Reviewerly/exHarmony huggingface dataset:
COMPONENTS OF ICT COMPETENCE OF TEACHERS OF MATHEMATICS AND INFORMATICS

Nowadays, the competency-based approach is becoming more and more traditional in Ukrainian education for every level – from preschool to postgraduate. Its focus is on two main concepts, “competency” and “competence”, interpreted in numerous ways in teaching and learning, which leads to inadequate educational results. Therefore, profound understanding and clear vision of the main concepts are very significant for the effective organization of the educational process. The article examines the ICT competency as a prerequisite for effective professional activities of pre-service teachers of mathematics and informatics in conditions of information society and digital world. The proper ICT competence level can considerably enhance both the teaching and learning processes, facilitate teachers’ professional development and their adaptation to changes in educational technologies. In this regard, defining the structure and content of ICT competency of pre-service teachers of mathematics and informatics is very important for clarification of educational process outcomes and students’ supporting personal characteristics needed to utilize ICT in their future job. The study introduces the conceptual model of structure of ICT competency and competence, which enlarges the focus of educational process to embrace value orientations and qualities of a person. In our opinion, the structure of ICT competency as well as of ICT competence should include the following criteria: knowledge, skills, habits, value orientations (motives, stimuli, values, interests and ambitions) and person’s qualities (abilities). Also, the article discusses the content of ICT competence of pre-service teachers of mathematics and informatics, based on the introduced model. ICT competence content data were acquired from empirical survey that involved students and practicing informatics teachers.
Information Security Evaluation based on Artificial Neural Network

In order to improve the information security ability of the network information platform, an information security evaluation method is proposed based on artificial neural networks.Based on the comprehensive analysis of the security events in the construction of the network information platform, the risk assessment model of the network information platform is constructed based on the artificial neural network theory.The weight calculation algorithm of artificial neural networks and the minimum artificial neural network pruning algorithm are also given, which can realize the quantitative evaluation of network information security.The fuzzy neural network weighted control method is used to control the information security, and the non-recursive traversal method is adopted to realize the adaptive training of the information security assessment process.The adaptive learning of the artificial neural network is carried out according, and the ability of information encryption and transmission is improved.The information security assessment is realized.The simulation results show that the method is accurate, and the information security is ensured.
Development and Efficiency Validation of Training Course on Smart Farm based on STEM Education: A Case Study of Abalone Mushroom

This research article aims to present the development and efficiency validation of the training course on Smart Farm based on STEM education. The abalone mushroom was selected as the case study of this research. This study comprised of a training set, a mobile phone application, information sheets and media presentation. The training course was organized with a sample group of 30 people. The results can be formulated as follows: 1) the quality of the developed training package was evaluated by the experts and obtained the average value of 4.34 (Good); 2) the efficiency of the developed training course met the standard criteria of Meguigans (1.18); and 3) the developed training course obtained the satisfaction at the average score of 4.46. In conclusion, it does not exaggerate to state that this training course on Smart Farm on STEM education can be applied in teaching and learning efficiently.
Cyberbullying Behavior Patterns in Adolescents in Jakarta

This research is motivated by the phenomenon of cyberbullying in adolescents. Cyberbullying has the potential to harm victims such as loss of self-confidence, depression and decreased performance. Therefore, this study aims to determine the behavior patterns of cyberbullying perpetrators and victims of adolescents in Jakarta. Cyberbullying behavior patterns in this study refers to the concept of Willard which classifies seven behaviors: flaming, online harassment, denigration, impersonation, outing and trickery, exclusion, and cyberstalking. This research has been conducted with descriptive research, by taking high school students in DKI Jakarta as sample. A total of 400 students have been chosen by using multistage random sampling. Data was collected through a questionnaire and measured using the Student Assessment Survey (SAS). The results showed the involvement of students as perpetrators and victims of cyberbullying. Behavior that tends to be used by students as cyberbullying is denigration (77.2%), harassment (74%), and flaming (70%). While students who are victims of cyberbullying, the treatment that tends to be experienced is denigration (79.2%), flaming (78.7%) and harassment (75.2). The results of this study suggest the need for strategies to prevent cyberbullying behavior by literacy using social media effectively.
An Analysis of Exports and Imports and Their Effect on the Economic Growth in Iraq

The Iraqi economy faces more challenges than opportunities, especially in recent years due to the civil war, while basic reforms for merging the private and public sector have commenced. This paper examines the causal relationship between exports, imports, and Iraq’s economic growth. The data are annual time series for the period 1980-2017. Thereafter, the data are stationary in different levels. Johansen cointegration is applied to figure out the long-run association among the variables. Moreover, Granger causality test has been used to direct the causality among variables. This paper finds that in the long run, exports and imports on gross domestic product are co-integrated and variables have a long-run association. The Granger causality result shows that exports affect economic growth, while imports also have a positive impact on Iraq’s economic growth. On the contrary, the relationship between exports and imports show that any increase in the volume of exports will increase the volume of imports. However, the converse is not true as the volume of imports does not influence exports in Iraq.
An Intrinsically-Motivated Approach for Learning Highly Exploring and Fast Mixing Policies

What is a good exploration strategy for an agent that interacts with an environment in the absence of external rewards? Ideally, we would like to get a policy driving towards a uniform state-action visitation (highly exploring) in a minimum number of steps (fast mixing), in order to ease efficient learning of any goal-conditioned policy later on. Unfortunately, it is remarkably arduous to directly learn an optimal policy of this nature. In this paper, we propose a novel surrogate objective for learning highly exploring and fast mixing policies, which focuses on maximizing a lower bound to the entropy of the steady-state distribution induced by the policy. In particular, we introduce three novel lower bounds, that lead to as many optimization problems, that tradeoff the theoretical guarantees with computational complexity. Then, we present a model-based reinforcement learning algorithm, IDE3AL, to learn an optimal policy according to the introduced objective. Finally, we provide an empirical evaluation of this algorithm on a set of hard-exploration tasks.
A study on Hierarchical Clustering and the Distance metrics for Identifying Architectural Styles

With rapid advances in information technology, explosive growth is recorded in data generation and data collection capabilities in all areas. For this purpose, it is also necessary in an adjacent field such as constructing a solution to facilitate the detection of architectural styles of buildings, so developing a solution for rapid identification of architectural style would add value to smart city. Keeping in mind the numerous types of buildings and their appurtenance in images, the identification of style represents an important problem to solve. The developed application that aims at studying clustering efficiency is based on CBIR. This study reviews the most used distances (Euclidean and Manhattan) in the Data Mining techniques, providing an overview of the impact on the classification.
Ranking-Based Semantics for Sets of Attacking Arguments

Argumentation is a process of evaluating and comparing sets of arguments. Ranking-based semantics received a lot of attention recently. All of the semantics introduced so far are applicable to binary attack relations. In this paper, we study a more general case when sets of arguments can jointly attack an argument. We generalise existing postulates for ranking-based semantics to fit this framework, define a general variant of h-categoriser, prove that it converges for every argumentation framework and study the postulates it satisfies. We also study the link between binary and hypergraph version of h-categoriser.
Forming Information Culture Of Senior Pupils While Teaching Information Technologies

The paper justifies timeliness of its subject by analysis of various points of view. Information technologies facilitate improvement of academic motivation and student knowledge enrichment. Information-based understanding of the world, comprehension of information vision, knowledge of a multitude of information sources and methods of working with them, skills in creating new information, searching and processing information from remote media, storing and transmitting available information. The information culture is analyzed as a component of general human culture, as a component in the information-based understanding of the world and system of knowledge and skills providing goal-oriented independent activities. An attempt has been made to justify original nature of the information culture. The main idea of this research is forming in senior pupils a culture in operation with information technologies and information within the framework of the learning process. The research involved studying theoretical sources. Forms and methods of forming the information culture in senior pupils were analyzed and the most efficient ways of forming the information culture of senior pupils are presented as elements of a continuous process. The essence and content of the concept of information culture have been established. A methodology has been proposed to use information technologies in teaching senior pupils. Methodological recommendations have been developed with the aim of assisting a teacher in developing the information culture in pupils. Many of the considered research materials may find practical use in teaching Information Technology in general education school.
Analysis Level of Digital Literacy of Digital Natives: How The Impact On Their Self-Regulated Learning?

This study aims to analyze the level of digital literacy of millennial generation and how it affects their abilities in their learning process (self-regulated learning). Data were obtained through an online questionnaire distributed to students at several universities in Yogyakarta. The number of questionnaires that can be processed is 100 questionnaires. Furthermore, the collected data is then analyzed using Structural Equation Modeling (SEM). The results showed that the level of digital literacy of female students could be said to be higher than that of male students. However, both male and female students have not so high scores on the dimensions of critical understanding as one dimension of digital literacy. The results of this study also show that digital literacy has a positive effect on self-regulated learning.
Combining Neural Language Models for Word Sense Induction

Word sense induction (WSI) is the problem of grouping occurrences of an ambiguous word according to the expressed sense of this word. Recently a new approach to this task was proposed, which generates possible substitutes for the ambiguous word in a particular context using neural language models, and then clusters sparse bag-of-words vectors built from these substitutes. In this work, we apply this approach to the Russian language and improve it in two ways. First, we propose methods of combining left and right contexts, resulting in better substitutes generated. Second, instead of fixed number of clusters for all ambiguous words we propose a technique for selecting individual number of clusters for each word. Our approach established new state-of-the-art level, improving current best results of WSI for the Russian language on two RUSSE 2018 datasets by a large margin.
Learning to Learn Morphological Inflection for Resource-Poor Languages

We propose to cast the task of morphological inflection—mapping a lemma to an indicated inflected form—for resource-poor languages as a meta-learning problem. Treating each language as a separate task, we use data from high-resource source languages to learn a set of model parameters that can serve as a strong initialization point for fine-tuning on a resource-poor target language. Experiments with two model architectures on 29 target languages from 3 families show that our suggested approach outperforms all baselines. In particular, it obtains a 31.7% higher absolute accuracy than a previously proposed cross-lingual transfer model and outperforms the previous state of the art by 1.7% absolute accuracy on average over languages.
Intelligent Fault Diagnosis of 3D Printers based on Reservoir Computing

Fault diagnosis is important for the working conditions of 3D printers, because the failure of 3D printers will have a great impact on the quality of printed products and result in unqualified printing.In this paper, the reservoir computing (RC) method and the data collected by the attitude sensor are analyzed to obtain the health status of a 3D printer.Considering the economics and viability of fault diagnosis, a low-cost attitude sensor is installed on the moving platform of the 3D printer to collect tri-axial angular velocity, tri-axial acceleration, and tri-axial magnetic field strength signals.Then, the collected data is divided into training data and test data.The training data is used to establish the optimization parameter of the RC model to improve its performance, and the test data is used to identify the failure patterns using the model.Finally, compared with the SAE and SVM intelligent diagnosis techniques, the RC method achieves the best fault recognition accuracy, which further proves its superiority.
Utilization of Scientific Publication Media to Improve the Quality of Scientific Work

Abstract&#x0D; The publication of scientific work is an absolute thing that must be owned and produced by academics at this time. Moreover, when referring to the Minister of Administrative and Bureaucratic Reform (PAN RB) Regulation No. 17 of 2013 and the Minister of Education and Culture Regulation No. 92 of 2004 which states that the increase in the academic level of lecturers requires publication of accredited national scientific journals and journals internationally reputable in their field. In addition to being very important for the performance of lecturers, the publication of scientific papers has become a government regulation through the Director General of Higher Education, which requires S1, S2 and S3 students to make a summary of scientific work published both online and in print as one of the graduation requirements. Seeing this, Raharja College has participated in providing publication media for scientific works, especially in online forms, one of which is iLearning Journal Center (iJC). Until now iLearning Journal Center has overseen 5 (five) journals in it with different scope of research. However, the problems that occur at this time are still a lack of the general public to know especially in the Higher Education environment regarding the iLearning Journal Center (iJC) as a publication media for online scientific work. In this study will be discussed about the steps or methods taken to maximize the use of iLearning Journal Center (iJC) as an online journal publication media to improve the quality and quantity of scientific works. This study uses SWOT analysis method and system design using the Unified Modeling Language (UML) and the applications used in this study by applying the Open Journal System (OJS) which is known as management software and publishing online journals. The results of this study are a governance or management that can be done as a step to maximize the increase of publication of online scientific works for the academic community.&#x0D; Keywords: iLearning Journal Center (iJC), Scientific Work Publication, Journal Online, Open Journal System (OJS)
Masked Sentence Model Based on BERT for Move Recognition in Medical Scientific Abstracts

Abstract Purpose Move recognition in scientific abstracts is an NLP task of classifying sentences of the abstracts into different types of language units. To improve the performance of move recognition in scientific abstracts, a novel model of move recognition is proposed that outperforms the BERT-based method. Design/methodology/approach Prevalent models based on BERT for sentence classification often classify sentences without considering the context of the sentences. In this paper, inspired by the BERT masked language model (MLM), we propose a novel model called the masked sentence model that integrates the content and contextual information of the sentences in move recognition. Experiments are conducted on the benchmark dataset PubMed 20K RCT in three steps. Then, we compare our model with HSLN-RNN, BERT-based and SciBERT using the same dataset. Findings Compared with the BERT-based and SciBERT models, the F1 score of our model outperforms them by 4.96% and 4.34%, respectively, which shows the feasibility and effectiveness of the novel model and the result of our model comes closest to the state-of-the-art results of HSLN-RNN at present. Research limitations The sequential features of move labels are not considered, which might be one of the reasons why HSLN-RNN has better performance. Our model is restricted to dealing with biomedical English literature because we use a dataset from PubMed, which is a typical biomedical database, to fine-tune our model. Practical implications The proposed model is better and simpler in identifying move structures in scientific abstracts and is worthy of text classification experiments for capturing contextual features of sentences. Originality/value T he study proposes a masked sentence model based on BERT that considers the contextual features of the sentences in abstracts in a new way. The performance of this classification model is significantly improved by rebuilding the input layer without changing the structure of neural networks.
Upcycling a Schol Comm Unit: Building Bridges with Creativity, Reallocations, and Limited Resources

Despite limited resources, the nascent Scholarly Communication Unit of the David L. Rice Library has focused on creatively developing the themes of NASIG’s Scholarly Communication Competencies within and outside the library in order to develop scholarly communication services at the University of Southern Indiana. This paper describes the creation and development of the unit, its strengths and weaknesses, and some lessons learned, in the hopes that more libraries like ours will see scholarly communication work as valuable and attainable.
An Objective for Hierarchical Clustering in Euclidean Space and Its Connection to Bisecting K-means

This paper explores hierarchical clustering in the case where pairs of points have dissimilarity scores (e.g. distances) as a part of the input. The recently introduced objective for points with dissimilarity scores results in every tree being a ½ approximation if the distances form a metric. This shows the objective does not make a significant distinction between a good and poor hierarchical clustering in metric spaces.Motivated by this, the paper develops a new global objective for hierarchical clustering in Euclidean space. The objective captures the criterion that has motivated the use of divisive clustering algorithms: that when a split happens, points in the same cluster should be more similar than points in different clusters. Moreover, this objective gives reasonable results on ground-truth inputs for hierarchical clustering.The paper builds a theoretical connection between this objective and the bisecting k-means algorithm. This paper proves that the optimal 2-means solution results in a constant approximation for the objective. This is the first paper to show the bisecting k-means algorithm optimizes a natural global objective over the entire tree.
Multi-Point Semantic Representation for Intent Classification

Detecting user intents from utterances is the basis of natural language understanding (NLU) task. To understand the meaning of utterances, some work focuses on fully representing utterances via semantic parsing in which annotation cost is labor-intentsive. While some researchers simply view this as intent classification or frequently asked questions (FAQs) retrieval, they do not leverage the shared utterances among different intents. We propose a simple and novel multi-point semantic representation framework with relatively low annotation cost to leverage the fine-grained factor information, decomposing queries into four factors, i.e., topic, predicate, object/condition, query type. Besides, we propose a compositional intent bi-attention model under multi-task learning with three kinds of attention mechanisms among queries, labels and factors, which jointly combines coarse-grained intent and fine-grained factor information. Extensive experiments show that our framework and model significantly outperform several state-of-the-art approaches with an improvement of 1.35%-2.47% in terms of accuracy.
Towards Interpretation of Pairwise Learning

Recently, there are increasingly more attentions paid to an important family of learning problems called pairwise learning, in which the associated loss functions depend on pairs of instances. Despite the tremendous success of pairwise learning in many real-world applications, the lack of transparency behind the learned pairwise models makes it difficult for users to understand how particular decisions are made by these models, which further impedes users from trusting the predicted results. To tackle this problem, in this paper, we study feature importance scoring as a specific approach to the problem of interpreting the predictions of black-box pairwise models. Specifically, we first propose a novel adaptive Shapley-value-based interpretation method, based on which a vector of importance scores associated with the underlying features of a testing instance pair can be adaptively calculated with the consideration of feature correlations, and these scores can be used to indicate which features make key contributions to the final prediction. Considering that Shapley-value-based methods are usually computationally challenging, we further propose a novel robust approximation interpretation method for pairwise models. This method is not only much more efficient but also robust to data noise. To the best of our knowledge, we are the first to investigate how to enable interpretation in pairwise learning. Theoretical analysis and extensive experiments demonstrate the effectiveness of the proposed methods.