Reviewerly/exHarmony huggingface dataset:
Transfer learning for Twitter sentiment analysis: Choosing an effective source dataset

Sentiment analysis on social media data can be a challenging task, among other reasons, because labeled data for training is not always available. Transfer learning approaches address this problem by leveraging a labeled source domain to obtain a model for a target domain that is different but related to the source domain. However, the question that arises is how to choose proper source data for training the target classifier, which can be made considering the similarity between source and target data using distance metrics. This article investigates the relation between these distance metrics and the classifiers’ performance. For this purpose, we propose to evaluate four metrics combined with distinct dataset representations. Computational experiments, conducted in the Twitter sentiment analysis scenario, showed that the cosine similarity metric combined with bag-of-words normalized with term frequency-inverse document frequency presented the best results in terms of predictive power, outperforming even the classifiers trained with the target dataset in many cases.
Lee at SemEval-2020 Task 12: A BERT Model Based on the Maximum Self-ensemble Strategy for Identifying Offensive Language

This article describes the system submitted to SemEval 2020 Task 12: OffensEval 2020.This task aims to identify and classify offensive languages in different languages on social media.We only participate in the English part of subtask A, which aims to identify offensive languages in English.To solve this task, we propose a BERT model system based on the transform mechanism, and use the maximum self-ensemble to improve model performance.Our model achieved a macro F1 score of 0.913(ranked 13/82) in subtask A.
Survey on Asymmetric Key Cryptographic Algorithms

Cryptography is an essential and effective method for securing information’s and data. Several symmetric and asymmetric key cryptographic algorithms are used for securing the data. Symmetric key cryptography uses the same key for both encryption and decryption. Asymmetric Key Cryptography also known as public key cryptography uses two different keys – a public key and a private key. The public key is used for encryption and the private key is used for decryption. In this paper, certain asymmetric key algorithms such as RSA, Rabin, Diffie-Hellman, ElGamal and Elliptical curve cryptosystem, their security aspects and the processes involved in design and implementation of these algorithms are examined.
Parallel Coordinates Visualization in the ELK Stack

Modern large-scale distributed computing systems, processing large volumes of data, require mature monitoring systems able to control and track in re-sources, networks, computing tasks, queues and other components. In recent years, the ELK stack has become very popular for the monitoring of computing environment, largely due to the efficiency and flexibility of the Elastic Search storage and wide variety of Kibana visualization tools. The analysis of computing infrastructure metadata often requires the visual exploration of multiple parameters simultaneously on one graphical image. Stacked bar charts, heat maps, radar charts are widely used for the multivariate visual data analysis, but these methods have limitations on the number of parameters. In this research the authors propose to enhance the capacity of Kibana, adding Parallel Coordinates diagram - one of the most powerful method for visual interactive analysis of high-dimensional data. It allows to compare many variables together and observe correlations between them. This work describes the development process of Parallel Coordinates as a Kibana plugin, and demonstrates an example of visual data analysis based on the Nginx logs metadata.
Deployment architecture for the local delivery of ML-Models to the industrial shop floor

Abstract Information processing systems with some form of machine-learned component are making their way into the industrial application and offer high potentials for increasing productivity and machine utilization. However, the systematic engineering approach to integrate and manage these machine-learned components is still not standardized and no reference architecture exist. In this paper we will present the building block of such an architecture which is developed with the ML4P project by Fraunhofer IFF.
ASSESSMENT AND ANALYSIS OF DIGITAL LITERACY OF TEACHERS AND UNIVERSITY STUDENTS AS A READINESS FACTOR TO USE DISTANT EDUCATIONAL TECHNOLOGIES

The article reveals the results of a sociological survey that allowed the authors to measure and analyze the levels of digital literacy of teachers and students of the Udmurt State University, as well as to assess the willingness of teachers and students to use digital technologies in the educational process and to the transition to the active use of distant learning technologies, including emergency transition to distant education due to the spread of coronavirus infection and rigid restrictions connected to self-isolation in the spring of 2020. In assessing the readiness of teachers and students for distance education, the authors proceed from the fact that it can be identified on the basis of their digital literacy index. According to the results of a sociological survey, the digital literacy indices of teachers and students are calculated, a detailed analysis of the components of these indices is given, the problem areas in the ICT competencies of teachers and students are identified, and the corresponding conclusions are drawn. The article provides a number of recommendations on the further use of digital technologies in the educational process of the university.
FORMS AND METHODS OF INTERACTION OF SCHOOL AND LOCAL SELF-GOVERNMENT IN THE PROCESS OF SCHOOLCHILDREN'S CIVIC EDUCATION

The article is dedicated to the revelation of forms and methods of interaction of school and local self-government when implementing schoolchildren's civic education. The main forms and methods of this interaction are: support of the activities of socially active schools aimed at providing the academic, health maintaining, entertaining and social services and providing training programs for people's professional activity, ensuring the implementation of projects and programs in civic education (in particular, Swiss-Ukrainian project "Development of Citizenship Competences in Ukraine", Ukrainian project "Zero Waste School", youth project "Deputy quest" etc.), support of the activities of regional youth school organizations ("Youth Parliamentary Club", in particular), conducting actions on schoolchildren's civic education (for instance, "For a Clean Environment", "Citizen" etc.), organization and facilitation of the work of museums, holding children's festivals, regional contests dedicated to different educational and cultural problems, schoolchildren's competitions in subjects, competitions (tourism and local history, sporting, intellectual etc.), tournaments, festivals, games, meetings, excursions and exhibitions dedicated to important dates and events in the life of the country, its history, outstanding people and nature, seminar-practicum in youth civic education, production and distribution of information materials about native land. Due to above-mentioned forms and methods of interaction of school and local self-government, schoolchildren can take active part in community life, introduce non-standard, innovative approaches to solving the problems of local communities and modern managerial and information technologies in local government.
Multiscale Detection of Cancerous Tissue in High Resolution Slide Scans

We present an algorithm for multi-scale tumor (chimeric cell) detection in high resolution slide scans. The broad range of tumor sizes in our dataset pose a challenge for current Convolutional Neural Networks (CNN) which often fail when image features are very small (8 pixels). Our approach modifies the effective receptive field at different layers in a CNN so that objects with a broad range of varying scales can be detected in a single forward pass. We define rules for computing adaptive prior anchor boxes which we show are solvable under the equal proportion interval principle. Two mechanisms in our CNN architecture alleviate the effects of non-discriminative features prevalent in our data - a foveal detection algorithm that incorporates a cascade residual-inception module and a deconvolution module with additional context information. When integrated into a Single Shot MultiBox Detector (SSD), these additions permit more accurate detection of small-scale objects. The results permit efficient real-time analysis of medical images in pathology and related biomedical research fields.
Satisfiability Problems on Sums of Kripke Frames

We consider the operation of sum on Kripke frames, where a family of frames-summands is indexed by elements of another frame. In many cases, the modal logic of sums inherits the finite model property and decidability from the modal logic of summands [Babenyshev and Rybakov 2010 ; Shapirovsky 2018 ]. In this paper we show that, under a general condition, the satisfiability problem on sums is polynomial space Turing reducible to the satisfiability problem on summands. In particular, for many modal logics decidability in PSpace is an immediate corollary from the semantic characterization of the logic.
Canceling the Big Deal: Three R1 Libraries Compare Data, Communication, and Strategies

Canceling the Big Deal is becoming more common, but there are still many unanswered questions about the impact of this change and the fundamental shift in the library collections model that it represents. Institutions like Southern Illinois University Carbondale and the University of Oregon were some of the first institutions to have written about their own experience with canceling the Big Deal several years ago, but are those experiences the norm in terms of changes in budgets, collection development, and interlibrary loan activity? Within the context of the University of California system’s move to cancel a system-wide contract with Elsevier, how are libraries managing the communication about Big Deals both internally with library personnel as well as externally with campus stakeholders? Three R1 libraries (University of Maryland, University of Oklahoma, and Kansas State University) will compare their data, discuss both internal and external communication strategies, and examine the impact these decisions have had on their collections in terms of interlibrary loan and collection development strategies. The results of a brief survey measuring the status of the audience members with respect to Big Deals, communication efforts with campus stakeholders, and impacts on collections will also be discussed.
Pengembangan Perangkat Pembelajaran Bercirikan CTL Berbantuan GeoGebra Menggunakan Model Flipped Learning

The purpose of this study was to develop valid, practical, and effective learning using the GeoGebra-assisted Contextual Teaching &amp; Learning (CTL) learning model using the Flipped Learning model to improve the mathematical representation abilities of junior high school students. This research is research and development (R&amp;D). This research has five stages: analyzing the developed product, developing the initial product, expert validation and revision, small-scale field trials and product revisions, and testing large-scale field and final products. Based on the results of several studies, including lesson plans and valid textbooks. From the limited test, the ten questions of the representation ability test were also valid and reliable. The lesson plans and textbooks developed are also practical in terms of teachers and students. Moreover, the average value of the mathematical representation in the experimental class is classified as effective in increasing students' mathematical representation ability. That way, the mathematics learning device characterized by GeoGebra assisted CTL using the Flipped Learning model can be said to be valid, practical, and effective.
E-Learning During Covid-19: Towards AlMaarif University College

E-learning is one way to make more learners more accessible and flexible and to close the divide between different fields or cultures or time management to the satisfaction of students. The purpose of this paper is to review the current literature on the benefits of online learning from the satisfaction of students in applied technology and the form of blended learning used in the learning process in order to build qualifications and enhance learning outcomes. The methodology focuses on explaining the different ways of e-learning methods and how to communicate through common communication models. The results of this paper demonstrate the effects of the 2019-2020 empiric study of e-learning at the University of Al-Maarif. The study further draws on its clear proof of the satisfaction of students with e-learning models.
Neural network integral representations with the ReLU activation function

In this effort, we derive a formula for the integral representation of a shallow neural network with the ReLU activation function. We assume that the outer weighs admit a finite $L_1$-norm with respect to Lebesgue measure on the sphere. For univariate target functions we further provide a closed-form formula for all possible representations. Additionally, in this case our formula allows one to explicitly solve the least $L_1$-norm neural network representation for a given function.
Parallelisms and Translations of (Affine) SL(2,q)-Unitals

Unitals can be obtained as closures of affine unitals via parallelisms. The isomorphism type of the closure depends on the chosen parallelism, which need not be unique. For affine $\operatorname{SL}(2,q)$-unitals, we introduce a class of parallelisms for odd order and one for square order. Translations are automorphisms of unitals, fixing each block through a given center. For each of the known parallelisms of affine $\operatorname{SL}(2,q)$-unitals, we compute all possible translations with centers on the block at infinity.
Which Heroes to Pick? Learning to Draft in MOBA Games with Neural Networks and Tree Search

Hero drafting is essential in MOBA game playing as it builds the team of each side and directly affects the match outcome. State-of-the-art drafting methods fail to consider: 1) drafting efficiency when the hero pool is expanded; 2) the multi-round nature of a MOBA 5v5 match series, i.e., two teams play best-of-N and the same hero is only allowed to be drafted once throughout the series. In this paper, we formulate the drafting process as a multi-round combinatorial game and propose a novel drafting algorithm based on neural networks and Monte-Carlo tree search, named JueWuDraft. Specifically, we design a long-term value estimation mechanism to handle the best-of-N drafting case. Taking Honor of Kings, one of the most popular MOBA games at present, as a running case, we demonstrate the practicality and effectiveness of JueWuDraft when compared to state-of-the-art drafting methods.
Linguistic vs Encyclopaedic Knowledge. Classification of MWEs from Wikipedia Articles

Abstract This paper reports on the first steps in the creation of linked data through the mapping between the synsets of BTB-WordNet and the articles in Bulgarian Wikipedia. The task of expanding the BTB-WordNet with encyclopaedic knowledge is done by mapping its synsets to Wikipedia articles with many MWEs found in the articles and subjected to further analysis. We look for a way to filter the Wikipedia MWEs in the effort of selecting the ones most beneficial to the enrichment of BTB-WN.
The Effectiveness of Discovery and Inquiry Learning Strategies in Improving Students’ Early Math Skills

The study aimed to find out the effectiveness of discovery and inquiry learning strategies on the efforts of improving the early math skills of students in kindergartens in West Java Province, Indonesia. This is experimental research. The sampling method was done by multi-stage random sampling with probability sampling and it obtained 72 samples of kindergarten students consisting of 36 children from kindergartens in Bandung, West Java who were treated by discovery learning strategy and 36 children from the kindergartens in West Bandung Regency who was treated using inquiry learning strategy. Data were obtained using a checklist of observation sheets for early math skills of kindergarten children. Data analysis used a quantitative descriptive with a t-test. The findings of the study indicate that discovery-learning strategy is better than inquiry learning strategy in improving early math skills of kindergarten students. It is evidenced by the results showing that the average score of group with discovery learning strategy was 111.63 and the average score of group n using inquiry learning strategy was 101.96 with the result of t-count of 5.551 at the 0.05 level of significance. The conclusion of this research there were differences in early math skills between groups of children with discovery learning strategy and those with the inquiry learning strategy. From the results of data processing and discussion of the study, it was concluded that discovery learning strategy was better than inquiry learning strategy in increasing the score of early math skills of kindergarten children.
ChemVox: Voice-Controlled Quantum Chemistry

Over the last decade, artificial intelligence has been propelled forward by advances in machine learning algorithms and computational hardware, opening up myriad new avenues for scientific research. Nevertheless, virtual assistants and voice control have yet to be widely utilized in the natural sciences. Here, we present ChemVox, an interactive Amazon Alexa skill that uses speech recognition to perform quantum chemistry calculations. This new application interfaces Alexa with cloud computing and returns the results through a capable device. ChemVox paves the way to making computational chemistry routinely accessible to the wider community
Optimasi Algoritma Naïve Bayes Classifier untuk Mendeteksi Anomaly dengan Univariate Fitur Selection

System and network security from interference from parties who do not have access to the system is the most important in a system. To realize a system, data or network that is safe at unauthorized users or other interference, a system is needed to detect it. Intrusion-Detection System (IDS) is a method that can be used to detect suspicious activity in a system or network. The classification algorithm in artificial intelligence can be applied to this problem. There are many classification algorithms that can be used, one of which is Naïve Bayes. This study aims to optimize Naïve Bayes using Univariate Selection on the UNSW-NB 15 data set. The features used only take 40 features that have the best relevance. Then the data set is divided into two test data and training data, namely 10%: 90%, 20%: 70%, 30%: 70%, 40%: 60% and 50%: 50%. From the experiments carried out, it was found that feature selection had quite an effect on the accuracy value obtained. The highest accuracy value is obtained when the data set is divided into 40%: 60% for both feature selection and non-feature selection. Naïve Bayes with unselected features obtained the highest accuracy value of 91.43%, while with feature selection 91.62%, using feature selection could increase the accuracy value by 0.19%.
E-TEXTBOOKS FOR THE NEW UKRAINIAN SCHOOL: IMPLEMENTATION EXPERIENCE AND PROPOSALS

The problem of using e-textbooks in educational institutions is becoming especially relevant today, due to the rapid pace of digitalization of education. The article analyses the current legislation on the introduction of e-textbooks in the educational process of modern schools, identifies requirements for their content, structural components and capabilities. Proposals of electronic teaching aids for general secondary education institutions have been analysed. It has been noted that the active development of the market of electronic publications is due to educational needs and the reform of the New Ukrainian School. An analysis of e-textbooks for primary school, recommended by the Ministry of Education and Science of Ukraine for use in educational institutions within the All-Ukrainian experiment «Electronic textbook for general secondary education», has been conducted, their compliance with national standards has been determined taking into account the requirements set out in «The Regulations on electronic textbooks». It has been found out that the state educational policy in Ukraine contributes to the digital transformation of education, determines the priorities of electronic educational resources, and outlines the mechanisms of transition from static scanned versions of paper textbooks to interactive multimedia tools that would meet modern challenges and tasks of providing qualitative educational service. The emphasis is placed on the need to take into account and borrow the best international experience in creating and implementing electronic textbooks in educational institutions, which is dictated by the need to form a competitive and competent person capable of responding quickly to today’s challenges of informatization of all spheres of public life.
A SIMPLE AND EFFICIENT TEXT SUMMARIZATION MODEL FOR ODIA TEXT DOCUMENTS

The present scenario witnesses an exponential growth in the research field pertaining to text mining and automatic text summarization is a relevant topic.Varied methods in this regard have been developed for English and other European languages, but Odia language maintains a nascent status.The complexity and the highly inflective property of the language restrict the existing models for being directly applied on it.This paper proposes effective extractive single document automatic summarizer for Odia text document.Both statistical and clustering methods are applied and their evaluation metric F scores are compared.The present work is the need of the existing scenario.The experimental documents belong to news domain.The proposed algorithms meticulously deal with the complexity of the language and solve the problem of summarization to an appreciable extent.
An analytical review and analysis for the data control and security in cloud computing

Now the major threat comes in the mind is the data security and the storage when the data is shared among several workstation remotely.So, the control on the data and the communication aspect is the major concern now days.
Educational Practice Using Projectile Motion VR Application

In recent years, virtual reality (VR) has become a popular entertainment medium as well as being used for worker-based education. However, VR has not yet been widely applied to school education and the effects of VR on school education have not been assessed. This paper proposes a VR framework for teaching materials that could be introduced into school education and especially secondary education. A projectile motion VR application “Fly Ball” was developed and its effectiveness confirmed through several evaluation experiments with university students. Then, a “Fly Ball” class was presented in three high school classes, after which it was evaluated by the students using a questionnaire. The evaluation results give guidance as to the usefulness of the VR teaching materials and the differences between the classes.