Reviewerly/exHarmony huggingface dataset:
Research on Digital Reading Experience with ‘PAGE-Turning’ Physical Feedback

In recent years, the Internet has become widespread, and many people have their own devices. The purpose of this research is to enhance the sense of physical reading sensation by introducing ‘physical touch’ to reading e-books on digital terminals, which are becoming more complex due to the various functions stored in e-books. In this paper, we focused on the ‘page-turning’ motion as a factor that enhances the reading experience, and conducted experiments and verifications. For the experiment, we used walnut binding, a common binding method, to verify the presence or absence of reading sensation, changes in reading sensation by the different number of pages, and convenience. The results show that the number of pages that provided the best reading experience while keeping the convenience of the digital terminal, was two sheets of paper used for ‘four page-turning’ operations. In addition, we conducted a verification of which position on the paper is touched and how it is turned when people turn pages for paper book reading, based on behavioral observations of 22 experiment participants. As a result, the page-turning motions were categorized into four patterns. Although there were some differences in the positions and used fingers, most people followed a pattern in which they hooked their thumbs to the bottom of the page, held the page with their other fingers, slid their thumbs under the page, and use another finger as a base point by holding the page between these fingers and turning.
Random Numbers and Monte Carlo Methods

Generators of sequences of pseudorandom numbers are introduced, after a general discussion about the transition of a numeric sequence from a regular to a chaotic and poorly predictable regime. The main methods to extract pseudorandom numbers distributed according to the desired density function are presented. Monte Carlo methods are introduced, in particular, the hit-or-miss Monte Carlo and importance sampling. The use of Monte Carlo method for numerical integration is presented. Markov Chain Monte Carlo are discussed.
Bayesian Probability and Inference

Bayes’ theorem is presented with its applications. Bayesian approach to probability is then introduced, with a discussion of possible applications. Prior and posterior probabilities are defined. The application to the continuous case is presented, and Bayesian inference is introduced, which can also be interpreted as learning process from multiple observations. The treatment of nuisance parameters with Bayesian inference is discussed. Credible intervals are defined, which determine uncertainty intervals with Bayesian inference. The assessment of different hypothesis with Bayes factors is introduced. The main critical issue with Bayesian inference is the dependence of the estimate result on assumed prior probability, which makes Bayesian probability intrinsically subjective. Jeffrey’s priors are introduced, which are invariant under variable transformation. The general approach to variable transformation and error propagation under the Bayesian approach is discussed.
Machine Learning

The main general concepts and terminology of machine learning are introduced, presenting supervised and unsupervised learning, with more emphasis on supervised learning, that is more widely used in physics for applications to classification and regression. A connection with hypothesis testing, covered in the previous chapter, is discussed. The issues with undertraining and overtraining are presented, illustrating the bias-variance trade-off with the aid of a polynomial fit. The main machine learning methods are then introduced. Logistic and softmax regression for classification problems are presented; after a very short section about support vector machines, artificial neural network is introduced, in particular, considering the potential gain offered by a deep-learning network structure. Applications of deep learning in physics are discussed, and the main deep-learning architectures are briefly presented: convolutional neural networks, recursive neural networks, and graph neural networks. An introduction to random forest and boosted decision trees completes the overview of supervised algorithm. The main unsupervised algorithms used for clustering and anomaly detection are introduced, with a discussion of autoencoders and reinforcement learning strategies. Generative adversarial networks are finally discussed.
Hypothesis Testing

Hypothesis testing is a frequentist method to compare two hypotheses based on observed data. The observed data is assigned to the most consistent hypothesis based on the determination of a single indicator, called test statistic, whose distribution determines the probability of correct and incorrect hypothesis assignment. The determination of the optimal test statistic is a key task. Fisher’s discriminant is discussed, whose performances are limited by the linearity of the model. The Neyman–Pearson lemma allows to determine the test statistic having the best performances, as the ratio of the likelihood functions evaluated in the two hypotheses, if those likelihood functions are known with sufficient precision, which is often not the case in realistic situations. The projective likelihood ration may provide acceptable performances if the variables can be approximated to be independent, otherwise, other methods need to be implemented, including machine learning techniques, that are discussed in the following chapter. Kolmogorov-Smirnov test to compare a data sample with a theoretical distribution is presented. Finally, Wilks’ theorem is introduced, which allows to approximate the distribution of test statistics based on likelihood ratios, under certain hypotheses, to a chi-square distribution.
Discrete Probability Distributions

The main properties of discrete probability distributions are presented. Joint, marginal, and conditional distributions are introduced, with the main statistical indicators: average, variance, etc. The problem of determining the probability distribution under variable transformation is presented. The most frequently used discrete distribution are introduced: Bernoulli, binomial, multinomial and Poisson, and their properties are discussed. The law of large numbers is introduced, and its importance under the frequentist approach to probability is discussed.
Solving the Traveling Salesman Problem with a Hybrid Quantum-Classical Feedforward Neural Network

Solving combinatorial optimization problems with the Quantum Approximate Optimization Algorithm (QAOA) is becoming more and more popular. The performance of the QAOA strongly depends on the initial parameters and the optimization procedure. This work presents a benchmark for solving the Traveling Salesman Problem (TSP) that introduces a hybrid feedforward neural network as the QAOA’s optimization routine. The strength of this method lies in training the optimization procedure on many instances of the problem and using mini-batch updates of the parameters. Although the learning process is costly, the advantage of this method is that after the neural network is trained, it immediately returns optimized parameters for new problem instances. We present the advantage of our method by evaluating it on two sets of initial parameters. The experiments demonstrated that the proposed hybrid quantum-classical feedforward neural network can be successfully used to solve the TSP.
Quantum Annealing to Solve the Unrelated Parallel Machine Scheduling Problem

Quantum computing has emerged in recent years as an alternative to classical computing, which could improve the latter in solving some types of problems. One of the quantum programming models, Adiabatic Quantum Computing, has been successfully used to solve problems such as graph partitioning, traffic routing and task scheduling. Specifically, in this paper we focus on the scheduling on unrelated parallel machines problem. It is a workload-balancing problem where the processing time of any procedure executed on any of the available processing elements is known. Here, the problem is expressed as Quadratic Unconstrained Binary Optimisation, which can be subsequently solved using quantum annealers. The quantum nonlinear programming framework discussed in this work consists of three steps: quadratic approximation of cost function, binary representation of parameter space, and solving the resulting Quadratic Unconstrained Binary Optimisation. One of the novelties in tackling this problem has been to compact the model bearing in mind the repetitions of each task, to make it possible to solve larger scheduling problems.
Introduction: Studying Political Debasement Discourse in the Public Sphere

This chapter presents a general introduction to the book. It draws upon and summarizes the key aspects of political debasement discourse as detailed in the previous volume in the series of two books on political debasement. The chapter first defines political debasement discourse with its aim to demean or degrade a certain target in the polity. This is followed by a short discussion on the ways to assess and examine political debasement, in its narrow and broad senses, detailing the sub-categories of the latter. The ensuing sections consider key aspects at the center of the interaction between debaser(s) and their target(s), the modes of interaction between the two main players, and distinguish between debasement, incivility, and hate speech. The final section of this chapter details the structure of the book and briefly describes each of the contributions.
An Innovative Approach to Develop Persona from Application Reviews

Software end users are diverse by nature and their different facets influence the way they use software.An understanding of the users and their needs are achieved by engaging with the users during requirement engineering.However, sometimes recruiting users during requirement engineering phase can be very challenging.An accessible way to understand a user's perspective and traits is through user application reviews.This research paper proposes an innovative approach to develop user personas from a data set of e-commerce application user reviews by using GPT-3 and PATHY.This enables the development teams to see different demographic data, as well as overall frustrations and expectations that users of their platform possess, so developers know how to enhance their software solutions.This is also helpful to developers of new e-commerce applications.
Incivility in the Language of the Powerful: Derisive and Debasing Discourse in Polish Politics

The objective of this chapter is to offer an exemplification of debasing, derisive comments made by politicians, mostly representing the ruling coalition parties in Poland (2015–2022). The examples are sourced from three genres—parliamentary speeches, press materials, social media posts. We trace how abusive attacks, vulgarities function as rhetorical devices deployed by the powerful to garner political capital or reinforce their hegemony. In the chapter we also discuss the normative shifts in the rules of deliberation that are being gradually skewed by the powerful in the public (parliamentary) sphere as well as in social media. We reflect on the possible implications of debasement for the condition of the public sphere, social relations in a relatively young democracy such as Poland’s.
Assessing the Politics of Debasement: From Impoliteness to Demonization in Political Communication

This chapter presents an introduction to the book. Following the first, brief introductory section, the chapter is divided into five additional sections. The second defines political debasement as the use of derogatory language, often by employing rhetorical devices as irony, cynicism, sarcasm, mockery, and ridicule, to strongly demean or degrade a certain target in the polity, especially those involved in the political process. The third distinguishes between debasement and incivility, offering specific examples for each type of discourse. The fourth section suggests a detailed account of how to assess and examine political debasement, including its narrow and broad senses, detailing important sub-categories of the latter. This is followed by a discussion on the key aspects at the center of the interaction between debaser(s) and their target(s). The final section of this chapter focuses on a proposed model consisting of eight elements or factors that need to be considered in studying political debasement. These elements are detailed one-by-one, suggesting questions that should be addressed regarding each element in future studies on the function, role, and effect of debasement language in the public sphere. Finally, the chapter details the structure of the book and briefly describes each of the contributions.
Minimizing Defaultation in Lending Using Blockchain-Based Loan Management System

The use of Blockchain and smart contracts ensures a safe and reliable transaction by preventing untrustworthy or malicious lending. Regular lending systems put lenders at risk of loan defaultation. However, in cryptocurrency lending, the value of the currency can fluctuate, resulting in the lender receiving less than the original lent amount when calculated in fiat or real-life currency. This research proposes using ERC-20 tokens as collateral to mitigate the risk for the lender. Our lending administration system based on the Ethereum blockchain is autonomous, making it a safe and reliable transaction option for users. The system tracks the fluctuation of the cryptocurrency lent and increases the value of the ERC-20 token accordingly. At the end of this paper, the effectiveness of the system is shown through tests conducted and presented in charts. The proposed system can prevent economic loss for lenders by addressing the decline in Ether’s exchange rate and incrementing collateral price.
Exploring ELT Teachers’ Behavioral Intention to Continue Using Technology in the Post-COVID-19 Era: A Case Study of Oman

Educators in Oman, at all levels of education, have adapted online education systems during COVID-19. As universities and colleges worldwide start to receive students back on their campuses post-COVID-19, it is crucial to rethink how our education is delivered, whether to continue using online education, backtrack to the conventional face-to-face mode, or integrate a hybrid approach. Therefore, this study seeks to explore the determinants that influence the behavioral intention of teachers to continue using technology in Oman’s higher education and identify teachers’ needs during the process of incorporating technology into their classes through the use of the Technology Acceptance Model and Theory of Reasoned Action. We used an online survey and semi-structured interviews through MS Teams for the quantitative and qualitative phases of the study. Data analysis was conducted through SmartPLS 3.7, and thematic analysis was implemented to report the findings of the interviews. This study shows a correlation between the participants’ willingness to continue using technology in their teaching after the pandemic and their perceived attitude, usefulness, and ease of use.
Deghosting Diffusion Model for Facial Attribute Editing via Differential Activations

Deghosting networks aim to eliminate the unavoidable ghosting effect for facial attribute editing via differential activations. Existing deghosting networks are based on GAN priors that combine the features of a pretrained StyleGAN generator with a decoder to generate the target images. However, these methods cannot effectively remove the ghosting effect and may produce other artifacts. In order to solve the above problems, we suggest a novel deghosting diffusion model that employs a conditional diffusion model to restore ghosting images to the face distribution, effectively eliminating ghosting. Experimental results outperform state-of-the-art facial attribute editing methods at a resolution of $$256 \times 256$$ in both qualitative and quantitative evaluations.
Mathematical Modeling of the Translation Process and Its Optimization by the Criterion of Quality Maximization

The paper substantiates the relevance of the issue of source texts optimizational pre-editing to improve machine translation quality. The paper provides in-depth research of applied linguistics fundamental concepts and describes a mathematical model of the translation process basing on set theory; a model for assessing translation task complexity and a strategy for source texts optimizational pre-editing are proposed; problem statement for further research is formulated.
Time to Be Represented: The Imperative for Investment in Igbo Indigenous Language Radio

The Igbo ethnic group is one of the three largest ethnic groups in Nigeria; occupying the southeastern part of the country. Other major ethnic groups in Nigeria have indigenous language radio except the Igbo. Igbo people are the only ethnic nationality that has large human and economic presence across all regions of Nigeria. It is estimated that about 50% of Igbos live and do business outside the Igbo homeland. The BBC and VOA in recognition of the influence the Igbo ethnic group across Nigeria and in diaspora have Igbo language programmes belt. Bond FM of Federal Radio Corporation of Nigeria, Lagos for indigenous languages has Igbo as one of the three indigenous languages. Igbo language channels in the Nollywood movies channels are very popular among Igbos, but they are no substitutes for indigenous Igbo language radio. Pioneer television station in Nigeria was established in 1959, in southwest Nigeria and second one in Igbo land, in 1960; this proves the Igbos follow development but it is not so with establishment of indigenous language radio. Investment in Igbo indigenous language radio will survive both in Igbo land and other parts of Nigeria with large Igbo business presence, and will ensure representation of Igbo language in radio segment.
Debasement Language as a Subset of Political Incivility: Characteristics and Effects

On the pretext of safeguarding democratic values in contemporary societies, various forms of debasement language have become an integral part of political discourse. Impregnating political speeches, statements, debates, and discussions with coarse language that normally targets political opponents as well as other potential victims is not a new trend. However, most of the literature dedicated to defining incivility in political discourse mainly focuses on the use of hate speech. The chapter at hand aspires to offer a deeper analysis of the concept of debasement language as part of incivility in politics and offers answers to several key questions: (1) what exactly is debasement language and what types of debasement language are used in political discourse; (2) why is such language used; (3) who is the target of political debasement discourse; and (4) how such language affects individuals and the democratic process in modern societies. Several instances of debasement language used by current and former politicians, from different countries in the world, generated on various platforms (TV, newspapers, social media, etc.), and appearing in different formats (press statements, political speeches, TV debates, etc.), will be used to illustrate the points discussed.
Exploring Music Videos in Indigenous Languages and Behavioural Change Towards Waste Management in Lagos State, Nigeria

This chapter examines the Sterling Environmental Makeover (STEM) music video and its creation of awareness. It seeks to determine whether the STEM music video impacted and inculcated behavioural change in the residents of Lagos state. The chapter specifically sought to investigate if the STEM music video created awareness of waste management, determine whether the STEM music video had any impact on the residents vis-à-vis waste management and ascertain if the STEM music video inculcated behavioural change towards waste management in Lagos state. The chapter utilizes simple random, stratified and purposive sampling techniques to distribute 400 questionnaires. Findings indicate that indeed, the Sterling Environmental Makeover music video created awareness and had an impact on and inculcated behavioural change in the residents of Lagos state towards waste management. Findings further reveal that music videos in indigenous languages are compelling, and other media can also be used to test their prowess. The chapter recommends that government at all levels use various communication media to create awareness and educate residents on the importance of waste management and its effects if not properly managed.
Adversarial Attacks in Spanish Politics: Direct and Indirect Forms

This chapter will focus on the effect on the politician addressed (debasement), understood as the use of derogatory language with the intention to degrade or discredit the politician’s image and identity. In Spain, social rules governing parliamentary behaviour respect, at least formally, the image of the other, and reject direct, aggressive confrontation. This means that everything occurs in a more attenuated tone but politicians look for ways to unleash debasement through more indirect means (accusation, irony, mockery, etc.), and construct their identity based on their ability to defeat others through attacks on their adversaries. These confrontations, on occasion approaching ridicule, are abundant among members of the opposition towards the State President, above all in the case of the far-right Vox, also in the replies by the government towards them. The effect sought is the debasement of the opponent. Regarding the arguments employed, accusations of lying, of not fulfilling the role of the government, of ignorance, ad hominem attacks (dickhead, witch, ugly, cheat) or ideological ones (communist, coup plotter, fear monger), particularly stand out.
Teacher Education in Mobile Assisted Language Learning for Adult Migrants: A Study of Provincial Centres for Adult Education in Italy

Provincial Centres for Adult Education (It. Centri Provinciali per l’Istruzione degli Adulti, CPIA) have in recent years been tasked with teaching Italian to adult migrants. With respect to other educational contexts, language courses in CPIAs exhibit a greater heterogeneity as regards literacy; this fact influences the way learners work with technology. Further, CPIA learners make large use of handheld devices; therefore, mobile assisted language learning constitutes a major issue in this context and teacher education for this technology is of paramount importance today. The chapter reports on a case study about how three CPIA language teachers use mobile technology in their classrooms and how contextual factors influence their work. Based on the findings, a tentative list of issues pertaining to teacher training for mobile assisted language learning (MALL) in this context is discussed.
Optimization with Quantum Algorithm that is Based on Grover’s Method

This article reviews the processes of solving the global optimization problems by Grover’s method of quantum algorithm, and presents all the solutions that are possible at the same time in solving global optimization problems by quantum algorithm and the methods of identifying the correct result. Searching for a solution to a global optimization task is typical for a systematic analysis. Adoption of optimal solutions and management of complex systems in information uncertainty and dangerous conditions have been developing for many years in various directions. In recent years, the solution to this problem is considered successful as a new form of intelligent computing. One of such intellectual computation is Grover’s method of quantum algorithm. The software for solving the optimization problems based on the quantum algorithm is developed and the obtained results are analyzed.