Reviewerly/exHarmony huggingface dataset:
Fractal feature extraction of English language based on semantic analysis

In order to solve the problems of low extraction accuracy and long extraction time cost in traditional English language fractal feature extraction methods, an English language fractal feature extraction method based on semantic analysis is proposed. The similarity matrix is constructed to determine the fractal similarity of English language. The weight of English language fractal similarity data is determined by analytic hierarchy process (AHP). According to its weight in the whole set, the information entropy and conditional entropy of English language fractal data are determined to realise the fusion of English language fractal similarity data. Singular value decomposition is used to improve semantic analysis, reduce the dimension of English language fractal, and extract the fractal features of English language. The experimental results show that the highest accuracy of the proposed method is about 98%, and the shortest extraction time is about 1.3 s.
Indian RTI Act-2005 Analysis by Machine Learning: A Bastar Zone Perspective

Indian residents now have an opportunity to obtain information that is kept in government offices thanks to the Right to Information (RTI) Act of 2005. The RTI query-log data, which records citizen interactions with the government, is available in every government office. The goal of this work is to analyses the RTI query-log data in order to find fundamental latent patterns, with the hope that these patterns may provide insight into prospective legislative changes. We gather RTI queries and reply-statistics from government educational institutions all throughout India for the first time. Three latent patterns are quantified: I the organizations' transparency as determined by RTI queries stats; (ii) the influences on organizations’ accessibility; and (iii) the success of the RTI law's execution. There is a suggestion for this double institute-query-category matrix representation of the RTI data. By employing an Evaluated Response Model to estimate the data matrix's maximum probability, the aforementioned three latent parameters are simultaneously measured. We find contradictions in the RTI legislation's application through the latent patterns that were identified, while also providing recommendations for possible RTI act reform.
Grey Wolf Optimization using Improved mutation oppositional based learning for optimization problems

Grey wolf optimization (GWO) algorithm is a swarm intelligence optimization technique that is developed by Mirjalili to mimic the hunting behavior and leadership hierarchy of grey wolves in nature.Oppositional-based learning (OBL) is a new concept, which has attracted many research efforts in the last decade, some optimization methods have already used the concept of OBL to improve their performance.This paper presents an efficient algorithm, namely, Improved Mutation Oppositional-based learning GWO (IMOGWO) based on Quasi Oppositional-based learning (QOBL) and Topological Oppositional-based learning (TOBL) with some parameter adjustments to improve exploration and exploitation.The experiments were executed 28 widely used benchmark test functions with various features. The results reveal that the proposed algorithm improves the exploration capability and is effective, ranking first in average value compared to the comparison algorithm.
LANDAMATICS AS A METHOD OF FORMING STUDENTS’ LITERATURE RESEARCH SKILLS

The article touches upon the problem of landamatics which is defined as an area of psychology that focuses on thinking algorithms as well as the algorithmic structure of human activity, including that which regards the organisation of learning process in school. However, each school subject is characterised by its own regularities of students’ development, therefore algorithms of the teacher’s and the children’s activities differ according to the subject. This paper argues for an application of landamatics in the process of developing students’ literature research skills. The author reflects on how to adapt the regularities identified in this area of psychology to the completion of subject-specific tasks, one of which is concerning students individual work (observation, analysis, generalisation) based on literary texts. To this end, the paper considers the general requirements suggested by landamatics scholars, as well as the special features of literature research, including already existing approaches to literary texts analysis. It is mentioned in the article that any work of art is associated with mathematical logic, which forms its integrity and provides artistic completeness and thus, the first aspect includes identifying stages (successive steps) of activities (thinking processes), the understandability of each stage to the students, their repetition during lessons and at home, the use of mnemonic aids as well as relatively similar materials that use the same research algorithm. The second aspect is no less important, consisting in adapting the regularities of landamatics to using the event-based, the thematic, the philosophical, the ideational-conceptual, the structural-stylistic and the genre-based ways of analysing literary works. The abovementioned ideas are the main focus of the paper.
Language Paradigms

The word “paradigm” comes from Greek $$\pi \alpha \rho \!\alpha ^{\!\!\!'}\delta \varepsilon \iota \!\gamma \mu \!\alpha $$ , meaning “pattern”, “example”, “instance”, or “object lesson”.
Higher Layers, Better Results: Application Layer Feature Engineering in Encrypted Traffic Classification

Encrypted traffic has become the primary carrier of network transmission, and encrypted traffic classification is essential for advanced network management and security protection. Existing studies mainly focus on encrypted traffic feature engineering and classification model design, aiming to select more expressive features from encrypted traffic and achieve high-performance classification. The most commonly used features in the feature engineering process are statistical features and sequence features obtained in network or transport layers, which are more inclined to represent the factors of network transmission rather than the data attributes of applications or services. As a result, the relevance of the features and application or services is not strong, leading to unsatisfactory performance. To solve this problem, we introduce the Application Data Unit (ADU) and put forward the application layer feature engineering, which uses the features of the highest protocol level - the application layer to achieve better HTTPS classification. In order to compare the classification effects of features of different layers, we carried out experiments on traditional machine learning models based on statistical features and deep learning models based on sequence features, respectively. The results show that the proposed ADU features are better than the segment granularity features of the TLS layer and far better than the packet granularity features both in statistical and length sequence features. The average F1-score increase in the encrypted traffic application classification scenario is more than 10%.
Plıthogenıc Combıned Dısjoınt Block Fuzzy Cognıtıve Maps (PCDBFCM)

This research article represents an innovative concept in Plithogenic Combined Disjoint Block Fuzzy Cognitive Maps (PCDBFCM) and its applications. PCDBFCM is a very useful tool in grouping the factors with contradiction degree of multiple attributes. A plithogenic fuzzy matrix is used to represent the connection matrix. The resultant vector is obtained while using plithogenic fuzzy operators. The produced results are very useful in making decisions since they include the degree of conceptual node contradiction with respect to the dominant node. For the plithogenic aggregation operators, the degree of dissimilarity between each attribute value and the main attribute value of the attribute leads to increased accuracy.
Monitoring laryngeal and respiratory adjustments during voice production using a simulation-based voice production inversion neural network

Previously we reported a simulation-based neural network for estimating vocal fold properties and subglottal pressure from the produced voice. In this study, we evaluate the feasibility of using this neural network to monitor laryngeal and respiratory adjustments during speech production in individual speakers. Acoustic and aerodynamic data were collected in human subjects while producing utterances of five repetitions of the syllable /pa/ at different loudness levels. Voice features were then extracted and used as input to the neural network to estimate changes in vocal physiology and subglottal pressure. The results showed all subjects increased the subglottal pressure and vocal fold adduction when producing a louder voice, although the degrees of laryngeal and respiratory adjustments were speaker-specific. The neural network also estimated on average shorter and thinner vocal folds in female subjects than male subjects. These results demonstrate the potential of this neural network toward monitoring and identifying potentially unhealthy vocal behaviors.
Using Augmented Reality to Reinforce the Learning of Installation of Electrical Equipment and Machines

The educational level of technical high schools has been affected by the current state of emergency caused by the Covid-19 pandemic, due to the lack of educational resources for subjects that require laboratory practice. The objective of the research is to describe a pedagogical strategy using interactive support material focused on augmented reality for equipment installations and electrical machines learning, in order to supply the lack of laboratories. The research carried out surveys to seven teachers, who teach practical subjects, and evaluations to 60 students who underwent the intervention with the application of augmented reality. The results of the pre-test and post-test were analyzed to verify the stated objectives, demonstrating that there are motivating results with the application of two tools Mywebar and Assemblr in the performance of the classes, affirming that the augmented reality tool impacts and motivates the teaching-learning process in the technical area in the professional figure of installations, equipment and electrical machines in a favorable way.
JoinTW: A Joint Image-to-Image Translation and Watermarking Method

The image-to-image translation tasks have been widely explored in recent years due to the rapid development of deep neural networks. Unfortunately, the misuse of image-to-image translation techniques may induce disastrous consequences. Although the existing proactive watermarking methods can give decent performances in tracing the manipulated images, they can be escaped by malicious users because they can only embed the watermarks after the generation of the deep manipulated images. Under such circumstances, we are motivated to propose a joint image-to-image translation and watermarking (JoinTW) framework with the correspondingly designed loss functions. Our JoinTW embeds the watermark as a fingerprint during the image-to-image translation process without being noticed or escaped, which enables the potential regulators to directly trace the producing source of the translated images once misuses happen. Experiments have demonstrated that our JoinTW can effectively embed watermarks without obvious degradations to the perceptual qualities of the generated images, as well as ensure the robustness of the watermark against various post-processing operations.
Improved cross-entropy research based on JS divergence: Iris flower data as an example

In the research of neural networks, the loss function as a standard to measure the gap between the real value and the predicted value has been widely studied by academia. However, how to increase the similarity between them is still an open issue. To address this problem, this paper proposes a new measurement method called Synthetic Cross Entropy (SCE). Inspired by JS divergence, this method innovatively introduces JS divergence into the calculation of loss function, which makes the method have comprehensive evaluation ability for real and predicted values, further reduces the gap between them, and improves the classification accuracy. The feasibility and rationality of the proposed method are verified by comparing it with the standard Cross Entropy (CE). In particular, the proposed SCE method can quickly converge the loss value and shows better accuracy than the traditional method. Eventually, under the test of the classical iris dataset, the algorithm can be effectively applied to the actual pattern classification problem.
COVID-19 Factors and Interest Rates Impacting on Bitcoin Price: A Case Study in ASEAN-5 Countries

This research aims to determine the COVID-19 factors and central banks' interest rates impacting on the Bitcoin price. A dearth study of measuring central banks' interest rate as Bitcoin determinants is the main research problem we aim to solve and hence produce the originality/value of this research. The COVID-19 factors utilized are COVID-19 cumulative cases and deaths. We further used the interest rate as a moderator variable. Feasible Generalized Least Square (FGLS) and moderated regression are applied as the first and second stage methodologies, respectively. We collected daily data from 2 March 2020 - 12 March 2021 and hence the total observations are 1,350. Focusing on the ASEAN-5 countries, this research found that Bitcoin can be an alternative investment. However, its character as a speculative asset may decrease investors' interest in Bitcoin when the condition is highly uncertain. Interest rate affects inversely on Bitcoin but insignificant. Surprisingly, the interest rate revealed a stronger inverse impact as a moderator variable to the Bitcoin price. Therefore, investors and central banks are highly suggested to watch the current condition for a better investment decision making and monetary policy adjustment to control and stabilize the economy.
Pipelines: magrittr

Data analysis consists of several steps, where your data moves through different stages of transformations and cleaning before you finally get to model construction. In practical terms, this means that your R code will consist of a series of function calls where the output of one is the input of the next. The pattern is typical, but a straightforward implementation of it has several drawbacks. The Tidyverse has for many years provided a “pipe operator” to alleviate this, and with R 4.1, there is also a built-in operator in the language itself. Although this chapter is mainly about the magrittr pipe operator, %>%, I will describe the native |> operator as well and point out a few places where they differ in their behavior.
The exposition of teaching the subject philosophy

«Philosophy» is one of the compulsory disciplines at the university. A student studying in any educational program masters the subject of philosophy, consisting of 5 credits. It is difficult to say that philosophy is understandable and attractive to all students, but it is possible to achieve certain results by transforming and updating the exposition of teaching the subject, i.e. the purpose of explanation, description, definition or message. The exposition of the philosophy of education or the exposition of the philosophy of learning is directly related to the attempt of the teacher of the subject «philosophy» to gain the trust and audience in mastering the process of teaching and learning. The article presents several options that contribute to achieving the main goal - involving students in the development of the subject «philosophy» through logical and cognitive games aimed at teachers and students of educational programs of non-philosophical and socio-humanitarian directions. The use of variants is presented and practiced as an interesting and informative way to increase the attractiveness of the subject «practical philosophy». «Practical philosophy» is the ability to think broadly and reflect on the student’s activities, so it pursues a long-term goal and result based on virtue. The article presents the exposition of teaching the discipline «philosophy» in combination with the philosophy of teaching the discipline «philosophy».
Experimental Robots

This chapter analyses, according to the presented framework, playful robots developed in research labs in the world and at Politecnico di Milano AI and Robotics Lab. In most cases, these have not reached the market nor development levels that could bring them to the real users in a real setting without the presence of the developers, but they have been used in experiments limited to labs or few, selected care centres or schools. However, it is interesting to analyse these experiences to understand what kind of playful activities have been explored, how these have been evaluated, what kind of outcomes have been obtained, and what kind of technologies have been used, since they trace the directions that are bringing to the next future products. At the end of the chapter, the scenarios to bring these innovative products to the market are analysed. These include the evaluation of effectiveness of this technology, and of the different characteristics that should be taken into account, such as playability, playfulness, accessibility, and usability.
High Quality Article Recognition Based on Ernie and Knowledge Mapping

Nowadays, with the explosive growth of we media articles, in the context of information distribution such as search and recommendation, how to identify high-quality articles and distribute them to users has important research significance and practical application value. The task of this competition is to introduce external Knowledge mapping, combined with the internal knowledge logic of the article, realizes high-quality article recognition on the basis of a deeper semantic understanding of the article. We mainly use Ernie pre training model and Article title - knowledge map - the modeling method of part of the article content, the use of pseudo label data sets and other ways to get the predicted value, and finally achieved the result of 0.82 in the B list.
SHAPING THE FUTURE OF FEDERAL SCIENCE: A LOOK BACK AND A LOOK AHEAD

In 1996, the American Geological Institute (AGI) convened a workshop gathering leaders of biological, geological, hydrological, and mapping societies to consider the scientific opportunities that could result from a congressionally mandated consolidation of the science functions of the Department of the Interior (DOI). The consolidation merged much of the National Biological Service (NBS) and several components of the U.S. Bureau of Mines into the U.S. Geological Survey (USGS). All three DOI bureaus had been targeted for elimination in congressional proposals the previous year, and the consolidation represented a compromise that would enable many of the functions of these bureaus to continue. By focusing on opportunities, the workshop helped set a positive course for the merged mission that has served the USGS well over the ensuing decades. The workshop participants embraced a vision that took advantage of the expanded breadth of the science that could be brought to bear to support decision-making by DOI and many others related to natural resources, environmental issues, and natural hazards. The workshop also endorsed the separation of science from regulatory authority that was a key driver in the establishment of the NBS in 1993, bringing together biological science activities in DOI land management bureaus to form a bureau that, like the USGS, had neither regulatory nor land management responsibilities but delivered independent, authoritative science. The resulting report identified five opportunity areas: credibility and quality assurance of scientific information, integration of information across disciplines and cross-disciplinary training, research agendas based on natural boundaries, development of data collection and analysis and information technologies, and assessment of hot spots and anticipation of environmental problems. The three primary recommendations were: Develop interdisciplinary initiatives and budgets that provide new approaches to problems; encourage partnerships and alliances; and implement stakeholder councils. Since that time, the USGS has continued to benefit from the active engagement of scientific and professional societies, most notably through the USGS Coalition that was started in 2002 as well as through participation in federal advisory committees.
Cloud simulation tools: A survey

Cloud computing is becoming more popular due to its ability to deliver low-cost services on a pay-as-you-go basis. Many techniques have been developed to improve services. However, one of the most significant challenges in this field is deciding how to compare new approaches based on various performance constraints. Experimenting in a real-world environment is challenging and costly. As a result, several studies concentrate on creating cloud simulation tools that can represent the real-world cloud environment. Therefore, this work presents a comprehensive analysis of 25 tools for cloud computing to select the appropriate one. This will advise readers on how to choose the right tool to test their technique. Furthermore, the review will allow researchers to compare the most common cloud simulators in terms of architecture, strength, limitations, and supported model.
Anomaly detection from images in pipes using GAN

In recent years, the number of pipes that have exceeded their service life is increasing. Therefore, earthworm-type robots have been developed to perform regularly inspections of sewage pipes. However, inspection methods have not yet been established. This paper proposes a method for anomaly detection from images in pipes using Generative Adversarial Network (GAN). A model that combines f-AnoGAN and Lightweight GAN is used to detect anomalies by taking the difference between input images and generated images. Subtraction images is used to estimate the location of anomalies. Experiments were conducted using actual images of cast iron pipes to confirm the effectiveness of the proposed method.
AUTOMATED PROGRAMMING IN THE TECHNOLOGICAL PROCESS AND ITS INFLUENCE ON THE PROGRAM LOGIC COMPILATION TECHNIQUE

The purpose of this work is to consider all the positive and negative nuances that automatic programming brings to the process of writing code and developing control systems. The article deals with such issues as the cause of programming, as well as the main features of automatic programming. A description of the labor costs associated with the training of young programmers and with a description of computer models and physical means to reduce the burden on the programmer, increase the quality of the program code and popularize such a method of developing control systems as automatic programming is given.
WORKING WITH CINEMA FAIRY TALES IN A PREPARATORY GROUP

The research reveals peculiarities using cinema fairy tales in the process of literary education in kindergarten. The author’s work with students creates new methods and techniques and also enriches with new content the existing ones: introduction of film terminology, screening and discussion of film footage, making a movie poster, guessing a film title by listening to an excerpt from film music, arranging a movie puzzle, guessing the name of a character from a movie line and etc. The introduction of film pedagogical accents raises the problem of the synchronous use of theater pedagogy, media pedagogy, library pedagogy and other scientific fields corresponding to pedagogical research.