Reviewerly/exHarmony huggingface dataset:
Ebook Boundaries: The Risk of Transfiguration a Digital Book into a Non-Book

The study tests the hypothesis that ebooks reduce the characteristics of “book” as a communication medium, thus becoming “non-books”. Two hypotheses have been tested: for the preconditions for the failure of the commercial (paid) ebook, and for the preconditions for an end to the ebook in the sense of “book” as a scientific category. Symptoms of the failure of commercial ebooks are as follows: lack of growth on the ebook market, transformation of the book from a commodity into a service, availability for free, as well as resistance by parents and readers. Key reasons for the said resistance are compromised knowledge and learning, and the infringement of readers’ rights. Furthermore, the study establishes the reason for the failure of the ebook in the sense of “book” as a category, which lies in the possibility for manipulation of both its form and content. The conclusion is that investment policies should be targeted at print book publishing while design innovations should be oriented towards physical books and incorporated into paper and print.
Specific features of the use of innovative pedagogical technologies in increasing the financial literacy of students

This article discusses the specifics of using innovative pedagogical technologies to increase the financial literacy of student youth, and mainly highlights the benefits of using collaborative technology. The views of pedagogical scientists on collaborative technology have also been studied. Methods of implementing collaborative technology are analyzed. It also reveals the specifics of the use of innovative pedagogical technologies in improving the financial literacy of students.
Line-of-Sight Rate Estimation Based on Strong Tracking Cubature Kalman Filter for Strapdown Seeker

This paper addresses the inability of the strapdown seeker to indirectly measure line-of-sight (LOS) rate and the strongly nonlinear relative motion equations of missile and target. Strong tracking theory is combined with cubature Kalman filter (CKF), and a novel nonlinear filter for LOS rate estimation is presented; this filter is called the strong tracking CKF (STCKF). On the basis of the relative motion relationship of missile and target, the LOS rate reconstruction model is derived. Then, the STCKF algorithm is proposed by introducing a suboptimal fading factor into the predicted error covariance of the CKF. Finally, the validity and feasibility of the proposed algorithm are verified by simulation. Simulation results show that the proposed STCKF maintains the strong tracking ability for abrupt state changes. A comparison with CKF shows that STCKF can improve the LOS rate estimation precision for a strapdown seeker with better robustness and adaptability.
Fraud Detection Model in Financial Statements BY Using Financial Equity Instruments

In this paper, the fraud detection model in financial statements is examined by using the financial ratios tool. The purpose of the fraud detection model is to examine the financial statements using the financial instrumnet tool. The period of this research is considered to be 1339-1394. The statistical population includes all companies accepted in Tehran Stock Exchange. The statistical sample consisted of 35 healthy companies with healthy , non fraudulent statements in the Tehran Stock Exchange, which had been listed on the Stock Exchange before 1387, and during this period, at least once in the list of the Top Vocational Companies Declared by the Stock Exchange. Research in terms of the purpose of the theoretical type and in terms of analytical-experimental methodology. In order to investigate the relationship between independent variables and dependent variable, two –way independent t-test and Mann-Whitney test as well as developed logistic regression model were used. The results of the research showed that by using the financial statements analysis of listed companies Securities can be discovered in fraudulent financial reporting. The results of the research indicate that all financial ratios include the ratio of total debt to total assets, the ratio of liquidity to current debt, the financial ratios of current assets to current debt, and the ratio of net profit to equity in determining fraud. Has an effective role.
Semantic Patches for Java Program Transformation (Artifact)

The program transformation tool Coccinelle is designed for making changes that is required in many locations within a software project. It has been shown to be useful for C code and has been been adopted for use in the Linux kernel by many developers. Over 6000 commits mentioning the use of Coccinelle have been made in the Linux kernel. Our artifact, Coccinelle4J, is an extension to Coccinelle in order for it to apply program transformations to Java source code. This artifact accompanies our experience report Semantic Patches for Java Program Transformation, in which we show a case study of applying code transformations to upgrade usage of deprecated Android API methods to replacement API methods.
Sparse Bayesian Learning via Stepwise Regression

Sparse Bayesian Learning (SBL) is a powerful framework for attaining sparsity in probabilistic models. Herein, we propose a coordinate ascent algorithm for SBL termed Relevance Matching Pursuit (RMP) and show that, as its noise variance parameter goes to zero, RMP exhibits a surprising connection to Stepwise Regression. Further, we derive novel guarantees for Stepwise Regression algorithms, which also shed light on RMP. Our guarantees for Forward Regression improve on deterministic and probabilistic results for Orthogonal Matching Pursuit with noise. Our analysis of Backward Regression on determined systems culminates in a bound on the residual of the optimal solution to the subset selection problem that, if satisfied, guarantees the optimality of the result. To our knowledge, this bound is the first that can be computed in polynomial time and depends chiefly on the smallest singular value of the matrix. We report numerical experiments using a variety of feature selection algorithms. Notably, RMP and its limiting variant are both efficient and maintain strong performance with correlated features.
Social Position of an Expert as a New Element of Science

The article focuses on analyzing the changes that have occurred in the procedures for assessing scientific knowledge during the period of their massive introduction into the economy, politics and everyday life, as well as the formation of a new social position of the expert. Up to this point, the assessment of scientific knowledge often took place within the scientific community. In that case, a special role was played by “authority” able to evaluate projects basing on criteria intrinsic to the scientific ethos. The active introduction of scientific knowledge into the social life encouraged the emergence of a new expert’s social position differing from an intrascientific “authority”. In their work, they have to evaluate projects that involve large financial resources and contribute to changes in social and economic life, and therefore, in addition to scientific criteria, such experts consider any possible economic, political and social consequences of the project implementation. Along with it, the formation of the new expert’s social position and its active use by scientists leads to several problems. First, the expertise and expert practices are beginning to be used by scientists to legitimize their own position in science and fight rival groups. Second, these processes can provoke a decrease in diversity both within science itself and in the number of cognitive practices; also they can limit the emergence of new approaches to the analysis of technological, economic, political and social innovations.
PSYCHOLOGICAL ANALYSIS FROM PORTRAIT

The article examines the issue of the portrait of a creative person and its role in psycho-psychological analysis on the basis of the interpretation in literature of the great thinker, sheikh, a great representative of mystical literature, the poet Ahmad Yassaviy. Methods of creating portraits and experiments in this area are interpreted on the form of comparative-analytical method. The combination of images of the hero and his biography highlights the creative human psyche. The monologues, in particular, highlight the artistic-aesthetic and artistic-conceptual role of monologue-memory and monologue-video in illuminating the heroic spiritual-psychological world.
Digital Communication and Metadiscourse. Changing Perspectives in Academic Genres

This collection of essays focuses on the interface of three key terms in today’s linguistic, semiotic and cultural framework: academic language, metadiscourse and digital communication. Specifically, it investigates dynamics of knowledge production and dissemination in international academia, by looking at recently established pure screen genres, as well as at the increasing digitalization of traditional genres
Defining and Detecting Toxicity on Social Media: Context and Knowledge are Key

As the role of online platforms has become increasingly prominent for communication, toxic behaviors, such as cyberbullying and harassment, have been rampant in the last decade. On the other hand, online toxicity is multi-dimensional and sensitive in nature, which makes its detection challenging. As the impact of exposure to online toxicity can lead to serious implications for individuals and communities, reliable models and algorithms are required for detecting and understanding such communications. In this paper, we define toxicity to provide a foundation drawing social theories. Then, we provide an approach that identifies multiple dimensions of toxicity and incorporates explicit knowledge in a statistical learning algorithm to resolve ambiguity across such dimensions.
Bidirectional Enhanced Selection Sort Algorithm Technique

Sorting algorithm refers to the arranging of numerical or alphabetical or character data in statistical order (ascending or descending). Sorting algorithm plays a vital role in searching and the field of data science. Most of the sorting algorithms with O(n2) time complexity are very efficient for a small list of elements. However, for large data, these algorithms are very inefficient. This study presented a remedy for the noted deficiencies of O(n2) sort algorithm for large data. Among the O(n2) algorithms, selection sort was the subject of the study considering its simplicity. Although selection sort is regarded as the most straightforward algorithm, it is also considered the second worst algorithm in terms of time complexity for large data. Several enhancements were conducted to address the inefficiencies of selection sort. However, the procedures presented in all the enhancements can still lead to some unnecessary comparisons, and iterations that cause poor sorting performance. The modified selection sort algorithm utilizes a Bidirectional Enhanced Selection Sort Algorithm Technique to reduce the number of comparisons and iterations that causes sorting delays. The modified algorithm was tested using varied data to validate the performance. The result was compared with the other O(n2) algorithm. The results show that the modified algorithm has a significant run time complexity improvement compared with the other O(n2) algorithms. This study has a significant contribution to the field of data structures in computer science and the field of data science.
Exploring links between geodynamics and climate change

&amp;lt;p&amp;gt;Combating global climate change remains one of the greatest challenges facing humanity in the coming decades. Whilst oceanographers, ice sheet dynamicists, and atmospheric modellers all have an obvious role to play in leading efforts to tackle this problem, there remain many aspects that require careful consideration and cross-disciplinary interaction in other areas of the geosciences. In this talk, I will use selected examples to illustrate important links between geodynamics and climate change, including improving our understanding of its potential impacts and mitigation. The first concerns the role of mantle convection in influencing palaeo sea-level records and ice sheet dynamics. For example, Pliocene interglacial periods are commonly invoked as potential climatic analogues for the near-future conditions expected in our warming world, but there is considerable uncertainty over the extent to which important sea-level indicator sites have been perturbed, post-deposition, by convection-induced dynamic topography. The second link involves the growing shortage of metals that are key to the manufacture of technologies for low-carbon energy generation and storage. Tackling this shortfall requires an improvement in our ability to locate new, high-grade metal deposits, particularly those buried beneath shallow sedimentary cover. Novel geodynamical insights into the geological processes responsible for ore genesis will form a core component of narrowing the exploration search-space, and we have recently demonstrated this approach for sediment-hosted metal deposits. Through these case studies, I will show that it is primarily through developing an environment of cross-disciplinary discussion and financial support that our community is most likely to progress in understanding the potential impacts of climate change and how we may mitigate against them. Although one of the least well-studied components, the solid Earth is increasingly being recognised as a critical part of the climate system. Researchers working in topics as diverse as rock mechanics, seismology, convection modelling, and geochemistry all have a crucial role to play.&amp;lt;/p&amp;gt;
Phenomenological Approaches in the Theory of Architecture in the Context of Modern Cognitive Science

The article is a fragment of the scientific research devoted to the analysis of mod­ern approaches to the understanding of architecture. It considers the problems of phenomenological approach to architecture. Applied to architectural issues, the phenomenological approach can be considered as very productive and promising, but only as it appears to be practiced by K. Norberg-Schulz and J. Pallasmaa. The article notes the fundamental unity of views, which is demonstrated by repre­sentatives of the phenomenological approach, despite the individual speci­ficity of the each representative’s position. Probably, the integrity and depth of the phe­nomenological concept (in its “primordial form”) is consonant with the comp­rehensiveness of each individual act of perception. The author also draws atten­tion to the significant circumstance, that the phenomenological approach is in fundamentally irreconcilable contradiction (conceptual and methodological) to the numerous attempts by “architectural semiotics and structuralists” to explain the whole by breaking it up into “semantic fragments”. He analyzes the corre­lation between the position of representatives of the phenomenological approach to architecture and scientific views in the sphere of modern psychology and ana­lytical philosophy. The article notes the basic similarity of the phenomeno­logical concept with M. Polanyi’s theory of “tacit knowledge”, in which those kinds of knowledge (and also practical skills) are considered and which cannot be formal­ized (partially or completely) for the purpose of transferring to others. The author also offers for consideration the introduction of concept of “pheno­menology of semi-instinctive behavior in its dynamic formation”.
A Few Shot Object Detection Method Based on Feature Pyramid Network and Graph Neural Network

Few shot learning has become a research focus in the field of computer vision. Few shot detection method has a very crucial value in practice. But so far, only few results have been achieved in this field, and more research is required. In this paper, we proposed a few shot object detection method, which consists of two subnets. The first subnet is the object detection subnet, which is only responsible for the object positioning. We adopted an anchor box clustering method to produce the proper initial size of anchor boxes. Furthermore, a general class classification method was proposed to make the model learn the common features of different classes. Therefore, it could detect unseen classes when testing. The second subnet is the few shot classification subnet, which is designed to classify the detected objects. Images were preprocessed before input to the few shot detection subnet to remove redundant information. Finally, end-to-end experiments were designed on the ImageNet LOC dataset. Our method reached a high mAP of 80.76% in 5-way-10-shot experiments and significantly outperformed the benchmark method.
Improving Creative Writing for Young Learners using CTL Post COVID 19

The research aims to implement contextual teaching and learning approach in writing skill which is integrated in the lesson plan. The methodology conducted in the study is a case study taken in one of elementary school in Indonesia. The purpose is to investigate the improvement of students’ writing skill post covid 19. There are five strategies of contextual teaching and learning approach, namely relating, experiencing, applying, cooperating, and transferring (REACT). The implementation is providing practices on descriptive and narrative writing. The assessment is the learning project related to real-world context and writing story telling. The results of the research are expected to give impact for enhancing the students’ creative writing, to provide learning process which is practical in real world situation, and to assign a lesson plan which is relevant with the current development in English language teaching.&#x0D; Keywords: contextual teaching and learning, creative writing, lesson plan, REACT
Predictive Intelligence for Learning and Optimization

Many different definitions of intelligence exist but, in the end, they all converge on the brain. In this chapter, we explore the implications of the simple idea that, ultimately, intelligence must help optimize the survival of the individual and of the species. Central to this evolutionary argument, intelligence must offer superior abilities to learn and flexibly adapt to new challenges in the environment. To enhance the possibility of survival, the brain must thus learn to make accurate predictions that optimize the amount of time and energy spent on choosing appropriate actions in a given situation. Such predictive models have a number of parameters, like speed, complexity, and flexibility, that ensure the correct balance and usefulness to solve a given problem (Deary, Penke, & Johnson, 2010; Friedman et al., 2008; Fuster, 2005; Houde, 2010; Johnson-Laird, 2001; Kringelbach & Rolls, 2004; Roth & Dicke, 2005). These parameters come from a variety of cognitive, affective, and social factors, but a main requirement is one of motivation to initiate and sustain the learning process. Finally, one thing is to survive, another is to flourish, and so we discuss whether the intelligent brain is also optimal in terms of wellbeing given that spending too much time predicting something that may never come to pass could be counterproductive to flourishing. Thus, in this perspective, intelligence can be thought of as the process of balancing and optimizing the parameters that allow animals to survive as individuals and as a species, while still maintaining the motivation to do so. Improving the predictive, intelligent brain is a lifelong process where there are important shifts throughout the lifespan in how different aspects and parameters are prioritized.
A Study on problems faced by the academicians in English while writing an academic paper

(Chien 2019, Huang, J. C. (2017)) English is the most predominant and widely used language for communication. It is the most common and widely accepted language of the journals. In order to convey the idea to the scientific society the researchers has to write their article only in English. (Reddy, M. S.et al.,2016)The common mistakes that a researcher makes in writing a research paper are grammatical mistake, using correct tenses and punctuation.
Extracting ground object information from remote sensing images based on DeepLabv3+ model integrating dual attention mechanism

Ground object information extraction is the key to remote sensing image applications. High-resolution remote sensing images contain complex feature information. However, traditional feature information extraction methods have certain accuracy limitations, while deep learning techniques largely make up for the shortcomings of traditional methods. Aimed at the slow speed and inaccurate boundary region segmentation in remote sensing image feature extraction by the DeepLabv3+ model, this paper introduces an attention mechanism, embedding the spatial and channel attention mechanism modules in the feature extraction network. The combined model was tested on the ISPRS remote sensing dataset and achieved 78.68% accuracy. The results show that the proposed network structure is capable of generalization and is feasible in ground object information extraction from high-resolution remote sensing images.
The Relationship Between Human Resource Practices and Organizational Performance

This study aims to analyses how Jordanian public universities take advantage of the relationship between HRM practices and organizational performance with their combined dimensions. This study is so important for different sectors to choose appropriate HRM practices which help to organizational performance in the organization. As well as, the organizational performance in the Jordanian universities is low comparing with expected results. However, HRM practices integration formulate strong and influential instruments to put a figure on ways Organizational performance. Thus, the problem of this research relates to the gap between the expected organizational performance and the actual organizational performance. The literature review outlined a wide range of relations between HRM practices and it is a positive relationship. The results display gaps in the plane and the implementation of researches. the researchers observed the dominance of positive results, in the studies that seek to evaluate the impact of HRM on performance.
Effective ways of assessment of reading comprehension

At the first sight, testing reading comprehension seems easy and straightforward compared to productive skills. In testing oral abilities or writing there is hearable and seeable data to assess, while in receptive skills it is difficult to assess the process which occurs consciously or unconsciously in the brain system. However, taking any passage and designing simple items and addressing it to students cannot give the expected results on the measuring reading comprehension of students. Due to the unobservable process in reading assessment, designing reading tests cause major difficulties for test designers. For instance, on measuring a particular skill it is impossible to measure that particular skill since it is cannot be observed cause of conscious process in the brain and the teacher cannot know exactly student uses that particular skill which is intended to measure by the test designer. While reading there is several stages to achieve ultimate comprehension of the text including decoding the symbols into words, transforming the words into meaningful content, and understanding them. As a second language reader, this process may contain extra stages.