Reviewerly/exHarmony huggingface dataset:
Index/Impressum: Vakuum in Forschung und Praxis 2/2022

Vakuum in Forschung und PraxisVolume 34, Issue 2 p. 51-51 Magazin Index/Impressum: Vakuum in Forschung und Praxis 2/2022 First published: 07 April 2022 https://doi.org/10.1002/vipr.202270210AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume34, Issue2April/May 2022Pages 51-51 RelatedInformation
Automatic Question and Answer Generation from Thai Sentences

Automatic questions and answers generated are challenging tasks that attract broad engagement. This paper presents an automatic question and answer generation from Thai sentences. We adopted question classification to predict the expected wh-question type before generating question and answer. This experimental study tested the question and answer generation on the sampling set of 300 Thai sentences based on the wh-question type that trained classify category from 15,000 samples. Automatic evaluation is performed on a reference set using the BLEU metrics and human evaluation. The experiment results show that the proposed method can generate questions and answers from Thai sentences. However, the performance based on the efficiency of Natural Language Processing includes tokenization, Named Entities Recognition, and Part of Speech tagging task because it could effectively extract focusing words correctly and suitable to answer the question that enhances the trustworthiness of future work in question and answer generation from Thai texts.
High-dimensional multi-input quantum random access codes and mutually unbiased bases

Quantum random access codes (QRACs) provide a basic tool for demonstrating the advantages of quantum resources and protocols, which have a wide range of applications in quantum information processing tasks. However, the investigation and application of high-dimensional $(d)$ multi-input $(n)$ $n^{(d)}\rightarrow1$ QRACs are still lacking. Here, we present a general method to find the maximum success probability of $n^{(d)}\rightarrow1$ QRACs. In particular, we give the analytical solution for maximum success probability of $3^{(d)}\rightarrow1$ QRACs when measurement bases are mutually unbiased bases (MUBs). Based on the analytical solution, we show the relationship between MUBs and $n^{(d)}\rightarrow1$ QRACs. First, we provide a systematic method of searching for the operational inequivalence of MUBs (OI-MUBs) when the dimension $d$ is a prime power. Second, we theoretically prove that, surprisingly, the commonly used Galois MUBs are not the optimal measurement bases to obtain the maximum success probability of $n^{(d)}\rightarrow1$ QRACs, which indicates a breakthrough according to the traditional conjecture regarding the optimal measurement bases. Furthermore, based on high-fidelity high-dimensional quantum states of orbital angular momentum, we experimentally achieve two-input and three-input QRACs up to dimension 11. We experimentally confirm the OI-MUBs when $d=5$. Our results open alternative avenues for investigating the foundational properties of quantum mechanics and quantum network coding.
Performance Analysis of Machine Learning, Deep Learning and Ensemble Techniques for Breast Cancer Diagnosis

Cancer is always a life-threatening disease. The lives of people can be saved only through proper treatment of cancer. Within the human body, there are different kinds of cancer that can form, one among them is breast cancer. Women are more at risk of breast cancer as compared to men due to the structure of the human body. To reduce the diagnosis time by the radiologists and increase the accuracy a computer aided diagnosis is required. In this study, we present a review on the different machine learning algorithms used in the past by the researchers and compare them. This paper will be beneficial for the newcomers who want to understand machine learning algorithms and develop the base for the deep learning.
METAPHORICAL MODELS IN ITALIAN POLITICAL DISCOURSE (BY THE EXAMPLE OF B. MUSSOLINI AND S. BERLUSCONI’S SPEECHES)

The article analyzes the metaphorical models in the Italian political discourse. The authors identify the frequency and dominant types of models based on excerpts from the speeches of two national leaders, B. Mussolini and S. Berlusconi. The results of the study show that the most common metaphorical model is the anthropomorphic metaphor, which, in turn, forms the recipients' perception of political life as a living and independent entity. The authors conclude that metaphorical modeling is a complex mechanism for making images that benefit political leaders.
Dynamical Simulation of a New Seiqr Covid-19 Model

The purpose of this article is to discuss the dynamics of Corona virus epidemic model. We first formulate the proposed model and obtain the value of threshold parameter R0 for the model. We then determine both the disease-free equilibrium (DFE) and endemic equilibrium (EE) and discuss the stability of the model. We show that both the equilibrium states are locally asymptotically stable. We use the Euler method, Runge-Kutta method of order 4 (RK4) and a non-standard finite difference (NSFD) scheme for the Corona virus epidemic model. In contrast to Euler, RK4, which fails for large time step size, it is found that the NSFD scheme preserves the dynamics of the proposed model for any step size used. Numerical results along with the comparison, using different values of step size h, are provided.
Safety and convenience of internet banking keyboard security technology

The keyboard is a traditional input device that transmits the values entered by the user to the computer. Attackers can find out the keyboard values entered by internet banking users, and some of the values are actually implemented and used. Although security is ensured as the complexity of keyboard security technologies increases, such technologies cannot be used in real life because user convenience cannot be guaranteed. In this paper, various technologies proposed to reinforce internet banking keyboard security are summarised, and the convenience and safety of the keyboard security technologies are examined.
Research on students' classroom performance evaluation algorithm based on machine learning

In order to overcome the poor accuracy of traditional classroom performance evaluation algorithm, a machine learning-based classroom performance evaluation algorithm was designed. This paper makes an empirical analysis of the statistical data and constructs a statistical information analysis model for students' classroom performance evaluation. According to the mining results of students' classroom performance evaluation information, the adaptive mining and feature clustering of students' classroom performance evaluation data are carried out. This paper uses quantitative game method to evaluate students' classroom performance, constructs the explanatory variable and control variable model of students' classroom performance evaluation, and then uses machine learning method to optimise the evaluation of students' classroom performance. The simulation results show that the evaluation accuracy of the proposed method is always above 0.77, which has high reliability and adaptability, and improves the quantitative evaluation ability of students' classroom performance.
Approaches and challenges to secure health data

The volume of health data being generated and stored is increasing massively. The growing number of data breaches is causing concern among patients and healthcare providers. Breach in security pertaining to health data will result in serious consequences for both patients and the providers. This critical piece of information is vital for cyber criminals to steal identity or make use of the information for malicious activities. This article discusses in detail the motivation of why healthcare data is of interests for attackers, followed by two common approaches to protect healthcare data which include blockchain and machine learning (ML). We discuss recent effort to protect healthcare data using blockchain and machine learning. Specific details are elucidated on how blockchain and ML can be game changers to secure health data and contribute positively to enhancing overall patient outcomes.
Prospects and foreign experience in the development of the pedagogical process aimed at strengthening active civil competence in future teachers

Today, the tasks of rational use of human capital in our country, increasing social activity in society, the role of young people in the implementation of democratic changes, the study of their duties in the formation of civil society, the use of pedagogical opportunities for the formation of active civil competence in them, the development ofpedagogical convictions for the development of civil culture.
Forensic Investigations of Popular Applications on Android and iOS Platforms

Since the turn of the 21st century, because of the tremendous growth in social media, online resources, and websites such as Facebook, there has been a vast number of users who have been sharing a huge amount of private data such as texts, pictures, videos, and calls on the Android and iOS platforms through instant messaging applications like Messenger, WhatsApp, and so on. This chapter investigates how much privacy these applications can provide for users, along with how much information can we get through forensic investigation using digital forensic tools like FinalMobile, Mobiledit, and so on.
Application of MATLAB Software in Higher Mathematics Teaching

To explore a teaching form of higher mathematics to adapt to the learning characteristics of college students, and create an efficient teaching classroom. Using MATLAB software powerful processing graphics and animation technology function to assist higher mathematics teaching in higher vocational colleges, using dynamic animation to assist static theoretical knowledge teaching, realizing the combination of theoretical teaching and mathematical experiment demonstration, creating an intuitive image angle to understand the definition and concept of higher mathematics teaching scene, reducing the difficulty of Higher Mathematics teaching, stimulating their interest in learning, and improving the teaching effect, To cultivate the ability of using higher mathematics to solve practical problems and further improve the teaching effect of higher mathematics.
A Deep Learning Implementation of End-to-End Image Denoising Steganography Model

Steganography is a methodology that essentially aims in hiding a secret data in a fitting carrier, essentially in such a way that the hidden data does not attract any attention toward it. The postulation in steganography is if the secret feature is perceptible, then so is the point of attack. Also, clean images when subjected to prolonged transmission, improper image acquisition or conditioned to multiple feature changes lead to image tarnishing due to unwanted noisy pixels. This proposes to be a major threat in steganography as the presence of noise itself could be a reason for the secret image to be revealed. In this work, a bi-objective end-to-end stacked denoising stenography model is implemented. The results are estimated by evaluating the average peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) of the trained images using COCO dataset. The stability of the proposed model is estimated using the average loss obtained by training the model for multiple iterations.
A New Reality through Virtual Reality

In this chapter, I will explain what virtual reality (VR) is and why it is worth your time to become a VR pioneer as well as some of the areas within which it is being used. You will also learn about three essential concepts in VR: the place illusion, the plausibility illusion, and the embodiment illusion, which are essential for creating genuine immersion in VR. Additionally, you will find out about the popular headsets currently on the market that you will need to follow along with this book and be introduced to the term degrees of freedom.
Mobile Forensic Investigation for WhatsApp

Due to the rapid development in smartphones and their applications resulting from the development of technology and communication, and due to their ease of mobility, cost, and energy consumption, smartphones have replaced computers in many uses. The WhatsApp (WA) application is considered to be the largest messaging application around the world and an important source of information; recently they added a new algorithm that works on end-to-end encryption features, which creates a major challenge for forensic investigators and analysts. This chapter explains how to extract the encryption key from WA, encrypt databases, and extract valuable artifacts that are presented and stored in the Android and iOS systems, as a way to present and analyze artifacts extracted from the latest version of WA to discover traces of new and valuable evidence and artifacts to help investigators and forensic analysts in the investigation. This means using many tools, devices, and types of software for portable devices specialized in digital forensics, as a result of the use of free trial tools for this chapter.
Image Deblurring Analysis Based on Deep Learning Algorithm

Nowadays, people take pictures more and more in daily life. However, since the objects they photograph are not always static, they will produce blur and impair image quality. In most cases, people are unlikely to get another opportunity to take pictures, so in order to solve the problem of deblurring blurred images, this article uses an algorithm based on Generative Adversarial Networks (GAN) to achieve this goal, mainly using DeblurGANv2 Code. By comparing the effects of using the Resnet, FPN-Mobile-net and FRN-Inception structures in the Generator, this article shows a relatively effective method of defuzzification, that is, using FPN-Mobile-net in the Generator, and in the generator Use Double-GAN discriminator.
Classification of Igneous Rocks

Building upon the award-winning second edition, this comprehensive textbook provides a fundamental understanding of the formative processes of igneous and metamorphic rocks. Encouraging a deeper comprehension of the subject by explaining the petrologic principles, and assuming knowledge of only introductory college-level courses in physics, chemistry, and calculus, it lucidly outlines mathematical derivations fully and at an elementary level, making this the ideal resource for intermediate and advanced courses in igneous and metamorphic petrology. With over 500 illustrations, many in color, this revised edition contains valuable new material and strengthened pedagogy, including boxed mathematical derivations allowing for a more accessible explanation of concepts, and more qualitative end-of-chapter questions to encourage discussion. With a new introductory chapter outlining the “bigger picture,” this fully updated resource will guide students to an even greater mastery of petrology.
Research of High-Speed Procedures for Defuzzification Based on the Area Ratio Method

The article discusses ways to build high-speed defuzzifiers. Two procedures for modifying the defuzzification model based on the area ratio method are presented. A limitation of the proposed method with two high-speed procedures is the use of only triangular or singleton membership functions. The main aim of the research is to test the hypothesis about the possibility of changing the type of the transient process during learning of the fuzzy MISO-system and to study the properties of the influence of the weight coefficient on the speed of its learning. The study tested the hypothesis of the presence of the additivity property in the high-speed defuzzifier with procedures I and II. Experimental researches have confirmed these hypotheses. The architecture of a fuzzy MISO-system based on the area ratio method with two high-speed procedures is shown in the article. Also in the article, firstly, graphs are presented that simulate the work of the center of gravity method, the area ratio method, and two high-speed procedures. Secondly, the article shows graphs that simulate the learning process of a fuzzy MISO-system.
Ensemble Method for Online Sentiment Classification Using Drift Detection-Based Adaptive Window Method

Textual data streams have been widely applied in real-world applications where online users’ expressed their opinions for online products. Mining this stream of data is a challenging task for researchers as a result of changes in data distribution, a phenomenon widely known as concept drift. Most of the existing classification methods incorporated drift detection methods that depend on the classification errors. However, these methods are prone to higher false-positive or missed detections rates. Thus, there is a need for more sensitive detection methods that can detect the maximum number of drifts in the data stream to improve classification accuracy. In this paper, we present a drift detection-based adaptive windowing for ensemble classifier, an adaptive unsupervised learning algorithm for sentiment classification, and opinion mining. The proposed algorithm employs four different dissimilarity measures to quantify the magnitude of concept drift in data streams, to improve the classification performance. Series of the experiments were conducted on the real-world datasets and the results demonstrated the efficiency of our proposed model.
Blockchain as the Catalyst in the Shift from Closed to Open Limited Liability Companies: The Case of Greyp Bikes

A limited liability company, as the most common form of incorporation of small and medium enterprises, has been historically praised as the staple of economy in most EU member states. It has regularly been recognized for its advantages of being a flexible and cost-effective way of doing business, which allows the members of the company to have elasticity in structuring the company management, while typically not being personally responsible for business debts and liabilities. However, unlike in the case of joint stock companies, the transfer of shares in limited liability companies is, in many EU member states, subject to a relatively lengthy and costly procedure and riddled with hurdles. Their transfer can include several formalities, such as the obligation to conclude the share transfer agreement in the form of a notarial deed, necessarily involving the participation of a notary. In addition to that, to be effective vis-à-vis third parties, the transfer usually must be registered in the relevant company register. Another prominent obstacle, very often prescribed by corporate laws of EU member states, is the fact that they cannot be expressed in the form of securities i.e. they cannot be incorporated in financial instruments to facilitate their circulation. Consequently, there is no secondary market for the shares in limited liability companies, which causes difficulties in relation to finding prospective buyers, to disclosing the relevant information about the related business, as well as to estimating the value and negotiating the price of the share to be sold. Regardless of the fact that shares can still be transferred on a contractual basis from one person to another, the shares in limited liability companies are virtually immobilized due to the described limitations and formalities. This is one of the reasons why limited liability companies are usually held by only a few individuals, who as a rule know each other personally, whereas none or only an insignificant number of shares are held by outsiders.
Analysis of the concept of “theatrical activity” from the perspective of preschool education

The analysis is conducted of the content of the concept of “theatrical activity”, which traditionally, since the 90s of the 20th century, has been actively used in methodological manuals devoted to the artistic and aesthetic development of children and intended for preschool educators. The meaning of this term could not be set as a specific aim in these works. The appearance of a large number of such works during this period caused a difference in approaches to the interpretation of the concept of “theatrical activity”, along with which such terms as “theatrical activity”, “theatricalization”, “theatrical play”, etc. began to be used synonymously in the texts of manuals. This circumstance actualized the terminological problem for the practice of preschool education, associated with a different understanding of key concepts and difficulties in creating new methods for the development of different abilities of children through theatrical activities. Since the beginning of the 21th century there have been scientific articles and dissertations devoted to the artistic and aesthetic development of children through theatrical activities. However, the concept of “theatrical activity” has not yet become the subject of special analysis, as a result of which the terminological problem for the theory of preschool education remains relevant. The definition of the term “theatrical activity” is given, emphasizing the prospect of its transformation from a formal into a meaningful concept, it is also distinguished from the concepts of “theatrical activity”, “theatricalization” and “theatrical play”.
Tracking the Evolution: Discovering and Visualizing the Evolution of Literature

A common task in research preparation is to survey related work in bibliographic databases. Scientists are finding the survey task notably difficult as the volume of the databases has been increased considerably over the past few decades. Making a good use of a survey paper of the research topic can vastly lower the difficulty but there may be no survey paper in some emerging research topics due to the rapid development. In this work, we propose a novel Literature Evolution Discovery (LED) process that aims to provide an explainable evolution structure of literature. The explainability is based on co-citation analysis and latent relationship extraction, which are done by Steiner tree algorithm and context-consistent factor graph model, respectively. The experiments show the superiority of our context-consistent factor graph model, compared with the state-of-the-art baselines. Our case studies and visualization results demonstrate the effectiveness and interpretability of our proposed algorithms in practice.
RESEARCH AND DEVELOPMENT OF A QUESTION AND ANSWER SYSTEM BASED ON THE BERT MODEL FOR THE KAZAKH LANGUAGE

This article presents the research and development of a question-and-answer system based on the BERT model for the Kazakh language. The article presents a new combination of normative and statistical approaches to the analysis of questions used in the system of answering questions of a closed subject area for agglutinative languages ​​such as Kazakh. Question analysis consists of focusing and classifying questions. To focus, we have several experts based on the rules of the Kazakh language FAQ. BERT is undoubtedly a useful model in using machine learning for natural language processing. It provides fast and accurate set-up and can be used in many practical and subsequent applications.
Integrity Verification Through File Container Analysis

Abstract In the previous chapters, multimedia forensics techniques based on the analysis of the data stream, i.e., the audio-visual signal, aimed at detecting artifacts and inconsistencies in the (statistics of the) content were presented. Recent research highlighted that useful forensic traces are also left in the file structure, thus offering the opportunity to understand a file’s life-cycle without looking at the content itself. This chapter is then devoted to the description of the main forensic methods for the analysis of image and video file formats.