Reviewerly/exHarmony huggingface dataset:
Recommendations to Improve the Usability of Mobile Learning for Preschool Teachers in Africa: A Systematic Scoping Review

Poor quality preschool teacher training is a significant contributor to children not achieving school readiness. With technological development, electronic and mobile learning offer accessible and affordable options for this training, however, there is a lack of design evidence for the African context. This interdisciplinary education- and computer-science study aims to provide evidence-based recommendations for improving the usability of mLearning modules for preschool teachers in Africa. A scoping review guided by Arksey and O’Malley’s framework, the PRISMA reporting guidelines for scoping reviews, and Levac et al. guidelines, was conducted. Databases searched included Africa Wide Information, ERIC, CIHNAL, and Academic Search Premier. Two researchers completed a blind-review process for article inclusion and framework analysis, using 10 usability attributes with inductive grouping of data under each attribute to form sub-categories. Overlapping themes were coded to formulate usability recommendations within WeftQDA. The search yielded 909 articles and, after title and abstract screening, 123 were selected for full text review. Seventeen articles (n=17) met the inclusion criteria and were included in the final review. All studies included an African partner, and largely included part-time learning for degree purposes and professional development using mLearning, eLearning, and videos. Nineteen recommendations were developed to improve the usability of mLearning training applications for preschool teachers in Africa, with contextual relevance emerging as a new usability attribute. Development of mLearning training modules for preschool teachers in Africa should include user centred design, heuristic evaluation and usability assessment, to ensure the sustainability of both mLearning and change in classroom teaching practices.
Challenges Encountered and Technical Assistance Needed by Parents and Learners Utilizing Modular Distance Learning: Basis for a Proposed Support Program

The shift from face-to face to distance learning brought about by the outbreak of the COVID-19 pandemic is a major adjustment to all school administrators, teachers, students and parents. The study aimed to determine the challenges encountered and needed support of parents and learners in an elementary school of the Schools Division of Meycauayan City as basis for crafting a contextualized support program. It employed a mixed method design using survey-questionnaire and interview guide as data gathering tools to 123 parents and 10 learners respectively. Frequency and percentage were used to describe the demographic profile, frequency counts and ranking were used on the challenges encountered and support/technical assistance needed by parents while thematic analysis was used to describe the experiences of the learners. The study found out that difficulty in independent learning, difficulty in concentrating on studies and parent’s limited knowledge on the content of child’s lessons are the top struggles of parents in the modular distance learning while almost all learner-respondents describe their experiences of distance learning as difficult because of difficulty in learning by themselves, lack of reference materials for more examples, limited guidance from parents and teachers, challenges in load to research and difficulty in understanding English. Moreover, both respondents are unanimous in declaring that provision of additional reference materials will greatly help lessen the struggles they encountered. Therefore, it is recommended that the support program crafted should be implemented and monitored to fully reinforce the continuity of learning amidst the challenges encountered in the first quarter.
Modern Approaches To Professional Training Of Future Teachers (The Personnel Reserve Project)

In the context of the digital transformation of general education, the successful professional activity of a modern teacher is not possible without strong motivation, cognitive and technological readiness to teach their subject in conditions of multitasking, uncertainty and situationality.At the same time, the modern education system puts forward certain regulatory requirements for the qualification and content of the professional activity of the teacher.All this imposes certain obligations on the university, which will organize the appropriate training of future teachers.The authors of the study analyze the Russian and foreign experience on the organization of effective training of the future teacher of the 21st century in the conditions of digital transformation of education.The article analyzes modern approaches to training future teachers, describes the results of their practical implementation.The article also presents the results of the survey, reflecting the state of motivational, cognitive and technological readiness of future teachers for professional activity in the digital educational environment.The presented results are the first stage of the project, the experience of which can serve as a reason for reviewing the content and organizational component of the professional training of future teachers.
“You’re too thick to change the station” – Impoliteness, insults and responses to insults on Twitter

Abstract This paper aims to propose a typology of replies to insults based on data retrieved from Twitter, which is ripe with offensive comments. The proposed typology is embedded in the theory of impoliteness, and it hinges on the notion of the perlocutionary effect. It assumes that what counts as an insult depends primarily on whether or not an utterance is evaluated as offensive by the insultee. The evaluation can be signalled behaviourally or verbally and includes expressed replies as well as so-called silent replies. The insults, regardless of the presence or absence of an insulting intention of the insulter (potential insult), that are not rendered as offensive by the target are only attempted insults, while those that are experienced as offensive amount to genuine insults. The analysis has illustrated select types of reactions and has shown that potential, attempted and genuine insults may be further divided into: in/direct insults, explicit/implicit, non-/pure, and non-/vocatives, whilst reactions can be subsumed by three overarching strategies: agreeing, attacking and rejection.
Application Of Machine Learning Techniques For Fake Customer Review Detection

Now-a-days with the increasing demand of the web , online marketing is additionally becoming progressively popular.This is often because; tons of products and services are easily available online.Hence, reviews of these products and services are vital for customers also as sellers.But, to gain profit or promotion, scammers produce fake reviews.These fake reviews written by scammers prevent customers and sellers reaching actual opinion about the products.Hence, fake reviews or spam reviews must be detected and eliminated so as to prevent misleading potential customers.In our work, supervised and semi supervised learning techniques are applied to detect spam review.
Universal Approximation Theorem for Tessarine-Valued Neural Networks

The universal approximation theorem ensures that any continuous real-valued function defined on a compact subset can be approximated with arbitrary precision by a single hidden layer neural network. In this paper, we show that the universal approximation theorem also holds for tessarine-valued neural networks. Precisely, any continuous tessarine-valued function can be approximated with arbitrary precision by a single hidden layer tessarine-valued neural network with split activation functions in the hidden layer. A simple numerical example, confirming the theoretical result and revealing the superior performance of a tessarine-valued neural network over a real-valued model for interpolating a vector-valued function, is presented in the paper.
From Face-to-Face to Digital Learning; Seen from EFL Student's Lenses Enrolled in Online Group Work

Numerous higher education institutions around the world must shift their instructions from face-to-face (FTF) to online learning due to Corona Virus Disease in 2019 (Covid19). Furthermore, with the increase in popularity of group work in higher education, especially in the language classroom, and the pivotal changes over the last years, the group works encounter new challenges as they transition to the online world. The study aims to explore the experiences of English as a Foreign Language (EFL) students' engagement in online group works to explore the benefits and challenges in a new learning environment. The qualitative research design provided an in-depth understanding of the students' perceived value of online group work. Further, an interview was used as the main technique in collecting the data from the entire participants. Furthermore, the findings demonstrated that students had generally good attitudes about online group work; however, some students noted that language and communications concerns presented difficulty at times. Nevertheless, the findings also demonstrated that the benefits of this experience surpassed the drawbacks. Thus, the study's findings can be valuable for practitioners and curriculum designers since they highlight the benefits of group work in an online learning setting and the students' problems they may confront during these classes.
Detection of circuit components on hand-drawn circuit images by using faster R-CNN method

In this study, one of deep learning methods, which has been very popular in recent years, is employed for the detection and classification of circuit components in hand-drawn circuit images. Each circuit component located in different positions on the scanned images of hand-drawn circuits, which are frequently used in electrical and electronics engineering, is considered as a separate object. In order to detect the components on the circuit image, Faster Region Based Convolutional Neural Network (R-CNN) method is used instead of conventional methods. With the Faster R-CNN method, which has been developed in recent years to detect and classify objects, preprocessing on image data is minimized, and the feature extraction phase is done automatically. In the study, it is aimed to detect and classify four different circuit components in the scanned images of hand-drawn circuits with high accuracy by using the Python programming language on the Google Colab platform. The circuit components to be detected on the hand-drawn circuits are specified as resistor, inductor, capacitor, and voltage source. For the training of the model used, a data set was created by collecting 800 circuit images consisting of hand drawings of different people. For the detection of the components, the pretrained Faster R-CNN Inception V2 model was used after fine tuning and arrangements depending on the process requirements. The model was trained in 50000 epochs, and the success of the trained model has been tested on the circuits drawn in different styles on the paper. The trained model was able to detect circuit components quickly and with a high rate of performance. In addition, the loss graphics of the model were examined. The proposed method shows its efficiency by quickly detecting each of the 4 different circuit components on the image and classifying them with high performance.
FOLIO and Smart Libraries: Meeting the future needs of Chinese Libraries

The vision and concept of FOLIO have gained the attention and expectation from libraries. Its open and flexible service platform provides an approach to the realization of the Smart Libraries in the future. On the basis of summarising the needs of Smart Libraries in China, the paper expounds the architecture and ideas of providing smart services based on FOLIO. By analyzing the problems and challenges in the implementation and localization of FOLIO in China, the author points out that the“Chinese Alliance for Library Service Platform”(also called YunHan Alliance) should play a very important role in running the Chinese FOLIO community. It should steward the direction of the community, carry out the minimum open source software application suite, cultivate an open platform ecology, so as to support the new business models for the prosperity of Chinese libraries in the future.
Use of Social Network Analysis for Tax Control in Spain

The Spanish Tax Agency is an experienced user of big data and has now deployed social network analysis (SNA) tools. SNA tools have led to a qualitative leap in such wide-ranging areas as tax collection, enforcement, control of ultra-high-net-worth individuals, and money laundering. This paper presents a comprehensive overview of the different lines of research, strategies and results of nine projects over the last five years, including the lessons learned. We present the best practices in pattern discovery, the tools developed for the control of big fortunes and the strategy developed to create a bridge between expert knowledge and SNA technologies. We highlight the results of investigating interposed entities used to channel personal remuneration, complex corporate structures, and opaque companies.
Mechanization of Incidence Projective Geometry in Higher Dimensions, a Combinatorial Approach

Several tools have been developed to enhance automation of theorem proving in the 2D plane.However, in 3D, only a few approaches have been studied, and to our knowledge, nothing has been done in higher dimensions.In this paper, we present a few examples of incidence geometry theorems in dimensions 3, 4, and 5.We then prove them with the help of a combinatorial prover based on matroid theory applied to geometry.
Experimental Research of the Sense-Value Regulation of Future Teachers in Academic and Professional Activity

The research is psychological-pedagogical experiment with factor analysis of value-sense meaningful parameters, preferences, expectations of future teachers (N = 140). The aim is to study the psychological rich in content features of formation of the value-sense sphere of future teachers. Values, while forming in the life experience of the individual, determinate the purpose-oriented and motivational program of behavior and provide sensory regulation of life activity. Personal sense, as a subject determinant, defines the effectiveness of professional activity. The development of system of values and senses is determined by rich in content process, the involvement of students in various types of social activities and purposeful impact by training facilities. Research methods: psychological and pedagogical experiment, standardized tests, factor analysis. The structure of value and sense regulation is determined. The forming stage provided changes in the experimental group (N = 17) from values oriented on social perception to values of personal and professional development. In the control group (N = 19), the dominance of personal comfort values was recorded. Compared to the ascertaining stage, the values of personal- professional development has emerged. The results obtained can be useful to stakeholders, leaders of educational institutions, researchers of self-regulation, axiogenesis, life creation and personality expectations.
Central Bank Digital Currencies: A Bibliometric Analysis

Rapid technological advances in electronic money and payment methods entice central banks to consider their role in an ever more digital world. Transformative changes to the financial and monetary system are met with an array of policy responses, including the issuance of central bank digital currencies. This paper examines the trends in the evolution of central bank digital currency literature and identifies the important features of the research area. A bibliometric analysis was used to analyze 77 journal articles on central bank digital currencies published from 2018 to 2020 in Scopus Database. Through performance, co￾citation, and co-word analysis of the retrieved literature, we document the main journals, countries, authors, and emerging fields of research in central bank digital currencies.
Knowledge based human resource management and employee creativity: the dual mediation of knowledge sharing and knowledge hiding

Introduction. Employee creativity is an essential element that is required for the dynamic work environments. Companies able to foster employee creativity through knowledge based human resource management practices enhance their competitive advantage over companies who stifle employee creativity. The conceptual model in this paper aims to demonstrate that knowledge sharing and knowledge hiding mediate the relationship between knowledge based human resource management practices and employee creativity. The implementation of knowledge based human resource management practices can lead to increased knowledge sharing and decreased knowledge hiding both of which will result in increased employee creativity.&#x0D; Aim and tasks. The aim of this paper is to build on social exchange theory as the foundational theory for understanding how knowledge based human resource management practices impact employee creativity through the mediators of knowledge sharing and knowledge hiding. &#x0D; Results. In previous research have studied the relationship between knowledge behaviors and employee creativity while accounting for motivational climate, transformational leadership, goal orientation, and various human resource management practices. Many of these factors have traditionally been exogenous variables to the individual employees working in organizations. While social exchange theory implies a dyadic exchange, it would be relevant to examine the endogenous variants within employees that might contribute to differential reactions to reciprocation. It is substantiated that individuals that have different exchange ideologies react differently to organizational support. The first section includes a literature review of the constructs - knowledge based human resource management, knowledge sharing, knowledge hiding, and employee creativity. The second section provides a model focusing on the impact social exchange theory has on the constructs, including theory-based propositions. In reviewing this model, the paper makes theoretical contributions to the constructs and social exchange theory. The final section provides direction for future research and discussion. &#x0D; Conclusions. Creativity is the fuel for the 21st century’s competitive organizations. Researchers and practitioners alike rely on creativity to solve problems and improve products and services around the globe. It is important to deeply understand the antecedents necessary for creativity so that organizations employ systems and process that are conducive for creative production. From the theoretical foundations laid out in this paper, knowledge based human resource management practices will increase employee creativity through increased knowledge sharing and decreased knowledge hiding.
Vision Transformer-Based Tailing Detection in Videos

Tailing is defined as an event where a suspicious person follows someone closely. We define the problem of tailing detection from videos as an anomaly detection problem, where the goal is to find abnormalities in the walking pattern of the pedestrians (victim and follower). We, therefore, propose a modified Time-Series Vision Transformer (TSViT), a method for anomaly detection in video, specifically for tailing detection with a small dataset. We introduce an effective way to train TSViT with a small dataset by regularizing the prediction model. To do so, we first encode the spatial information of the pedestrians into 2D patterns and then pass them as tokens to the TSViT. Through a series of experiments, we show that the tailing detection on a small dataset using TSViT outperforms popular CNN-based architectures, as the CNN architectures tend to overfit with a small dataset of time-series images. We also show that when using time-series images, the performance of CNN-based architecture gradually drops, as the network depth is increased, to increase its capacity. On the other hand, a decreasing number of heads in Vision Transformer architecture shows good performance on time-series images, and the performance is further increased as the input resolution of the images is increased. Experimental results demonstrate that the TSViT performs better than the handcrafted rule-based method and CNN-based method for tailing detection. TSViT can be used in many applications for video anomaly detection, even with a small dataset.
English Machine Translation Model Based on an Improved Self-Attention Technology

English machine translation is a natural language processing research direction that has important scientific research value and practical value in the current artificial intelligence boom. The variability of language, the limited ability to express semantic information, and the lack of parallel corpus resources all limit the usefulness and popularity of English machine translation in practical applications. The self-attention mechanism has received a lot of attention in English machine translation tasks because of its highly parallelizable computing ability, which reduces the model’s training time and allows it to capture the semantic relevance of all words in the context. The efficiency of the self-attention mechanism, however, differs from that of recurrent neural networks because it ignores the position and structure information between context words. The English machine translation model based on the self-attention mechanism uses sine and cosine position coding to represent the absolute position information of words in order to enable the model to use position information between words. This method, on the other hand, can reflect relative distance but does not provide directionality. As a result, a new model of English machine translation is proposed, which is based on the logarithmic position representation method and the self-attention mechanism. This model retains the distance and directional information between words, as well as the efficiency of the self-attention mechanism. Experiments show that the nonstrict phrase extraction method can effectively extract phrase translation pairs from the n-best word alignment results and that the extraction constraint strategy can improve translation quality even further. Nonstrict phrase extraction methods and n-best alignment results can significantly improve the quality of translation translations when compared to traditional phrase extraction methods based on single alignment.
Assessing the Higher Education Settings after the Transition to Online Learning: Exploring Teaching, Assessments, and Students’ Academic Success

This empirical research report intended to explore the impact of the sudden transition to online learning and teaching and assessment at higher education institutions in the United Arab Emirates (UAE). Furthermore, the study assessed the difference in students’ academic achievement and their perception of the transition to online learning. A mixed-method research design was adopted to achieve the purpose of this research paper. Content analysis, the Correlation Coefficient, and Simple Linear Regression were supportive analysis tools. Data analysis proved that the higher education system in the UAE embraced appropriate teaching and assessment approaches to the online mode of teaching and learning. Moreover, higher education students maintained high academic success after transitioning to online learning. Students’ perception of the transition to online learning positively correlated to students’ academic success. Thus, higher education students maintained a decent level of academic success after transitioning to the online mode of learning.