Reviewerly/exHarmony huggingface dataset:
Using New Technologies to Teach English in Malaysia - Issues and Challenges

The adoption of new technologies is critical to the advancement of the field of education, and to support the process of teaching and learning. New technologies in education aid in the delivery of educational contents, for instance in making them interactive and immersive. Interactive and immersive educational contents are needed to cater to the needs and wants of ‘Generation Z’ (young people who were born between 1995 and 2015) and now ‘Generation Alpha’ (children born between 2011 and 2025). They are children, teenagers and adolescents who were born at a time of great technological changes, particularly those brought on by the Fourth Industrial Revolution in human history. In developing and developed nations, members of Generation Z and particularly Generation Alpha are born into technology. Learning by rote do not cut it for these youngsters whose attention span might seem to be shorter, yet they are able to quickly sieve through information and make sense of knowledge faster compared to previous generations; technology is part of who they are due to the fact that they are were born in the digital age. This empirical study explores the use of new technologies to teach English to Malaysian students from Generation Z and also Generation Alpha. Focusing on qualitative data from educational instructors at Malaysian tertiary institutions and informal classroom observation data from a number of Malaysian tertiary institutions, using English as a case in point, this study highlights issues and challenges faced by educational instructors (tertiary lecturers) who are vigorously trying to apply new technologies to the teaching and learning dyad within their tertiary institutions. Though they face tough challenges, especially in terms of limited financial resources and low technological knowledge, the resolve of these instructors is commendable given the fact that they understand how essential new technologies are for their students.
Conditions for conceivability of individualization within the new paradigm of education

This article discusses why the traditional pedagogical paradigm of education failed to consider individualization: this was contradicted by the fundamental principles of pedagogy of the XIX century (interpretation of a student as a generalized individual, rather than personality; uniform curriculum, understanding of key goal of education as digestion of knowledge, etc.); these principles were also supported by project of modernity. New paradigm of education should take into account the analogy of phylo- and ontogenesis, in the context of which the author differentiates the culture that includes sociality (phylogenetic plan), and development of the individual in education and culture (ontogenetic plan). At the same time, culture and sociality, should be viewed within the framework of semiotic, activity, and environmental approaches, a detailed characteristic of which is presented. The author distinguishes between the &amp;ldquo;before-personality&amp;rdquo; cultures (Ancient World) and &amp;ldquo;with personalities&amp;rdquo; cultures (since the Antiquity). The first one did not allow autonomous behavior; while the second, in addition to collective forms of life, initiate private forms driven by &amp;ldquo;private patterns&amp;rdquo;. From ontogenetic perspective, the author delineates the three &amp;ldquo;cultures of life&amp;rdquo;: the culture of &amp;ldquo;prama&amp;rdquo; (childhood), the culture of &amp;ldquo;personal becoming&amp;rdquo; (teenage period and adolescence), the culture of &amp;ldquo;personal enhancement&amp;rdquo; (adulthood). Three phases of personal becoming are discusses: 1) formation of intersubjective situation, 2) response of an individual to such situation, as a yet unsconsious way of its resolution; 3) comprehension, outlining, action (this phase includes learning). The author introduces an important concept of &amp;ldquo;evolution of an individual&amp;rdquo;, which implies spontaneous and conscious changes in personality of a student under the influence of school, family, media (Internet), friends, and reflection. Based on the aforementioned differences and concepts, the author gives characteristics to the sources of individualization, and describes the space it is fulfilled within.
Organization of a system of digital education practices in the municipal sphere of general education

Abstract This article is devoted to solving the problem of accumulation, systematization and translation of the practices of using digital educational technologies in municipal educational organizations. The solution to this problem was carried out through the implementation of the municipal project "Organization of a system of digital education practices in the urban district of the city of Yelets" in 2019. The project was aimed at involving at least 350 managing and teaching staff, 4000 students into the active practice of using digital educational technologies in educational organizations of the urban district of the city of Yelets by the end of 2019. As a way to achieve the goal, the implementation of a system of digital education practices in educational organizations of the urban district of the city of Yelets was determined. Continuity and concession are key features of this system. Consequently, the stakeholders are preschool educational organizations, general educational organizations, organizations of additional education, secondary vocational education and higher education. This system involved the use of such organizational forms as a resource center, a municipal network platform, a pivotal school, an author’s school, within which approbation, tuning and dissemination of the experience of using software products, the LECTA digital educational platform, the digital educational environment "Mobile e-education" and directions 3D modeling, digital video and robotics. The key results of the conducted research were not only quantitative but also qualitative indicators. The main problem that has blocked the adoption of digital education tools among the pedagogical community is the belief that they are ineffective compared to full-time education.
Impossibility of Partial Recovery in the Graph Alignment Problem

Random graph alignment refers to recovering the underlying vertex correspondence between two random graphs with correlated edges. This can be viewed as an average-case and noisy version of the well-known graph isomorphism problem. For the correlated Erd\"os-R\'enyi model, we prove an impossibility result for partial recovery in the sparse regime, with constant average degree and correlation, as well as a general bound on the maximal reachable overlap. Our bound is tight in the noiseless case (the graph isomorphism problem) and we conjecture that it is still tight with noise. Our proof technique relies on a careful application of the probabilistic method to build automorphisms between tree components of a subcritical Erd\"os-R\'enyi graph.
The relationship between management and control indexes of distribution network construction based on Apriori algorithm

There are a large number of distribution network construction projects with different types, and a large number of complex data relationships are generated in the construction process. This paper combs the key management and control indicators of distribution network construction projects, and constructs the management and control index system of each stage of the project. Based on Apriori algorithm, this paper studies the relationship between the key management and control indicators, and provides reference for the management and control of distribution network construction.
Unsupervised Dual Adversarial Learning for Anomaly Detection in Colonoscopy Video Frames

The automatic detection of frames containing polyps from a colonoscopy video sequence is an important first step for a fully automated colonoscopy analysis tool. Typically, such detection system is built using a large annotated data set of frames with and without polyps, which is expensive to be obtained. In this paper, we introduce a new system that detects frames containing polyps as anomalies from a distribution of frames from exams that do not contain any polyps. The system is trained using a one-class training set consisting of colonoscopy frames without polyps -- such training set is considerably less expensive to obtain, compared to the 2-class data set mentioned above. During inference, the system is only able to reconstruct frames without polyps, and when it tries to reconstruct a frame with polyp, it automatically removes (i.e., photoshop) it from the frame -- the difference between the input and reconstructed frames is used to detect frames with polyps. We name our proposed model as anomaly detection generative adversarial network (ADGAN), comprising a dual GAN with two generators and two discriminators. We show that our proposed approach achieves the state-of-the-art result on this data set, compared with recently proposed anomaly detection systems.
Subjectively Experienced Time and User Satisfaction: An Experimental Study of Progress Indicator Design in Mobile Application

In the user's interaction with systems, waiting and interruptions often constitute a source of negative experiences.However, system response time can be difficult or impossible to control.This study explores "subjective experienced time", which refers to the users' assessment of system response timeliness.The aim of this study is to gain increased knowledge of user satisfaction and subjectively experienced time in interaction with mobile applications.Thirty participants used and evaluated three mobile applications, containing unique stimuli in progress indicators.The results show correlation between progress indicators' degree of feedback and the subjectively experienced time and user satisfaction.Contributions include increased insight into the somewhat complex connection between the degree of feedback, subjectively experienced time and user satisfaction, as well as design implications for usercentred design.
An Overview of Digital Entrepreneurship in Central and Eastern European Countries

The aim of the study is to explore and present an overview of digital entrepreneurship in Central and Eastern European countries and to examine how certain components of the DESI index affect GDP per capita in CEE countries and in what way modern information technologies affect their economies. The paper uses secondary data sources, mostly scientific and professional journals from the studied area, DESI reports, Eurostat data, and other Internet sources. The first part of the paper presents a short introduction on digitization digital entrepreneurship and digital technologies. The second part provides a descriptive analysis of digital entrepreneurship indicators and explores business demography in the ICT sector while the third part refers to the analysis of the DESI index. The panel method on data from 2015 to 2019 was used to show the influence of the different DESI index components on the observed countries’ GDP per capita. The hypothesis that the components of the DESI index have a positive impact on GDP per capita has been partially confirmed. DESI rank, Connectivity and Human capital did not prove to be significant, while Use of internet services, Integration of digital technology, and Digital public services proved their significant positive effect.
On Baselines for Local Feature Attributions

High-performing predictive models, such as neural nets, usually operate as black boxes, which raises serious concerns about their interpretability. Local feature attribution methods help to explain black box models and are therefore a powerful tool for assessing the reliability and fairness of predictions. To this end, most attribution models compare the importance of input features with a reference value, often called baseline. Recent studies show that the baseline can heavily impact the quality of feature attributions. Yet, we frequently find simplistic baselines, such as the zero vector, in practice. In this paper, we show empirically that baselines can significantly alter the discriminative power of feature attributions. We conduct our analysis on tabular data sets, thus complementing recent works on image data. Besides, we propose a new taxonomy of baseline methods. Our experimental study illustrates the sensitivity of popular attribution models to the baseline, thus laying the foundation for a more in-depth discussion on sensible baseline methods for tabular data.
Geochemistry of termite mounds in the sediment-hosted Lead-Zinc Mining District of Yolo, Gongola Sub-basin: A guide for lead-zinc exploration in the Upper Benue Trough, Nigeria

Yolo lead-zinc mining district is an important area in Gongola Sub-basin of the Upper Benue Trough with paucity of rocks outcrops but abundant termite mounds. The termite mounds and their adjoining surface soils were analyzed for Pb, Zn, Ti, Cu, Sb, As, U, Cr, Zr and Li in an attempt to test their effectiveness in defining favorable areas for lead-Zinc mineralization in the Upper Benue Trough. The result shows a general trend of higher elemental concentration in termite mounds relative to their adjoining surface soils. The ore elements Pb, Zn together with Ti have the highest average concentrations of 503, 2136.5, and 6285 ppb in termite mounds compared to 356, 1662 and 2250 ppb respectively in adjoining surface soils. Biological Absorption Coefficient, calculated to evaluate their degree of concentration, shows 141 values of Biological Absorption Coefficient within enrichment category with only 59 values in the depletion category. The relatively high concentration of the ore elements Pb, Zn and the associated trace elements in termite mounds and the elevated contents of BAC values in the enrichment category together suggest that Pb, Zn and Ti in termite mounds can be effectively used for lead-zinc exploration in the Upper Benue Trough. Key words: Lead-Zinc, termite mound, Gongola Sub-basin, Upper Benue Trough.
Analysis of Smart Grid Stability and Security Management Based on Data Mining

Abstract In the process of the operation and construction of the smart grid, the development of science and technology and the improvement of social productive forces have put forward higher requirements for the development of the power system. In this process, the scale and form of the power system have also changed to a certain extent, and power security has also become the most concerned issue. In the information society, through the application of the Internet of things and big data technology in power security management and control. It can simplify the operation process of the staff and reduce the occurrence of accidents. Distributed smart grid is a new technology proposed for power networks with elastic nodes, which can realize dynamic electricity price demand response without large-scale transformation of infrastructure. In order to analyze the system stability of DSG, six representative machine learning classification models are applied to analyze the stability data of 10000 samples of 4-node system. Combined with the requirements of power system security, stability and economy, the effect of each classification model on the prediction of DSG system stability is tested.
Digital Divide and Digital Barriers in Distance Education during COVID-19

Digital divide exists between the underserved student population and their peers, yet our knowledge about digital barriers and digital divide in distance education remains limited.In this study, we examine digital divide and digital barriers in distance education in the context of the coronavirus pandemic by addressing two questions: (1) What digital barriers are emerging in distance education during COVID-19?(2) Do underserved students experience digital barriers differently from their peers?Informed by distance education and digital divide literature, this study uses qualitative research method to analyze survey data collected from 206 college students in a four-year public university in the United States.Results revealed five major digital barriers and showed that the distribution of these digital barriers varied by demographic background and socioeconomic status of the students.Practical implications are provided to educators and policymakers to implement equityminded teaching practices and enhance digital inclusion of the underserved student population in distance education.
Learning under Distribution Mismatch and Model Misspecification

We study learning algorithms when there is a mismatch between the distributions of the training and test datasets of a learning algorithm. The effect of this mismatch on the generalization error and model misspecification are quantified. Moreover, we provide a connection between the generalization error and the rate-distortion theory, which allows one to utilize bounds from the rate-distortion theory to derive new bounds on the generalization error and vice versa. In particular, the rate-distortion based bound strictly improves over the earlier bound by Xu and Raginsky even when there is no mismatch. We also discuss how auxiliary loss functions can be utilized to obtain upper bounds on the generalization error.
Automated Gold Grain Counting. Part 1: Why Counts Matter!

The quantitative and qualitative assessment of gold grains from samples of glacial till is a well-established method for exploring gold deposits hidden under glaciated cover. This method, widely used by the industry and which produced numerous successes in locating gold deposits in glaciated terrain, is still based on artisanal gravity separation techniques and visual identification. However, being artisanal, it is limited by inconsistent recoveries and the difficulty to visually identify the predominantly occurring small gold grains. These limitations hinder its capability to deceipher subtle or complex signal. To improve detection limits through recovery of small gold grains, a new approach has recently been introduced in the industry (commercially referred as &amp;ldquo;ARTGold&amp;rdquo; procedure) using an optimized miniature sluice box coupled with an automated scanning electron microscopy routine. The capabilities of this improved method are highlighted by comparing till surveys conducted around the Borden gold deposit (Ontario, Canada) using the conventional and improved methods at both local and regional scales. Relative to the conventional approach, the improved method recovered almost one order of magnitude more gold grains from samples (regional and down-ice mineralization), dominantly in small size fractions. Increasing the counts in low-abundance regional samples enables better discrimination between background signals and significant dispersions. The method offers an alternative to improve characterization of gold dispersal in glaciated terrain and the related gold deposit footprints.
Convolutional Neural Network Interpretability with General Pattern Theory

Ongoing efforts to understand deep neural networks (DNN) have provided many insights, but DNNs remain incompletely understood. Improving DNN's interpretability has practical benefits, such as more accountable usage, better algorithm maintenance and improvement. The complexity of dataset structure may contribute to the difficulty in solving interpretability problem arising from DNN's black-box mechanism. Thus, we propose to use pattern theory formulated by Ulf Grenander, in which data can be described as configurations of fundamental objects that allow us to investigate convolutional neural network's (CNN) interpretability in a component-wise manner. Specifically, U-Net-like structure is formed by attaching expansion blocks (EB) to ResNet, allowing it to perform semantic segmentation-like tasks at its EB output channels designed to be compatible with pattern theory's configurations. Through these modules, some heatmap-based explainable artificial intelligence (XAI) methods will be shown to extract explanations w.r.t individual generators that make up a single data sample, potentially reducing the impact of dataset's complexity to interpretability problem. The MNIST-equivalent dataset containing pattern theory's elements is designed to facilitate smoother entry into this framework, along which the theory's generative aspect is naturally presented.
Intelligent Text Mining to Sentiment Analysis of Online Reviews

In the prevailing days social networking sites helps people to connect easily across the world and gain knowledge and also share their interests.But, unfortunately in some cases these sites became a platform for cyber bullying.Cyber bullying is the act which causes emotional and psychological distress leading to depression, anxiety, fear and low selfesteem to the victims.Cyberbullying can be elucidated as usage of digital communication typically by sending messages to threaten, defame, harass or intimidate someone.Common social media platforms like twitter, facebook, instagram are exposed to cyberbullying which has become very common now-a-days.This can be reduced to an extent if such intimidating messages or comments are segregated.The process of classifying a sentence whether it is positive, negative or neutral is known as sentiment analysis.It helps in determining emotional tone behind a sentence.To classify these intimidating messages this paper proposes a hybrid classifier approach which classifies reviews into positive or negative.Experimental results show that the accuracy of the classifier for considered dataset is 89.36%.
An Enhanced Method for Review Mining Using N-Gram Approaches

In the current scenario, when a person wants to purchase a product, they try to discover information concerning the product. For finding such information, they use reviews about the products on the Internet. This information always influences the purchase of products. The proposed work introduces a method for the classification of reviews using the relevant aspects of the product. The novelty of the work lies in exploiting the combination of higher-level N-approach for finding the relevant aspects from the product reviews. The most commonly used unigram approach fails to provide adequate accuracy when the dataset is very large. Proposed work uses a higher level of N-gram, a combination of unigram, bigram, and trigram for the extraction of relevant aspects from the reviews. The proposed method also calculates the polarity score of emoji in the review for the classification. The experimental analysis shows that the proposed method is better in terms of accuracy and execution time.
A Novel Blockchain-Based Authentication and Access Control Model for Smart Environment

With the increase of smart factories and smart cities following the recent 4th industrial revolution, internal user authentication and authorization have become an important issue.The user authentication model using the server-client structure has a problem of forgery of the access history caused by the log manipulation of the administrator and unclearness of the responsibility.In addition, users must independently manage the authentication method for each service authentication.In this paper, to solve the above problem, the researchers propose an integrated ID model based on a hybrid blockchain.The proposed model is implemented as two layers of Ethereum and Hyperledger Fabric: the former layer is responsible for integrated authentication, and the latter layer is responsible for access control.The physical pass or application for user authentication and authorization are integrated to one ID through the proposed model.In addition, the decentralized blockchain ensures the integrity and transparency of the stored access history, and it also provides non-repudiation of authority and access history.