Reviewerly/exHarmony huggingface dataset:
The Effectiveness of the Blended Learning Approach Based on Edmodo to Enhance Student Learning Outcomes in English Courses

&lt;div class="WordSection1"&gt;&lt;p&gt;&lt;span lang="EN-US"&gt;A learning model called blended learning mixes online and face-to-face instruction. It includes multiple implementation techniques, instructional models, and learning styles. &lt;/span&gt;&lt;span lang="EN-US"&gt;The Edmodo application media is one of the educational chances supplied by exploiting current ICT advances. The purpose of this study was to determine the effectiveness of learning using the Edmodo-based blended learning model in improving student learning outcomes in English courses. &lt;/span&gt;&lt;span lang="EN-US"&gt;This study employed an experimental research methodology. Data are grouped and extraneous information is reduced due to the descriptive qualitative approach's framework, which collects data through testing, observation, and documentation. In this study, data analysis started with data collecting and was then condensed, including summarizing, choosing the most crucial information, concentrating on it, searching for themes and patterns, and removing irrelevant information. Based on the research findings, two approaches—online via the Edmodo application and offline—are recommended for implementing the blended learning strategy based on Edmodo (face to face). Students use Edmodo to interact with lecturers and other students during online lectures. Beforehand, the lecturer created a virtual class on Edmodo and connected all the students to it. The in-person meetings include group discussions based on the material covered in the online classes. Events held in person can also be hosted using the Edmodo app. Based on test results and an analysis of the learning outcomes of students who used the blended learning strategy built on Edmodo, it is rated in the very good category.&lt;/span&gt;&lt;/p&gt;&lt;/div&gt;
EML for Unsupervised Learning

This chapter introduces the use of Evolutionary Machine Learning (EML) techniques for unsupervised machine learning tasks. First, a brief introduction to the main concepts related to unsupervised Machine Learning (ML) is presented. Then, an overview of the main EML approaches to these tasks is given together with a discussion of the main achievements and current challenges in addressing these tasks. We focus on four commonly found unsupervised learning tasks: Data preparation, Outlier detectionOutlier detection, Dimensionality reductionDimensionality reduction, and Association rule mining. Finally, we present a number of findings from the review. These findings could guide the reader at the time of applying EML techniques to unsupervised ML tasks or when developing new EML approaches.
Evolutionary Computation and the Reinforcement Learning Problem

Evolution by natural selection has built a vast array of highly efficient lifelong learningLifelong learning organisms, as evidenced by the spectacular diversityDiversity of species that rapidly adapt to environmental change and acquire new problem-solving skills through experience. Reinforcement Learning (RL) is a machine learning problem in which an agent must learn how to map situations to actions in an unknown world in order to maximise the sum of future rewards. There are no labelled examples of situation $$\rightarrow $$ action mappingsMapping to learn from and we assume that no model of environment dynamics is available. As such, learning requires active trial-and-error interaction with the world. Evolutionary Reinforcement Learning (EvoRL), the application of evolutionary computation in RL, models this search process at multiple time scales: individual learning during the lifetime of an agent (i.e., operant conditioning) and population-wide learning through natural selection. Both modes of adaptation are wildly creative and fundamental to natural systems. This chapter discusses how EvoRL addresses some critical challenges in RL including the computational costComputational cost of extended interactions, the temporal credit assignmentCredit assignment problem, partial-observability of state, nonstationary and multi-task environments, transfer learningTransfer learning, and hierarchical problem decomposition. In each case, the unique potential of EvoRL is highlighted in parallel with open challenges and research opportunities.
Evolutionary Supervised Machine Learning

This chapter provides an overview of evolutionary approaches to supervised learning. It starts with the definition and scope of the opportunity, and then reviews three main areas: evolving general neural network designs, evolving solutions that are explainable, and forming a synergy of evolutionary and gradient-based methods.
Evolutionary Machine Learning in Science and Engineering

Evolutionary machine learning (EML) has been increasingly applied to solving diverse science and engineering problems due to the global search, optimization, and multi-objective optimization capabilities of evolutionary algorithms and the strong modeling capability of complex functions and processes by machine learning (ML) and especially deep neural network models. They are widely used to solve modeling, prediction, control, and pattern detectionPattern detection problems. Especially EML algorithms are used for solving inverse designInverse design problems ranging from neural network architecture search, inverse materials design, control system design, and discoveryMaterials discovery of differential equations.
Exploration and Struggle in Advancing Historical Research Methodologies

Within the purview of “Retrospection and Prospect,” analyses of research findings from 2021 to 2022 reveal several notable trends. Firstly, scholars in various domains emphasize the intensification of collaborative research, reevaluation of hypothetical and traditional research paradigms, and the promotion of structural history. Simultaneously, there is a discernible trend towards individualized research pursuits. Secondly, the quantitative growth in research output has led to the diversification of research methodologies. However, it is worth reiterating that the mere increase in quantity has not invariably resulted in an enhancement of research quality. An enduring concern is the challenge faced by emerging researchers in establishing and expanding their unique research domains, exacerbated by limitations in accessing new source materials and research topics. To address this predicament, there is a pressing call to explore novel research avenues, revisit foundational scholarship, and develop innovative methodological approaches. Thirdly, the papers highlight the aspiration for collaborative and collective research endeavors that envisage a ‘chemical synergy,’ fostering the emergence of diverse research domains with the aim of forging a novel historical paradigm in the 21st century.BR Despite the ongoing earnest endeavors of Korean historians, the hope remains for historical research communities in Korea that prioritizes empirical and profound analyses over mechanistic, superficial, and fragmented pursuits.
Dreaming of Chemical Bonds at the End of the Growth Era

It may differ depending on whether defining ‘Korean ancient history’ in a narrow or broad sense, but according to Korean History Research Report, a total of 1,307 articles related to ‘ancient Korea’ were published between 2021 and 2022. It was recognized that the annual quantitative flow of articles on ‘Korean ancient history’ peaked in 2018 and has entered a downward or plateauing period. It seems to have slightly shrunk compared to the number of articles on ‘Korean history’ and history as a whole showing a recovery in quantitative growth during the same period. However, considering expected decline in the inflow of new researchers due to the decrease in population, it is difficult to deny that we are all standing at or moving towards the end of the Growth Era.BR The decentralization of research topics has been criticized consistently, but diversification is an irreversible trend. Expecting researchers to become part of a large theoretical framework or great discourse again seems to be a hope of the past Growth Era. Instead, researchers may naturally gather around the materials or themes that their own pursuing leads to. It goes without saying that joint research will be more important, but simply putting each arguments together is not enough. It is necessary to increase the results from ‘chemical bonding’ among researchers.
Adversarial Evolutionary Learning with Distributed Spatial Coevolution

Adversarial Evolutionary Learning (AEL) is concerned with competing adversaries that are adapting over time. This competition can be defined as a minimization–maximization problem. Different methods exist to model the search for solutions to this problem, such as the Competitive Coevolutionary Algorithm, Multi-agent Reinforcement LearningReinforcement Learning, Adversarial Machine Learning, and Evolutionary Game Theory. This chapter introduces an overview of AEL. We focus on spatially distributed competitive coevolutionCompetitive coevolution for adversarial evolutionary learning to deal with the Generative Adversarial Networks (GANs)Generative Adversarial Network (GAN) training challenges. A population of multiple individual solutions, parameterized artificial neural networks (ANN), provides diversityDiversity to the gradient-based GAN learning and increases the robustness of the GAN training. The computational complexity is reduced by using a spatial topology that decreases the number of evaluations and facilitates scalability. In addition, the topology enables diverse hyper-parameters, objectives, search operators, and data. We present a design and an implementation of an AEL system with spatial competitive coevolutionCompetitive coevolution and gradient-based adversarial learning. We demonstrate how the increase in diversityDiversity improves the performance of generative learning tasks on image data. Moreover, the distributed population in AEL can help overcome some hardware limitations for ANN architectures.
Illocutionary Acts in Recorded Presentations of English Education Master's Program Students

This study aims to identify the illocutionary acts used by English Education Master’s Program students and the underlying reasons for them to use the acts in their recorded presentations. There is little study on master’s degree students, especially in the pandemic era. Therefore, this study attempted to identify the illocutionary acts performed by master’s degree students in the recorded presentation. This study used qualitative research in order to collect and analyze the data. Searle’s (1979) theory of the illocutionary act is used. The data was taken from the videos which were transcript. The transcription was used to analyze the illocutionary acts used by the students. The qualitative data showed that the students mainly used representatives (66%), directives (26%), and expressive acts (8%) in the video 1 of the recorded presentation. Similarly, in the second video, the student used representatives (68%), directives (25%), and expressive (6%). Besides, none of the videos were showing the students’ commissive and declarative acts. Results showed that the students used it for explaining to the audiences about facts or theories and examples.
The Image of Children in Roald Dahl's Matilda

his research aims to explore the image of the children as it is constructed by the educational institution particularly by the teachers in the novel Matilda. This research is a descriptive qualitative study with applies the theory of imagology by Milan Kundera and the image of the child by Loris Malaguzzi. This research used the close textual analysis method to describe and analyze the meaning, structure, and function contained in the text. The result of this research shows that the different images of the children constructed by two teacher characters of the novel. The image of the children constructed by Miss Honey is that the children are innocent, potential, and dependent. While the image of the children constructed by Mrs. Trunchbull is that children are a source of trouble, disgusting, disturbing, and should not exist in the world. Based on the image of the children constructed by Miss Honey and Mrs. Trunchbull, these two teacher figures show different behaviors when they interact with children. Miss Honey tends to pay special attention to children, teach them new things in a fun way, and be open to children. While Mrs. Trunchbull shows abusive behavior in children such as shouting, cursing, and giving harsh punishments to children. Therefore, based on the results of research conducted, it can be concluded that the image of children is not always the same in the eyes of everyone and the image they construct in children affects the way they treat children.
Run-on and Fragment Sentences: An Error Analysis in Indonesian English Learners' Writings

Run-on and fragment sentences provide clues as to how English learners understand English sentences and how English learners organize information and shape their essays. This research was based on a group case study of run-on and fragment sentences in English college studentsâ€™ essays, using an English Sentence Completeness approach to investigate how English college students make an error on making a sentence in their English essays and discusses the further pedagogical implications based on the errors pattern found. English college students were found to do run-on in their sentences more often than fragments. They write more than one subject and one verb in a sentence and significantly forget to limit their sentences. The carelessness in punctuation and a period between sentences make the sentence permitted to â€œrun onâ€ into the next. Besides run-on, fragments were also found in studentsâ€™ essays: Missing subject and verb conditions are significantly found in the studentsâ€™ sentences. These findings revealed that English college students lack knowledge of how to make complete sentences in ordering information and building up a textâ€™s structure. Therefore the learning process of English writing should be informed of the importance of English sentence completeness in organizing a text and introduce the strategies for avoiding and making less run-on and fragment sentences through practice activities and writing assignments. This research is qualitative descriptive-explorative research where descriptive and explorative are intended to describe and explain the situation based on collected data and facts, which are then analyzed and arranged systematically to get conclusions in detail to be a hypothesis.
An Analysis Of Extrinsic Elements In The Collection Of Short Stories Berjuta Rasanya by Tere Liye

The collection of short stories Berjuta Rasanya is one of the literary masterpieces of an Indonesian writer named Tere Liye. This study aims to determine the extrinsic elements that influence the writing of each short story in the book Berjuta Rasanya. This study aims to describe the various extrinsic elements contained in the collection of short stories Berjuta Rasanya. The type of this research is a qualitative research with a descriptive method. The data source of the research is a collection of short stories Berjuta Rasanya by Tere Liye. The data collection techniques in this study were: (1) reading and understanding the collection of short stories Berjuta Rasanya by Tere Liye, (2) coding by underlining sentences containing extrinsic elements in the short story collection Berjuta Rasanya by Tere Liye. The steps of analyzing the data are: (1) reading the recorded data, (2) classifying or recording data according to the researcher's objectives based on the collection of short stories Berjuta Rasanya by Tere Liye, (3) interpreting the data with research objectives, namely: analyzing the collection of short stories Berjuta Rasanya by Tere Liye, and (4) making research conclusions. The results of this study indicate that the extrinsic elements that can be found in the collection of short stories Berjuta Rasanya by Tere Liye are the background of the author's life, psychological elements of the author, the author's social environment, social values, religious values, moral values, and cultural values.
Utilization of the complete blood count in diagnosing endemic diseases in Pakistan

Numerous infectious diseases, including enteric fever, malaria, dengue fever, and, most recently, coronavirus disease-2019, are prevalent in Pakistan. All these diseases have overlapping clinical symptoms and can present a diagnostic challenge for the general practitioner. Since definitive testing for these disorders is time-consuming and expensive, basic clinical testing, such as a complete blood count, should be utilised to supplement clinical diagnosis, when possible. The current narrative review was planned to present specific alterations in haematological parameters for each of these disorders. The review was intended as a guide for practising physicians in their decision-making.
An Analysis of Cooperative Principles on Asumsi.co's Interviews on Russia-Ukraine War Issue

Cooperative Principle is one of the pragmatics disciplines that should be known by everyone to conduct effective communication, especially diplomats. This research studied the utterances which were uttered by two Russian diplomats who are actively working at the Russian Embassy in Indonesia, Denis Tetyushin and Lyudmila Vorobyova in their interviews with Asumsi.co. The objectives of this research were to identify the maxims of cooperative principle which are produced by the interviewees in the Asumsi.co interviews, to figure out the types of maxims disobedience which are produced by the interviewees, and to recognise the motives behind the interviewees obeying and disobeying the maxims. This study was conducted with a qualitative approach assisted by a statistical descriptive method to see the distribution of the obedience and disobedience utterances. The result of the research revealed 47 data obey and disobey the maxim of cooperative principles. The most obeyed maxim was the maxim of manner, followed by the maxim of quantity, the maxim of quality, and there was no utterance which followed the maxim of relevance rule.
Students' Responses to Plot Device of Going/Trapped to Another World Seen in The Wonderful Wizard of Oz by Frank L Baum: Reading Interest and Popular Culture Knowledge

This study discusses the plot device of ‘going/trapped to another world’ as seen in The Wonderful Wizard of Oz by Frank L. Baum about reading interest and popular culture knowledge by the students’ responses from the English Department Universitas Andalas. It aims to find the correlation between their knowledge and interest in the plot device of ‘going/trapped into another world’ and the popularity of the Isekai genre among them. The result shows that the majority of the respondents were familiar with and interested in the genre, and the genre’s ability to facilitate imaginative escapism, served as coping mechanism from real life challenges, it helps their opinion that the plot device remains popular and relevant in the future with strong confidence in its enduring appeal as relatable experiences become the primary reason for its popularity to stay in the pop culture world.
Using a Chatbot to learn English via Charades: the correlates between social presence, hedonic value, perceived value, and learning outcome

Referring to the advantages of intelligent personal assistants, or virtual agents, Charades can be implemented with a Chatbot to enhance students’ vocabulary mastery. However, only a few studies have addressed the effectiveness of learning through practice with chatbots based on the Charades approach. We designed a Charades game using Google Assistant for L2 learners of English to practice vocabulary, and explored the role of social presence, hedonic value, perceived value, and learning outcome, and their correlates. Data were collected from 153 respondents and were analyzed by confirmatory factor analysis. The results showed considerable efficacy of the learning outcomes, revealing a positive correlation between social presence, hedonic value, perceived value, and learning outcomes. The study’s results suggest that incorporating well-designed Charades games via a chatbot with a virtual agent may enhance the perception of hedonic and perceived value and help L2 learners deepen their English vocabulary learning.
Genetic Programming as an Innovation Engine for Automated Machine Learning: The Tree-Based Pipeline Optimization Tool (TPOT)

One of the central challenges of machine learning is the selection of methods for feature selectionFeature selection, feature engineeringFeature engineering, and classification or regression algorithms for building an analytics pipeline. This is true for both novices and experts. Automated machine learning (AutoML)AutoML has emerged as a useful approach to generate machine learning pipelinesMachine learning pipelines without the need for manual construction and evaluation. We review here some challenges of building pipelines and present several of the first and most widely used AutoMLAutoML methods and open-source software. We present in detail the Tree-based Pipeline Optimization Tool (TPOT)TPOT that represents pipelines as expression trees and uses genetic programming (GP) for discovery and optimization. We present some of the extensions of TPOTTPOT and its application to real-world big data. We end with some thoughts about the future of AutoMLAutoML and evolutionary machine learning.
Evolutionary Generative Models

In the last decade, generative models have seen widespread use for their ability to generate diverse artefacts in an increasingly simple way. Historically, the use of evolutionary computation as a generative model approach was dominant, and recently, as a consequence of the rise in popularity and amount of research being conducted in artificial intelligence, the application of evolutionary computation to generative models has broadened its scope to encompass more complex machine learning approaches. Therefore, it is opportune to propose a term capable of accommodating all these models under the same umbrella. To address this, we propose the term evolutionary generative modelsEvolutionary generative model to refer to generative approaches that employ any type of evolutionary algorithm, whether applied on its own or in conjunction with other methods. In particular, we present a literature review on this topic, identifying the main properties of evolutionary generative modelsEvolutionary generative model and categorising them into four different categories: evolutionary computation without machine learning, evolutionary computation aided by machine learning, machine learning aided by evolutionary computation and machine learning evolved by evolutionary computation. Therefore, we systematically analyse a selection of prominent works concerning evolutionary generative modelsEvolutionary generative model. We conclude by addressing the most relevant challenges and open problems faced by current evolutionary generative modelsEvolutionary generative model and discussing where the topic’s future is headed.