Reviewerly/exHarmony huggingface dataset:
Teacher Competencies for Dyslexia Students

This study aims to identify the competencies that teachers have in dealing with dyslexic students.Method: The type of research used is quantitative which uses a research design survey that is strengthened by the results of interviews to support the results of quantitative data.Where the total number of samples from this study were 91 teachers taken based on the total sampling technique.Data were then analyzed with the help of the SPSS 21 application to look for descriptive statistics in the form of mean, median, min, max, and category.Findings: From the results of the analysis conducted, it is found that there is a teacher's ability to handle dyslexic students in the good category, which is indicated by a good category of 64.8% (59 of 91) students.Implications and Recommendations: For this reason, it is recommended that teachers need to have good competence in order to help dyslexic students.Students need to be given the opportunity to develop direct experiences and thoughts in learning activities.
Network of Steel: Neural Font Style Transfer from Heavy Metal to Corporate Logos

We introduce a method for transferring style from the logos of heavy metal bands onto corporate logos using a VGG16 network.We establish the contribution of different layers and loss coefficients to the learning of style, minimization of artefacts and maintenance of readability of corporate logos.We find layers and loss coefficients that produce a good tradeoff between heavy metal style and corporate logo readability.This is the first step both towards sparse font style transfer and corporate logo decoration using generative networks.Heavy metal and corporate logos are very different artistically, in the way they emphasize emotions and readability, therefore training a model to fuse the two is an interesting problem.
Rethinking the quality of children’s bilingual story books

Books are one of the learning media for children, in this case, specifically, bilingual books were discussed. Bilingual books are used to introduce children to the world of Indonesian and English as well, where the book is used by teachers or parents to read stories for children. Bilingual books are numerous and easily found in various bookstores and online stores. However, the quality of the books is questionable whether the English translation used in the book is correct. In this short article, the author takes the example of 7 children's bilingual story books with 376 sentences in them. The author found that the majority of translations used were word-by-word translation and with complex translation because of the use of sentences that are too long and the vocabulary was not appropriate to use in that context. In fact, story books should be books that were easily read and understood by both those who read and those who listen. With the complexity of these books, it will be difficult for teachers and parents to read and understand, let alone convey them to children. The author suggested publishers and the government to pay more attention to the quality of books by selecting competent writers in their fields.
Servant leadership and its impact on organizational citizenship behavior: Exploring the mediating role of job satisfaction

This research aims to recognize the impact of servant leadership on organizational citizenship behavior in Jordanian Islamic Banks where job satisfaction is a mediating variable.The research sample is selected from three Jordanian Islamic Banks operating in Jordan.The sample selected 168 people for the current research based on a random sample technique.The results indicate that there is a positive correlation between servant leadership and organizational citizenship behavior.There is also a significant impact of the servant leadership on the organizational citizenship behavior and a significant impact of servant leadership on the organizational citizenship behavior through job satisfaction at the Jordanian Islamic Banks.
Brown University at TREC Deep Learning 2019.

This paper describes Brown University's submission to the TREC 2019 Deep Learning track. We followed a 2-phase method for producing a ranking of passages for a given input query: In the the first phase, the user's query is expanded by appending 3 queries generated by a transformer model which was trained to rephrase an input query into semantically similar queries. The expanded query can exhibit greater similarity in surface form and vocabulary overlap with the passages of interest and can therefore serve as enriched input to any downstream information retrieval method. In the second phase, we use a BERT-based model pre-trained for language modeling but fine-tuned for query - document relevance prediction to compute relevance scores for a set of 1000 candidate passages per query and subsequently obtain a ranking of passages by sorting them based on the predicted relevance scores. According to the results published in the official Overview of the TREC Deep Learning Track 2019, our team ranked 3rd in the passage retrieval task (including full ranking and re-ranking), and 2nd when considering only re-ranking submissions.
Professional training of future music art teachers for inclusive education: theoretical and technological aspects

The article is devoted to the actual problem of contemporary art education ἦ#x201C; to improve the professional training of future music art teachers for children’s inclusive education. Its purpose is to present the results of theoretical understanding of the developmental and healing potential of music, to develop technological support in future music art teachers’ professional training for children’s inclusive education. According to the results of the study, the basic concept of the study was defined as: “professional training of future music art teachers for inclusive education”, which is seen as an educational process aimed at ensuring students’ readiness to provide a system of educational services to persons with special educational needs in their future professional activity, and it involves mastering their inclusively oriented musical and pedagogical knowledge, skills and personal qualities. To improve the professional training of future music art teachers for children’s inclusive learning, the findings of the latest research in the field of art and musicpedagogical theory concerning the harmonizing and music-therapeutic influence of music on the personality of a child in need of educational inclusion were used; criteria of musical works selection for children with psycho-physiological disabilities have been identified; there was developed and substantiated developmental and corrective technique of training singing that can be used in inclusive classes of different age, gender and cognitive abilities of children. Developmental and corrective technique of training singing includes innovative content of children’s teaching, comprising a specially selected vocal repertoire; lesson and extracurricular forms of training; specific teaching methods for students with special educational needs.
Neuro-network technologies as a mean for creating individualization conditions for students learning

The study is devoted to the analysis of the current level of use of neural network tools to create conditions for individualization of student learning. The author presents the results of the analysis of scientific developments of scientists in the questions of the use of neural network approaches in the educational process, presents a range of basic modern directions of research and development, in particular: adaptive tests, individualization of learning, improvement of the level of distance education and others. The author emphasizes that modernity is characterized by an active increase in the number of students who choose for themselves individual and distance forms of education, which leads to the need for the development of modern technological systems of distance learning. For this purpose, the scientist conducts a comparative analysis of distance learning tools, their features and capabilities in terms of implementation of an automated intellectual system of individualization of the educational process.
Bayesian Estimation in Piecewise Constant Model with Gamma Noise by Using Reversible Jump MCMC

A piecewise constant model is often applied to model data in many fields.Several noises can be added in the piecewise constant model.This paper proposes the piecewise constant model with a gamma multiplicative noise and a method to estimate a parameter of the model.The estimation is done in a Bayesian framework.A prior distribution for the model parameter is chosen.The prior distribution for the parameter model is multiplied with a likelihood function for the data to build a posterior distribution for the parameter.Because a number of models are also parameters, a form of the posterior distribution for the parameter is too complex.A Bayes estimator cannot be calculated easily.A reversible jump Monte Carlo Markov Chain (MCMC) is used to find the Bayes estimator of the model parameter.A result of this paper is the development of the piecewise constant model and the method to estimate the model parameter.An advantage of this method can simultaneously estimate the constant piecewise model parameter.
THE EFFECT OF INVESTMENT EDUCATION AND INVESTMENT EXPERIENCE ON INVESTMENT DECISION WITH FINANCIAL KNOWLEDGE AS INTERVENING VARIABLE

This research aims to explain the influence of investment education and investment experience on investment decision with financial knowledge as intervening variables.This research was conducted on college students who have invested in the capital market with 229 respondents.Used four variables, the data collected tested using path analysis.This study shows that investment education and investment experience have a positive effect on investment decision.This study also investigates the indirect effect between investment education and investment experience on investment decision through financial knowledge.The result shows that the direct effect is stronger than indirect effect on the influence of investment education and investment experience on investment decision.The higher the investment decision and investment experience of the college students, the better the financial knowledge of the students in making investment decision.
Curriculum Data Association Organization and Knowledge Management Method for Unstructured Learning Resources

To solve the problems in the curriculum of unstructured learning resources, data association organizations and knowledge management methods were proposed. First, according to functions and requirements, the associated course data system was designed. Then, the table conversion test and the storage index test were performed. Finally, the merger test of the entity was carried out. Results showed that the data association organization and knowledge management methods effectively solved the problem of the curriculum of unstructured learning resources. In summary, the online learning environment provides conditions for unstructured learning resources.
Spark-based Parallelization of Basic Local Alignment Search Tool

This paper continues series of research on application of the novel approach of InterCriteria Analysis (ICrA) to medical data.It describes a new method of analyzing the treatment results of patients with Behterev's disease in order to aid the decision making process for treatment course -InterCriteria Analysis.The ICrA analysis is applied on the results of medicine, physiotherapeutic treatment and kinesitherapeutical program characteristics.The main goal is an improvement of general quality of patients' life through practices of specific methodology of kinesitherapy and ergotherapy.The object of empirical study is health status of patients suffering Rheumatoid spondylitis in relation to their current life conditions.Here are analyzed the data from observation about 25 patients (14 women and 11 men, aged 25 to 67).The results confirm previously performed research, showing that the muscles in the human body are closely connected and the improving of one movement will lead to improving another.The obtained results also prove applicability of ICrA to the researched problem, give grounds for extending its application and potential of further in-depth study.
Big Data Oriented Mining and Implementation Analysis for Online Education Information

With the advent of the big data, the amount of data generated during the development of online education is also amazing. Therefore, online education based on big data guidance has become a hot research trend. Focusing on educational mining of big data and learning analysis techniques, this paper uses Apriori algorithm running on Hadoop to improve the efficiency of parallel computing, by studying the data set segmentation idea and optimization from the key value reduction. Then, we analyze and study the characteristics of educational big data, and perform data set processing for user-course access frequency data, which improves the accuracy of association rule mining and provides instructive information for school education decision-making and teaching management.
Bipartite Structured Gaussian Graphical Modeling via Adjacency Spectral Priors

Learning a graph with a bipartite structure IS essential for interpretability and identification of the relationships among data in numerous applications including document clustering, network medicine, etc. To learn a bipartite structure is equivalent to a max-cut problem, which is an NP-hard problem. Existing methods employ a two-stage procedure and are computationally demanding as they require solving semi-definite programming. In this paper, we introduce a bipartite graph learning framework lying at the integration of Gaussian graphical models (GGM) and spectral graph theory. The proposed algorithms are provably convergent and practically amenable for large-scale unsupervised graph learning tasks. Numerical experiments demonstrate the effectiveness of the proposed algorithm over existing state-of-the-art methods. An R package containing code for all the experimental results is available at https://cran.r-project.org/package=spectralGraphTopology.
Key Quality Indicators Prediction for Web Browsing with Embedded Filter Feature Selection

In this paper, the prediction of over-the-top service quality is discussed, which is a promising way for mobile network engineers to tackle service deterioration as early as possible. Currently, traditional mobile network operation often takes appropriate remedial measures, when receiving customers’ complaints about service problems. With the popularity of over-the-top services, this problem has become increasingly serious. Based on the service perception data crowd-sensed from massive smartphones in the mobile network, we first investigated the application of multi-label ReliefF, a well-known method of feature selection, in determining the feature weights of the perception data and propose a unified multi-label ReliefF (UML-ReliefF) algorithm. Then a feature-weighted multi-label k-nearest neighbor (ML-kNN) algorithm is proposed for the key quality indicators (KQI) prediction, by combining the UML-ReliefF and ML-kNN together in the learning. The experimental results for web browsing service show that UML-ReliefF can effectively identify the most influential features of the data and thus, lead to better performance for KQI prediction. The experiments also show that the feature-weighted KQI prediction is superior to its unweighted counterpart, since the former takes full advantage of all the features in the learning. Although there is still much room of improvement in the precision of the prediction, the proposed method is highly potential for network engineers to find the deterioration of service quality promptly and take measures before it is too late.
Enhancing interest in research activities in mathematics students in teacher training universities

The article looks into the issue of developing an interest in mathematics students’ research activities in the teacher training universities. The study is dedicated to the feasibility of involving the 5E Instructional Model to the organization of students’ scientific research during the workshop on the Approximation Theory and Fourier Series. The research considers the results of the survey among students who helped to evaluate their emotional state during the workshop. To collect the data we used the tool of express evaluation of positive and negative emotionality the Differential Emotion Scale. The article discusses the positive influence of the environment developed through the 5E Instructional Model on students’ emotional state and forming their interest in scientific research while organizing workshop classes. We have grounds to conclude that there is the efficiency of implementing workshops based on the 5E Instructional Model. The index reduction of students’ negative emotions encouraged their activity during the workshop and the improvement of interest in research activities.
Pengaruh Penggunaan Media Berbasis Information and Communication Technology (ICT) terhadap Hasil Belajar Siswa pada Materi Dimensi Tiga

These paper have purposed to know the difference of result between the students that have learned by using IT and the students who had subject follow by using conventional learning method in dimension three subject. The research method used in this study is an experimental method using Posttest-Only Control Group Design. The results showed that the average learning outcomes of students who get learning by using ICT media is higher than the average learning outcomes of students who get learning by using conventional learning models on the three-dimensional material.
Mentoring Matters in Workplace: The Impact of Formal Mentoring Program on EFL Instructors’ Performance at ELI, King Abdulaziz University, Saudi Arabia

An unprecedented acceleration in globalization, cross-culture integration and intensified innovation are a few elements that have triggered the need for availability of mentoring as the professional identity of any institution of higher learning. It has got the status of a foundation stone of mutual accomplishments between universities in the provision of teacher development. Therefore, this research study was carried out to evaluate the experiences of faculty members who participated in a formal mentoring program organized by the English Language Institute (ELI) at King Abdulaziz University (KAU) from 2017 to 2019. In this mixed-method study, a questionnaire and semi-structured interviews were used to gather data in order to respond to questions connected to the effectiveness of the mentoring program for mentors and mentees. The study particularly sought to discover the character of the work issues discussed and the worth judged by participants as emerging from their contribution to a programmed mentoring correlation. Data analysis transpired that mentoring promoted all of those who participated in the program. The study concluded that mentoring could assist in constructing capability in two ways: featured and standardized mentoring of trainee teachers through overt mentoring practices, and demonstrating and deconstructing teaching methods and practices for mentors&amp;#39; pedagogical progression. This study emphasizes the worth and value of the formal mentoring program as a valued and fitting professional development approach.&amp;nbsp;
Using weaker consistency models with monitoring and recovery for improving performance of key-value stores

Abstract Consistency properties provided by most key-value stores can be classified into sequential consistency and eventual consistency. The former is easier to program with but suffers from lower performance whereas the latter suffers from potential anomalies while providing higher performance. We focus on the problem of what a designer should do if he/she has an algorithm that works correctly with sequential consistency but is faced with an underlying key-value store that provides a weaker (e.g., eventual or causal) consistency. We propose a detect-rollback based approach: The designer identifies a correctness predicate, say P , and continues to run the protocol, as our system monitors P . If P is violated (because the underlying key-value store provides a weaker consistency), the system rolls back and resumes the computation at a state where P holds.We evaluate this approach with graph-based applications running on the Voldemort key-value store. Our experiments with deployment on Amazon AWS EC2 instances show that using eventual consistency with monitoring can provide a 50–80% increase in throughput when compared with sequential consistency. We also observe that the overhead of the monitoring itself was low (typically less than 4%) and the latency of detecting violations was small. In particular, in a scenario designed to intentionally cause a large number of violations, more than 99.9 % of violations were detected in less than 50 ms in regional networks (all clients and servers in the same Amazon AWS region) and in less than 3 s in global networks.We find that for some applications, frequent rollback can cause the program using eventual consistency to effectively stall . We propose alternate mechanisms for dealing with re-occurring rollbacks. Overall, for applications considered in this paper, we find that even with rollback, eventual consistency provides better performance than using sequential consistency.
Software Defect Prediction Model Based on Improved BP Neural Network

This paper proposes a software defect prediction algorithm based on improved BP neural network, which can effectively improve the prediction accuracy caused by the imbalance of the category distribution of data within the project. In this paper, in order to improve the data imbalance in the project, we use SMOTE algorithm to increase the minority samples (defective software modules), the ENN (Extended Nearest Neighbor Algorithm ) data cleaning algorithm is performed for the post-sampling data noise problem. The SA ( Simulated Annealing ) algorithm is used to optimize the four- layers BP neural network to establish the classification prediction model on the AEEEM database. We use cross validation to evaluate the performance of the proposed algorithm on AEEEM database. The results show that the proposed algorithm can effectively improve the performance of the model in predicting unbalanced data.
Publishing Video Data with Indistinguishable Objects.

Millions of videos are ubiquitously generated and shared everyday. Releasing videos would be greatly beneficial to social interactions and the community but may result in severe privacy concerns. To the best of our knowledge, most of the existing privacy preserving techniques for video data focus on detecting and blurring the sensitive regions in the video. Such simple privacy models have two major limitations: (1) they cannot quantify and bound the privacy risks, and (2) they cannot address the inferences drawn from the background knowledge on the involved objects in the videos. In this paper, we first define a novel privacy notion ϵ-Object Indistinguishability for all the predefined sensitive objects (e.g., humans and vehicles) in the video, and then propose a video sanitization technique VERRO that randomly generates utility-driven synthetic videos with indistinguishable objects. Therefore, all the objects can be well protected in the generated utility-driven synthetic videos which can be disclosed to any untrusted video recipient. We have conducted extensive experiments on three real videos captured for pedestrians on the streets. The experimental results demonstrate that the generated synthetic videos lie close to the original video for retaining good utility while ensuring rigorous privacy guarantee.