Reviewerly/exHarmony huggingface dataset:
Powering Elementary Civics Education: Enhancing Active Learning and Performance

This research addresses the issue of passive student engagement and teacher-centered learning approaches in primary schools. To overcome these challenges, the study employs Microsoft PowerPoint as an instructional medium to enhance the learning experience. A Pre-Experimental Design research method is employed, conducted at SD Negeri Pejangkungan with thirty fourth-grade students as participants. Data collection involves the use of tests, and the obtained results are subjected to t-test analysis. The findings reveal a significant positive impact of utilizing Microsoft PowerPoint media on students' civics learning outcomes, as indicated by tcount (24.686) exceeding ttable (2.045). Additionally, eta squared analysis demonstrates a considerable influence of Microsoft PowerPoint media on learning outcomes (0.95&gt;0.14). These results emphasize the potential of Microsoft PowerPoint as an effective tool to promote active learning and enhance students' academic achievements in various educational settings.&#x0D; Highlights:&#x0D; &#x0D; &#x0D; Active Engagement: This research explores the use of Microsoft PowerPoint as a means to promote active learning among elementary students, addressing the issue of passive student engagement in the classroom.&#x0D; &#x0D; &#x0D; Academic Improvement: The study investigates the impact of utilizing Microsoft PowerPoint on students' academic performance, specifically focusing on civics education, to determine the effectiveness of this instructional medium.&#x0D; &#x0D; &#x0D; Experimental Design: The research employs a Pre-Experimental Design methodology, conducted at SD Negeri Pejangkungan with fourth-grade students, to gather data through tests and analyze the outcomes using statistical measures such as t-tests and eta squared.&#x0D; &#x0D; &#x0D; Keywords: Active learning, Academic performance, Microsoft PowerPoint, Experimental study, Civics education.
Big Books: Enhancing Grade 4 Reading Skills Through Descriptive Texts

This study discusses the use big book in learning descriptive text skills for 4th grade students at SDN GRABAGAN. The purpose of this study was to determine the effect of big book on the ability to reading descriptive text. The method used in this study is quantitative with a pre-experimental design that applies pre-test and post-test instrument. By using the paired sample T-test, it shows that there are differences in the results of the average scores of the pre-test and post-test, namely for average value of the pre-test, and, for the post-test. This shows that the use of big book has an influence in learning reading skills.&#x0D; Highlights: &#x0D; &#x0D; The study explores the utilization of big books to enhance reading skills in grade 4 students through descriptive texts.&#x0D; A pre-experimental design with pre-test and post-test instruments was used to assess the impact of big book usage.&#x0D; The results indicate a significant difference in the average scores between the pre-test and post-test, demonstrating the positive influence of big book on learning reading skills.&#x0D; &#x0D; Keywords: Big book, Descriptive text, Grade 4 students, Reading skills, Elementary schools.
Compulsoriness and Energy Efficiency in the Decentralized World of Smart Things

Energy-efficient, self-sufficient and intelligent networked nodes and IoT devices can be found almost everywhere in everyday life today. These wide range of internet-enabled household and entertainment devices that can be centrally controlled and managed, through medical devices that monitor the personal condition, to the monitoring of entire machine parks and production streets in Industry 4.0 as well as supply chain management issues. Blockchained IoT contributes to the compulsoriness, commitment in the decentralized world of smart things. Secured IoT devices can only be achieved with the combination of known crypto-technologies. Thought a little further, the step-by-step provision of different blockchain-based platforms and solutions can help to reach the desired protection goals.
TrustedCloud

Applications executing in the cloud are prone to numerous attacks including network, malware and data manipulation which are not immediately apparent. The Dwell Time for an attack to be noticed is currently 211 days where human error is reported to be a factor of 95% of data breaches [1]. In this abstract, we present TrustedCloud, a novel framework for run-time trust state analysis of a virtual machine (VM). TrustedCloud has four components, a) Trust Agent, b) Kernel Module and c) Trusted Platform Module (TPM) executing in all the VMs while d) Verification Cluster, a centralised entity verifying the VM's state. We will show the functioning of the TrustedCloud from the stage of VM creation to the detection of unauthorised service execution.
Quantum field theory and the structure of the SM

The Standard Model of the electroweak and strong interactions of particle physics is a quantum field theory.Elementary particles are not indivisible 'pieces' of matter but energy bundles of fields, whose properties and interactions are a consequence of the principles of symmetry.These lecture notes provide a brief introduction to the construction of the Standard Model from its basic ingredients: Poincaré symmetry, gauge invariance and spontaneous symmetry breaking.The full Lagrangian is derived in detail and the most relevant aspects of the electroweak phenomenology are discussed with special emphasis on the determination of the input parameters and the consistency checks of the model.Some exercises are proposed to fix the main ideas.
Eigenstates of two-level systems in a single-mode quantum field: from quantum Rabi model to N-atom Dicke model in the Coulomb gauge

Abstract In the present paper we show that the Hamiltonian describing the resonant interaction of N two-level systems with a single-mode electromagnetic quantum field in the Coulomb gauge can be diagonalized with a high degree of accuracy using a simple basis set of states. This allows one to find an analytical approximation for the eigenvectors and eigenvalues of the system, which interpolates the numerical solution in a broad range of the coupling constant values. For the first time, we show that the energy spectrum of Dicke model (DM) as a function of N (with <?CDATA $N \gg 1$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>N</mml:mi> <mml:mo>≫</mml:mo> <mml:mn>1</mml:mn> </mml:math> ) satisfies the simple scaling relation. In addition, the introduced basis states provide a regular way of calculating the corrections and estimating the convergence to the exact numerical solution. Analytical approximations and numerical results describing the energy spectrum are valid for both quantum Rabi model ( N = 1) and DM for <?CDATA $N \geqslant 2$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mi>N</mml:mi> <mml:mo>⩾</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> atoms.
Busting Trolls

This chapter examines the social media experiences of comedian and actress Leslie Jones in the months following the debut of the trailer for the 2016 Ghostbusters film. Critical discourse analysis is used as a method for understanding the discursive practices rooted in harmful ideologies present in Western culture that manifested through social media mobbing and normalized hate speech directed at Jones over several months. The release of the first trailer and a Breitbart review of the film are viewed as critical incidents that inspired social media mobbing and eventually led to Jones temporarily leaving Twitter. Twitter users who responded to Jones fell into categories of detractors and supporters. Within those categories, subgroups were discovered along with underlying commonalities between those groups. Nearly, all responses to Jones’ tweets were characterized by a sense of personal familiarity on the part of the users which speaks to the nature of Twitter communication. Hostile messages directed at Jones were typically characterized by racist and/or misogynist language along with some sort of justification as to why the abuse was warranted. This chapter concludes with a discussion about the implications for ideological discursive formations within the social media context.
Why a bilingual writer corpus? Motivations and approaches

This chapter provides context for the rest of the book by explaining the rationale for creating and using bilingual user corpora: large collections of authentic language use by people in more than one of the languages which they frequently use. In the first part of this chapter, I review the nature of language corpora, the types of corpus currently available and the kinds of research question for which they are relevant. I then review the focus in recent years on multilingualism as a key topic in applied linguistics, and the lack of existing suitable corpora to support research on multilingual language users. Following this, I explain the characteristics of Zayed Arabic-English Bilingual Undergraduate Corpus (ZAEBUC) as an example of a bilingual writer corpus. Finally, I outline the chapters in the rest of this book, which focus on aspects of methodology for such a corpus and present a range of research studies using ZAEBUC.
Teaching ESP: The English Lecturers’ Communicative Activities in Business English Instructions

This corrigendum article focuses on enhancing the communicative competence of ESP learners using a wide range of tasks or activities. The target audience for this article is students enrolled in the Business Administration program at the State Polytechnic of Ujung Pandang. By carefully designing activities based on each student’s level and the standards they must meet, we facilitated them to connect with their classmates in the classroom and improve their speaking ability. The purpose of this study was to demonstrate that students can improve their speaking skills by actively participating in the communicative activities that take place in the classroom. It was found that students were engaged in the activities and using the language when interacting with others in discussion sessions, group projects, and role-playing activities.
7. Narrative Experiences of Social Media and the Internet from Men with Eating Disorders

Eating disorders remain little understood by the public, and sensationalist stories in the media have done little to dispel simplistic and reductionist perspectives. This edited volume uses a range of language-centred approaches to provide much needed critical in-depth analysis and interdisciplinary synthesis. The book
11. ‘I’ll Never Be Skinny Enough’: A Fantasy Theme Analysis of Pro-Anorexia Discourse

Eating disorders remain little understood by the public, and sensationalist stories in the media have done little to dispel simplistic and reductionist perspectives. This edited volume uses a range of language-centred approaches to provide much needed critical in-depth analysis and interdisciplinary synthesis. The book
GENESIS

GENESIS embarks on an emotionally intense virtual reality journey to experience the dramatic milestones in the evolution of Earth and mankind. As a 360°, stereo 3D film, GENESIS has been pre rendered (no real time) and is usable as a three Degrees of Freedom (3DOF) experience. The project can be watched on all VR glasses that can play 3DOF, like 360° movies. Glasses such as Pico G2 or Oculus Go were developed specifically for 3DOF content. Of course, all glasses which are able to present 6DOF experiences can also play 3DOF content. The users can watch the content while standing or sitting.
Gaussian process regression in the flat limit

Gaussian process (GP) regression is a fundamental tool in Bayesian statistics. It is also known as kriging and is the Bayesian counterpart to the frequentist kernel ridge regression. Most of the theoretical work on GP regression has focused on a large-n asymptotics, characterising the behaviour of GP regression as the amount of data increases. Fixed-sample analysis is much more difficult outside of simple cases, such as locations on a regular grid. In this work, we perform a fixed-sample analysis that was first studied in the context of approximation theory by Fornberg and Driscoll (2002), called the “flat limit”. In flat-limit asymptotics, the goal is to characterise kernel methods as the length-scale of the kernel function tends to infinity, so that kernels appear flat over the range of the data. Surprisingly, this limit is well-defined, and displays interesting behaviour: Driscoll and Fornberg showed that radial basis interpolation converges in the flat limit to polynomial interpolation, if the kernel is Gaussian. Subsequent work showed that this holds true in the multivariate setting as well, but that kernels other than the Gaussian may have (polyharmonic) splines as the limit interpolant. Leveraging recent results on the spectral behaviour of kernel matrices in the flat limit, we study the flat limit of Gaussian process regression. Results show that Gaussian process regression tends in the flat limit to (multivariate) polynomial regression, or (polyharmonic) spline regression, depending on the kernel. Importantly, this holds for both the predictive mean and the predictive variance, so that the posterior predictive distributions become equivalent. For the proof, we introduce the notion of prediction-equivalence of semiparametric models, which lets us state flat-limit results in a compact and unified manner. Our results have practical consequences: for instance, they show that optimal GP predictions in the sense of leave-one-out loss may occur at very large length-scales, which would be invisible to current implementations because of numerical difficulties.
CONCEPTION OF TEACHING THE COURSE NUMERICAL METHODS AND STATISTICS AT THE FACULTY OF MECHANICAL ENGINEERING, UNIVERSITY OF ŽILINA AND ITS TRANSFORMATION DURING THE COVID 19 PANDEMIC

CONCEPTION OF TEACHING THE COURSE NUMERICAL METHODS AND STATISTICS AT THE FACULTY OF MECHANICAL ENGINEERING, UNIVERSITY OF ŽILINA AND ITS TRANSFORMATION DURING THE COVID 19 PANDEMIC
TEACHING APPLIED THROUGH REMOTE BOOT CAMP: AN EXPERIENCE REPORT

Appears in: ICERI2022 Proceedings Publication year: 2022Pages: 5700-5704ISBN: 978-84-09-45476-1ISSN: 2340-1095doi: 10.21125/iceri.2022.1406Conference name: 15th annual International Conference of Education, Research and InnovationDates: 7-9 November, 2022Location: Seville, Spain
ROBOTSOLUTION: A KIT FOR THE DEVELOPMENT OF COMPUTATIONAL THINKING SKILLS IN HIGH SCHOOL STUDENTS

Appears in: ICERI2022 Proceedings Publication year: 2022Pages: 5716-5722ISBN: 978-84-09-45476-1ISSN: 2340-1095doi: 10.21125/iceri.2022.1410Conference name: 15th annual International Conference of Education, Research and InnovationDates: 7-9 November, 2022Location: Seville, Spain
THE VALUE OF BICHRONOUS ONLINE FACILITATION TO PROMOTE STUDENT ENGAGEMENT IN A TEACHER EDUCATION COURSE

THE VALUE OF BICHRONOUS ONLINE FACILITATION TO PROMOTE STUDENT ENGAGEMENT IN A TEACHER EDUCATION COURSE
Automated Room Occupancy Prediction Using Fuzzy-Rough Set Theory-Based Supervised Learning

Among the many research problems that appeal to the scientific community toward smart, IoT-enabled developments, human activity recognition has always been relevant and interesting. This human activity recognition of active research offers several enticing challenges, such as single person activity recognition, group activity recognition, monitoring and tracking toward indoor healthcare, and room occupancy estimation. Out of these, room occupancy classification from passive data is an important one. While most works focus on the use of conventional learning or deep learning algorithms toward developing an automated solution, the recent progress in fuzzy-set and rough-set theory can be advantageous in solving data-driven problems. The proposed work leverages this mathematical complexity of the convergence of fuzzy-rough set theories along a conventional machine learning algorithm to provide a smart solution to the problem of room occupancy estimation. The experiments reveal that the conventional learning algorithm alone is outperformed by the chosen fuzzy-rough set theory-based classification approach, with a high accuracy of 99.96%.
Organizational, Technological, and Pedagogical Conditions for Differentiated Instruction of Teaching English as a Foreign Language

The aim of this study is to examine the relationships between differentiated instruction, pedagogical and technological competencies of students studying in the foreign language department. Differentiated instructional self-efficacy, pedagogical and technological competencies of students studying in foreign languages departments were examined in terms of grade level and perception of academic achievement within the scope of the comparative relational survey model. In the study, the relationships between differentiated instructional self-efficacy and pedagogical and technological competencies of students studying in foreign language departments were also examined on the basis of the relational survey model. The study was carried out on 234 students studying in foreign languages departments of different universities in Kazakhstan. Differentiated instructional self-efficacy, pedagogical and technological competence scales in the Likert-form were used to collect the research data. In the study, descriptive statistics F test and Regression analysis techniques were used in the analysis of the data. According to the research findings, the differentiated instruction self-efficacy, pedagogical and technological competencies of foreign language students were found to be moderate. Differentiated instructional self-efficacy, pedagogical and technological competencies of foreign language students differ according to their classroom and academic achievement perceptions. Finally, the pedagogical and technological competencies of foreign language students significantly affect their self-efficacy towards differentiated instruction.
Anti‐forensics for double JPEG compression based on deep reinforcement learning

DJPEG (double JPEG compression) detection has received attention in image forensics. In order to study the limitations of forensics detectors under attacks, it is essential to develop DJPEG anti-forensics techniques. Compared with anti-forensics operations performed in spatial domain, existing DCT-based methods have not brought their potential to full play due to the difficulty of directly manipulating DCT coefficients. In recent years, DRL (deep reinforcement learning) has developed rapidly in image-related tasks. Its ability of decision making can turn an unpredictable restoration step into multiple simple trial-and-error modification steps. In this paper, it is investigated how to efficiently modify DCT coefficients for anti-forensics purpose with the help of DRL, and propose a method called AFDJ-DRL (Anti-forensics Framework for DJPEG based on DRL). Specifically, an agent utilizes a policy network to extract DCT inter-block and intra-block features and learn a coefficient-level policy. An environment assigns rewards from multiple sources. Via maximizing such rewards, the agent can learn to modify DCT coefficients in several rounds to obtain images with anti-forensics capability. Experimental results show that the AFDJ-DRL is superior to existing DCT-based methods, and can be applied as a post-processing step for spatial ones for further performance improvement.
Tool for Parsing Important Data from Web Pages

This paper discusses the tool for the main text and image extraction (extracting and parsing the important data) from a web document. This paper describes our proposed algorithm based on the Document Object Model (DOM) and natural language processing (NLP) techniques and other approaches for extracting information from web pages using various classification techniques such as support vector machine, decision tree techniques, naive Bayes, and K-nearest neighbor. The main aim of the developed algorithm was to identify and extract the main block of a web document that contains the text of the article and the relevant images. The algorithm on a sample of 45 web documents of different types was applied. In addition, the issue of web pages, from the structure of the document to the use of the Document Object Model (DOM) for their processing, was analyzed. The Document Object Model was used to load and navigation of the document. It also plays an important role in the correct identification of the main block of web documents. The paper also discusses the levels of natural language. These methods of automatic natural language processing help to identify the main block of the web document. In this way, the all-textual parts and images from the main content of the web document were extracted. The experimental results show that our method achieved a final classification accuracy of 88.18%.
From Counterpoint to Small Forms

Instructors seeking to diversify music and topics discussed in their curricula may turn to popular music examples to supplement the comparative lacuna of analyzed western classical works by Black artists. With this change in focus comes at least three pedagogical challenges: (1) how to approach musical organization in popular music, (2) how to discuss the similarities and differences between popular and classical music styles without superimposing the “rules” of the latter onto the former, and (3) how to create a meaningful progression toward more advanced topics when works by Black artists are currently under-theorized and thus less accessible to instructors. This chapter proposes a sound-first approach that promotes an emphasis on listening and the use of protonotation. By removing a reliance on scores or transcriptions, students with varying levels of notational fluency can approach classical and popular music styles on equal footing. We utilize a spiral curricular design that starts with contrapuntal reductions and moves through cadences and phrase types to small forms. Throughout, we explore several examples by Black artists for each topic and provide lists of excerpts for further listening.