Reviewerly/exHarmony huggingface dataset:
Reinforcement Learning Page Prediction for Hierarchically Ordered Municipal Websites

Public websites offer information on a variety of topics and services and are accessed by users with varying skills to browse the kind of electronic document repositories. However, the complex website structure and diversity of web browsing behavior create a challenging task for click prediction. This paper presents the results of a novel reinforcement learning approach to model user browsing patterns in a hierarchically ordered municipal website. We study how accurate predictor the browsing history is, when the target pages are not immediate next pages pointed by hyperlinks, but appear a number of levels down the hierarchy. We compare traditional type of baseline classifiers’ performance against our reinforcement learning-based training algorithm.
Visualizing Probability Distributions Across Bivariate Cyclic Temporal Granularities

Deconstructing a time index into time granularities can assist in exploration and automated analysis of large temporal datasets. This article describes classes of time deconstructions using linear and cyclic time granularities. Linear granularities respect the linear progression of time such as hours, days, weeks and months. Cyclic granularities can be circular such as hour-of-the-day, quasi-circular such as day-of-the-month, and aperiodic such as public holidays. The hierarchical structure of granularities creates a nested ordering: hour-of-the-day and second-of-the-minute are single-order-up. Hour-of-the-week is multiple-order-up, because it passes over day-of-the-week. Methods are provided for creating all possible granularities for a time index. A recommendation algorithm provides an indication whether a pair of granularities can be meaningfully examined together (a “harmony”), or when they cannot (a “clash”). Time granularities can be used to create data visualizations to explore for periodicities, associations and anomalies. The granularities form categorical variables (ordered or unordered) which induce groupings of the observations. Assuming a numeric response variable, the resulting graphics are then displays of distributions compared across combinations of categorical variables. The methods implemented in the open source R package gravitas are consistent with a tidy workflow, with probability distributions examined using the range of graphics available in ggplot2. Supplementary files for this article are available online.
Multi-Model Ensemble-Based Fault Prediction of Telecommunication Networks

Fault prediction is the critical method to ensure the stability and reliability of the whole communication networks. This paper proposes an ensemble model combining traditional algorithms and deep neural networks with multiple functions of feature combination, text learning, local feature extraction, and sample imbalance processing for fault prediction according to the characteristics of data with attribute relevance, imbalance, and text type. The experiments validate the proposed method can effectively improve fault recognition by 4–9%.
Revealing the Human Direction of Managers’ Vocational Training at Bryansk State Technical University

The problem of training managerial personnel at a technical university during the integration of the humanitarian, economic and technological components of education is considered. The proposed approach allows training economists-managers of the industrial production possessing a high level of professional competence, economic culture, humanistic views, who are able to carry out highly effective activities in the field of creating and functioning the competitive organizations. The results of the sociological studies carried out among the students of the economic direction, the profile “Economics and running the Enterprise” at Bryansk State Technical University aimed at finding the humanitarian interests, the ways to improve the process of teaching humanitarian disciplines are presented. The findings of the sociological study are a good basis for seeking ways to improve the humanitarian training of the production managers, for forming the humanistic orientation of the student’s personality, for using the most powerful tools to maintain the students’ motivation.
Denoising and Motion Artifact Removal Using Deformable Kernel Prediction Neural Network for Color-Intensified CMOS

Image intensifiers are used internationally as advanced military night-vision devices. They have better imaging performance in low-light-level conditions than CMOS/CCD. The intensified CMOS (ICMOS) was developed to satisfy the digital demand of image intensifiers. In order to make the ICMOS capable of color imaging in low-light-level conditions, a liquid-crystal tunable filter based color imaging ICMOS was developed. Due to the time-division color imaging scheme, motion artifacts may be introduced when a moving target is in the scene. To solve this problem, a deformable kernel prediction neural network (DKPNN) is proposed for joint denoising and motion artifact removal, and a data generation method which generates images with color-channel motion artifacts is also proposed to train the DKPNN. The results show that, compared with other denoising methods, the proposed DKPNN performed better both on generated noisy data and on real noisy data. Therefore, the proposed DKPNN is more suitable for color ICMOS denoising and motion artifact removal. A new exploration was made for low-light-level color imaging schemes.
Analysis of price correlation of two kinds of graded funds

With the growth of economy and the improvement of per capita income, investment activities have attracted more and more attention. As an investment tool, classified funds have also been developing continuously. Because its sub funds have different returns and risks, which can meet the different needs of investors, it is very important to strengthen investors' understanding of graded funds. This paper takes all existing classified funds in China as the object of empirical test, from January 1, 2016 to December 1,2016, to analyze the correlation between class Ashare price and class B share price. The empirical test results show that the price of class A shares and class B shares is low correlation. Therefore, when the price of class A shares goes up or down, investors cannot infer whether the price of class B shares will rise or fall according to the price changes of class A shares, so as to obtain more profits.
Research on the Reform of College Public English Teaching Management Based on Computer Network

Abstract Many universities offer public English courses. Today’s society requires the continuous improvement of the level of English talents. The structure of Public English teaching management needs to be reconstructed. The teaching system of College English should be constantly strengthened and reformed. In order to meet the needs of today’s social development, universities should also strive to cultivate applied English talents[7]. With the continuous progress of network technology, the research on the reform of Public English teaching management based on network has become a hot topic for scholars. This paper describes the characteristics of English Teaching under the network environment. Finally, this paper mainly describes the main strategies of teaching management reform.
Content Based E Mail Classification

Electronic Mail (E-mail) has established a significant place in information user’s life. Mails are used as a major and important mode of information sharing because emails are faster and effective way of communication. Email plays its important role of communication in both personal and professional aspects of one’s life. The rapid increase in the number of account holders from last few decades and the increase in the volume of mails have generated various serious issues too. The content base mail classification can be classified into four ways namely Private, Public, Newsletter, and Anonymous. Every user has the right to choose their keyword (a semi-private password). Those contacts who know the user’s keyword will be classified as private contacts and those users who are unknown them classified anonymous contacts. A contact can be classified as public or private, upon verification of an anonymous contact. Any newsletter or group mails are classified into newsletter contacts. It is highly likely that the rests are junk mail or spam. In this project, a spam detector to identify an email as either spam or ham is built using n-gram analysis. The system involves the classification of mails based on user’s contacts. This way any mail from a contact whom the user knows very well is being displayed.
Bibliometric of Semantic Enrichment

Much research has been done on the Semantic Web. Semantic enrichment is one branch of this field that has gotten much attention recently. This study aims to use bibliometrics to look at the current state of research in this field. Bibliometrics is the study of scientific sources' bibliographic data, which can be used to assess the current state of a field. Various bibliometric analyses were performed after extracting the metadata required for bibliometry from the Scopus database. According to the study's findings, more articles in this field have been published since 2018, and they have recently received special attention. Ontology/semantics/semantic web/semantic enrichment are also becoming more important keywords. Furthermore, based on the countries that published the articles, the United States, Germany, the United Kingdom, France, Italy, and Brazil published the most in this field. The article goes on to provide more analysis and illustrations, which will be helpful to researchers in this field.
The Dudeney–Stockmeyer Conjecture

Consider a variant of the famous Tower of Hanoi problem where moves of the discs are allowed along the edges of a connected simple graph D whose vertices represent the vertical pegs of the puzzle. The task is to transfer a tower of n∈N discs from one peg to another according to the well-known rules of the classical game corresponding to D≅K3. Apart from its recreational appeal, the problem brings about fascinating integer sequences engendered by the respective minimum number of moves, i.e. the distance in the canonical metric of the state graph HDn. We construct an algorithm which solves the task for all cases but the exceptional instance of the Linear Tower of Hanoi (i.e. where D is a path graph) in which the transfer is between the two end vertices of D. Optimality of this algorithm is claimed in our central conjecture which is supported by large-scale computational explorations.
Delphi research on the textbook authorization system for elementary Korean language textbook

The aim of this study is to examine the validity of textbook authorization system for elementary Korean language textbook. To this end, Delphi surveys were conducted with 30 experts. The first Delphi survey consisted of questions related to the current common standards, the characteristics of Korean language education, and problems with the textbook authorization system. Based on the results of the first survey, the 2nd Delphi survey was organized mainly on the problems that could arise when the system was switched to the textbook authorization system, including the timing of the transition of the textbook publication system, and matters to be considered in relation to the selection and development of textbooks, and their solutions. As a result, it was confirmed that experts negatively perceived the role of the government publication system in relation to the autonomy and diversity of interpretation of the curriculum and educational neutrality. In addition, in relation to the competency development of the Korean language and the effectiveness of teaching and learning using various materials, the application of various and creative teaching and learning methods and evaluations, and the individualized evaluation of learners, the textbook authorization system develops better textbooks than the government publication system. In addition, experts say that if the system of textbook publication is switch to authorization system, it should be changed immediately from the next revised curriculum. However, despite the validity of such conversion, many experts pointed out problems related to the selection of textbooks at a school and over/underinterpretation of the curriculum may occur. As a solution, specific measures related to the provision of textbook guide (publicity) materials, curriculum explanatory materials, and development of publication standards and authorization standards were proposed.
A Survey of Class Imbalance Problem on Evolving Data Stream

Learning from data streams with both online class imbalance and concept drift (OCI-CD) is receiving much attention in today's world. Due to this problem, the performance is affected for the current models that learn from both stationary as well as non-stationary environments. In the case of non-stationary environments, due to the imbalance, it is hard to spot the concept drift using conventional drift detection methods that aim at tracking the change detection based on the learner's performance. There is limited work on the combined problem from imbalanced evolving streams both from stationary and non-stationary environments. Here the data may be evolved with complete labels or with only limited labels. This chapter's main emphasis is to provide different methods for the purpose of resolving the issue of class imbalance in emerging streams, which involves changing and unchanging environments with supervised and availability of limited labels.
Multiple Face Recognition Using Self-adaptive Differential Evolution and ORB

Face recognition (FR) applications have been intensively studied in the areas of Computer Vision specially with the wide use of bio-metrics as a method of security access. Hence, working on still images with multiple faces (SIMF) is still a challenge due to the diversity of features that may be present in the images and different conditions from which the images are usually captured. In addition, FR algorithms may not perform well under uncontrolled conditions due to the illumination variation and other related image conditions. Hence robust approachesshould be developed to perform well under complex conditions. Based on this context, in the present work, we proposed a new approach for recognizing faces in SIMF obtained under different conditions using an optimization algorithm with feature extraction method based on interest points (IP). Our algorithm is constructed using the Self Adaptive Differential Evolution (SaDE) algorithm and ORB, well-known key point detector and descriptor. A specific base of SIMF obtained under different illumination conditions, rotation, scale, and occlusion is used to validate the algorithm. The recognition process is conducted with the aid of optimization algorithm searching for a possible best match of a template face image (TFI) in the SIMF. During the search process, numerous target cut images (TCI) from SIMF are evaluated using descriptor distance measure. According to the experiment results, it can be observed the present approach is suitable for real-world FR applications with complex conditions.
An Overview of Crowd Counting on Traditional and CNN-based Approaches

Crowd counting, which is one of the primary research lines of computer vision, has achieved significant advancement due to the affordable computational cost, and it is a particularly useful means of ensuring public security in public places, especially in crowded places. In recent years, crowd counting methods based on public places have emerged one after another given crowd congestion. Firstly, this paper introduces in detail from traditional approaches to deep learning approaches, focusing on crowd counting approaches based on the convolution neural network (CNN), and compares and analyzes the advantages and disadvantages of each approach. Next, this paper summarizes the commonly used datasets and the main indicators of evaluating crowd counting algorithms. Finally, this paper expounds on the challenges existing in the field of crowd counting and the possible research directions in the future.
The Impact of Note-Taking on the Learning Process in Intelligent Tutoring System Tutomat

Abstract The research investigates how note-taking practice affects the learning process in Tutomat, an intelligent tutoring system. The complete analysis includes (i) the identification of learning analytics variables to describe student-Tutomat interaction; (ii) the description of experimental student groups using learning analytics variables; (iii) data-driven clustering and (iv) the comparison of the experimental groups and revealed clusters. The results show that there is a difference in how a student interacts with Tutomat based on note-taking practice. It is revealed that the note-taking practice can be detected using the proposed learning analytics variables with the prediction accuracy of the clustering approach of 85 %.
Mimicking Learning for 1-NN Classifiers

We consider the problem of mimicking the behavior of the nearest neighbor algorithm with an unknown distance measure. Our goal is, in particular, to design and update a learning set so that two NN algorithms with various distance functions $$\rho _{p}$$ and $$\rho _{q}$$ , $$0<p,q<\infty ,$$ classify in the same way, and to approximate the behavior of one classifier by the other. The autism disorder-related motivation of the problem is presented.
Aspects of Requirements Elaboration to Basic Competences of a Personality in the Digital Economy Environment

The rapid development of digital economy influences the society in general and ordinary life of a human particularly. Rapid changes are challenges for the system of education. They course the problem of elaborating the system for basic competences of a personality that would provide person with a possibility of fast adaptation to social and economic changes and also their successful socialization, professionalization and well-being under the conditions of digital economy. The article focuses on methods that allow constructing of a possible relevant model of requirements to basic competences of a personality in the environment of digital economy in the system of the adaptive control of professional education quality – the educational framework of qualifications. The proposed methods for determining the content and levels of competencies have been successfully tested in the development of requirements and evaluation of the results of formal education. The article is aimed at researchers who are dealing with the problem of adaptive control of the quality of human resources and with results of formal and informal training.
Mini Jarvis Patrick -Based Graph Clustering for Scientific Institutions

The competition between scientific institutions is increased every day. Every institution tends to improve its reputation by producing and publishing high-quality scientific research. Clustering and evaluating the educational institutions are important for professors, policymakers, as well as students. This research aims to develop a Jarvis-Patrick algorithm for scientific institutions clustering, which is one of the graph-based techniques. It suffers from the problem of a large number of clusters. In addition to the Shared Nearest Neighbor (SNN) similarity included in the standard Mini Jarvis-Patrick (MJP) algorithm, the merging clusters of low separation are proposed to improve algorithm performance. The SNN similarity measures the number of shared neighbors between every two points in the data. Besides that, the merging is implemented by combining the clusters that have low separation. The proposed algorithm takes advantage of cluster validity measures (separation) to produce rational and reasonable clusters. The SciVal dataset for USA scientific institutions 2016-2018 dataset is used. The proposed MJP detected 8 clusters (Cluster0 %6, Cluster16%, Cluster2 6%, Cluster3 2%, Cluster4 7%, Cluster5 7.3%, Cluster6 26.6%, Cluster7 32%). In addition to the standard MJP, the proposed technique is compared with known methods; the cobweb, DBSCAN, and HierarchicalClusterer. The results have proved that the MJP is superior to other methods.
K-Means Algorithm for Clustering Third-party Funds of Conventional Banking

Abstract Banking business activities in Indonesia are very important for Indonesia’s economic growth, one of which is by growing the investment sector through obtaining funds from the general public via third party funds. In this study, the third party funds discussed were third party funds from conventional banking. The purpose of this study is to group the Third party funds of Conventional Banking to obtain information on the development of Indonesia’s economic growth, especially in conventional banking using the K-means. Research data in the form of conventional banking third party funds were obtained from the Financial Services Authority. The data are divided into 2 clusters: C1 (high) and C2 (low). Results show that 5 years have a low cluster (C1) and 6 years have a high cluster (C2). The findings of the analysis come in the form of information on the grouping of national banking third party funds to provide input to the Government of Indonesia, the Financial Services Authority, and Bank Indonesia in making policies on national banking.
Development and Application of Sports Video Analysis Platform in Sports Training

Abstract The sports video analysis platform can accurately extract the training parameters of the trainer, build a model with personality differences based on the actual training actions, conduct an in-depth analysis of the training parameters, and fully display the standard degree of the trainer’s actions in the analysis results. Trainers and coaches can have a comprehensive and intuitive understanding of the problems in sports training through sports videos and analysis results. Coaches can adjust their training plans based on the analysis results. Trainers can make corrections to sports actions, thereby promoting the quality of sports training. constantly improving. This article mainly elaborates the development of the sports video analysis platform in sports training from the three aspects of hardware structure, function and target tracking model. At the same time, simple performance tests of the platform are carried out, and then the platform is applied to common long jump, race walking, badminton and gymnastics, I hope to have a certain reference significance for the effective development and promotion of sports video analysis platform.
Review of Imbalanced Data Classification and Approaches Relating to Real-Time Applications

In today's era, multifarious data mining applications deal with leading challenges of handling imbalanced data classification and its impact on performance metrics. There is the presence of skewed data distribution in an ample range of existent time applications which engrossed the attention of researchers. Fraud detection in finance, disease diagnosis in medical applications, oil spill detection, pilfering in electricity, anomaly detection and intrusion detection in security, and other real-time applications constitute uneven data distribution. Data imbalance affects classification performance metrics and upturns the error rate. These leading challenges prompted researchers to investigate imbalanced data applications and related machine learning approaches. The intent of this research work is to review a wide variety of imbalanced data applications of skewed data distribution as binary class data unevenness and multiclass data disproportion, the problem encounters, the variety of approaches to resolve the data imbalance, and possible open research areas.