Reviewerly/exHarmony huggingface dataset:
Theoretical and Practical Aspects of Integrating the Computer and Music Technologies into The Instrumental Performance Training of The Pedagogical University Students

The work reveals the general social and technological prerequisites for integrating computer technologies into Russian education at various levels and profiles of educational institutions. The reasons for the basic contradictions in traditional and innovative pedagogical principles in professional training of performing musicians have been substantiated. Productive ways to solve the problem have been described based on the leading developments of education researchers in the computer and music technologies as well as in electronic music. The essence of the computer and music technologies concept as a specialized field of music pedagogy in Russia has been revealed. Features of hardware and software have been defined based on the digital interface of musical instruments and the prospects for their use in pedagogical activities. The methods, forms, and conditions for practical implementation of experimental training of pianist students from the Institute of Culture and Arts of the Moscow City University have been presented. The generalized results and conclusions of the experimental training in instrument performance of the pedagogical university students using the software and hardware components of the computer and music technologies have been analyzed and discussed.
EFL learners� perspective on English Honorifics (EHs) in Indonesia

Indonesian EFL learners (IELs) have been familiar with the practice of English Honorifics/EHs (Mr, Mrs, Miss, Sir and Ma�am) for decades. Mr, Mrs, Miss are followed by family name as the clan identity and Sir and Ma�am are for respectful address for adult man and woman. However, in Indonesia, it�s been the phenomenon because there seem to be a collision between how EFL learners practice EHs with what EHs are supposed to be used in English culture. The purpose of this study is to clarify and verify how Indonesian EFL learners perceive on EHs that have been interpreted and practiced for decades in Indonesia. This study applies double approaches. First, cross cultural study which employs content analysis by means of analyzing how EHs are interpreted and practiced under the setting of Indonesian culture. The sources to be analyzed are obtained from English handouts, chats and texts in social media among EFL students, as well as script deriving from teacher-EFL students� classroom interaction. Second, case study which employs triangulation technique to examine some aforementioned sources. The result reveals that Indonesian EFL learners perceive that the practice of EHs (Mr, Mrs, Miss, Sir, Ma�am) have no difference with the ones applied in Indonesian culture. It means that there is no more difference in function between Mrs and Miss, Ma�am, Mr and Sir. Those have been considered the same in practice with Indonesian Honorifics (IHs) like ibu (Ma�am), bapak (Mr), nyonya (Mrs), dan nona (Miss) even though they have different function. �These have been conducted beyond EFL learners� culture awareness of how native speaker of English uses EHs because there is an assumption that learning English doesn�t include learning its culture. For most Indonesian EFL learners, learning English as a foreign language is a matter of learning basic skills and linguistic features. So, if there are any further cultural encounters during learning English, they will be switched automatically to Indonesian culture. This study recommends English teachers to keep teaching English culture during teaching English in order to avoid learners� misunderstanding in communication with English speaking people.
BUILDING, VISUALIZING AND EXECUTING DEEP LEARNING MODELS AS DATAFLOW GRAPHS

In recent years many frameworks have appeared, which enable users to easily build, visualize and execute deep learning networks on graphical interfaces.However, they do not always provide enough opporunities to automate this process.Generally, data processing programs can be organized into dataflow graphs that define the operations to be performed sequentially on the data.The operation of deep learning neural networks can also be interpreted in a similar way, in which the input data to be processed is a specific data set and the operations to be performed on the data are the layers of the net.Due to architectural reasons, the entire deep learning neural network graph must be built before actual running, thus it is necessary to change topological execution of dataflows to evaluation preceding graph building since knowing the layers separately is not enough to operate the nets.As a solution for displaying editable program graphs, we created a framework in which data processing related to Python packages can be described and the programs built from them can be visualized and executed (mostly) automatically.
Single-Image Crowd Counting using Multi-Column Neural Network

Crowd scene understanding is an important and challenging problem in computer vision.Most of studies based on tracking individuals, crowd counting, finding region of motion and alarming crowd flaws have came into existence.The task of crowd counting and density map estimation is riddled with many challenges such as occlusions, non-uniform density, intra-scene and inter-scene variations in scale and perspective.Nevertheless, over the last few years, crowd count analysis has evolved from earlier methods that are often limited to small variations in crowd density and scales to the current state-of-the-art methods that have developed the ability to perform successfully on a wide range of scenarios.The success of crowd counting methods in the recent years can be largely attributed to deep learning and publications of challenging datasets.One of the appropriate method that can accurately estimate the crowd count from an image with arbitrary crowd density and arbitrary perspective is using the state-of-the-art i.e. convolution neural network.The technique used for the crowd detection and crowd density estimation is through the Multicolumn Convolution Neural Network architecture.The model allows the input image to be of arbitrary size or resolution with high accuracy and produces a state of art results.The proposed work is implemented with the Shanghaitech dataset, which is among the largest dataset.The model produces highly precise and accurate results with the estimate crowd count and density map.
Доповнення даних контентом на іноземних мовах в текстовій класи- фікації з використанням машинного навчання

The object of research is the data augmentation method in text classificationproblems using machine learning methods. The method is considered on the example of sentiment analysis of visitor reviews of hotels. It is shown that datasets with insufficient volume or representativeness requires special methods for increasing the amount of data in it.The aim of the work is to improve the accuracy of the neural network in thetasks of text classification by increasing the amount of data. To achieve the goal, it was proposed to use text data written in languages of other families, which will be translated into the target language using Google translator. Russian was chosen as the target language. To level the effect of the model on the results, a simple neural network is used - a multilayer perceptron with variations in the parameters of its structure.The article investigated the influence of the considered data augmentationmethod on the resulting accuracy of the network. According to experimental results, the expediency of using this method in a number of tasks is shown.Ref. 7, pic. 3, tab. 3
Need Analysis on English for Hotel at the Faculty of Tourism and Hospitality at Universitas Negeri Padang

The importance of English proficiency in tourism sector, especially for hotel staff is undeniable.This study aims to explore the needs of English for tourism sector, in particular English needs for hotels.The result of the analysis of the needs will become the factors to be considered in developing English teaching materials for English subject at Faculty of Tourism and Hospitality.The sample of the study was selected using purposive random sampling technique, involving 41 staff.The data of the study were collected through questionnaire.The results of the study show that Listening skill is mostly needed.The second most needed skill is Speaking skill; while the third needed skill are Reading and Writing skills.The results of the study also reveal the topics, functions needed for each skill.
STUDY OF EYE MOVEMENT CHARACTERISTICS FOR GETTING FOCUS POINT OF HUMAN EYE

Eye movement recognition is still an area under research to achieve higher accuracy. As technology has advanced many device allows human to pass mouse based actions in form of eye movement. This paper presents a study of three main eye movement characteristics: blinks, fixations and saccades. This paper also presents different fixations features and saccades features.
Equidistribution Among Cosets of Elliptic Curve Points in Intervals

Abstract In a recent paper devoted to fault analysis of elliptic curve-based signature schemes, Takahashi et al. (TCHES 2018) described several attacks, one of which assumed an equidistribution property that can be informally stated as follows: given an elliptic curve E over 𝔽 q in Weierstrass form and a large subgroup H ⊂ E (𝔽 q ) generated by G ( x G , y G ), the points in E (𝔽 q ) whose x -coordinates are obtained from x G by randomly flipping a fixed, sufficiently long substring of bits (and rejecting cases when the resulting value does not correspond to a point in E (𝔽 q )) are close to uniformly distributed among the cosets modulo H . The goal of this note is to formally state, prove and quantify (a variant of) that property, and in particular establish sufficient bounds on the size of the subgroup and on the length of the substring of bits for it to hold. The proof relies on bounds for character sums on elliptic curves established by Kohel and Shparlinski (ANTS–IV).
On the impact of lightweight ciphers in automotive networks (S)

The ever more exposure of modern vehicles to computer networks has led, in recent years, to increase risks due to cyberattacks.The internal computer network of vehicles, used to connect several electronic components present on cars, is even more threatened as it is potentially exposed to external attacks.Securing the Controller Area Network (CAN) protocol, used to govern those networks, is becoming increasingly important to ensure a safe driving experience.CAN is an ISO standard that dates back to 1983, over the years it has undergone very few changes coming to be outdated.It has been designed to minimize latency and data transmission errors through two essential features: small frames and unencrypted information transfer.The latter feature, in particular, appears to be the weak point of this protocol.Securing the communication channel is needed, but it must be done by preserving all the main features that ensure the performance of this protocol, in particular the low latency.Furthermore, the modification cannot introduce low-level alterations.CAN security can be improved by acting at a higher level.In this work we investigate the feasibility of using symmetric encryption algorithms for securing messages exchanged on the CAN-bus.In particular, this paper evaluates the effectiveness of using lightweight ciphers, designed with the aim of introducing encryption also on devices, which have limited hardware and software resources, such as microcontrollers.
Reducing the Complexity of the Multilayer Perceptron Network using the Loading Matrix

In this paper, Researchers present three models of a Multilayer Perceptron Network (MLPs) based on the Factor Analysis with Principal Components method (PC) to reduce the degree of complexity of the neural network.In the first model, a neural network was built with all the variables in the input layer.In the second model, the results of the FA were adopted instead of the basic variables in the input layer, and in the third model, the Loading matrix was used to determine the number of nodes in the hidden layer and the weights that are associated with the input layer.Then compared the results of the models by determining the number of network weights that reflect the complexity of the network, in addition to the time of building and training the model and the accuracy of classification.The results of applying the models to a hypothetical database for the purposes of scientific research titled Bank Marketing showed that the model that inserted the factors in the hidden layer and preserved the high loading factors only is the best model in terms of low degree of complexity and maintaining classification accuracy.
Efficient Breast Cancer Classification Using Tumor Area and Boundaries Calculation

Many of the women across the world suffer from breast cancer. Since the causes of this disease remain unknown, early detection and diagnosis are essential to control the breast cancer, increase the success of the treatment, save live, and reduce costs. Unfortunately, the methods proposed in the literature are suffering from low detection rates. In this paper, we proposed two new methods called C-Comp and CRoI. Both methods efficiently detect benign and malignant breast cancer tumors using shape circularity. The experimental results show that, the proposed C-Comp method achieved a detection rate of 12.3% higher than other previous work. However, the proposed CRoI method achieved a detection rate of 5.6 % higher than other previous work.
REMAP-RT in the Circulatory System Material to Improve Students Cognitive Learning Output

Learning outcomes are still a benchmark of learning success, with good learning outcomes signifying mastery of knowledge that can be implemented in life. Students require to be accustomed to reading before beginning learning, to be able to manage information from learning resources.. This study aims to study the Remap-RT learning model to improve student learning outcomes by looking at the results of pretest and posttest students of class VIII E Muhammadiyah 2 Kalasan. The method used in this research is CAR (Classroom Action Research). The subject in this study was a student at VIII E of SMP Muhammadiyah 2. Data obtained by using the test twice in both cycle, cycle I and cycle II. Based on the results of the pretest and posttest the average value of class VIII E in the last cycle is higher than the first cycle. Based on these findings it can be concluded that there is an improvement in the learning process and differences in student learning outcomes using the Remap-RT learning model.
ASEG Research Foundation: Looking to the future

The ASEG Research Foundation continues to back students in their degree studies. Through a competitive process, the Foundation makes annual grants to support the laboratory and fieldwork necessary ...
PENGEMBANGAN SISTEM PENDUKUNG KEPUTUSAN PENENTUAN KONSENTRASI BIDANG KEAHLIAN MAHASISWA DENGAN INTEREST INVENTORY

Universities are designed to prepare graduates who are ready to enter the workforce and are able to develop a professional attitude. Educational institutions such as the University need a form of decisions in determining the right concentration for students, so that the learning process can be achieved well. The decision is very influential on the process of handling the choice of alternative concentration, choosing an appropriate concentration of interest will also have an impact on the research focus for the final assignment of students. This research develops student concentration selection system in Electrical. Currently the concentration determination system is based only on academic assessment alone, regardless of student interest, so that it can impact on student learning outcomes. The system was developed by combining academic judgment and interest inventory with three criteria, ie, interest tests using interest inventory, prerequisite concentration course grades, and GPA. The system is built using an intelligent system model that is Fuzzy Multiple Attribute Decision Making (FMADM), which helps the Department in the selection process and helps the process of career guidance on students. With this selection system, the Department can be provide the most suitable concentration decisions with interest in student.
Human Action Recognition in Videos

The goal of this project is to develop a Human Action Recognition system with a video classification approach.Data is gathered by downloading images from google, analyze the frames of the video and predict the actions being performed.This is achieved by using Computer vision -a field of computer science that works on enabling computers to see, identify and process images similar to human vision, and then provide appropriate results.It is like imparting human intelligence and instincts to a computer.
Considering Psychological And Pedagogical Characteristics Of Young Learners In Improving Professional Competence Of Primary School English Teachers

The article looks into the importance of taking into account psychological, pedagogical and age characteristics of children of primary school age was caused by the world phenomena of introducing English at younger ages.One of the main aspects of creating necessary conditions for ensuring the effectiveness of the in-service teacher training courses is considering the level of language and methodological training of English teachers and bringing the goals, objectives and content of the inservice courses in line with the goals and objectives of the education.Teachers who teach English in primary schools, along with general pedagogy, theory and methods of teaching English, need to know the specifics of working with children of primary school age, including psychological and age characteristics.The process of teaching English has changed over the past decades, affecting both teachers and students.This influence is especially noticeable when children start learning a foreign language at the primary school level, where not only the basics of a foreign language are laid, but also the foundation of the child's educational system.The role of the teacher in the process of teaching primary school children is very important, as it should cover many aspects, including: designing children's activities in the classroom that maximize their use of English; selecting language tasks that will ensure linguistic progress; maintaining motivation and a positive attitude of learners to learning a foreign language.
Improved Software Defect Prevention using Transfer Learning

This paper presents a highly effective technique to handle data insufficiency issue in software defect prevention using machine learning techniques.Extracting knowledge using data mining techniques in software engineering is a difficult task as the data available from software projects for research is not only less but also outdated.Generally Software engineering activities like defect prediction, effort estimation etc., were done on data available from open source datasets which is less in volume.All researchers and data scientists tend to agree on one common thing i.e. they always need more quality data to produce accurate results.When the data used to construct models is lesser than essential quantity, the results predicted will be inaccurate and unstable.In this paper, transfer learning technique has been employed to tackle this data insufficiency issue using techniques, by transferring knowledge from related similar task, where sufficient data is available and this extracted knowledge is made use in the pursuing task to create more accurate prediction.From the experimental results, it is evident that transfer learning technique employed show considerable improvement in defect prevention even when the data available for that problem is limited.
Design and Implementation of Basic Department Operation Model

The article deals with an approach to designing and implementing the operation model for the basic department in the education quality control system at higher education institutions. The approach refers to university structure within the framework of network interaction between university and school employees. The authors describe the designed structure and main components of the operation model of the university basic department and list organizational and pedagogical conditions for the implementation of the basic department model at university. The authors give a brief description and characterize the components required in the process of establishing the interaction: content-related, managerial and resulting. Within the framework of the model, the authors provide a brief personal characteristic of the head of the basic department that is essential and determines the key direction for development. The developed model has been implemented at the Institute of pedagogy and psychology of education of the State autonomous general education institution of higher education “Moscow City University” in cooperation with Moscow schools.
Land Administration and Registration using Blockchain Technology

Land organization and title enlistment framework is the framework for putting away land title data and overseeing exchanges including area titles. Because of the affectability of land issues, land organization and title enlistment framework ought to be solid to stay away from any report falsification, accessible constantly, and set aside a short effort to finish undertakings. Along these, this examination targets planning a model for such framework dependent on blockchain innovation. The proposed model incorporates the Land Database Management System (ILMIS) with Blockchain which empowers encryption of data from LDMS to get the data of each land title and store it to the blockchains. The model further scrambles the land data from LDMS when required and afterward contrast it and people customer's information from blockchains for verification.Furthermore, the general public is relied upon to profit by this examination as the time and cost for enrolling area title will diminish and the conceivable outcomes of a land parcel having more than one proprietor won't be there.
Iron in Well Water: Case Study in Dulalowo and Heledulaa Districts

This study aims to determine the iron content of water wells in Dulalowo and Heledulaa, Gorontalo City. Ten well water samples were taken randomly from five different wells in each district. Water samples at each well consist of top, middle, and bottom water. Determination of iron level using Atomic Absorption Spectrophotometer (AAS) at a wavelength of 248.33 nm. The results showed that the iron concentration of well water in Dulalowo was 0.030 (D1), 0.265 (D2), 0.224 (D3), 0.158 (D4), 0.149 ppm (D5). Meanwhile, iron levels in Heledulaa were 0.100 (H1), 0.039 (H2), 0.159 (H3), 0.198 (H4), and 0.235 ppm (H5). These results prove that the Fe content in the well water in the two districts is still fulfil the drinking water standard which is a maximum of 0.3 mg/L iron.