Reviewerly/exHarmony huggingface dataset:
HUBUNGAN KECERDASAN EMOSIONAL DAN MOTIVASI BELAJAR DENGAN HASIL BELAJAR SISWA PADA MATA PELAJARAN EKONOMI KELAS XI DI SMA NEGERI 3 GORONTALO

The research aims to determine the relationship of emotional intelligence and learning motivation with student learning outcomes in economic subjects in class XI SMA Negeri 3 Gorontalo, partially and simultaneously. This research is a quantitative study. The data source in this study is primary data through questionnaires. The data analysis technique used is multiple regression analysis. The results showed that (1) Emotional intelligence has a positive and significant relationship with student learning outcomes in Economy class XI IPS subject in Senior High School (SMA) Negeri 3 Gorontalo City with a correlation value of 0.524. (2) Motivation to learn has a positive and significant relationship with student learning outcomes in Economics subject for class XI IPS in Senior High School (SMA) Negeri 3 Kota Gorontalo with a correlation value of 0.479. (3) Emotional intelligence and learning motivation together have a significant relationship with student learning outcomes in Economics subject for class XI IPS at Senior High School (SMA) Negeri 3 Gorontalo City. The simultaneous correlation value is 0.562 which was interpreted as strong. This shows that there will be an increase in learning outcomes if students have high emotional intelligence and learning motivation. As for other variables that have the potential to influence student learning outcomes, namely learning models, interest in learning, teacher teaching skills, parental attention, social factors, learning environment, peer support, intellectual and spiritual intelligence.
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Active Bagging Ensemble Selection

As technology progresses with more and more data collected, the need of finding the appropriate label for them increases. However, many times the labeling process is a very difficult or/and expensive task and in most cases a help of an expert or expensive equipment is needed. For this reason the need of labeling only the most appropriate instances rises. Active Learning techniques can accomplish this by querying only those instances that a trained model finds the greatest amount of information and providing them to a human expert in order to label them. Combining these techniques with a fast ensemble classifier, a very performant in terms of classification accuracy schema can emerge where a trained model in a small amount of labeled instances can grow by adding only the most informative instances from a much greater pool of unlabeled instances. In this paper, we will propose such a schema using Bagging Ensemble Selection that uses REPTree as base classifier under Active Learning techniques and we will compare it to four well-known ensemble classifiers under the same techniques on 61 real world datasets.
An Ontology based Optimized Semantic Web Service Discovery using TensorFlow in Overlay Cell Networks

Semantic Web Services (SWS) proposes to expand traditional web service technology to integrate ontology and semantics. Services adapt to changes without human intervention. The dissemination of information about traffic services is very important to the average consumer. The current system provides semantic web services for travel information through ontology at the request of travelers. However, the current system has problems with high-level semantic web services using the Palochi algorithm. So it does not provide proper services and reliability is declining. To overcome this error, Word Sense Disambiguation (WSD) suggests that, under certain circumstances, semantic similarity results should be improved by reducing ambiguity. Introduced the Genetic Algorithm (GA) to improve optimally based web services. The focus is on providing road accident information through oncology. It selects close to the right services and provides high quality services in terms of fast response time and high accuracy results.
Attitudes of Modern Youth to the Innovative Activities: Based on the Materials of Sociological Research

Introduction . Orientation of the Russian to innovative way of development requires the provision of the state of human resources – qualified specialists with a certain set of qualities, skills and competencies. Researchers studying the matter, concluded that such a resource is the youth as the most active and creative social and demographic groups. Important components of the innovative potential of young people are the open mindedness, self-interest and involvement in innovative processes. The author carries out a case study, the aim of which is to study the attitudes and engagement of young people of the Ivanovo region in innovation activities. Methodology and sources. The methodological basis of this work is the sociological researches conducted by the author. The subject of these studies were representatives of youth of the Ivanovo region in age from 14 to 30 years. During the research, several sub-groups divided the youth group: senior pupils, students and representatives of working youth. Sampling error does not exceed 5 %, for a given level of significance of 95 %. Results and discussion. The study showed that only half of the youth in the region would like to take part in innovative activities. At the same time over the past 3 years, about 44 % of young people have taken part in some form of innovation activity. Revealed that innovative practices are most common for pupils and students. Young people engaged in professional activities in more than half of the cases had no experience of participation in innovative activities. Conclusion. The results of the author of sociological studies show varying degrees of readiness of youth to innovative activities. Pupils and students largely than older youth, interested in innovative practices, due to its involvement in these processes through the use of new educational technologies. The interest of working youth is determined by the content of the profession.
Backtracking (the) Algorithms on the Hamiltonian Cycle Problem

Even though the Hamiltonian cycle problem is NP-complete, many of its problem instances aren't. In fact, almost all the hard instances reside in one area: near the Komlos-Szemeredi bound, of $\frac{1}{2} v\cdot ln(v) + \frac{1}{2} v\cdot ln( ln(v))$ edges, where randomly generated graphs have an approximate 50\% chance of being Hamiltonian. If the number of edges is either much higher or much lower, the problem is not hard -- most backtracking algorithms decide such instances in (near) polynomial time. Recently however, targeted search efforts have identified very hard Hamiltonian cycle problem instances very far away from the Komlos-Szemeredi bound. In that study, the used backtracking algorithm was Vandegriend-Culberson's, which was supposedly the most efficient of all Hamiltonian backtracking algorithms. In this paper, we make a unified large scale quantitative comparison for the best known backtracking algorithms described between 1877 and 2016. We confirm the suspicion that the Komlos-Szemeredi bound is a hard area for all backtracking algorithms, but also that Vandegriend-Culberson is indeed the most efficient algorithm, when expressed in consumed computing time. When measured in recursive effectiveness however, the algorithm by Frank Rubin, almost half a century old, performs best. In a more general algorithmic assessment, we conjecture that edge pruning and non-Hamiltonicity checks might be largely responsible for these recursive savings. When expressed in system time however, denser problem instances require much more time per recursion. This is most likely due to the costliness of the extra search pruning procedures, which are relatively elaborate. We supply large amounts of experimental data, and a unified single-program implementation for all six algorithms. All data and algorithmic source code is made public for further use by our colleagues.
The Analysis Optimization Analysis of Budget Absorption of State University Operational Assistance Funds (BOPTN) in FEB, FISIP, and FH Lambung Mangkurat University

This study aims to describe the optimization model of BOPTN budget absorption on FEB, FISIP and ULM FH 2016 S / D 2018 and identify constraints faced in optimizing the budget absorption. This research is a qualitative research with descriptive methods and quantitative data presentation. The research results of budget absorption in FEB, FISIP, and FH ULM Banjarmasin units from 2016 S / D in 2018 experienced a significant increase in BOPTN fund absorption optimization. This is intended from the percentage numbers in the recapitulation of budget realization in the FEB, FISIP, and ULM Banjarmasin work units which reached 100% in 2017 and 2018. Employee competencies and budget absorption performance have a unidirectional relationship, if employee competencies are higher, the higher also the level of performance of budget absorption. Similarly, the role of information technology is so important in the budget process. Maintenance and repair of hardware and software that are compiled in a comprehensive, clear and periodic manner.
Extremal behaviour of ± 1-valued completely multiplicative functions in function fields

We investigate the classical Pólya and Turán conjectures in the context of rational function fields over finite fields 𝔽q. Related to these two conjectures we investigate the sign of truncations of Dirichlet L-functions at point s=1 corresponding to quadratic characters over 𝔽q[t], prove a variant of a theorem of Landau for arbitrary sets of monic, irreducible polynomials over 𝔽q[t] and calculate the mean value of certain variants of the Liouville function over 𝔽q[t].
Conceptual Service Level Agreement Mechanism to Minimize the SLA Violation with SLA Negotiation Process in Cloud Computing Environment

Online service is used to be as Pay-Per-Use in Cloud computing. Service user need not be in a long time contract with cloud service providers. Service level agreements (SLAs) are understandings marked between a cloud service providers and others, for example, a service user, intermediary operator, or observing operators. Since cloud computing is an ongoing technology giving numerous services to basic business applications and adaptable systems to manage online agreements are significant. SLA maintains the quality-of-service to the cloud user. If service provider fails to maintain the required service SLA is considered to be SLA violated. The main aim is to minimize the SLA violations for maintain the QoS of their cloud users. In this research article, a toolbox is proposed to help the procedure of exchanging of a SLA with the service providers that will enable the cloud client in indicating service quality demands and an algorithm as well as Negotiation model is also proposed to negotiate the request with the service providers to produce a better agreement between service provider and cloud service consumer. Subsequently, the discussed framework can reduce SLA violations as well as negotiation disappointments and have expanded cost-adequacy. Moreover, the suggested SLA toolkit is additionally productive to clients so clients can secure a sensible value repayment for diminished QoS or conceding time. This research shows the assurance level in the cloud service providers can be kept up by as yet conveying the services with no interruption from the client's perspective
FLEA: Provably Fair Multisource Learning from Unreliable Training Data

Fairness-aware learning aims at constructing classifiers that not only make accurate predictions, but do not discriminate against specific groups. It is a fast-growing area of machine learning with far-reaching societal impact. However, existing fair learning methods are vulnerable to accidental or malicious artifacts in the training data, which can cause them to unknowingly produce unfair classifiers. In this work we address the problem of fair learning from unreliable training data in the robust multisource setting, where the available training data comes from multiple sources, a fraction of which might be not representative of the true data distribution. We introduce FLEA, a filtering-based algorithm that allows the learning system to identify and suppress those data sources that would have a negative impact on fairness or accuracy if they were used for training. We show the effectiveness of our approach by a diverse range of experiments on multiple datasets. Additionally we prove formally that, given enough data, FLEA protects the learner against unreliable data as long as the fraction of affected data sources is less than half.
Improving visibility of hazy images using image enhancement-based approaches through the fusion of multiple exposure images

Haze is an atmospheric obscurity in which very small particles like dust, smoke, etc. are suspended in the air. These suspended particles reduce the visibility of image/video. This reduced visibility is the main cause of failure of various applications such as intelligent transportation, object recognition, target identification, underwater image enhancement, remote sensing, and so on. In this paper, we have proposed an image enhancement method to remove the effect of haze rather than a restoration-based method. The restoration-based methods first calculate the transmission based on some assumptions and are then followed by costly refinement process. The success of these methods depends on these assumptions. The proposed method utilizes three approaches, namely white balancing, multi-scale Retinex, and Gamma correction. The proposed method is validated through many experiments on hazy images. This analysis reveals that the proposed method performs well as compared to the existing methods in the field of dehazing.
ESTABLISHING A VIRTUAL FEEDBACK WITH STUDENTS DURING THE LECTURE

Since the main form of teaching in higher education is a lecture, the main didactic goal of the lecture is to form the necessary basis for teaching students teaching materials. The most vulnerable aspect of traditional education is the students' indifference to the one-sided high activity of the teacher. For this reason, the number of lecture types has recently increased. Lectures using feedback methods are one way to energize students. One of the ways to effectively use information technology is to provide virtual feedback to students during lectures. In this work, an experiment was carried out using a virtual feedback tool. The importance of virtual feedback is emphasized and practical examples are given
AI-Based Container Orchestration for Federated Cloud Environments.

This paper proposes an architecture for artificial-intelligence-based container orchestration in cloud federation environments. The paper adapts the architecture proposed by Kurze et al. [1] and enhances it with two subcomponents in the federation layer to enable resource identification and container orchestration across cloud federation members. These two subcomponents are the AI Resource Identifier and the Container Orchestrator. The AI Resource Identifier is responsible for identifying the available resource in the federated environment whenever needed. It optimizes resource allocation of federation members by using Artificial Intelligence (AI) techniques for learning from the past. The Container Orchestrator sub-component is responsible for organizing jobs to be allocated along with all the necessary files and dependencies in a containerized way. This, in turn, enhances scalability and portability in a federated environment.
Synthesis: Making magic with GenieMo

GenieMo is a real-time communication tool based on LiveScan3D, created to empower volumetric content creation and communication for social good.It presents a potentially democratising set of tools that can demystify technological processes that seem out of reach when presented by corporate "big tech".This paper explains how working with GenieMo develops some of the potentials in earlier works of telematic art, using a capture system, social hot-casting with additional real-time brainwave recording and visualisations to create something new that is also rooted in the history of immersive art.
QFCNN: Quantum Fourier Convolutional Neural Network

The neural network and quantum computing are both significant and appealing fields, with their interactive disciplines promising for large-scale computing tasks that are untackled by conventional computers. However, both developments are restricted by the scope of the hardware development. Nevertheless, many neural network algorithms had been proposed before GPUs become powerful enough for running very deep models. Similarly, quantum algorithms can also be proposed as knowledge reserves before real quantum computers are easily accessible. Specifically, taking advantage of both the neural networks and quantum computation and designing quantum deep neural networks (QDNNs) for acceleration on Noisy Intermediate-Scale Quantum (NISQ) processors is also an important research problem. As one of the most widely used neural network architectures, convolutional neural network (CNN) remains to be accelerated by quantum mechanisms, with only a few attempts have been demonstrated. In this paper, we propose a new hybrid quantum-classical circuit, namely Quantum Fourier Convolutional Network (QFCN). Our model achieves exponential speed-up compared with classical CNN theoretically and improves over the existing best result of quantum CNN. We demonstrate the potential of this architecture by applying it to different deep learning tasks, including traffic prediction and image classification.
News Content Completion with Location-Aware Image Selection

News, as one of the fundamental social media types, typically contains both texts and images. Image selection, which involves choosing appropriate images according to some specified contexts, is crucial for formulating good news. However, it presents two challenges: where to place images and which images to use. The difficulties associated with this where-which problem lie in the fact that news typically contains linguistically rich text that delivers complex information and more than one image. In this paper, we propose a novel end-to-end two-stage framework to address these issues comprehensively. In the first stage, we identify key information in news by using location embeddings, which represent the local contextual information of each candidate location for image insertion. Then, in the second stage, we thoroughly examine the candidate images and select the most context-related ones to insert into each location identified in the first stage. We also introduce three insertion strategies to formulate different scenarios influencing the image selection procedure. Extensive experiments demonstrate the consistent superiority of the proposed framework in image selection.
METHODOLOGICAL APPROACHES TO THE FORMATION OF DIGITAL LITERACY OF STUDENTS

The article discusses the functioning of the information society, the main aspects of the development of the socio-cultural society from the perspective of digital literacy, as well as a set of methodological approaches to solving the problem of the formation of digital literacy of students of various types of educational organizations, the level structure of the formation of digital literacy is presented. A detailed description of the anthropocentric, subject-activity and modular approaches that ensure the development of basic characteristics in the formation of digital literacy is given.
An Empirical Analysis of the Effects of Population Growth on Economic Growth in Ethiopia using an Auto Regressive Distributive Lag (ARDL) Model Approach

Abstract Objective The study examines An Empirical Analysis of the Effects of Population Growth on Economic Growth in Ethiopia using an Auto Regressive Distributive Lag (ARDL) Model Approach from the period of 1980 through 2019 with specific focus on total population, Growth Domestic Product, population growth rate, and foreign direct investment, inflow. This study investigated to understand the effects of total population on economic growth, and to analyze the short run and long run relationship of economic growth with respect to population growth. Results From the results of the study, personal remittance is stationary at level, while total population, FDI net inflows, population growth rate, rate of inflation, and gross capital formation are stationary at first difference. From the finding of long run equilibrium relationships between RGDP, population number, FDI, personal remittance, population growth rate, rate of inflation and GCF is existed since the value of F-statics is greater than the upper boundary line. Finally, to increase the economic growth of Ethiopia; the government should adopt policies that can attract the foreign investors. The government also should put a standard to guarantee that the economy grows at a larger rate than the population growth.
METHODS OF TEACHING THE COURSE OF INFORMATICS OF THE NATURAL-MATHEMATICAL DIRECTION ON THE UPDATED CONTENT

This article is devoted to the analysis of methods of teaching computer science of natural and mathematical direction on the updated content, the solution of actual problems, methods and techniques aimed at the formation of students ' skills. The essence of the updated educational program is the formation of functional literacy of the child. One of the current problems is the formation of professional competence of students in grades 10-11, the formation and provision of the necessary resources for conscious improvement of the course of professional activity in practice. Today in the school learning of science and mathematics in profile classes accompanied by a number of difficulties, due to the breach of the educational-cognitive characteristics of students of different profiles, orientation of training programs to increase scientific information. In accordance with this, it is very important to conduct a comparative analysis and create a scientific and theoretical basis for the unification of computer science education. As a result, we believe that the student should use this knowledge in life. The results of teaching and learning can only be achieved if the topics studied in computer science are understandable, clear and specific to the student.
The Effectiveness of KWL Strategy for Reading Comprehension

Some strategies used by teachers when teaching English seemed to be less effective, causing the students to have a comprehension difficulty, including in reading. This issue was also found among the students of SMA Negeri 1 Darul Imarah, Aceh Besar. This study was therefore conducted to find out whether or not the eleventh grade students of SMA Negeri 1 Darul Imarah taught reading comprehension by using KWL Strategy will receive a higher score than those taught without KWL Strategy. This study used the quantitative method with experimental design. Sixty students were taken as the sample. The data were analyzed by using t-test, and the result showed that the t-score of two means between post-test of experimental and control group was 2.19 thus, the t-test was higher than the critical value of the t-test for the degree freedom of 58 (167) at the level of significance 0.05.