Reviewerly/exHarmony huggingface dataset:
Practice-Oriented Technology for the Formation of Professional Competencies of Future Physical Education Teachers

The article presents the results of testing the practice-oriented technology for the formation of professional competencies of future physical education teachers. Strengthening the practical orientation of modern education is considered by researchers as a key change in the educational space. The greatest difficulty in the process of forming professional competencies is the search for mechanisms for preparing students to implement the requirements of the standard of general education for meta-subject learning results in a specific subject area – physical culture. The aim of the research was to develop and experimentally substantiate a practice-oriented technology for the formation of professional competencies necessary for the implementation of the requirements of the general education standard for students with a major in physical culture. The study was conducted for 2 years, and involved 45 third-year students majoring in physical culture. The research methods were analysis and generalization, independent pedagogical experiment, testing, observation, survey methods, and methods of mathematical statistics. The developed technology is aimed at mastering students’ professional competencies, including the skills of forming students’ universal educational actions in physical education classes. The technology is based on including students in professional tests, bridging the gap between theoretical and practical training, learning and gaining experience on the basis of a step-by-step formation of competencies. The technology includes three modules: 1) goal-setting for individual educational tasks and various didactic cycles; 2) selection and programming of physical education methods; 3) implementation of practical activities. A system for monitoring and evaluating the level of professional competence formation using interactive methods and techniques was developed for each module. During the experiment, problem areas, typical difficulties and students’ mistakes were identified and classified. As a result of the research, a significant increase in the qualitative values of the level of professional competence formation of future physical education teachers has been established. At the same time, the most significant changes in the level of readiness to solve professional tasks have been noted in the third module, in which professional tests were implemented. The results of the research show that the implementation of the practice-oriented training technology (which involves students’ inclusion in professional tests, the use of activity and interactive methods, simulation and design, technology for solving situational problems and cases) has contributed to the formation of the main professional competencies in future physical education teachers.
LIBRARY ONLINE SERVICES DURING COVID-19: THE EXPERIENCE OF LIBRARIES IN CROATIA AND UKRAINE

The purpose of the article is to identify activities in providing online services during forced quarantine on the example of public libraries’ activities in Croatia and university libraries in Ukraine. The study aimed to identify, analyse, and characterise online services offered by libraries to establish remote customer service during the forced quarantine. The results of the study are presented in three sections. The first analyses the work of Croatian public libraries; the second, presents the experience of Ukrainian libraries; in the third, there is the case of the Scientific Library of Kyiv National University of Culture and Arts is displayed. The scientific novelty of the article is to identify practices for providing online services by libraries in Croatia and Ukraine during the COVID‑19 pandemic. The study shows that Croatian public librarians, as well as Ukrainian librarians, have demonstrated they can quickly adapt and use digital technologies on behalf of their supporters. Libraries went from developing imaginative practices to create online spaces in a matter of weeks. The result was a massive amount of produced content like workshops for children and adults; streamings of online events, organisation of virtual exhibitions, and collecting verified reference sources for research and learning. Another one of the impacts of this crisis was the accelerated development of digital library services, including online membership registration and the acquisition of e-books by public libraries. Public response was more significant than ever, including following, commenting, and sharing digital content created or shared by public libraries. The study found that the challenges of the COVID‑19 pandemic have intensified the digitisation of Ukrainian libraries and led to changes in activities. University libraries quickly adapted to remote work, offering their users a variety of remote services. The authors hope that the considered practices will contribute to disseminating better experience of libraries during quarantine in general and online services in remote maintenance in particular.
Analyzing the Impact of GDPR on Storage Systems

The recently introduced General Data Protection Regulation (GDPR) is forcing several companies to make significant changes to their systems to achieve compliance. Motivated by the finding that more than 30% of GDPR articles are related to storage, we investigate the impact of GDPR compliance on storage systems. We illustrate the challenges of retrofitting existing systems into compliance by modifying Redis to be GDPR-compliant. We show that despite needing to introduce a small set of new features, a strict real-time compliance (e.g., logging every user request synchronously) lowers Redis' throughput by 20x. Our work reveals how GDPR allows compliance to be a spectrum, and what its implications are for system designers. We discuss the technical challenges that need to be solved before strict compliance can be efficiently achieved.
Rethinking the Implementation Tricks and Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learning

Many complex multi-agent systems such as robot swarms control and autonomous vehicle coordination can be modeled as Multi-Agent Reinforcement Learning (MARL) tasks. QMIX, a widely popular MARL algorithm, has been used as a baseline for the benchmark environments, e.g., Starcraft Multi-Agent Challenge (SMAC), Difficulty-Enhanced Predator-Prey (DEPP). Recent variants of QMIX target relaxing the monotonicity constraint of QMIX, allowing for performance improvement in SMAC. In this paper, we investigate the code-level optimizations of these variants and the monotonicity constraint. (1) We find that such improvements of the variants are significantly affected by various code-level optimizations. (2) The experiment results show that QMIX with normalized optimizations outperforms other works in SMAC; (3) beyond the common wisdom from these works, the monotonicity constraint can improve sample efficiency in SMAC and DEPP. We also discuss why monotonicity constraints work well in purely cooperative tasks with a theoretical analysis. We open-source the code at \url{https://github.com/hijkzzz/pymarl2}.
On the automorphism groups of simple polarized abelian fourfolds over finite fields and Jordan constants

We give a classification of maximal elements of the set of finite groups that can be realized as the full automorphism groups of simple polarized abelian fourfolds over finite fields. As an application, we compute the Jordan constants of the automorphism groups of simple abelian fourfolds over finite fields.
일제 강점기 동안의 우리나라 영어교육 - 공립 중등학교를 중심으로

English teaching in Korea during the Japanese colonial period has often been described as the victim of a dark age. Most previous studies on the topic tended to cover diverse aspects of English teaching in Korea at different school levels and in different contexts, thus making it impossible to characterize the nature of English teaching at public schools. In an attempt to better understand the nature of English teaching in Korea during the Japanese colonial period, this study focused on English teaching at Korean public secondary schools during the period. First, the study looked at weekly instructional hours as dictated by four educational edicts to evaluate the commonly made claim that English teaching in Korea experienced a dark age throughout the Japanese colonial period. In addition, the study examined why the grammar-translation method was the most commonly adopted approach to teaching English at public secondary schools. An analysis of the four educational edicts showed that there were substantial differences in the number of instructional hours across different periods of Japanese colonial rule. Thus, characterizing the entire colonial period as a dark age of English teaching seemed to lack validity. In addition, considerations of various contextual factors suggested that the adoption of the grammar-translation when teaching English at Korean public secondary schools during the Japanese colonial period was a forced choice, not a choice mandated by any political entity. Big class size, Japanese teachers’ inadequate English proficiency, high-stakes English examinations, and other factors seemed to be responsible for widespread use of the grammar-translation method.
Best Computer Desks with a Hutch

Make no mistake about it, you have a lot of stuff to store and no place to put it. Time to get a hutch to store it in. Kidding aside, a hutch ...
Blockchain Technology: Methodology, Application and Security Issues

Blockchain technology is an interlinked systematic chain of blocks that contains transaction history and other user data. It works under the principle of decentralized distributed digital ledger. This technology enables cryptographically secure and anonymous financial transactions among the user nodes of the network enabling the transactions to be validated and approved by all the users in a transparent environment. It is a revolutionary technology that earned its emerging popularity through the usage of digital cryptocurrencies. Even though Blockchain holds a promising scope of development in the online transaction system, it is prone to several security and vulnerability issues. In this paper, blockchain methodology, its applications, and security issues are discussed which might shed some light on blockchain enthusiasts and researchers.
Few-shot Font Generation with Localized Style Representations and Factorization

Automatic few-shot font generation is a practical and widely studied problem because manual designs are expensive and sensitive to the expertise of designers. Existing few-shot font generation methods aim to learn to disentangle the style and content element from a few reference glyphs, and mainly focus on a universal style representation for each font style. However, such approach limits the model in representing diverse local styles, and thus makes it unsuitable to the most complicated letter system, e.g., Chinese, whose characters consist of a varying number of components (often called "radical") with a highly complex structure. In this paper, we propose a novel font generation method by learning localized styles, namely component-wise style representations, instead of universal styles. The proposed style representations enable us to synthesize complex local details in text designs. However, learning component-wise styles solely from reference glyphs is infeasible in the few-shot font generation scenario, when a target script has a large number of components, e.g., over 200 for Chinese. To reduce the number of reference glyphs, we simplify component-wise styles by a product of component factor and style factor, inspired by low-rank matrix factorization. Thanks to the combination of strong representation and a compact factorization strategy, our method shows remarkably better few-shot font generation results (with only 8 reference glyph images) than other state-of-the-arts, without utilizing strong locality supervision, e.g., location of each component, skeleton, or strokes. The source code is available at https://github.com/clovaai/lffont.
APPLICATION OF INNOVATIVE TEACHING TECHNOLOGIES IN FORMING COGNITIVE ACTIVITY OF STUDENTS

The article deals with the practiced methods of teaching in the learning process: the active use of innovative methods of teaching the professional Russian language through new technologies. The author indicates the relevance of modern methods of teaching in the formation of cognitive and creative activity of students, in increasing professional competence, personality-oriented development. The practical significance of this article is connected with the use of the results of experience in the application of innovative methods and educational technologies in the process of language teaching and learning, as modern society needs customized competitive specialists who have a professional command of several languages.
Towards Extracting Graph Neural Network Models via Prediction Queries (Student Abstract)

Graph data has been widely used to represent data from various domain, e.g., social networks, recommendation system. With great power, the GNN models, usually as valuable properties of their owners, also become attractive targets of the adversary who covets to steal them. While existing works show that simple deep neural networks can be reproduced by so-called Model Extraction Attacks, how to extract a GNN model has not been explored. In this paper, we exploit the threat of model extraction attacks against GNN models. Unlike ordinary attacks which obtain model information via only the input-output query pairs, we utilize both the node queries and the graph structure to extract the GNNs. Furthermore, we consider the stealthiness of the attack and propose to generate legitimate queries so the extraction can be applied discreetly. We implement our attack by leveraging the responses of these queries, as well as other accessible knowledge, e.g., neighbor connectives of the queried nodes. By evaluating over three real-world datasets, our attack is shown to effectively produce a surrogate model with more than 80% equivalent predictions as the target model.
COMPARISON OF WIRELESS ADAPTERS INTERFERENCE BASED ON DIFFERENCES OF FLOOR POSITION

The wireless network adapter has now been widely developed. The wireless adapter receives signal strength has several levels. It depends on the signal strength of the device with respect to the wireless signal and access point. The external adapter is used by computers that are not equipped with a wireless network adapter. Usually, internet users use a USB wireless adapter. An external adapter can also amplify signal reception from hotspots. The problem is how users consider using an external wireless adapter or just an onboard wireless adapter when there are many networks interfering with the weak signal strength of the access point. The method is implemented by measuring the value of the Received Signal Strength Indicator (RSSI). The purpose of this study is to compare the received signal strength in conditions where the signal before interference and after experiencing interference (interference). Meanwhile, the measured RSSI value is divided into four floors and with different distances for each floor. Each floor is measured by five distances. The average value of signal strength at a distance of 10 meters is -74 dBm using the onboard wireless adapter and -69 dBm using a USB wireless adapter. The value obtained after the interference is -75 dBm and the USB wireless adapter gets -61 dBm. The reduction of the RSSI value between interference and non-interference is 3%. The onboard wireless adapter is affected by interference while the external wireless adapter is not affected.
Large Deviations For Randomly Weighted Sums of Random Measures

Let $\{{\bf \mathcal{Z}}_n:n\geq 1\}$ be a sequence of i.i.d. random probability measures. Independently, for each $n\geq 1$, let $(X_{n1},\ldots, X_{nn})$ be a random vector of positive random variables that add up to one. This paper studies the large deviation principles for the randomly weighted sum $\sum_{i=1}^{n} X_{ni} \mathcal{Z}_i$. In the case of finite Dirichlet weighted sum of Dirac measures, we obtain an explicit form for the rate function. It provides a new measurement of divergence between probabilities. As applications, we obtain the large deviation principles for a class of randomly weighted means including the Dirichlet mean and the corresponding posterior mean. We also identify the minima of relative entropy with mean constraint in both forward and reverse directions.
Revealing Pre-service Teachers’ Reading Skills by Utilizing CEFR in their Self-assessment

An English teacher should meet the standard English skills requirement since proficiency constitutes the foundation of non-native English teachers' professional confidence. This research aims to measure the pre-service teachers’ reading skills by using CEFR level and to investigate pre-service teachers’ awareness regarding their reading skills as their preparation as future English teachers. The study involved 40 (forty) students from the English program in a private University in Yogyakarta, and used Descriptive Mixed method to collect the data. The researcher used the questionnaire for the students' self-assessment, and the EF SET test to assess their reading skill level. The result of the questionnaire obtained shows 20% of students are in level A1, 25% are in level A2, 25% are in level B1, 12% are in level B2, 10% are in level C1, 8% are in the level C2). The test result shows 17% of the students are in level A1, 25% in level A2, 25% in level B1, 20% are in level B2, 8% are in level C1, and 5% are in the level C2. Six students joined the survey, of which 3 (three) rated themselves at a reading level above the result obtained on the test, and 3 (three) others rated themselves at a level lower than the result they got on the test. The result of this research showed that pre-service teachers should improve in English language proficiency and self-assessment. This knowledge expects to be used as information to those involved in the Educational field.
Transfer-based adaptive tree for multimodal sentiment analysis based on user latent aspects

Multimodal sentiment analysis benefits various applications such as human-computer interaction and recommendation systems. It aims to infer the users' bipolar ideas using visual, textual, and acoustic signals. Although researchers affirm the association between cognitive cues and emotional manifestations, most of the current multimodal approaches in sentiment analysis disregard user-specific aspects. To tackle this issue, we devise a novel method to perform multimodal sentiment prediction using cognitive cues, such as personality. Our framework constructs an adaptive tree by hierarchically dividing users and trains the LSTM-based submodels, utilizing an attention-based fusion to transfer cognitive-oriented knowledge within the tree. Subsequently, the framework consumes the conclusive agglomerative knowledge from the adaptive tree to predict final sentiments. We also devise a dynamic dropout method to facilitate data sharing between neighboring nodes, reducing data sparsity. The empirical results on real-world datasets determine that our proposed model for sentiment prediction can surpass trending rivals. Moreover, compared to other ensemble approaches, the proposed transfer-based algorithm can better utilize the latent cognitive cues and foster the prediction outcomes. Based on the given extrinsic and intrinsic analysis results, we note that compared to other theoretical-based techniques, the proposed hierarchical clustering approach can better group the users within the adaptive tree.
Safer and More Efficient Parallel Cryptographic Algorithm and its Implementation in the GPU

In the digital world, the demand for data security during communication has increased. Hash functions are one of the cryptographic algorithms that provide data security in terms of data authenticity and integrity. Nowadays, most online applications require user authentication. These authentications are done on the server-side, which he must manage. As the number of applications increases, building a one-way function will be faster for calculating a hash value for small data such as passwords. In this paper, we will present a sequential cryptographic algorithm and its parallel implementation. We performed security analyses, executed comparisons for different amounts of data, and provided steps for further developing this algorithm. With the construction of this one-way function, we have provided the calculation of hash value in a shorter time for data in small quantities, which speeds up the authentication process on the server and thus speeds up the online services provided by the respective applications. A comparison was made between sequential implementation, parallel implementation on the CPU, and parallel implementation on the GPU using CUDA (Computer Unified Device Architecture) platform.
Detection and Prevention of Cancer through Artificial Intelligence and Machine Learning

In the field of mathematical biology, Cancer research is done to study the growth dynamics of cancerous cells as well as to develop the strategies for its prevention, diagnosis and treatment. Though cancer is one of the life-threatening diseases, early detection can help to control its growth with proper treatment strategies. With the recent advances in Artificial Intelligence (AI), research studies have been initiated to use different Machine Learning (ML) techniques in the detection and classification of different types of cancer in the human body. The main objective of this study is to address the recent developments of different ML techniques to detect and categorize different types of cancer like breast, brain, lung, liver and skin cancer in the human body. In this study, we have highlighted how MLs with supervised, unsupervised and deep learning techniques are used in the detection and treatment of different types of cancer for lowering cancer risk to the patient. For this purpose, we have also studied different types of cancer and their growth processes in the human body.
Boosting Video Representation Learning with Multi-Faceted Integration

Video content is multifaceted, consisting of objects, scenes, interactions or actions. The existing datasets mostly label only one of the facets for model training, resulting in the video representation that biases to only one facet depending on the training dataset. There is no study yet on how to learn a video representation from multifaceted labels, and whether multifaceted information is helpful for video representation learning. In this paper, we propose a new learning framework, MUlti-Faceted Integration (MUFI), to aggregate facets from different datasets for learning a representation that could reflect the full spectrum of video content. Technically, MUFI formulates the problem as visual-semantic embedding learning, which explicitly maps video representation into a rich semantic embedding space, and jointly optimizes video representation from two perspectives. One is to capitalize on the intra-facet supervision between each video and its own label descriptions, and the second predicts the "semantic representation" of each video from the facets of other datasets as the inter-facet supervision. Extensive experiments demonstrate that learning 3D CNN via our MUFI framework on a union of four large-scale video datasets plus two image datasets leads to superior capability of video representation. The pre-learnt 3D CNN with MUFI also shows clear improvements over other approaches on several downstream video applications. More remarkably, MUFI achieves 98.1%/80.9% on UCF101/HMDB51 for action recognition and 101.5% in terms of CIDEr-D score on MSVD for video captioning.