Reviewerly/exHarmony huggingface dataset:
An Analysis of Voice-Controller’s Implementation in Teaching DLP Form-One Mathematics: A Case Study

This case study aims to analyze voice-controller (VC)’s implementation of five, national-type secondary schools, Kinta Utara, Ipoh, Perak, teachers in teaching mathematics form-one (T1) Dual Language Programme (DLP). VC consists of three elements: sound level and voice level practice (SL-VL-P); marker and voice level practice (M-VL-P); and activity and voice level practice (A-VL-P). Students’ voice level barrier disturbs teaching and learning. Teacher should control students’ voice level. This study contributes an alternative classroom control strategy for teachers and reflection to Secondary School Standard Curriculum planner about learning and facilitating’s situation in school from communication perspective. Data collection methods were teaching observation, interview, and document analysis. Content analysis method was used to analyze data. The research’s findings indicate the number of participants who had the VC’s goal: cognitive domain (five), affective domain (four), and psychomotor domain (none). Affective domain’s level is limited within receiving and giving response. In VC implementation: (i) The number of participants who implemented SL-VL-P using: routine class explanation (three), reminder (five), oral order (two), repeated conversation (two), blame (one), positive provocation (one), and questioning (two). (ii) The number of participants who implemented M-VL-P using: voice tone (three), body language (three), and teaching aids (none)(iii) The number of participants who implemented A-VL-P using: group size plan (two), activity’s voice level explanation (one), oral order (two), motivation (one), and questioning (two). ‘Giving reminder’ is the common way for all participants. No formal VC drill for students. Two participants faced problem in implementing VC. Conclusion: Five participants’ VC goals are not holistic. Five participants implemented VC undirectedly. There are students unable to perform VC. Implication: Teachers should: (i) plan holistic goals. (ii) plan formal VC. (iii) help students who suffer from voice level barrier. Future study is required to study the relation between voice-level practice and student’s ability to control voice-level.
The Influence of Hot Point on MTU CB Condition at the Pgeli-Giugur 1 Bay Line (PT. PLN Paya Geli Substation)

Local heating that occurs in substation equipment is caused by the current flowing in the conductor due to resistance. The parts that often experience heating are the terminals and connections at the substation, especially between two different metals, as well as the conductor cross-section which decreases due to corrosion. So that part must be considered. As for how to control or check the temperature is done by using thermovision. The heat temperature of the Circuit Breaker (CB) equipment at the Bay line PGELI-GIUGUR1 at Paya GeIi Substation is still in normal conditions where the temperature ranges from 20oC-43oC. But one phase need attention if temperature more than 70oC, it is needed for improvement plans. The temperature difference between the phases of the CB equipment at Bay Iine P. GELI-GLUGUR 1 at Paya Geli Substation has reached condition 5 (five), so it needs serious attention and a repair plan, to avoid short circuits between phases (R, S, T).
Prototype of the request in the service queue

The development of a visual prototype of an application in a single queue for service is given. The pre-image connects the accounting for the growth of losses due to the duration of waiting with such sides of the importance of the application as urgency and priority. Varieties of change of urgency in time are discussed. The possibility of interpreting service as a lasting good is considered. On this basis, it is proposed to use relative losses as the main measure for determining the place of an application in the queue - the ratio of losses due to waiting to the duration of service. The example shows that the order in which service requests arrive depends on the time when the service facility starts accessing the queue and on the ratio of the service durations of individual requests. For the case of returning the service facility to the request after a forced interruption, the possibility of rivalry with it by other requests in the queue that have not yet arrived for servicing is considered. The possibility of working with the queue when servicing in small parts with a change in serviced requests is shown. For this type of work, expressions for step-by-step calculations are given. Calculations are made for two complicated cases of servicing a queue of five requests with a contradictory combination of the initial data of requests. A diagram of the switching of service facilities between requests is given. Taking into account the four sides of the description of the request in the queue at once provides the prototype with greater flexibility than most widely used queuing disciplines. The proposed prototype can be taken as a basis for building multi-parameter models of applications and algorithms for automatic control of the queue of applications for social, economic and computational applications.
Evaluation applying ConvLSTM neural network for the prediction of epileptic seizures

The COVID-19 pandemic has affected many aspects of people's lives and territories. It has a destabilizing effect on the economy due to both direct and indirect factors. In order to develop an effective policy to combat such emergencies, the problem of studying its consequences seems urgent.To form effective responses to the consequences of the pandemic, adequate scientific tools are needed to assess them. In many of the studies conducted, the impact of the pandemic was assessed without considering the previously existing socio-economic trends in the regions.The purpose of the article is to conduct a retrospective analysis of the consequences of economic instability caused by the COVID-19 pandemic on the socio-economic development of the region on the example of the Sverdlovsk region. The monthly values of twelve main socio-economic indicators from January 2016 to December 2021 were analyzed. The study is based on the use of regression models with dummy variables and seasonal autoregression for forecasting, which allow considering the seasonal dependence of socio-economic indicators.To determine the seasonality of individual indicators, a graphical method, autocorrelation correlograms and spectral analysis were used. When determining the uniformity between the predicted and actual values during the pandemic, the parametric Student's criterion and the nonparametric Wilcoxon criterion were applied, which allowed us to obtain a statistically significant result regardless of the normality of the compared aggregates.The retrospective analysis made it possible to establish a significant heterogeneous impact of the COVID-19 pandemic on most of the analyzed spheres of life in the Sverdlovsk region.
Educational values of postmodernism

This article is an attempt to highlight the essence of the postmodernist phenomenon in education and what are its values that must be promoted in the process of training students. The challenges of contemporary society call for a new modernity, and the problematic aspects associated with postmodernity (constructivism, multiple perspectives, realism, contextualization, etc.) from a conceptual perspective can senitize the field and offer wide possibilities not only for theorizing, but also for school practice currently.
Innovative Teaching-Learning Method for Engineering Mathematics Through Video Lectures in Rural Areas

Abstract : Mathematics is an important subject required for the foundation and has applications in all branches of engineering. Mathematical knowledge and skills are required for higher study, developing new technology and innovations. But creating students' interest in learning activities is always a challenging task for every mathematics teacher. Many engineering students are failing in the first year of their studies. This problem becomes more important and challenging in rural areas. There is always a concern about the problem that why students are not taking interest in learning? Out of many possible ways to deal with this problem, the use of flipped classrooms (FCR) plays an important role. This paper deals with the use of FCR for engineering students from rural areas. In addition to classroom teaching, for technology-based learning, video lectures (VLs) are provided to students of first-year engineering for thesubject of engineering mathematics. Starting with a review of FCR, experimentation of VLs, and its impact on learning activities and result has been presented. Various surveys at different levels and examination results are considered for conclusions. Through this action research, we have shown that VL is one of the most useful tools for enhancing learning and improving the results of students in rural areas. It has been further shown that this method is very much accepted by students. Keywords: Educational technology, Mathematics learning, Short VLs, Flipped classroom, Engineering Education.
Analysing the Impact of Emerging Technology Course on Student Satisfaction by Exposing Freshmen to Dynamic Industry Trends

Abstract : Technology is an inescapable necessity; it dominates almost every aspect of human existence. Emerging technol ogy hel ps to unl ock new possibilities for connecting things/people. At the same time, technological advancement changes industrial trends and the demand for engineering graduates' expertise. Hence, it becomes necessary for the students to imbibe the skills of continuous learning and understand the emerging trends, right from the inception year of engineering education. Also, with an amendment to the national education policy (NEP), the Indian Government has shown its clear intention to promote a holistic and multidisciplinary approach to technological developments through engineering education. Therefore, to accomplish this, it is critical to bridge the gap between industry and academia. The proposed course in this article is an initiative taken by Chitkara University Punjab to bring industry mentors and first-year students face-to-face and promote holistic learning amongst themselves. A total of 1000freshman engineering students took part in this course and participated in the post-course survey to express their satisfaction level with the course thus introduced. The Cronbach's Alpha coefficient α = 0.957 shows the high Reliability of data obtained through a survey questionnaire. Students expressed high motivation levels after completion of the course. The survey shows that the course gave the students clarity about the attributes required by the industry, which can help them decide their action points towards their career development. In the future, a hybrid model of teaching-learning can be employed to provide an added advantage of industry mentor interaction with students in live physical space. Keywords:Emerging-technology, NEP2020, Reliability, Engineering, Student-satisfaction, Motivation.
Contextual Emotion Detection of E-Learners for Recommendation System

Abstract : In the recent pandemic times, there was an impactful transformation in imparting education which required everyone to become online learners. There has been an exponential growth in the number of e-learners attending classes online and taking MOOCs courses. This has opened an avenue for research to analyze the emotion of e-learners through reviews of students to evaluate the learning outcomes and performance of the course. Most challenging task is to find the exact pulse of the e-learners’ emotions from the huge data of the e-learners reviews. The reviews on all online platforms are mostly textual and this qualitative data needs to be quantified for analysis. There is a necessity to propose contextual emotion detection of e-learners by extracting the relevant information which can be correlated to the performance of the course on e-learning platform. Further, it can be a recommendation system to the aspiring e-learners to make decision based on thesatisfaction index of previous e-learners. This paper leverages deep learning techniques to train various models for academic emotion detection using dataset E-Learners Academic Reviews (ELAR) prepared from online textual feedback of e-learners and MOOCs course reviews. The Bidirectional Encoder Representations from Transformers (BERT) transfer learning model used to detect the emotions outperformed the other models. This proposed method using ELAR dataset is a novel approach to identify the right emotion of e-learners from the course reviews available on e-learning platform. The results were discussed with a benchmark of ISEAR (International Survey on Emotion Antecedents and Reactions) and GoEmotion Dataset. Keywords : Academic emotions; Digital natives; Deep learning; E-learners; Textual emotions
Comparative Study between Partial Bayes and Empirical Bayes Method in Gamma Distribution

Though the name Partial Bayes was used earlier in a different context, but in statistics this is started from 2021, (Banerjee and Seal, 2021). Also, we know that empirical Bayes method was studied extensively for several decades. In this paper, these two methods are compared in two parameter gamma distribution having shape and scale parameter. As expected, it is found that empirical Bayes method is good in some cases. However, partial Bayes method performs even better in some cases where the shape parameter is sufficiently small, i.e. variation in the data is small. Even, overall performances of these two methods do not differ too much. But whenever we have information that shape parameter is small, then in that case partial Bayes method performs well. These results are also found by extensive simulation technique. The performances of these two estimators are also compared using two real datasets.
Proposed Two-Steps Procedure of Classification High Dimensional Data with Regularized Logistic Regression

The field of Bioinformatics has developed in response to the rapid increase in biological data, particularly high-dimensional gene expression data. Bioinformatics utilizes optimization, computational science, and statistical methods to effectively address challenges in the field of molecular biology. Numerous genes (variables) in gene expression are irrelevant to their study. Gene selection has been demonstrated to be an effective means of enhancing the performance of numerous methods of classification. The job of acquiring significant variables via the use of ranking variable selection (RVS) techniques and then picking the most effective classifier is an enormous challenge in the context of high-dimensional data. in this study, we proposed a new ranking filter method using smooth clipped absolute deviation depending on the resampling technique (RSVS) to obtain a proficient subset of genes with strong classification abilities. This is achieved by merging A screening technique employed as a filtering method in conjunction with Regularized Logistic Regression, such as LASSO,ALASSO,ENET, and MCP. The study involved the utilization of both simulated and real datasets to conduct an empirical evaluation of the proposed approach. The findings indicated that the proposed method outperformed other established methods. it was tested using three publicly data sets about Cancer. The Results demonstrate that the suggested approach is highly effective and viable, thus showing a strong level of performance with regards to accuracy, geometric mean, and the area under the curve. Furthermore, The findings suggest that the genes most often chosen are physiologically associated with the specific form of cancer. Therefore, the method that has been suggested has potential advantages for the classification of cancer via the use of DNA gene expression data within a clinical setting.
Poverty Alleviation Policy Strategy Through Sustainability Development Goals (SDGs) and Indonesia's Economic Resources

The purpose of this research is to examine and provide guidelines for local governments, communities, academics, and the private sector in planning and implementing poverty alleviation activities that are more effective, efficient, and on target. In addition, the specific objectives of this study are: 1) increasing the rate of regional economic growth through optimizing potential sources of regional income, 2) increasing per capita income, and 3) reducing poverty, unemployment, and socioeconomic inequality of the community. The research was conducted in Palembang City, South Sumatra, Indonesia, in the last 10 years, namely 2017-2022. The research approach uses qualitative descriptive analysis. Data sources include secondary data and Regional Statistics. The results of this study are based on the Sustainability Development Goals (SDGs) indicators, there are four priority scales in poverty alleviation, namely, Health and Infrastructure (Priority I), Education (Priority II), Food Stability (Priority III), and Population and Employment (Priority IV). Therefore, as a solution to poverty alleviation strategies, a budget approach through optimizing the regional economy and regional sources of income as well as community empowerment factors is important. In addition, involvement between elements (government, organizations, communities, universities, and institutions) is expected to continue as an effort to realize poverty alleviation can be overcome optimally.
Machine Learning Identification on Twitter Towards Combating Covid-19 (SARS-CoV-2): Pandemic Attacks and Urban Resilience in Indonesia

The purpose of this study is to detect the topic and discourse of the development of covid-19 and its resistance on social media Twitter in Indonesia. This research method is surveyed through Machine Learning to filter, track, and predict the spread of Covid-19 (SARS-CoV-2) on Twitter, and is assisted by Nvivo 12 pro and Ncapture analysis tools to collect data from big data on Twitter. The results of this study show ML (Machine Learning) plays a role in fighting the virus, especially looking at it from the perspective of screening, forecasting, and vaccines spread across various social media accounts on Twitter. A comprehensive survey of ML algorithms and models used in covid-19 (SARS-CoV-2) development expeditions on Twitter can help fight the virus. This research shows that the hashtag #lawancovid19 (Fight Covid-19) has relevance to several new hashtags that are still relevant in campaigning against covid-19 on Twitter to combat community and urban vulnerabilities in Indonesia. Collectively, characteristics such as Tagar #ayovaksin (let's get vaccinated), #jagajarak (keep your distance), and #ayopakaimasker (let's wear masks) support the resistance to covid-19 in Indonesia the account (@Username) that is most often @mention in the covid-19 cloud l action is the account of the @jokowi (President of Indonesia). The social media movement (Twitter) in encouraging community resilience and urban resilience through digital communication against Covid-19 (SARS-CoV-2) has predominantly succeeded in shaping understanding, behavior, and prudence in interacting directly in public spaces. This is important in the context of assessing messages and communications within the overall social media (Twitter) communication activities in response to online resistance measures in support of public health as well as to digitally address the global pandemic. This research contributes to providing insight into the dynamics of covid-19 (SARS-CoV-2) combat communication on Social-Media (Twitter) and supporting public health measures.
Emerging Trends and Digitization in the Education System

Education plays a key role in nation-building as it decides the direction for the society to develop. Education is the driver for achieving different societal milestones. Advancement and development are the need of the day and both cannot be achieved without education. For this reason, education has caught the limelight and turned out to be of paramount significance. Education, provides a conducive environment required to ensure all round development of an individual. In the current context of the education environment, digitization is a powerful tool to bring about modernization. Digitization refers to the introduction of new technological tools to be used as resources while imparting education. In the 21st century when the world is testing technology like 6G we are using decades-old techniques of imparting education. In developing countries where there is a lack of resources,the educational system is being run in a very old-fashioned way. In this research paper an attempt is being made to discuss the reforms being made towards digitization of education and further emerging trends and their system.
Impact of Artificial Intelligence on our Indian Society

hese days, everyone in our nation and around the world is familiar with the term Artificial Intelligence (AI) in the context of science and technology. In daily life, people encounter this word in a variety of settings, including corporations, software businesses, universities, schools, and higher education institutions. We have attempted to explain artificial intelligence (AI) in full in this review article, along with some of the positive and negative effects it may have on Indian society in the future.
The Impact of Cloud Computing on Firms: A Case Study of Cloud Adoption in Saudi Airline Navigation Services Company

The world has become increasingly digital. Companies rely on technology to assist them in improving their business processes. They are searching for an information system capable of handling massive work. An Enterprise Resource Planning system (ERP) comes into play at this point. Most ERP systems have been implemented on-premise, also known as traditional ERP systems. ERP system offers a variety of advantages, including integrated submodules, mature system functionality, and enhanced customization and integration capabilities. Other software applications have shifted to cloud computing over the past decade, which has become one of the fastest-growing segments of the IT industry. In addition, cloud computing is a modern trend that reveals the architecture of the next generation of applications. In this paper, we present a case study of Saudi Air Navigation Services company regarding their migration to cloud computing, highlighting the key concepts and services that cloud computing provides today and its impact on their businesses. This research aims to provide a better understanding of the cloud computing transformation.
Facial Expression Recognition Based on Well-Known ConvNet Architectures

The Convolution Neural Network (CNN) is the most widely used deep learning architecture as it has broken most world records for recognition tasks. Facial Expression Recognition (FER) systems that use classical feature-based techniques, especially CNN’s, is best for classifying images. This paper used three CNN-based methods, which are VGG-16, Inception-v3, and Resnet50-V2 network architectures, to classify facial expressions into seven classes of emotions: happy, angry, neutral, sad, disgust, fear, and surprise. The face expression dataset from Kaggle and JAFFE dataset were used to compare the accuracy between the three architectures to find the pretrained network that best classifies models. The results showed that VGG-16 network architecture produced a higher accuracy (93% in JAFFE and 54% in Kaggle) than the other architectures.