Reviewerly/exHarmony huggingface dataset:
A Study on Korean Life and Culture Adaptation of Muslim International Students in Seoul

In this study, two in-depth interviews were conducted in April 2023 on five Muslim foreign students enrolled at a university (graduate school) in Seoul with focus on college life, cultural adaptation, and religious life. The study was conducted as a prelude to Korea’s globalization and multicultural era in an attempt to understand Muslim culture and narrow the psychological distance between Koreans and Muslims. The results of this study were as follows. First, the students decided to study abroad based on perceptions of Korea and the recommendations of relatives, and found their colleagues and professors treated them fairly, but regretted that exchanges with Korean students were limited to formal classroom exchanges. The excellent Korean language abilities of international students simplified obtaining scholarships and meeting living expenses in Korea through part-time work, implying an ability to find jobs in Korea and their home countries. The students had strong religious beliefs and demonstrated a traditional, conservative tendency to prioritize marriage over employment. Second, in terms of adaptation to Korean culture, diet was the most difficult issue for religious reasons, particularly the availability of halal food. Because Koreans consume much pork, Muslim students could not eat school meals, rarely ate out, and found it difficult to buy appropriate snacks at convenience stores, and thus cooked meals themselves. As regards dress, female Muslim students do not expose skin except for that of the face and hands. The wearing of a hijab is a serious religious topic and symbolizes piety. Third, Muslim international students experience difficulties securing prayer time and space and complying with the requirements of Ramadan and thus choose courses that are compatible with prayer time and space requirements. Students also experienced Islamic/Korean cultural difficulties but exercised constraint and conformed with Korean expectations. The number of Muslim international students included in this study was limited, and thus generalizations are not possible. In addition, individual student characteristics and factors associated with countries of origin and culture were not considered. Therefore, we suggest follow-up studies be performed to address these shortcomings.
Efficiency of Machine Translation in the Language Processing Process; Using Context Clues in Finding the [Exact] Meaning of Quranic Words [In Persian]

Efficiency of Machine Translation in the Language Processing Process; Using Context Clues in Finding the [Exact] Meaning of Quranic Words [In Persian]
A Semantic Analysis of the Verb dowr zædæn (to turn around) in Persian: A FrameNet-Based Account [In English]

A Semantic Analysis of the Verb dowr zædæn (to turn around) in Persian: A FrameNet-Based Account [In English]
Machine learning techniques for nanoparticles transport

Machine learning is a branch of artificial intelligence concerned with creating and developing algorithms that enable computers to learn behaviors or patterns from empirical data. The aim of this chapter is the implementation of machine learning algorithms in predicting nanoparticle transport in the oil reservoir. We used Jupyter Notebook for the implementation, which utilizes Python programming language. Jupyter Notebook is an open-source web tool that allows you to write live code while creating statistics and machine learning models. This chapter contains selected machine learning techniques that can be used for nanoparticle transport in porous media. It starts with the fundamentals of a number of machine learning methods, followed by basic metrics that are frequently used. After that, we discuss datasets and their analysis. Finally, we explain how to implement machine learning techniques in the Jupyter Notebook environment using Python programming language.
Typical applications of cognitive engines

This chapter focuses on two typical examples of cognitive engines. One is the cognitive engine design based on the genetic algorithm and the other is the cognitive engine design based on case reasoning.
Hybrid inference integration mechanism based on data drive

The data-driven hybrid reasoning integration mechanism has the advantages of different traditional hybrid reasoning integration mechanisms. Because of the introduction of a data-driven mechanism in the underlying instruction structure and parsing, the reasoning process is not given in a fixed way in the design, but is dynamically arranged at runtime.
Cognitive engine knowledge base design

The large intelligent system of the cognitive engine introduces the knowledge base module in data management. As the information storage and information exchange center of the entire intelligent learning decision-making system, the knowledge base can be divided into short-term knowledge and long-term knowledge according to the visual length of data statistics.
Intelligent language knowledge for cognitive engine

In this chapter, knowledge regarding intelligent language for the cognitive engine is described and various theories and methods are explained that realize effective communication between humans and computers through natural language.
Revisiting sensor technology for a safe and smart living environment for the aged and infirm at home

This chapter reviews sensor technologies for safe and smart home automation to serve the needs of people to remain independent. Independence is not only for older adults but also people with chronic conditions or disabilities who wish to increase their quality of life. Satisfying these needs through advance technology helps improve their quality of life, promoting an autonomy that inevitably gets reduced with time. Smart and safe home automation can greatly assist older people who wish to continue residing in their homes and adapt to their specific needs. The use of advanced information technology of sensors plays an integral part, becoming an essential tool to promote independent living and improve the quality of life of the elderly, who usually experience a progressive loss of functions due to age or chronic illnesses. Such losses hinder daily chores, forcing them to depend on outside assistance. Thus, the need for a safe and smart living environment for older people at home is key to enhancing their quality of life.
Basic theory and technology of reasoning engine

This chapter describes the basic process, principle knowledge, advantages, and disadvantages of rule reasoning and case diagrams and introduces the highly scalable parallel processing application of massive data.
Object-oriented knowledge representation

The object-oriented knowledge representation method has three main concepts: the framework class, concept class, and object. The framework class is used to instantiate the concept class and object. The final knowledge base is composed of the concept class and object.
Artificial intelligence technology

This chapter introduces the technology of cognitive engine in the field of artificial intelligence and the concepts of intelligence and autonomy. It describes the autonomous levels of spacecraft intelligence and introduces the development stage, direction of development direction, and technology forecast of artificial intelligence.
Large Language Models are Prone to Methodological Artifacts

Generative AI, particularly Large Language Models (LLMs) such as GPT, is poised to replace humans for many tasks. However, as the adoption of generative AI grows, so do concerns about the biases it might perpetuate. Here, we propose that in addition to biases that reflect how humans think and process the world around them, LLMs are also subject to methodological artifactsâ€”biases due to the specificities of a given research design. Across multiple large-scale experiments using GPT-4 and GPT-3, we find that LLMs are susceptible to biases in both response order and label selection. These results call into question the validity of LLM research findings stemming from single-prompt designs and demonstrate the need for full factorial designs, systematic experimentation, and replication with different prompts. While LLMs offer valuable insights, their use requires caution, and researchers and practitioners should be aware of and account for methodological artifacts to ensure the validity and reliability of findings.
Shape parameterizations for reduced order modeling in biophysics

This chapter provides a detailed framework for anatomical shape parametrization for reduced order modeling of living tissues and organs biophysics. In particular, the parameterization of the anatomy is obtained using statistical shape analysis on data sets of medical images, thus offering a fundamental tool for patient-specific modeling in reduced-order complexity. The goal of parametric shape models is twofold: extracting a low dimensional set of shape features describing the variability of organs anatomies from image collections and creating generative models capable of producing anatomically consistent shapes for an arbitrary choice of the shape parameters for the reliable training of reduced-order models
Transition operator

The transition operator typically generates new candidate solutions; as such, it has a strong impact on the search ability of a metaheuristic algorithm. This chapter starts with the description of different types of transition operators that are used to generate new candidate solutions from other solutions, from a random process, toward another solution, and from accumulated information. Partially mapped crossover (PMX), cycle crossover (CX), order crossover (OX), and modified one-point crossover (MOPC) operators of GA for solving the traveling salesman problem are used as examples to show how a transition operator may have a strong impact on the results of a metaheuristic algorithm. The source code for PMX, CX, OX, and MOPC is also given to show how to realize them.
Conclusion and future research directions

Many metaheuristic algorithms have been presented, and they all have their own distinguishing features. Although we can classify metaheuristic algorithms as single-solution-based or population-based, most metaheuristic algorithms presented after 2000 are population-based; therefore, this classification is not particularly suitable for classifying metaheuristic algorithms proposed recently. A simple classification based on the abbreviations, names, and inspirations of metaheuristic algorithms is given in this chapter, dividing them into six groups, namely, (1) neighborhood heuristics, (2) evaluation computation, (3) swarm intelligence, (4) human intelligence, (5) astronomy and physics, and (6) natural phenomena. A family tree and a summary table based on this classification are presented to provide a concrete overview of metaheuristic algorithms developed from the 1960s to the present. Last but not least, some promising research directions and open issues are given to discuss the future of metaheuristic algorithms.
Local search algorithm

Local search (LS) is widely used in metaheuristic algorithms, for it is typically simple and thus easy to implement for fine-tuning the searched solutions to further improve the end results. Several well-known LS algorithms are included in this chapter. Among them are iterated LS, guided local search, variable neighborhood search, and k-opt. Some examples using metaheuristic algorithms with LS are also described. The source code of a genetic algorithm with 2-opt (GA+2-opt) for solving the traveling salesman problem is given to explain how to add LS to a metaheuristic algorithm. GA+2-opt and GA are compared to show the possibilities and potential of metaheuristic algorithms with LS.
Heterogeneous cardiological data communication in telecardiology with music-key generation scheme

The novel coronavirus had entirely changed the health sector. A parallel wing of telemedicine had blossomed rapidly in this critical phase to treat noninvasive and nonemergency patients. In the field of COVID-19 telehealth, Internet technology and nature-motivated innovations help to transmit the private and confidential information present in multiple cardiological reports. These reports can be sent to different cardiologists for better treatments, opinions, and research purposes. These cardiac reports must be protected against intruders in order to maintain patients’ confidentiality. The cardiac reports should be converted into nonreadable text for the intruders, which is the main objective of this chapter. Music-key has been derived through a nature-motivated harmony search metaheuristic algorithm followed by classical AES cryptography. Cardiovascular diseases (CVDs) are one of the cardiac diseases connected to the blockage of arteries and veins in the human body. Cardiac comorbid patients were at the largest risk zone during the COVID-19 period. Those patients were treated properly with the help of telecardiology in the critical times of COVID-19. A secure transmission of multiple cardiac reports of the patients can be carried out without the loss of patients’ data. It has been crafted in this chapter with the help of metaheuristic music-key generation followed by AES encryption. The time complexity of the proposed music-key generation module was found to be O(m ∗ log n). Thus, pushing the COVID-19 telecardiology system with more security and robustness attributes.
Pattern reduction

An efficient framework for reducing the computation time of metaheuristic algorithms, called pattern reduction (PR), is described in this chapter. Compared with parallel technologies that can only be used to reduce the “response time” of metaheuristic algorithms, PR can be used to reduce the “computation time” of metaheuristic algorithms by eliminating redundant computations during the convergence process. Motivated by the observation and assumption that subsolutions of metaheuristic algorithms will reach their final states at different times, the major task of PR is to detect and remove subsolutions that have reached their final states and for which any further computations are thus essentially redundant. The basic idea and source code of PR for GA, called pattern reduction enhanced genetic algorithm (PREGA), for solving the data clustering problem are given to show how to realize it. Simulation results of PREGA and GA for the data clustering problem are also given to show the possibilities and potential of PR for most, if not all, of the metaheuristic algorithms.
Introduction

Since we encounter many complex optimization problems in our daily lives and scientific studies, the development of high-performance search algorithms to solve these complex optimization problems, especially those that are either NP-hard or NP-complete, has been an interesting research topic for years. That is why this chapter begins with an optimization problem, named the traveling salesman problem, as an example to explain how hard such problems can be. This is followed by a discussion of why metaheuristic algorithms are used to solve optimization problems and the main research issues in the implementation of metaheuristic algorithms. The organization of the whole book and of each chapter is explained to show how to use this book to maximize learning.
Ant colony optimization

The focus of this chapter is on another well-known population-based metaheuristic algorithm, called ant colony optimization (ACO), which is also a popular algorithm of swarm intelligence. The search behavior of ACO is inspired by the behavior of ant colonies. Examples are given to show how ants use and share distance and pheromone information to find a good path. In addition to the pseudocode for illustrating the basic idea of ACO, the equations for calculating the transition probability are provided to show how a candidate solution is constructed by an ant. The source code and simulation results of ACO will also be presented to show how to use it to solve the traveling salesman problem. The historical development, applications, and recent research trends of ACO are discussed in the end of this chapter.
Simulated annealing

The focus of this chapter is on a single-solution-based algorithm, named simulated annealing (SA). A simple example is first given to show the search behavior of SA in the landscape of the solution space. The basic idea and pseudocode of SA as well as the probability equations for accepting a non-improving candidate solution are also given. The source code and simulation results of SA are then presented to show how to realize SA to solve the one-max and deceptive optimization problems. Finally, a brief survey on the historical development of SA and its applications is given to show the possibilities of SA.
Parallel metaheuristic algorithm

The focus of this chapter is on the realization of parallel metaheuristic algorithms. Some classifications of parallel metaheuristic algorithms are given. Several models of parallel metaheuristic algorithms, such as master–slave, coarse-grained (island model), and fine-grained, are described. The source code of the master–slave parallel genetic algorithm (GA) is given as an example to show how to convert a metaheuristic algorithm to a parallel metaheuristic algorithm. We compare the master–slave parallel GA with simple GA to show the potential of parallel metaheuristic algorithms.
Genetic algorithm

The focus of this chapter is on a well-known population-based metaheuristic algorithm, called the genetic algorithm (GA). Since the search behavior of GA is inspired by the natural selection theory of Darwin, examples are first given to show the solution structure of GA, that is, what the terms population, chromosome, gene, and fitness mean in GA. Other examples are then given to illustrate the major search operators and the search behavior of GA in the landscape of the solution space. The basic idea and pseudocode of GA are also given to explain the key ideas of fitness values, selection, crossover, and mutation. The source code and simulation results of GA are also presented to show how to use it for solving the one-max and deceptive optimization problems. The last section is focused on the historical development, applications, and recent research trends of GA.
Search economics

A new promising metaheuristic algorithm, named search economics (SE), is described in this chapter. The unique characteristics of SE are that it first divides the solution space into a set of subspaces (regions) and then uses the expected value of a region to replace the objective or fitness value of a solution to determine later search directions. The expected value is inspired by the return on investment (ROI), which is composed of the following pieces of information: (1) status of investment, (2) potential of investment, and (3) the landscape of each region. The major operators of SE—namely, resource arrangement, vision search, and marketing research—are also described. SE is applied to the one-max problem and compared with other metaheuristic algorithms to show the possibilities and potential of SE.
Particle swarm optimization

This focus of this chapter is on a population-based metaheuristic algorithm called particle swarm optimization (PSO), which is also a popular swarm intelligence algorithm. Because PSO emulates the social behavior of a swarm, such as a flock of birds or a school of fish, the positions of particles and their velocities are used to represent the candidate solutions and how they move. Examples and equations for updating the velocities and positions of particles are provided to describe how the particles are moved around by using the following pieces of information: personal trajectory, personal best, and global best. The pseudocode is presented to explain the basic ideas of PSO. The source code and simulation results of PSO are presented to show how to use it for solving the single-objective optimization problem. The historical development, applications, and recent research trends of PSO are also given in the end of this chapter.
Differential evolution

The focus of this chapter is on a very popular population-based metaheuristic algorithm, called differential evolution (DE). DE can be regarded as a branch of evolutionary computation (EC). DE has inherited the characteristics of EC; as such, DE algorithms contain crossover, mutation, and selection operators. However, its solution encoding is typically a set of real numbers and its search operator is designed for such a solution encoding schema. Some examples and equations of the mutation and crossover operators are given to describe how the current vectors are used to generate offspring vectors. Pseudocode is given to explain the basic idea of DE. Source code and simulation results of DE are also presented to show how to use it for solving the single-objective optimization problem. The historical development, applications, and recent research trends of DE are also given in the end of this chapter.
Evaluation and determination operators

The evaluation and determination operators are two different operators of a metaheuristic algorithm; however, they are typically inseparable (the results of the evaluation operator may affect the differentiation ability of the determination operator), and thus they are designed together. This chapter begins with discussing a number of evaluation operators to show why different types of evaluation operators are needed. The determination operators of simulated annealing, tabu search, the genetic algorithm, ant colony optimization, particle swarm optimization, and differential evolution are then discussed to show how they use different ways to determine the search directions or regions. To further explain the impact of the determination operator and the relationship between search algorithm and optimization problem, the schema theorem and fitness landscape analysis are also discussed.
Freedom to Share: How Government’s Data Sharing Policies Concerning Publicly Available Data Impact Academic Research and Journalism in the Public Interest

Our societies rely on quality academic and scientific research and journalism to thrive and prosper. A necessary condition for public-interest research and journalism is access to data unencumbered by unnecessary legal barriers. Commemorating the ten-year anniversary of the tragic death of data access activist and digital rights champion Aaron Swartz, this article examines the policy and legal landscapes in the European Union and in the United States regarding access to data. It offers recommendations to strike a necessary balance between data protection and copyright, on the one hand, and research and journalism freedoms on the other, as a means to support better sharing of data in the public interest.