Reviewerly/exHarmony huggingface dataset:
Validity and Reliability of Holding Bow Digitec Test

This study aims to: 1) determine the validity of the digitec holding bow test 2) determine the reliability of the digitec bow holding test 3) create a new breakthrough that the "Holding Bow Digitec Test" is a tool for measuring arm muscle strength and endurance with high validity and reliability for sports.archery.This research is a type of research and development (R&D) research that uses the Borg & Gall development model which consists of 10 procedures.The subjects involved in this study amounted to 33 subjects consisting of 18 male archers and 15 female archers.Data collection techniques using tests and measurements. .The validity test uses content validity which is analyzed using the Aiken's v formula and concurrent validity which is analyzed using product moment correlation.The reliability test used a test-retest approach which was analyzed using product moment correlation.The results of the study were a tool for measuring the strength and endurance of the arm muscles in archery called the "Holding Bow Digitec Test".The strength test is declared valid with r arithmetic > from r table, with details of 0.961 > 0.344.The arm muscle endurance test was declared valid with r count > from r table, with details of 0.895 > 0.344.The strength test was declared reliable with Cronbach's alpha value > 0.60, with details of 0.978 > 0.60.The endurance test was declared reliable with Cronbach's alpha value > 0.60, with details of 0.944 > 0.60.
Implementasi Data Mining Dengan Algoritma Multiple Linear Regression Untuk Memprediksi Penyakit Diabetes

According to WHO, diabetes is a metabolic disorder characterized by high levels of sugar in the blood. Diabetes is a deadly disease if the sufferer cannot control it and it will become a complication. Many people are affected by diabetes and find out too late, so that at the time of treatment the condition has complications. Early detection of diabetes is very helpful for sufferers to avoid complications that will occur. Therefore we need a data mining technique that can process data and prevent diabetes from an early age. Data mining is a process of extracting knowledge from a number of data to find a pattern. Data mining has been widely used, one of which is the prediction method to find out people with diabetes. There are so many prediction methods available, one of which is linear regression, where this method uses dependent and independent attributes. In this study, the multiple linear regression method is used to predict diabetes, and evaluates using RMSE (root mean square error). The results of this study produce an RMSE value of 0.403, the RMSE test uses cross validation by changing the number of validation value
Analytic “Newton’s cradles” with perfect transfer and fractional revival

Analytic mass–spring chains with dispersionless pulse transfer and fractional revival are presented. These are obtained using the properties of the para-Racah polynomials. This provides classical analogs of the quantum spin chains that realize important tasks in quantum information: perfect state transfer and entanglement generation.
The Effect of U-Dictionary on Vowel Pronunciation Ability of the Tenth Grade Students

The objective of this research was to find out the empirical data which could determine whether there is a significance of using U-Dictionary application on vowel pronunciation ability of the tenth-grade students of SMA Kristen Palangka Raya or not. The method used quantitative with pre-experimental research method with One Group Pretest-Posttest Design. The samples of this research were 30 students of the tenth grade. The samples were selected by total sampling. To collect the data, this research used a pronunciation test which had been tried out provided in pretest and posttest as the research instrument. The result of this research revealed that the mean score of the posttest was greater than that of the pretest, namely 66.33 &gt; 49.17. Then the result of hypotheses testing showed that the critical value for the one-tailed test is ta with df = n-1, 30-1 = 29. The value of the test statistic, is t* = -6.809 and the critical value of t0 is t.05 {29} = - 1.699. Because t*&lt; t.05 {29} = -6.809 &lt; -1.699, we reject H0. The test results were statically significant at the 5% level. It means that there is a significant effect of using the U-Dictionary application on the vowel pronunciation ability of the students of SMA Kristen Palangka Raya.
“Does blockchain technology matter?”: Understanding consumer purchase intention in a blockchain technology for olive oil context

Background: Blockchain, which was once primarily connected with financial services, is now affecting other businesses as well.Blockchain is gaining power as a tool to trace and monitor food as it moves through the supply chain, confirming its provenance.Consumers are becoming increasingly interested in learning where their purchases are created and how they are made.Consumers in the food business can use blockchain to verify the source of their purchases.Despite some blockchain studies on innovation uptake in finance settings, little is known about how consumers view blockchain in terms of food traceability and transparency, particularly olive oil products.Purpose: To look why people would want to buy olive oil with blockchain code, using an adapted version of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) that includes the perceived trust among other variables. Materials and Methods: A survey methodology is used to collect responses from a sample of 115 olive oil consumers in France and analyzed via AMOS 23. Linear regression was used to assess the causal relationship between measured variables. Results:The results indicate that the trust value generated by the blockchain technology has a strong effect on olive oil product's purchase intention.The unexpected result is that the other values, such as performance expectancy or effort expectancy, have no impact on that behavioural intention among French population.Conclusions: For researchers, this study lunches a new call to discussion on the predictive effectiveness of the UTAUT2 model.In addition, it provides new insights to practitioners on how to increase the purchase intention towards olive oil brands that uses blockchain technology.
PETROLOGY AND GEOCHEMISTRY OF THE LATE CRETACEOUS PAB FORMATION, WESTERN SULAIMAN FOLD- THRUST- BELT, PAKISTAN: IMPLICATIONS FOR PROVENANCE AND PALEO-WEATHERING

Late Cretaceous sandstone succession of the Pab Formation in western Sulaiman Fold Thrust belt Pakistan was investigated for petrology and bulk rock chemistry to determine its source terrain, paleo-weathering and tectonic setting. The formation is mainly comprised of sandstone with reddish to maroon color shale and arenaceous limestone. Texturally, the sandstone is fine to coarse grained, sub-angular to well-rounded and moderately to well sorted. The sandstone is petrologically and geochemically classified as quartz arenite to sub lithic arenite. The detritus was mainly derived from plutonic acidic source. QtFL and QmFLt suggests that recycled orogeny and Craton Interior setting were major sources of sediments. Geochemical models support that the detritus was derived from quartzose sedimentary source terrain, suggest deposition in a passive continental margin setting. Average values of chemical indices are CIA 59% CIW 67% and CIV 12.70%, which suggest moderate to high degree of chemical weathering in source area, that may reﬂect humid climate condition in the source area. The petrographic study and geochemical models demonstrate that the Pab Formation is mostly composed of mature sandstone from acidic plutonic and low-grade metamorphic rocks terrain in recycled and Craton Interior setting deposited on western passive margin of Indian plate in Tethys Ocean.
Deep Kinship Verification and Retrieval Based on Fusion Siamese Neural Network

Automatic kinship analysis, which aims to judge the kinship of different individuals, has been widely used in many real world applications such as helping missing persons reunite with their families and social media analysis. In this work, we focus on three practical and challenging tasks related to kinship analysis, i.e., kinship verification, tri-subject kinship verification and kinship retrieval. A deep fusion Siamese neural network is proposed to address these tasks in a flexible and progressive manner. Firstly, we propose a basic deep Siamese neural network for kinship verification to judge the kinship between individuals based on face images. More specifically, the Siamese neural network takes two input face images and then outputs the similarity between them. To improve the performance, a jury system is also introduced for multi-model fusion. Secondly, we integrate two basic deep Siamese neural networks for tri-subject kinship verification(father, mother and child), which is intended to decide whether a child is related to a pair of parents or not. Specifically, the kinship similairty score of the triplet for verification is obtained by weighting the similarity scores of the father-child and mother-child ones. Thirdly, the proposed deep Siamese neural network can be used to quantify the similarity between any two persons. Thus, it is natural and easy to extend its application to the kinship retrieval task by sorting the similarities between the candidates and faces in the database. We conduct experiments on the RFIW2021 dataset, and final results validate the effectiveness of our solution.
Password Guessability as a Service (PGaaS)

This paper presents an adaptable password guessability service suited for different password generators according to what a user might need when using such a service. In particular, we introduce a flexible cloud-based software architecture engineered to provide an efficient and robust password guessability service that benefits from all the features and goals expected from cloud applications. This architecture comprises several components, featuring the combination of a synthetic dataset generator realized via a generative adversarial network (GAN), which may learn the distribution of passwords from a given dictionary and generate high-quality password guesses, along with a password guessability estimator realized via a password strength estimation algorithm. In addition to detailing the architecture’s components, we run a performance evaluation on the architecture’s key components, obtaining promising results. Finally, the complete application is delivered and may be used by a user to estimate the strength of a password and the time taken by an average computer to enumerate it.
The Influence of School Library Utilization and Reading Interest on Student Achievement

This study aimed at determining influence of using the school library and reading interest on student achievement fourth grade students of SD 04 OKU partially and simultaneously. This is a quantitative research with sampling using all class IV totaling 105 students. The variabels studied were the use of the school library (X1), reading interest (X2) and learning achievement (Y). Methods of data collection using a questionnaire. Data analysis using validity and reliability tests using the SPSS 22 for windows program. The results showed that there was an influence between the influences of the use of the school library on student achievement. This paper recommends to more focus on library and reading interest of students where school would have good students’ achievement.
Blended learning for doctoral training in the context of the COVID-19 pandemic

Distance learning has been an option to mitigate the global spread of the highly contagious disease, named COVID-19 (coronavirus), and declared a Pandemic by the World Health Organization (WHO) in March 2020. Social confinement as a public health strategy to address the health crisis has led to the adoption of new teaching methods in the new social sphere. Higher education contexts have gradually incorporated the opportunities offered by information and communication technologies (ICT). The purpose of the article is to present some theoretical reflections on the inclusion of the Blended Learning (B-Learning) methodological model in higher education, considering the changes that have occurred in the current situation due to the pandemic. Based on the review of the specialized scientific literature in relation to the integration of ICT in educational environments, the relevance of the advantages of technological tools in blended learning in the current context of the COVID-19 pandemic is recognized, specifically for the doctoral training process.
EW-CACTUs-MAML: A Robust Metalearning System for Rapid Classification on a Large Number of Tasks

This study aims to develop a robust metalearning system for rapid classification on a large number of tasks. The model-agnostic metalearning (MAML) with the CACTUs method (clustering to automatically construct tasks for unsupervised metalearning) is improved as EW-CACTUs-MAML after integrated with the entropy weight (EW) method. Few-shot mechanisms are introduced in the deep network for efficient learning of a large number of tasks. The process of implementation is theoretically interpreted as “gene intelligence.” Validation of EW-CACTUs-MAML on a typical dataset (Omniglot) indicates an accuracy of 97.42%, performing better than CACTUs-MAML (validation accuracy = 97.22%). At the end of this paper, the availability of our thoughts to improve another metalearning system (EW-CACTUs-ProtoNets) is also preliminarily discussed based on a cross-validation on another typical dataset (Miniimagenet).
Electronic Medical Record–Based Machine Learning Approach to Predict the Risk of 30-Day Adverse Cardiac Events After Invasive Coronary Treatment: Machine Learning Model Development and Validation

Although there is a growing interest in prediction models based on electronic medical records (EMRs) to identify patients at risk of adverse cardiac events following invasive coronary treatment, robust models fully utilizing EMR data are limited.We aimed to develop and validate machine learning (ML) models by using diverse fields of EMR to predict the risk of 30-day adverse cardiac events after percutaneous intervention or bypass surgery.EMR data of 5,184,565 records of 16,793 patients at a quaternary hospital between 2006 and 2016 were categorized into static basic (eg, demographics), dynamic time-series (eg, laboratory values), and cardiac-specific data (eg, coronary angiography). The data were randomly split into training, tuning, and testing sets in a ratio of 3:1:1. Each model was evaluated with 5-fold cross-validation and with an external EMR-based cohort at a tertiary hospital. Logistic regression (LR), random forest (RF), gradient boosting machine (GBM), and feedforward neural network (FNN) algorithms were applied. The primary outcome was 30-day mortality following invasive treatment.GBM showed the best performance with area under the receiver operating characteristic curve (AUROC) of 0.99; RF had a similar AUROC of 0.98. AUROCs of FNN and LR were 0.96 and 0.93, respectively. GBM had the highest area under the precision-recall curve (AUPRC) of 0.80, and the AUPRCs of RF, LR, and FNN were 0.73, 0.68, and 0.63, respectively. All models showed low Brier scores of <0.1 as well as highly fitted calibration plots, indicating a good fit of the ML-based models. On external validation, the GBM model demonstrated maximal performance with an AUROC of 0.90, while FNN had an AUROC of 0.85. The AUROCs of LR and RF were slightly lower at 0.80 and 0.79, respectively. The AUPRCs of GBM, LR, and FNN were similar at 0.47, 0.43, and 0.41, respectively, while that of RF was lower at 0.33. Among the categories in the GBM model, time-series dynamic data demonstrated a high AUROC of >0.95, contributing majorly to the excellent results.Exploiting the diverse fields of the EMR data set, the ML-based 30-day adverse cardiac event prediction models demonstrated outstanding results, and the applied framework could be generalized for various health care prediction models.
Teacher Education in the Digital Transformation Process in North Cyprus: A Situation Analysis Study

While the world was already moving towards a digital future before 2020, the coronavirus pandemic accelerated that significantly in many sectors. This is certainly true with regard to digital transformation in the classroom, which gathered pace almost overnight when schools shutdown and lessons first moved online. At the time, the shift served to highlight how unprepared most of the sector was for digital transformation. At this point, both teacher and student skills and competencies for digital transformation have been questioned and many academic studies for literature have been put forward in this context. In this research, teacher education and competencies are questioned in the transition to the digital transformation process in Northern Cyprus. In addition, tools for measuring digital competencies and teacher-oriented changes will be introduced. It is thought that determining the competencies of teachers and the tools measuring these competencies within the scope of the digitalization process will be effective in ensuring quality in education on behalf of Northern Cyprus in the future and will shed light on future research. In the literature review, although the existence of studies belonging to Northern Cyprus in measuring the digital competence of teachers/teacher candidates&amp;rsquo; is remarkable, it has been determined that there are not enough numbers according to the importance of the subject. Considering the rapid transition and adaptation to the digital transformation process, especially during the pandemic period, since teachers are the most important part of digital education, it is foreseen that more qualitative or quantitative research is needed to interpret and measure digital competencies.
'Censorship-free' platforms: Evaluating content moderation policies and practices of alternative social media

Following the development and implementation of mainstream social media platforms' election-related speech policies, a renewed wave of criticism emerged from the U.S. ideological right.Several months before the 2020 U.S. presidential election, conservative politicians, pundits, and self-described patriots alleged that their speech was being censored by "Big Tech."This resulted in right-leaning influencers, and many of their followers, migrating to alternative online platforms to avoid moderation.Alternative social media, such as Parler, Bitchute, Gab, and Gettr, describe themselves as unmoderated hubs for "free speech," signalling an invitation for users to voice everything from unpopular opinions, to misinformation, to hate speech.Yet when pushed by technology infrastructure platforms like Apple's App Store and Google's Play Store to address missing or substandard moderation practices, "alt-tech" platforms were forced to create or adapt ad hoc, often minimalistic, content moderation policies.Our research explores and evaluates these policies in comparison to mainstream platforms, and analyses how moderation policies interact with the ideological framework asserted at an alternative platform's nascence.Our work provides necessary insight into the potential motivations for one potential source of U.S. internet platform oversight.With few immediately available regulatory options, assessing the viability of alternatives is crucial.This is particularly true as severe legislative gridlock stalls meaningful reform to the federal law perhaps most capable of improving platforms' moderation practices.Because private regulation appears to be the most immediate solution to address new breeding grounds for mis-and disinformation, inquiry into alternative platforms' adoption and enforcement of moderation policies is needed.Our paper concludes with questions for future research into the efficacy of alternative platforms' policy implementation; it is imperative to distinguish legitimate moderation from mere shells constructed to retain profit in parallel with ideological posturing.
The Correlation Between Self-Control and Cyberbullying at Private High School X in Bogor

Teenagers try to develop their identity. Adolescents who fail to realize their identity may develop problematic behaviors. Technological developments have had a major impact, such as the emergence of new forms of bullying, namely cyberbullying carried out through electronic media. Juvenile delinquency such as cyberbullying can be influenced by several factors, including the lack of individual self-control. Individuals who have good self-control should be able to regulate their behavior in a more positive direction. This study aimed to determine whether there is a correlation between self-control and cyberbullying in Private High School X in Bogor. The study sample consisted of 95 students recruited through purposive sampling. The study employed a quantitative method with a Cyberbullying Scale comprised of 26 items with a 0.873 reliability, and a Self-Control Scale comprised of 22 items with a 0.853 reliability. Validity was evaluated by testing content with expert lecturers. Data were analyzed using the product-moment correlation technique. The study results produced an r of –0.465 (p &lt; 0.05), indicating a significant negative correlation between self-control and cyberbullying in students at Private High School X in Bogor.&#x0D; Keywords: cyberbullying, self-control, teenagers
ALLD: Acute Lymphoblastic Leukemia Detector

Acute Lymphoblastic Leukemia (ALL) is a life-threatening type of cancer wherein mortality rate is unquestionably high. Early detection of ALL can reduce both the rate of fatality as well as improve the diagnosis plan for patients. In this study, we developed the ALL Detector (ALLD), which is a deep learning-based network to distinguish ALL patients from healthy individuals based on blast cell microscopic images. We evaluated multiple DL-based models and the ResNet-based model performed the best with 98% accuracy in the classification task. We also compared the performance of ALLD against state-of-the-art tools utilized for the same purpose, and ALLD outperformed them all. We believe that ALLD will support pathologists to explicitly diagnose ALL in the early stages and reduce the burden on clinical practice overall.
sSfS: Segmented Shape from Silhouette Reconstruction of the Human Body

Three-dimensional (3D) shape estimation of the human body has a growing number of applications in medicine, anthropometry, special effects, and many other fields. Therefore, the demand for the high-quality acquisition of a complete and accurate body model is increasing. In this paper, a short survey of current state-of-the-art solutions is provided. One of the most commonly used approaches is the Shape-from-Silhouette (SfS) method. It is capable of the reconstruction of dynamic and challenging-to-capture objects. This paper proposes a novel approach that extends the conventional voxel-based SfS method with silhouette segmentation-segmented Shape from Silhouette (sSfS). It allows the 3D reconstruction of body segments separately, which provides significantly better human body shape estimation results, especially in concave areas. For validation, a dataset representing the human body in 20 complex poses was created and assessed based on the quality metrics in reference to the ground-truth photogrammetric reconstruction. It appeared that the number of invalid reconstruction voxels for the sSfS method was 1.7 times lower than for the state-of-the-art SfS approach. The root-mean-square (RMS) error of the distance to the reference surface was also 1.22 times lower.