Reviewerly/exHarmony huggingface dataset:
Eye movement pattern of reading Chinese Classics

Abstract This article explores the use of eye movement technology in studying the reading pattern of Chinese classical text. Written in a character-based ancient language, Classical Chinese or wenyanwen, the Chinese classics are known to be difficult to apprehend for most contemporary readers. In this study, a new character-oriented eye movement technology, Region of Interest in Reading (ROIR), was invented to detect readers’ attention allocation in reading. Based on ROIRs, first fixation duration, gaze duration, and total fixation duration were formulated to discuss readers’ cognitive processes and reading patterns when reading Classical Chinese text. The empirical study was conducted using Tao Te Ching (道德经) as the reading material. The study provides foundational technology for the construction of Chinese-oriented eye movement control model, and the findings also shed light on the teaching of Chinese Classics.
Fuzzy Production Rules on Estimations of Buck DC-DC Power Converter Parameters

The paper presents fuzzy production rules, which model estimations of power converter parameters. A generation of such rules with authors’ software is proposed. All models are implemented in .NET and MATLAB applications; this process continues works on software for index matrices. The approach is useful on power electronic device modeling; in this way it is possible both power circuits and controllers to be handled by a single tool.
Stopping Floods with Buckets: Attack and Countermeasure for IOTA Autopeering

Distributed Ledger Technology (DLT) is one of the most promising technology at present with applications in different domains, such as Cyber-Physical Systems (CPS) and Internet of Thing (IoT). IOTA is an example of Directed Acyclic Graph (DAG)-based DLTs applicable to IoT, addressing the issues of scalability, consensus complexity, and mining incentives of blockchains. In order to join the network and to later disseminate fresh information, nodes exploit IOTA's autopeering module. The security of this module is hence of fundamental importance for the network liveness, however this requirement has never been analyzed in the literature. In this paper, we provide the first security analysis of the autopeering module of the Coordicide, IOTA's main framework. We divide the contribution in two parts: i) we show that the peer-discovery process in the autopeering module is vulnerable to flooding attack, a type of Denial of Service (DoS), and ii) we propose Time Constrained Identity Match (TID), a novel protocol to mitigate the flooding attack. In particular, we compare two implementations of TID: leaky-TID based on leaky bucket, and token-Tid based on token bucket. We discuss their suitability to IOTA both in terms of effectiveness in mitigating the flooding attack and in terms of resource consumption (memory and CPU). Based on the results, we observe that leaky-TID shows the better performance in preventing flooding attacks, reducing the traffic under attack by 67% and reducing memory consumption by 12.5%.
How Bitcoin Works

The former Soviet Union used to be the largest country in the world, with its territory occupying about one-sixth of the entire Earth’s land surface and controlling about half of Europe (all the modern Eastern European countries). As we can see today, most subjects of the “Evil Empire” were not joined voluntarily. Therefore, the communists also had to have the biggest army in the world to be able to watch over such a vast territory. So, they had a draft, a mandatory enrollment into the armed forces, when every young man (well, almost everyone) had to spend two or three years (depending on the military branch they were drafted into) at the most dangerous place you can even imagine.
Testing Systems

The chapter conducst a case study of an automated voice recognition system used to detect English language testing fraud.It showcases the errors, mistakes and harm caused by automated decision making.
A Study on the Comparison of Elementary Preservice Teachers’ Noticing to a Mathematics Lesson

The purpose of this study is to understand and compare the characteristics of elementary preservice teachers' noticing to a mathematics lesson. This study investigated the noticing of 21 1st-year preservice teachers and 21 3rd-year preservice teachers who were taking the elementary teacher education program in a University of Education in Korea, with focus on the categories such as Actor, Topic, Stance, and Knowledge. As results of the study, the 3rd-year preservice teachers showed more advanced noticing than the 1st-year preservice teachers, in particular, in terms of lesson design and teacher questioning in the pedagogy of Topic category, mathematical content and mathematics process in the mathematical thinking of Topic category, reflection in Stance category, and pedagogical content knowledge in Knowledge category. On the other hand, as aspects to be improved in the noticing of 3rd-year preservice teachers, mathematics tasks in mathematics lesson and each student’s mathematics learning process were revealed. This study suggested implications for elementary mathematics teacher education based on the findings.
Visible Mending

In a series of photographic vignettes, we consider several forms of visible mending, keeping in mind the contrasts between the physical world and virtual worlds. Visible mending means repair that is intended to augment rather than merely salvage the value of a design, as a matter of aesthetics or activism for examples. The contribution of this pictorial critique is the presentation and illustration of an inventory of several forms of visible mending, sometimes literal and sometimes abstract, sometimes digital and sometimes physical.
Perceptions of human movement researchers and clinicians on the barriers and facilitators to health research data sharing in Africa

Introduction The benefits of research data sharing abound in the literature. However, some factors define how researchers and clinicians approach the challenges surrounding sharing human movement health research data.Purpose To describe the perceptions of human movement researchers and clinicians on the barriers and facilitators to research data sharing in Africa.Method A qualitative descriptive design with a purposive sampling method was used. In-depth interviews with human movement researchers and clinicians across Africa were conducted online via Microsoft Teams. Sixteen (n = 16) participants took part in this study. This sample size was representative of East, West, Northern, and Southern Africa. Efforts made to engage with participants in Central Africa were unsuccessful.Result Five themes emerged: 1) the researcher-clinician gap; 2) technological pros and cons in Africa; 3) cost matters; 4) bureaucracy and ethical factors; and 5) the unique African perspective. Mainly, barriers rather than facilitators to data sharing exist among African human movement researchers and clinicians.Conclusion There needs to be a societal and psychological shift through reorientation to encourage data sharing among African human movement researchers and clinicians.
Elements of crisis e-learning: Perspectives of Polish teachers

The aim of the research was to investigate teachers' perspectives on the elements of emergency e-learning during the COVID-19 pandemic. The research was conducted with 134 teachers from different types of schools in Poland during the first wave of crisis e-learning (March - May 2020). The variables included in the analysis comprise teachers' use of differentiated teaching methods, student collaboration, school support for modern ICT, and teachers' digital competence. The findings are summarized as follows: (1) about a third of students did not develop the ability to work together in emergency e-learning; (2) more than two-thirds of teachers underlined that their schools actively promoted the idea of implementing ICT in education; (3) more than two-thirds of teachers emphasized that their school principals had systematically modernized the IT facilities necessary for effective teaching; (4) approximately half of the teachers were supported by the school authorities in strengthening their digital competence; and (5) teachers used various teaching methods in emergency e-learning, and the most popular methods were videos, presentations, e-learning platforms, and interactive games and applications; (6) teachers who did not differentiate digital teaching methods did not believe in the development of opportunities for soft skills among students; (7) the schools invested in technological facilities and supported the development of digital competence among teachers; and (8) the intensive use of e-learning platforms by teachers increased their positive attitude towards the development of soft competences (e.g., collaboration skills) among students.
Product usability testing and outcomes

The vast majority of literature on usability testing comes from the literature on human–computer interaction, which is coincidentally the youngest of the design professions. The inclusion of utility and usefulness-related outcomes as a component of usability is likely derived from the International Organization for Standardization definition of usability, which describes usability broadly as comprising effectiveness and efficiency, but also implicitly as including utility. It is important to note that the term usability has a very narrow interpretation based on the fit between a product's functioning and users' functional abilities and needs. Like rehabilitation engineering, usability and human-centered design have long been recognized as an important component of industrial design, although its history of product usability testing as an important factor in making successful products is somewhat more recent. Heuristic evaluation uses a small set of evaluators to assess a prototype product for compliance with usability design principles and any applicable usability standards specific to the target audience.
Combinatorial Approach for the Access Points Map Correcting in the Mines Positioning Systems

Due to human factor localization systems in underground mines may be incorrectly installed. Positions of access points may be measured incorrectly. One of the most common errors is mixed access points: several access points replace each other. As a result, localization system output may be inaccurate. This article proposes an algorithm for resolving mixed access points positions problem. While the most obvious solution, brute-force search, is execution time-consuming, the proposed algorithm allows to significantly reduce this time by applying brute-force search to smaller set of access points chosen by observations of mobile object tracked by the localization system. To evaluate the proposed solution and compare the algorithm to brute-force search, several experiments with |synthetic data were conducted. The experimental results show the execution time advantage of the proposed algorithm over common brute-force search.
Automatic Image Stitching for Stereo Spherical Image

This paper deals with automatic image stitching of two or more images using OpenCV algorithm in computing environment Python. The aim of this work was to create a new image from various input pictures, from multiple camera positions using existing method proposed by OpenCV. The main goal is to prepare algorithm for image stitching where objects are relatively close to the camera. After testing the algorithm, we pointed out some disadvantages of existing method of image stitching and proposed some adjustments for existing algorithm to achieve better results in 3D image reconstruction.
Radio Frequency Fingerprinting based on Circulant Singular Spectrum Analysis

Radio Frequency fingerprinting is a technique to identify wireless devices on the basis of their intrinsic physical features, which can be extracted by signals generated during transmission. In recent times, a number of studies have explored this identification approach using a variety of techniques, features and machine learning algorithms. This paper proposes a new technique based on the adoption of Circulant Singular Spectrum Analysis (CSSA), which is a recent extension of Singular Spectrum Analysis (SSA). To the knowledge of the author, this is the first time in literature that CSSA is applied to the problem of Radio Frequency fingerprinting. The proposed technique is applied to a recently published data set with signals extracted from 16 Bluetooth wireless devices. The experimental results show that the CSSA based approach outperforms significantly, in terms of accuracy, the original SSA and the other approaches based on the time domain and frequency domain features usually adopted in literature.
Dynamic memory network with spatial-temporal feature fusion for visual tracking

Recently, single object tracking has demonstrated great success. However, due to various problems caused by fast motion, occlusion, and deformation, it is still intractable for traditional trackers to adapt to changes in object appearance. In this work, a dynamic memory network is proposed for visual tracking to handle the template matching process globally. More specifically, we first build a memory model, which consists of a memory feature fusion module and a memory bank. By the memory model, the network not only accepts the first frame as initial information but also memorizes the selective frames in the video sequence to provide rich time-domain information. Second, an innovative sampling strategy is adopted in the tracking process. By updating the template and guiding the selection of memory frames, our model can output higher-quality features. In addition, a spatial-channel fused attention module that effectively improves the representational capability and discriminability of the model is introduced. Our proposed method obtains compelling results on six challenging tracking benchmarks, including the OTB100, VOT2019, UAV123, NFS, GOT-10k, and LaSOT datasets. Extensive experiments demonstrate that our approach shows satisfactory robustness and leading application potential in real-time speed.
Methodology of Training Managers in the Higher Education Institution of Physical Culture According to Statistics in Accordance with the Relevance of the Transition to Domestic Software

The article describes the content, structure and features of the work program of the discipline “Statistics” in the direction of training 38.03.02 “Management”, the training profile “Management of the organization”, updated taking into account the new requirements of the digital economy, methodological recommendations of the Ministry of Digital Development, Communications and Mass Media of the Russian Federation for the transition of state-owned companies to the predominant use of domestic software and professional standards prepared in accordance with the requirements of the Federal State Educational Standard and professional standards. General professional competencies, labor functions, labor actions, necessary knowledge and skills are given. To demonstrate the acquired knowledge, skills and ability to perform labor functions in professional practice, achievement indicators are formulated.
Tongue-Tied by Authorities: Library of Congress Vocabularies and the Shakespeare Authorship Question

Despite the existence of a vast literature reflecting hundreds of years of scholarship questioning the authorship of the works of Shakespere, the conventional Library of Congress Name Authority File and Library of Congress Subject Headings (LCSH) are unable to accurately describe this literature owing to their assumption that the author was William Shakspere of Stratford-upon-Avon. Adopting a pragmatic, philosophically realist perspective based in social epistemology, this article highlights past and current deficiencies in the authority records concerning Shakespere and proposes changes that would better reflect the nature and purpose of this literature, as well as the historic signifiers of the named persons in question.
Critical Success: The narrative weight of chance in TTRPG based shows (actual play)

In recent years TTRPG (Tabletop Role Playing Game) based shows or actual plays have seen amazing growth, with shows like Critical Role being the number one grossing channel on the streaming platform Twitch .TV and Dimension 20 becoming the flagship content for the newly rebranded Dropout.TV. This shows put in the forefront of their storytelling, chance, and the players´ ability to improvise, to the detriment of a traditional screenplay. The failure or success of every action and the making of every player character (the stats and backstory of the characters are rolled and fleshed out in pre-production phases) is often determined by luck of the draw (dice roll, card draw…), thanks to the rules provided by the TTRPG system of choice (in most cases Dungeons &amp; Dragons 5e). The system allows for Dungeon Masters (the players that run the game and consequently the story) to step out of the rules set and change the impact of some dice rolls, allowing the chance of a natural 20 on D20 roll to completely restructure the narrative of the story. Is this rolling of the die aspect and more important natural 20s and 1s where this study will focus, trying to comprehend how special they really are and how much impact they can have in the result of the show.
Multimodal Genetic Optimized Feature Selection for Online Sequential Extreme Learning Machine

Extreme learning machine (ELM) is a rapid classifier evolved for batch learning mode unsuitable for sequential input. Retrieving data from the new inventory leads to a time-extended process. Therefore, online sequential extreme learning machine (OSELM) algorithms were proposed by Liang et al.. The OSELM is able to handle the sequential input by reading data 1 by 1 or chunk by chunk mode. The overall system generalization performance may devalue because of the amalgamation of the random initialization of OS-ELM and the presence of redundant and irrelevant features. To resolve the said problem, this paper proposes a correspondence multimodal genetic optimized feature selection paradigm for sequential input (MG-OSELM) for radial basis function by using clinical datasets. For performance comparison, the proposed paradigm is implemented and evaluated for ELM, multimodal genetic optimized for ELM classifier (MG-ELM), OS-ELM, MG-OSELM. Experimental results are calculated and analysed accordingly. The comparative results analysis illustrates that MG-ELM provides 10.94% improved accuracy with 43.25% features compared to ELM.
The impact of COVID-19 on the Spanish education system

The crisis caused by COVID-19 has tested the Spanish educational system. On the one hand, it has delved into some of the pre-existing weaknesses: lack and inadequate distribution of resources, high segregation of the students considering their socio-economic origin, elevated levels of year repetition linked to their social origin, and high early school dropout, among others. On the other hand, this situation has shaped a new social and educational reality that has arisen recently, following the restrictions on attendance during the successive waves of the pandemic. The traditional functions of the educational system have been transferred to the families, leading to a growing and unbalanced technologisation of the formative process, increasing the disengagement of the most vulnerable students and worsening educational inequity. In addition to the educative function, it has been weakened the role played by schools as a socialisation environment, caretaker of children and where inequalities are balanced. This context has led to widening the existing learning gaps, and also it has affected the mental health of the actors involved, students and teachers, in a situation that can also be described as a pandemic. In this scenario, different administrations have developed measures and mechanisms that aim to offer a response to face the new challenges which have aroused. The question is whether these measures will be satisfactory, not only to face the specific needs of the pandemic but also the new educational context.
Countering Cybercrime during the Pandemic

The accelerated informatisation of public and private entities and the widespread and often enforced use of electronic communications during the pandemic have highlighted certain cybersecurity-related issues. The sudden increase in users’ online activity is coupled with an increase in the activity of cybercriminals or groups involved in the spread of disinformation. The following chapter presents an analysis of the impact of the pandemic on cybercrime, discussing the most common attack scenarios involving COVID-19. It discusses both attacks observed locally in Poland and global trends and identifies regulatory gaps that facilitate committing prohibited acts. The conducted quantitative research indicates that the growing number of incidents and cybercrimes in Poland is related to the digital transformation of society and economy. In addition to the lack of sufficient legal instruments to combat cybercrime in the face of the pandemic, the research shows that in Poland the pandemic has not served as an impetus for legislative changes that could increase cybersecurity.
CT Image Denoising Using Bilateral Filter and Method Noise Thresholding in Shearlet Domain

Computed tomography (CT) proves to be one of the important apparatuses to diagnose disease in clinical science. The X-beam intensity decides the nature of CT images, if the X-beam portion is higher, the nature of CT picture is improved yet it might create bad effect on the patients. Low portion CT pictures are noisy because of some significant reasons, for example, measurable vulnerability in all physical estimations. The major problem arises that if we lower the CT dose the quality of our image deteriorates and if we increase the dosage the risk on the patient increases. Thus in this paper, a technique is proposed in which bilateral filter and shearlet-based thresholding are used. For better edge safeguarding, we have used the concept of method noise thresholding. From different output metrics such as PSNR, IQI, the accuracy of the resulting CT images is checked and evaluated. The findings are also contrasted with the related state of the art and some recent works as well. After review of the findings, it is noted that our proposed algorithm is better compared to existing algorithms.
Problems of Digital Transformation of Universities in Times of Pandemic

In the wake of the spread of the SARS-CoV-21 virus and the introduction of a temporary limitation in the functioning of universities related to preventing, counteracting and combating COVID-19, the university’s functioning has changed. Due to the urgent situation, universities were forced to seek and implement digital solutions that would enable them to operate. The study aims to demonstrate the introduced statutory and regulatory solutions and assess their implementation and effectiveness. The basic legal acts regulating the principles are the Anti-Crisis Shield 2.0 and Shield 4.0 acts. The implementing measures for universities were also the Regulations of the Minister of Higher Education on the temporary limitation of the functioning of some higher education and science system entities concerning the prevention, counteraction and combating of COVID-19, issued from 11 March 2020, as amended. Internal university regulations settled detailed solutions. Universities had to implement and use digital solutions and programmes. Part of the introduced solutions will permanently support the functioning of the higher education institution.