Reviewerly/exHarmony huggingface dataset:
Google Classroom Training in Supporting Online Learning During the COVID-19 Pandemic at Bimbingan Belajar Hudallinnas

This year the world is experiencing a COVID-19 pandemic, so educators are witnessing the development of technology in all fields, including in the field of education. During the COVID-19 pandemic, educators must be brave in taking steps to make online learning an opportunity to transform education. There are several steps that can be considered together in transforming education, especially regarding online learning. The purpose of this service is to take this step by sharing knowledge regarding online learning methods which are currently very much needed during the COVID-19 pandemic by making e-learning training based on virtual classes with Google Classroom as a learning medium in Hudallinnas tutoring. Google Classroom is an internet-based service provided by Google with an e-learning system. So that it can be used as an efficient, effective and interactive learning media to support technology-based learning. Hudallinnas Tutoring plays a role in maintaining the continuity of learning so that it continues to run well during the pandemic. While the method used is the practical training method through the transfer of knowledge through training and simulations of Google Classroom application training which are carried out virtually with the hope of achieving equitable abilities in understanding and mastering the Google Classroom application to support online learning with the aim of increasing and increasing the knowledge and abilities of students. educators in using the Google Classroom application so that online learning activities can run effectively.
COMPARISON OF CLASSIFICATION ALGORITHM IN CLASSIFYING AIRLINE PASSENGER SATISFACTION

In order to revive the airline industry, which is being hit by the current recession, it is essential to restore passenger confidence in airlines by improving the services provided by airlines. With the influence of technology in all industrial fields, airlines can now use Machine Learning to find the essential points that can make passengers feel satisfied with airline services and classify passenger satisfaction. This study presents the making of Machine Learning models starting from Data Acquisition, Data Cleaning, Exploratory Data Analysis, Preprocessing, and Model Building. It is concluded that Random Forest is the best algorithm used in this case study, with an F1 accuracy score of 89.4, ROC-AUC score of 0.90, and a shorter modeling period than other algorithms used in this study.
Essential Metals of Public Health Concern in Nigeria

Essential metals are imperative at every stage of human life. Their inclusion in consumer products has been seen over the years. Essential metals are used as fillers in paints. Fillers are granular solids incorporated to impart toughness and texture. The present study was conducted to evaluate the concentrations of essential metals from different manufacturers of paints with special emphasis on their health effects. Six metals, including Cr, Co, Ca, Ti, Mg and Al, were quantified using Inductively Coupled Plasma-Optical Emission Spectrometry. On comparative basis, fourteen paint manufacturers depicted the white paints had the highest concentrations of Ca (10,400.0 mg/kg), Ti (3,170.0 mg/kg) and Al (4,300.0 mg/kg) while green, chocolate and pink paints had the highest concentrations of Co (37.4 mg/kg), Mg (99, 510.0 mg/kg) and Cr (225.0 mg/kg), respectively. The highest concentrations were found in unregistered manufacturers except for Ti. Ti as a filler must have been used to thicken the film, support its structure and simply increase the volume of the paint. The problem of metals knowing what could be a safe exposure and recognising a hazardous exposure has been noted. While we could not make these judgments, it would be wise to adopt a precautionary approach and reduce human exposure to essential metals in paints to a practicable minimum. The toxicity of these metals can cause potential health hazards. Keywords: Essential Metals; Fillers; Inductively Coupled Plasma-Optical Emission Spectrometry; Paint Manufacturers
LIFE IN SCIENCE ACCORDING TO THE CALL OF THE SOUL

In 2021, the 70th anniversary of the Dean of the Faculty of History and Philosophy, Doctor of History Kushnir Vyacheslav Grigoryevich. A sincere man with a great soul, extremely friendly and always smiling, he is an example for all of us.
Sudan's National Launch and Access to Dimensions [Recording]

Training Centre in Communication was honored to work with the Sudanese Academic Libraries Consortium (SALC) in the introduction of open access research discovery solution, Dimensions, to the Sudanese academic community.
Awareness among employees towards investment products

The study aims to explore the awareness of various investment products and evaluate the effect of demographic and socio-economic factors on the awareness of investment products among government employees in Haryana, India. Primary data have been collected from 394 government employees with the help of a self-structured questionnaire. Exploratory factor analysis has been used to extract the dimensions of investment product awareness. One-way ANOVA and t-test have been used to identify the influence of demographic and socio-economic factors on investment product awareness. Exploratory factor analysis extracted eight dimensions of investment product awareness. The result showed that age significantly influences the awareness of all investment products except for balance growth products. However, education has a significant effect only on the awareness of risk-averse products. The awareness level of employees covered under different pension schemes is significantly different in risk-averse products, specific purpose products, and NPS.
Omnivariant Generalized Least Squares Regression: Theory, Geochronological Applications, and Making the Case for Reconciled Δ47 calibrations

Least-squares regression methods are mathematically powerful, conceptually and computationally simple, and widely used in many fields. However, none of the commonly-used flavors of least-squares regression, such as York regression or Generalized Least Squares (GLS), take into account the full set of covariances between all observed (x,y) values. Here we describe the Omnivariant Generalized Least Squares (OGLS) method to fit a model of the form y=f(x), accounting for the full error correlation structure of the (x,y) data, based on a first-order linear propagation of the uncertainties in all variables into errors in y residuals, followed by minimizing the vector of y residuals with respect to the Mahalanobis norm defined by its covariance matrix. This approach may be described as a generalization of both York regression and GLS. It is mathematically exact for straight-line fits, and is also suitable for many non-linear models. Here we describe the principles of OGLS regression and discuss its properties, caveats, and practical use, and provide two consistent open-source implementations in Python and R. To illustrate how various fields of geochronology and stable-isotope geochemistry may benefit from this new method, we discuss how OGLS may specifically apply to 40Ar/39Ar dating and how it provides robust mathematical evidence that Δ47 carbonate calibrations in the recently defined I-CDES metrological scale are statistically indistinguishable, effectively solving long-standing methodological discrepancies.
Convergence and Recovery Guarantees of Unsupervised Neural Networks for Inverse Problems

Neural networks have become a prominent approach to solve inverse problems in recent years. While a plethora of such methods was developed to solve inverse problems empirically, we are still lacking clear theoretical guarantees for these methods. On the other hand, many works proved convergence to optimal solutions of neural networks in a more general setting using overparametrization as a way to control the Neural Tangent Kernel. In this work we investigate how to bridge these two worlds and we provide deterministic convergence and recovery guarantees for the class of unsupervised feedforward multilayer neural networks trained to solve inverse problems. We also derive overparametrization bounds under which a two-layers Deep Inverse Prior network with smooth activation function will benefit from our guarantees.
Nilai Moral dalam Novel Hati Suhita Karya Khilma Anis

The study of this novel uses a literary sociology approach that takes into account the importance or interrelationship between literature and society. The method used in this research is descriptive qualitative method, which means describing words, phrases, sentences and paragraphs contained in the novel. This research is a type of library research because the source of the research is from Novels. The data collection technique used in this study is the reading and note taking technique. From the analysis of the data obtained in this study, it can be concluded that the moral values ​​contained in the novel Hati Suhita by Khilma Anis include: a. moral values ​​related to God include being grateful, surrendering, praying, praising the majesty of God, b. Moral values ​​related to oneself include patience, self-confidence, steadfastness in maintaining marwa, never giving up (tough), admitting mistakes, c. Human relations with other humans in the social sphere include parental love for children, affection for friends/relatives, advice between friends/relatives, help, children's affection for parents, courtesy, respect and gratitude.
Lived experiences among high school students in playing 4 pics 1-word mobile game

This qualitative study unraveled the lived experiences among Grade-10 students of Tuyan National High School in playing the mobile word game 4 Pics 1 Word. The study made use of the descriptive phenomenological research method that looked into their experiences playing the word game. The data were gathered through one-on-one interviews using researcher-made questionnaires and analyzed using the Braun and Clarke six-phase framework for doing thematic analysis employing semantic and latent approaches. Based on the findings of the study, the themes that emerged from the students' responses were: challenged, engaged, enhanced critical thinking skills, and increased vocabulary skills, which reflect that the 4 Pics 1 Word mobile application is conducive and lucrative for students in learning vocabulary. Moreover, the study found that the students’ experiences in playing 4 Pics 1 Word helped them develop an interest in learning new words and increased their self-confidence in expressing themselves and their ideas. Keywords: Gamification, mobile games, vocabulary learning, words.
Development of Digital Literacy on Role of the Instagram Platform in Elementary School Students

The purpose of this study is to describe the role of the Instagram platform in increasing digital literacy among grade IV students at Elementary School Country 2 Wameo. Researchers used a qualitative descriptive research method. The subjects of this study were 25 grade IV students. This study uses instruments in collecting data, namely: observation, interviews, and documentation. The data analysis techniques used include: (1) Data reduction (2) Data presentation (3) Conclusion. The results of the study show the role of the Instagram platform in increasin students digital literacy in class IV Elementary School Country 2 Wameo creating interest in students as well as teachers and students being more creative in using technology, making use of up to date learning resources, students' skills in using new media, students obtaining a variety of relevant information. more broadly, and students have experience from the internet so they can improve students' digital literacy skills by fulfilling competencies in the form of technical abilities, critical understanding, and the ability to communicate and participate.
Planning time in Relation to Complexity of the Writing Task Performance of EFL learners in Non-English Country: Case Study at English Department Khairun University, Indonesia

Writing is a place to express feelings and thoughts in relatively permanent form. However, it becomes problematic because it requires certain linguistic features such as fluency, accuracy, and complexity, (the latter being the dependent variable for the study). One way to overcome these problems is through planning. Thus, this research is focused to find out the effect of planning on complexity in the writing performance especially in EFL learners. There are 20 final-year of undergraduate students of Unkhair involved and are selected through individual IELTS prediction writing-test. This study applies Independent t-test for the first variable of complexity: richness and diversity, and the second variable: size and elaborateness uses Wilcoxon-Mann-Whitney U test. The study uses two different groups as independent variables: 1) Planning group will be coded P1, P2, P3, … and so on, while 2) without planning group will be coded WP1, WP2, WP3, … and so on. The result of the study presents that the mean score in planning groups of both number of words per T-unit and number of clauses per T-unit are generally higher with 10.684 and 1.487, respectively, than in without planning group with just 9.403 and 1.225, respectively. The figures conclude that through planning which happened before the writing, helps the learners to accomplish a better performance regarding the number of words and clauses occur per T-unit.
PROJECT LEARNDEMIC (Learning Assessment and Reading Development through Enhanced Modified Intervention and Collaboration)

The CoViD-19 pandemic has brought serious impact to the various sectors of society especially in education where teachers experience difficulties in the teaching-learning process with their learners. Due to lack of internet connection, teachers of Ansulag Elementay School opted to use modular distance learning delivery mode where learners have to answer every activity or learning task provided in their module at home and submit such to their teacher as scheduled without the usual teacher-learner interactions. This affect learners’ access to quality instruction that affect their learning outcome. Thus, the learning gap between the abilities that learners would have developed in the context of standard educational practice and their actual learning is significantly noted. In line with the 2021 National Reading Month was themed “Bawat Bata Bumabasa Sa Kabila ng Hamon ng Pandemya,” (Every Child Reading Despite the Challenge of the Pandemic) as per DepEd Memorandum No.348, s.2021, the innovators crafted a reading innovation which is the Project LEARNDEMIC (Learning Assessment and Reading Development through Enhanced Modified Intervention and Collaboration). This is aligned with ECARP- Every Child A Reader Program, 3Bs or Bawat Bata Bumabasa, the Regional Innovation banner program: LIBRO or Literacy Initiatives Bridging Reading Opportunities and the division banner program E-RENE (Enhancement of REading and Numeracy towards Excellence). All these initiatives is in support to the DepEd’s mission and vision as well as the regional mantra that every child a champion as per RM Order No.753 s.2021.
Systems with Homogeneous Workloads

Abstract In order to smoothly create the modeling knowledge in the readers, in this chapter we analyze some elementary problems that can be considered as a reference for the implementation of more complex ones. To start with simple models, we consider a load consisting of requests having similar characteristics, i.e., single class workloads. We hope that readers can gain experience step by step analyzing models of increasing complexity. It must be pointed out that even with these simple models many interesting performance problems can be addressed. Two capacity planning studies are presented for a web server and a data center, respectively. A version of the latter parameterized at system-level with service demands is also analyzed. The identification of the workload that allows a server to work in the optimal operating point concludes the chapter.
The Tussle Between Artistic Creativity and Commerce in the Realm of Copyright Law; Exploring Copyright Existing in the Work of Director of a Film

The Indian film industry, renowned for its diversity and prolificacy, stands as a testament to the creative process of its directors. This paper delves into the intricate realm of copyright law as it pertains to the director's work in Indian cinema. In a country where filmmaking is both an art form and a thriving industry, the protection and regulation of the director's creative contributions are of paramount importance. This analysis examines the legal framework governing copyright in directorial works, encompassing elements such as screenplay, cinematography, choreography, and overall visual aesthetics. It sheds light on the Indian Copyright Act of 1957 and its subsequent amendments, elucidating the rights granted to directors and the duration of their protection. Furthermore, the abstract explores notable cases and legal precedents, showcasing the evolving nature of copyright jurisprudence in India. It also discusses the challenges faced by directors in asserting their rights, including issues related to authorship, moral rights, and disputes with producers and studios. The comparative study of the UK Copyright Law provides number of new jurisprudence which can find a place in Indian ecosystem. By navigating the intricate legal landscape and addressing emerging challenges, this paper aims to present a more comprehensive understanding of the evolving copyright paradigm in India's dynamic film industry. Keywords: Copyright, Economic Interest, Joint Authorship, Director, UK CR Law.
Mount and Update

In Chap. 7 , we mainly introduced the basic concepts and overall architecture of the renderer. In this chapter, we will explain the core function of the renderer: mounting and updating.
Compilation Optimization

Compilation optimization refers to the process of extracting as much key information as possible in the process of compiling templates into rendering functions by the compiler, and using this to guide the process of generating the optimal code. The strategy and specific implementation of compilation optimization are determined by the design ideas of the framework. Different frameworks have different design ideas, so the compilation optimization strategy is not the same. However, the direction of optimization is basically the same, that is, distinguish dynamic content from static content as much as possible, and adopt different optimization strategies for different content.
Parsers

In Chap. 15 , we first discussed how a parser worked and learned that a parser was essentially a state machine. However, we also mentioned that regular expressions were actually a finite-state machine. Therefore, using regular expressions can save us a lot of code when writing parsers. In this chapter, we will use regular expressions more to implement HTML parsers. In addition, a complete HTML parser is more complicated than expected. We know that browsers parse HTML text, but how does it do it? In fact, there is a specification for parsing HTML text, that is, the WHATWG specification for parsing HTML, which defines the complete error handling and the state transition process of the finite-state machine, and also mentions some special states, such as DATA, CDATA, RCDATA, RAWTEXT, etc. So, what do these states mean? What are the effects on the parser? What is an HTML entity, and how does the Vue.js template parser need to handle HTML entities? These issues will be discussed in this chapter.
Video Transformation for the Facilitation of Skill Learning in Sport

This study was conducted on an video image transformation method that can facilitate skill learning in sport using recorded competition videos. The proposed method for the skill learning targeting karate competition are described in this paper. The method consists of two stages. The first stage compensates for partially filmed karate competition field in the input video image by generating a complete field image via a deep neural network (DNN) based image-generator. The generator was developed based on a pix2pix framework to perform this image transformation. The second stage utilizes the output image of the generator and the players’ stick models to convert the output image into a front view image. The transformed video is useful for observing not only the players’ motions, but also the entire flow of the competition. The evaluation experiment showed that the transformed video promotes ease of observation. By the proposed method, it becomes possible to simultaneously observe players' movement skills and the overall tactics on the field such as formations, thereby opening up new possibilities for video-based sports learning.
Overview of Core Technologies of Compilers

Compilation technology is a huge subject, and we cannot fully explain it in a few chapters. However, the difficulty of compilers or compilation technologies for different purposes may vary greatly, and the knowledge requirements will vary greatly. If you want to implement a general-purpose language such as C and JavaScript, then you need to master more knowledge of compilation technology. For example, understanding context-free grammars, writing grammar rules using Bacos Normal Form (BNF), Extending Bacos Normal Form (EBNF), performing grammar derivation, understanding and eliminating left recursion, recursive descent algorithms, and even knowledge of type systems. But as front-end engineers, the scenarios where we apply compilation techniques are usually custom formula calculators in tables, reports, designing a domain-specific language (DSL), etc. Among them, the implementation of the formula calculator even involves only the compilation front-end technology, and the difficulty of domain-specific languages will vary according to their specific usage scenarios and target platforms. Vue.js templates and JSX are domain-specific languages, and their implementation difficulty is medium and low, as long as you master the basic compilation technology theory, you can achieve these functions.
An Unsupervised Gradient-Based Approach for Real-Time Log Analysis From Distributed Systems

We consider the problem of real-time log anomaly detection for distributed system with deep neural networks by unsupervised learning. There are two challenges in this problem, including detection accuracy and analysis efficacy. To tackle these two challenges, we propose GLAD, a simple yet effective approach mining for anomalies in distributed systems. To ensure detection accuracy, we exploit the gradient features in a well-calibrated deep neural network and analyze anomalous pattern within log files. To improve the analysis efficacy, we further integrate one-class support vector machine (SVM) into anomalous analysis, which significantly reduces the cost of anomaly decision boundary delineation. This effective integration successfully solves both accuracy and efficacy in real-time log anomaly detection. Also, since anomalous analysis is based upon unsupervised learning, it significantly reduces the extra data labeling cost. We conduct a series of experiments to justify that GLAD has the best comprehensive performance balanced between accuracy and efficiency, which implies the advantage in tackling practical problems. The results also reveal that GLAD enables effective anomaly mining and consistently outperforms state-of-the-art methods on both recall and F1 scores.