Reviewerly/exHarmony huggingface dataset:
The conceptual-technological model of formation of future foreign language teachers’ awareness of the monitoring activity

This article is devoted to the elucidation of the essence and results of a pedagogical experiment focused on the formation of future foreign language teachers’ awareness for monitoring activity. The pedagogical conditions which are believed to enhance the effectiveness of the process in question, - have been revealed therein. Among the said conditions, the following ones have been determined as leading: the intensification of the role of the pedagogical monitoring and organization of relevant pedagogical educational researchbased activities within the framework of pedagogical disciplines studied; the support of professional development of a teacher as the participant, organizer and facilitator of quality foreign language education and multi-cultural upbringing. Based on of the above-mentioned pedagogical conditions a conceptual model named “Formation of Future Foreign Language Teachers’ Awareness for Monitoring Activity in the Process of Their Professional Training” has been elaborated. The said model functions as a unity of the following components: motivational, didactic-strategic, organizational, content-technological, evaluative. The results of this model’s approbation in the process of the pedagogical experiment carried out have proved its high effectiveness. Furthermore, we have been able of revealing the essence of future foreign language teachers' awareness for monitoring activity being an important professional new development of their personality as consciously acting organizers, dispatchers and facilitators of quality foreign language teaching, deemed important for the future teachers' capability to plan, implement, correct and assess the results achieved during relevant educational tests carried out.
Causes of Traffic Accidents in Taif City and Strategies Required for Treatments

The aim of this important scientific paper is to study the causes of traffic accidents in Taif city and the strategies required to reduce them due to the strategic location of Taif city among the general cities of Saudi Arabia. The methodology used in this study was based on data from the competent authorities and through an electronic questionnaire distributed to a large number of community members in Taif city. The paper discussed the results of the online questionnaire efficiently and concluded with important strategies to improve traffic flow as well as reducing traffic accidents in the future on Taif city. The results obtained were analyzed and discussed. The results proved the technical feasibility of the causes of traffic accidents and important recommendations that should be applied and adhered to reduce them completely.
Portable System for Sampling Liquid Atmospheric Precipitation

The paper considers the possibility of determining the permanent and random sources of pollution of the environment by the level of pollution of liquid atmospheric precipitation. The problems of liquid atmospheric precipitation testing and monitoring of pollution concentration during precipitation are outlined. The expediency of developing of a portable system that allows collecting samples of liquid atmospheric precipitation in autonomous modes at different time intervals and periods of rainfall, regardless of the place of its installation, is substantiated. The design features of the electric and mechanical parts of the device, as well as the algorithm of their work, are described. The results of modeling and checking of the working capacity of the portable device, which determine the total mass flow of water, are described.
A Study on Fitness Function for Substation Fault Restoration

In order to reduce blackout recovery time and economic losses, intelligent methods for substation rapidly fault recovery are required. Genetic Algorithm is one of the approaches to solving optimization problems.BR In this paper, we will study on the fitness function for applying the genetic algorithm for substations recovery. First, the theory(principle) of genetic algorithm is introduced, and then the SOP (Standard Operation Procedure) of the substation is reviewed. A fault recovery algorithm for substation using fitness functions to transformer capacity, DS (Disconnector) switching, transformer and D/L (Distribution Line) is proposed. Finally, the performance of the proposed GA-based fault recovery algorithm is evaluated through several simulation.
Peningkatan Akurasi Klasifikasi Ketidaktepatan Waktu Kelulusan Mahasiswa Menggunakan Metode Boosting Neural Network

Supervised learning in Machine learning is used to overcome classification problems with the Artificial Neural Network (ANN) approach. ANN has a few weaknesses in the operation and training process if the amount of data is large, resulting in poor classification accuracy. The results of the classification accuracy of Artificial Neural Networks will be better by using boosting. This study aims to develop a Boosting Feedforward Neural Network (FANN) classification model that can be implemented and used as a form of classification model that results in better accuracy, especially in the classification of the inaccuracy of Terbuka University students. The results showed the level of accuracy produced by the Feedforward Neural Network (FFNN) method had an accuracy rate of 72.93%. The application of boosting on FFN produces the best level of accuracy which is 74.44% at 500 iterations
DESIGN AND ANALYSIS OF DATA CLASSIFICATION WITH CONTROL OF SAFETY SCHEMES

Effective and efficient safety of control system statistics, in terms of both operational difficulty and cost, involves that the types of data used in the system be recognized and classified according to their importance in operating the control system safety.Lastly, the practical execution particulars has defined that will run the level of safety stated by the security profile.The method it has presented in this research paper considers both the criticality of the statistics to the usual processes of the control system and exclusive features of a control system.The goal is a description of a practice for emerging an efficient, effective safety scheme for process control statistics.Our aim is to certify suitable levels of data safety while minimizing influence on the control system processes.
Basic Model for Memory Retention of Advertising Content in Outdoor Advertising

The production of signboard advertisements with high advertising effectiveness has been achieved with some craftsmanship. The authors have consistently studied advertising effects on signboards in order to optimize the advertising effects of signboard advertisements. In this paper, we focused on modeling the memory retention structure among the advertising effects. This shows that it is possible to design billboard advertisements according to each purpose. Our final goal is to develop a model that can be used in practice.
Modeling and Publishing Finnish Person Names as a Linked Open Data Ontology

Funding Information: Acknowledgments This work is part of the Anoppi project36 funded by the Ministry of Justice in Finland. Thanks to Aki Hietanen, Saara Packalen, Tiina Husso, and Oili Salminen of the Ministry of Justice, and Risto Talo, Jari Linhala, and Arttu Oksanen of Edita Publishing Ltd. for collaboration. Thanks also to Aleksandra Konovalova from University of Helsinki and Esko Kirjalainen from The Finnish Digital Agency for insightful discussions. CSC – IT Center for Science, Finland, provided us with computational resources. Funding Information: This work is part of the Anoppi project36 funded by the Ministry of Justice in Finland. Thanks to Aki Hietanen, Saara Packal?n, Tiina Husso, and Oili Salminen of the Ministry of Justice, and Risto Talo, Jari Linhala, and Arttu Oksanen of Edita Publishing Ltd. for collaboration. Thanks also to Aleksandra Konovalova from University of Helsinki and Esko Kirjalainen from The Finnish Digital Agency for insightful discussions. CSC - IT Center for Science, Finland, provided us with computational resources. Publisher Copyright: Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Learning to Support Derivation of Adaptable Products in Software Product Lines

Software product line engineering is a large scale development paradigm based on mass production.It consists in building a common platform from which a set of products can be derived.Under the constraints of continuous evolution and costs optimization, the derivation process must be able to answer customers' requirements and provide adequate products in a short time without defects.However, this purpose cannot be achieved if the customer must wait for the change is implemented in the common platform.In this paper, we describe our work which proposes a framework to manage derivation of adaptable products.An adaptable product is obtained by deriving the most similar product from the common platform and changing it to support the new requirements.The aim of the framework is to give quick response to the customers need while the new requirements are being implemented in the common platform.The framework includes tools for processing natural language requirements, computing the similarity between products on the basis of their requirements, and the product adaptation effort measuring.
Parallel processor scheduling: formulation as multi-objective linguistic optimization and solution using Perceptual Reasoning based methodology

In the era of Industry 4.0, the focus is on the minimization of human element and maximizing the automation in almost all the industrial and manufacturing establishments. These establishments contain numerous processing systems, which can execute a number of tasks, in parallel with minimum number of human beings. This parallel execution of tasks is done in accordance to a scheduling policy. However, the minimization of human element beyond a certain point is difficult. In fact, the expertise and experience of a group of humans, called the experts, becomes imminent to design a fruitful scheduling policy. The aim of the scheduling policy is to achieve the optimal value of an objective, like production time, cost, etc. In real-life situations, there are more often than not, multiple objectives in any parallel processing scenario. Furthermore, the experts generally provide their opinions, about various scheduling criteria (pertaining to the scheduling policies) in linguistic terms or words. Word semantics are best modeled using fuzzy sets (FSs). Thus, all these factors have motivated us to model the parallel processing scenario as a multi-objective linguistic optimization problem (MOLOP) and use the novel perceptual reasoning (PR) based methodology for solving it. We have also compared the results of the PR based solution methodology with those obtained from the 2-tuple based solution methodology. PR based solution methodology offers three main advantages viz., it generates unique recommendations, here the linguistic recommendations match a codebook word, and also the word model comes before the word. 2-tuple based solution methodology fails to give all these advantages. Thus, we feel that our work is novel and will provide directions for the future research.
코칭리더십이 조직구성원의 혁신행동에 미치는 영향: 긍정피드백과 창의적 자기효능감의 다중 매개효과를 중심으로

The purpose of this study is to explore the effect of coaching leadership on employee's innovative behavior and to empirically verify the multi-mediated effects of positive feedback and creative self-efficacy in the process. To this end, a total of 396 subordinate staff members of Korean companies were surveyed, 345 members surveys we completed. The validity of the questionnaire was verified using SPSS 25.0 and AMOS 25.0 using Process Macro 3.0.Research has shown that the coaching leadership of the leader has a positive influence on employee’s positive feedback, creative self-efficacy and innovative behavior. In particular, employee’s positive feedback has a positive effect on creative self-efficacy, and has a positive (+) mediation effect on the relationship between the coaching leadership and innovative behavior of the leader. Also creative self-efficacy mediates the relationship between coaching leadership and innovative behavior. In addition, positive feedback and creative self-efficacy have a multi-mediated effect between coaching leadership and innovative behavior.The implications of this study are as follows. First, coaching leadership, which is different from traditional leaderships, is proven empirically to improve innovative behavior through positive feedback and creative self-efficiency. Second, the findings of this study suggests that coaching leadership improves members' positive feedbacks and creative self-efficiency, which can help to increase their potential and to produce creative results, and consequently, to contribute positively to the development of the organization's human resources.
How Chaotic Are Recurrent Neural Networks?

Recurrent neural networks (RNNs) are non-linear dynamic systems. Previous work believes that RNN may suffer from the phenomenon of chaos, where the system is sensitive to initial states and unpredictable in the long run. In this paper, however, we perform a systematic empirical analysis, showing that a vanilla or long short term memory (LSTM) RNN does not exhibit chaotic behavior along the training process in real applications such as text generation. Our findings suggest that future work in this direction should address the other side of non-linear dynamics for RNN.
Semantic Analysis: Can MD&amp;A Word Choice Increase Stock Price of American Publicly Traded Banks Disseminating Using Twitter?

Social media has become a part of the International framework.Use has risen exponentially and organizations that have relied on traditional channels of communication are considering its use.Researchers studied the impact of semantics on organizations disseminating financial information using Twitter.This study sought to understand the impact of word strength in communicating with investors.This study included 34 American publicly traded banks over four quarters or a sample size of 136.Seventeen used Twitter while seventeen did not.The researchers analyzed the semantic content of these messages.Using a predictive model, researchers compared the cumulative abnormal returns associated with the announcement.Researchers concluded that using Twitter did not positively impact stock price.Researchers also found that banks using Twitter used more powerful words in communicating.This research advances understanding the role social media and semantics can play to disseminate financial information.
Evolving a Weighted Combination of Text Similarities for Authorship Attribution

Authorship Attribution (AA) also known as Authorship Identification is the problem of identifying the author of an anonymous text based on its characteristics or features. Among notable features extraction methods used to this end, one can cite, the bag of words methods (BOW) and the semantic and syntactic methods (SSM). BOW methods consider the text as a sequence of tokens and disregard the semantics of the language, whereas SSM rely on advanced natural language processing (NLP) techniques. The features extracted from an anonymous text are compared to features extracted from a corpus of texts written by known authors using several similarity measures. In this paper, we combine multiple results generated using conventional methods (chosen from the literature) and we use a genetic algorithm (GA) to find the optimal weighting distribution. The optimal combination obtained by the GA is then applied, and the author attributed to the anonymous text is selected among a set of known authors based on the highest similarity. The fitness of our GA is the resulting accuracy of the authorship attribution task. A numerical application on a corpus consisting of 3036 books written by 142 authors shows that the proposed method has higher accuracy than conventional methods and achieved satisfying performance.
Exploring the Suitability of Semantic Spaces as Word Association Models for the Extraction of Semantic Relationships

Given the recent advances and progress in Natural Language Processing (NLP), extraction of semantic relationships has been at the top of the research agenda in the last few years. This work has been mainly motivated by the fact that building knowledge graphs (KG) and bases (KB), as a key ingredient of intelligent applications, is a never-ending challenge, since new knowledge needs to be harvested while old knowledge needs to be revised. Currently, approaches towards relation extraction from text are dominated by neural models practicing some sort of distant (weak) supervision in machine learning from large corpora, with or without consulting external knowledge sources. In this paper, we empirically study and explore the potential of a novel idea of using classical semantic spaces and models, e.g., Word Embedding, generated for extracting word association, in conjunction with relation extraction approaches. The goal is to use these word association models to reinforce current relation extraction approaches. We believe that this is a first attempt of this kind and the results of the study should shed some light on the extent to which these word association models can be used as well as the most promising types of relationships to be considered for extraction.
Detection of Breast Region of Interest via Breast MR Scan on an Axial Slice

Breast cancer is one of the most common cancer types especially met in women. The number of breast cancer patients increases every year. Thus, to detect breast cancer at its early stages gains importance. Breast region detection is the pioneering step of breast cancer diagnosis researches performed via image processing techniques. The performance of computer-aided breast cancer diagnosis systems can be improved by exactly determining the breast region of interest. In this study, the goal is to determine a region of interest for breast MR images, in which one or more lesion can appear. The achieved region includes two breasts and lymph nodes. The proposed region of interest detection system is fully automatic and it utilizes several image processing techniques. At first, the local adaptive thresholding technique is applied to the noise-filtered grey level breast magnetic resonance images taken with ethical permissions from Sakarya Education and Research Hospital. After adaptive thresholding, connected component analysis is performed to exclude extra structures around the breast region as thorax area. This analysis selects the largest area in the binary image which corresponds to a gyrate region including breast area and lymph nodes over the backbone. Then, the integral of horizontal projection is calculated to determine an optimum horizontal line that allows setting the region of interest apart. In the following step, sternum midpoint is detected to separate the right breast from the left one. Finally, a masking operation is applied to get corresponding right and left breast regions in the original MR image. To evaluate the performance of the proposed study, the results of automatic region of interest detection system are compared with the manual region of interest selection performed by an expert radiologist. Dice similarity coefficient and Jaccard coefficient are used as performance criteria. According to the results, the proposed system can detect region of interest for computer-aided breast cancer diagnosis researches, exactly.
A Framework for Feature Selection to Exploit Feature Group Structures

Filter feature selection methods play an important role in machine learning tasks when low computational costs, classifier independence or simplicity is important. Existing filter methods predominantly focus only on the input data and do not take advantage of the external sources of correlations within feature groups to improve the classification accuracy. We propose a framework which facilitates supervised filter feature selection methods to exploit feature group information from external sources of knowledge and use this framework to incorporate feature group information into minimum Redundancy Maximum Relevance (mRMR) algorithm, resulting in GroupMRMR algorithm. We show that GroupMRMR achieves high accuracy gains over mRMR (up to \({\sim }\)35%) and other popular filter methods (up to \({\sim }\)50%). GroupMRMR has same computational complexity as that of mRMR, therefore, does not incur additional computational costs. Proposed method has many real world applications, particularly the ones that use genomic, text and image data whose features demonstrate strong group structures.
Hardware Architecture Design for Template Matching

This paper presents the hardware architecture for ORB (Oriented FAST and Rotated BRIEF) based template matching that can overcome delay problems due to the high computation inherent in existing template matching techniques. The proposed method converts the extracted Canny edge image and FAST corner image into a bit string. It then compares the bit string data to locate the template image in the input image. Template matching is a technology used in various fields, such as object tracking and motion tracking. Because template matching has a long computation time, a solution to this issue has been widely researched in many industrial fields. To solve this problem, we designed a hardware structure based on XILINX Virtex7 (XC7V2000TT1FG1925-1 FPGA using Verilog-HDL).
Research Guides: Boolean Operators: A Cheat Sheet: Home

Boolean logic (named after mathematician George Boole) is a system of logic to designed to yield optimal search results. The Boolean operators, AND, OR, and NOT, help you construct a logical search. Boolean operators act on sets -- groups of records conta
Paraphrasing vs Coreferring: Two Sides of the Same Coin

We study the potential synergy between two different NLP tasks, both confronting predicate lexical variability: identifying predicate paraphrases, and event coreference resolution. First, we used annotations from an event coreference dataset as distant supervision to re-score heuristically-extracted predicate paraphrases. The new scoring gained more than 18 points in average precision upon their ranking by the original scoring method. Then, we used the same re-ranking features as additional inputs to a state-of-the-art event coreference resolution model, which yielded modest but consistent improvements to the model's performance. The results suggest a promising direction to leverage data and models for each of the tasks to the benefit of the other.
Tinjauan Sistematis: Faktor-Faktor Gaya Atribusi Prestasi Akademik

Attribution style is one of the most relevant concepts in predicting academic achievement. Various researches have tried to study the factors that influence the attribution style. Several factors such as gender, ethnicity, and social class have been widely discussed in scientific publications. However, since Bernard Weiner first developed attribution theory more than 40 years ago, there have been changes in a social condition that might have affected various attribution factors. This review aims to see the latest developments in the factors that influence the attribution of academic achievement. A systematic search of articles published in 3 English-language international publishers since 2010 identified a total of 6 studies, which included 132.293 students from various nations. An assessment that measured qualities of a research in 4 areas: research design, sampling, measurement, and analysis identified 3 high-quality research and 3 moderate-quality research. The result shows that ethnic factors confirm the findings from previous studies. Researches on gender factors provide new insights regarding the attribution style of female students. Besides, new factors emerge, such as school stratification and the attribution style of parents over their life events.