Reviewerly/exHarmony huggingface dataset:
Substantiation of the methodology for studying the level of formation of STEM-competencies in students

The article presents the substantiation of the methodology for studying the level of formation of STEM-competencies in students of basic general secondary education. The authors assume that the study of this problem will help to correct the conditions for the effective implementation of educational tasks of educational institutions.&#x0D; The authors noted that natural-mathematical education (STEM-education) is an integral system of natural and mathematical educational branches, the purpose of which is the development of personality through the formation of STEM-competencies, a natural-science picture of the world, based on the practical application of scientific, mathematical, technical and engineering knowledge to solve practical problems&#x0D; STEM-competencies are defined as a dynamic system of knowledge, skills and attitudes of the individual, which forms the ability to effectively innovate. The basis for studying the features of the levels of formation of STEM-competencies of students is to determine the content of the studied phenomenon. The components of the formation of STEM-competencies were defined as semantic - knowledge of mathematical competence and competence in the field of natural sciences, engineering and technology, engineering; emotional-motivational - attitude to STEM-competencies; behavioral - practical implementation of STEM skills.&#x0D; The study of the levels of formation of structural components of STEM-competencies is proposed to be carried out on a number of indicators, namely: semantic was analyzed for completeness, adequacy, awareness of knowledge of mathematical competence and competence in science, technology and engineering; emotional and motivational - for a positive or negative attitude to the application of mathematical competence and competence in the field of natural sciences, technology, engineering, its stability and effectiveness; behavioral - on the adequacy, stability and independence of the skills of STEM-competencies.&#x0D; The study of STEM-competencies formation levels of students will help to correct the conditions for the effective implementation of educational and educational tasks of educational institutions.
(inf, sup)-Hesitant Fuzzy Ideals of BCK/BCI-Algebras

In this paper, we introduce the concept of (inf, sup)-hesitant fuzzy ideals, which is a generalization of the concept of interval-valued fuzzy ideals, in BCK/BCI-algebras and its related properties are investigated. The concept is established in terms of sets, fuzzy sets, negative fuzzy sets, interval-valued fuzzy sets, Pythagorean fuzzy sets, bipolar fuzzy sets and hesitant fuzzy sets. Moreover, characterizations of ideals, fuzzy ideals, anti-fuzzy ideals, negative fuzzy ideals, Pythagorean fuzzy ideals and bipolar fuzzy ideals of BCK/BCI-algebras are discussed in terms of (inf, sup)-hesitant fuzzy ideals and interval-valued fuzzy ideals.
PENERAPAN MODEL PEMBELAJARAN DIDI (DISCOVERY &amp; DIGITAL LEARNING) UNTUK MENINGKATKAN KEMAMPUAN PEMAHAMAN KONSEP SISTEM PENCERNAAN PADA SISWA KELAS V SD

Identification of problems that arise in the material of the human digestive system in class V SDN Mekarjati IV is the low level of understanding of students' concepts in the material of the human digestive system. One of the learning models that can be used to overcome the above problems is to use the Didi learning model (Discovery &amp; Digital learning). The goal to be achieved is to improve the ability to understand the concept of digestion in fifth grade students at SDN Mekarjati IV by applying the Didi learning model. The type of research conducted is classroom action research. This study aims to improve educational practices that appear in the learning process in the classroom. The design used in this research is a cyclical method. The subjects in this study were the fifth grade students of SDN Mekarjati IV with a total of 22 students. The results of increasing students' understanding of concepts after applying the Didi improvement learning model are categorized as good. In the first cycle, the results of the fifth grade students' concept understanding test were still far from what was expected with the percentage level of understanding of the concept of new students reaching 61.79%. And after the improvement in the second cycle, the percentage of students' conceptual understanding level increased to 81.32%. Thus, students' understanding of the concept of the digestive system material for class V at SDN Mekarjati IV after applying the Didi learning model (Discovery &amp; Digital learning).
Proposing a Research Model on Factors Affecting Customers’ Trust in E-Commerce Transactions in Binh Dinh

Although online shopping is convenient, has many advantages, and is the trend of the times, consumers still have not put their whole trust in these goods and service providers. It is essential to study the factors affecting customer trust in e-commerce transactions. To have the basis to carry out this study, the authors have analyzed relevant research at home and abroad. On that basis, propose a model to study the factors affecting the trust of customers in e-commerce transactions in Binh Dinh province, which are: website reputation, website design, information quality, transaction safety, and social impact.
English Online Hybrid Teaching Based on Internet-of-Things Information Technology

In recent years, due to the epidemic, the country is vigorously developing online teaching, which further promotes the development of hybrid English teaching. Online and offline hybrid teaching combines face-to-face classroom and online teaching. Offline traditional classroom and online auxiliary teaching can complement each other. However, in actual teaching, mixed teaching still suffers some defects, which can undermine the learning outcomes. In this paper, the Internet of Things information technology is used to study hybrid teaching. Firstly, relevant core concepts are clarified. After that, this paper designs an English online hybrid teaching model based on Internet of Things information technology and verifies the feasibility and effectiveness of this model through experiments. The results show that, compared with the simple mixed teaching, the average score of students’ final evaluation under this mode increased by 5.11 points. And the proportion of students with classroom interaction and autonomous learning also rose by 12.1%. This shows that under the promotion of Internet of Things information technology, the online hybrid teaching mode has great guiding significance for middle school English teaching.
Effect of Grid Search and Hyper Parameter Tuned Pipeline with Various Classifiers and PCA for Breast Cancer Detection

Background: The study of breast cancer detection begins with the WBCD dataset for most researchers, as it is a very well-known dataset. We use this dataset as a benchmark in this paper to study ML algorithms like SVM, DT, RF, KNN, NB classifiers, Logistic Regression, Extra Trees, Bagging Classifiers with hard and soft voting, Ensemble techniques and Extreme Gradient Boosting classifiers like XG Boost and 2 deep learning models with regularization and without regularization. Objective: The primary objective is to revisit how the existing classifiers fare with the WBCD dataset and suggest a method with Grid search and Randomized search by selecting the best hyper-parameters to apply with and without PCA and check if WBCD dataset can be classified in lesser time without compromising accuracy. Method: We explore PCA as a feature extraction technique in this dataset and use techniques like Feature Scaling K Fold stratified cross-validation technique, K best etc. We implement Grid search CV along with PCA in the pipeline to tune the hyper parameters across various classifiers and reduce the training and prediction time without compromising accuracy. Last but not the least, this paper also compares the accuracy, precision and recall of various ML techniques for manually selected features by observing the feature importance score and the correlation matrix. Results: In our experiment with all features, we get an accuracy of 97.9 per cent for Extra trees and Ensemble techniques with RF, KNN and Extra Trees with soft voting strategy and using feature selection with PCA and grid search we get an accuracy of 99.1 per cent with SVM (kernel trick). We also demonstrate that the running time of training and prediction also reduces if hyper parameters of classifiers are tuned appropriately which is taken care of by Grid and Randomized Hyper Parameter Grids. Conclusion: It is shown in this paper that Feature subset selection or feature ranking may not be the best way and not the only way to be applied on WBCD dataset along with PCA. In datasets where features are closely correlated , a method for hyper parameter tuning using either Grid or Randomized Search can be accompanied with PCA to extract the best feature combinations and then fed into the classifiers to get good accuracy scores and can be executed in a much quicker time.
Govi preprint

In this universe there are some theories and concepts are there, those are beyond our thinking and very tough to understand. Like these, there is one concept that explains “Einstein’s two postulates of light” without including the ‘space-time dilation' and ‘special theory of relativity '. This concept is unknown yet also because of tough to find. And also this concept gives a new property to electromagnetic waves.
PERFORMANCE OF LOW-COST SOLAR WATER HEATER

This study investigated the performance of a low-cost solar water heater (SWH). The SWH consists of a solar collector with a simple flat plate thermosiphon and water tank. The solar collector has, a glass cover, painted black on an aluminium absorber plate, water pipes and the sides insulated. During the testing process, the temperatures in the SWH were recorded at 1-second intervals by using Advantech Software DAQ Navi Data Logger, which is connected to a laptop via USB-4718, 8-Channel Thermocouple Input Module. The results that the efficiency of the solar collector ranged from 5% to 23%, with an average of 16% at water flow rate of 0.0019 kg/s. In addition, the time required by low-cost SWH to heat the water in a tank of 20 liters to reach its maximum temperature of 48.3°C is 8 hours 36 minutes
Patents at the University of Mosul as a Source of Information: an analytical study

Patents are among the scientific contributions that researchers are distinguished in from various aspects. The study aims to identify the scientific and research productivity in the field of patents at the University of Mosul. It also aims to measure the level of the university research productivity in the field of patents. In addition, analytical approach was used for-the years 2000-2019. Results of this shows that the level of productivity of researchers at the university of Mosul reached (85) patents during the period of research. Meanwhile, the years (2006 – 2019) recorded the lowest level of patent decline in the university. The patent researcher, Ihab Salem Ahmed completed (10) patents during the period of time researched with a percentage of 10.3% of the total patents.
Comparative Discourse Analysis of Meme Tv Headlines

Linguistic creativity is realised in our everyday text and it allows formulation and expression of ideas. Online content creators (humourists) have created meme tags or headlines posited to done by Nation TV comparing them to other unspecified TV stations. These humourists utilise language. This paper does a comparative analysis of the meme TV headlines to comprehend how language has been utilised in this scenario by determining the structural differences in the process of formation, determining whether the grammar constructed would say practical and sensible things about the written text, identifying figures of speech, and determine the source of amusement in the meme. This paper was guided by functional grammar (Halliday, 1985), a theory that is oriented toward describing how language makes contextual meanings. It analyses the grammatical structure, the communicative situation, participants, and context of the discourse. This paper has identified that the online humourists or content creators have conveyed meaning artistically with creativity by use of figures of correlation such as metonymy, symbolism, figure of comparison, truncation, wordplay and paronyms. The data that was utilised in this paper was collected from Kenyan Facebook fun pages.
Organizational identification and impact on supporting workers towards digital transformation

The purpose of the study is to diagnose the causes of the weakness of the digital transformation of workers and to analyze a research model that includes Organizational Identification, which is an independent variable, and its dimensions are (feeling connected to the objectives of the organization, the desire to be similar to the values of the organization, the feeling of workers caring at work With the dependent variable of supporting workers towards digital transformation, its seven dimensions are (independence, ability, relationship, performance, well-being, attitude, intention to support digital transformation), with the interactive variable of grumbling in the workplace. The researcher chose a sample of the workers in the education of Najaf Ashraf because of the significant and influential role played by this directorate in the movement of education in the province and the country. The model, consisting of three main variables, was formulated to achieve the study's goal, and three main hypotheses of impact were devised. The researcher used the questionnaire as a critical tool to obtain data related to the research sample. Suppose the study community reached 852 workers, the questionnaires were distributed (265) in the study sample and received (240) questionnaires that can be statistically analyzed.
Soft skills and comprehensive training in teachers

The purpose of this study was to analyze and evaluate in a systemic way and then proceed to choose and summarize the scientific support regarding soft skills as a catalyst for the comprehensive training of teachers at the JEC institution in the city of Chiclayo. The following repositories were consulted: CONCYTEC, DIALNET, PROQUEST, EMERALD INSIGTH, SCIENCE DIRECT, EBSCO, CONACYT AND REDALYC. Regarding the language, it was not restricted, but the year in which it was developed was between the years 2018 - 2021. Articles that had results on the soft skills model were included as a catalyst for comprehensive training in teachers in the JEC institution of the city of Chiclayo, as well as documents with reasons why the soft skills model should contribute to the comprehensive training of teachers in the JEC institution of the city of Chiclayo. For its part, the results were found in the following way, it was chosen according to the relevance of the variables under study, and the articles that contain them were also chosen randomly.
URGENSI KOMUNIKASI INTERPERSONAL PUSTAKAWAN TERHADAP PELAYANAN PEMINJAMAN KOLEKSI PERPUSTAKAAN UNIVERSITAS ANDALAS

This article discusses the communication skills of librarians at the Andalas Padang University Library. The purpose of this study was to analyze the implementation of librarian interpersonal communication to users in providing collection lending services. This study uses a qualitative descriptive method with a qualitative approach. Data collection was carried out through complete participatory observation, namely the researcher was fully involved in several activities carried out by the informants. In addition, researchers also conducted in-depth interviews with librarians and students who act as users in order to strengthen the data in the study. In addition, researchers also use the method of documentation through previous research, manuals and various literatures related to research. Two findings were obtained from these results. First, the lack of use of written verbal communication by new students so that they do not have an understanding of the use of collection lending services. Second, the comparison of the number of officers in loan services with the users they face per day is inadequate.
Blockchain Based Framework for COVID-19 Vaccine Delivery using Smart Contracts

Abstract A blockchain is a continuously expanding list of documents known as blocks that are connected together via cryptography. A smart contract is a computer program that executes itself in accordance with the terms of a contract. In Traditional vaccine delivery systems, vaccine delivery information is not visible to all the supply chain entities, thereby prone to data tampering. Thus, transparency is the biggest concern in this system. In the case of the COVID-19 pandemic, the rapid launch of a vaccine and the implementation of a worldwide vaccination campaign is crucial, but their success depends on the availability of an operable and transparent distribution chain that all necessary stakeholders can assess. In the proposed work, we show how blockchain technology may be used to enable transparent tracing of COVID-19 vaccination registration, storage, and distribution and self-reporting of side effects by constructing a prototype using smart contracts.
Social networks as a source of Arabic digital content in Oman: A case study on Sultan Qaboos university’s students

This paper aims to identify the role of social media in the digital transformation of the Omani society, as social media represents an essential source of information that many rely on to create and disseminate knowledge and digital transformation. The study focuses on Sultan Qaboos University students, who are consumers and producers through social networks. The study attempts to identify the contribution of the study sample to these sites and the quality of information shared through those networks. The study also sought to identify the possibility of relying on Arabic information available in those networks and employing it as a source of information. The purpose of this study is to identify the information practices of the study sample on social networks, which may affect Arabic digital content on the Internet. The result of this study shows that the social networks are a rich source of information and have a significant role in enriching the Arab digital transformation. However, despite being a source of digital content, many users treat their content with caution due to doubts regarding the credibility of the information. The study also finds that social networks can be exploited in disseminating digital Arabic knowledge content on the Internet through codified practices through which the information can be verified.
Distance Learner: Incorporating Manifold Prior to Model Training

The manifold hypothesis (real world data concentrates near low-dimensional manifolds) is suggested as the principle behind the effectiveness of machine learning algorithms in very high dimensional problems that are common in domains such as vision and speech. Multiple methods have been proposed to explicitly incorporate the manifold hypothesis as a prior in modern Deep Neural Networks (DNNs), with varying success. In this paper, we propose a new method, Distance Learner, to incorporate this prior for DNN-based classifiers. Distance Learner is trained to predict the distance of a point from the underlying manifold of each class, rather than the class label. For classification, Distance Learner then chooses the class corresponding to the closest predicted class manifold. Distance Learner can also identify points as being out of distribution (belonging to neither class), if the distance to the closest manifold is higher than a threshold. We evaluate our method on multiple synthetic datasets and show that Distance Learner learns much more meaningful classification boundaries compared to a standard classifier. We also evaluate our method on the task of adversarial robustness, and find that it not only outperforms standard classifier by a large margin, but also performs at par with classifiers trained via state-of-the-art adversarial training.
Estimating sparse direct effects in multivariate regression with the spike-and-slab LASSO

The multivariate regression interpretation of the Gaussian chain graph model simultaneously parametrizes (i) the direct effects of $p$ predictors on $q$ outcomes and (ii) the residual partial covariances between pairs of outcomes. We introduce a new method for fitting sparse Gaussian chain graph models with spike-and-slab LASSO (SSL) priors. We develop an Expectation Conditional Maximization algorithm to obtain sparse estimates of the $p \times q$ matrix of direct effects and the $q \times q$ residual precision matrix. Our algorithm iteratively solves a sequence of penalized maximum likelihood problems with self-adaptive penalties that gradually filter out negligible regression coefficients and partial covariances. Because it adaptively penalizes individual model parameters, our method is seen to outperform fixed-penalty competitors on simulated data. We establish the posterior contraction rate for our model, buttressing our method's excellent empirical performance with strong theoretical guarantees. Using our method, we estimated the direct effects of diet and residence type on the composition of the gut microbiome of elderly adults.
Refign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditions

Due to the scarcity of dense pixel-level semantic annotations for images recorded in adverse visual conditions, there has been a keen interest in unsupervised domain adaptation (UDA) for the semantic segmentation of such images. UDA adapts models trained on normal conditions to the target adverse-condition domains. Meanwhile, multiple datasets with driving scenes provide corresponding images of the same scenes across multiple conditions, which can serve as a form of weak supervision for domain adaptation. We propose Refign, a generic extension to self-training-based UDA methods which leverages these cross-domain correspondences. Refign consists of two steps: (1) aligning the normal-condition image to the corresponding adverse-condition image using an uncertainty-aware dense matching network, and (2) refining the adverse prediction with the normal prediction using an adaptive label correction mechanism. We design custom modules to streamline both steps and set the new state of the art for domain-adaptive semantic segmentation on several adverse-condition benchmarks, including ACDC and Dark Zurich. The approach introduces no extra training parameters, minimal computational overhead -- during training only -- and can be used as a drop-in extension to improve any given self-training-based UDA method. Code is available at https://github.com/brdav/refign.
Breaking Correlation Shift via Conditional Invariant Regularizer

Recently, generalization on out-of-distribution (OOD) data with correlation shift has attracted great attentions. The correlation shift is caused by the spurious attributes that correlate to the class label, as the correlation between them may vary in training and test data. For such a problem, we show that given the class label, the models that are conditionally independent of spurious attributes are OOD generalizable. Based on this, a metric Conditional Spurious Variation (CSV) which controls the OOD generalization error, is proposed to measure such conditional independence. To improve the OOD generalization, we regularize the training process with the proposed CSV. Under mild assumptions, our training objective can be formulated as a nonconvex-concave mini-max problem. An algorithm with a provable convergence rate is proposed to solve the problem. Extensive empirical results verify our algorithm's efficacy in improving OOD generalization.