Reviewerly/exHarmony huggingface dataset:
Factual Question Generation System Using Pro Semantic Ranker

In today’s internet world the amount of materials available to learn and gain knowledge is immense in numbers, which has given access to a lot of people to gain knowledge easily. Is it possible to find out if someone has read this sentence? To arrive at a conclusion of whether or not someone has read something, we can ask them to summarize its contents or question them about it. We can start with basic questions that deal with the title or the abstract, and progress to more challenging questions. A good example of this process would be a primary school teacher questioning his or her students on the basics of what they are reading to make sure that they are learning. It is a time-consuming process to create the basic questions and reading assessments that are commonly used, and can be quite taxing on the educator. This paper focuses on automating that time-intensive process. To be precise, this paper deals with the problem of generation of the factual questions in an automated manner from stand-alone texts.
An approach to the automatic linguistic ontology construction to determine the interests of social networks users

Social networks provide researchers with the opportunity to obtain an array of text data for further analysis within a certain subject area. Each subject area has its own specific professional vocabulary and writing style. When defining the subject area of text material there is a big problem with building dictionaries, thesauri, and ontologies. In this article a linguistic ontology is considered under ontology and which is aimed to determine the subject area of text material. An algorithm for the automatic construction of an ontology based on the Wikidata knowledge graph is presented. The task is to map a set of objects of the Wikidata knowledge graph to a set of entities of a linguistic ontology. The article proposes an algorithm for determining the degree of belonging of the text material to the subject area. Experiments on assessing the time of building an ontology and the applicability of the obtained linguistic ontologies to the problem of determining the degree of belonging of text materials in the subject area have shown: the running time of the algorithm and the number of terms in the formed ontology are directly proportional to the number of analyzed properties and Wikidata objects; the formed linguistic ontology is applicable to the problem of determining the degree of belonging of a text to a subject area.
COVID-19 as an Opportunity to Expand the Instructional Portfolio of STEM Librarians

The pivot to online teaching caused by the COVID-19 pandemic enabled science and engineering librarians at Vanderbilt University to expand their teaching roles within graduate-level courses in biomedical engineering, chemistry, and physics. In addition to addressing traditional information literacy skills related to information retrieval and resource evaluation, these new lessons addressed important science process skills such as academic reading, responsible conduct of research, and research data management. A facility with cloud-based teaching tools such as Zoom breakout rooms and Excel for Microsoft 365 allowed for engaging instructional experiences, even within synchronous online instructional environments. By integrating these topics into the graduate curricula, these guest lectures supported the professional development of early career graduate students and deepened relationships with the course instructors of record.
Tsunami mitigation – online learning effectiveness by using ombak learning model

Many communities in Indonesia are potentially affected by tsunamis but do not have adequate preparedness in dealing with such disasters. This study aims to improve the preparedness capacity of the people living around the coast that are potentially affected by the tsunami through tsunami mitigation learning by using OMBAK learning model. The learning is done online to avoid the transmission of covid-19. Before the online learning begins, the student community is asked to work on the tsunami mitigation pre-test. The number of people living around the coast who took part in this learning was 62 people, ranging in age from 13 to 55 years old. Furthermore, students were briefed via a zoom meeting (synchronous) about the implementation of this online learning, especially relating on how to learn tsunami mitigation by using disaster mitigation video media. In this tsunami mitigation learning process, students are allowed to learn asynchronously. We also allow students to discuss tsunami mitigation materials through the WhatApp Group (WAG) during the learning process. After completing the learning process, students were asked to work on post-test questions. Based on the results of the percentage analysis on the learning implementation, it was found that the implementation of tsunami mitigation learning by using OMBAK learning model was carried out very well. In addition, based on the analysis of t-test and gain score from pre-test and post-test data, it was obtained that there was a significant increase in the students’ tsunami mitigation ability. © 2021, Tsunami Society. All rights reserved.
Analysis of micro-motion of nanofluid particles based on machine vision

In order to accurately analyze the motion characteristics of nanofluid particles in the flow state, a method for micro-motion analysis of nanofluid particles based on machine vision was proposed. The influence of temperature on the micro-motion of nanoparticles was studied by establishing a water-based near-wall flow simulation model of nanofluids, and the Lennard-Jones potential parameters of nanofluids were obtained. Machine vision imaging technology is used to establish a reference system for nanofluid particles micro-motion image acquisition. Based on this system, the micro-motion images of nanofluid particles are collected, and the micro-motion characteristics of nanofluid particles are accurately detected. An experimental measuring platform composed of optical system and electronic system is established. The rotational and translational motions of nanoparticles and the velocity and temperature distributions of nanofluids are obtained. It was found that the velocity gradient of the nanofluid near the wall was higher than that of the base liquid, and the temperature of the nanofluid near the wall was significantly higher than that of the single-phase base liquid. The experimental results show that the velocity and temperature distributions of nanofluids at different temperatures can be obtained by using this method.
Attitudes of primary and secondary students towards online lessons during Covid-19

The spread of the coronavirus (COVID-19) forced the closure of schools nationwide at the outset of the pandemic. The aim of this paper is to examine the attitudes of primary/secondary school students in Novi Sad, Serbia towards online lessons during pandemic. However, the online course on geography was open to non-Serbian students i.e. students from other countries who used Preply platform. The research was conducted based on the online survey regarding the attitudes of students. Main results of data analysis indicate that students prefer face to face lessons. Around 15.8% of students recommend using only online lessons in the future. Live lesson e.g. Google Meet, Zoom was mostly given to respondents.
TYPES OF LITERACY LEARNING IN EARLY GRADES: WHAT DOES THE TEACHER DO?

Literacy has become a major component of learning. In early elementary schools, children must be taught early reading skills to build a higher grades literacy foundation. The teacher determines literacy learning performance in the classroom because it plays a vital role in the front line of learning. The main objective of this research was to describe the process of learning literacy in elementary schools. This study used a survey method involving five principals and thirty elementary school teachers. Interviews were used as instruments to describe literacy learning in elementary school. Data obtained from the field were tabulated and presented. This study showed that the methods or models and learning media used by the teacher were different. The teacher had also shown an effort to increase children's motivation in participating in literacy learning. Keywords: Early literacy, early grade elementary school, elementary school teachers .
RESPON SISWA TERHADAP MEDIA PEMBELAJARAN BERBASIS MULTIMEDIA PADA MATERI PEWARTAAN KELAS XI DI SMA DON BOSCO SANGGAU

This study aims to know students' response to the aspect of attraction, materials and attention to multimedia study-based learning materials on don bosco sanggau high school in reporting materials. Research uses descriptive methods with a qualitative approach. The data-gathering technique used was indirect communication. The data-collection tool used was an angket sheet. The data analysis techniques used in this study are data reductions, data presentation and deduction drawing. Research shows that don bosco's high school student response rates on multimedia study-based learning materials include an average interest aspect of 90.60%, material 89.20% and attention content 95%. The conclusion of the study is that students' response to the aspect of attraction, materials and attention to multimedia study-based learning in reporting materials on don bosco's high school has very good criteria.
FED Para Politikalarının Finansal Piyasalara ve Emtia Fiyatlarına Etkileri: Gelişmiş ve Gelişmekte Olan Ülkeler İçin Yapısal Kırılmalı Bir Ekonometrik Analiz

In this study, the effects of the US Federal Reserve's (Fed) interest and quantitative easing (QE) policies on stock markets, exchange rates, and commodity prices were examined applying structural break time series analysis methods using the data of 17 countries for the January 2003-January 2021 period.The external reflections of monetary expansion in the USA, which constitutes approximately 25% of the world economy, and whose money also symbolizes an important power, is worth examining as related to the 2008 subprime financial crisis and the 2020 Covid-19 pandemic, particularly in selected countries.It was determined that Fed increases in interest rate positively affect all countries' stock markets in the short run.Fed interest rates were observed to negatively affect the exchange rates in South Korea in the long run and in Canada, Brazil, Russia, India, South Africa, Mexico, South Korea, and Indonesia in the short run; they positively affect the UK and Japan in the short run.The Fed's implementation of QE policy was found to positively affect stock markets in Canada and Italy in the short run and in the USA, Canada, the UK, Germany, India, China, South Africa, Mexico, South Korea, Indonesia, Malaysia, and Turkey in the long run.These results suggest that the liquidity in the market generated by Fed QE policies particularly affects the stock markets of developing countries.The effect of Fed QE policies on exchange rates was determined to be generally positive, with short run effects that are higher than the long run.In sum, Fed monetary policies generally exert extremely weak long-term effects on financial markets, the existing effects are concentrated in developing countries, and such effects differ in developed countries.From the policy perspectives, the policymakers of other countries should consider the monetary policies of the USA, which affect other countries to a great extent, and set their monetary policies to be compatible with ones of the USA.In addition, the results of this study are expected to serve as a basis for future research to determine the degrees of sensitivity to the monetary policies of the USA by grouping the developing countries according to their homogeneity in terms of fragility.
Big Bang Big Crunch Based Development of Machine Learning Models: A Soft Computing Based Novel Approach

This paper proposes a new metaheuristic approach to design and develop Artificial Neural Network (ANN) based machine learning models. The proposed approach is a data-driven technique that works in three phases. Phase 1 of the proposed algorithm evolves the optimal architecture of an ANN model. In this phase, we evolve the number of hidden layers and the number of neurons in every hidden layer of the ANN model. Phase 2 is the model optimization phase in which we optimize the weights of the ANN architecture, evolved in phase 1. To evolve the architecture and weights for the specific ANN architecture, we used Big Bang Big Crunch (BB-BC) optimization algorithm. In Phase 3 of the approach, the optimized model is implemented. For implementation purposes, the proposed approach writes the code for the optimally evolved ANN model. We validated our proposed approach by developing machine learning model from a nickel cadmium battery charging training data set and compared its performance with manually configured ANN, Multi-Regression, and KNN (K Nearest Neighbors) based machine learning models. We observe that the proposed BB-BC based automated approach completely outperforms the other three machine learning models.
Complex Data: Learning Trustworthily, Automatically, and with Guarantees

Machine Learning (ML) achievements enabled automatic extraction of actionable information from data in a wide range of decisionmaking scenarios.This demands for improving both ML technical aspects (e.g., design and automation) and human-related metrics (e.g., fairness, robustness, privacy, and explainability), with performance guarantees at both levels.The aforementioned scenario posed three main challenges: (i) Learning from Complex Data (i.e., sequence, tree, and graph data), (ii) Learning Trustworthily, and (iii) Learning Automatically with Guarantees.The focus of this special session is on addressing one or more of these challenges with the final goal of Learning Trustworthily, Automatically, and with Guarantees from Complex Data.
PENERAPAN MODEL PEMBELAJARAN ROLE PLAYING UNTUK MENINGKATKAN AKTIVITAS DAN HASIL BELAJAR SISWA DALAM MENGGALI INFORMASI DARI DONGENG BINATANG (FABEL) PADA SISWA KELAS II SDN PARANG 4 KEDIRI

Based on data obtained by researchers at SDN Parang 4, Banyakan sub-district, it was found the fact that there was a low level of activity and student learning outcomes in the material of digging information from animal tales (Fable).. In addition to this in everyday learning the teacher only conveys the material conventionally. Teachers only tend to give monotonous lectures and do not use learning models when carrying out learning in the classroom. Student activity is also very low, because when the teacher gives practice questions to students there are some students who just sit and don't seem eager to learn. The purpose of this research is toto find out the Application of the Role Playing Method for mimprove student activity and learning outcomes in dig up information from animal tales (Fables). The approach used is qualitative research with Classroom Action Research (CAR) design, with stages: 1) planning, 2) implementation, 3) observation, 4) reflection. This research is planned to last for 2 cycles, each cycle 2 meetings with thematic learning materials. Based on the results of research in the implementation of the first cycle of student learning completeness reached 50% and in the second cycle of student learning completeness increased to 91.60%. Based on the results of the study, it can be concluded that the Role Playing learning model can increase student activity and learning outcomes in extracting information from animal tales (fables) in class II SDN Parang 4, Banyakan District.
Towards Expert-Inspired Automatic Criterion to Cut a Dendrogram for Real-Industrial Applications

Hierarchical clustering is one of the most preferred choices to understand the underlying structure of a dataset and defining typologies, with multiple applications in real life. Among the existing clustering algorithms, the hierarchical family is one of the most popular, as it permits to understand the inner structure of the dataset and find the number of clusters as an output, unlike popular methods, like k-means. One can adjust the granularity of final clustering to the goals of the analysis themselves. The number of clusters in a hierarchical method relies on the analysis of the resulting dendrogram itself. Experts have criteria to visually inspect the dendrogram and determine the number of clusters. Finding automatic criteria to imitate experts in this task is still an open problem. But, dependence on the expert to cut the tree represents a limitation in real applications like the fields industry 4.0 and additive manufacturing. This paper analyses several cluster validity indexes in the context of determining the suitable number of clusters in hierarchical clustering. A new Cluster Validity Index (CVI) is proposed such that it properly catches the implicit criteria used by experts when analyzing dendrograms. The proposal has been applied on a range of datasets and validated against experts ground-truth overcoming the results obtained by the State of the Art and also significantly reduces the computational cost.
Using “Panel Reports” to Advance Scholarly Discourse: A Change in Editorial Policy and Guidelines for Panel Report Authors

“Panel reports” reflect a particular category of submissions that authors can make to the Communications of the Association for Information Systems (CAIS). As the CAIS website states, panel reports (i.e., papers that report on panels, debates, symposia, workshops, and similar events) differ from traditional research papers in that they “have to clearly position the matter of discussion at the event, highlight the relevance of event and topic and outline the different views on the topic that emanated at the events” (see https://aisel.aisnet.org/cais/panel_reports.pdf). While this definition has persisted for some time and still holds true, it leaves room for interpretation as to what constitutes a contribution and how one knows that a particular paper has made enough of one. In this editorial, we interpret and elaborate on these principles based on our collective experience with such reports.
sVerify: Verifying Smart Contracts Through Lazy Annotation and Learning

Smart contracts have recently attracted much attention from industry as they aim to assure anonymous distributed secure transactions. It also becomes clear that they are not immune to code vulnerabilities. As smart contracts cannot be patched once deployed, it is crucial to verify their correctness before deployment. Existing approaches mainly focus on testing and bounded verification which do not guarantee the correctness of smart contracts. In this work, we develop a formal verifier called sVerify for Solidity smart contracts based on a combination of lazy annotation and automatic loop invariant learning techniques. The latter is essential as explicit or implicit loops (due to fallback function calls) are common in smart contracts. Patterns and features which are specific to smart contracts are used to facilitate invariant learning. sVerify has been evaluated with 4670 Solidity smart contracts, and the evaluation result shows that sVerify is effective and reasonably efficient for verifying smart contracts .
Development of Learning Media Android-Based for Physical Education with the Topic of Illicit Drugs in Facing the New Normal Era

Teaching material about drugs, data showed that student score were lower.Passive students in the learning process tend to exhibit idleness in school duties and reluctant to pay much attention to learning.There is a plethora of factors that plague the ineffective of material delivery to the beneficial.The lack of supporting media in teaching, the disparity in teaching material in schools, inequality in the distribution of facilities, scarcity of professional teachers.The present study wants to intervene and help the learning process to overcome such shortcomings by introducing a certain learning application known as Smart Application Creator 3 (SAC 3).Based on the validation results, construct validation by material experts is 75%, content validation by material experts is 79.41%, validation by media experts is 90.63%,This media is stated to be very interesting, seen from the results of the trial questionnaire, it is found that a percentage of 81.11%.
Translating traditional Malay pottery motifs to inspire ceramic surface decoration design / Siti Maryam Ali Yasin ... [et al.]

A particular ceramic product offered by the designers and manufacturers is measured based on its appearance and performance from aesthetic value, design and craftsmanship quality. Products in our daily life play an essential role in bringing us happiness, comfort, convenience and satisfaction to the consumers and buyers. In a Malay community in Malaysia, the Malay Traditional Pottery is one of the Malay arts heritages. Its products used to have a high demand among the locals as well as people from abroad. However, today, demand for Malay Traditional Pottery from the locals has declined significantly. Hence, to improve the product appearance, this study highlights one of the essential factors in ceramic design features, namely surface decoration, by using motifs derived from the Malay Traditional Pottery onto the new contemporary design of the local ceramic products. The surface decoration consists of motifs, patterns, colours, techniques and materials. Surface decoration study would give designers and local ceramic entrepreneurs the product appearance improvement. With such improvements, local ceramic products would become a preferred choice for the consumer’s daily use. This study also aimed to entice the designer to be involved in pattern design and ceramic product surfaces.
Convolutional Neural Networks for event classification

Cutting-edge Artificial Intelligence is being implemented in a wide range of tasks in High Energy Physics (HEP) in order to facilitate the analysis of large datasets.However, visual recognition has not been explored as much in HEP for event classification.This study shows how Convolutional Neural Networks could be applied for such an important task, for which a novel method to represent the event information in images is explored.This technique is applied for a classification problem corresponding to a search for Dark Matter in proton-proton collisions.The results obtained with this technique are also compared with the performance of a Boosted Decision Tree.