Reviewerly/exHarmony huggingface dataset:
Stochastic model and method of optimization of operating modes groups of artesian wells in water supply systems

Water supply of large cities is carried out, as a rule, from open reservoirs - lakes, reservoirs, large rivers, ie from sources with significant water reserves. In this case, the water supply scheme includes: pumping stations of the first rise, which take water directly from the water; water treatment system (purification); pumping stations of the second and third rise, pumping clean water over long distances and supplying it to settlements [1]. If the settlement is located in an area where there are no large open water sources, or the geographical location of the settlement allows the use of groundwater, in which case artesian wells are used as a source of water supply. Usually small settlements are supplied with water from artesian wells. Artesian wells are widely used for water supply of villages, industrial water supply, for irrigation (irrigation systems), in the mining industry, to reduce the level of groundwater. The advantage of using artesian wells to supply the population with drinking water is that the water is extracted from great depths and purified using natural filters [2]. Thus, the cost of water treatment is significantly reduced. The disadvantage of using artesian wells is usually low productivity of the well, the strong dependence of water volumes in the well on climatic and weather conditions, depletion of the well as a result of large-scale construction or extraction of natural minerals such as shale gas [3].
Stylistic approaches to predicting Reddit popularity in diglossia

Past work investigating what makes a Reddit post popular has indicated that style is a far better predictor than content, where posts conforming to a subreddit's community style are better received.However, what about diglossia, when there are two community styles?In Singapore, the basilect ('Singlish') co-exists with an acrolect (standard English), each with contrasting advantages of community identity and prestige respectively.In this paper, I apply stylistic approaches to predicting Reddit post scores in diglossia.Using data from the Singaporean and British subreddits, I show that while the acrolect's prestige attracts more upvotes, the most popular posts also draw on Singlish vocabulary to appeal to the community identity.
Product Review Translation: Parallel Corpus Creation and Robustness towards User-generated Noisy Text

Reviews written by the users for a particular product or service play an influencing role for the customers to make an informative decision.Although online e-commerce portals have immensely impacted our lives, available contents predominantly are in English language-often limiting its widespread usage.There is an exponential growth in the number of e-commerce users who are not proficient in English.Hence, there is a necessity to make these services available in non-English languages, especially in a multilingual country like India.This can be achieved by an in-domain robust machine translation (MT) system.However, the reviews written by the users pose unique challenges to MT, such as misspelled words, ungrammatical constructions, presence of colloquial terms, lack of resources such as in-domain parallel corpus etc.We address the above challenges by presenting an English-Hindi review domain parallel corpus.We train an English-to-Hindi neural machine translation (NMT) system to translate the product reviews available on e-commerce websites.By training the Transformer based NMT model over the generated data, we achieve a score of 33.26 BLEU points for English-to-Hindi translation.In order to make our NMT model robust enough to handle the noisy tokens in the reviews, we integrate a character based language model to generate word vectors and map the noisy tokens with their correct forms.Experiments on four language pairs, viz.English-Hindi, English-German, English-French, and English-Czech show the BLUE scores of 35.09, 28.91, 34.68 and 14.52 which are the improvements of 1.61, 1.05, 1.63 and 1.94, respectively, over the baseline.
Detection of Fake Indian Currency Notes using Deep Learning

A normal human being can easily see and distinguish any banknote, however doing the same job is extremely difficult for someone who is visually challenged or blind. Because money plays such an essential part in our everyday lives and is required for any commercial transaction, real-time detection and recognition of banknotes is a must for anyone who is blind or visually impaired. The mobilenet based CNN model-based Indian currency detection and identification system is presented for this purpose, and it is quick and accurate. To make the system more resilient, pictures of various denominations and situations were collected first, and then these images were supplemented with various geometric and image modifications. These augmented pictures are then manually tagged, and training and validation image sets are created from them. Later, the trained model's performance was assessed on a real-time scenario as well as a test dataset. The suggested mobile net model-based technique exhibits detection accuracy of 91.33% according to the test results. This standalone system operates in real-time.
A Performance Analysis of Face and Speech Recognition in the Video and Audio Stream using Machine Learning Classification Techniques

Biometric authentication is an emerging technology that utilizes biometric data for the purpose of person identification or recognition in security applications.A number of biometrics can be used in a person authentication system.Among the widely used biometrics, voice and face traits are most promising for pervasive application in every life, because they can be easily obtained using unobtrusive and user-friendly procedures.The low-cost audio and visual capture sensors on smart phones, laptops, and tablets has made the advantages of voice and face biometrics more outstanding compared with others.For quite a long time, the use of acoustic information alone has been a great success for speaker authentication applications.Meanwhile, the last decades or two also witnessed great advancement in face recognition technologies.Object detection and tracking is usually the first step in applications such as video surveillance.The static camera face recognition and tracking system's main purpose is to estimate the speed and distance parameters.We propose a general detection and tracking method for motion based on the visual system and using the image difference algorithm.Then recognize the person's voice to get feedback from the corresponding person.The process focuses on detecting people on stage and then completes the voice signal processing.We propose a new person recognition technology that uses face and voice fusion Compared to a single biometric recognition, and this technology can greatly improve the recognition speed.Development of security systems uses the Viola-Jones face recognition algorithm.The proposed method uses the Local Binary Pattern (LBP) as a function extraction technique to calculate local functions.Our project uses Mel Frequency Divergence Coefficient (MFCC) extraction technology for speech recognition.The extracted functions are used as input to the multi-SVM classifier to provide a gender to identify individuals and display the results.The new system can be used in various areas, such as identity verification and other potential commercial applications.
A Hybrid MultiLayer Perceptron Under-Sampling with Bagging Dealing with a Real-Life Imbalanced Rice Dataset

Classification algorithms have shown exceptional prediction results in the supervised learning area. These classification algorithms are not always efficient when it comes to real-life datasets due to class distributions. As a result, datasets for real-life applications are generally imbalanced. Several methods have been proposed to solve the problem of class imbalance. In this paper, we propose a hybrid method combining the preprocessing techniques and those of ensemble learning. The original training set is undersampled by evaluating the samples by stochastic measurement (SM) and then training these samples selected by Multilayer Perceptron to return a balanced training set. The MLPUS (Multilayer perceptron undersampling) balanced training set is aggregated using the bagging ensemble method. We applied our method to the real-life Niger_Rice dataset and forty-four other imbalanced datasets from the KEEL repository in this study. We also compared our method with six other existing methods in the literature, such as the MLP classifier on the original imbalance dataset, MLPUS, UnderBagging (combining random under-sampling and bagging), RUSBoost, SMOTEBagging (Synthetic Minority Oversampling Technique and bagging), SMOTEBoost. The results show that our method is competitive compared to other methods. The Niger_Rice real-life dataset results are 75.6, 0.73, 0.76, and 0.86, respectively, for accuracy, F-measure, G-mean, and ROC with our proposed method. In contrast, the MLP classifier on the original imbalance Niger_Rice dataset gives results 72.44, 0.82, 0.59, and 0.76 respectively for accuracy, F-measure, G-mean, and ROC.
Infrastructure as a code - on Demand Infrastructure

Infrastructure as code (IaC) is the devops process to automate every single need of the infrastructure to enable better deployment speed, secured, high scalability, automated backup and restores.IaC is about writing code that describes the infrastructure where resources can easily be created, destroyed, resized, replaced and moved.It is the process of scripting environments -from installing an operating system, to installing and configuring servers on instance, to configuring how the instances software communicate with one another, and much more.This paper discusses various tools and technology sets that can be used to attain an efficient infrastructure across all the industries ensuring the security using public and private clouds.Questions like how easily we can adapt, how to ensure security, how to choose the correct tools and technology set, deployment methods, benefits and implementations of the IaC, would be answered in this paper.Infrastructure as code (IaC) is the devops process to automate every single need of the infrastructure to enable better deployment speed, secured, high scalability, automated backup and restores.IaC is about writing code that describes the infrastructure where resources can easily be created, destroyed, resized, replaced and moved.It is the process of scripting environmentsfrom installing an operating system, to installing and configuring servers on instance, to configuring how the instances software communicate with one another, and much more.This paper discusses various tools and technology sets that can be used to attain an efficient infrastructure across all the industries ensuring the security using public and private clouds.Questions like how easily we can adapt, how to ensure security, how to choose the correct tools and technology set, deployment methods, benefits and implementations of the IaC, would be answered in this paper.
Research and extension completed and on-going registered programs database system (RECORDS)

The study designed and developed RECORDS (Research and Extension Completed and On-going Registered programs Database System which will serve as the central storage of data of SLSU Research and Extension offices. The development of the system used VB.Net as a programming language and MySQL as database of the system application to function properly. This research followed the process of developmental research. It was conducted during the school year 2019-2020 at SLSU Campus Lucena The developed software and data base system were evaluated by IT experts, selected members of Research and Extension Council and researchers and extensionists of SLSU. The parameters used in evaluating the developed system were functionality, reliability, usability, efficiency, maintainability, portability and security. The evaluation result showed that the 30 respondents highly accepted the system. Overall rating of the RECORDS evaluation got the average weighted mean of 3.79 with equivalent interpretation of highly acceptable. This implied that, the developed RECORDS (Research and Extension Completed and On-going Registered programs Database System) can be utilized by the SLSU Research and Extension Offices.
Customer Response towards QR Codes Used in Marketing: An African Perspective

Literature indicates QR code use in marketing is growing and needs attention. The objective of his research is to study QR codes use in marketing via accessibility of product information(PI), advertising(Ad), sales promotion(SP), billing and payment(BP) from African viewpoint. Correlation analysis paved way to factor analysis for data reduction. Both EFA and CFA were performed. Cronbach alpha and composite reliability were assessed for data reliability. Fit indices RMSEA, RFI, TLI and NFI were computed to check model fit. Regression slope was used for testing study hypothesis. Results found, customers use &amp;ldquo;product information, advertisements, sales promotion and billing &amp;amp; payment through QR codes&amp;rdquo; has significant positive influence on perceived usefulness(PUM). Further, customers use of marketing QR codes proved perceived usefulness having significant positive influence on intention to use (IUM) in future course of time. &amp;ldquo;Tesco Homeplus&amp;rdquo; at global level succeeded in QR codes implementation for marketing activities is discussed and referred in the conclusions. Since respondents are positive towards perceived usefulness and intention to use QR codes, and the cost of QR technology and implementation is very less and affordable, the onus relies on marketing decision makers to adopt and promote QR codes in marketing aggressively for benefiting companies, customers and to further elevate African markets on mobile technological front.
Zero-Shot Compositional Concept Learning

In this paper, we study the problem of recognizing compositional attribute-object concepts within the zero-shot learning (ZSL) framework.We propose an episode-based cross-attention (EpiCA) network which combines merits of cross-attention mechanism and episode-based training strategy to recognize novel compositional concepts.Firstly, EpiCA bases on cross-attention to correlate conceptvisual information and utilizes the gated pooling layer to build contextualized representations for both images and concepts.The updated representations are used for a more indepth multi-modal relevance calculation for concept recognition.Secondly, a two-phase episode training strategy, especially the transductive phase, is adopted to utilize unlabeled test examples to alleviate the low-resource learning problem.Experiments on two widelyused zero-shot compositional learning (ZSCL) benchmarks have demonstrated the effectiveness of the model compared with recent approaches on both conventional and generalized ZSCL settings.
Improved Local Search Based Grey Wolf Optimization for Feature Selection

These days’ technologies like machine learning have come into sight as promising areas of research, in which the system is trained using a dataset; that may comprise redundant and inappropriate features and may require more memory. Feature Selection aids to increase the accuracy during the process of classification while considering minimum number of features, that can be modeled as an optimization task. Currently, metaheuristic category of algorithms is being widely used by researchers to solve various optimization problems. In this context, this research proposes an improved variant of Grey Wolf optimization Algorithm using mutation based local search for getting into the bottom of the problem of selecting smallest set of features while increasing the accuracy. Moreover, the presented algorithm is weighed against several other algorithms for solving problem of selecting smallest set of features with increased accuracy.
Leadership Qualities Of A Teacher – A Factor Of Student Success

The article is devoted to topical issues of education modernization in the direction of the formation of a socially active and successful student personality.New time makes new requirements for the content and methods of educational and upbringing activities.Competitiveness and success in society means initiative, the ability to think creatively, find non-standard solutions, be able to choose a professional path and be ready to learn throughout life.A modern school should form, first of all, a self-confident personality, resistant to stress and troubles, able to cope with difficulties, to cope with anxiety, insecurity and fear.The presence of such qualities determines the setting of adequate socially and personally significant goals, planning activities and taking actions to achieve them.In this aspect, the leading role belongs to the personal qualities of the teacher, the style of his leadership, the ability to inspire students, lead, form motivation and interest in independent activity and self-development.In this aspect, the role of the development of the teacher's leadership qualities is considered as a factor in increasing the success of students -both in educational activities and in the social, cultural, and leisure sphere.The components of personal success and the components of the educational space that contribute to its development are highlighted.The main indicators and criteria for diagnosing the leadership qualities of teachers are indicated.Correlation analysis has been carried out, which showed the presence of a close relationship between the leadership qualities of a teacher and the level of student success.
Modelling, Simulation, and Implementing ROS for Autonomous Navigation of Tracked Robot

This paper presents autonomous navigation and its implementation based on Robotic Operating System (ROS) for a nonholonomic autonomous tracked robot, whereas it handles a full design, implementation, dynamic modeling, and kinematic modeling of the robot.This paper applies robot localization using Adaptive Monte Carlo Localization (AMCL) which uses a particle filter to track the pose of a robot against a known map.It also uses mapping based on the mapping package.It provides laser-based SLAM (Simultaneous Localization and Mapping) mapping.The LIDAR is used to create a 2-D occupancy grid map.Path planning is established using the Dijkstra algorithm to plan a path to a goal position, using a persistent map created by the robot during the mapping process.Also, the robot was equipped with a camera module for image processing and detection of different objects for security purposes.The proposed system shows its capability for adaptation to road segments of different curvatures and the transitions between them.
English Trainee Teachers’ Perspective on Soft Skills Development in Ukraine

The integration of soft skills in training pre-service teachers ensures higher academic performance and provides them with better career opportunities. However, many university teachers pay insufficient attention to developing soft skills in higher education. The article aims to investigate the correlation between the soft skills included in professional teacher training and undergraduates’ viewpoint on soft skills development at university. The authors formulated three research questions: (a) How do English trainee teachers assess their soft skills? (b) How much attention, in students’ opinion, do teachers pay to developing soft skills? (c) Is there a gap between teachers’ work on developing students’ soft skills and trainee teachers’ expectations? To answer these questions, the researchers analized the empirical data received through a questionnaire. The questionnaire was completed by 153 English trainee teachers studying at H.S. Skovoroda Kharkiv National Pedagogical University, Ukraine. The respondents answered two close-ended questions regarding the level of soft skills they believe to possess and the amount of time devoted to soft skills development. The findings suggest that most students consider the level of their soft skills intermediate or high. Nevertheless, a significant number of the respondents still feel that teachers pay insufficient or hardly any attention to improving these skills. It is necessary to conduct further research to analyze university teachers’ viewpoint on improving students’ soft skills and organize the development of soft skills which would meet trainee teachers’ expectations.
Perception of teaching performance in the virtual learning environment

&lt;span&gt;This article aims to analyze the perception of the students of the professional school of business administration about the teaching performance in the virtual learning environment during the COVID-19 pandemic. When developing the research, it was determined that there is a good teaching performance, according to the perception of the students. However, it was identified that the factors of domain of the topic and class session management, present a better perception in this context of virtual learning; while the didactic strategies factor is the one that presents a not so favorable perception. In addition, through a comparative analysis, it was determined that the didactic strategies and planning factors have suffered a negative variation with respect to the perception per teacher, when moving from face-to-face to virtual learning, since, of the total of 17 specialty teachers, seven of them one negative variation. These results are validated by the linear regression test, where an R2 value of 0.965 is calculated, with respect to the didactic strategies factor. It means that this factor influences 96.5% on the perception of students with the global factor and an R2 value of 0.921 for the planning factor, which indicates an influence of 92.1%.&lt;/span&gt;
Automatic Control System for Swallow's Nest Cultivation Using the Internet of Things

In swallow nest cultivation, swallow breeders still have difficulty in tropical climates, one of which is maintaining the temperature and humidity in the swallow nest room to be stable and to know the value of swallow population. When the temperature is hot, the swallow room will become dry which impacts on the nest is damaged and the swallow is not comfortable living there. Therefore, a system that can be easily managed by swallow farmers is made so it can help reduce hot temperature and lack of humidity in the swallow nest room by using a sprayer that works automatically. In this system, there are temperature and humidity detection module which functions to detect the temperature valueand humidity when the room is hot / dry, the detected value will be processed by the microcontroller so that it gives an active sprayer command if the room temperature is hot, while the swallow detection module that uses infrared sends the movement value if there is a moving object. The value of the system will be sent to a cloud database which will be conveyed via the farmer wallet smartphone application. The accuracy level of the infrared detector sensor module and the air humidity sensor is very good, so that it can control the temperature conditions in the swallow room which is 27 degrees C and a stable humidity at a value of 80%. The stability of temperature and humidity produces swallow nests which have high selling value.