experimental class 1 and 2 scored higher than the control class, according to the results of the study. Controlled writing technique and clustering technique are both successful in boosting writing descriptive text, according to the result. The statistics also indicate that the clustering technique outperforms the controlled writing technique.
Computing the Cost Economics of Agricultural Machinery in Farms

Farm mechanization accelerates faster agricultural growth through the efficient use of machines by ensuring the timeliness of agricultural operations, reducing the cost of operation, and by reducing human drudgery. Farm machinery ownership and operating costs represent a substantial portion of total crop production expenditure. Cost economics becomes important for farm machinery managers to control the machinery costs per hectare. The manual procedure of determining the cost economics of agricultural machinery is a time-consuming process and finally making a smart decision whether to buy a farm machine or to go for custom hiring is a cumbersome process. The web application was designed for naive users to help the users in making the decision. The user is asked to enter the basic data pertaining to the farm machine, and the software decides whether to acquire the farm machine or to go for custom hiring. The cost of operating a farm machine can be computed with ease by using the web application. Further, the break-even-point, payback period and benefit-cost ratio can also be determined with a single click
SOCIAL AND PSYCHOLOGICAL TRAINING AS A METOD OF ACQUIREMENT OF SOCIAL COMPETENCES

This article defines the list and significance for individuals of social competences that can be acquired during social-psychological training. The main goal of the conducted research is to highlight the characteristic features and conceptual foundations of social and psychological training. The systematization of literary sources and approaches to solving the problem proved that in the process of conducting socio-psychological training, participants can acquire the following social competencies: respect for socially accepted systems of values and principles, decision-making abilities and willingness to take responsibility for their behavior and behavior of the team entrusted to them, the ability to properly organize their own work and harmonious cooperation in the team, etc. In the materials of the article, it is determined that the crisis conditions of the functioning of national economies, intra-personal and interpersonal conflicts, the uncertainty of the future determine the need to conduct various forms and types of social-psychological training. The urgency of solving this scientific problem lies in the fact that the complication of interpersonal relations at the macro and micro levels, global and local uncertainty of the environment, leads to a growing need for individuals to acquire and develop social competences that would reduce the level of interpersonal and intrapersonal conflicts. The study of the list and characteristics of social competences acquired in the process of social-psychological training made it possible to reveal its influence on social and emotional intelligence, to determine approaches and methods of formation and development of liquid and crystallized intelligence. It was determined that the intensity of social-psychological training depends, on the one hand, on the certain temperament and personal traits of all its participants, and on the other hand, on the influence of the external environment in which the individual functions. Historical and logical methods, methods of analysis and synthesis became the methodical tools of the conducted research. The results of the conducted research can be useful for the formation of a program of socio-psychological trainings for particularly vulnerable categories of the population.
Design Pipeline SHA-3 MIPS Processor using FPGA

The purpose of this study is to implement and evaluate the security of the Keccak hash function in a Microprocessor without Interlocked Pipelines (MIPS) processor using Field Programmable Gate Arrays (FPGA). The design methodology involves selecting a set of instructions that are required to run the SHA-3 Keccak algorithm on the MIPS processor. The findings of this study demonstrate that the Keccak hash function can be efficiently implemented on a MIPS processor using FPGA technology. The research limitations include the fact that the study only focuses on the implementation of the Keccak hash function on a MIPS processor using FPGA, and does not consider other platforms or technologies. The practical implications of this study are that it provides a cost-effective solution for implementing secure hash functions on embedded systems that require low power consumption and high performance. The originality and value of this study lie in its contribution to the field of cryptography by demonstrating the feasibility of implementing secure hash functions on low-cost processors using FPGA technology.
An automatic density peaks clustering based on a density-distance clustering index

&lt;abstract&gt;&lt;p&gt;The density peaks clustering (DPC) algorithm plays an important role in data mining by quickly identifying cluster centers using decision graphs to identify arbitrary clusters. However, the decision graph introduces uncertainty in determining the cluster centers, which can result in an incorrect number of clusters. In addition, the cut-off distance parameter relies on prior knowledge, which poses a limitation. To address these issues, we propose an improved automatic density peaks clustering (ADPC) algorithm. First, a novel clustering validity index called density-distance clustering (DDC) is introduced. The DDC index draws inspiration from the density and distance characteristics of cluster centers, which is applicable to DPC and aligns with the general definition of clustering. Based on the DDC index, the ADPC algorithm automatically selects the suitable cut-off distance and acquires the optimal number of clusters without additional parameters. Numerical experimental results validate that the introduced ADPC algorithm successfully automatically determines the optimal number of clusters and cut-off distance, significantly outperforming DPC, AP and DBSCAN algorithms.&lt;/p&gt;&lt;/abstract&gt;
Problem of person in the history of philosophical thought

This article examines the problem of a man in the philosophy of different historical periods — the philosophy of the Ancient World, antiquity, the Middle Ages, the Renaissance, the philosophy of the New Age, modernity. Initially, the first philosophical views on the problem of person developed in ancient Eastern philosophy (in the philosophy of Ancient India and China) are considered. A man as an object of study in the beliefs of ancient people is characterized. The main Western European approaches of ancient philosophy to the allocation of a person as a separate and special philosophical problem (Protagoras, Socrates, Plato, Aristotle) are determined. It is also emphasized that the Western European Middle Ages were marked by the most powerful influence of the Christian worldview on all aspects of people’s lives, particularly on the spiritual life, which was inseparable from the religious worldview (A. Augustine). It is shown that the philosophy of the New Age is formed by the fact that the path to the rational interpretation of human essence was opened (J.O. La Mettrie, P. Holbach, D. Diderot, I. Kant). Features in the philosophy of person of the nineteenth century are highlighted. In addition, the pecularities of Russian philosophy in the second half of the XIX — early XX century and anthropological problems of the 20th century are presented (Belinsky, Chernyshevsky, Berdyaev, Soloviev). Finally, the article also considers the problem of man in Kazakh philosophy and describes viewpoints of Abu Nasr Al-Farabi and Abay. Anthropological problems of the XX century in Western philosophy are presented.
Battle report from a corporate psychologist: Why inaccurate methods are easier to sell and what we can do about it

As a psychologist working in consulting, dealing with various types of organizational research is my daily bread. The amount of low-quality methods and tools lacking adequate validation that I encounter in the business world is staggering. Even more worrying is their popularity in business and their resistance to being replaced by validated methods, even if they are available. For some reason business seems to prefer using questionable tools. This paper explores why such tools dominate the market. Several reasons are discussed for why these methods may have an advantage over those designed and validated by academia. Possible solutions to overcome this problem are also discussed.
Development of mortality prediction model using electronic health record (EHR) data and machine learning algorithm in intensive care unit (ICU)

In this study, a mortality prediction model was developed and evaluated using Electronic Health Record (EHR) data and the XGBoost (eXtreme Gradient Boosting) machine learning algorithm. The research dataset comprises EHR records of a total of 5,211 patients who were admitted to the medicine and surgery intensive care units of a tertiary medical institution from January 1, 2017, to February 29, 2020. Models were developed based on the accessibility of variable data, their high correlation with mortality, and their diversity. The research results indicated that variables such as respiratory pattern, pulse rate, RASS (Richmond Agitation-Sedation Scale), and post-surgery admission status showed relatively high importance compared to other variables. Models demonstrated higher predictive power compared to APACHE II, Random Forest and logistic regression models (AUC .93 ~ .98, accuracy .91 ~ .95). The model utilizing various variables showed the highest predictive power, but even a model based solely on accessible physical examination records demonstrated sufficient predictive capability. It was confirmed that patient observations recorded by nurses holds high importance and accessibility in mortality prediction. Also using simplified variables with machine learning techniques can efficiently predict mortality. It is expected that healthcare professionals can reduce the burden of record-keeping for mortality prediction and receive more accurate information about patients.
Analysis of main variables affecting the 112 Emergency Call

With the development of data science, various machine learning and deep learning models are being utilized to predict the number of 112 emergency calls with high accuracy. Predicting emergency response volume can help law enforcement agencies efficiently allocate police resources to high-risk areas and dates, thereby preventing crime. However, it is also important to understand the factors that contribute to high call volume on specific dates and times. Police officers may have a sense of whether call volume will be high or low based on their experience and intuition. Similarly, the general public may make assumptions about which factors contribute to higher call volumes. In fact, there is a significant difference in call volume between holidays and weekdays, as several studies have confirmed that holidays have a significant impact on call volume. However, clear variables can reduce the impact of other variables on the prediction model and can become the sole criterion for all predictions. Additionally, variables with relatively low variability may not have a significant impact on call volume prediction. For these reasons, it is important to understand the impact of each variable on prediction results, select the necessary variables for predicting 112 emergency calls, and understand why the model selects certain variables to build an effective prediction model. Through this study, we can examine the key variables that influence the model and their explanatory power, and provide the essential variables needed to construct a 112 emergency call prediction model, without relying solely on model performance improvement.
Sparse Least Absolute Deviation Support Vector Machine

The support vector machine solves a quadratic programming problem with linear inequality and equality constraints. However, it is not trivial to solve the quadratic problem. The least squares support vector machine(LS-SVM) solves a linear system by equality constraints instead of inequality constraints. LS-SVM is a popular method in regression and classification problems, because it effectively solves simple linear systems. There are two issues with the LS-SVM solution : the lack of robustness to outliers and the absence of sparseness. In this paper, we propose a sparse and robust support vector machine for regression problems using the least absolute deviation support vector machine (LAD-SVM) and recursive reduced LS-SVM (RR-LS-SVM). The split-Bregman iteration gives the exact solution for the LAD-SVM problem, while RR-LS-SVM gives a sparse solution with a much smaller number of all support vectors. Numerical experiments with simulation and benchmark data demonstrate that the proposed algorithm can achieve comparable performance to other methods in terms of robustness and sparseness.
Social Media Sentiment Analysis

Abstract: In today's digital landscape, understanding and harnessing customer sentiment expressed across social media platforms is essential for the success of businesses, especially in the aviation and retail industries. This article delves into the profound significance of sentiment analysis in shaping strategic decisions and enhancing customer engagement in this digital age. Sentiment analysis emerges as a potent instrument for extracting invaluable insights from the vast reservoir of unstructured data that pervades social media platforms. We begin a thorough investigation of several social media sentiment analysis approaches in the context of this study. Both supervised and unsupervised procedures are included in our trip. In order to ensure a thorough knowledge of the applicability and subtleties of supervised approaches like Long Short-Term Memory (LSTM), which require labeled data for classification, we closely examine them. With SentiWordNet, we simultaneously explore the unsupervised domain, focusing on lexical sentiment analysis to sharpen the understanding of sentiment represented in the text. We provide a novel hybrid strategy that combines linear support vector machines (SVM) with latent semantic analysis (LSA). This combination delivers sentiment analysis that is more comprehensive and nuanced and is especially suited to the changing requirements of companies working in the digital era