Reviewerly/exHarmony huggingface dataset:
Reading engagement, achievement and learning experiences through kahoot

Side-synchronizing between English reading teaching and learning and latest technology is highly encouraged to happen nowadays in order to get better learning achievement. The main purpose of this study was to find out the effect of Kahoot! on reading comprehension. This study involved 244 students of one of the established private universities in East Kalimantan. Data analysis was conducted using descriptive statistics in the form of frequency, mean, standard deviation and t – test. The results showed that the students’ reading comprehension taught using Kahoot! was higher than that of those without Kahoot!. Furthermore, their cognitive, psychomotor and affective learning attitudes showed positive and significant effects as well. Based on the findings, it implies that online media like Kahoot! is strongly recommended to be used in reading teaching and learning process. It is expected that the finding would enrich and enlighten the relevant literature of the area.
Machines Imitating Human Thinking Using Bayesian Learning and Bootstrap

In the field of cognitive science, much research has been conducted on the diverse applications of artificial intelligence (AI). One important area of study is machines imitating human thinking. Although there are various approaches to development of thinking machines, we assume that human thinking is not always optimal in this paper. Sometimes, humans are driven by emotions to make decisions that are not optimal. Recently, deep learning has been dominating most machine learning tasks in AI. In the area of optimal decisions involving AI, many traditional machine learning methods are rapidly being replaced by deep learning. Therefore, because of deep learning, we can expect the faster growth of AI technology such as AlphaGo in optimal decision-making. However, humans sometimes think and act not optimally but emotionally. In this paper, we propose a method for building thinking machines imitating humans using Bayesian decision theory and learning. Bayesian statistics involves a learning process based on prior and posterior aspects. The prior represents an initial belief in a specific domain. This is updated to posterior through the likelihood of observed data. The posterior refers to the updated belief based on observations. When the observed data are newly added, the current posterior is used as a new prior for the updated posterior. Bayesian learning such as this also provides an optimal decision; thus, this is not well-suited to the modeling of thinking machines. Therefore, we study a new Bayesian approach to developing thinking machines using Bayesian decision theory. In our research, we do not use a single optimal value expected by the posterior; instead, we generate random values from the last updated posterior to be used for thinking machines that imitate human thinking.
Improving Professional Skills of Music Teachers Through the Use of Distance Learning

This paper presents some possibilities of distance learning in music teacher education. The research presented two teacher training programs at Kazan Federal University. The distance learning tools allowed to realize some important subject-oriented principles include self-education, self-realization, and creative approach. The result data were collected from tests results and analysis of self-assessment and questionnaires by quantitative and qualitative experimental methods. The results of the level of musical knowledge and creative activity were increased in the second distance learning group, but not in the first traditional group. Based on the experimental research work, the authors concluded that distance learning environment is a perspective way for develop education system. A distance tool allows us to reflect modern society changes where online format became the priority way for getting new competencies including professional skills in music teacher education.
Mutual Information based Fuzzy Inference System for Classification Problems

Fuzzy inference system (FIS) is one of the most powerful inference systems that is widely used in the field of classification. Indeed, in this approach, FIS is engaged to create a mapping from features (inputs) onto classes (outputs) using fuzzy set theory. So far, many efforts have been made to improve classification accuracy performed by FIS. Generally, these efforts have been put in the following areas: efficient fuzzy rule generation, fuzzy membership function tuning, fuzzy rule weight tuning, feature selection for the antecedent part of fuzzy rules, and so on. In this paper, we consider this issue and propose a method based on mutual information for applying the impact factor of input parameters on the fuzzy inference process for improving the accuracy of fuzzy classification. Finally, we test our proposed method for boosting classification on six different problems using manual and auto-generated FIS. The method provided promising classification results confirming its correctness.
Detection of Stylometric Writeprint from the Turkish Texts

Authorship attribution studies aim to extract information about the author by analyzing the data in the text form. With the increase of anonymous authors in digital environments, the need for these works is increasing day by day. Although there exists lots of studies focuse on stylometric writeprint detection in different languages using different attributes, there is no standard feature set and detection algorithm to be evaluated in these studies. Giving priority to Turkish texts, in this study, which features are more distinctive for determining stylistic writeprint of text, and which methods will contribute to increase the success to be achieved are shown with experimental studies.
To GitHub or Not to GitHub?

While preparing students for careers upon graduation, faculty often have to manage the priorities of bridging the gap between the level of student preparation and the expected level of preparedness. The prioritization of these tasks is influenced by discipline and industry specific guidelines and does not always align with the experience of students at Historically Black College and Universities (HBCU) and Minority Serving Institutions (MSI). The Association of Computing Machinery (ACM) 2013 Computer Science Curricula guidelines highlight version control as one of many core tools in the Social Issues and Professional Practice (SP) Knowledge Area students should experience in their upper-level software engineering courses. Research has demonstrated that at this level, students receive exposure to the tool but are not able to gain sufficient mastery of the tool. Other researchers have advocated for version control being introduced earlier in the computer science curricula. There is limited research that indicates that this particular tool improves the learning outcomes for students or significantly complements the other ACM Knowledge Areas. This presentation seeks to challenge the notion that the use of version control systems improves HBCU students' computational thinking (CT) skills and in turn prepares them for careers beyond graduation. It is important to note, there is no research that directly addresses the use of version control in predominately African American classrooms.
Diffraction-based Ringing Suppression in Image Deblurring

Digital processing of images are essential parts of numerous applications ranging from biomedical image diagnosis to applications in machine vision and artificial intelligence. One of the common problems is image blurring effects due to degradation. Image blurring can inherently be caused by equipment (camera lens), motion of the target object or vibration of the image acquisition device while the image is being captured or other degradation processes. A number of image restorations algorithms have appeared in the literature using both blind and nonblind deconvolution techniques to restored a sharp latent image from its degraded blurred version. Most of these observed deblurred images exhibits an artifact (ringing effect) in the areas around the edges of the restored image. In this study, a method for decreasing these effects is proposed to improve the clarity of the objects in the image and reduce image obscurity. The method makes use of the realization that the ringing artifact also known as the Gibbs phenomenon are mostly caused by the distortion or loss of frequency contents of input image which is unavoidable as most of the deconvolution algorithms operations are performed in frequency domain with help of Fourier transforms. Area with high gradients magnitude (high frequency region) whose frequency contents are likely going to be distorted in the subsequent operations are estimated and modeled from the blurred input image using Gaussian low-pass filter. Then, concept of wave diffraction model using Fraunhofer diffraction equation is used to infused the estimated edge maps to compute the scaled and less distorted Fourier transform of the of the blurred image. Results from the experiment using MMIP ringing dataset show an improved result with suppressed ringing effect in both blind and nonblind deconvolution where the proposed method was used.
Refactoring Cost Estimation for Architectural Technical Debt

Paying-off the Architectural Technical Debt by refactoring the flawed code is important to control the debt and to keep it as low as possible. Project Managers tend to delay paying off this debt because they face difficulties in comparing the cost of the refactoring against the benefits gained. These managers need to estimate the cost and the efforts required to conduct these refactoring activities as well as to decide which flaws have higher priority to be refactored. Our research is based on a dataset used by other researchers that study the technical debt. It includes more than 18,000 refactoring operations performed on 33 apache java projects. We applied the COCOMO II:2000 model to calculate the refactoring cost in person-months units per release. Furthermore, we investigated the correlation between the refactoring efforts and two static code metrics of the refactored code. The research revealed a weak correlation between the refactoring efforts and the size of the project, and a moderate correlation with the code complexity. Finally, we applied the DesigniteJava tool to verify our research results. From the analysis we found a significant correlation between the ranking of the architecture smells and the ranking of refactoring efforts for each package. Using machine learning practices, we took the architecture smells level and the code metrics of each release as an input to predict the levels of the refactoring effort of the next release. We calculated the results using our model and found that we can predict the ‘High’ and ‘Very High’ levels, the most significant levels from managers’ perspective, with [Formula: see text] accuracy.
Transforming Hotel Supply Chain Using Intelligent Decision Support System: Prospects and Challenges

Modern technological innovations are driving the trends to digitalize the hotel supply chains. The most famous and used technologies are artificial intelligence, robotics, blockchain, and advanced predictive analytics. These technologies provide the tools that will inevitably redesign and transform many aspects of the hotel supply chains. Thus, this study aims to investigate the opportunities and chart the path to adopt the Intelligent Decision Support System (IDSS) in hotel supply chain management. IDSS is a computerized information system that facilitates the decision-making process by creating alternatives, which can then be evaluated using a set of predetermined patterns to make reliable and fact-based decisions. To that end, this study engaged in 12 Delphi survey with hotel managers and key industry experts in Egypt and received additional inputs from 70 structured surveys conducted amongst four and five-star hotels located in Cairo, Giza, Alexandria, Sharm El-Sheikh, and Hurghada which allow a deeper insight into the prospective scenarios of how IDSS could be applied in hotel supply chain management. The results unveiled that the hotel supply chains in Egypt are moving towards digital transformation, albeit with slow steps. Also, the results revealed that data quality, analytical decision-making capabilities, and transformational impacts appear to be critical factors that influence the adoption of IDSS. A key finding of this study is that Egyptian hotels cannot reap the integration advantage of IDSS technology without additional engagement with supply chain partners.
A fine-tuning approach research of pre-trained model with two stage

A Fine-tuning method has been mention in BERT, which is a pre-trained model use widely in NLP. In BERT and GPT, they hold that a standard fine-tuning model should there have a minimal difference between pre-trained architecture and the final downsteam architecture, and the task-special model will harm the result. In this paper, we mention two stream model which use hidden state pre-trained in BERT. In order to facilitate the validity of the verification method, We use sentiment analysis tasks to verify the results, which is a very simple text classification task in natural language process. Experiments on Yelp-review-poliarty show that using the same training data and other fine-tuning method, we can reduce ERROR by 0.21%. With the same setup, we can reduce ERROR of Amazon-review-poliarty by 0.13 %.
System Of Continuing Education "School-University-Enterprise" In The Focus Of National Security Issues

This article is devoted to the current problem of education, namely, distance learning in the context of the global coronavirus pandemic.The article presents the experience of the implementation of distance learning, within the framework of the developed system of continuing professional education "schooluniversity-enterprise".This system was implemented at the Belgorod State University named after Shukhov, most of the classes are held in e-learning mode, on specially created online platforms.After the introduction of quarantine, almost all classes were adapted and transferred to a distance format, which makes it possible to study at any time and in any place.In the article, we examined many educational tools of a distance nature: online monitoring of the quality of education, a circle of additional education for children "NIKA", information and analytical system for identifying and supporting gifted children, information and educational portals (website of the advanced training program; website of the professional retraining program; website of additional educational courses for senior students), educational process management system.A sociological analysis of the effectiveness and efficiency of distance learning in this system of continuing professional education "school-university-enterprise" in the context of national security was carried out; the degree of satisfaction of the participants in this system was revealed.A comparative assessment of the data obtained was carried out with data from similar studies conducted at various sites on the Internet in 2020.
Optimize Your React App

Optimizing your code is an advanced topic and is needed to ensure we deliver a quality product that lowers our footprint and loads our app faster. In this chapter, I will highlight some optimizing techniques you should be aware of before you write your first line of code. Topics covered include precaching, lazy loading, code splitting, tree shaking, prefetching, and sprite splitting, to name just a few.
The Power of Cross-unit Data Sharing: Nontraditional Uses for ILLiad

ILLiad, an online interlibrary loan service widely used in libraries, generates detailed reports that support the assessment of interlibrary loan workflows and services. The data generated by these reports may also be utilized in nontraditional ways to inform e-resource troubleshooting workflows and collection development procedures. Louisiana State University assessed ILLiad data over the span of a year to determine best practices for e-resource troubleshooting, as well as to enhance staff training. Austin Peay State University reviewed ILLiad data to develop and implement a new collection development program. This combined presentation details the impact of ILLiad data in shaping new procedures for each respective university and provides insight into the potential of nontraditional applications of ILLiad report data.
CRYPTOCURRENCY MARKET DEVELOPMENT IN LATVIA AND THE BALTIC STATES

The use of digital money creates not only threats, but also opportunities for economic development, but in Latvia and the Baltic countries, they are not sufficiently researched and not widely spread. The paper aims to identify the reasons for the lagging behind of the Baltic countries and Latvia in the development of the cryptocurrency market and to develop recommendations for improving its regulation and functioning. Based on the methods of cross-country, statistical and sociological analysis, the national factors of differences in the functioning and regulation of cryptocurrencies in Latvia, Estonia, and Lithuania have been identified and the risks of its use for illegal activities have been assessed. It is shown that the role of cryptocurrencies in the financial markets of the Baltic States is determined by the degree of their use for payments and investments, which depends on their recognition by the state and investors. It has been revealed that the definition of the concept of cryptocurrency in the EU and the Baltic countries, primarily in the money laundering and terrorist financing (ML/TF) directives, does not stimulate the use of its positive features. A correlation between the restrained attitude towards cryptocurrencies in Latvia and its large-scale business relations with the CIS countries with a higher proportion of shadow economy, caused by the peculiarities of its history and geographical location, has been revealed. The Latvian banking system, under the influence of international regulators, has become less appropriate for operations of non-residents from the East, which increases the risk of using cryptocurrency for ML/FT purposes. The factors of motivation, advantages and difficulties of the businessmen of the Baltic countries they face when dealing with cryptocurrency have been identified, and a discrepancy in the regulation of taxation of transactions with cryptocurrency and the respective activities in Latvia has been revealed. Recommendations are given to the state and private investors pertaining to the integration of cryptocurrencies into the economic space, the implementation of which will contribute to the development of the digital economy and growth of wealth of Latvian residents. The results of the study can be used to improve government regulation and private investments in the cryptocurrency market, as well as in the study and further research of this problem
Mengejar Pekerjaan Impian: Pelatihan Daring Membuat Curriculum Vitae dan Cover Letter

Freshly graduated students are expected to get a job soon, but the competition in the job-seeking world is getting tougher because the number of fresh-graduate continues to increase from year to year. The first stage of employee selection is administrative selection, which can be done from Curriculum Vitae and Cover Letter sent by applicants. Considering that this first stage is crucial for determining the next stage, CV and CL must be made seriously and with consideration. This training aims to provide provisions in the form of strategies for making CV and CL for students who have just graduated from college. The training was attended by 27 students who are currently taking thesis courses and those who have finished their thesis trial using the online method using cyber.unika.ac.id. The training participants had a positive reaction to this training. In addition, their skills in making CV and CL have also improved, seen from the CV and CL results that were collected by the participants after the training ended.
Incremental learning of iterated dependencies

We study some learnability problems in the family of Categorial Dependency Grammars (CDG), a class of categorial grammars defining dependency structures. CDG is a formal system, where types are attached to words, combining the classical categorial grammars’ elimination rules with valency pairing rules defining non-projective (discontinuous) dependencies; very importantly, the elimination rules are naturally extended to the so called “iterated dependencies” expressed by a specific type constructor and related elimination rules. This paper first reviews key points on negative results: even the rigid (one type per word) CDG cannot be learned neither from function/argument structures, nor even from dependency structures themselves. Such negative results prove the impossibility to define a learning algorithm for these grammar classes. Nevertheless, we show that the CDG satisfying reasonable and linguistically valid conditions on the iterated dependencies are incrementally learnable in the limit from dependency structures. We provide algorithms and also discuss these aspects for recent variants of the formalism that allow the inference of CDG from linguistic treebanks.
Shrinkage Under Random Projections, and Cubic Formula Lower Bounds for AC0 (Extended Abstract)

Hastad showed that any De Morgan formula (composed of AND, OR and NOT gates) shrinks by a factor of O(p²) under a random restriction that leaves each variable alive independently with probability p [SICOMP, 1998]. Using this result, he gave an Ω(n³) formula size lower bound for the Andreev function, which, up to lower order improvements, remains the state-of-the-art lower bound for any explicit function. In this work, we extend the shrinkage result of Hastad to hold under a far wider family of random restrictions and their generalization - random projections. Based on our shrinkage results, we obtain an Ω(n³) formula size lower bound for an explicit function computed in AC⁰. This improves upon the best known formula size lower bounds for AC⁰, that were only quadratic prior to our work. In addition, we prove that the KRW conjecture [Karchmer et al., Computational Complexity 5(3/4), 1995] holds for inner functions for which the unweighted quantum adversary bound is tight. In particular, this holds for inner functions with a tight Khrapchenko bound.Our random projections are tailor-made to the function’s structure so that the function maintains structure even under projection - using such projections is necessary, as standard random restrictions simplify AC⁰ circuits. In contrast, we show that any De Morgan formula shrinks by a quadratic factor under our random projections, allowing us to prove the cubic lower bound.Our proof techniques build on the proof of Hastad for the simpler case of balanced formulas. This allows for a significantly simpler proof at the cost of slightly worse parameters. As such, when specialized to the case of p-random restrictions, our proof can be used as an exposition of Hastad’s result.
Some graph theoretical aspects of generalized truncations.

A broader definition of generalized truncations of graphs is introduced followed by an exploration of some standard concepts and parameters with regard to generalized truncations.
Welcome to the Jungle: A Reference Model for Blockchain, DLT and Smart-Contracts (Short Paper)

Blockchain technology has gained increasing attention from research and industry over the recent years. This interest is mainly due to its core property that allows users to perform transactions without a Trusted Third Party (TTP), while offering a transparent and fully protected tracking of these transactions. However, there is a lack of reference models to describe and compare various Blockchain technologies, leading to some confusion between different kinds of solutions. We propose in this paper a reference model aiming to assess and compare different kind of Blockchain-based ecosystems, including Decentralized Applications (DApp).