Reviewerly/exHarmony huggingface dataset:
The possibilities of the school Quantorium for practice-oriented training of schoolchildren in information technologies

Currently, the information technology industry is one of the most dynamically developing industries in Russia. Our country needs highly qualified specialists for the introduction of digital technologies in various spheres of life in order to improve its quality and the economic development of the state. In this regard, there is a need to train future personnel who will have the necessary competencies. The article discusses the possibilities of the school Quantorium for the implementation of practice-oriented training of schoolchildren in information technologies and its advantages in preparing students for a future career in the IT industry. The article analyzes the experience and methods of work that allow students to effectively use modern equipment, as well as gain experience in developing projects in the field of information technology. The authors describe an example of the implementation of a project in Almetyevsk, the Republic of Tatarstan, which allows students to form key practical skills in the field of information technologies and skills of interaction with a real customer.
Cancer prognosis with machine learning-based modified meta-heuristics and weighted gradient boosting algorithm

Cancer is typically a disease that is instigated when cells partition uncontrollably, thereby spread to surrounding tissues. Among different kinds of cancer, breast cancer, cervical and lung cancer have found to experience less screening rate. Hence, detecting such kinds of cancer has become crucial to save individual’s life. With the ability of ML (Machine Learning) for solving complicated tasks in dynamic configurations have contributed to its application in prognosticating cancer with diverse meta-heuristic algorithms. Though conventional researches have attempted to perform this, feature selection process has been inefficient that negatively impacted the accuracy rate. To resolve such consequences, the study considers Wisconsin breast cancer dataset, lung cancer dataset and cervical cancer dataset from UCI ML repository and proposes Modified QPSO-LDA (Quantum Particle Swarm Optimization-Linear Discriminant Analysis) to select suitable and relevant features. Following this, WGB (Weighted Gradient Boosting) is proposed for classification that works based on weighted column subsampling. To validate the performance of this system in prognosticating breast cancer, cervical cancer and lung cancer, analysis is undertaken that confirm its effectiveness with 0.9912 as accuracy for breast cancer dataset, 0.99 as accuracy for cervical cancer dataset and 0.97 as accuracy for lung cancer dataset.
Features of the religious education of a child in the family based on the views of V. Zinkivsky

Modern conditions of life remove the child from religious upbringing in the family, since all Orthodox upbringing is completely contained in the general rule of Christian life: the Spirit of peace will prevail, and thousands around you will be saved. On the contrary, the father’s desire to have a pedagogical manual in his hands and be guided by it in relation to his son or daughter is a sign of how artificial parental religiosity is, how impoverished is the personal attitude towards children and the spirit of love, which is the true essence of the patriarchal tradition, the center of all Orthodox spirituality. The purpose of the research is to determine the peculiarities of the religious upbringing of children and youth in the pedagogical heritage of V. Zinkivskyi, based on the analysis and literature, as well as archival sources. Research methods: hermeneutic (for studying and commenting on pedagogical ideas of V. Zenkivskyi); phenomenological (for the objective disclosure of historical facts); biographical (for studying the creative work of a scientist); synergistic (to determine changes in the evolution of the teacher’s views); paradigmatic (to characterize the religious and pedagogical ideas of V. Zenkivskyi); comparative-historical (to reveal and compare pedagogical-historical ideas); bibliographic (for analysis and archival and library funds). The work presents arguments in favor of strengthening the religious component of family upbringing of children. Based on the views of V. Zenkivskyi, it was noted that it is necessary to establish and strengthen the spiritual connection of children with their parents. For this purpose, V. Zinkivskyi suggests constant communication with children, involving them in the world of family worries and difficulties, assigning responsible and urgent tasks, generally involving children in active work for the family. If they are not under compulsion, but go for it themselves, then with a properly created spiritual atmosphere, it is not difficult to achieve this. A family can give religious education to its children, but for this it must itself acquire the religious power that is communicated to it in the sacrament of marriage. Beyond this, only the socio-psychological reality of the family remains, and its religious-ontological basis is hidden in an inacces- sible depth, and therefore children grow without involvement in spirituality, which contains the necessary forces for their growth. According to V. Zinkivskyi, the problem of religious education in the family is the problem of returning the family itself to its religious basis. Religious upbringing of children is carried out mainly by personal example and a prayerful atmosphere of love in the home.
An Analysis of Students' Linguistic Difficulties in Writing Recount Text at the Tenth Grade Teknik Pengeboran Minyak and Gas (TPMG) SMK Negeri 1 Mandau

The purpose of this study is to analyze what are the students' difficulties in writing English using recount text. This research is a quantitative approach descriptive research design involving 33 Tenth grade students of TPMG. The data collection technique is a test. There are several steps to analyze the data: analyzing the students' work, calculating the average score using descriptive statistics and the last step is descriptive analysis. The results showed that the students' difficulties in writing English in the tenth grade of TPMG were Vocabullary 33%, Grammar 40% and language use 27%. So the highest difficulty experienced by these students lies in grammar with a percentage of 40%
STUDENTS’ ANXIETY IN LEARNING ENGLISH ( A Descriptive Study to the Seventh Grade Students of SMPN 03 Mempawah Hilir in the Academic Year of 2021/2022 )

This research is the result of research on students’ anxiety in learning English by students of SMPN 03 Mempawah Hilir, West Kalimantan. Anxiety in learning English is often reported to negatively affect English learning. This research aims to determine the level of anxiety and the factors that dominate the anxiety of students at SMPN 03 in Mempawah Hilir, West Kalimantan in learning English, which will then be carried out further research to respond to the findings in this research. The method used in this research is descriptive approach, where the findings are presented in the form of a description of the calculations from the data obtained from questionnaires and interviews. This research took junior high school students in one class as respondents. They are class VII A students in 2021/2022. Questionnaires and interviews were distributed to collect data on students' anxiety in learning English. The questions in the questionnaire were made to determine the level of students' anxiety in learning English but interviews were conducted to determine the factors that dominate students' anxiety in learning English. The data obtained from the questionnaire will be calculated the average and percentage. Then the value obtained will be categorized such as mild anxiety, moderate anxiety, severe anxiety, and panic. From the results of interviews with students, conclusions will be drawn to see the similarities experienced by students. It was found that level of students’ anxiety in this school is severe anxiety. In mild anxiety, the average result is (59%) with enough criteria. On moderate anxiety, the average result is (57%) with enough criteria, (67%) of severe anxiety belongs to the strong criteria. There is a (47%) average score on panic anxiety that belongs to the enough criteria. Based on the results of these percentages, students are in the Severe Anxiety. From the results of the interview, it was found that the factor that dominates students' anxiety in learning English is test anxiety. All respondent are 24 students of class VII A in 2021/2022.
Word Of Wonders: Crossword Game-Based In Optimizing The Students’ Vocabulary

The most crucial part of English learning is having and understand a proper vocabulary. To be involved in English academic as the learning topic, students must be able to have and understand words collection sufficiently. The purpose of this study is to find out that, the vocabulary learning through the game of Words of Wonders: Crossword in groups learning has been declared to perform better than individual learning. This study arises the quantitative analysis approach with a comparison design to reveal the students' vocabulary. The use of pre-test and post-tests vocabulary were utilized as the role of instruments within this study. The samples were the faculty of Management Secretary A and B of the academic year in 2019/2020, in Ciwaringin, Bogor. The results of this study infer that, the students who are taught by using Words of Wonders: Crossword in group learning has performed better than the students who learn individually. It is shown by the pre-test scores of 59.58 and 59.00, and post-test scores of 83.47 and 73.62
Android-based AKSI application usage to improve achievements in accounting learning

The purpose of this mini-research is to find out the use of learning media for Android-based Acquired Knowledge Systems Inc. (AKSI) applications can improve accounting learning achievement in students of class XII Accounting class of Sekolah Menengah Kejuruan University, Post-secondary (AKL 1 SMK N 1 Kebumen). This research is qualitative. Data collection techniques include interviews, observation, and documentation analysis. The data analysis technique used qualitative descriptive analysis, including data collection, data reduction, data presentation, and conclusion drawing. The study findings are as follows. 1) The teachers’ observations about using learning media for Android-based AKSI applications in learning activities indicate that they have implemented the learning process well. 2) The results of interviews with students have shown that: a) the learning process using the Android-based AKSI application media has been implemented well; b) student motivation has increased so that students feel their value increases. 3) The results of the analysis of the value documentation indicate an increase in accounting learning achievement by students, namely, a) before the use of teaching media Android-based AKSI applications are an average of class 66 with students who complete grades above Minimum Completeness Criteria (MCC) 16, b) after the use of learning media android-based AKSI Applications the average quality achieved by 75 with students who scored above MCC 23, then increased again the grade average value to 79 with students who achieved grades above MCC to 26.
What Makes Entities Similar? A Similarity Flooding Perspective for Multi-sourced Knowledge Graph Embeddings

Joint representation learning over multi-sourced knowledge graphs (KGs) yields transferable and expressive embeddings that improve downstream tasks. Entity alignment (EA) is a critical step in this process. Despite recent considerable research progress in embedding-based EA, how it works remains to be explored. In this paper, we provide a similarity flooding perspective to explain existing translation-based and aggregation-based EA models. We prove that the embedding learning process of these models actually seeks a fixpoint of pairwise similarities between entities. We also provide experimental evidence to support our theoretical analysis. We propose two simple but effective methods inspired by the fixpoint computation in similarity flooding, and demonstrate their effectiveness on benchmark datasets. Our work bridges the gap between recent embedding-based models and the conventional similarity flooding algorithm. It would improve our understanding of and increase our faith in embedding-based EA.
NFTVis: Visual Analysis of NFT Performance

A non-fungible token (NFT) is a data unit stored on the blockchain. Nowadays, more and more investors and collectors (NFT traders), who participate in transactions of NFTs, have an urgent need to assess the performance of NFTs. However, there are two challenges for NFT traders when analyzing the performance of NFT. First, the current rarity models have flaws and are sometimes not convincing. In addition, NFT performance is dependent on multiple factors, such as images (high-dimensional data), history transactions (network), and market evolution (time series). It is difficult to take comprehensive consideration and analyze NFT performance efficiently. To address these challenges, we propose NFTVis, a visual analysis system that facilitates assessing individual NFT performance. A new NFT rarity model is proposed to quantify NFTs with images. Four well-coordinated views are designed to represent the various factors affecting the performance of the NFT. Finally, we evaluate the usefulness and effectiveness of our system using two case studies and user studies.
An Adapted Cascade Model to Scale Primary School Digital Education Curricular Reforms and Teacher Professional Development Programs

Many countries struggle to effectively introduce Digital Education (DE) to all K-12 students as they lack adequately trained teachers. While cascade models of in-service teacher-professional development (PD) can rapidly deploy PD-programs through multiple levels of trainers to reach all teachers, they suffer from many limitations and are often ineffective. We therefore propose an adapted cascade model to deploy a primary school DE teacher-PD program throughout an administrative region. The model relies on teacher-trainers who (i) are active teachers in the region, (ii) have a prolonged trainer-PD with experts who piloted the teacher-PD program to acquire adult-trainer and DE-related competences, and (iii) are supported by the experts throughout the deployment. To validate the deployment model we used data from 14 teacher-trainers, the 700 teachers they trained, and 350 teachers trained by experts. The teacher-trainer findings demonstrate that the adapted cascade model effectively addresses most cascade models' limitations. The teacher-related findings further validate the adapted cascade model in terms of perception, motivation and adoption which are at least equivalent to those obtained with the experts. To conclude, the adapted cascade model is an effective means of spreading primary school DE PD-programs at a large scale and can be used in other DE reforms.
Representation-agnostic distance-driven perturbation for optimizing ill-conditioned problems

Locality is a crucial property for efficiently optimising black-box problems with randomized search heuristics. However, in practical applications, it is not likely to always find such a genotype encoding of candidate solutions that this property is upheld with respect to the Hamming distance. At the same time, it may be possible to use domain-specific knowledge to define a metric with locality property. We propose two mutation operators to solve such optimization problems more efficiently using the metric. The first operator assumes prior knowledge about the distance, the second operator uses the distance as a black box. Those operators apply an estimation of distribution algorithm to find the best mutant according to the defined in the paper function, which employs the given distance. For pseudo-boolean and integer optimization problems, we experimentally show that both mutation operators speed up the search on most of the functions when applied in considered evolutionary algorithms and random local search. Moreover, those operators can be applied in any randomized search heuristic which uses perturbations. However, our mutation operators increase wall-clock time and so are helpful in practice when distance is (much) cheaper to compute than the real objective function.
The Algorithm of Student Grade Evaluation Based on Clustering

Aiming at the current assessment methods of college courses, an algorithm of student grade evaluation based on clustering is proposed. Using course performance data from multiple platforms, after preprocessing and integration, K-means algorithm is used to classify the results, and then Mahalanobis distance is used to achieve the grade evaluation of the results. The experimental results show that the proposed algorithm has a good effect, the algorithm has a guiding significance for the students in the edge of classification, and has a good application value.
The Annual Quiz [Microwave Surfing]

As New Zealander geophysicist Gillian Turner’s book <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">North Pole, South Pole: The Epic Quest to Solve the Great Mystery of Earth’s Magnetism</i> <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> shows, our planet’s magnetic properties have intrigued people since ancient times. This quiz offers a quick tour of the early history of geomagnetism. It is now time to grab a pen and test your own knowledge of this fascinating subject! Google and ChatGPT are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">not</i> allowed. The answers appear elsewhere in this issue.
Energy Audit of Flouresen Lights at School Building C III Floor Politeknik Negeri Semarang

&lt;p dir="ltr"&gt;&lt;span&gt;Energy audit is very necessary to calculate the level of energy consumption of a building or buildings. The research includes the amount of energy consumption based on the initial energy audit and detailed energy audit as well as saving opportunities based on conditions in the field. Based on the initial energy audit, the value of the Energy Consumption Intensity (IKE) is 0.699 kWH/m2month, while in the detailed audit the IKE value is 0.935 kWH/m2month. Based on the results of a detailed energy audit, the lighting measurement of 325 lux still meets the requirements for a classroom with a standard between 250 lux to 500 lux.&lt;/span&gt;&lt;/p&gt;&lt;div&gt;&lt;span&gt;&lt;br /&gt;&lt;/span&gt;&lt;/div&gt;&lt;script id="stacks-wallet-provider" type="text/javascript" src="chrome-extension://ldinpeekobnhjjdofggfgjlcehhmanlj/inpage.js"&gt;&lt;/script&gt;&lt;script id="stacks-wallet-provider" type="text/javascript" src="chrome-extension://ldinpeekobnhjjdofggfgjlcehhmanlj/inpage.js"&gt;&lt;/script&gt;
Pseudo-label based and transformation-consistent self-ensembling model for underwater life images

With the continuous development of underwater vision technology, high-precision object segmentation has received more attention from scholars. Due to the complexity of the underwater environment, the segmentation of underwater objects has become a new challenge. In order to alleviate such problems, this paper proposes a semi-supervised segmentation model based on pseudo-labels and transformation-consistent(PSTM). Due to the complexity of underwater images, PSTM preprocesses underwater biological images through an image enhancement module. A semi-supervised segmentation network with pseudo-labels and transformation-consistent is then used to obtain a more robust segmentation result. The method in this paper achieves better performance on the public underwater biological dataset DUT-USEG.
Privacy Preserving Approach to Communication Data Mining Based on Federated Learning

Current traditional privacy protection methods for communication data achieve functions such as anti-tampering and resistance to single point of failure attacks through edge computing, which leads to poor data protection due to the lack of aggregation processing of user behavior. In this regard, a privacy protection method for communication data mining based on federated learning is proposed. A secure aggregation protocol is constructed to aggregate user interaction behaviors, and a user sensitivity scoring mechanism is constructed to establish a privacy protection model for communication data mining. In the experiments, the privacy-preserving effect of the proposed method is verified. The analysis of the experimental results shows that the data privacy protection model constructed by the proposed method has a high degree of convergence of the model and has a more excellent data privacy protection performance.
Nonparametric Iterative Machine Teaching

In this paper, we consider the problem of Iterative Machine Teaching (IMT), where the teacher provides examples to the learner iteratively such that the learner can achieve fast convergence to a target model. However, existing IMT algorithms are solely based on parameterized families of target models. They mainly focus on convergence in the parameter space, resulting in difficulty when the target models are defined to be functions without dependency on parameters. To address such a limitation, we study a more general task -- Nonparametric Iterative Machine Teaching (NIMT), which aims to teach nonparametric target models to learners in an iterative fashion. Unlike parametric IMT that merely operates in the parameter space, we cast NIMT as a functional optimization problem in the function space. To solve it, we propose both random and greedy functional teaching algorithms. We obtain the iterative teaching dimension (ITD) of the random teaching algorithm under proper assumptions, which serves as a uniform upper bound of ITD in NIMT. Further, the greedy teaching algorithm has a significantly lower ITD, which reaches a tighter upper bound of ITD in NIMT. Finally, we verify the correctness of our theoretical findings with extensive experiments in nonparametric scenarios.
PENGARUH MODEL PEMBELAJARAN KOOPERATIF-STAD BERBANTUAN GAME KAHOOT TERHADAP HASIL BELAJAR SISWA

&lt;p&gt;&lt;em&gt;This study aims to know the effect of cooperative learning model type Student Team Achievement Divisions (STAD) using smartphone game kahoot application toward students learning outcome. This research is quasi experimental design. The population of this study were students of class VIII SMP totaling 4 classes. The sampling was conducted by random sampling technique and obtained 2 classes as samples, class VIII-C and class VIII-D. Class VIII-C as the experimental class totaling 24 students and class VIII-D as the control class totaling 25 students. The test instrument used to determine student learning outcomes is a multiple choice questions in the form of pre-test and post-test which consists of 20 questions. The results showed that the class taught by STAD learning model using kahoot game obtained an average score of 82.917, while classes taught with conventional learning obtained an average score of 50.25. Hypothesis test obtained t count = 4.509 &amp;gt; t table = 4.047 then H0 is rejected and Ha is accepted. So it can be concluded that there is an effect on student learning outcomes through the Student Team Achievement Divisions (STAD) type cooperative learning model using the Kahoot game smartphone application on the learning outcomes of class VIII students on the Materials Structure and Function of Plants.&lt;/em&gt;&lt;/p&gt;