Reviewerly/exHarmony huggingface dataset:
Mobile-Assisted Language Learning (MALL): Students’ Perception and Problems towards Mobile Learning in English Language

Mobile-Assisted Language Learning (MALL) is the latest learning way in the language education where applications or websites are used to facilitate students learning activities. Mobile phone has been studied years by researchers in its connection with education-related activities. This research will focus on its main purposes, they are: 1) students' perceptions in using mobile phone in English language learning classroom activities; 2). the problem of using mobile phone to support classroom activities, especially in English language learning. The method of the research was quantitative method which used 70 students as research object. The data were collected through a 5-point Likert Scale questionnaire. The research found; first, the students' majority had positive perception on the usage of MALL to support classroom activities, especially in learning English language. Second, the problem in internet connectivity is the biggest problems that students faced in terms of using MALL in English language classroom. But, It is hoped that MALL will be used as one of the teaching aids that could assist students in learning English as a Foreign Language (EFL) more effectively.
Perfect fluid model with <i>g</i> = 2

A perfect fluid model with a shell of charge is presented which yields g = 2 for low angular velocity. This model is not intended to represent a classical model of the electron but to show that a simple model based on equations consistent with special relativity can yield a value of g = 2.
Weighted Deep Neural Network Ensemble Approach for Multi-Domain Sentiment Analysis

The sentiment analysis is a subtask of text classification that is known as a domain dependent problem. In order to obtain an accurate classifier for a particular domain, a large labelled dataset is needed. To tackle the challenge of data scarcity in some domains, in the area of multi-domain problems, the classifier is trained on a set of labelled data from some domains and then it is applied to the target domains. In addition, another important issue in classification-based approaches in order to reach the better performance is that the nature of train and test data should be similar. So, a model trained by data from a specific domain, leads to poor results when it comes to another domains. This paper proposes three Weighted(deep)Neural Networks Ensemble approaches for multi-domain sentiment classification to address the mentioned issues, by training individual deep learning models (including CNN, LSTM and Bi-GRUCapsule) on specific domains. Using a weighted score of DBD and the initial polarity of the sample test data on each domain, a new aggregated score of final polarity is obtained. The DRANZIERA protocol is used for evaluation of the proposed models. The results have shown more than 0.03 improvements in average accuracy in comparison to the other state-of-the-art approaches.
Toward Personalization in Intelligent Learning Systems

This research emphasizes how technologies enable creating an intelligent learning environment where the pedagogical practice is more flexible and attractive, adaptive and personalized, and most of all—effective. Improving the quality of education is related to teaching and learning in intelligent education system (IES). Based on intelligent approaches, IES provides intelligent management of the learning process, smart workspaces or laboratories, adaptive learning, virtual pedagogical agents, smart data collection, immediate feedback to the user, and control of learning environment parameters. The paper presents an overview of IES from five different perspectives end user (teacher, student), technology (applications, devices, networks), data (collection, analysis, storage), smartness (personalization, decision making), and management (security, quality). The essential aspects of IES, such as modeling sensor networks and systems, ensuring reliability and service quality, knowledge presentation, and adaptive training, are also considered. Of particular importance for an effective teaching and learning process in an IES is the possibility for offering personalized education, so this aspect is discussed in detail. The paper proposes an open general knowledge model based on a network of mapped ontologies, which serves to personalize the learning process in IES.
Efektivitas Google Classroom dalam Pembelajaran Lesen Und Schreiben di Masa Pandemi Covid-19

Abs trak. The study's purpose is to know the effectiveness of google classroom in learning Lesen und Schreiben courses during the COVID-19 pandemic for students of the German education department of the Faculty of Language and Literature Makassar State University. The research design of this study is quasi-experiment. The research was managed at the Faculty of Language and Literature by adopting online learning when giving treatment. A study sample of 25 students of the 2020/2021 academic year. Data on the study was obtained through reading and writing tests and documentation. This research data was analyzed by using the t-one tile test. The results of the analysis showed that Google classroom had significance to the competence of reading and writing German students with t score (10,975) > t table (1,714) in reading competence and t score (8,292) > t table (1,714) on writing competence. Based on the research data analysis, it can be concluded that google classroom is effective in learning Lesen und Schreiben for students of the German Language Education Study Program of the Faculty of Language and Literature UNM during the COVID-19 Pandemic. K ata Kunci : Google Classroom, Learning Lesen und Schreiben , the COVID-19 Pandemic
Multi-Screen Thinking Environment for Supporting Inventive Problem Solving

Out of the box thinking is known to produce results in today’s era. Many organizations are putting an emphasis on out of the box ideas. More often than not, an individual who is in need of a problem solving tool settles for any one of the fairly inefficient thinking tools available on the internet. A system is proposed and designed to help create a more consistent problem solving environment for this Final Year Project 1. The methodology used in this proposed system is the Rapid Application Development (RAD). A literature review with three reviewed systems is conducted to obtain a better understanding of the proposed system. The requirements will be gathered through observation and reviewed system in requirement analysis phase. In system design phase, context diagram, Data Flow Diagram for Level 0and Level 1, Entity Relationship Diagram, and user interface design for the web-based system were drawn. In the end of Final Year Project 1, the idea and design of proposed system is identified. The implementation and testing phase of proposed system has been defined in Final Year Project 2 based on the requirement analysis and system design stated earlier. This project can be a useful tool for students and researches alike in assisting them with inventive problem solving. In addition, this project has a lower learning curve compared to the existing systems.
Dual-Arm Adversarial Robot Learning

Robot learning is a very promising topic for the future of automation and machine intelligence. Future robots should be able to autonomously acquire skills, learn to represent their environment, and interact with it. While these topics have been explored in simulation, real-world robot learning research seems to be still limited. This is due to the additional challenges encountered in the real-world, such as noisy sensors and actuators, safe exploration, non-stationary dynamics, autonomous environment resetting as well as the cost of running experiments for long periods of time. Unless we develop scalable solutions to these problems, learning complex tasks involving hand-eye coordination and rich contacts will remain an untouched vision that is only feasible in controlled lab environments. We propose dual-arm settings as platforms for robot learning. Such settings enable safe data collection for acquiring manipulation skills as well as training perception modules in a robot-supervised manner. They also ease the processes of resetting the environment. Furthermore, adversarial learning could potentially boost the generalization capability of robot learning methods by maximizing the exploration based on game-theoretic objectives while ensuring safety based on collaborative task spaces. In this paper, we will discuss the potential benefits of this setup as well as the challenges and research directions that can be pursued.
Are models trained on temporally-continuous data streams more adversarially robust?

Task-optimized convolutional neural networks are the most quantitatively accurate models of the primate visual system. Unlike humans, however, these models can easily be fooled by modifying their inputs with human-imperceptible image perturbations, resulting in poor adversarial robustness. Prior work showed that modifying a model's training objective or its architecture can improve its adversarial robustness. Another ingredient in building computational models of sensory cortex is the training dataset and, to our knowledge, its effect on a model's adversarial robustness has not been investigated. Motivated by observations that newborn chicks (Gallus gallus) develop more invariant visual representations when reared with more temporally-continuous visual experience, we here evaluate a model's adversarial robustness when it is trained on a more naturalistic dataset---a longitudinal video dataset collected from the perspective of infants (SAYCam; Sullivan et al., 2020). By evaluating the adversarial robustness of models on 26-way classification of a set of annotated video frames from this dataset, we find that, across multiple objective functions, models that have been pre-trained on SAYCam video frames are more adversarially robust than those that have been pre-trained on ImageNet. Our results suggest that to build models that are more adversarially robust, additional efforts should be made in curating datasets that are more similar to the natural image sequences and the visual experience that infants receive.
From Cluster Distributions Through Kernel Density Estimate to Driving Behaviour Scores: A Complete Data Science Pipeline

The study of driving behaviour is an element of great interest for all those companies that provide a fleet management service. Careful observations allow us to find imperfections that, if corrected, entail a reduction of risks and an increase in profits. In order to objectively evaluate this type of behaviour, there are countless indicators to be taken into consideration. In this work, the most significant indicators among those available are used to produce an evaluation of the driving behaviour through a pipeline of operations related to the world of data science. ?? This thesis focuses on the last stage of the process. Machine learning techniques are used to explore a cluster distribution resulting from previous stages. Then that distribution is exploited to calculate a final Key Performance Indicator (KPI) for each trip to be evaluated. This is not done through conventional techniques, such as comparison with a reference approved by a domain expert, but through the application of a Kernel Density Estimation (KDE) function that allows us to produce an assessment based on proximity to the majority of similar behaviours. ?? The results of the experiments demonstrated the validity of the automatically selected set to be considered as a reference, thus confirming the reliability of the KPIs. Furthermore, the analyses on the various types of features have highlighted which are the indicators that most influence the evaluation of driving behaviour.
Blockchain as a new digital paradigm

This chapter provides a synthesis of the blockchain literature in view of making accessible the main concepts of this technology that are still obscure to many. We study in a pedagogical way the main implications that the different generations of blockchain can have on the analysis of the decentralised coordination modes of agents. We advance that if blockchain has been seen as a disruptive technology since the start, it appears now a general purpose technology as well, which strongly shapes economic implications both in terms of transactions, organizations, and occupations.
Effect of marketing capabilities on innovation performance of small and medium enterprises (SMEs) In Jordan

Ownership of marketing capabilities can translate into the performance of the organization that excels in numerous business disciplines and sectors of industries. Nevertheless, studies indicate different conclusions with lack of consensus in the literature, which calls for additional research in the field of marketing capabilities. The objective of the present research is to examine the influence of marketing capabilities on innovation performance among SMEs in Jordan. Data have been collected through an online questionnaire. A total number of 385 samples been collected. SPSS version 23 was used to analyze the data from this Study. The result obtained shows that marketing capability has a significant influence on innovation performance. The conclusion from this study is that marketing capabilities is essential variable that affect SMEs innovation performance. Due to that it is very critical for managers and owners of SMEs to focus on enhancing business strategies that would recognize competitor moves and attempt to increase the quality standards of products and services through innovation performance.
XpulpNN: Enabling Energy Efficient and Flexible Inference of Quantized Neural Networks on RISC-V based IoT End Nodes

Published in "IEEE Transactions on Emerging Topics in Computing, Volume: 9, Issue: 3, JulySeptember 2021" and orally presented at ARITH 2021.
CHANGING THE ATTITUDE OF SCHOOLCHILDREN TO A HEALTHY LIFESTYLE AS A NECESSARY CONDITION OF THE ECOLOGICAL COMPONENT OF A SAFE EDUCATIONAL ENVIRONMENT WHEN IMPLEMENTING A DISTANCE LEARNING FORMAT

Information about the paper titled "CHANGING THE ATTITUDE OF SCHOOLCHILDREN TO A HEALTHY LIFESTYLE AS A NECESSARY CONDITION OF THE ECOLOGICAL COMPONENT OF A SAFE EDUCATIONAL ENVIRONMENT WHEN IMPLEMENTING A DISTANCE LEARNING FORMAT" at IATED Digital Library
Application of Expert System in Diagnosing Polycystic Ovary Syndrome

PCOS (Polycystic Ovary Syndrome) or polycystic ovary syndrome is a condition of impaired ovarian function in women of childbearing age. This condition causes the hormones of women suffering from PCOS to become imbalanced due to unknown reasons. The early signs of PCOS are irregular ovulation or fertility, increased levels of male hormones (androgens) in a woman's body, and the appearance of many cysts (fluid-filled sacs) on the ovaries. Things like that are very feared by women because women's nature is to conceive and have offspring, if it is not taken care of from the start or early on, it is not impossible for this to happen. But in fact, public knowledge is still low about Polycystic Ovary Syndrome, information about Polycystic Ovary Syndrome is still not well socialized to the public so that people still do not know how to handle and treat it. In diagnosing Polycystic Ovary Syndrome, there are several methods that can be used, including the Dempster Shafer method. From the results of the study, it can be concluded, among others: The expert system built can provide convenience for users to diagnose polycystic ovary syndrome, and can provide treatment solutions for the disease. The results of the diagnosis using the Dempster Shafer method have a better percentage value.
Tractable Approximate Gaussian Inference for Bayesian Neural Networks.

In this paper, we propose an analytical method for performing tractable approximate Gaussian inference (TAGI) in Bayesian neural networks. The method enables the analytical Gaussian inference of the posterior mean vector and diagonal covariance matrix for weights and biases. The method proposed has a computational complexity of $\mathcal{O}(n)$ with respect to the number of parameters $n$, and the tests performed on regression and classification benchmarks confirm that, for a same network architecture, it matches the performance of existing methods relying on gradient backpropagation.
PERFIL EPIDEMIOLÓGICO DOS PACIENTES ATENDIDOS EM UNIDADES DE ATENDIMENTO AO AVC AGUDO NO BRASIL: UMA REVISÃO DE LITERATURA

INTERSUBJETIVIDADE EM TERMOS DA ÉTICA E DA ALTERIDADE: UM ESTUDO JURÍDICO-FILOSÓFICO E EDUCACIONAL (DHIEA), presents the results of a research that analyzed the problem of child and teenage abandonment under a legal-humanist perspective, through human rights and the Child and Adolescent Statute.In this context, we seek, firstly, to define the concept of abandonment analyzed by this scientific project, and then to understand the position of
ЦИФРОВАЯ ТРАНСФОРМАЦИЯ ЭКОНОМИКИ В СОВРЕМЕННЫХ УСЛОВИЯХ

The authors illustrate that in the context of the pandemic COVID-19 in early 2020, an active appeal to the cyber space led to a significant acceleration of the transformation that began several decades ago. At the same time, in addition to unconditional positive aspects, digitalization is accompanied by certain negative phenomena: an increase in the digital gap between countries; inequalities related to age, education, gender, income, disability and geography; increased unemployment among low-skilled workers, etc. To ensure a smooth and painless transition to a economy, a comprehensive package of coordinated measures is needed to redistribute the workforce, attract additional investment in education, strengthen social protection and maintain employment.
Periodical digitization projects at Perm Territorial Library

Periodical digitization projects of Perm Territorial Library are discussed. The Library has both initiated several projects and participated in those as a partner. The scheme of collaboration with municipal libraries in digitizing local periodicals (newspapers) is characterized. The experience of Perm e-library demonstrates the user demand for digital newspaper collection.
COMPARISON OF TERTIARY STUDENTS' HOME SCHOOLING DURING THE TWO CORONAVIRUS WAVES

Appears in: ICERI2021 Proceedings Publication year: 2021Pages: 3363-3370ISBN: 978-84-09-34549-6ISSN: 2340-1095doi: 10.21125/iceri.2021.0826Conference name: 14th annual International Conference of Education, Research and InnovationDates: 8-9 November, 2021Location: Online Conference
Research on University Ideological Education Platform Based on Data Mining Technology

Cloud platform can not only provide massive teaching resources, but also retain a large amount of data about teachers and students during online learning. Data mining is a hot topic in the field of database application in recent years. Many achievements have been made in both theory and application, and the technology tends to be more mature. As a widely used new discipline, data mining technology has a good application prospect in university education informationization, which provides a new and scientific analysis way for the management, construction and service process of universities. K-means algorithm is used to track learners' learning behavior, analyze learners' learning patterns and cognitive level, classify learners, and provide automatic push of learning resources, so that online learning platform can teach learners in accordance with their aptitude and provide personalized services. According to the mining results, some suggestions are put forward for personalized design of online learning platform.
Discovering core terms for effective short text clustering

This thesis aims to address the current limitations in short texts clustering and provides a systematic framework that includes three novel methods to effectively measure similarity of two short texts, efficiently group short texts, and dynamically cluster short text streams.
HUBUNGAN PROGRAM GERAKAN LITERASI SEKOLAH (GLS) DENGAN LITERASI BACA-TULIS SISWA KELAS VIII SMP NEGERI 1 TARAKAN

This study aims to determine the relationship and closeness between the School Literacy Movement Program (GLS) on the Reading and Writing Literacy of Class VIII Students of SMP Negeri 1 Tarakan and the implementation of the GLS Program at SMP Negeri 1 Tarakan. This type of research is descriptive quantitative. The population in the study were all class VIII students of SMP Negeri 1 Tarakan in the 2020/2021 academic year, totaling 345 students. Samples were taken based on the Simple Random Sampling technique. Based on the results of data analysis, 69 of 86 students showed a supportive attitude in the GLS and Literacy and Literacy Program at school and based on the results of Kendall's Tau-B correlation test at a significant level of 5% (N = 86) obtained a p-value of less than 0.05 ( 0.000 0.05) means that there is a relationship between the GLS Program and Literacy. Then, the result of Z count is greater than Z table (4,972 1.65). This shows that there is a significant and strong relationship between the GLS Program and the Literacy and Literacy of students in schools. Thus, it can be concluded that "There is a significant and strong relationship between the School Literacy Movement Program (GLS) and the Literacy of Class VIII Students of SMP Negeri 1 Tarakan".Keywords: Literacy Movement, GLS, Read and Write Literacy
Implementasi Model Pembelajaran Inklusi bagi Peserta Didik Learning Disability di Sekolah Dasar Negeri Kota Mataram

The aims of research are to; 1) describe the types of learning difficulties of SNS, 2) Identifying the learning model applied by the classroom teacher to students with learning disabilities, and 3) describe the development of students' learning behavior academically and socially towards students with learning disabilities. The research was conducted on inclusive students in elementary schools throughout the city of Mataram. This research method uses qualitative and quantitatif research methods. The research subject consist of 67 inculsive students. Data were collected through direct observation, interviews and documentation. Data analysis was carried out descriptively to measure proportions and averages. The result of study show that; 1) types of learning difficulties experienced by students in inclusive learning at Public Elementary Schools in Mataram include cognitive learning difficulties such as perceptual disorders, cognitive thinking disorders, symbolization, language reasoning; solving academic learning problems which include difficulties in learning to read (dyslexia), difficulties in learning to write (dysgraphia) and difficulties in learning to count; and emotional and social behavior disorders, 2) the inclusive learning model used is a regular learning model to change students' views about children who have learning problems and do not experience learning problems so that class conditions are cooperative, and 3) the application of inclusive learning improves the development of the learning abilities of SNS between odd and even semesters.
Pedagogical conditions of creativity development of future preschool teachers in the process of pedagogical practice

The article reveals the pedagogical conditions for the development of creativity of future preschool teachers in the process of pedagogical practice. Creativity is a necessary component of the work of a preschool teacher and a condition of his self-knowledge and development, the formation of the personality of the future specialist in preschool education. The development of creativity ofpreschool teachers in the process of pedagogical practice is the main component of the whole system of vocational training and education in higher education. The concepts of «creativity», «teacher's creativity» are revealed. The main directions of creative activity of the preschool teacher are presented.