Reviewerly/exHarmony huggingface dataset:
User Perception of Digital Money Solutions - Impact of the Complementary Usage to Fiat Currencies on Prospective Use Behavior

Since the monetary world and its currencies are changing in the age of digitization, so do costumers. With various possibilities to pay and store value, users have many opportunities when it comes to Money Solutions. This study aims to question the drivers of prospective usage behavior in the field of Digital Money Solutions by modelling a benefit and risk framework for technology acceptance. Thus, we investigate the determinants driving the future usage intention of Digital Money Solutions. It is based on the Unified theory of acceptance and use of technology and on the framework of benefit and risk. Furthermore, we categorize users of the technology of digital money solutions into groups to find out whether the parallel usage of fiat currencies and cryptocurrencies has an impact on the future usage intention. With the aim to find evidence on the drivers of usage behaviour of digital money users we conduct a structural model. We aim to find evidence on the competitive advantages of digital money and fiat currency, parallel in circulation. Thus, we find that the perception of benefit has a significantly positive influence on the future usage intention of digital money solutions, yet even stronger for the complimentary usage to fiat currencies. Thus, we contribute to the existing literature of behavioural intention and coexistence theory and deduce a future research agenda.
How to Train BERT with an Academic Budget

While large language models a la BERT are used ubiquitously in NLP, pretraining them is considered a luxury that only a few well-funded industry labs can afford. How can one train such models with a more modest budget? We present a recipe for pretraining a masked language model in 24 hours using a single low-end deep learning server. We demonstrate that through a combination of software optimizations, design choices, and hyperparameter tuning, it is possible to produce models that are competitive with BERT-base on GLUE tasks at a fraction of the original pretraining cost.
Online and Offline Reinforcement Learning by Planning with a Learned Model

Learning efficiently from small amounts of data has long been the focus of model-based reinforcement learning, both for the online case when interacting with the environment and the offline case when learning from a fixed dataset. However, to date no single unified algorithm could demonstrate state-of-the-art results in both settings. In this work, we describe the Reanalyse algorithm which uses model-based policy and value improvement operators to compute new improved training targets on existing data points, allowing efficient learning for data budgets varying by several orders of magnitude. We further show that Reanalyse can also be used to learn entirely from demonstrations without any environment interactions, as in the case of offline Reinforcement Learning (offline RL). Combining Reanalyse with the MuZero algorithm, we introduce MuZero Unplugged, a single unified algorithm for any data budget, including offline RL. In contrast to previous work, our algorithm does not require any special adaptations for the off-policy or offline RL settings. MuZero Unplugged sets new state-of-the-art results in the RL Unplugged offline RL benchmark as well as in the online RL benchmark of Atari in the standard 200 million frame setting.
IMPROVING STUDENTS’ READING COMPREHENSION OF NARRATIVE TEXT THROUGH COLLABORATIVE STRATEGIC READING TO THE TENTH GRADE STUDENTS AT MA AL MUNAWAR KUNCI BOJONEGORO IN ACADEMIC YEAR 2019/2020

AbstractClass of the tenth grade students often got low score in their examination where the questions were full of reading text. The researcher thinks that Collaborative Strategic Reading (CSR) can be an alternative technique for students when doing reading comprehension in achieving their academic success later. The objectives of the study are 1) To find out how Collaborative Strategic Reading can improve the students’ reading comprehension of narrative text to the tenth grade students at MA Al Munawar Kunci Bojonegoro in academic year 2019/2020, 2) To find out the improvement of tenth grade students’ reading comprehension through the implementation of Collaborative Strategic Reading at MA Al Munawar Kunci Bojonegoro in academic year 2019/2020. This research was collaborative classroom action research (CAR). Because of there is only one classroom, so this classroom to be the sample of the research by total sampling. The respondent is X class’ students that has 23 students. The results of this research were 1) Collaborative Strategic Reading can improve the students’ reading comprehension of narrative text to the tenth grade students at MA Al Munawar Kunci Bojonegoro in academic year 2019/2020; 2) There was significant improvement of tenth grade students’ reading comprehension through the implementation of Collaborative Strategic Reading at MA Al Munawar Kunci Bojonegoro in academic year 2019/2020.Key words: students’ reading comprehension, narrative text, CSR
The contributions of decoding skill and lexical knowledge to the development of irregular word reading.

Two recent computational models of reading development propose that irregular words are read using a combination of decoding and lexical knowledge but differ in assumptions about how these sources of information interact and about the relative importance of different aspects of lexical knowledge. We report developmental data that help to adjudicate these differences. Study 1 adopted a correlational approach to investigate the item-level relations between the ability to read a word aloud, general decoding ability, and knowledge of the word's phonological form (lexical phonology) or meaning (lexical semantics). We found that the latter three factors all influenced accuracy of oral reading. We observed trends indicating that the impact of differences in decoding skill and lexical knowledge were more prominent for irregular words. Study 2 comprised two experiments in which novel irregular words were taught; in Experiment 1 we compared phonological to no pretraining, whereas in Experiment 2 we compared phonological to phonological plus semantics pretraining. Exposure to the phonological form of the word had a substantial impact in the early stages of learning, whereas the impact of adding semantics was more modest and emerged later. Our findings provide strong evidence that irregular words are read using a combination of decoding and lexical knowledge, with a greater contribution from lexical phonology than lexical semantics. Computational models of learning to read are currently unable to fully account for our data, therefore we propose some modifications. We advocate an instructional approach whereby phonics and vocabulary teaching are combined to support irregular word reading. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Amortized Variational Inference for Simple Hierarchical Models

It is difficult to use subsampling with variational inference in hierarchical models since the number of local latent variables scales with the dataset. Thus, inference in hierarchical models remains a challenge at large scale. It is helpful to use a variational family with structure matching the posterior, but optimization is still slow due to the huge number of local distributions. Instead, this paper suggests an amortized approach where shared parameters simultaneously represent all local distributions. This approach is similarly accurate as using a given joint distribution (e.g., a full-rank Gaussian) but is feasible on datasets that are several orders of magnitude larger. It is also dramatically faster than using a structured variational distribution.
On the use of Computer Technologies in Studying Mathematics

A mathematics lesson using ICT is visual, colorful, informative, interactive, saves the teacher and the teacher's time, allows the teacher to work at his or her own pace, allows the teacher to work with students in a differentiated and individual manner, and allows for quick monitoring and evaluation of learning outcomes. The computer can be employed at any point in the learning process, such as when introducing new content, consolidating, repeating, or monitoring ZUN, while it can also serve as a teacher, a working tool, a learning object, a collaborative team, or a leisure (game) means for students. The characteristics of the employment of computer technology in the study of mathematics are revealed in this article.
Reducing the Covariate Shift by Mirror Samples in Cross Domain Alignment

Eliminating the covariate shift cross domains is one of the common methods to deal with the issue of domain shift in visual unsupervised domain adaptation. However, current alignment methods, especially the prototype based or sample-level based methods neglect the structural properties of the underlying distribution and even break the condition of covariate shift. To relieve the limitations and conflicts, we introduce a novel concept named (virtual) mirror, which represents the equivalent sample in another domain. The equivalent sample pairs, named mirror pairs reflect the natural correspondence of the empirical distributions. Then a mirror loss, which aligns the mirror pairs cross domains, is constructed to enhance the alignment of the domains. The proposed method does not distort the internal structure of the underlying distribution. We also provide theoretical proof that the mirror samples and mirror loss have better asymptotic properties in reducing the domain shift. By applying the virtual mirror and mirror loss to the generic unsupervised domain adaptation model, we achieved consistent superior performance on several mainstream benchmarks.
TEACHING INDUSTRY: ANALISIS PENERAPAN SOP PRODUKSI ANIMASI PADA VIDEO PEMBELAJARAN

Polibatam's policy of implementing independent learning and PBL requires maximum infrastructure preparation. One of the facilities that have been prepared is the DigiArs studio for practical activities. The studio is equipped with production equipment, including animation production business process flows to support the realization of ACI. Through the application of this business process flow, the products produced can be of industry standard and can be utilized by the wider community. One of the projects carried out As the beginning of the implementation of PBL in the department of informatics engineering, one of the projects that was carried out was an English learning video. The research method is Action Research and Evaluation, which begins with product creation and analysis of the application of the animation business process flow. In Action Research, the product is made using the Sharewood-Rout approach by applying the animation production business process flow for video assets in the form of motion graphics. Furthermore, in the evaluation, an analysis of the application of the process flow is carried out. The results show that the business process flow for the production of animation products at the DigiArs studio is very dynamic, so for the manufacture of multimedia products other than animation products, you can follow the business process specifically for products that have assets in the form of animation styles, such as videos, games, and simulations.
The Effect of E-learning Media on the Quality of Learning of the Library Science Students Amidst the COVID-19 Pandemic

The aim of this study is to investigate the effect of e-learning on the quality of learning of the Library Science students amidst the COVID-19 pandemic. This study was conducted in Universitas Islam Negeri Sumatera Utara Medan (UINSU), Indonesia. Data was obtained from 83 respondents, that were selected from a total population of 497 students using questionnaires, and, was analyzed using descriptive statistical and simple linear regression techniques. Furthermore, the analysis was carried out through descriptive and quantitative methods. The empirical results of the hypothesis test was obtained using the SPSS Version 23 software and it showed that the results of tcount was greater than ttable (8.337 > 1.989) with a significant level below 0.05, namely 0.000. Therefore, this means that the e-learning variables significantly influenced the quality of learning. The results of the coefficient of determination (R2) which showed the R2 value of 0.462/46.2%, indicated that the percentage of e-learning contributions on the quality of learning of the Library Science students was 46.2%. Meanwhile the remaining 53.8% was influenced by other independent variables that were not discussed. The finding reveals that E-learning has a positive and significant effect on the quality of learning of the Library Science students at UINSU Medan during the pandemic COVID-19. Therefore, it is recommended that university policies in utilizing e-learning needs an in-depth study and design and the decision makers of the university should improve the learning facilities to make the learning process better. © 2021
On the Expected Complexity of Maxout Networks

Learning with neural networks relies on the complexity of the representable functions, but more importantly, the particular assignment of typical parameters to functions of different complexity. Taking the number of activation regions as a complexity measure, recent works have shown that the practical complexity of deep ReLU networks is often far from the theoretical maximum. In this work we show that this phenomenon also occurs in networks with maxout (multi-argument) activation functions and when considering the decision boundaries in classification tasks. We also show that the parameter space has a multitude of full-dimensional regions with widely different complexity, and obtain nontrivial lower bounds on the expected complexity. Finally, we investigate different parameter initialization procedures and show that they can increase the speed of convergence in training.
Shaking Syntactic Trees on the Sesame Street: Multilingual Probing with Controllable Perturbations

Recent research has adopted a new experimental field centered around the concept of text perturbations which has revealed that shuffled word order has little to no impact on the downstream performance of Transformer-based language models across many NLP tasks. These findings contradict the common understanding of how the models encode hierarchical and structural information and even question if the word order is modeled with position embeddings. To this end, this paper proposes nine probing datasets organized by the type of controllable text perturbation for three Indo-European languages with a varying degree of word order flexibility: English, Swedish and Russian. Based on the probing analysis of the M-BERT and M-BART models, we report that the syntactic sensitivity depends on the language and model pre-training objectives. We also find that the sensitivity grows across layers together with the increase of the perturbation granularity. Last but not least, we show that the models barely use the positional information to induce syntactic trees from their intermediate self-attention and contextualized representations.
Hasil Plagiasi Using Picture and Guided Questions to Improve Students' Ability in Writing Descriptive Text

This research is classroom action research. It is carried out at 3rd semester students PGSD study program in Panca Marga University Probolinggo, consisting of 32 students. The objective of the research is to find out how can pictures and guided questions be implemented to improve students’ ability in writing descriptive text. This research is conducted in 2 (two) cycles. The procedure of the research consist of: preliminary study, planning, action, observation, and reflection. Qualitative data are obtained from observation checklist, field notes and interview. While quantitative data are gained from the product of students’ descriptive text writing. The result of the research shows that using picture and guided questions can improve students’ ability in writing descriptive text. As the indicator of success, the researcher adapted scoring rubric consisting 4 level: Level 1 = Very Poor, Level 2 = Fair to Poor, Level 3 = Good to Average, and Level 4 = Excellent to Very Good. It is considered success if 63 % or more than 63% students have reached level 3. In preliminary study only 28% students belongs to level 3. In cycle 1 there a slight significant showing percentage improvement in level 3 (47% students reached level 3). In second cycle, the result of students’ writing finally shows a high improvement, 69% students have reached level 3. It indicates that using picture and guided questions can improve students’ ability in writing descriptive text.
Understanding Model Robustness to User-generated Noisy Texts.

Sensitivity of deep-neural models to input noise is known to be a challenging problem. In NLP, model performance often deteriorates with naturally occurring noise, such as spelling errors. To mitigate this issue, models may leverage artificially noised data. However, the amount and type of generated noise has so far been determined arbitrarily. We therefore propose to model the errors statistically from grammatical-error-correction corpora. We present a thorough evaluation of several state-of-the-art NLP systems’ robustness in multiple languages, with tasks including morpho-syntactic analysis, named entity recognition, neural machine translation, a subset of the GLUE benchmark and reading comprehension. We also compare two approaches to address the performance drop: a) training the NLP models with noised data generated by our framework; and b) reducing the input noise with external system for natural language correction. The code is released at https://github.com/ufal/kazitext.
Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints

Evaluating adversarial robustness amounts to finding the minimum perturbation needed to have an input sample misclassified. The inherent complexity of the underlying optimization requires current gradient-based attacks to be carefully tuned, initialized, and possibly executed for many computationally-demanding iterations, even if specialized to a given perturbation model. In this work, we overcome these limitations by proposing a fast minimum-norm (FMN) attack that works with different $\ell_p$-norm perturbation models ($p=0, 1, 2, \infty$), is robust to hyperparameter choices, does not require adversarial starting points, and converges within few lightweight steps. It works by iteratively finding the sample misclassified with maximum confidence within an $\ell_p$-norm constraint of size $\epsilon$, while adapting $\epsilon$ to minimize the distance of the current sample to the decision boundary. Extensive experiments show that FMN significantly outperforms existing attacks in terms of convergence speed and computation time, while reporting comparable or even smaller perturbation sizes.
PENGARUH MODEL PEMBELAJARAN TALKING STICK TERHADAP HASIL BELAJAR IPS KELAS IV SDN KERAMAT MINA KABUPATEN BANJAR

The purpose of this study was to determine whether there is an effect of the talking stick learning model on social studies learning outcomes in grade IV SDN Keramat Mina, Banjar Regency. The object of this research is the implementation of the talking stick learning model on social studies learning outcomes for class IV SDN Keramat Mina, Banjar Regency. The subjects in this study were all students at SDN Keramat Mina Banjar Regency. The sampling technique used purposive sampling with a total of 22 class IV students. The test of the instrument was carried out by testing the validity and reliability testing using the help of a computer application, namely SPSS 20 for windows. The data mining tool used was a questionnaire. The data analysis used was simple linear regression technique. Based on the data analysis, it is concluded that there is an effect of the talking stick learning model on the analysis rhitung = 0.471> rt 0.432 at the 5% significance level of N = 22, which means that the hypothesis (Ha) is accepted. , and the null hypothesis (Ho) is rejected.
Silence Is Silver, but Speech Is Golden: Intelligent Voice Assistants (IVAs) and Their Impact on a Brand’s Customer Decision Journey with a Special Focus on Trust and Convenience – A Qualitative Consumer Analysis in the Netherlands

Intelligent Voice Assistants (IVAs) have become a new phenomenon in Marketing. The paper analyzes how IVAs can be used in different stages of a brand’s consumer decision journey. Since trust and convenience are seen as major drivers for usage adoption, their impact on IVAs is investigated. For this purpose, primary in-depth interviews with eight IVA users in the Netherlands were conducted in July/August 2019. The results show that IVAs have a significant impact on all stages of a brand’s consumer decision journey. In the pre-purchase stage, consumers refuse to be exposed to advertisements over IVAs, but find recommendations helpful. For gathering information about products and services, the phone or laptop is still preferred to the IVA because voice-proof SEO is still seen as inferior. When it comes to purchase, consumers can imagine buying simple, lower-price, daily-use items over an IVA, such as groceries, personal care products, food delivery and transportation. Customers do not trust their IVA yet with more expensive or complex products (e.g., electronics) because they see a higher risk if the IVA does not interpret the order correctly. Also, products that require visualization (clothes, hotel) are less considered for conversational commerce. Regarding payment, consumers hardly have any trust issues with providing an IVA personal information or payment details. In the post-purchase step, informational services (such as information on delivery times, the return process or warranty) and providing reviews are identified as key functionalities of IVAs. The most potential for conversational commerce currently lies in the repurchase of products or services customers have already bought. At this point, the customer saves time and effort through a simplified decision journey.
La Educación Híbrida: como sistema educativo y medio de educación alternativa, en las IES del Ecuador

Higher education has had a 360-degree turn from 2020 to the first semester of 2021, as a result of the Covid-19 pandemic. Thus, Higher Education Institutions (HEIs) have gone from face-to-face teaching to online (hybrid), which represents an opportunity to do things in a different way but always with the vision and conviction of improvement. continues as a strategic ally and competitive advantage.The changes to which the (IES) have been subjected have ensured the well-being of the students and the entire university community, using digital pedagogy as a key factor so as not to paralyze their administrative and academic activities.The objective of this line of research was to describe the digital teaching scenario and the advantages that (HEI) have, compared to the hybrid modality, without neglecting the quality of teaching and institutional prestige with all the key actors of the university community. Through the descriptive-analytical method we can indicate that the Higher Education Institutions of Ecuador, went from the traditional in a classroom to digital innovation, using all existing technological resources and in turn acquiring new ones with the respective accesses to platforms of Educational digital environments to facilitate the adaptation process between the teacher and the students.