Reviewerly/exHarmony huggingface dataset:
Migrant’s remittance and investment financing nexus in Africa: Does investment climate matter?

Abstract Development economists have enjoined Africans to leverage on remittance as their main source of investment financing due to its constant and undisrupted inflows despite structural distortions and economic weaknesses compared to other sources of financial flows in recent time. This ignited the motivation for this study, to unveil the nexus between migrant’s remittance and investment financing and the modulating effect of the investment climate in this relation on a panel of 28 sub-Saharan African countries over the period 1995 to 2017. Using the panel autoregression distributive lagged estimation technique, the following empirical findings were established. First, the theoretical supposition underpinning the assumption in investment climate as a factor that motivates migrant’s remittance inflow to be channelled to investment received a clear empirical support. Second, it was established that the interaction between remittance and components of investment climate (government size and open market) enhanced the growth of private investment. Last, remittance is found to exert a positive effect on private investment in as much as the former does not exceed the threshold of 78%, above which private investment would decline off the quadratic curve. The study suggests the policy-makers channel deliberate efforts at improving not only the efficiency of the market, but government participation especially in the area of tax policy and fiscal financing.
Efficient Use of Randomisation Algorithms for Probability Prediction in Baccarat Using: Monte Carlo and Las Vegas Method

Randomised algorithms use a degree of randomness in their logic. Such algorithms provide a solution which may not always be optimal, but is often produced faster than a brute force process. This paper explores the two classes of randomised algorithms: Las Vegas and Monte Carlo. A Las Vegas algorithm always produces the correct result, but its running time is based on a random value. A Monte Carlo algorithm has a deterministic running time and produces an answer that has a probability of ≥ 1/3 of being correct. By means of this paper, we develop algorithms that predict the winning probabilities of the betting options in the casino table card game of Baccarat. Both classes of algorithms have been implemented in two ways each, varying in space and time complexities. We also propose and implement a novel approach to reduce the time complexity of a typical Las Vegas algorithm through the use of multithreading. A comparative study for the algorithms has also been done.
Adaptive Fuzzy Control for IM Stochastic Nonlinear Systems Based on CFC

In this chapter, an adaptive fuzzy control method based on the command filtered is proposed to solve the problems of stochastic disturbance and input saturation of the IM drive system. Firstly, the fuzzy logic system (FLS) is employed to cope with the stochastic nonlinear functions in IM drive systems. Secondly, the adaptive backstepping method is used to design controllers and the quartic Lyapunov function is selected as the stochastic Lyapunov function. Then the command filtered control (CFC) technology is utilized to deal with the “explosion of complexity” in conventional backstepping, and the compensation signal is designed to eliminate the filtering error. Finally, the simulation results verify the effectiveness and superiority of the proposed method.
Research and Application of Algorithm Based on Maximum Expectation and Collaborative Filtering In Recommended System

Abstract The problem of sparse information is easily found in information searching for new users in recommended system, and it also has difficulty in recommending related modules. In view of the problem, the maximum expectation algorithm in demographics is adopted to cluster users for neighbouring users, and then it is regarded as an input of collaborative filtering algorithm. As the users’ scores on different projects represent their preference, certain demand relevance exists in similar users when evaluating the same project. And this kind of relevance degree varies with the change of individual demand. Therefore, a cooperative filtering algorithm based on the change of user’ demand is put forward by introducing a time weight function, which alleviates the shortness of traditional cooperative filtering recommendation algorithm. By tracking the needs of users, the scoring matrix can be predicted. According to experiments comparison, this algorithm can help solve the problem of sparseness of user’s scoring matrix and the recommendation quality is improved.
AutoRec: A Comprehensive Platform for Building Effective and Explainable Recommender Models

This paper presents a comprehensive platform named AutoRec, which can help developers build effective and explainable recommender models all in one platform. It implements several well-known and state-of-art deep learning models in item recommendation scenarios, a AutoML framework with a package of search algorithms for automatically tuning of hyperparameters, and several instance-level interpretation methods to enable the explainable recommendation. The main advantage of AutoRec is the integration of AutoML and explainable AI abilities into the deep learning based recommender algorithms platform.
VIRTUAL REALITY GLASSES DESIGN FOR STUDENTS

It becomes difficult for students living in the interactive world, the world of technology, to adequately perceive the curriculum in a standard format. Thanks to the creation of new, modern methods of teaching and presenting information, it is possible to approach the educational process at a different level. The object of the research is the features of designing for students to increase motivation as well as creative and cognitive abilities and the results of their educational activities. The methodological basis of the research is the following: statement of the problem, analysis of existing solutions, choice of the topic of graphic support and its implementation for secondary and high school students, description of the developed product, and research results
Behavior Trend Analysis Method of Software Runtime Environment Elements Based on ARIMA

Abstract Behavior trend analysis method based on auto-regressive integrated moving average (ARIMA) is presented for software runtime environment elements (REEs). The history data of environment element behavior is collected to build behavior trend analysis model. Then, behavior trend of environment elements is predicted before the environment elements actually change. Thus, according to the analysis results, we can determine the corresponding adaptive strategy before the environment elements actually change, which provides a reference for the selection and implementation of active adaptive strategy for the self-adaptive software.
An IoT Based Cloud Deployment Framework for Effective Classification of Machine Conditions

Cloud services are proposed for real-time data acquisition, data classification, data processing and decision making, which are highly interconnected services for effective condition monitoring of electrical machines. The proposed Software as a Service, Storage as a Service and Platform as a Service layers address the challenges of data storage and scalability while making analysis on the cluster of machines in an Industrial Environment. An experimental setup consisting of two DC motors coupled to AC Generator operating at different locations is considered to evolve the proposed model for effective integrated monitoring and decision making. This cloud-based vibration monitoring model provides services for data acquisition from the IoT devices mounted on the shafts of the DC motors, data storage to store the enormous amount of acquired signal data from multiple sensors, data classification of vibration signals for effective statistical analysis to estimate adaptive cluster of thresholds and appropriate decision-making services on demand over the Internet to utilize the reliable service of the machines in a persistent way. The computational engine will do inherent statistical analysis of the vibration signals to estimate the cluster of thresholds adaptive to various operating conditions. The services have been deployed without any limitation in a cloud environment and the industrial applications can share information using the deployed services from anywhere on demand basis. The deployed cloud service for the enhanced statistical classification algorithm eliminates the false identification of failures, which not only increase the availability of machines for intended operations but also reduce the maintenance cost. The resulting threshold values are compared with that of the vibration analysis carried out on the machine beds locally using myRIO for data acquisition in LabVIEW and the proposed model ensures the integrity in appropriate decision making with assured scalability.
Analysis on Teacher-student Interaction with Computer Aid in College English Language Teaching Class

Abstract In recent years, College English teaching has made remarkable achievements. Some long-term problems have been properly solved, and students’academic performance has improved to a certain extent. Nevertheless, there are still many problems in College English classroom teaching, which not only hinder the improvement of College English classroom teaching efficiency, but also affect the cultivation of students’English language ability. The author finds that the interaction between teachers and students should be strengthened in the classroom, which has a very good auxiliary role in improving the efficiency of English teaching. Firstly, this paper analyses the problems existing in the current college English classroom teaching. Secondly, it analyses the role of improving teachers’ and students’ interaction ability in College English classroom teaching with the computer aid. Finally, the corresponding solutions are proposed.
Magnet-Free Routes to Nonreciprocal Photonics

In this talk, we discuss our recent progress in the context of integrated photonic systems that exploit temporal modulations, opto-mechanical phenomena and nonlinearities to enable nonreciprocity without the need for a magnetic bias, and their implications for robust topological propagation of light on chip.
Pengenalan Konsep Asta Kosala-Kosali Pada Bade Di Bali Berbasis Multimedia Interaktif

Asta Kosala Kosali is one of the Balinese local materials in Balinese traditional building lessons. Asta Kosala Kosali is also used in the concept of making Bade, where the concept of measuring Bade is based on the measurements contained in Asta Kosala Kosali. As time goes by, there are still many people who do not know or understand the rules of Asta Kosala Kosali, so the standard Balinese building will gradually diminish in the future. Therefore interactive learning information media can help the community in the process of delivering material that is deemed indispensable and as a supporting medium for Asta Kosala Kosali and Bade materials.In making this interactive media the writer uses Coreldraw X7 software for character creation in the learning media, then for the animation process, button creation, and backsound giving is done using the Adobe Flash CS 6 application. This information media has been tested by distributing questionnaires to the community as many as 30 people, with the aim of finding out how feasible this application can be used. From the results of the questionnaires that have been distributed, it was found that 97% of the people stated that it was very good with the introduction of information media for the introduction of Bade in Bali based on interactive multimedia as a learning information medium.
Study of the hydrodynamic modes of ejectors for flotation water treatment at space industry enterprises

The paper considers the problem of determining the hydrodynamic modes in the mixing chamber of ejectors with a rectangular mixing chamber by solving the Navier - Stokes equation. The problem is considered for the case of absence of mass forces. To describe the steady-state flows of a viscous fluid in the isothermal approximation, the Navier - Stokes system of equations was used, which includes the continuity equation and the equation for the change in momentum. The solution was obtained using the Maple computer modeling tool. The proposed method adequately describes the process in the mixing chamber of an ejector with rectangular mixing, which potentially indicates the possibility of describing the hydrodynamic modes in the mixing chambers with a different section.
Web-based Educational Administration System for Student Affairs: An Initial Step to Develop Data-Driven Decision Making

The Engineering Faculty of Jakarta State University (UNJ) has twenty departments. The students of each department always increase year by year. The administration system today uses many platforms such as email, google forms, messenger and mostly using manual ways. We found some of problems for example: difficult to collect the data completely and quickly. During the pandemic, the administration process for student can't proceed like usual. In this study, we developed student administration system based on website and its database. The system is the first step to create data-driven decision making for student affairs. This system using the prototype method with CodeIgniter framework and MySQL for the database. In this research, the student administration system was successfully launched. The system generates several services to access achievement reports, scholarships, and administration.
Media competence of an editor as a factor of the effective promotion of scientific journals in the international information environment

Abstract The article gives insights into the concept of media competence regarding the profession of an editor of a scientific journal. Currently, the media competence is gaining the same relevance as other important competencies of publishers of scientific periodicals. The ability to find the required information quickly and efficiently, the ability to work professionally with international scientometric databases, a good understanding of the process of promoting a publication in the information environment, an ability to see if the publication corresponds to the research request and bibliographic description - all these and other skills and knowledge become crucial in organizing the work of the editorial staff of a scientific journal. At the same time, it should be recognized that the editorial staff of scientific journals acquire professional media competences directly in the process of work on the basis of their own successful or less successful experience. The country does not have a training system for such personnel, despite the fact that the challenges faced by the publishers are ambitious. These challenges require an integrated approach, including the increase of the media competence of editors of scientific journals.
On Top-Down Versus Bottom-up Personalisation and Evaluation of Augmented Reality Learning Systems

The paper aims to analyse the problem of personalisation and evaluation of quality of Augmented Reality (AR) learning systems. AR is often used in education to enhance students’ motivation by visualizing learning content and activities. In the paper, first of all, systematic review of relevant scientific literature on the research topics was conducted. The author’s AR learning systems quality personalisation and evaluation frameworks are presented in the paper. Evaluation of quality of AR learning systems should be based on applying both expert-centred (top-down) and user-centred (bottom-up) quality evaluation methods consisting of creating quality models (systems of criteria) and evaluation methods. AR-based learning systems including learning content (i.e. learning objects) and activities should be suitable, acceptable and usable for particular learners. Personalisation of AR learning systems should be based on learners’ models/profiles using students’ learning styles (bottom-up method), and educational data mining (top-down method). AR personalisation method is aimed to personalise learning by applying well-known learning styles models, educational data mining methods and techniques, and intelligent technologies, and thus to ensure that suitable AR-based learning systems should be selected for particular users to improve their learning motivation and thus—quality and efficiency. The method of identifying students preferring to actively use AR-based learning systems is based on identification of probabilistic suitability indexes to choose the most suitable AR-based learning systems for particular students. Experimental research is also performed, and its results are presented in the paper. The research is multidisciplinary, including computer science, education, operations research, and educational psychology areas.
Effects of Visual Location of Information on The Performance of Monitoring Task Searhing in Digital Interactive Interface

The visual location of the information influences the searching efficiency of the monitoring task. In this paper, from the division of human eye’s visual regions, the task searching experiments of visual location in digital interactive interface are conducted. The experimental results show that, for target information blocks in the foveal and the parafoveal regions, the operators can finish the task searching efficiently and rapidly. However, when the target task is away from present fixation range’s parafoveal region, it will easily lead to sequence searching that will cost extra unnecessary task searching time, or even lead to failure of task searching. Therefore, the information layout design of digital interactive interface should be set successively in effective visual locations, i.e., the foveal and the parafoveal regions according to task order. This will satisfy the visual location rule and will efficiently improve the performance of task searching.
Application of knowledge test control in external independent evaluation format at chemistry

Pedagogical control is one of the components of the teacher's practical activity, which reveals the advantages and disadvantages of new teaching methods. It allows you to match the planned and achieved level of knowledge, to evaluate student achievement, to identify gaps in his knowledge and skills. The forms and methods of pedagogical control are quite diverse. Currently, testing is increasingly used in the practice of school assessment. Testing process has more advantages than classical methods of knowledge control because it is the most objective way of knowledge assessing. Objectivity of testing achieves by standardizing the process and by minimizing the subjective component of student knowledges assessment. The mentioned advantages of the testing control form became the basis for the test system of the External Independent Еvaluation while entering higher education institutions. It is advisable to introduce such a method of knowledge control as testing in the form of External Independent Еvaluation into the practice of school education for the gradual adaptation of the student to the form of testing that they will expect while entering the higher education institutions, as well as to reduce the negative psychological impact of the testing procedure on the student. To achieve this goal, eight sets of test assignments have been created on different topics of the program for 11th grade students who study chemistry at the profile level. The test tasks are designed in the format of External Independent Еvaluation. The methodological materials that have been created can be used for the current and final control of the knowledges in the practice of General Secondary Education, like any other instruments of student knowledges control. As a result of the pedagogical experiment, it was found that testing in the External Independent Evaluation format, was more successful and effective to students with higher levels of education, since the total percentage of good and excellent grades obtained by students at testing was higher than in writing classic control work. The main disadvantages and advantages of using the test system for monitoring the quality and success of knowledge acquisition by students of the institution of general secondary education are presented.
A comparison between Suzuki invariant code and one-point Hermitian code

In this paper we compare the performance of two algebraic geometry codes (Suzuki and Hermitian codes) constructed using maximal algebraic curves over [Formula: see text] with large automorphism groups by choosing specific divisors. We discuss their parameters, compare the rate of these codes as well as their relative minimum distances, and we show that both codes are asymptotically good in terms of the rate which is in contrast to their behavior in terms of the relative minimum distance.
Unsupervised Human Pose Estimation on Depth Images

Human pose estimation is a widely studied problem in the field of computer vision that consists in regressing body joints coordinates from an image. Most state-of-the-art techniques rely on RGB or RGB-D data, but driven by an industrial use-case to prevent musculoskeletal disorders, we focus on estimating human pose based on depth images only. In this paper, we propose an approach for predicting 3D human pose in challenging depth images using an image-to-image translation mechanism. As our dataset only consists in unlabelled data, we generate an annotated set of synthetic depth images using a human3D model that provides geometric features of the pose. To fit the challenging nature of our real depth images as closely as possible, we first refine the synthetic depth images with an image-to-image translation approach using a modified CycleGAN. This architecture is trained to render realistic depth images using synthetic depth images while preserving the human pose. We then use labels from our synthetic data paired to the realistic outputs of the CycleGAN to train a convolutional neural network for pose estimation. Our experiments show that the proposed unsupervised framework achieves good results on both usual and challenging datasets.
Valid Prediction Intervals for Course Grades with Conformal Prediction

Conformal prediction is a statistical-learning framework that complements predictions with a reliable measure of confidence [22]. It allows the construction of prediction intervals for any type of regression models under the assumption that the data are exchangeable. Conformal prediction intervals have provably valid frequentist coverage for finite data; i.e. they contain the true value of the response variable for any test data with probability at least 1 - ε for any significance level ε.Due to the validity of its prediction intervals, we argue that the conformal-prediction framework has to be used for course-grade prediction. In this paper, we experiment with two conformal predictors: Inductive Conformal Machines (ICMs) [11] and Cross-Conformal Machines (CCMs) [21]. They are compared in terms of informational efficiency (width and stability of prediction intervals) and computational efficiency on course-grade prediction tasks from a liberal education program of Maastricht University, the Netherlands. We show that ICMs enjoy substantial computational benefits while CCMs has a better informational efficiency. Moreover, CCMs extend the applicability of the conformal-prediction framework to course-grade prediction tasks for data sets of as few as 30 instances.