Reviewerly/exHarmony huggingface dataset:
An Approach to Developing Mathematical Software of On-Board Helicopter Flight Simulator Decision Support System

Abstract The work explores methods and develops aids to improve a pilot flight training level using Full Flight Simulators. It examines their didactic advantages over real helicopters by implementing on-board intelligent decision support systems (IDSS). These simulation programs monitor actions of learner, find the best ways to correct errors and generate appropriate instructions. The training is carried out by reference point methods while the helicopter flight dynamics model simulates a flight and helps an instructor in parallel. Sufficiently simple and accurate model of helicopter flight dynamics was selected and tested to assess the proposed methods and aids. Our analysis introduces a functional structure of IDSS as an adaptive control system with a reference model. We further conducted a study on developing models, methods and means of automatic analysis, forecasting, optimization and correction of actions. Combining single-criteria conditional and vector optimization methods, we found out optimal flight parameters. The research findings revealed that the chosen optimization method requires too many system resources for the optimal solution to be found in a short time. The study thereby comes up with applying ANNs to solve this problem.
Extracurricular activities for rural children: accessibility, content, forms

Additional education of children is currently one of the priority areas for the development of educational organizations. The availability of additional education for children and youth is one of the important factors in ensuring the social integrity and stability of the development of each region and the country as a whole. Accessibility of additional education is characterized, first of all, by the scale of participation of children and youth in additional education programs. This article examines the features of rural children's participation in additional education: coverage in general, at school and outside of school by various forms of educational programs. The article provides an analysis of various parameters of the involvement of children living in rural areas in additional education. According to the data of the study, the involvement of children in rural areas in classes on additional general education programs implemented in anetwork form is less than in urban areas. The geography of the study is quite extensive: more than forty-five thousand students from fifty-one subjects of the Russian Federation took part in the survey. The article also analyzes the results of a survey of schoolchildren’s parents on the subject of satisfaction with the educational services received when mastering additional general development programs of all directions in educational organizations. The article describes the assessment of the participation of rural schoolchildren in additional education in the context of various directions, forms of participation, age and gender of children. Changes in the possibilities of obtaining additional education for children in rural areas within the framework of federal projects are discussed. The features of infrastructural and personnel support of additional educational programs implemented in rural areas are considered.
What Works and Doesn’t Work, A Deep Decoder for Neural Machine Translation

Deep learning has demonstrated performance advantages in a wide range of natural language processing tasks, including neural machine translation (NMT).Transformer NMT models are typically strengthened by deeper encoder layers, but deepening their decoder layers usually results in failure.In this paper, we first identify the cause of the failure of the deep decoder in the Transformer model.Inspired by this discovery, we then propose approaches to improving it, with respect to model structure and model training, to make the deep decoder practical in NMT.Specifically, with respect to model structure, we propose a cross-attention drop mechanism to allow the decoder layers to perform their own different roles, to reduce the difficulty of deep-decoder learning.For model training, we propose a collapse reducing training approach to improve the stability and effectiveness of deep-decoder training.We experimentally evaluated our proposed Transformer NMT model structure modification and novel training methods on several popular machine translation benchmarks.The results showed that deepening the NMT model by increasing the number of decoder layers successfully prevented the deepened decoder from degrading to an unconditional language model.In contrast to prior work on deepening an NMT model on the encoder, our method can deepen the model on both the encoder and decoder at the same time, resulting in a deeper model and improved performance.
A Proposed Fraud Detection Model based on e-Payments Attributes a Case Study in Egyptian e-Payment Gateway

As per Payfort's 2017 report, titled State of payments in the Arab world; Egypt had a 22% yearly increase in the overall volume of internet payments in 2016, which was assessed at $6.2 billion.e-Payments are the major point of life nowadays in Egypt and the whole world; with tens of e-payments companies in Egypt and more than 5 million transactions done every day and 60 billion EGP volume of payments in 2018.Online and mobile fraud was estimated at $10.7 billion in 2015, as per Juniper Research, and is expected to reach $25.6 billion by the end of the decade.As the whole e-payments business is affected by fraud, e-payments firms and their consumers lose a lot of money.On the other hand, one of the most powerful techniques that could be used for fraud predictive is data mining techniques such as the decision tree.This paper introduces a prediction model for managing the risk of fraud in the Egyptian e-payment market that helps to reduce the loss of money.This model is developed using a real dataset from one of Egypt's top epayment gateways based on the e-payment transaction attributes importance like transaction time, transaction amount, transaction limit, and transaction customer No. repetition limit.The importance of these attributes was determined using IBM SPSS modeler's decision tree and its predictors' importance.The model significantly assisted in the reduction of fraud cases by a very high rate, with an accuracy of 88.45% and a precision of 93.5% resulting in a savings of 101970.52EGP out of 131297.83EGP.
Automatic Scoring Model of Subjective Questions Based Text Similarity Fusion Model

Abstract AI In this era, scene based translation and intelligent word segmentation are not new technologies. However, there is still no good solution for long and complex Chinese semantic analysis. The subjective question scoring still relies on the teacher's manual marking. However, there are a large number of examinations, and the manual marking work is huge. At present, the labor cost is getting higher and higher, the traditional manual marking method can't meet the demand The demand for automatic marking is increasingly strong in modern society. At present, the automatic marking technology of objective questions has been very mature and widely used. However, by reasons of the complexity and the difficulty of natural language processing technology in Chinese text, there are still many shortcomings in subjective questions marking, such as not considering the impact of semantics, word order and other issues on scoring accuracy. The automatic scoring technology of subjective questions is a complex technology, involving pattern recognition, machine learning, natural language processing and other technologies. Good results have been seen in the calculation method-based deep learning and machine learning. The rapid development of NLP technology has brought a new breakthrough for subjective question scoring. We integrate two deep learning models based on the Siamese Network through bagging to ensure the accuracy of the results, the text similarity matching model based on the birth networks and the score point recognition model based on the named entity recognition method respectively. Combining with the framework of deep learning, we use the simulated manual scoring method to extract and match the score point sequence of students’ answers with standard answers. The score recognition model effectively improves the efficiency of model calculation and long text keyword matching. The loss value of the final training score recognition model is about 0.9, and the accuracy is 80.54%. The accuracy of the training text similarity matching model is 86.99%, and the fusion model is single. The scoring time is less than 0.8s, and the accuracy is 83.43%.
Bullyspoiler: Detect and Block Cyberbullying Tweet and Bully using Deep Learning

Social networking site is being hastily expanded in latest years, which provides platform to join human beings all over the global and share their hobbies. however, Social Networking sites is providing possibilities for cyberbullying activities. Cyberbullying is harassing or insulting a person through sending messages of hurting or threatening nature the usage of digital communication. Cyberbullying poses full-size chance to bodily and intellectual fitness of the victims. as a result, it's far crucial to reveal person's posts and clear out the detest speech related publish earlier than it's far spread. Detection of cyberbullying and the supply of next preventive measures are the main courses of movement to fight cyberbullying. This paper proposed a system for computerized detection and prevention cyberbullying thinking about the primary traits of cyberbullying such as intention to damage an character, repeatedly and over time and the use of abusive curl language or hate speech the use of LSTM set of rules. The proposed version is capable to stumble on hate content on Twitter robotically. This method is based on a bag of phrases and TFIDF (term frequency-inverse record frequency) technique. Those capabilities are used to educate deep mastering classifiers. The detest stage encapsulates the level of hate in a given virtual surroundings. We present methods to robotically decide the dislike stage, and block submit and users of the post with the aid of the utilising transfer mastering on pre-trained language fashions with annotated information to create automatic hate detectors.
Model-Based Algorithm Configuration with Adaptive Capping and Prior Distributions

Many advanced solving algorithms for constraint programming problems are highly configurable. The research area of algorithm configuration investigates ways of automatically configuring these solvers in the best manner possible. In this paper, we specifically focus on algorithm configuration in which the objective is to decrease the time it takes the solver to find an optimal solution. In this setting, adaptive capping is a popular technique which reduces the overall runtime of the search for good configurations by adaptively setting the solver’s timeout to the best runtime found so far. Additionally, sequential model-based optimization (SMBO)—in which one iteratively learns a surrogate model that can predict the runtime of unseen configurations—has proven to be a successful paradigm. Unfortunately, adaptive capping and SMBO have thus far remained incompatible, as in adaptive capping, one cannot observe the true runtime of runs that time out, precluding the typical use of SMBO. To marry adaptive capping and SMBO, we instead use SMBO to model the probability that a configuration will improve on the best runtime achieved so far, for which we propose several decomposed models. These models also allow defining prior probabilities for each hyperparameter. The experimental results show that our DeCaprio method speeds up hyperparameter search compared to random search and the seminal adaptive capping approach of ParamILS.
A Survey of Artificial Intelligence Techniques for User Perceptions’ Extraction from Social Media Data

Measuring and analyzing user perceptions and behaviors in order to make user-centric decisions has been a topic of research for a long time even before the invention of social media platforms. In the past, the main approaches for measuring user perceptions were conducting surveys, interviewing experts and collecting data through questionnaires. But the main challenge with these methods was that the extracted perceptions were only able to represent a small group of people and not whole public. This challenge was resolved when social media platforms like Twitter and Facebook were introduced and users started to share their perceptions about any product, topic, event using these platforms. As these platforms became popular, the amount of data being shared on these platforms started to grow exponentially and this growth led to another challenge of analyzing this huge amount of data to understand or measure user perceptions. Computational techniques are used to address the challenge. This paper briefly describes the artificial intelligence (AI) techniques, which is one of the types of computational techniques available for analyzing social media data. Along with brief information about the AI techniques, this paper also shows state-of-the-art studies which utilize the AI techniques for measuring user perceptions from the social media data.
Structure of a chain ring as a ring of matrices over a Galois ring

&lt;abstract&gt;&lt;p&gt;The structure of a finite chain ring has already been described by Wirt in 1972 and others later. The purpose of this article is to describe another structure of a finite chain ring as a ring of square matrices over Galois ring using the companion matrix of a certain Eisenstein polynomial over Galois ring. Such a companion matrix generates the unique maximal ideal of the corresponding matrix chain ring.&lt;/p&gt;&lt;/abstract&gt;
Development Of Natural Science Learning Media Based on Interactive Multimedia Power Point in Blood Circulation System Material Humans at Primary 5th SDN 101928 Rantau Panjang Academic Year 2020/2021

This research aims to 1) Develop interactive multimedia-based learning media power point, 2) know the feasibility of interactive multimedia-based learning media power point, and 3) know the effectiveness of interactive multimedia-based learning media power point in blood system materials in humans in Primary 5th SDN 101928 Rantau Panjang based on the results of field trials.Based on the results of research 1) interactive multimedia power points developed with 10 stages, namely potential problems, data collection, product design, design validation, design revision, product trials (small scale), product revisions, field trials (large scale), revisions, and mass products, 2) the results of expert validation of materials including excellent categories (3.7) the percentage of feasibility of 92.5% belongs to the category is very feasible, the average assessment of interactive multimedia-based learning media power points is declared "Very Good" (3.78) and "Very Decent" (94.6%).
Suum Cuique: Studying Bias in Taboo Detection with a Community Perspective

Prior research has discussed and illustrated the need to consider linguistic norms at the community level when studying taboo (hateful/offensive/toxic etc.) language.However, a methodology for doing so, that is firmly founded on community language norms is still largely absent.This can lead both to biases in taboo text classification and limitations in our understanding of the causes of bias.We propose a method to study bias in taboo classification and annotation where a community perspective is front and center.This is accomplished by using special classifiers tuned for each community's language.In essence, these classifiers represent community level language norms.We use these to study bias and find, for example, biases are largest against African Americans (7/10 datasets and all 3 classifiers examined).In contrast to previous papers we also study other communities and find, for example, strong biases against South Asians.In a small scale user study we illustrate our key idea which is that common utterances, i.e., those with high alignment scores with a community (community classifier confidence scores) are unlikely to be regarded taboo.Annotators who are community members contradict taboo classification decisions and annotations in a majority of instances.This paper is a significant step toward reducing false positive taboo decisions that over time harm minority communities.⋆ This paper examines taboo language as a case study.The reader is cautioned that the paper contains strong language.
Transnational online education and digital inequality: Issues of pedagogical design

Digitalization of education is commonly seen as a process that contributes to societal progress rather than as a factor that gives rise to the digital divide in society. This paper therefore considers how the digital divide can impact students and their learning in transnational online education which has been in greater demand since the onset of the pandemic. Methodologically, the paper draws on the digital inequality stack model showing how each level of the stack — physical access to information and communication technologies; digital skills and competency; purposes and motivations for technology use — can influence learning experiences in distance online education. This paper contributes to the literature on digital inequalities in higher education in the following three ways: by further articulating the levels and aspects of digital inequalities in the context of online learning; by demonstrating how pedagogical design can contribute to redressing some of the challenges experienced by students as a result of digital inequalities; and by offering a practical model of pedagogical design in the form of a checklist that helps identify and mitigate the effects of digital inequalities on online learning. The paper also makes some critical remarks about the concept of the digital divide that are relevant to the development and implementation of online programs and courses. This paper may be of interest to education providers planning to participate in transnational online education.
Darkness can not drive out darkness: Investigating Bias in Hate SpeechDetection Models

It has become crucial to develop tools for automated hate speech and abuse detection.These tools would help to stop the bullies and the haters and provide a safer environment for individuals especially from marginalized groups to freely express themselves.However, recent research shows that machine learning models are biased and they might make the right decisions for the wrong reasons.In this thesis, I set out to understand the performance of hate speech and abuse detection models and the different biases that could influence them.I show that hate speech and abuse detection models are not only subject to social bias but also to other types of bias that have not been explored before.Finally, I investigate the causal effect of the social and intersectional bias on the performance and unfairness of hate speech detection models.
Commissives Speech Act of Vehicle’s Sales’ Advertisement on Electronic Media and Implementation of Indonesian Teaching Material in Junior High School

The problem in this study is how the discourse of the commissives speech act implemented in vehicle's sales' advertisement on electronic media and how to deliver existing advertisements of vehicle's sales on electronic media.The purpose of this study is to describe the actions of commissives in the discourse of vehicle's sales' advertisement on electronic media and the implicit and also explicit meaning of vehicle's sales' advertisement on electronic media.Thus, this study will improve language skills in serving advertisements.The method of the study uses a qualitative descriptive approach with data analysis studies.Data are in the form of speech act which has a discourse analysis in vehicle's sales' advertisement on electronic media that related to the actions of the commissives.The technique used in this study is through listening and recording techniques obtained from Instagram by screen capturing (screenshot) or listening speech act in Instagram video.Based on the analysis of the commissives speech act, it can be found three categories, namely, the act of offering speech, the act of promise speech, and the act of vowing or swearing.For readers or buyers, the results of this study can be used as a reference to be more careful in responding to various advertisements on electronic media.It is because most advertisements use commissives language that is difficult to understand or less logical to be accepted.For further studies, although this study still has very limited discussion of the discourse in vehicle's sales' advertisement, it is expected that the next researchers can develop and focus the study on the field of advertisement.
Intelligent teaching integrated service platform based on Artificial Intelligence

Guided by the actual demand, focusing on the construction requirements of "intelligent education informatization", this paper constructs the innovative teaching mode of recording and broadcasting classroom according to the information center of colleges and universities and the teaching buildings and classrooms.The supporting environment of educational informatization can promote the great leap forward development of teaching level.Using the toplevel design method, relying on the cloud platform, further integrate the educational resources and applications of the whole school, create a technologically advanced, fair and open, resource rich, safe and reliable recording and broadcasting educational resource sharing platform, and realize the balance of education.
Developing Scientific Based Pop Up Media for Children Speaking Skill Development

Learning media is growing with many variations.However, learning media that are devoted to the development of speaking skills in children aged 5-6 years using a scientific approach are very limited.The innovation of learning media in Early Childhood Education should be encouraged.The purpose of this research is to produce a scientificbased Pop-Up Book to improve children's speaking skill.This research is a research and development (R&D) with aims to produce the pop up media that can be implemented for speaking activities.The data collection techniques used are observation, and questionnaires.Then data will be analyzed to determine the level of effectiveness, accuracy and attractiveness of the product.The result obtained from this research is the production of Pop-up Book media that can help improve child speaking skill.
Color and Texture Segmentation Using an Unified MRF Model

The color image segmentation problem has two main issues to be solved.The proper choice of a color model and the choice of an appropriate image model are the key issues in color image segmentation.In this work, Ohta ( ), , I I I is taken as the color model and different variants of Markov Random Field (MRF) models are proposed.In this regard, a Compound Markov Random Field (COMRF) model is porposed to take care of inter-color-plane and intra-color-plane interactions as well.In continuation to this model, a Constrained Compound Markov Random Field Model (CCOMRF) has been proposed to model the color images.The color image segmentation problem has been formulated in an unsupervised framework.The performance of the above proposed models has been compared with the standard MRF model and some of the state-of-the-art methods, and found to exhibit improved performance.