Reviewerly/exHarmony huggingface dataset:
A Study on Exploring the Possibility of Learner-tailored Learning in Classics Education

The purpose of this paper is to obtain implications for improving the teaching method of classics education by analyzing the case of the learner-tailored individual and team-based activity parallel classes for the cultivation of core competencies in classics education, as well as to gain an better understanding of the students’ perceptions of classics education. The research question raised in this paper is first, what is learner-customized education in classics education and how is it possible? Second, how should reading notes be structured and utilized as learner-tailored education? Third, how should the discussion be operated as a team-customized education? Fourth, what should be the relationship between individual activities and team activities, and what is the synergistic effect of the two activities?As a result of the research on the problems raised, the instructor can provide 1:1 feedback on the students’ reading notes written before class by using the online bulletin board reflecting the learners’ characteristics, that is, the characteristics of learners who prefer immediate feedback due to exposure to various media. First, this can help to form a rapport between instructors and learners and to cultivate the learners’ sense of achievement, self-confidence, and challenging spirit in the classics whole reading class. In addition, the instructor can understand the characteristics of each learner and prepare effective lectures. Second, by setting the weight of reading notes and the team activity evaluation to 50%, evaluation based on the process rather than the result can be made. Third, reading notes combined with prior learning lead to active participation in this class and team activities. Fourth, it was confirmed through the questionnaire responses of learners that individual activities help to cultivate creativity and self-directed learning skills, and that team activities help to cultivate communication and collaboration skills that can be neglected in individual activities. However, in order for the learner-tailored class to become more active, the task remains to reduce the number of students and select only one work, rather than teaching two works at the same time, so that lectures and discussion activities can proceed smoothly.
A Comparative Analysis of Mathematics Achievement in Content Areas of Pre-calculus Before and After Covid-19 －A case study for first-year engineering students at a local university

This study examined the changes in mathematics achievement in certain content areas of freshmen before and after Covid-19. For this purpose, in 2019 and 2021, the diagnosis assessment and the mid-term and final exam scores for the first semester of the local engineering college students were converted into 100 points and compared in various aspects. As a result, first, the scores seem to confirm a decline in mathematics achievement in 2021 when compared to the scores achieved before the Covid-19 pandemic. Second, in the case of the upper group, online classes were more effective concerning the study of transcendental function, function and limit, derivatives, and the application of derivatives. Third, in the lower level group, the face-to-face classes were more effective regarding the study of function, transcendental function, function and limit, and integrals. Fourth, the results of the comparison of contents between the upper and lower level groups in 2019 showed that the lower level group’s achievement was higher than the upper level group’s in all areas, but that there was no statistically significant difference. Finally, the results of the comparison between the upper and lower level groups in 2021 showed that the upper level group’s achievement was higher than the lower level group’s in all areas, but that there was a statistically significant difference only when it came to the learning of integrals., in addition to the content characteristics of courses in the liberal arts area regarding the basic nature of a major.
A Study on Teaching for Writing a Self-introduction －Focusing on the lecturer's reflective narrative

This study is a qualitative research that examines the limitations and difficulties experienced as an instructor in the field of a self-introduction class through the use of reflective narrative technology. As a result of analyzing my teaching, my class was buried in the ‘difficulty and complexity of writing a self-introduction’, resulting in the poor achievement of students. Accordingly, I tried to find ways to improve the class while keeping the following in mind. First, the dimness in the students’ perception when it comes to their future employment is considered more carefully and reflected in the class design. In addition, with the advent of the AI era, various engineering discussions related to self-introductions are being announced. Thus, major discussions, such as developing a deep learning model that can identify good self-introductions, will be actively used in the class. It is hoped that the students’ vague fear of self-introductions can be offset through more sophisticated and colorful changes in teaching methods.
A Case Study on the Development and Operation of Liberal Arts Subjects Using Micro-Learning Content

This study analyzes the experiences of instructors and learners who were using micro-learning content, and suggests the development and operation plan of micro-learning to apply it to actual classes. In order to achieve the research purpose, K-MOOC courses operated by S University in Gyeonggi-do were developed and operated as micro-learning content. Of the 213 students who were enrolled in the lecture, 107 students participated in the survey. The research results covered learner perception of existing online learning, their experience of micro-learning content, and their course satisfaction.Learners perceived that the micro-learning content was short and focused only on the core material, improving their concentration compared to existing online lectures, and that repetitive classes were not burdensome. Thus, they were generally satisfied with the program. However, the instructors expressed regret over time constraints and the lack of environmental support for designing courses and developing content through micro-learning. On the other hand, instructors said that they would continue to develop micro-learning content in consideration of the high satisfaction of the learners and that they would consider various ways to better utilize the content. Based on these results, examples of how to develop and operate micro-learning content in universities and/or standardized classes were presented and the implications were discussed.
An Investigation of the Identity of Character Through the Fundamental Cognitive Mechanism

Character education in Korea has been strengthened since the Promulgation of the Character Education Promotion Act in 2015. But the concept of character itself is not clear and there is no consensus regarding it, causing confusion and certain side issues when it comes to character education. As part of resolving this essential problem of character education, this study looked at what character is from its cognitive origin, from which various human minds are formed. It also looked at the mechanism of how character emerges from the cognitive origin.The results showed that character, that is, human nature, is composed of two contradictory natures: a nature for ‘emptiness,’ which is a mind that one tries not to think and thus any cognitive structure (knowledge) does not appear internally, and a nature for ‘appearance,’ which is a mind that one try to think to form knowledge and thus knowledge appears internally. Furthermore, humans are born with natures of sensibility, memory, thinking and knowledge, and innate abilities such as sensory ability, thinking ability, and knowledge formation ability (intelligence) for the manifestation of appearance. Due to these natures and innate abilities, human beings form knowledge about the world and form various fields of mind based on this knowledge. Thus, we have the so called ‘virtues in character education.’Based on these results, this thesis discusses the debates related to character, such as nature, as well as the debate of good versus evil, and suggests the right direction and method of character education.
A Study on the University Life Adaptation Program for International Students －Focusing on the New Student Seminar Training Module and Satisfaction Analysis

This study aims to examine the university life adaptation program according to the admission of international students to domestic universities, which has almost doubled over the past ten years. The number of international students at domestic universities is rapidly increasing due to the combination of the Wave of Korean Culture, the government led globalization policy, and the sense of crisis in the university itself due to the decrease in the school-age population. However, the reality is that the proper curriculum system to educate such students still has problems. In particular, programs for international students in the first year, such as those pertaining to students adapting to college life and those promoting academic capability, are crucial because the first year determines the success or failure of college life since international students experience unfamiliar cultural environments.Accordingly, this study proposes a module containing the following three themes for the international students' college life adaptation program: the module that introduces various learning resources provided by the university where international students are enrolled and which they can learn how to use more effectively; the module that overcomes and resolves cultural conflicts based on the understanding of university culture including the peculiarities of Korean culture; and the module concerning the methods and examples of report writing using Korean to increase the excellence of university studies.In addition, this study provides a foundation for the advancement of the university life adaptation program for international students by checking and suggesting improvement directions through satisfaction analysis according to the results of providing these educational modules to students.
Can the Government Compel Decryption?

If a court knows that a respondent knows the password to a device, can the court compel the respondent to enter that password into the device? In this work, we propose a new approach to the foregone conclusion doctrine from Fisher v. U.S. that governs the answer to this question. The Holy Grail of this line of work would be a framework for reasoning about whether the testimony implicit in any action is already known to the government. In this paper we attempt something narrower. We introduce a framework for specifying actions for which all implicit testimony is, constructively, a foregone conclusion. Our approach is centered around placing the burden of proof on the government to demonstrate that it is not "rely[ing] on the truthtelling" of the respondent. Building on original legal analysis and using precise computer science formalisms, we propose demonstrability as a new central concept for describing compelled acts. We additionally provide a language for whether a compelled action meaningfully entails the respondent to perform in a manner that is 'as good as' the government's desired goal. Then, we apply our definitions to analyze the compellability of several cryptographic primitives including decryption, multifactor authentication, commitment schemes, and hash functions. In particular, our framework reaches a novel conclusion about compelled decryption in the setting that the encryption scheme is deniable: the government can compel but the respondent is free to use any password of her choice.
Continuous self‐adaptation of control policies in automatic cloud management

Summary Deep reinforcement learning has been recently a very active field of research. The policies generated with the use of this class of training algorithms are flexible and thus have many practical applications. In this article we present the results of our attempt to use the recent advancements in reinforcement learning to automate the management of resources in a compute cloud environment. We describe a new approach to self‐adaptation of autonomous management, which uses a digital clone of the managed infrastructure to continuously update the control policy. We present the architecture of our system and discuss the results of evaluation which includes autonomous management of a sample application deployed to Amazon Web Services cloud. We also provide the details of the training of the management policy using the Proximal Policy Optimization algorithm. Finally, we discuss the feasibility to extend the presented approach to further scenarios.
An Architecture Model on Artificial Intelligence for Ground Tactical Echelons

This study deals with an AI architecture model for collecting battlefield data using the tactical C4I system. Based on this model, the artificial staff can be utilized in tactical echelon. In the current structure of the Army's tactical C4I system, Servers are operated by brigade level and above and divided into an active and a standby server. In this C4I system structure, the AI server must also be installed in each unit and must be switched when the C4I server is switched. The tactical C4I system operates a server(DB) for each unit, so data matching is partially delayed or some data is not matched in the inter-working process between servers. To solve these issues, this study presents an operation concept so that all of alternate server can be integrated based on virtualization technology, which is used as an source data for AI Meta DB. In doing so, this study can provide criteria for the AI architectural model of the ground tactical echelon.
Movie Recommendation Using Clustering and Nearest Neighbour

Due to the abundance of items available and online information, a user cannot easily choose which product is ideal for him. A recommender system assists users in finding what is best for them. A recommender system uses information about a user's activity. It uses it to suggest movies to users based on their individual interests. This paper provides an overview of a recommender system that uses the K-means and KNN algorithm. Without wasting time exploring, both algorithms rapidly and effectively recommend movies to customers based on their likes. There are many uses for recommender systems worldwide. K-means algorithm is used to get beyond some of the restrictions of content-based and collaborative work. The K-means algorithm creates clusters of individuals with similar interests, and KNN, which includes nearest neighbors, recommends movies to each group. This is used in well-known fields like books, news, music, videos, and movies, among others. These search engines allow users to find movies of their choice. K-mean, KNN, and hybrid algorithms have been covered in this study. K-means algorithm results based on metrics like "average Genre Rating" and "User Movie Rating". The RMSE feature has been used to KNN algorithm. A hybrid algorithm combines the two algorithms mentioned above. K-means is given an input, and the output of this method serves as the input for the KNN algorithm, which is more accurate than both K-means and KNN.
Two Enhancement Levels for Male Fertility Rate Categorization Using Whale Optimization and Pegasos Algorithms

Recently, diseases and health problems that were common only in the elderly became common also among the youth. Some of these medical problems causes include behavioral, environmental, and lifestyle factors. The decrease in fertility rates especially among the male population is one of those problems. Now, machine learning and artificial intelligence algorithms are emerging methodologies as computer-aided decision systems in medical diagnosis and health problems. In this chapter, the incorporation of the bio-inspired whale optimization algorithm (WOA) and Pegasos algorithm are used to enhance the male fertility rate categorization in two levels. Results show that implementing WOA as the second level of enhancement gives better accuracy than the first level of enhancement in Pegasos algorithm with a prediction accuracy value of 90%. Using two machine learning algorithms to categorize the male fertility rate helped in the overall improvement of the proposed system performance to give results that exceeded all recent research results for fertility data.
Severity of Breast Mass Prediction in Mammograms Based on an Optimized Naive Bayes Diagnostic System

Mammography is the most effective tool for breast mass screening. It is a special CT scan technique used only to detect breast tumors early and accurately. Detecting tumors in its early stage has improved the survival rate for breast cancer patients. Computer-aided diagnostic systems help physicians to detect breast cells abnormalities earlier than other traditional procedures. The main aim of this chapter is to increase physicians' ability to determine the severity of a mammographic mass lesion from the BI-RADS features and the patient's age using the bio-inspired chicken swarm optimization (CSO) algorithm for Naive Bayes (NBC) classifier. The mammographic mass dataset is used to analyze the proposed method (CSO-NBC). The dataset is preprocessed and divided to train the CSO-NBC system and test it by the 5-fold cross-validation technique. The performance of the proposed classification system is compared with the results from other research to show the efficiency of the system in predicting the severity of breast tumors with the highest accuracy.
Going Through the Motions

With the learning slippage that occurred in the aftermath of the SARS-CoV-2/COVID-19 pandemic, learning advantages that may be achieved are a topic of special interest. The popularization of online learning has riveted focus to digital learning methods and contents. In formal higher education, various contemporary digital infographics are in use: static, motion, interactive, and immersive. This work explores some of the publicly available and open infographics used in formal learning in higher education to better understand these digital contents. The search for “infographics” is based on a popular referatory for web-hosted digital learning resources, but with a new search feature that goes beyond the curated and peer-reviewed contents that captures the newest relevant contents from the web. The works are analyzed for modalities, topics, pedagogical value, and design for transience to protect against cognitive overload. This is an exploratory research work.
Infographics for Information Conveyance

To help the world emerge from the COVID-19 pandemic, an older tool has come back to the fore: analog and digital informational graphics. Infographics (information + graphics) have been used for many decades to convey data, knowledge, information, and learning. In the latest phase, there are now motion (animated) and interactive and immersive infographics that offer richer ways. This work explores the basic mechanisms of information conveyance in infographics from early days through the contemporaneous moment with the richer. Finally, a summary graphic captures the general sequence in the design, development, and deployment of modern motion, interactive, and/or immersive infographics.
On Analyzing Complex Data Within Clinical Decision Support Systems

Clinical decision support systems (CDSSs) represent digital health tools applicable to important tasks within the clinical decision-making process. Training data-driven CDSSs requires extracting medical knowledge from the available information by means of machine learning. The analysis of the complex (possibly big or high-dimensional) training data allows knowledge relevant to be obtained for clinical decisions related to the diagnosis, therapy, or prognosis. This chapter is devoted to training CDSSs by machine learning based on complex data. Remarkable recent examples of CDSSs including those based on deep learning are recalled here. Principles, challenges, or ethical aspects of machine learning are discussed here in the context of CDSSs. Attention is paid to dimensionality reduction, deep learning methods for big data, or explainability of the data analysis methods. Data analysis issues are discussed also for two particular CDSSs on which the author of this chapter participated.
Information Security Governance

In today's IT-regulated era, data is nothing less than money. It is rightly said that a person who has data can become a millionaire. That is the power of data. For business organisations, information and business-related data is like blood. If there is any shortage or leakage, it could risk the life of the business. The business information has become a part of the valuable assets of the company. Thus, information security should be given high importance in the corporate governance system of the company. For this purpose, the role of the management of the company comes into focus. The management and the board of directors work towards the success of their company; thus, it is their sole responsibility to protect the valuable assets including the confidential business information. The board needs to make decisions regarding corporate governance by keeping the information security in consideration. Thus, information security governance is the need of the hour. The present chapter focuses on the importance of the information security governance in today's information-driven world.
KYC Fraud

Know your customer (KYC) is a measure adopted by financial institutions like banks to render timely services to their legitimate customers. Cybercriminals are resorting to KYC fraud through Vishing and SMiShing attacks. Customers have been falling prey to such frauds in the name of KYC updation. The chapter proposes the application of the supervised machine learning model to detect KYC fraud related to SMS and prevent such frauds. A dataset of sample KYC fraud messages and non-fraud messages is taken to train the machine learning model. The model is trained to extract the relevant features that distinguish fraud messages from valid messages, thus detecting fraud messages. The model was tested with popular supervised machine learning algorithms. The proposed model may be made as part of a security patch in the messaging service. KYC fraud has become rampant in recent times, and the chance of an individual falling for such attacks are highest given that it is a mandatory process. Previous research studies provided generic solutions for such frauds.
CDSS Architecture

Patient-oriented data-driven CDSS architecture, based on adaptive ontology, is proposed as a perspective for a future development of intelligent medical decision support systems. A human body (anatomy and physiology) knowledge base should be the basic component of the system with the possibility to permanently automated update the deeply structured data, both general and personal, using the technologies of ontology learning, natural language processing, and automated planning. Already existing information technologies, standards, and protocols allow implementing such an approach in a healthcare domain in a framework of FHIR HL7.org standard.