Reviewerly/exHarmony huggingface dataset:
TECHNOLOGIES FOR CRITICAL THINKING DEVELOPMENT OF A STUDENT-PHILOLOGIST IN THE PROCESS OF LITERARY EDUCATION

One of the priority educational tasks of both secondary and higher school is the critical thinking development. In the concept of «New Ukrainian School», critical thinking is one of the basic skills of the student, and the law «On Higher Education» defines the ability to think freely and self-organize in modern conditions, which is, in other words, the ability to think critically. In this way, the appropriate orientation of modern education in Ukraine raises the question of technologies for the critical thinking development. The article considers the technologies for critical thinking development of a student-philologist in the process of literary education. In the pedagogical perspective, critical thinking is understood as the ability, in particular, to analyze, compare, evaluate, identify problems, make hypotheses, justify decisions, and so on. All these skills are organically activated in the analysis of a literary work, and its correct course develops criticality as effectively as possible. Traditionally, there are three stages of critical thinking technology, such as a challenge stage, where it is important to arouse students’ interest in the problem; comprehension, where students’ activities are organized in such a way that they explore, comprehend, and actively work; and reflection, when students summarize the importance and functionality of acquired knowledge. The author gives examples of analysis of a literary work that forms criticality, taking into account the stages of challenge, comprehension, and reflection. The stages of critical thinking technology are determined on the example of the analysis of J. D. Salinger’s story «Pretty Mouth and Green My Eyes» and the passage «To Be or Not to Be» from Shakespeare’s tragedy «Hamlet». The study does not cover all the theoretical and practical aspects of the problem of technologies for the critical thinking development, which are the prospects for further exploration. According to the tasks of higher education, the formation of critical thinking of a student of philology should become one of the priority tasks. In the process of literary teaching, critical thinking is effectively developed during the analysis of a work of art, the well-thought-out course of which is able to maximize the ability to analyze, compare, evaluate, identify problems, hypothesize, justify decisions and more. The study does not cover all the theoretical and practical aspects of the problem of technologies for the development of critical thinking, which are the prospects for further exploration.
Simulation and Analysis of Network Security Performance Using Attack Vector Method for Public Wifi Communication

The use of wifi networks in public spaces has a risk of robbery of user access data in cyberspace, such as banking transactions, social media and other online access. The threat of Man In The Middle Attack (MITM) attacks is carried out on public wifi networks to gain access to user information by illegal means. The attack vector simulation process is carried out on the site access exampleriset.com/dvwa/login.php. ARP Poisoning attack with Ettercap device performs interception and manipulation which provides 08: 00: 27: 25: 22: 99 MAC address information to the target. Session Hijacking attack simulations are carried out using a cookie manager plugin on the HTTP and HTTPS protocols. SSL Stripping attacks by better intercepting and downgrading HTTPS to HTTP communication protocols. Poisoning ARP attacks get information from targets such as Mobile IP MAC Address 192.168.3.249 F4: 09: D8: EA: EC: E7 and Server 192.168.3.5 08: 00; 27: CC: 59: OE and user: admin Passed: password. The results of the Session Hijacking attack on the HTTP protocol get a session id in the form of phpsessiid = 4f1pnfr081e4jero11truspb60 \ r \ n which is used the specified session id time without entering user authentication. The Session Hijacking attack on the HTTPS protocol was unsuccessful and the SSL Striping attack on the HTTPS protocol was unsuccessful
Wargames: Analyzing the Act of War Exclusion in Cyber Risk Insurance Coverage and Its Implications for Cybersecurity Policy

Cyber risk insurance coverage has become an increasingly vital tool permitting both public and private-sector organizations to mitigate an array of cyber risks, including the prevalent issue of ransomware. However, despite the rapid uptake of these policies, a series of have emerged, which have in turn only been exacerbated given the COVID-19 pandemic. Litigation has centered on issues ranging from what constitutes “covered computer systems” as many employees are working from home, to questions of negligence. Among the most vexing issues, though, with arguably wide-ranging implications for not only the cyber risk insurance industry, but on U.S. cybersecurity policy generally, consist of when a cyber attack that has been attributed back to a foreign nation constitutes an act of war thus excluding coverage. As one example, the 2017 NotPetya cyber attack resulted in more than $10 billion in damages globally, including more than $100 million to the multination food conglomerate, Mondelez International. However, when Mondelez filed a claim with its insurance firm, Zurich International, to recover these costs its claim was denied because NotPetya was considered a “hostile or warlike action” by a “government or sovereign power.” Mondelez countersued, alleging breach of contract, and the case remains pending in Illinois state court as of this writing. A similar case involving damage from NotPetya on Merck is likewise pending in New Jersey. Yet, the literature to date has largely ignored this vital issue. This Article makes several original contributions to this debate. First, it couches this issue as part a set of cybersecurity dilemmas facing organizations that are manifest in the ransomware epidemic. Relatedly, it summarizes findings from a statewide cybersecurity survey that was conducted in collaboration with the Indiana Attorney General’s Office. Second, it analyzes current pending litigation related to the act of war exclusion, and the impact of the 2019 Eleventh Circuit’s Universal Cable Productions LLC v. Atlantic Specialty Insurance Company holding, which called into question the efficacy of these exclusions in certain cases. Third, it brings in lessons not only from U.S. cybersecurity policy, but also on the applicable international law on defining acts of cyber war and related challenges of attribution. By way of conclusion, a standard is suggested to help guide courts, policyholders, and insurance companies in navigating these issues going forward.
Research on identification technology of encircled serial tags based on big data and semantic analysis

In order to further improve the accuracy of tag recognition, this paper proposes a tag recognition technology based on big data and semantic analysis. Firstly, the big data technology is used to establish the analysis model, identify the behavior of the surrounding tags, and collect and classify the characteristics of the surrounding tags. Based on the complex network, the model of circle recognition is established. Secondly, based on the principle of multi-objective and semantic analysis, the intelligent association rule mining model is constructed to optimize the identification steps of encirclement and collusion in big data. Through the data experimental results, the hidden rules behind the transaction data are revealed, and the transaction track and behavior characteristics of surround bidding and collusion are depicted, so as to effectively reflect the internal institutional root of inducing surround bidding and collusion behavior, which can provide reference for the future The next step of the bidding supervision department is to suit the remedy to the case, establish a comprehensive and effective mechanism for the prevention and control of bidding collusion, and provide strong technical support.
School Literacy Movement

Literacy is an ability that students must develop from an early age.This literacy is related to reading and writing.This ability will later become provisions for future children's lives in their daily lives.However, the level of literacy in Indonesia is still low.This condition shows that the Indonesian people are still weak in accepting various sources of information and even easily accepting hoax news among the public.In early childhood, literacy education can be started with the habit of reading story books and fairy tales to children regularly.Efforts that can be done by implementing the school literacy movement, the implementation of the school literacy movement can include the habituation stage, development and learning.The teacher can invite children to write, listen and communicate a theme or topic in the book that the child reads.Although this activity is a simple activity, however, reading books to children is the initial stage of their introduction to the world of literacy.
Protective Football Headgear and Peripheral Visuomotor Ability in NCAA Football Athletes: The Role of Facemasks and Visors

The purpose of this investigation was to determine the effects of varying facemask reinforcement and visor tint on peripheral visuomotor abilities in collegiate football players. Division I NCAA football players (n = 14) completed two peripheral visuomotor experiments: (1) Varying facemask reinforcement, (2) Varying visor tinting. In experiment 1, participants were tested under the following conditions: baseline (no helmet; BL), helmet + light (HL), helmet + medium (HM), helmet + heavy (HH), and helmet + extra heavy (HXH) reinforced facemasks. In experiment 2, participants were tested under the following conditions: baseline (no helmet; BL), helmet only (HO), helmet + clear (HCV), helmet + smoke-tinted (HSV), and helmet + mirror-tinted (HMV) visors. For each condition, a 60 s peripheral visuomotor test was completed on a Dynavision D2 visuomotor board. For experiment 1, the BL peripheral reaction time (PRT) was faster than all facemask conditions (p &lt; 0.05). Furthermore, PRT was impaired with the HXH compared to HL (p &lt; 0.001), HM (p &lt; 0.001), and HH (p = 0.001). Both HH and HXH resulted in the potentiation of PRT impairments in the outermost and inferior peripheral visual areas (p &lt; 0.05). In experiment 2, BL PRT was faster than all helmeted conditions (p &lt; 0.05). Additionally, PRT was slower in HSV (p = 0.013) and HMV (p &lt; 0.001) conditions compared to HO. HMV resulted in slower PRT in all peripheral areas (p &lt; 0.05) while PRT was impaired only in outer areas for HSV (p &lt; 0.05). Wearing protective football headgear impairs peripheral visuomotor ability. Lighter reinforced facemasks and clear visors do not appear to exacerbate impairment. However, heavier reinforced facemasks and tinted visors further decrease visuomotor performance in outer and inferior visual areas, indicating a potential need for considerations of on-field player performance and safety.
Dynamic Tensor Recommender System

In this talk, we propose a new dynamic tensor recommender system, which aims particularly at long-term recommendation.The proposed method utilizes a tensor-valued function of time to integrate time information and contextual information, and creates a time-varying coefficient canonical polyadic decomposition with grouping effects through polynomial spline approximation.Major advantages are that the proposed method is able to provide competitive long-term recommendations and prediction intervals.Specifically, it provides smooth extrapolation through penalized polynomial splines effectively.In theory, we establish asymptotic consistency of the estimated parameter and asymptotic normality of the estimated coefficients.The proposed method is applied to simulations and IRI marketing data.Numerical studies demonstrate that the proposed method outperforms existing methods in long-term forecasting.
Multi-group Flower Pollination Algorithm Based on Novel Communication Strategies

Multi-group Flower Pollination Algorithm (MFPA) based on novel communication strategies was proposed with an eye to the disadvantages of the Flower Pollination Algorithm (FPA), such as tardy convergence rate, inferior search accuracy, and strong local optimum. By introducing a parallel operation to divide the population into some groups, the global search capability of the algorithm was improved. Then three new communication strategies were proposed. Strategy 1 combined high-quality pollens of each group for evolution and replaced the old pollens. Strategy 2 let each group’s inferior pollens approaching to the optimal pollen. Strategy 3 was a combination of strategies 1 and 2. Then, experiments on 25 classical test functions show that MFPA based on novel communication strategies has a good global optimization ability, improving the convergence speed and accuracy of the FPA. Thus, we compare MFPA using three strategies with FPA and PSO, its result shows that MFPA is better than FPA and PSO. Finally, we also applied it to two practical problems and achieved a better convergence effect than FPA.
Teaching Design and Implementation Based on R Language Under the Background of Big Data

With the rapid development of big data, R language course has become one of the important data processing courses offered by colleges and universities at home and abroad. The development of R language foundation course combines the needs of post and industry development, and reorganizes the teaching contents and methods of the course. It can change the traditional cramming teaching, adopt the student-centered teaching mode, and strengthen the cultivation of students' professional ability.
Heterogeneous Information Access System with a Natural Language Interface in the Context of Organization of Events

The article presents an innovative approach to providing information to event and conference participants using a natural language interface and chatbot. The proposed solution, in the form of chatbots, allows users to communicate with conference/event participants and provide them with personalized event-related information in a natural way. The proposed system architecture facilitates exploitation of various communication agents, including integration with several instant messaging platforms. The heterogeneity and flexibility of the solution have been validated during functional tests conducted in a highly scalable on-demand cloud environment (Google Cloud Platform); moreover the efficiency and scalability of the resulting solution have proven sufficient for handling large conferences/events.
Differences in Colonial Experience and the Institution-Economic Growth Nexus in West Africa

Does the quality of institutions affect economic growth in West African countries? Which institutional variable aids or harms economic growth in the region? Is the effect of institutions on economic growth in former French-colonised countries different from that of British-colonised countries? This study addresses these questions. Specifically, we first examined the effect of six institutional variables on economic growth for each of the 13 West African countries. Then, we employed panel data estimation techniques to examine the overall effect of the quality of institutions on the economies of the region. Finally, we grouped the 13 countries into French-colonised and British colonised countries following the argument of Acemoglu, Johnson and Robinson (2001,2005) and then examined the impact of institutional quality on the economic growth of these subgroups. Our findings reveal that the effect of institutional variables on the economy of each country varies. Overall, we find that government stability and democratic accountability have a positive and significant influence on economic growth, while control of corruption and socioeconomic conditions have deleterious effects on economic growth. Finally, institutions contribute positively to economic growth in French-colonised countries compared to British-colonised countries. The results imply that there is a need to strengthen institutions in West Africa, especially in former British colonies.
A Brief Review of Intelligent Rule Extraction Techniques

Rule extraction is a process of extracting rules which helps in building domain knowledge. Rules plays an important role in reconciling financial transactions. This paper presents a brief study of intelligent methods for rule extraction. The paper touches upon heuristic, regression, fuzzy-based, evolutionary, and dynamic adaptive techniques for rule extraction. This paper also presents the state-of-the-art techniques used in dealing with numerical and linguistic data for rule extraction. The objective of the paper is to provide directional guidance to researchers working on rule extraction.
Objects of intellectual property rights created by artificial intelligence: international legal regulation

In modern conditions of development of public relations, the creation of objects of intellectual property rights by artificial intelligence is becoming more widespread. With this in mind, it is important to analyze the international legal experience of regulating the use of artificial intelligence as the author of intellectual property, to further borrow it for domestic laws, as well as to pay attention to problematic aspects of such regulation and make proposals to resolve inconsistencies. The study clarifies the international legal regulation of intellectual property rights created by artificial intelligence, as well as analyses the problematic issues of regulation of artificial intelligence by international law and the features of such regulation in Ukraine and presents positions on the development of artificial intelligence systems and prospects, as well as the prospects for its impact on world society.
Modeling of monitoring processes with uneven and fuzzy observation intervals

The paper presents the results of applying a separable mathematical model for analyzing fuzzy time series with uneven and fuzzy data sampling intervals. The study of the efficiency of an advanced quantile modeling algorithm is presented. The implementation of models of measurement sequences with fuzzy steps is conducting by applying the approach based on α-levels. The center of weight method was used for scalarization the fuzzy result. A separable model was used for modeling the processes of clinical monitoring of patients with diabetes.
LMS MOODLE USE EXPERIENCE DURING DISTANCE LEARNING IN HIGHER EDUCATION INSTITUTIONS

High competition in the educational service market and the necessity to provide advanced level of quality in training of specialists require introducing into traditional educational form a new format based on information technologies and interactive methods of educational process by research and educational staff of higher education institution. The specific of MOODLE using for distance learning during the quarantine COVID-19 is considered and its advantages and disadvantages for training management and administration specialists are discussed in the article. The questionnaire which was carried out by authors among students of the Accounting and Finance Faculty of the Centralukrainian National Technical University in terms of their attitude about LMS MOODLE use for distance training during spring semester of 2019–2020 academic year students shows the next results: Most of the students had a positive attitude to distance learning – 48,3 %, negative – 34,5 %, neutral – 17,2 %. From students prospective the main advantages were the opportunity to study and work at the same time (32.4 %) to plan more flexible study schedule (29.7 %). Existence of such negative situation as wish to cheat while performing tests and tasks in MOODLE. Difficulties for lecturers to control stand-alone basis tests and tasks performance by students. The main problem was the independent study of lectures and practical tasks by students without teachers’ support. To improve the effectiveness of training asynchronous Moodle or Google Classroom platforms and learning sessions in Zoom, Meet, Skype, etc should be combined. This approach allows visualizing educational material with presentations and simplifying training materials by students, as well as expanding opportunities for students with special educational needs to obtain professions in management and administration.
MODEL PREDIKSI LEVEL AIR DI LAHAN PERKEBUNAN KELAPA SAWIT DENGAN JARINGAN SARAF TIRUAN BERDASARKAN PENGUKURAN SENSOR RAIN GAUGE DAN ULTRASONIK

Monitoring and regulating water levels in oil palm swamps has an essential role in providing sufficient water for crops and conserving the land to not easily or quickly deteriorate. Presently, water level is still manual and has weaknesses, one of which is the accuracy of the data taken depending on the observer. Technology such as sensors integrated with artificial neural network is expected to observe and regulate water levels. This study aims to build a prediction model of water levels in oil palm plantations with artificial neural networks based on the rain gauge and ultrasonic sensors installed on Automatic Weather Station (AWS). The obtained results showed that the prediction model runs well with an R2 value of 0,994 and RMSE 1,16 cm. The water level prediction model in this research then tested for accuracy to prove the model's success rate. Testing the water level prediction model's accuracy in the dry season obtained an R2 value of 0,96 and an RMSE of 1,99 cm. Testing the water level prediction model's accuracy in the rainy season obtained an R2 value of 0,85 and an RMSE value of 4,2 cm. Keywords : artificial neural network, automatic weather station, palm oil, water level
Learning discrete state abstractions with deep variational inference

Abstraction is crucial for effective sequential decision making in domains with large state spaces. In this work, we propose an information bottleneck method for learning approximate bisimulations, a type of state abstraction. We use a deep neural encoder to map states onto continuous embeddings. We map these embeddings onto a discrete representation using an action-conditioned hidden Markov model, which is trained end-to-end with the neural network. Our method is suited for environments with high-dimensional states and learns from a stream of experience collected by an agent acting in a Markov decision process. Through this learned discrete abstract model, we can efficiently plan for unseen goals in a multi-goal Reinforcement Learning setting. We test our method in simplified robotic manipulation domains with image states. We also compare it against previous model-based approaches to finding bisimulations in discrete grid-world-like environments. Source code is available at this https URL.
Meta Analis Efektivitas Model Discovery Learning terhadap Hasil Belajar Kognitif Siswa SD

The research was conducted to understand the influence of discovery learning models on student cognitive learning outcomes. The development of a learning model is needed to improve the quality of students in school so that it can directly improve the learning outcomes of students, the application of less attractive learning models will make students feel bored and affect the learning outcomes of students be less than optimal. This study uses the Meta-Analysis design, Meta-Analysis in this study to examine the effectiveness of the Discovery Learning learning model on student cognitive learning outcomes. From the results of the analysis of 20 studies, it was found that the effect size obtained was 1.86, so it could be said that learning carried out using the Discovery Learning model affected improving cognitive learning outcomes of elementary school students.