Reviewerly/exHarmony huggingface dataset:
Implementation of a Continuous Integration and Deployment Pipeline for Containerized Applications in Amazon Web Services Using Jenkins

Today, cloud computing is the preferred option for the majority of businesses. DevOps techniques have been developed as a result, requiring developers to collaborate closely with network engineers to ensure the quick and secure deployment of their applications. Many organizations adopt DevOps and Continuous Integration and Continuous Deployment (CI/CD) techniques to address the slow delivery of services to customers as in Traditional IT approaches, which better meets customer needs. Thanks to the high flexibility and scalability of the infrastructure in the Cloud, DevOps, which unifies developers and operations teams, can bridge the gap between them. Automation is a representation of the CI/CD central approach. This does away with the requirement for human checks, but it does not come without security risk.
PEMANFAATAN EDMODO UNTUK MEMPERSIAPKAN GURU MENGHADAPI PEMBELAJARAN ABAD 21 DI MGMP MATEMATIKA SMA KABUPATEN PONOROGO

In order to prepare students who are able to reason at a high level as well as skilled in using technology, it is necessary to hold a training activity for teachers to design and implement HOTS-oriented learning by utilizing Edmodo as a technology-based teaching medium. The training consisted of material reinforcement (the use of Edmodo, HOTS-oriented learning, preparation of HOTS questions) and workshops (teachers were guided in creating accounts and utilizing the features available in Edmodo as well as developing HOTS-oriented learning modules). The participants were able to inform more about the HOTS after training and about 18 teachers agreed that the participants had been able to construct their learning. In addition, there was also an increase in the ability of teachers to use Edmodo as a technology-based learning media. Of the 22 questionnaires submitted, 14 participants agreed that their understanding of Edmodo had increased and more than 90% of the training participants had been able to understand HOTS-based learning. In addition, although not many participants understand the use of Edmodo, the percentage was still above 50%. In general, 90% of the training participants believe that HOTS-based learning can be applied to the Edmodo as a learning medium.
CONSUMER PERCEPTION TOWARDS E-LEARNING WITH SPECIAL REFERENCE TO ONLINE COURSES PROVIDING COMPANIES AT BANGALORE CITY

Exam preparation, reskilling and certification, casual learning, higher education language, and primary and secondary supplemental education are some of the areasthat make up the online education sector. With a predicted CAGR of 46.48 percent, the market for online primary and secondary supplemental education would increase in value from INR 11.99 billion in 2018 to INR 123.65 billion by 2024. A shift in consumer behaviour toward detailed learning and an increase in demand from tier IIand tier III cities are what are fuelling the growth of this industry. From 2019 to 2024, the market for online reskilling and certification is projected to expand at a CAGR of 36.95 percent, reaching INR 93.81 Bn. The growing skill gap among workers brought on by the growing business environment is driving up the demand for reskilling programmes. The market for online higher education is expected to increase at a CAGR of 40.74 percent from 2019 to 2024 to reach INR 40.63 billion, from an estimated value of INR 5.01 billion in 2018. Because the traditional educational system cannot meet the demands of the growing population, students are turning to online higher education courses. This paper throws light on the importance of online education, and percentage of people relying on online education and their dependency on the various courses and the features of it in particular. Keywords: E-learning, Online, Certifications, Programmes.
Symmetric, Asymmetric and Hash Functions

The field of cryptography offers numerous methods for transmitting data securely through networks. It usually builds and evaluates several protocols that deal with the numerous facets of information security, such as confidentiality, integrity, etc. Modern cryptography includes a number of engineering and scientific areas. Everyday uses of cryptography include things like computer passwords, ATM cards, and electronic commerce. The term "cryptography" refers to a method of protecting data from unauthorized parties by converting readable and stable information into an unknowable form that can be deciphered on the other end to obtain the needed information using the decoding method supplied by the message's creator. Keywords: Hash function, encryption, symmetric key, and cryptography
Correlation and projection analysis between total population and total electrical energy consumption in Province of Bangka Belitung Island

Abstract This study aims to analyze the projected total population and its correlation to the level of electricity consumption in the Province of Bangka Belitung Islands. The correlation of total population to the level of electrical energy consumption and its projections are very important as basic information in designing sustainable regional development, especially in the energy and economic sectors. The time series data used in this study starts from 2007 and ends in 2021 with a projected value starting from 2022 to 2030. The projection method used is the exponential smoothing method with the results of model fitting that the Holt’s Exponential Smoothing model is the best model with the smallest Root Mean Square Error (RMSE), R Squared and Mean Absolute Percentage Error (MAPE) values among other models, namely 15381,641 for the RMSE value, 0.979 for R Squared value and 0.936 for the MAPE value. The projection results obtained that the Total Population and Total Electrical Energy Consumption variables in the Province of Bangka Belitung Islands have a significant increase namely 0.20% for total percentage increase and there was a strong correlation between the two variables with a significance value of less than 0.05 and a Pearson Correlation value of 0,792.
Interaction of Environmental Uncertainty, Characteristics of Management Accounting Information Systems and Decentralization on Managerial Performance

This study aims to examine the effect of the characteristics of management accounting information systems and decentralization on managerial performance with environmental uncertainty as a moderating variable. This research is a type of quantitative research, the data used using primary data and data collection using a questionnaire to obtain information from respondents, namely middle managers who work in manufacturing companies in the feedmill sector in Banten Province. The analytical method used is Structural Equation Model (SEM) with Partial Least Square (PLS) Path Modeling with SmartPLS 3.0 analysis tool. The results of this study are 1) The effect of the characteristics of the management accounting information system on managerial performance, a) Broadscope has an effect on managerial performance, b) Timeliness has no effect on managerial performance, c) Aggregation has no effect on managerial performance, d) Integration has no effect on managerial performance, 2) Decentralization has an effect on managerial performance, 3) The effect of environmental uncertainty moderates the relationship between the characteristics of management accounting information systems on managerial performance, a) Environmental uncertainty does not moderate the relationship between broad- scope management accounting information characteristics and managerial performance, b) Environmental uncertainty does not moderate the relationship between characteristics of management accounting information timeliness with managerial performance, c) Environmental uncertainty does not moderate the relationship between the characteristics of aggregation management accounting information with managerial performance, d) Uncertainty environmental assurance does not moderate the relationship between the characteristics of integrated management accounting information and managerial performance, 4) Environmental uncertainty moderates the relationship between decentralization and managerial performance
Human Rights and Artificial Intelligence

Artificial intelligence (AI) is developing every day with a new vision and mission in this competitive world. It is an area of computer science that creates intelligent machines that are programmed to work and act like humans. It involves them solving problems and improving themselves. It is expected that they will eventually be able to mimic and perform the same tasks as a human would. As artificial intelligence continues to find its way into our daily lives, its propensity to interfere with human rights only gets more severe. With this in mind and noting that the technology is still in its infant stages, Access Now conducts this preliminary study to scope the potential range of human rights issues that may be raised today or in the near future.
Optimization of Cr(VI) Adsorption on Eugenol-Silica Gel Composites Using Behnken Box Design

This study aims to find the optimum conditions for the adsorption of Cr (VI) ion to the eugenol-silica gel composite as an adsorbent and to determine the adsorption parameters using the Langmuir and Freundlich isotherm model. The three optimization parameters analyzed were pH, contact time, and weight of the adsorbent of the eugenol-silica gel composite. The optimization experiment was designed using the Box Behnken Design Response Surface Methodology (RSM) on the MINITAB software. Then, the maximum adsorption capacity was evaluated using the Langmuir and Freundlich isotherm equation model. The results showed that the optimum conditions for the adsorption process were the weight of the eugenol-silica gel composite adsorbent of 0.15 grams, contact time of 35 minutes and pH of 5. The adsorption process under these conditions was favorable because the RL obtained was 0.0297. The maximum absorption capacity of Cr (VI) was indicated by the qm value of 1.0328 mg/g, and the KF, KL, and n values ​​observed 0.7961, 1.0885 and 14.0056, respectively. Adsorption of Cr(VI) ion by eugenol-silica gel composite followed the Langmuir isotherm equation.
Application of classifier based on DRNG algorithm

With the advent of the era of big data, semi-supervised learning algorithms are becoming more and more popular and widely used. Self-training is the most widely used semi-supervised learning framework. The performance of the classifier obtained by self-training mainly depends on the selection of high-confidence samples during the self-training process. To study the effect of the base classifier on the performance of the DRNG algorithm, we use KNN, decision tree, and SVM base classifier to conduct experiments on four medical images. The experimental results show on the most datasets, the classification performance of the DRNG algorithm on the KNN (K=1) base classifier is higher than the other three base classifiers. The DRNG algorithm matches the KNN(K=1) base classifier better.
Character Extraction and Character Type Identification from Summarised Story Plots

Identifying the characters from free-form text and understanding the roles and relationships between them is an evolving area of research. They have a wide range of applications, from summarising narrations to understanding the social network from social media tweets, which can help in automation and improve the experience of AI systems like chatbots and much more. The aim of this research is twofold. Firstly, we aim to develop an effective method of extracting characters from a story summary, to develop a set of relevant features, then, using supervised learning algorithms, to identify the character types. Secondly, we aim to examine the efficacy of unsupervised learning algorithms in type identification, as it is challenging to find a dataset with a predetermined list of characters, roles, and relationships that are essential for supervised learning. To do so, we used summary plots of fictional stories to experiment and evaluate our approach. Our character extraction approach successfully improved on the performance reported by existing work, with an average F1-score of 0.86. Supervised learning algorithms successfully identified the character types and achieved an overall average F1-score of 0.94. However, the clustering algorithms identified more than three clusters, indicating that more research is needed to improve their efficacy.
A Knowledge-Based Model for Polarity Shifters

Polarity shifting can be considered one of the most challenging problems in the context of Sentiment Analysis. Polarity shifters, also known as contextual valence shifters (Polanyi and Zaenen 2004), are treated as linguistic contextual items that can increase, reduce or neutralise the prior polarity of a word called focus included in an opinion. The automatic detection of such items enhances the performance and accuracy of computational systems for opinion mining, but this challenge remains open, mainly for languages other than English. From a symbolic approach, we aim to advance in the automatic processing of the polarity shifters that affect the opinions expressed on tweets, both in English and Spanish. To this end, we describe a novel knowledge-based model to deal with three dimensions of contextual shifters: negation, quantification, and modality (or irrealis).
Differential neural networks prediction using slow and fast hybrid learning: application to prognosis of infections and deaths of covid-19 dynamics

Abstract This essay discusses a potential method for predicting the behavior of various physical processes and uses the COVID-19 outbreak to demonstrate its applicability. This study assumes that the current data set reflects the output of a dynamic system that is governed by a nonlinear ordinary differential equation. This dynamic system may be described by a Differential Neural Network (DNN) with time-varying weights matrix parameters. A new hybrid learning scheme based on the decomposition of the signal to be predicted. The decomposition considers the slow and fast components of the signal which is more natural to signals such as the ones corresponding to the number of infected and deceased patients who suffered of COVID 2019 sickness. The paper results demonstrate the recommended method offers competitive performance (70 days of COVID prediction) in comparison to similar studies.
DEVELOPMENT OF CREATIVE THINKING OF PRIMARY SCHOOL STUDENTS

The article deals with the development of children's creative thinking in elementary school. The role of the teacher in the formation of the creative abilities of primary school students is described. The methods of interaction between the teacher and students for the development of creative potential are revealed.
Different approaches to visualising trends using Covid-19 data: a study comparing cumulative line graphs with non-cumulative line graphs

In the past years the public was frequently informed about the current status of the covid-19 pandemic through visualisations. Daily numbers of infected, positively tested and deceased persons were provided either in cumulative charts or non-cumulative charts. In this paper, we study which data visualisation technique works better for informing the general public. We find clear evidence that non-cumulative visualisations work better.
Analisis Prioritas Penanganan Banjir Kali Konto, Provinsi Jawa Timur

Flood in Kali Konto is a disastrous hazard which strikes a lot of housing, infrastructures and paddy fields along its river banks almost every year. In 2021, along with destroyed revertment at roolagh 70 area, flood inundated more than 10 villages in Kabupaten Jombang and several more in Malang and Kediri. However, flood management at Kali Konto needs special consideration because several conditions, include watershed area pattern, extremely slope condition changing, numerous materials from Kelud mountain eruption in several part of Konto river, and a lot of infrastructures which laid in Konto river. This paper discussed about appropriate infrastructures which derived from hydrology and hydraulics modeling for Q2 and Q50 flood return periods. Since the model resulted 49 locations, Analytical Hierarchy Process (AHP) model used for determining priority for scaling down flood hazard in Kali Konto. The most priority comes to Karang Tengah, Blaru and Bugasur Kedaleman area based on the most losses occurred.
Learning from Self-Discrepancy via Multiple Co-teaching for Cross-Domain Person Re-Identification

Employing clustering strategy to assign unlabeled target images with pseudo labels has become a trend for person re-identification (re-ID) algorithms in domain adaptation. A potential limitation of these clustering-based methods is that they always tend to introduce noisy labels, which will undoubtedly hamper the performance of our re-ID system. To handle this limitation, an intuitive solution is to utilize collaborative training to purify the pseudo label quality. However, there exists a challenge that the complementarity of two networks, which inevitably share a high similarity, becomes weakened gradually as training process goes on; worse still, these approaches typically ignore to consider the self-discrepancy of intra-class relations. To address this issue, in this paper, we propose a multiple co-teaching framework for domain adaptive person re-ID, opening up a promising direction about self-discrepancy problem under unsupervised condition. On top of that, a mean-teaching mechanism is leveraged to enlarge the difference and discover more complementary features. Comprehensive experiments conducted on several large-scale datasets show that our method achieves competitive performance compared with the state-of-the-arts.
A Training-Based Mutual Information Lower Bound for Large-Scale Systems

We provide a mutual information lower bound that can be used to analyze the effect of training in models with unknown parameters. For large-scale systems, we show that this bound can be calculated using the difference between two derivatives of a conditional entropy function. The bound does not require explicit estimation of the unknown parameters. We provide a step-by-step process for computing the bound, and provide an example application. A comparison with known classical mutual information bounds is provided.
Current Situation and Optimized Suggestions for English Translation of Linguistic Landscape in Fulai Mountain Geopark, Shandong Province, China

In order to understand the overall situation of the English translation of the linguistic landscape and sort out the problems in the English translation of the linguistic landscape, this study tries to carry out a field investigation on the current situation of the English translation of linguistic landscapes in the Fulai Mountain Geopark from the perspective of the current situation of the English translation of linguistic landscapes in domestic tourist attractions .The results show five kinds of errors in the English translation of the linguistic landscape in the Fulai Mountain Geopark, such as Chinglish, pragmatic failure, punctuation, missing translation, and spelling error.There are three reasons for the errors in the English translation of linguistic landscape in Fulai Mountain Geopark.Firstly, the administrative department of the scenic spot does not pay enough attention to linguistic landscapes; secondly, the lack of standardization in the design and production of linguistic landscape; thirdly, the lack of English professionals in the construction of linguistic landscape.In the future, the administrative department of the scenic spot should attach importance to the value of the linguistic landscape, formulate the standards for linguistic l andscape construction, and conduct regular surveys and updates of the linguistic landscape.The authors also hope to awaken the sense of responsibility of the management department of Fulai Mountain Geopark through this work, make them realize the important value of the linguistic landscape, strengthen the landscape supervision and management ability, and enhance the popularity and reputation of Fulai Mountain Geopark.
The Construction of Robot Experiment Teaching Course for the Cultivation of Outstanding Talents in Intelligent Manufacturing

With the continuous progress of science and technology, intelligent manufacturing has become one of the main directions of industrial development, and training outstanding talents for it has become a historical mission that relevant universities must complete.As an important basis for realizing intelligent manufacturing, robot learning and application naturally become a key link in the cultivation of outstanding talents in intelligent manufacturing.On the basis of theoretical learning, the construction of robot experimental teaching courses can truly cultivate outstanding talents with practical application ability.This paper takes the construction of "Autonomous Navigation and Obstacle Avoiding Mobile Robot System Design Experiment" as an example to analyze how to build a student centered, interest oriented,
Language between Modularity and Subsystems as Self-Contained Linguistic Components: Wh-Words as a Case Study

Modern linguistics has always witnessed the linguists' tendencies to break down language into different modules or subsystems because they are self-contained and domain free having their own specific characteristics.In fact, all the natural languages evoke a variety of sub-systems, which are highly functional, operational, demarkable, identifiable, teachable, learnable, and highly pragmatic; the fact that helps to make of them an integral part of the students' linguistic knowledge.However, the focus of this study is on the Wh-words as a sub-system through which we tried to answer the question about our purpose behind their analysis by using a linguistic corpus based on three novels.The analysis is done by being based on four linguistic factors, the form, the meaning, the structure, and the pragmatic use, a long side with the textual context of these terms and their exploration in terms of the semantic field they evoke in different contexts.
An Analysis of the Supervised Learning Approach for Online Fraud Detection

Illegal online financial transactions are now more sophisticated and global in scope, which costs both parties—customers and businesses. For fraud prevention and detection in the online setting, many different strategies have been proposed. While all of these techniques aim to detect and stop fraudulent online transactions, they differ in terms of their features, advantages, and disadvantages. This study assesses the current fraud detection research in this area to detect the employed algorithms and assessing in accordance with predetermined standards. The systematic quantitative literature review methodology was used to assess the research studies in the subject of online fraud detection. A hierarchical typology is created based on the supervised learning methods in scientific articles and their properties. Therefore, by integrating three selection criteria—accuracy, coverage, and costs—our research presents the best methods for identifying fraud in a novel approach. Index Terms : Detection, Online fraud, Online transaction, Supervised Learning Algorithm.