Reviewerly/exHarmony huggingface dataset:
The Use of Virtual Reality as A Substitute for The Pre-School Students’ Field Trip Activity During the Learning from Home Period

This study aims to examine the use of virtual reality as a substitute for the pre-school students' field trip activity during the learning from the home period. This research used a literature study as the research methodology. We conducted this study by observing and analyzing several journals on the use of Virtual Reality as a medium of learning. The various results of the journal analysis were used to determine whether virtual reality could be a substitute for the pre-school students’ field trip learning activity in this learning from the home period. By using Virtual Reality, the students would still be able to participate in field trips while still pays attention to the steps of conducting a field trip, such as (1) planning and preparation period; (2) the execution of the event, and; (3) the return time. Besides being considered as an effective tool to increase students’ learning interest and improve their learning outcomes, virtual reality can also be a substitute for the real field trips during this Covid-19 pandemic. Therefore, the students do not need to leave the house to participate in field trips. Hopefully, the result of this research would be able to provide the readers with new insights and knowledge on the utilization of virtual reality as a substitute for the pre-school students’ real field trip activity especially in this learning from the home period.
Eager functions as processes

We study Milner's encoding of the call-by-value λ-calculus into the π-calculus. We show that, by tuning the encoding to two subcalculi of the π-calculus (Internal π and Asynchronous Local π), the equivalence on λ-terms induced by the encoding coincides with Lassen's eager normal-form bisimilarity, extended to handle η-equality. As behavioural equivalence in the π-calculus we consider contextual equivalence and barbed congruence. We also extend the results to preorders. A crucial technical ingredient in the proofs is the recently-introduced technique of unique solutions of equations, further developed in this paper. In this respect, the paper also intends to be an extended case study on the applicability and expressiveness of the technique.
Catastrophic Forgetting in Deep Graph Networks: A Graph Classification Benchmark

In this work, we study the phenomenon of catastrophic forgetting in the graph representation learning scenario. The primary objective of the analysis is to understand whether classical continual learning techniques for flat and sequential data have a tangible impact on performances when applied to graph data. To do so, we experiment with a structure-agnostic model and a deep graph network in a robust and controlled environment on three different datasets. The benchmark is complemented by an investigation on the effect of structure-preserving regularization techniques on catastrophic forgetting. We find that replay is the most effective strategy in so far, which also benefits the most from the use of regularization. Our findings suggest interesting future research at the intersection of the continual and graph representation learning fields. Finally, we provide researchers with a flexible software framework to reproduce our results and carry out further experiments.
University English Teacher Personality In The Context Of Digitalization

The article discusses the education conditions changing due to the digitalization and gives the definition of digital competence.The purpose of the article is to determine the level of digital competence of a modern foreign language teacher at a university and to consider this competence in educational process.It is argued that a teacher should be extremely flexible and have "digital" competence for effective teaching.We highlight the areas of teacher activity that require digital competence (scientific and curricular activities).We study the level of digital competence of foreign language teachers at the university and propose pedagogical options for using digital technologies both in education and in scientific research of a modern teacher.This describes the skills of the allocated levels (basic, high, advanced).The article highlights possible digital services for using in writing research papers, study guides and creating content, as well as for classroom work and for conducting distance learning.It is noted that the using of digital services in some cases helps the teacher to reduce the time for the revision process, increases students' motivation and consequently achieves better learning outcomes.
Improving Students’ Speaking Skill through Role-Play Technique in 21st Century

Speaking is one of hard skill that should be mastered by students in English. In the speaking class, students faced several problems such as the learners show a low enthusiasm and motivation in speaking, afraid and shy to speak. It arises because of some factor such a low interest, material, and improper technique which apply in the class. In solving those problems, it needed an attractive teaching technique. The implementation of role-play as an appropriate teaching technique in the 21st century that can stimulate students in learning especially in speaking class can be one alternative. Role-play serve many advantages, one of them is to urge students in speaking. This study will use library research as the method for collecting the data. The findings show that role-play teaching technique strongly can improve students speaking skill in the classroom.
A Development, Validity and Reliability of Safe Social Networking Scale

Social networks has become in the spotlight for millions of people since they create a dynamic and rich interaction environment between users, and allow free access and usage of fake identity information. Distance education and working from home around the world due to COVID-19 pandemic has affected the increase in the rate and duration of social media usage in the last year. Unfortunately, this increase has offered criminals, who have the potential to pose risks and dangers on social media, more opportunities. Therefore, it has become even more important to use social media as a safe environment. The objective of this study was to develop a Safe Social Networking Scale for determining the security levels in social network usage. The validity and reliability studies of the scale were conducted with 585 social media users. As a result of the validity study, 28 items under five factors were obtained. These factors were being “Time Tunnel/Wall Sharing”, “Safety of Social Network Profile Information and Sharing”, “Social Network Friends List and its Safety”, “Safety of Social Network Information Input”, “Safe Login to Social Network Account”. The items obtained were capable of discriminating the individuals in terms of the features to be measured by the scale. There has not been any scale that directly determines the level of safe use of social media in the literature. The developed scale is expected to fill this scale gap in the literature. Keywords: safety, social networks, scale development, reliability, validity
Axiomatization of BLRI Determined by Limited Positive Relational Properties

In the paper a generalised method for obtaining an adequate axiomatic system for any relating logic expressed in the language with Boolean connectives and relating implication (BLRI), determined by the limited positive relational properties is studied. The method of defining axiomatic systems for logics of a given type is called an algorithm since the analysis allows for any logic determined by the limited positive relational properties to define the adequate axiomatic system automatically, step-by-step. We prove in the paper that the algorithm really works and we show how it can be applied to BLRI.
Utility of Patient-Generated Health Data and Artificial Intelligence in the Prediction of Asthma Exacerbations

With the advent of the EMR, patient-generated health data (PGHD) has become more accessible, allowing for a patient-centered approach to the management of chronic asthma. Combined with artificial intelligence and remote monitoring, this data has the potential to predict asthma exacerbations and prognosis in individualized patient populations. Utilizing PRISMA guidelines, a literature search was conducted using the search terms “asthma,” “artificial intelligence,” “PGHD,” “exacerbations,” “prognosis,” and related terms. The search for this scoping review was conducted on PubMed, Cochrane, Web of Science, and EMBASE. Inclusion criteria consisted of articles published from January 2011 onwards and written in English. Duplicates were removed and review papers, abstracts, presentations, and grey literature were excluded. The literature search yielded 377 articles. A preliminary review was conducted based on inclusion criteria, resulting in 15 articles. The top three variables for PGHD included vital sign monitoring via wearables (7 articles), patient-reported symptomatology (6 articles), and an element of pulmonary function testing (6 articles). Demographics and environmental exposures were each reported in 2 articles. Various forms of AI were used, the most common being random forest classifiers (6 articles). Existing literature has demonstrated that the utilization of PGHD in combination with AI promotes the adoption of precision medicine in asthma management. Given the heterogeneity of asthma, the ability to compute and relay real-time data to the patient and the provider can aid in the prediction and management of asthma exacerbations, but more data on demographics and environmental exposures should be considered to improve individualized prognostics.
Management of HEIs during pandemic

This article aims to assess the changes in the HEIs management system elements during the pandemic. The methodology was built on the analysis of management cases of Kyiv National University named after Taras Shevchenko and National University "Lviv Polytechnic". The internal documentation of HEIs management: strategies and development plans, internal documentation of quality management system implementation defining the main challenges of HEIs management were also used for case analysis. Results. It was determined that the pandemic generated significant structural changes in the system of HEIs management caused by changes in students' needs, development, and spread of innovative learning technologies, reduction in the number of international students, closing, merging, and restructuring of universities due to funding cuts. The pandemic has put forward challenges to the core business of higher education in supporting students through a wide range of curricula. These challenges make it much more difficult to implement interactive, person-centered traditional classroom instruction based on years of university experience. To adapt to a long-term pandemic, HEIs require flexible educational methods that will continuously adapt to different stages. COVID-19 led to the activation of long-term pedagogical trends, created a natural experiment to test and evaluate numerous innovations.
System Design of Certified Digital Document Sharing Platform Using Blockchain

This paper aims to build the design of a secure and trustworthy platform of digital document sharing system, where students may share their officially obtained educational documents, e.g. Certificates or Academic Transcripts, with potential employers. The system was designed on top of the Ethereum Blockchain, coupled with IPFS as the document storage system. The design has been arranged in such a way as to work as a nationwide solution.
Sentimental event detection from Arabic tweets

This article presents and evaluates an approach to detect sentimental events from Twitter Arabic data streams. Sentimental events attract strongly opinionated responses from the online community; therefore, we aim at detecting the association of a topic with a positive or a negative sentiment at a particular time. To achieve that, we build sentimental time series where the frequencies of that association (between topics and sentiment) are recorded. And then, we use several algorithms to locate possible events. Events in positive timelines will be considered as positive, and similarly for negative events. Our approaches use Shannon diversity index and hill climbing peak finding. We experimented our proposed algorithms with the domain of football (soccer) news. The results showed good precision and recall considering mainstream media as a reference. The success of such experiment can open the door for many useful applications including reputation and brand monitoring systems for various domains and languages.
Implementation of Importance-Performance Analysis on Integrated Information System Institut Teknologi Telkom Purwokerto

Improving information systems is essential to increase user satisfaction which then has a positive impact on the institution. This study aims to measure the importance and performance of website service quality attributes on the i-Gracias ITTP using Importance Performance Analysis. This study integrate e-ServQual and WebQual to measure the level of student satisfaction with i-Gracias as a web-based information system service comprehensively. Generally, the satisfaction score shows that the performance of quality attributes does not meet the expectations of students as users. Of the 30 quality attributes measured, there is one attribute in quadrant I, 12 in quadrant II, 13 in quadrant III, and 3 in quadrant IV. The attributes that describe the fundamental functions of i-Gracias as an online service system are efficiency, security, fulfillment, information quality, and accessibility. The next priority for improvement for i-Gracias is efficiency improvement and process simplification.
Stability analysis and synchronization of incommensurate fractional-order neural netwroks

This paper develops a theoretical framework for analyzing the stability of nonlinear incommensurate fractional- order neural networks. A necessary theorem for asymptotical stability is established using the characteristic equation for a nonlinear fractional-order system, and how to employ this theorem in stabilization is also presented. With the suitable control, the difficulties of stabilization and synchronization of fractional-order chaotic incommensurate fractional-order neural networks may be readily overcome. Two numerical examples have been shown to demonstrate how the established theory may be used to investigate stability and construct stabilization controllers.
Machine Learning Model for Human Activity Analysis

Human Activity Recognition is an active subject of research and scientific progress in which several models have been presented for identifying and categorizing activities using Machine Learning utilizing various methodologies. The purpose of human activity recognition is to look at activities in video or still photos. Human activity recognition systems are motivated by this fact, and their goal is to appropriately classify input data into its underlying activity category. Human activities are classified as (a) gestures, (b) atomic actions, (c) human-to-object or human-to-human interactions, (d) collective actions, (e) behaviors, and (f) events, depending on their complexity. Today, health informatics is a critical field for improving healthcare efficiency by streamlining the collecting, storage, and retrieval of critical patient health data. In this paper, an intelligent smart healthcare system is provided that uses machine learning approaches to deliver ubiquitous human activity recognition (HAR) in an automated manner. The goal is to model and recognize activities of everyday living in a precise and efficient manner. Furthermore, for HAR purposes, we focus on a dataset collecting body motion and vital sign recordings from volunteers of various profiles while performing various physical activities. This research has demonstrated that identifying human activity from sensor data is extremely difficult, even with the availability of a number of machine learning approaches. When it comes to machine learning techniques, there is no one-size-fits-all approach.
Interactive Models for Post-Editing

Despite the increasingly good quality of Machine Translation (MT) systems, MT outputs require corrections.Automatic Post-Editing (APE) models have been introduced to perform these corrections without human intervention.However, no system has been able to fully automate the Post-Editing (PE) process.Moreover, while numerous translation tools, such as Translation Memories (TMs), largely benefit from translators' input, Human-Computer Interaction (HCI) remains limited when it comes to PE.This research-in-progress paper discusses APE models and suggests that they could be improved in more interactive scenarios, as previously done in MT with the creation of Interactive MT (IMT) systems.Based on the hypothesis that PE would benefit from HCI, two methodologies are proposed.Both suggest that traditional batch learning settings are not optimal for PE.Instead, online techniques are recommended to train and update PE models on the fly, via either real or simulated interactions with the translator.
Implementation of a priori algorithm for book lending at state high school library I Silima Pungaga-Punga Parongil

The library plays a role in helping students to enjoy reading books. The availability of books in various fields motivates students to come to visit the library, students / i can read or borrow library books. For this reason, the purpose of this research was carried out including helping library officials apply the rules of how to visit the libraries.&#x0D; In carrying out research methods, including conducting direct observations, conducting interviews to collect the necessary data. The pattern that will be analyzed is the pattern of borrowing what books are often borrowed so that library officials know the information of books that are often borrowed. The result obtained is with the application of a priori algorithms, book data is processed to produce a pattern of borrowing books. After all high frequency patterns are found, then the association rules are sought that meet the minimum requirement for associative confidence A→B minimum confidence = 25%. The final association rules are ordered based on minimum support and minimum confidence, if borrowing IPA, then borrowing MTK Support = 15%, Confidence = 42.8%.
AN ENHANCED WAVELET BASED METHOD FOR NUMERICAL SOLUTION OF HIGH ORDER BOUNDARY VALUE PROBLEMS

The Legendre wavelet collocation method (LWCM) is suggested in this study for solving high-order boundary value problems numerically. Eighth, tenth, and twelfth-order examples are used as test problems to ensure that the technique is efficient and accurate. In comparison to other approaches, the numerical results obtained using LWCM demonstrate that the method's accuracy is very good. The results indicate that the method requires less computational effort to achieve better results.
CRIMINOLOGICAL ANALYSIS OF THE PHENOMENON OF CYBERCRIME

Пивоваров В.В., к.ю.н., доцент, доцент кафедри
Perception of Foreign Language Learners in the Use of Internet-Based Tools

This study is aimed at understanding the perception of foreign language learners on the use of internet-based tools in learning in general and learning a foreign language in particular.The study is focused on investigating the learners' view on the strengths, significant uses and problems with integrating internet-based tools in classroom learning and personal supplementary learning.A survey was used in collecting data by providing a 35-item self-assessment questionnaire to a whole class of respondents who are taking a Mandarin language course from the College of Business and Financial Science of the University of Makati.The data collected was analyzed by getting the frequency and mean to determine which strengths, significant uses, and problems with integrating internet-based tools perceived to be true for the learners.Although foreign learners have a generally positive view of integrating internet-based tools in foreign language learning, they are not using it sufficiently.
Military Dataset Processing Approaches or Trauma Risk Mitigation in Machine Learning Practitioners

To develop new computer vision capabilities leveraging artificial intelligence, we will increasingly need to use operationally realistic training and validation datasets. Although operational full motion video and imagery datasets present information regarding their provenance and classification level, these designations are often not indicative of the presence of potentially offensive or traumatizing content. As machine learning and data scientists increasingly need to work with operational unsanitized operational video and imagery data, they will have a higher risk of being exposed to sensitive and traumatic content. In this paper, we first raise awareness about this risk within the Defense community. Then, we propose several approaches for mitigating machine learning practitioner's exposure to offensive and traumatizing media, including dataset preprocessing procedures and viewing tool design considerations.
Potensi Penggunaan Eco Enzyme pada Degradasi Deterjen dalam Air Limbah Domestik

Environmental pollution is one of the factors causing environmental damage. The environment can accept a certain level of pollutant load and can neutralize it within a certain period. But along with the development of human activities, the amount and level of pollutants are increasing inversely with the capacity of the environment and the ability of the environment to neutralize pollutants. One of the factors causing environmental pollution is domestic wastewater. This research uses orange waste to be processed into the eco enzyme. Making eco enzyme lasts for 3 months. Eco enzyme is made using orange waste, brown sugar, water in a ratio of 3:1:10, and fermentation time is 3 months. This study aims to determine whether the eco-enzyme of orange waste can be used to reduce the concentration of detergent in domestic wastewater. The method used in this research is an experimental method with a laboratory scale. From the experiments carried out, primary data will be obtained. Based on the eco-enzyme characterization of orange waste used, it is known that the concentration of detergent is 0.2534 mg/L. The concentration of detergent in domestic waste is 2.1464 mg/L. The concentration of detergent in domestic waste after added eco-enzyme of orange waste 5% becomes 0.68 mg/L and 10% becomes 0.30 mg/L with a detention time of 7 days.
Network and layer experiment using convolutional neural network for content based image retrieval work

In this study, a test will be conducted to find out how the results of experiments on the network and layer used on the convolutional neural network algorithm. The performance and accuracy of the retrieval process method that was tested using the algorithm approach to do an object image retrieval. The expected results of this study are the techniques offered can provide relatively better results compared to previous studies. The results of the classification of object images with different levels of confusion on the Caltech 101 database resulted an average accuracy value. From the experiments conducted in the study, content based image retrieval work (CBIR) work using convolutional neural network (CNN) algorithm in terms of execution time, loss testing and accuracy testing. From several experiments on layers and networks shows that, the more hidden layers used, then the result is better. The graph of validation loss decreases at fewer epochs, slightly fluctuating at more epochs. Likewise, validation accuracy increases insignificantly on epochs with small amounts, but tends to be stable on more epochs.