Reviewerly/exHarmony huggingface dataset:
Digital Conflict in Business Infrastructure

The human and technology have integrated, and distinctive characteristics of different societies have intertwined with technology. Thus, technology constitutes the reflection of the society, where it exists, in economic, social, and political domains. The desires, expectations, and needs of people have affected the change of technology in time. This effect contains different domains in itself. Life has changed since the 1980s under the leadership of means of informatics, and following the 1990s, internet technology have opened the doors of an informatics era whose effect is being highly sensed today. In addition to its importance, it creates significant changes and effects on individuals, society, culture, and other domains. When significant changes in technology and digitalization have combined with the globalization of information and communication, the resulting problems have begun to spread faster.In this study, which was performed for examining the digital conflicts arising in business infrastructure, literature was reviewed thoroughly. In the first part of the study, the conceptual framework of business infrastructure was examined and tried to be clarified. Moreover, it was intended to convey the commencement of the technologies used, and their following processes. Then, it was urged upon clarifications regarding business processes and digitalization of business processes, and by referring to the implementations of technologies – used in business processes – in the domain of digitalization, structural examinations were conveyed from the literature. Finally, the issues of digital conflicts and cyber security occurring in business infrastructure, which are being expressed as more prominent factors, were clarified.
Getting Started with Apache

In this chapter, you learn how to install Apache safely on macOS, Linux, and Windows and get it up and running in its default configuration.
Organisational skills in the structure of universal pedagogic competences as a result of training future pedagogues at different stages of education

Organisational abilities can be considered to be among the important components of the pedagogic potential of a specialist in the field of education. Their development is one of the most important tasks for pedagogue training. The allocation of these characteristics in the structure of competences, the presence of which among graduates of pedagogic classes, colleges and universities, determines their success in the profession, thus helping build a continuous and successive process of forming the readiness of future pedagogues to solve organisational problems. Analysis of psychological and pedagogic literature, including the works of Anatoliy Lutoshkin, Lev Umanskiy, Lev Spirin, the results of the study of the priorities of the teaching profession of future and current pedagogues made it possible to single out individual components of organisational abilities characterising the readiness of a specialist to organise the activities of participants in the educational process and orient them towards self-organisation and professional self-development. Working with indicators of universal pedagogic competences helped to identify characteristics (knowledge, attitudes, qualities) that may indicate manifestations of the organisational potential of students at certain stages of training. The highlighted landmarks will ensure the purposefulness of the development of the organisational skills of future pedagogues in the framework of the implementation of educational programmes of a pedagogic orientation, and will also allow to design the process of their formation on the basis of the results already achieved.
Discretizing Numerical Attributes: An Analysis of Human Perceptions

AbstractTo partition numerical attributes, machine learning (ML) has used a variety of discretization approaches that partition the numerical attribute into intervals. However, an effective method for discretization is still missing in various ML approaches, e.g., association rule mining. Moreover, the existing discretization techniques do not reflect best the impact of the independent numerical factor on the dependent numerical target factor. The main objective of this research is to develop a benchmark approach for partitioning numerical factors. We present an in-depth analysis of human perceptions of partitioning a numerical factor and compare it with one of our proposed measures. We also examine the perceptions of various experts in data science, statistics and engineering disciplines by using a series of graphs with numerical data. The analysis of the collected responses indicates that \(68.7\%\) of the human responses were approximately close to the values obtained by the proposed method. Based on this analysis, the proposed method may be used as one of the methods for discretizing the numerical attributes.KeywordsMachine learningData miningDiscretizationNumerical attributesPartitioning
Block the Chain: Software Weapons of Fighting Against COVID-19

This article proposes an architecture for vaccination information validation and tracking with a fog and cloud-based blockchain system, providing a privacy-aware and scalable approach for interoperable and effective data management. It evaluates the scalability of the underlying blockchain system by means of simulation.
Understanding Conversational Artificial Intelligence

As we become ever more trusting and reliant on conversational artificial intelligence in our teaming with machines, how do we ensure that this reliance does not lead to unintended consequences?
Maximum Relevancy and Minimum Redundancy Based Ensemble Feature Selection Model for Effective Classification

Feature selection algorithms are widely used in most of the critical applications of machine learning, pattern recognition, disease diagnosis and fraud identification. Specifically, due to the existence of redundant and irrelevant attributes, the performance of the underlying model will be degraded with higher time complexity and lower accuracy. Thus, feature selection has become one of the most widely used pre-processing techniques as it improves performance concerning time and accuracy. Several methods exist in identifying the optimal subset of features from the large feature set. However, none of the single feature selection techniques is proved to be effective for all types of the training set. Each method will be effective with specific classifiers and for specific datasets. This paper utilizes the maximum relevancy and minimum redundancy based ensemble feature selection for effective classification with maximized accuracy. The proposed model collects the ranks from different filtering techniques and convert them to scores using the rank-sum approach. The accumulated scores signify the feature importance for which the mutual information for each selected feature with the class variable as well as the selected optimal features are computed. The model has been validated using various experiments by different classifiers, and it is proved to be more effective in improving the performance of the classification results than the existing single rank techniques.
A Qualitative Case Study on the Adaptation of Chinese Students to University Life in Corona Era

Objectives This study define the characteristics of the adaptation of Chinese students in South Korea during COVID-19, so suggest the direction about deep understanding on overseas students in a serious incident situation. Methods The researchers selected 5 Chinese students and 1 sub informant and conducted a qualitative case study. Collecting data with participation observation and in-depth interview and analyzing the characteristics of adapting experience in COVID-19 era by coding and categorizing the data. Results The adaptation phenomenon of Chinese students studying in South Korea during COVID-19 is shown as “chaos and confusion,” “difficult to adapt,” “increasing stress,” “overcome and move on”. Chinese international students have encountered a series of difficulties due to the changes in teaching methods and learning environment after the COVID-19 outbreak, and interpersonal communication is more online. Conclusions Chinese overseas students have new psychological pressure and the previous methods to relieve the pressure are no longer applicable and new solutions must be found. In order to create a better education environment in a unexpected situation like COVID-19, in addition to the efforts of Chinese students themselves, schools and society should also improve the relevant policies and systems.&#x0D;
Changes in University Life and Labor Market Performance of Secondary Teacher Education Graduates

Objectives The study purposed to analyze the changes in university life and labor market performance of secondary teacher education graduates. Methods Among the data of the Korea Employment Information Service's The Graduates Occupational Mobility Survey, 728 graduates of 2019 GOMS and 552 graduates of 2009 GOMS 10 years ago were used, changes in university life, labor market performance, and factor influencing labor market performance were analyzed using frequency analysis, desciptive statistics, and hierarchical regression analysis methods. Results First, as a result of analyzing the change in university life of secondary teacher education graduates, the proportion of university and major selection due to external motivation rather than internal motivation increased slightly, the proportion of job preparation such as career selection and job preparation program participation, and vocational education and training increased, the proportion of experience in the teacher employment examination decreased but the proportion of 9th grade civil service examination increased. Second, as a result of analyzing the change in labor market performance of secondary teacher education graduates, the proportion of economic activity decreased, the proportion of job achievement planned as a job goal while attending school decreased, and the proportion of entering a school teacher also decreased slightly. Third, as a result of analyzing the changes in labor market performance influencing factors of secondary teacher education graduates, the variables that had a positive effect on both job satisfaction and major congruence were curriculum satisfaction and vocational education and training experience. Conclusions It is meaningful that basic data were provided by analyzing the changes in university life and labor market performance of secondary teacher education graduates, and it is necessary to conduct follow-up studies in university life and labor market performance for secondary teacher education students and graduates.&#x0D;
Introduction to Blockchain for Internet of Things

Blockchain is a decentralized or distributed platform for sharing information and computational capabilities. Records of transactions are duplicated among the participating workstations in the network, which coordinate and collaborate with each other. As such, any attempt to change the stored data requires access to all the workstations in the network, thereby ensuring higher privacy and security of the underlying data. Internet of Things refers to the connectivity of numerous physical objects or devices (either sensors or actuators) over the Internet to ascertain a densely connected network infrastructure capable of making decisions on its own. Thus, a huge amount of sensitive data needs to be processed and stored for further use in an Internet of Things network. Given its robust security, Blockchain finds widespread applications in several fields of applications ranging from science and engineering to medical and business applications, and Internet of Things is no exception in this regard. This chapter introduces the fundamentals of Blockchain and Internet of Things with special reference to the synergy between the two in yielding a robust and secure distributed data processing environment.
Effects of Science, Mathematics, and Informatics Convergence Education on Creative Problem-solving

The Fourth Industrial Revolution led to accelerated development and promotion of software (SW) usage in various fields. SW has been integrated into various areas to address multiple problems. As the importance of SW convergence increased, the need for SW convergence talent concomitantly increased. Korea implemented the “Science, Mathematics and Information Education Promotion Act” in 2018 to cultivate SW convergence talents. The Act includes not only the promotion of individual subjects but also science, mathematics, and information convergence education (SMICE). With the Act's enforcement, education programs for SMICE have been formulated. A study analyzed student satisfaction and perception regarding SMICE, but its effect on student education was not analyzed. Therefore, the present study analyzed the effects of SMICE on middle school students. The subjects comprised 163 middle school students who were divided into experimental and control groups. The control (n=83) received general informatics education. The experimental group (n=80) received both informatics education and SMICE. Creative problem-solving (CPS), a common competency of the three subjects, was selected as the test factor to analyze the educational effect. Changes in student CPS were examined using pre-and post-tests. The results showed no difference in the pre-test CPS between the experimental and control groups. The post-test results showed that the experimental group had higher CPS than the control. Notably, there was a significant increase in problem discovery and analysis, idea generation, persuasion, and communication metrics. These findings demonstrated that SMICE is effective in the CPS development of middle school students.
Aerobic Exercise System for Home Telerehabilitation

The COVID-19 pandemic has impacted every aspect of health delivery and encouraged to replace in-person clinical visits with telecommunications. By providing wireless communication between embedded electronic devices and sensors, telerehabilitation enables constant monitoring of vital body functions, and tracking of physical activities of a person and aids physical therapy. In this paper, we designed and tested two remotely controlled versions of interactive bike (iBikE) systems which communicate through either Wi-Fi or BLE and give the clinical team the capability to monitor exercise progress in real time using simple graphical representation. We used the same hardware and user interface for both designs. The software uses either Wi-Fi or BLE protocol to connect the iBikE equipment and PC tablet. The bike can be used for upper or lower limb rehabilitation. A customized tablet app was developed to provide user interface between the app and the bike sensors. Both bikes were tested with a single group of nine individuals in two separate sessions. Each individual was asked to hand-cycle for three separate sub-sessions (1 minute each for slow, medium, and fast pace) with one-minute rest. During each sub-session, speed of the bikes was measured continuously using a tachometer, in addition to reading speed values from the iBikE app, to compare the functionality and accuracy of the measured data. Measured RPMs in each sub-session from iBikE and tachometer were further divided into 4 categories: 10-second bins (6 bins), 20-second bins (3 bins), 30-second bins (2 bins), and RPMs in each sub-session (1 minute, 1 bin). Then, the mean difference of each category (iBikE, tachometer) was calculated for each sub-session. Finally, mean and standard deviation (SD) of the calculated mean differences were reported for all individuals. We saw decreasing trend in both mean and SD from 10 second to 1 minute measurement. For BLE iBikE system, minimum mean RPM difference was <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.2 \pm 0.3$</tex> in one-minute sub-session with medium speed. This number was <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.21 \pm 0.21$</tex> in one-minute sub-session with slow speed for Wi-Fi iBikE system. Thus, testing confirmed high accuracy of our interfaces.
Galois groups of certain even octic polynomials

Let $f(x)=x^8+ax^4+b \in \mathbb{Q}[x]$ be an irreducible polynomial where $b$ is a square. We give a method that completely describes the factorization patterns of a linear resolvent of $f(x)$ using simple arithmetic conditions on $a$ and $b$. As a result, we determine the exact six possible Galois groups of $f(x)$ and completely classify all of them. As an application, we characterize the Galois groups of irreducible polynomials $x^8+ax^4+1 \in \mathbb{Q}[x]$. We also use similar methods to obtain analogous results for the Galois groups of irreducible polynomials $x^8+ax^6+bx^4+ax^2+1 \in \mathbb{Q}[x]$.
Giyilebilir Sensörler ile İnsan Hareketlerini İzleme Human Activity Monitoring via Wearable Sensors

In recent years, the use of wearable devices in many areas has emerged. Recognition of human behavior and movements has become almost the essential component of wearable devices. Monitoring human behavior enhances human life in many fields, especially in the health sector. Wearable sensors are preferred for motion tracking because they can work independently of the location, cause less discomfort to users in terms of privacy than other sensing devices, and are inexpensive. In this study, using data from wearable sensors, human behavior has been predicted with deep learning methods. The contributions of the spectrogram, wavelet transform and time-based feature spaces to the prediction performance are analyzed. The prediction performance of our developed model is comparable to the state-of-art studies in the literature.
Named Entity Recognition for Defense Industry

Named Entity Recognition (NER) is a natural language processing task which aims to detect persons, locations, organizations, and similar useful attributes in a given text. There are language-specific named entity recognition studies as well as subject-specific ones. In Turkey, the number of companies and their projects in the field of the defense industry increases rapidly. Therefore, the same trend can also be observed in the amount of textual data produced in this field. Due to the need to better understand this data, the task of recognizing named entities, which is specific to the field of the defense industry, has gained importance. In this study, a deep learning-based named entity recognition model with a focus on the Turkish defense industry is proposed. The created and used dataset was prepared using the product descriptions in the Turkish Defense Industry Product Catalogue of the Presidency of Defense Industries (SSB). The goal is to identify four types of labels: product, company, related term, and unit. In the experiments, the BiLSTM-CRF word sequence tagging model, which uses the combination of BERT and FastText word embedding models, gave the highest performance when micro and macro F1 scores were considered as performance metrics. Sample results of this study are shared at https://defencener.github.io.
Assessment of a New Adaptation Approach for Scalable 3D Video

Transparent access of a 3 Dimensional (3D) video content considering heteregeneoity of the user terminals, preferences, network technologies, and environment conditions is provided by the Universal Multimedia Access (UMA) concept, which has a vision of providing maximized Quality of Experience (QoE) of the video contents. In order to support the UMA vision, the Scalable Video Coding (SVC) standart provides spatial, temporal, and quality scalability options. When these scalability options are associated with the video adaptation, achieving the vision of the UMA concept can be eased. However, the (Network Abstraction Layer Unit (NALU)) prioritization used by the latest SVC standard is not performed in compliance with the video contents. Moreover, Joint Scalable Video Model (JSVM), which is a software implementation of the SVC, supports an adaptation approach not flexible in terms of bit rate options. Therefore, in this study, an adaptation approach applying NALU prioritization considering 3D video contents and using flexible bit rate options and in this way enabling highly efficient results is proposed.
Blockchain and IoT-Based Diary Supply Chain Management System for Sri Lanka

The central problem to be addressed in this research is to investigate how blockchain technology can be used in today's food supply chains to deliver greater traceability of assets. The aim is to create a blockchain model in the dairy supply chain that can be implemented across any food supply chains and present the advantages and limitations in its implementation. Blockchain allows monitoring all types of transactions in a supply chain more safely and transparently. Acceptance of blockchain in the supply chain and logistics is slow right now because of related risks and the lack of demonstrable models. The proposed solution removes the need for a trusted centralized authority, intermediaries and provides records of transactions, improving high integrity, reliability, and security efficiency and protection. All transactions are registered and maintained in the unchangeable database of the blockchain with access to a shared file network.
Visualization of the description of a cryptocurrency (doacc) ontology with RelOntoUML

This article transforms the Description of a Cryptocurrency (DOACC) ontology OWL into RelOn-toUML model. The ontology (DOACC) can be downloaded from github, an open-source project. The RelOntoUML model is a combination of the relational database model, the ontology model, and the UML model. The relational model stores data in relations (tables). Ontology is a representation of knowledge and also has an inference engine. The UML model is a popular and frequently used model-ling by software developers. The article also presents the doacc ontology and the conversion of the ontology.
A Comprehensive Survey of Datasets Used for Spam and Genuineness Views Detection in Twitter

Social media is one of the evolving platforms to share the views. The social media such as Twitter, Instagram, Facebook, and other microblogging sites floats lot of information from one corner of the world to another corner. This information can be used for the various purposes. Social media plays major role in collection of wide variety of information. This wide variety of information can be used to extract sentiments, opinions, spam, and genuineness of views shared by the users. To perform the experimentation, proper datasets should be available. In this survey paper, we have throws light on the recent datasets used for experimentations. We have analyzed the results obtained by using various datasets and its performance. By comparing the results and performance, we try to analyze the suitable domain datasets which will give the better results after applying various methods, techniques, and algorithms.