Reviewerly/exHarmony huggingface dataset:
Development of Blended Learning-Based Learning Media to Improve the Language Capability of Children Aged 5-6 Years Old at Kindergarten Bunda Gemilang, Bogor Regency In 2021

This study aims to analyze: 1) The process of developing learning using the blended learning method to improve the language skills of children aged 5-6 years, 2) The feasibility of multi-language interactive multimedia learning based on Android on the ADDIE model in an effort to apply language skills to children aged 5-6 years at Bunda Gemilang Kindergarten, Bogor Regency. This research method is development research or also called Research and Development (RD). By using the ADDIE development model (Analysis, Design, Development, Implementation, Evaluation). The subjects in this study were Bunda Gemilang Kindergarten students, Bogor Regency. The results shown are the assessment of the feasibility of multimedia by material experts obtaining a score of 84.3% (Eligible). The feasibility assessment by media experts obtained a score of 74, 8 and the feasibility assessment by learning design experts obtained a score of 74.3%. Based on a feasibility assessment by media and instructional design experts, this multilingual dictionary is considered feasible to use. The response of Bunda Gemilang Kindergarten students with the multilingual learning dictionary in the Gemilang Multilingual Dictionary application in the individual trial was categorized as good (BSH) with a score of 71. In the small group trial, it showed an average score of 72 which was categorized as Good as Expected (BSH). And in the field trial, the N-Gain value was obtained in the range of 0.3 g 0, so it can be concluded that the development of blended learning-based learning media applied to students in Bunda Gemilang Kindergarten is very feasible and good in improving children's language skills. 5-6 years old.
Development of Teaching Materials for Science Courses Based on Digital Comic on Temperature, Heat, and Expansion Materials for Class Vii Smpit Al Kahfi, Bogor Regency

This study aims to 1) determine the procedure for developing digital comic-based science teaching materials on temperature, heat, and expansion grade VII materials, 2) determine the feasibility of digital comic-based science teaching materials on temperature, heat, and expansion grade VII materials, 3) find out the effectiveness of science teaching materials based on digital comics on the material of temperature, heat, and expansion for class VII. This study uses research and development methods (Research and Development). The results of this study are 1) the development of digital comic-based teaching materials using the ADDIE model (analyze, design, development, implementation, evaluation). This product is based on an application that can be installed on a computer or device, making it easy to use anywhere. The material presented is temperature, heat, and expansion. In the development of digital comics teaching materials, family characters (mother and children) are used and are equipped with images that match the storyline. There are also materials, practice questions, and evaluations that are tailored to the curriculum of the driving school. 2) the feasibility of teaching materials is obtained from the results of the validation of the material experts who show a value of 80% which means that it enters the criteria very valid/feasible, while the linguist shows a value of 96% which means that this product is very valid/feasible, while the results from media and learning design experts by 76% and 79% which means valid/feasible. 3) The average pretest obtained in the field trial was 59, while the posttest was 74. Based on the normality test, that the significance value was 0.2 0.05 that the resulting data were normally distributed. Based on the hypothesis test data.
Online Learning of Early Childhood Language Development Courses Third Semester Students of the Early Childhood Education Teacher Education Study Program (PGPAUD) STKIP Muhammadiyah Bogor

This study aims to analyze: 1) To analyze the process of developing google classroom learning courses early childhood language development courses third semester students of the Early Childhood Education Teacher Education Study Program (PGPAUD) STKIP Muhammadiyah Bogor. 2) To analyze the feasibility of online learning google classroom The Early Childhood Language Development course based on the ADDIE model is integrated with the PEDATI model for third semester students of the Early Childhood Education Teacher Education Study Program (PGPAUD) STKIP Muhammadiyah Bogor. 3) To analyze the effectiveness of google classroom-based online media for learning the Early Childhood Language Development course for third semester students of the Early Childhood Education Teacher Education Study Program (PGPAUD) STKIP Muhammadiyah Bogor. This research method is development research or also called Research and Development (RD). By using the ADDIE (Analysis, Design, Development, Implementation, Evaluation) development model using integrated with the PEDATI learning system design model. The subjects in this study were students in the third semester of the Early Childhood Education Teacher Education Study Program (PGPAUD) STKIP Muhammadiyah Bogor. The result shown is a multimedia feasibility assessment by the material expert obtained a score of 83% (good). The feasibility assessment by media experts obtained a score of 89% (good). Based on the results of the researchers, it can be concluded that the online learning activities of STKIP Muhammadiyah Bogor students are effective. From this calculation, the average N-Gain of students was obtained as much as online learning of STKIP Muhammadiyah Bogor students in the High N-Gain category, which was 0.78.
Leveraging Bias in Pre-trained Word Embeddings for Unsupervised Microaggression Detection

Microaggressions are subtle manifestations of bias (Breitfeller et al. 2019).These demonstrations of bias can often be classified as a subset of abusive language.However, not much focus has been placed on the recognition of these instances.As a result, limited data is available on the topic, and only in English.Being able to detect microaggressions without the need for labeled data would be advantageous since it would allow content moderation also for languages lacking annotated data.In this study, we introduce an unsupervised method to detect microaggressions in natural language expressions.The algorithm relies on pre-trained word-embeddings, leveraging the bias encoded in the model in order to detect microaggressions in unseen textual instances.We test the method on a dataset of racial and gender-based microaggressions, reporting promising results.We further run the algorithm on out-of-domain unseen data with the purpose of bootstrapping corpora of microaggressions "in the wild", perform a pilot experiment with prompt-based learning, and discuss the benefits and drawbacks of our proposed method.
Extract Similarities from Syntactic Contexts: a Distributional Semantic Model Based on Syntactic Distance

Distributional Semantics (DS) models are based on the idea that two words which appear in similar contexts, i.e. similar neighborhoods, have similar meanings.This concept was originally presented by Harris in his Distributional Hypothesis (DH) (Harris 1954).Even though DH forms the basis of the majority of DS models, Harris states in later works that only syntactic analysis can allow for a more precise formulation of the neighborhoods involved: the arguments and the operators.In this work, we present a DS model based on the concept of Syntactic Distance inspired by a study of Harris's theories concerning the syntactic-semantic interface.In our model, the context of each word is derived from its dependency network generated by a parser.With this strategy, the co-occurring terms of a target word are calculated on the basis of their syntactic relations, which are also preserved in the event of syntactical transformations.The model, named Syntactic Distance as Word Window (SD-W2), has been tested on three state-of-the-art tasks: Semantic Distance, Synonymy and Single Word Priming, and compared with other classical DS models.In addition, the model has been subjected to a new test based on Operator-Argument selection.Although the results obtained by SD-W2 do not always reach those of modern contextualized models, they are often above average and, in many cases, they are comparable with the result of GLOVE or BERT.
MODEL INDEX CARD MATCH TERHADAP KEAKTIFAN DAN HASIL BELAJAR SISWA PADA PEMBELAJARAN MATEMATIKA TENTANG BANGUN DATAR KELAS IV SD NEGERI 140 PALEMBANG

ABSTRACT: This study aimed to determine the effect of using The Index Card Match learning model on the activeness and learning outcomes of the fourth graders at Public Elementary School number 140 Palembang . This study was the Quasi Experimental Design with the type of Pretest-posttest Control Group Design. The sample of study consisted of 41 fourth graders. The instruments were a test and observation sheet. Data analysis this study by used t-test formula, comparing pretest and posttest obtained scores. The observation results of activeness at the experiment class reaches an average value of 65.13 and the control class reaches an average value of 47.08 . The average value of the experimental class pretest was 48 and pretest was the control class 44,2. The average posttest of the experimental class was 70.4 and the control class was 59. The results of pretest and posttest the experimental class were normally distributed with a value of 0.317 and 0.605. The results of the pretest and posttest of the control class were normally distributed with a value of 0.404 and 0.05. Calculation of hypotheses using the t-test and obtained t.count of 3.72 at a significant level of 0.05 and t.table of 2.022. The data showed that the model had significant effect values was indicated by t.count &gt; t.table.
INTERPRETING MOET’S 2018 GENERAL EDUCATION ENGLISH CURRICULUM

In this paper as the chief developer of the document, I will attempt to interpret and clarify some essential points of the General Education English Curriculum (GEEC) promulgated by the Vietnamese Ministry of Education and Training (MoET) on December 26, 2018. Three questions raised for exploration are: (1) “What are the backgrounds to the development and promulgation of MoET’s 2018 GEEC?”, (2) “What are the prominent characteristics of MoET’s 2018 GEEC?”, and (3) “How is MoET’s 2018 GEEC structured and why is it structured in the way it is?” These questions will be addressed in detail throughout the paper.
STUDENTS’ PREDISPOSITION TO BEING A VICTIM OF INTERNET CRIME IN TERTIARY INSTITUTIONS OF NIGERIA

The intensive and pervasive use of the use of the internet among the youth is an incontrovertible fact. Electronic or cybercrime among the youth has figuratively become endemic beating most security preventive networks and putting online users at the risk of becoming unsuspecting victims. This paper examines students’ Students’ Predisposition to Being a Victim of Internet Crime in Tertiary Institutions of Nigeria. The study employs quantitative research methods, 150 students were selected using multistage sampling procedure. Data were gathered on User’s purpose of using Internet and predisposition to internet Victimization; and Student Socio-economic status and Victimization Experience. Data were analyzed using frequency counts, percentage and correlation analysis. information from the respondents revealed that, students who engaged in online banking had been victimized at least in which their accounts was debited by unknown person without their knowledge/ consent and also, those who engaged in chatting and performing e-business transaction with high social economic status are more susceptible to e-victimization experience, more so, users’ motives of surfing internet are directly related to their vulnerability to victimization experience. Finally, from the study, it was observed that there is positive relationship between individual socio-economic status and their victimization experienced. The study concludes that students who engaged in online banking transaction, both via app, point of sales POS or e-transfer is exposed to fraudsters and more vulnerable to internet fraud experiences therefore recommended that student should try as much as possible not to display wealth or any of their socio-economic information on social media.
Performance Test of Homemade Control Materials from Addition and Spike Placebo Simulation on Urine Chloride Examination with Fantus Method

The background of this research was to examine the performance of homemade urine control materials on urine chloride examination. This study was aimed to test the performance of homemade control materials from addition and spike placebo simulation on urine chloride examination using the Fantus method. This research applied an experimental research method. The stages of the study started with standardization of AgNO3 solution, preparation of urine control materials from addition and spike placebo simulation, as well as calculation of accuracy (%R), bias (%d) and coefficient of variation (%CV). The results showed that the normal and pathological levels of accuracy in the urine control materials from addition were 109.55% and 104.30%, while the accuracy values (%R) ​​for urine control materials from spike placebo simulation, for the normal and pathological levels, were high, 110.07% and 104.54%. The bias values (%d) obtained in the urine control materials from addition with normal and high pathological levels were 9.55% and 4.30% bias, while the bias values (%d) in the urine control materials from spike placebo simulation with normal and high pathological levels were 10.07% and 4.54%. The coefficient variation (%CV) values ​​in urine control materials from addition at normal and pathological high levels were 3.75% and 3.07%, while the %CV values ​​in urine control materials from spike placebo simulation at normal and high pathological levels were 4.55% and 3.11%. This study concludes that based on the accuracy (%R) and bias (%d) parameters, the homemade control materials from addition and spike placebo simulation have relatively good performance for urine chloride examination using the Fantus method. Meanwhile, based on the parameter of coefficient of variation (%CVV), the homemade control materials from the addition and spike placebo simulation have slightly lower performance for examining urine chloride with the Fantus method.
Retraction: RNN based prediction of spatiotemporal data mining (IOP Conf. Ser.: Mater. Sci. Eng. 981 022027)

This article has been retracted by IOP Publishing following an allegation that this article may contain tortured phrases [1]. IOP Publishing has investigated and agrees the article contains a number of nonsensical phrases that feature throughout the paper to the extent that the article makes very little sense. This casts serious doubt over the legitimacy of the article and the expertise of the authors in this topic. IOP Publishing wishes to credit PubPeer commenters [2] for bringing the issue to our attention. The authors neither agree nor disagree to this retraction. [1] Cabanac G, Labbe C, Magazinov A, 2021, arXiv:2107.06751v1 [2] https://pubpeer.com/publications/EB7C68106400A4BD1BA7093DB1F0DA# Retraction published: 15 February 2023
Learning Invariant Representations with Missing Data

Spurious correlations allow flexible models to predict well during training but poorly on related test distributions. Recent work has shown that models that satisfy particular independencies involving correlation-inducing \textit{nuisance} variables have guarantees on their test performance. Enforcing such independencies requires nuisances to be observed during training. However, nuisances, such as demographics or image background labels, are often missing. Enforcing independence on just the observed data does not imply independence on the entire population. Here we derive \acrshort{mmd} estimators used for invariance objectives under missing nuisances. On simulations and clinical data, optimizing through these estimates achieves test performance similar to using estimators that make use of the full data.
Anticanonical codes from del Pezzo surfaces with Picard rank one

We construct algebraic geometric codes from del Pezzo surfaces and focus on the ones having Picard rank one and the codes associated to the anticanonical class. We give explicit constructions of del Pezzo surfaces of degree 4, 5 and 6, compute the parameters of the associated anticanonical codes and study their isomorphisms arising from the automorphisms of the surface. We obtain codes with excellent parameters and some of them turn out to beat the best known codes listed on the database codetable.
A Human-AI Loop Approach for Joint Keyword Discovery and Expectation Estimation in Micropost Event Detection

Microblogging platforms such as Twitter are increasingly being used in event detection. Existing approaches mainly use machine learning models and rely on event-related keywords to collect the data for model training. These approaches make strong assumptions on the distribution of the relevant micro-posts containing the keyword -- referred to as the expectation of the distribution -- and use it as a posterior regularization parameter during model training. Such approaches are, however, limited as they fail to reliably estimate the informativeness of a keyword and its expectation for model training. This paper introduces a Human-AI loop approach to jointly discover informative keywords for model training while estimating their expectation. Our approach iteratively leverages the crowd to estimate both keyword specific expectation and the disagreement between the crowd and the model in order to discover new keywords that are most beneficial for model training. These keywords and their expectation not only improve the resulting performance but also make the model training process more transparent. We empirically demonstrate the merits of our approach, both in terms of accuracy and interpretability, on multiple real-world datasets and show that our approach improves the state of the art by 24.3%.
Uncertainty-Aware Deep Classifiers using Generative Models

Deep neural networks are often ignorant about what they do not know and overconfident when they make uninformed predictions. Some recent approaches quantify classification uncertainty directly by training the model to output high uncertainty for the data samples close to class boundaries or from the outside of the training distribution. These approaches use an auxiliary data set during training to represent out-of-distribution samples. However, selection or creation of such an auxiliary data set is non-trivial, especially for high dimensional data such as images. In this work we develop a novel neural network model that is able to express both aleatoric and epistemic uncertainty to distinguish decision boundary and out-of-distribution regions of the feature space. To this end, variational autoencoders and generative adversarial networks are incorporated to automatically generate out-of-distribution exemplars for training. Through extensive analysis, we demonstrate that the proposed approach provides better estimates of uncertainty for in- and out-of-distribution samples, and adversarial examples on well-known data sets against state-of-the-art approaches including recent Bayesian approaches for neural networks and anomaly detection methods.
Generalised learning of time-series: Ornstein-Uhlenbeck processes

In machine learning, statistics, econometrics and statistical physics, cross-validation (CV) is used asa standard approach in quantifying the generalisation performance of a statistical model. A directapplication of CV in time-series leads to the loss of serial correlations, a requirement of preserving anynon-stationarity and the prediction of the past data using the future data. In this work, we proposea meta-algorithm called reconstructive cross validation (rCV ) that avoids all these issues. At first,k folds are formed with non-overlapping randomly selected subsets of the original time-series. Then,we generate k new partial time-series by removing data points from a given fold: every new partialtime-series have missing points at random from a different entire fold. A suitable imputation or asmoothing technique is used to reconstruct k time-series. We call these reconstructions secondarymodels. Thereafter, we build the primary k time-series models using new time-series coming fromthe secondary models. The performance of the primary models are evaluated simultaneously bycomputing the deviations from the originally removed data points and out-of-sample (OSS) data.Full cross-validation in time-series models can be practiced with rCV along with generating learning curves.
DeepFIB: Self-Imputation for Time Series Anomaly Detection

Time series (TS) anomaly detection (AD) plays an essential role in various applications, e.g., fraud detection in finance and healthcare monitoring. Due to the inherently unpredictable and highly varied nature of anomalies and the lack of anomaly labels in historical data, the AD problem is typically formulated as an unsupervised learning problem. The performance of existing solutions is often not satisfactory, especially in data-scarce scenarios. To tackle this problem, we propose a novel self-supervised learning technique for AD in time series, namely \emph{DeepFIB}. We model the problem as a \emph{Fill In the Blank} game by masking some elements in the TS and imputing them with the rest. Considering the two common anomaly shapes (point- or sequence-outliers) in TS data, we implement two masking strategies with many self-generated training samples. The corresponding self-imputation networks can extract more robust temporal relations than existing AD solutions and effectively facilitate identifying the two types of anomalies. For continuous outliers, we also propose an anomaly localization algorithm that dramatically reduces AD errors. Experiments on various real-world TS datasets demonstrate that DeepFIB outperforms state-of-the-art methods by a large margin, achieving up to $65.2\%$ relative improvement in F1-score.
Machine Learning Model Attribution Challenge

We present the findings of the Machine Learning Model Attribution Challenge. Fine-tuned machine learning models may derive from other trained models without obvious attribution characteristics. In this challenge, participants identify the publicly-available base models that underlie a set of anonymous, fine-tuned large language models (LLMs) using only textual output of the models. Contestants aim to correctly attribute the most fine-tuned models, with ties broken in the favor of contestants whose solutions use fewer calls to the fine-tuned models' API. The most successful approaches were manual, as participants observed similarities between model outputs and developed attribution heuristics based on public documentation of the base models, though several teams also submitted automated, statistical solutions.