Reviewerly/exHarmony huggingface dataset:
A Robust approach based on fractional variable-order Total Variation and anisotropic diffusion for Poisson image restoration

Abstract In this work, we propose a novel approach for Poisson image denoising based on a fractional variable-order total variation functional combined with an anisotropic diffusion operator. Poisson noise is particularly challenging to remove since it depends heavily on the image intensity. Due to the region geometry in images, efficient regularization terms capable of preserving piecewise smoothness in details and features such as Total Variation technique are necessary. The proposed model is discretized, and the Alternating Direction Method of Multipliers algorithm is used to split our optimization variables. We conduct various numerical experiments to demonstrate the superiority of our proposed approach over state-of-the-art methods in Poisson image denoising.
An Active-Set-Based Recursive Approach for Solving Convex Isotonic Regression with Generalized Order Restrictions

This paper studies the convex isotonic regression with generalized order restrictions induced by a directed tree. The proposed model covers various intriguing optimization problems with shape or order restrictions, including the generalized nearly isotonic optimization and the total variation on a tree. Inspired by the success of the pool-adjacent-violator algorithm and its active-set interpretation, we propose an active-set-based recursive approach for solving the underlying model. Unlike the brute-force approach that traverses an exponential number of possible active-set combinations, our algorithm has a polynomial time computational complexity under mild assumptions.
THE METHOD OF SCIENTIFIC MODELLING AND THE LIMITS OF ITS APPLICATION IN THE RESEARCH OF THE MIND

This article can be considered as an attempt to investigate the complex problem of the epistemic essence and typologization of models in science, as well as an attempt to determine the scope and boundaries of the modeling method in the research of the human psyche. The solutions to these issues led the author of the article to believe that the methodological analysis of the development of models in various sciences is far from complete and will continue in the coming years. The author connects the prospects of such an analysis with the logical clarification of a number of concepts and approaches related to the nature of models in science and the modeling method in general. A separate part of the article is devoted to the genesis of the idea of modeling the functions of consciousness. In particular, in this context, the article analyzes behaviorism, the models of which do not meet the requirements of the principle of completeness. A special place is also given to the idea of computer modeling of consciousness and its justification in the form of functionalism in psychology. The spread of functionalism in the article is associated with the use of the concept of machine modeling of rational functions proposed by A. Turing and his followers. At the same time, the author of the article dwells on the critical analysis of functionalism from the standpoint of emergent naturalism of J. Searle. In this context, it is shown that the creation of effective computer programs in itself is not a sufficient basis for the absolutization of the functionalism approach. The main argument of this article is aimed at substantiating the fact that any attempt to create a strong AI cannot be successful, since the internal causal relationship between the human brain and its psyche remains unclear. Thus, the application of functionalism should be accompanied by an understanding of its scope and boundaries.
OPENING REMARKS TO THE READERS OF THE SPECIAL ISSUE “BALTIC JOURNAL OF LEGAL AND SOCIAL SCIENCES”

OPENING REMARKS TO THE READERS OF THE SPECIAL ISSUE “BALTIC JOURNAL OF LEGAL AND SOCIAL SCIENCES”
“Managerization” of school education: towards formulation of the problem

Introduction. The article attempts to combine seemingly disparate and isolated social problems in the institutions of school education in a single conceptual matrix. Namely, to find in the generalized trend of decreasing the quality of general education signs of the implementation of the installations of such a managerial ideology as managerialism. The stated conceptual matrix, justified as adequate as a hypothesis, will become the theoretical basis for conducting new, including empirical studies. Methods. As a methodology, the concept of state managerialism is used by A.V. Dyatlov, V.V. Kovalev, Ya.A. Aslanov, as well as the institutional ideas of D. North. The methods for research are the analysis of normative documents, thematic academic literature and reviews of the observation. Results and its discussion. In the reviewed scientific literature, thematically similar to the problem posed by us, gaps have been identified. The latter can be conceptually eliminated when considering the social phenomena and management practices studied by the authors within the framework of the concept of state managerialism. A review of the normative document regulating the activities of school management is proposed, as well as a primary analysis of such a method of checking the effectiveness of teachers and schools as All-Russian knowledge testing programs. And as a result, both there and there were signs that allow us to attribute the existing strategies of school education management to the implementation of the ideology of managerialism.
RISK FACTOR SIMULATOR MODEL AFFECTING TODDLER DENTAL AND ORAL HEALTH

The period five years in the child's development stage is the golden age, which is a golden period in the period of child growth and development. During this period, everything that is poured out and absorbed by the child will become a basis and a sharp memory for the child. Matters related to dental health, if during this golden age children have formed memories, behaviors, habits and attitudes about how to care for teeth and mouth, then this attitude of life will be carried over later in adulthood, so that knowledge about how to live a clean and healthy life, including maintaining health Teeth need to be implanted in infancy. The purpose of this study was to design a risk factor simulator model that affects the oral and dental health of toddlers. This research method uses research and development methods (Research and Development (RnD)). Product trials were carried out on kindergarten children with a total of 20 children. Knowledge after the intervention was carried out on students who got 16 (80%) students with good criteria. The trial use was carried out to parents of kindergarten children with a total of 20 respondents. Knowledge after the intervention on parents of students found 18 (90%) of students' parents had good criteria. The results of statistical tests using Wilcoxon to determine the significance of changes in the pre-post knowledge variable obtained sig values ​​of 0.000 and Z -3.742 which can be interpreted as meaningful results. The risk factor simulator model that affects oral and dental health in toddlers is effective in increasing the knowledge of preschool children (TK) and their parents.
Grid-Uniform Copulas and Rectangle Exchanges: Bayesian Model and Inference for a Rich Class of Copula Functions

Copula-based models provide a great deal of flexibility in modelling multivariate distributions, allowing for the specifications of models for the marginal distributions separately from the dependence structure (copula) that links them to form a joint distribution. Choosing a class of copula models is not a trivial task and its misspecification can lead to wrong conclusions. We introduce a novel class of grid-uniform copula functions, which is dense in the space of all continuous copula functions in a Hellinger sense. We propose a Bayesian model based on this class and develop an automatic Markov chain Monte Carlo algorithm for exploring the corresponding posterior distribution. The methodology is illustrated by means of simulated data and compared to the main existing approach.
Down-sampling from hierarchically structured corpus data

Resource constraints often require researchers to restrict their attention to a subset of the tokens returned by a corpus query. This paper sketches a methodology for down-sampling and offers a survey of current practices. The most prevalent approach, drawing a random sample from the list of corpus hits, has been shown to be inefficient if tokens are clustered by text file. We extend the evaluation of down-sampling designs to settings where tokens are also clustered by lexical item. Our case study, which deals with the replacement of third-person present-tense verb inflection -(e)th by -(e)s in Early Modern English, focuses on five predictors: time, gender, genre, frequency, and phonological context. Assuming we are able to analyze only 2,000 (out of 12,244) tokens, we compare two strategies for selecting a sub-sample of this size: simple down-sampling, where each hit has the same probability of being selected; and structured down-sampling, where this probability is inversely proportional to the author- and verb-specific token count. We form 500 sub-samples using each scheme and compare estimates based on mixed-effects logistic regression to a reference model fit to the full set of cases. We observe that structured down-sampling outperforms simple down-sampling on several evaluation criteria.
Advancing our understanding of dispersion measures in corpus research

While our inventory of dispersion measures is continuously growing, corpus linguists find little guidance for the choice among them. This paper offers a survey of recent corpus-based work, which shows that dispersion is typically measured across the text files in a corpus. Systematic insights into the behavior of measures in such distributional settings are currently lacking, however. After a thorough discussion of six prominent indices, we investigate their behavior on relevant frequency distributions, which are designed to mimic actual corpus data. Our evaluation considers different distributional settings, i.e. various combinations of frequency and dispersion values. The primary focus is on the response of measures to relatively high and low sub-frequencies, i.e. texts in which the item or structure of interest is over- or underrepresented (if not absent). We develop a simple method for constructing sensitivity profiles, which allow us to draw instructive comparisons among measures. We observe that these profiles vary considerably across distributional settings. While D and DP appear to show the most balanced response contours, our findings suggest that much work remains to be done to understand the performance of measures on items with normalized frequencies below 100 pmw.
PENGEMBANGAN LABORATORIUM STATISTIKA BERBASIS ANDROID

The importance of practicum as a forum for theoretical explanations has strong support in the knowledge transfer process, so it needs to be embodied in the learning process in a form that is easily accessible to students. The development of a statistics-based laboratory is one of the solutions to make it easier for students to access statistical software which is difficult and has a large capacity. The aim of developing an Android-based statistics laboratory is to implement statistical science and is supported by practicality and effectiveness tests for students and lecturers. The research method is carried out through five stages, namely assessment, design, development, implementation, and evaluation. The research samples were taken from two study programs (prodi) at two tertiary institutions in Ambon, namely the Statistics study program at Pattimura University and the Sharia Economics study program at the State Islamic Institute (IAIN) Ambon. The results showed that the Android-based Statistics Laboratory had a significant percentage of the feasibility, practicality and effectiveness of statistical practicum with a feasibility rating of 71%, practicality of 79% and 75% indicating the effectiveness of using the application.
A Long-term Prediction of Covid-19 by Building a Transition Probability Matrix

The outbreak of the covid-19 pandemic caused a devastating scenario in the human lives for a long period of time. Since the outbreak of the pandemic, many researchers have published papers on the prediction of covid-19 using machine learning techniques, artificial intelligence, statistical techniques such as regression analysis, Markov chain models etc. In this paper a transition probability matrix is introduced by building a first order Markov chain. This first order Markov chain is built by generating sequences from the data. Later with the help of Chapman Kolmogorov theorem and ergodic theorem, higher order transition probabilities and limiting probabilities are obtained.
A Study on Stress Management of Employees of IT Field with Reference to InfoPark, Kakkanad

Stress is a complex problem in the IT industry, and the underlying reasons are difficult to comprehend. This research is being conducted to investigate the elements that lead to stress among IT employees. The key stress factor has a direct impact on employee productivity at work. As a result, personnel should be aware of the factors that contribute to stress. Once the criteria are recognised, the task may be completed efficiently and successfully. Employee stress can be reduced, but it cannot always be avoided. As a result, the effective release strategy must be used in this investigation. Employees can lessen stress by utilizing an efficient release mechanism. Employees may devote more time to issue solving and work. There are several types of successful releasing methods. As a result of this discovery, it is critical to distinguish the ways that may be utilized to successfully reduce stress, which helps to improve productivity, create work harmony, and minimize stress factors experienced by employees. To study the effectiveness of stress management of employees of the IT field at Infopark by examining the employee perception of various stress management methods, and to identify the areas that needed to be improved and to suggest measures to improve conditions of stress levels of employees.
‘Experience Norfolk! Experience Fun!’ vs. ‘Doživi više od očekivanog’ – A Corpus-Based Contrastive Study of Reader Engagement Markers on the Web

The paper investigates how reader engagement markers (Hyland 2005; Zou and Hyland 2020) are used in tourism promotion to establish interaction with potential customers on the web. The markers are extracted using AntConc software from two comparable corpora in English and Serbian compiled from the web texts of regional tourism organizations. Normalized frequencies per 1,000 words are calculated, followed by a quantitative and qualitative analysis of the most frequent markers. The results are interpreted in view of the differences and similarities in the two corpora considering the distribution and communicative functions of the markers, and the cultural aspects of this kind of interaction with the reader. The findings shed light on the implied concepts underlying reader-oriented engagement and written e-communication practices in the context of tourism discourse. The results can be used for the data-driven teaching of writing and translation studies.
Particle trajectory simulation using python and spreadsheet as an online learning alternative

Education today is required to utilize technological knowledge and skills in preparation for global competition. Along with the rapid development of technology, educators are required to develop learning alternatives. The purpose of this research is to create a particle trajectory simulation that is used as an alternative to online learning. The simulation uses Python programming language and Origin Pro assisted spreadsheet. Simulation in Python programming uses the Euler Cromer method to describe particle trajectories affected by electric and magnetic fields. This paper has successfully simulated particle trajectories affected by electric and magnetic fields with the Python programming language and Spreadsheet. The case where the motion of a charged particle is affected by a combination of electric and magnetic fields is when a positively charged particle moves perpendicular to the magnetic field, it will form a helical trajectory. However, when the electric field is in the direction of the magnetic field, the motion in the direction of the magnetic field will be accelerated by the electric force in the direction of the magnetic field which causes the helix to increase in width.
Impoliteness on Twitter by Malaysians

This study investigates impoliteness on Twitter in the context of Malaysian users. The objectives of the study are to examine the impoliteness strategies and triggers of impoliteness found in tweet replies on tweets on issues related to COVID-19. The data consist of 440 tweet replies on COVID-19, posted from May 2020 to May 2021 which contain elements of impoliteness. The study uses Culpeper’s (2005) impoliteness model and Culpeper’s (2011) framework for examining impoliteness triggers. The study is motivated by the dearth of studies on impoliteness on Twitter focusing on Malaysian users. The findings show that four types of impoliteness strategies are used in the tweet replies: bald-on record impoliteness, positive impoliteness, negative impoliteness, and sarcasm or mock impoliteness. The most dominant type of impoliteness strategy is positive impoliteness, while bald-on record impoliteness is the least employed impoliteness strategy. Pointed criticism is found to be the most often occurring impoliteness trigger in the study, followed by insult, negative expressive, and challenging or unpalatable question and/or presupposition. The findings suggest culture and the communication platform may play a role in the use of impoliteness strategies and impoliteness triggers in the tweets.&#x0D; &#x0D; &#x0D;
Monte Carlo Simulation and Derivation of Chi-Square Statistics

Computer simulation has become an important tool in teaching statistics.Teaching using computer simulation would enhance the understanding of the concept using visual illustrations.This paper describes how to use simulation in Rprogramming language to perform a chi-square test.We try to show the distribution of most commonly used chi-square statistics we often found in statistical methods in both derivation and simulation.In statistical methods in such cases as test of independency, test of goodness of fit, test of significance, log likelihood ratio test, significance test and model selection we use chi-square statistic.The approach of the paper will enhance the students' and researchers' ability to understand simulation and sampling distribution.The paper contains an expository discussion of chi-square statistic, its derivation and distribution and its derivatives such as t-distribution and F-distribution.We consider two chi-squares, the empirical chi-square statistic and the theoretical chi-square distribution.The empirical distribution of chi-square statistic agrees closely with the theoretical chisquare distribution for large simulations, only the empirical distribution near to zero has lower density compared to the theoretical one for one degree of freedom.This is because the theoretical chi-square distribution at 1 degree of freedom has infinite density near to zero, but for any number of simulation the empirical distribution has finite density near to zero.Chisquare itself turns to normal distribution as the degree of freedom is large.
Position Data Estimation System Based on Recognized Field Landmark Using Deep Neural Network for ERSOW Soccer Robot

One of the problems faced by soccer robots is how to find out the position of the robot itself and other robots on the field.A simple way to find out the robot's position is to use the odometry method.However, odometry is weak in accumulating position errors that reduce the accuracy of the moving robot's absolute position estimation and orientation.This paper presents a robot position data estimation system that is to be implemented on the ERSOW wheeled soccer robot.The robot can determine its position based on a unique landmark: an L-shaped line on the soccer robot field.We use a deep neural network method to recognize landmark L-shaped.Vision systems and deep learning inferences run on the Robot Operating System platform.After obtaining the distance of the robot to the L-shaped landmark, the robot's orientation and position relative to the field can be accurately determined based on the omnidirectional camera's perception.The results of the position estimation system in this study can be used to reduce position errors resulting from the odometry method.Based on the landmark L-shape recognition test results conducted on 641 datasets, the validation accuracy value is 0.806.The results of testing the robot position generated by vision obtained the largest error x about 2.32 cm and y about 1.99 cm.
Comparative Study of 3D Assets Optimization of Virtual Reality Application on VR Standalone Device

The progress of VR technology is undeniably rapid and reaches many sectors unrelated to what it first came out of entertainment.Today, many educational, health care, company training, etc., use and utilize VR in one way or another as their first step to familiarize a concept or procedure with their members or workers.The advantages of implementing educational content with VR are ease of development, cheap operational cost, and safety.This kind of approach is a good step considering the impact of VR technology on those cases.However, because the leading device for VR is a standalone VR, some things to consider are performance and visualization.Some early adaptations have these problems, performance issues, and the realism of visualization shown on the VR application.We can minimize those problems by meticulously optimizing 3D assets used in VR applications.The optimization method improved the average FPS on CBIVT by 14.02% on Quest 1 and 8.99% on Quest 2. The GPU utilization level percentage of Quest 1 decreased by 6.73%, and the Quest 2 GPU utilization level percentage decreased by 11.72%.On the other metrics, the user's comfortability also increases because of the enhancement of performance on the CBIVT application.These changes are marked by the increase in Important and Satisfactory levels to 4.26 and 4.16, respectively.
Sentiment Analysis of E-commerce Product Review through Machine Learning

Sentiment analysis is a way which is used to determine that a piece of information is positive, negative or neutral. In the analysis of text or any kind of information natural language processing (NLP) and machine learning (ML) both are combined to assign sentiment scores to the topics, categories and any kind of entities within a given phrase. In today's day and time online shopping has become well received among the people. If we talk about ordinary people, they always think too much before buying any product through any e-commerce application. The trust of the people of e-commerce applications is not very high. So, people turn to reviews to fill this confidence gap. And to ensure this trust Sentiment Analysis were used. Our project can be a great option for people to maintain their trust. In this project we are analyzing products of respective e-commerce apps on the behalf of their reviews and ratings. Users will get information for each product whether it can be a good one or not on the basis of reviews and ratings. A great number of internet sources are present on categorical views of activities, goods and services, beliefs or perhaps the mind created by the online dwellers. In this fierce business world, various industries, especially e-commerce, exceptionally use sentiment analysis to increase productivity and make better business decisions. This project tackles a comprehensive overview of sentiment analysis and relevant techniques in the e-commerce sector that is always keen to find out about the consumers' opinions of their goods and services. It starts with the view of an assessment of sentiments that have come out as a method for understanding clients’ emotions.