Reviewerly/exHarmony huggingface dataset:
Motivation and Willingness to Communicate in English amongst ESL Engineering Pre-university Students in Malaysia

This study investigated the relationship between motivation and willingness to communicate (WTC) in English and motivation towards the WTC in English amongst pre-university Engineering students. The instruments used to investigate the motivation and the WTC in English and the correlation between motivation and WTC in English were administered amongst 109 respondents. There was a significant relationship between the variables. The multivariate regression results implied that 48.8% of WTC variance could be predicted from the motivation to communicate in English. The study provided important implications concerning the extension of the corpus of knowledge and pedagogical enhancement. Keywords: Motivation to communicate, Willingness to communicate, Engineering students eISSN: 2398-4287© 2021. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning &amp; Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v6iSI4.2906
Computer Forensic Using Photorec for Secure Data Recovery Between Storage Media: a Proof of Concept

Data plays the important role, so that data recovery and data security be prioritized. Computer users often lose their data due to personal errors or by attacks. Digital forensics has one sub-field called computer forensic, which has an important role in the process of secure data recovery. USB Flashdisk as the most widely used storage media has a probability of data loss. It is necessary to do computer forensic actions, especially secure data recovery, on it so that it can restore data securely to other media while protecting it by giving privilege root. In this research, computer forensic testing the 2781 files of various data formats that were erased on a 32 GB USB flash drive using Photorec. The media for collecting data recovery results using an Intel computer, 2 GB RAM, 1.8 GHz processor, the Linux operating system Xubuntu 20.04. Testing is carried out following the test scenarios that have been designed, then observed, recorded, and analyzed. Photorec places recovery data in 6 recup_dir subdirectories. Test results and analysis of the test results show that Photorec is a reliable tool for computer forensic, especially secure data recovery because it can restore 100% of data, accompanied by privilege root for all data recovery results, so they cannot be changed and deleted by an end-user without granted access.
How Does Physical Exercise Affect Academic Performance? The Mediating Role of Non-Cognitive Abilities

Previous studies have found that good physical exercise can promote academic performance, but the underlying mechanism behind this lacks large-scale empirical data. Based on this, we used the 2020 Jiangsu Province academic quality monitoring data to construct an OLS regression. Then, the non-cognitive abilities variable was built with the NEO-FFI Theory, and the mediation effect diagram was drawn through the Amos22.0 software. The study found that: (i) Physical exercise brought academic improvement to elementary and middle school students. (ii) The influence mechanism of physical exercise was different between elementary school and middle school. Students at the elementary school were directly and indirectly affected. (iii) In non-cognitive specific dimensions, openness played a significant and great mediating role.
The effect of visual attention on stereoscopic lighting of museum ceramic exhibits： A virtual environment mixed with eye-tracking

Stereoscopic lighting is one of the essential visual quality factors affecting the presentation of ceramic exhibits. This research adopted the method of bidirectional verification ： psychophysical experiment and virtual environment with eye-movement tracking. We proposed a special questionnaire for the stereoscopic visual experience, obtained the nonlinear equations of the different volumes of exhibits. The eye-tracking data show that the visual fixation duration with stereoscopic lighting is (M=5.18s), and the number of fixation points accounted for 63.48% of the whole number. Both indexes were higher than the situation without stereoscopic lighting. The result shows that the accuracy of finding the detail changes with or without stereoscopic lighting was 12.5% through the comparison of visual identification tasks, and finding modeling changes was 11.5%. In the situation with stereoscopic lighting, the first identification frequency increased by 15.9% for small pattern changes of exhibits, as for local modeling changes increased by 19%. These results indicated that the stereoscopic lighting set according to the exhibits' volume could effectively improve the viewer's visual guide, strengthen the visual attention of the details, and model elements of the exhibits to achieve the optimal appreciation. These findings should contribute to offer critical insights into its evaluation and elaborate measurement. stereoscopic sense lighting , virtual environment, eye-movement tracking, ceramic exhibit
On the Relation Between Approximation Fixpoint Theory and Justification Theory

Approximation Fixpoint Theory (AFT) and Justification Theory (JT) are two frameworks to unify logical formalisms. AFT studies semantics in terms of fixpoints of lattice operators, and JT in terms of so-called justifications, which are explanations of why certain facts do or do not hold in a model. While the approaches differ, the frameworks were designed with similar goals in mind, namely to study the different semantics that arise in (mainly) non-monotonic logics. The First contribution of our current paper is to provide a formal link between the two frameworks. To be precise, we show that every justification frame induces an approximator and that this mapping from JT to AFT preserves all major semantics. The second contribution exploits this correspondence to extend JT with a novel class of semantics, namely ultimate semantics: we formally show that ultimate semantics can be obtained in JT by a syntactic transformation on the justification frame, essentially performing some sort of resolution on the rules.
Coping With Imbalanced Data in the Automated Detection of Reminiscence From Everyday Life Conversations of Older Adults

Reminiscence—the act of recalling or telling others about relevant personal past experiences—plays an important role in the well-being of older adults. Therefore, it is relevant to develop intelligent systems aiming at improving the well-being of the elderly by reliably detecting reminiscence in their everyday life conversations. Data imbalance is one of the main challenges in the automatic detection of reminiscence from everyday conversations, as reminiscing is a rare event. In this paper, we address the problem by proposing a methodology for coping with imbalanced data in the detection of reminiscence in conversations of older adults. The methodology combines data augmentation using BERT (Bidirectional Encoder Representations from Transformer) and feature extraction techniques leveraging natural language processing for the German language. We evaluate the proposed methodology on a dataset comprising transcripts of social conversations of older adults held in German. We compare our results with a previous work addressing the problem on the same dataset and we show that our approach strongly outperforms the baseline. The results in this study may support the development of intelligent systems for the real-time detection of reminiscence in everyday life of older adults and the design of digital health interventions to support their well-being.
exPrior: An R Package for the Formulation of Ex-Situ Priors

The exPrior package implements a procedure for formulating informative priors of geostatistical properties for a target field site, called ex-situ priors and introduced in Cucchi et al. (2019).The procedure uses a Bayesian hierarchical model to assimilate multiple types of data coming from multiple sites considered as similar to the target site.This prior summarizes the information contained in the data in the form of a probability density function that can be used to better inform further geostatistical investigations at the site.The formulation of the prior uses ex-situ data, where the data set can either be gathered by the user or come in the form of a structured database.The package is designed to be flexible in that regard.For illustration purposes and for easiness of use, the package is ready to be used with the worldwide hydrogeological parameter database (WWHYPDA) Comunian and Renard (2009).
The Sixth International Workshop on Deep Learning on Graphs - Methods and Applications (DLG-KDD'21)

Deep Learning models are at the core of research in Artificial Intelligence research today. A tide in research for deep learning on graphs or graph neural networks. This wave of research at the intersection of graph theory and deep learning has also influenced other fields of science, including computer vision, natural language processing, program synthesis and analysis, financial security, Drug Discovery, and so on. However, there are still many challenges regarding a broad range of the topics in deep learning on graphs, from methodologies to applications, and from foundations to the new frontiers of GNNs. This international workshop on "Deep Learning on Graphs: Method and Applications (DLG-KDD'21)" aims to bring together both academic researchers and industrial practitioners from different backgrounds and perspectives to the above challenges.
Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent  Learning Systems

Multiagent reinforcement learning (MARL) has achieved a remarkable amount of success in solving various types of video games. A cornerstone of this success is the auto-curriculum framework, which shapes the learning process by continually creating new challenging tasks for agents to adapt to, thereby facilitating the acquisition of new skills. In order to extend MARL methods to real-world domains outside of video games, we envision in this blue sky paper that maintaining a diversity-aware auto-curriculum is critical for successful MARL applications. Specifically, we argue that \emph{behavioural diversity} is a pivotal, yet under-explored, component for real-world multiagent learning systems, and that significant work remains in understanding how to design a diversity-aware auto-curriculum. We list four open challenges for auto-curriculum techniques, which we believe deserve more attention from this community. Towards validating our vision, we recommend modelling realistic interactive behaviours in autonomous driving as an important test bed, and recommend the SMARTS/ULTRA benchmark.
Integration of Virtual Reality in the Instructive-Educational Process in Primary Education

Today's children live immersed in the digital environment and build in this environment connections, both social connections and connections with their fields of interest. The classroom could be that submarine that students sail safely in the ever-deeper waters of digital technology and the Internet and that will lead students on the fascinating journey of knowledge. How prepared are teachers and parents to be children's guides? The aim of this paper is to identify the availability of teachers who teach in primary education and parents who have children enrolled at this level on the use of virtual reality in education, more precisely in the instructional-educational process. The research findings support both the positive opinions of teachers and parents regarding the use of virtual reality in education, and their willingness to integrate this type of technology in the instructional-educational process.
New Approach for Online Examination Conduction System Using Smart Contract

Today's era is the smart era where every person is trying to execute the process smartly. Then how the education system will be in a back place. The online conduction of courses either by engaging online classes or by introducing the MOOC courses. This made learning easier but the major concerning issue in the online system is the conduction of online examination. This paper discusses the current treads in an online examination system as well as the new approach introduced for smooth conduction of online examination at any place anywhere at any time. This new approach is based on blockchain technology such as the smart contract. The smart contract will be helpful to the universities, institutes for conduction of online examination at any place at any time. This will keep regress monitoring on the examinee such as posture analysis as well as control panel processes. By using Compare Hash And Password() the authentication of the examinee password will be possible. GoCV package is used to authenticate the examinee through video capturing. dlib toolkit used to monitor the continuous posture of the examinee during the examination.
Design Thinking Mini Project Approach Using Factor Analysis

This study used factor analysis to investigate relationships between educator to student interactions, the relationship between students, skills to critical thinking, and motivation. Factor analysis showed that there are three factors; passion, assistance, and guidance. Meanwhile, the adequacy is 0.945 surpassing the recommended value of 0.50 using Kaiser Meyer Olkin. The statistical significance from the test of Bartlett’s Test of Sphericity indicated that 0.00 and χ2 = 3552.973. Adequacy measurement of Anti-Image Correlation Matrix ranges from 0.49 to 0.69. It is significantly important that educators guide the students in becoming more creative and innovative by using and applying DTMP during the learning process. Keywords: design thinking;creative;innovative;factor analysis eISSN: 2398-4287© 2021. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning &amp; Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v6i17.2797
Exploring Neural Architecture Search Space via Deep Deterministic Sampling

Recent developments in Neural Architecture Search (NAS) resort to training the supernet of a predefined search space with weight sharing to speed up architecture evaluation.These include random search schemes, as well as various schemes based on optimization or reinforcement learning, in particular policy gradient, that aim to optimize a parametric architecture distribution and the shared model weights simultaneously.In this paper, we focus on efficiently exploring the important region of a neural architecture search space with reinforcement learning.We propose Deep Deterministic Architecture Sampling (DDAS) based on deep deterministic policy gradient and the actor-critic framework, to selectively sample important architectures in the supernet for training.Through balancing exploitation and exploration, DDAS is designed to combat the disadvantages of prior random supernet warm-up schemes and optimization schemes.Gradient-based NAS approaches require the execution of multiple short experiments in order to combat the random stochastic nature of gradient descent, while still only producing a single architecture.Contrary to this approach, DDAS employs a reinforcement learning-based agent and focuses on discovering a Pareto frontier containing many architectures over the course of a single experiment requiring 1 GPU day.Experimental results for CIFAR-10 and CIFAR-100 on the DARTS search space show that DDAS can depict in a single search, the accuracy-FLOPs (or model size) Pareto frontier, which outperforms random sampling and search.With a test accuracy of 97.27%, the best architecture found on CIFAR-10 outperforms the original second-order DARTS while using 600M fewer parameters.Additionally, DDAS finds an architecture capable of achieving 82.00% test accuracy on CIFAR-100 while using only 3.14M parameters and outperforming GDAS.
The Effectiveness of Using Blended Learning with Google Classroom and Zoom Media on Students' Understanding

The teaching and learning process is the core of education. Everything that has been programmed will be carried out in the teaching and learning process where interaction and involvement is needed between educators and students with materials as the medium. Teaching activities at the strata 1 level involve educators called lecturers and students called students. In general, teaching activities are carried out offline, which involves direct interaction between lecturers and students. At the beginning of 2021, rumors began to appear that there was a global disaster of the Corona Virus Disease (COVID-19) pandemic, which initially Indonesia considered that the Corona virus was impossible to enter Indonesia. However, the development of the virus is so fast. Indonesia finally announced in early March 2021 as a country experiencing the impact of the global disaster due to the COVID -19 pandemic. This study wants to find out how effective it is to use blended learning by using google classroom and zoom to improve the understanding of FEB students at Muhammadiyah University of Gresik. This research is a quantitative research using causal clause relationship, using SPSS version 23.0 analysis tool. The results obtained from this study are that the Google Classroom and Zoom applications partially and simultaneously have a significant positive effect on student understanding.
Pedagogical Conditions for Digital Citizenship Formation among Primary School Pupils

The digitalization of the world community implies the need for preparing the citizens for active activities in the virtual environment, critical evaluation of information content, and safe behavior on the internet. Additional importance, in this context, is to be attributed to the need for the formation of digital intelligence (DQ). The initial stage of DQ development is specifically important, as involves the development of digital citizenship. The article presents the results of scientific theoretical and experimental research on the problem of primary school pupils’ digital citizenship formation. The scientific novelty is that for the first time the concepts of “primary school pupils’ digital citizenship” and “a digital citizen”, as well as the criteria, indicators, and levels of its development, have been specified. In addition, the pedagogical conditions of digital citizenship formation have been defined as the project method implementation, based on digital citizenship content; the use of parental control applications; the use of didactic infographics in the informational and educational environment. The article presents the pedagogical model of school pupils’ digital citizenship formation in the educational process. The study was conducted on the basics of the Poltava V. G. Korolenko National Pedagogical University (2018-2020).
Factors affecting students’ speaking performance in English department of Jawzjan University

This study aims to recognize the problems that students of English department face when they speak English. To achieve the objective of the investigation a quantitative research was designed. A questionnaire was used as the main data collection tool. The findings of the study showed that majority of the students have a positive perception about importance and necessity of speaking skill. According to the findings most of the students do not have the opportunity of practicing English speaking out of the class. Moreover, dissimilarity in English education background of the students are another challenge which is recognized in outcome of the study. Furthermore, the findings showed that most of the students feel motivated and confident in speaking performance. Moreover, the findings also revealed that the teachers have significant role for motivating the students in speaking performance. To sum up, the result exposed that students are appreciated and praised when they perform a speaking task efficiently.
Expertise based skills management system to support resource allocation

Skills Management is an essential concept of human resource management in which a skill inventory may be created for each employee and managers can assign tasks to workers based on worker's abilities. This concept is not fully practiced for two reasons: i) employee's skills are not effectively evaluated and documented, ii) tool support is deficient to manage this complex task. Ineffective skill management of an organization fizzle tasks assigned to the incompetent employees and this may lead to project failure. To fill up this gap, a survey is conducted across various software organizations to find out the best practices for the skill management and to gather requirements for skills management framework. Based on survey findings, a mathematical framework is proposed that calculates the soft and hard skills of employees automatically based on time and achievements as skill increases or decreases over time. In this framework, the Skills Calculation Engine (SCE) is developed for the managers to enhance the capacity of appropriate decisions making in assigning tasks to the rightly skilled workers. This framework is also useful for organizations as it can increase profitability as tasks are assigned to the most appropriate employees. The SCE is implemented as a Windows-based application to calculate skills, store skills in skills inventory, and assign tasks based on an employee's skills. The skills management tool is evaluated in a facilitated workshop; furthermore, a feature-wise comparison of the tool is also made with existing tools.
Comparative research in the field of professional education of preschool teachers: comparative content analysis as a method of authentic interpretation of concepts

The article is devoted to the disclosure of psycholinguistic aspects of comparative research, in particular the specifics of speech and thinking activities of the comparativist in the application of comparative content analysis of Ukrainian and English scientific pedagogical terminology. Theoretical aspects of comparative content analysis in comparative pedagogical research are revealed, which consist in: objectivity of system and quantitative description of the available content of the term; counting the semantic units represented in the information array that are determined; objectivity of conclusions and rigour of a procedure; quantification of text processing with the subsequent interpretation of the results of the analytical review of the phenomenon under study. It is determined that the speech-thinking activity of a comparativist is characterised by a step-by-step and complex of complementary, interconnected operations, which consist of the following components: motivational-target, cognitive-semantic, reproductive-activity, creative-active, evaluative-reflexive. It was determined that the methodological aspects of the application of comparative content analysis are the necessity to separate units for analysis, the formation of a sample that will ensure the authenticity of the interpretation of the concept in the domestic research-pedagogical thesaurus.