Reviewerly/exHarmony huggingface dataset:
Reform and counter-reform of mathematics education in the USSR in 1960-1970

Introduction. In the 1960s and 1970s, a secondary school in the USSR became involved in the international mainstream for the reform of mathematics education. Both the content of mathematics education and its ideology and structure were subject to change. Today, the historical events of the last 50 years require a retrospective view. The relevance of the study is determined by the need to rethink the reform of mathematics education of the 1960s and 1970s in the new conditions of radical changes and modernization of the modern education system. The purpose of the study is to restore the objective history of the reform and counter-reform of mathematics education in the USSR, identify the prerequisites for the reform, analyze the reasons for its failure and identify the positive methodological aspects of transformations. Research methods. More than 100 sources were used, including documents from the Archive of the Russian Academy of Sciences, textbooks on mathematics (pre-reform, reformist and post-reform textbooks), certificates of the participants of reform and counter-reforms. The leading methodological approaches included theoretical, philosophical, theoretical-mathematical and experimental-practical. Research results. The analysis of the facts made it possible to establish that the main motives for the reform in the USSR were, on the one hand, the international movement for the new content of school mathematics education based on the ideas of an international group of mathematicians who called themselves Bourbaki, and on the other, the excessive involvement of academic structures (mathematicians and academicians) in solving this issue. Results and discussion. The desire of the top leadership of the USSR not to lag behind the trends in science, education and technology in the world played a negative role in the field of Soviet mathematics education. The initiative in choosing the content of mathematics education “according to Western patterns” to academic workers and structures was erroneous, since the opinions of a community of thousands of teachers, practitioners, methodologists were not taken into account. According to the authors, another reason for the failure of the reform was the excessive involvement of academic structures. Conclusion. Historical experience shows that the greatest successes in education and science are achieved during the periods of stabilization. In the mathematics education of Russia, the stabilizing factor is textbooks written in the traditions of a systematic course, going back to the Elements of Euclid, which is a kind of cultural and historical standard. One of the main conclusions of the study is that the quality of training is closely related to the preservation of the domestic pedagogical tradition, its fundamental principles of learning (systematics and consistency).
Penerapan Elektronik Customer Relationship Management (E-CRM) Dalam Penjualan Roti Berbasis Web

UD Roti Ali is a home industry engaged in the production of bread making. The rapid development of technology in the world has had a major influence on the performance of bread making in all business fields, especially in the food manufacturing business. The business strategy of retaining old customers is more profitable than finding new customers, so it is necessary to maintain relationships with customers. UD Roti ali does not yet have a strategy to get new customers to find out information about products and prices. Currently UD Roti Ali does not yet have the right relationship with customers, one of which is the sales and ordering process is still done manually, where customers directly come to UD Roti Ali or call to place an order for products, then recording customer order data is still done by recording in the memorandum, so errors often occur in recording customer order data. This affects the bakery business, so UD Roti Ali needs an electronic system using the CRM method to build and manage the right relationship between UD Roti Ali and its customers. Therefore, UD Roti Ali needs a special strategy regarding proper customer service and product marketing in electronic form using a web-based CRM (E-CRM) method which is useful for making it easier for UD Roti Ali and customers, for UD Roti Ali it is easier to manage sales data and customer data, while for customers it is easier for customers to find out information about products, make it easier to make orders, payments, as well as the discount/promo and bonus features provided by UD Roti Ali to customers. So that this web-based system can help UD Roti Ali in maintaining customer loyalty.
Concept, Orientation and System Mechanism: The Construction of On-Campus Training Base from the Essence of Vocational Education

Taking Beijing Vocational College of Social Management as an example, and according to the essence analysis of vocational education, this paper analyzes the concept, functional orientation and institutional mechanism of the construction of on-campus training base.And this paper also points that the construction of the on-campus training base should adhere to the principle of social service and resource advantage allocation.The on-campus training base should serve the practical training teaching and social services.
KNN Based Peak-LOF for Outlier Detection

Data mining is a concept of getting relevant data from massive databases. Nowadays, there is a lack of Prediction of disease is increased predominantly. Hence, we need to remove the inaccurate data that has been spread over in the dataset, which leads to inaccurately predicting disease. Outliers are more prone to prediction mechanisms. This paper includes the detection of outliers in disease prediction mechanisms. The goal of this paper is to bring out exact data instead of getting invalid data by finding outliers and handling them with care. Outliers can be detected using a KNN based peak-LOF based approach with manhattan distance metrics. In this paper, a proper study on outlier detection mechanisms has been done.
Aspect-Sentiment-Guided Opinion Summarization for User Need Elicitation From Online Reviews

Abstract Extracting and analyzing informative user opinion from large-scale online reviews is a key success factor in product design processes. However, user reviews are naturally unstructured, noisy, and verbose. Recent advances in abstractive text summrization provide an unprecedented opportunity to systematically generate summaries of user opinions to facilitate need finding for designers. Yet, two main gaps in the state-of-the-art opinion summarization methods limit their applicability to the product design domain. First is the lack of capabilities to guide the generative process with respect to various product aspects and user sentiments (e.g., polarity, subjectivity), and the second gap is the lack of annotated training datasets for supervised learning. This paper tackles these gaps by (1) devising an efficient and scalable methodology for abstractive opinion summarization from online reviews guided by aspects terms and sentiment polarities, and (2) automatically generating a reusable synthetic training dataset that captures various degrees of granularity and polarity. The methodology contributes a multi-instance pooling model with aspect and sentiment information integrated (MAS), a synthetic data assembled using the results of the MAS model, and a fine-tuned pretrained sequence-to-sequence model “T5” for summary generation. Numerical experiments are conducted on a large dataset scraped from a major e-commerce retail store for sneakers to demonstrate the performance, feasibility, and potentials of the developed methodology. Several directions are provided for future exploration in the area of automated opinion summarization for user-centered product design.
Analysis of Internal Process Innovations with Emphasis on the Introduction of Technologies Associated with Industry 4.0 and the Digitalization

The paper contains new knowledge about the expansion of technologies associated with Industry 4.0 and digitalization in selected companies including metallurgical companies in the Czech Republic and evaluates the intensity of business process innovation with a focus on internal processes implemented in companies.A probe was implemented which was purposefully focused on the analysis of statistical data on innovation activities in innovative companies including metallurgical companies in the Czech Republic in the period under study by.The processing of statistical data obtained by the CZSO by a survey made it possible to obtain more detailed overviews of the relative frequencies of enterprises that implemented individual types of process innovations in the period under study and that have introduced some of the elements or tools of Industry 4.0 and the digitalization.The importance of innovation for the continued existence and development of the company is recognized especially by large companies and companies under foreign control.Many of these companies are already applying technologies associated with Industry 4.0 and the digitalization and they are moving towards the creation of a digital enterprise.On the contrary, the implementation of innovation activities and the use of technologies associated with Industry 4.0 in small and medium-sized companies is low.The main obstacle is insufficient funding for new technologies and human resources.
Debias the Black-Box: A Fair Ranking Framework via Knowledge Distillation

Deep neural networks can capture the intricate interaction history information between queries and documents, because of their many complicated nonlinear units, allowing them to provide correct search recommendations. However, service providers frequently face more complex obstacles in real-world circumstances, such as deployment cost constraints and fairness requirements. Knowledge distillation, which transfers the knowledge of a well-trained complex model (teacher) to a simple model (student), has been proposed to alleviate the former concern, but the best current distillation methods focus only on how to make the student model imitate the predictions of the teacher model. To better facilitate the application of deep models, we propose a fair information retrieval framework based on knowledge distillation. This framework can improve the exposure-based fairness of models while considerably decreasing model size. Our extensive experiments on three huge datasets show that our proposed framework can reduce the model size to a minimum of 1% of its original size while maintaining its black-box state. It also improves fairness performance by 15%–46% while keeping a high level of recommendation effectiveness.
AN IOT-BASED WEARABLE FALL DETECTION SYSTEM AND ALERTING SYSTEM

Seamless monitoring is one of the major challenges for human community because of increasing the strength of sixty years old people and occurrence of many accidents in falls-risk related works like construction, manufacture and others. Wearable IoT devices are useful for continuous health monitoring, analyzing the behaviours, and fall detection. Falls are one of the foremost causes for injuries and death in our life. Present work proposes the human movement monitoring by using the pressure sensor for avoiding human falls and also the health report prepared from historical data’s uploaded in cloud by support-vector machine (SVM) algorithm. The proposed wearable IoT device for seamless monitoring system is proving 99% accuracy, less complex and compatible to use over other IoT wearable devices.
THE STUDY OF LITERACY CULTURE THROUGH ANDROID-BASED WEBTOON APPLICATION AS AN EFFORT TO IMPROVE ENGLISH CAPABILITY OF HIGH SCHOOL STUDENTS IN PANDEMIC TIMES

Webtoon is a type of comic currently in demand by young people (students). If it is used properly, webtoon can be used as one of the teaching media in learning English. In addition to improve English competence, the efforts to foster literacy culture in students are grown here. With the development of android-based mobile phones that are easy to use by students, researchers combine android-based application with webtoon learning media to improve students' English competence and literacy. The problem in this research is how to increase reading interest in high school students through android-based interactive learning media during the pandemic. This study aims to develop android-based interactive learning media through webtoon application as an effort to improve the English language skills of high school students through literacy culture during the pandemic. This study also aims to foster reading interest in high school students through smartphone technology. The subjects of this study were high school students. The research method used is triangulation method which includes data triangulation, inter-researcher triangulation and theoretical triangulation. The data collection techniques used were interview, observation and documentation which were analyzed with descriptive qualitative analysis techniques. The results showed that the respondents' English skills and reading interest increased with the use of the webtoon application. Based on the results of the study, it can be concluded that the use of android-based webtoon application makes students not only learn the existing material, but can also increase their reading interest.
Implementasi Jaringan Saraf Tiruan Dalam Prediksi Penjualan Kue pada UD. Mak Kembar Pematang Siantar Dengan Backpropagation

UD.Mak Kembar is a shop engaged in the sale of cakes and bread, located in Rambung Merah. Until now, UD. Mak Kembar only uses a simple system in marketing information and recording, causing delays in marketing information and in recording sales calculations. Based on the problems above, the authors design and create a system at UD.Mak Kembar. This system applies predictions in its sales, and is built using an artificial neural network with the Backprogation algorithm. The results obtained with the best architecture 4-5-1 with an iterative process (epoch) during training with an epoch value = 75% and SSE achievement at the time of testing with SSE = 0.23950.
Sufficient conditions for the existence of equilibrium in the form of Hamilton-Jacobi equations

The article discusses the application of the theory of optimal control for solving Hamilton-Jacob equations with phase constraints. A method for constructing generalized solutions using optimal control problems is proposed. The results and analysis of numerical experiments, conditions for the existence of equilibrium situations in noncooperative differential games of several persons, namely the conditions for the existence of equilibrium situations in noncooperative differential games of several persons, defining the action according to Hamilton, are stated. Necessary conditions in the form of Hamilton-Jacobi equations are obtained. Game theory as an applied mathematical theory is used to understand and explain the mechanisms that are used when people make decisions. The theory contributes to the functioning of the logic of strategic planning and the relationship between individuals. Game theory as a method of applied mathematics is used for behavioral studies in various conditions, and helps understand the behavior of economic agents. The theory has many applications and can be used in different areas such as: strategy games, administration, economics and artificial intelligence research. The article describes a mathematical method for studying optimal situations in game theory.
LA3: Efficient Label-Aware AutoAugment

Automated augmentation is an emerging and effective technique to search for data augmentation policies to improve generalizability of deep neural network training. Most existing work focuses on constructing a unified policy applicable to all data samples in a given dataset, without considering sample or class variations. In this paper, we propose a novel two-stage data augmentation algorithm, named Label-Aware AutoAugment (LA3), which takes advantage of the label information, and learns augmentation policies separately for samples of different labels. LA3 consists of two learning stages, where in the first stage, individual augmentation methods are evaluated and ranked for each label via Bayesian Optimization aided by a neural predictor, which allows us to identify effective augmentation techniques for each label under a low search cost. And in the second stage, a composite augmentation policy is constructed out of a selection of effective as well as complementary augmentations, which produces significant performance boost and can be easily deployed in typical model training. Extensive experiments demonstrate that LA3 achieves excellent performance matching or surpassing existing methods on CIFAR-10 and CIFAR-100, and achieves a new state-of-the-art ImageNet accuracy of $$79.97\%$$ on ResNet-50 among auto-augmentation methods, while maintaining a low computational cost.
Hand Gesture Classification on Praxis Dataset: Trading Accuracy for Expense

In this paper, we investigate hand gesture classifiers that rely upon the abstracted 'skeletal' data recorded using the RGB-Depth sensor.We focus on 'skeletal' data represented by the body joint coordinates, from the Praxis dataset.The PRAXIS dataset contains recordings of patients with cortical pathologies such as Alzheimer's disease, performing a Praxis test under the direction of a clinician.In this paper, we propose hand gesture classifiers that are more effective with the PRAXIS dataset than previously proposed models.Body joint data offers a compressed form of data that can be analyzed specifically for hand gesture recognition.Using a combination of windowing techniques with deep learning architecture such as a Recurrent Neural Network (RNN), we achieved an overall accuracy of 70.8% using only body joint data.In addition, we investigated a long-short-term-memory (LSTM) to extract and analyze the movement of the joints through time to recognize the hand gestures being performed and achieved a gesture recognition rate of 74.3% and 67.3% for static and dynamic gestures, respectively.The proposed approach contributed to the task of developing an automated, accurate, and inexpensive approach to diagnosing cortical pathologies for multiple healthcare applications.
Adaptive Learning System (ALS) using Fuzzy Logic and K-means

E-learning is increasing in popularity due to its accessibility and reduction in spatial and/or temporal boundaries.Notwithstanding these benefits, e-learning platforms must consider the student's level of commitment, the delivery and assessment of course materials, along with the underlying pedagogical approach.It is of paramount importance to use this information in order to evaluate the quality of teaching and modify the way that the information is presented to the students.To tackle these challenges, an automated adaptive learning system is proposed according to the selection concepts of a customised learning path using integrated the Fuzzy Logic based classifier and the K-means clustering algorithm.This paper presents the architecture for an integrated Fuzzy Logic and K-means clustering adaptive learning system which allows learning paths to be adapted to the learner, according to the evaluation criteria set by the teacher.
KORELASI KEBIASAAN MEMBACA TERHADAP KEMAMPUAN MEMBACA PEMAHAMAN PADA SISWA KELAS VII SMP NEGERI 1 MAMBI

This study aims to determine the relationship between reading habits and the ability to read grade VII students of SMP N 1 Mambi. The analytical method used in this study is a quantitative research method with a correlational design. The population in this study were all students of SMP N 1 Mambi, the sample of this study was class VII students with a total of 57 students. The instruments used are test sheets and questionnaires. Data collection techniques in this study are test and non-test techniques—data analysis techniques, namely descriptive statistics and inferential statistics parametric inferential Pearson correlation. The descriptive statistical analysis of reading habits results showed that the highest score was 92.50 and the lowest score was 54.17. Then the total number obtained is 4254.13, and the average is 74.6339. The results of descriptive statistical analysis of reading comprehension ability showed the highest score obtained was 89.29 and the lowest score was 25.00. Then the total number obtained is 3510.68, and the average is 61.5909. shows that when the Pearson Correlation is -0.120 and sig. (2-tailed) is 0.347. Because of sig. (2-tailed) is higher than (0.05) then it can be stated that there is no relationship between reading habits and reading comprehension.
Minat Baca Masyarakat saat Pandemi Covid-19 di Taman Baca Masyarakat MIZAN

This research is a quantitative descriptive, where the problem in this writing is How is the Use of the Mizan Community Reading Park as a source of learning in increasing thecommunity's interest in reading in the Mannuruki urban village of Makassar ?. Thepurpose of writing is to describe the use of community reading parks as a source oflearning in increasing public interest in reading. The approach used in this paper isquantitative with quantitative descriptive research type, the population in this study is allvisitors to the Mizan community reading park as many as 480 people, the sample istaken ten percent of the existing population of 48 people, data collection techniques usingquestionnaire and technique techniques documentation, and data managementtechniques using descriptive this shows that many people who use the Mizan CommunityReading Park in the midst of a global pandemic that hit Indonesia.
CT-DA: A Knowledge Extraction Method for Cultural Industry Big Data

Knowledge extraction is the core work of constructing a knowledge graph, but most knowledge extraction methods assume perfect data support. Therefore, this paper analyzes the characteristics of big data in the cultural industry. In addition to the consensus characteristics of big data, these data also highlight the features of sectors such as low resources and intense data boundary fuzziness. Therefore, this paper proposes a knowledge extraction method for cultural industry data (CT-DA). Firstly, design a labeling strategy for big data in the cultural industry. Secondly, according to the low resource characteristics of data, create the counter transfer learning layer to realize resource transfer. Considering the intense fuzziness of data, design the dynamic attention mechanism layer for learning the critical attention of entities in the cultural field. Finally, build an experimental platform. The experiments show that this method has performance advantages in accuracy, recall, and F1.
Graph Embedding Augmented Skill Rating System

This article presents a framework for learning player embeddings in competitive games and events. Players and their win–loss relationships are modeled as a skill gap graph, which is an undirected weighted graph. The player embeddings are learned from the graph using a random walk-based graph embedding method and can reflect the relative skill levels among players. Embeddings are low-dimensional vector representations that can be conveniently applied to subsequent tasks while still preserving the topological relationships in a graph. In the latter part of this article, graphical Elo (GElo) is introduced as an application of player embeddings when rating player skills. GElo is an extension of the classic Elo rating system. It constructs a skill gap graph based on player match histories and learns player embeddings from it. Afterward, the rating scores that were calculated by Elo are adjusted according to player activeness and cosine similarities among player embeddings. GElo can be executed offline and in parallel, and it is nonintrusive to existing rating systems. Experiments on public datasets show that GElo makes a more reliable evaluation of player skill levels than vanilla Elo. The experimental results suggest potential applications of player embeddings in competitive games and events.