Reviewerly/exHarmony huggingface dataset:
IMPLEMENTASI ALGORITMA C 4.5 DAN ALGORITMA ID3 UNTUK MENGANALISIS KEPUASAN PELANGGAN TERHADAP PELAYANAN BENGKEL KENDARAAN BERMOTOR

AbstractThe growth of motor vehicle repair services business is a business that can be said to have a very high growth in Indonesia. This is due to the large number of motor vehicles in the country. Customer satisfaction in a business is important because of the satisfaction of the customer who will move the business forward. In this study results of consumer satisfaction survey in the workshop was analyzed using classification method with C 4.5 algorithm and Iterative Dichotomiser algorithm 3 (ID3). The results of the C 4.5 algorithm and the Iterative Dichotomiser algorithm 3 (ID3) Customers are satisfied with the reliability, Assurance (assurance) and responsiveness (responsiveness) attributes, while the less attributes are tangible (physical evidence). The C 4.5 algorithm and the Iterative Dichotomiser algorithm 3 (ID3) can be used to find the level of customer satisfaction with good value, enough, and less. From the calculation result two methods namely algorithm method C 4.5 and ID3 algorithm Get the result of the same decision tree, although the formula of two methods is different. While the suggested improvement of the results of this research is an improvement on the tangible attribute (physical evidence).Keywords : Workshop, C 4.5, Dichotomiser Iterative 3 (ID3), Classification.
A Resource Estimation and Verification Workflow in Q# Special session paper

An important branch in quantum computing involves accurate resource estimation to assess the cost of running a quantum algorithm on future quantum hardware. A comprehensive and self-contained workflow with the quantum program in its center allows programmers to build comprehensible and reproducible resource estimation projects. We show how to systematically create such workflows using the quantum programming language Q#. Our approach uses simulators for verification, debugging, and resource estimation, as well as rewrite steps for optimization.
Technical properties of Ranked Nodes Method

This paper presents analytical and experimental results on the ranked nodes method (RNM) that is used to construct conditional probability tables for Bayesian networks by expert elicitation. The majority of the results are focused on a setting in which RNM is applied to a child node and parent nodes that all have the same amount discrete ordinal states. The results indicate on RNM properties that can be used to support its future elaboration and development.
The Effect of Electronic Information Sharing on Innovative Behavior of Employees in Organizations

The purpose of this study was to investigate the effect of electronic information sharing on employee's innovative behavior in specialized clinics of Baqiyatallah Hospital in 1396. Type of research according to the purpose  is applied and In terms of data collection method is a correlation type of descriptive method that using the two methods of field studies (collecting questionnaire data) and library (compilation of theoretical bases), the present situation has been studied. The statistical population includes employees employed in the studied population in 1396, numbering 267 people. In analyzing the findings of the questionnaires, Pearson correlation coefficient and multivariate regression analysis were used. For this purpose, SPSS software has been used. The results of the analysis of the findings show that the electronic information sharing dimensions have been able to predict the variations of all five dimensions of innovative behavior (providing useful and constructive ideas, promoting new ideas, creativity and innovation, an interest in creativity and innovation, and the use of existing facilities) with different coefficients.
Exploring and Mining Attributed Sequences of Interactions

We consider entities interacting over time: individuals meeting, customers buying products, etc., each entity being labeled with some information that may depend on time, and possibly extracted from the interaction nature. Capturing the dynamics as well as the structure of these interactions is of crucial importance for analysis. We are interested here in mining sequences of such interactions. For that purpose, we define core closed patterns in this context and introduce algorithms to enumerate them on a labeled stream graph. We run experiments on two real-world datasets, one representing interactions among students and the other representing citations between authors.
DEVELOPMENT OF PSYCHOEMOTIONAL STABILITY OF FUTURE TEACHERS

Appears in: EDULEARN21 Proceedings Publication year: 2021Pages: 11343-11348ISBN: 978-84-09-31267-2ISSN: 2340-1117doi: 10.21125/edulearn.2021.2367Conference name: 13th International Conference on Education and New Learning TechnologiesDates: 5-6 July, 2021Location: Online Conference
프로젝트 관리자의 유형과 역량이 성과에 미치는 영향 : 방위산업을중심으로

The subject of this study is to determine whether the leader's leadership style and competency have a significant effect on the defense industry project performance in order to successfully perform project management in the Defense Acquisition Program Administration's integrated project management team. The Defense Acquisition Program Administration, which is at the center of the defense industry, has been playing an important role in order to increase defense capabilities in Korea in the face of continuous North Korean provocations such as the Cheonan incident and the 5th nuclear test. Therefore, in this study, leadership styles of defense industry leaders were divided into charisma, intellectual stimulation, conditional reward, and exception management. The purpose of this study is to classify the leadership style of defense industry leaders into charisma, intellectual stimulation, conditional reward, and exception management, and divide the leadership competency into management capability, interpersonal relationship capability, and intellectual capability to find out the impact of each factor on project performance.
A Study on a Three-Dimensional Path Planning Algorithm for a Large-Scale Swarm

In this paper, we propose a three-dimensional path planning algorithm for a large-scale swarm. The proposed algorithm is based on the Eulerian approach that controls spatial characteristics which is an agent density vector. This algorithm has three phases, which are environment abstraction, optimal transport, and collision avoidance, respectively. The algorithm can be applied to a largescale swarm of 1000 agents or more, and it guarantees collision avoidance between obstacles existing in the given flight environment and the surrounding agents. We carried out simulation using two scenarios, and as a result, it was confirmed that the proposed algorithm is suitable for three-dimensional path planning for a large-scale swarm.
Probabilistic Trust Intervals for Out of Distribution Detection

Building neural network classifiers with an ability to distinguish between in and out-of distribution inputs is an important step towards faithful deep learning systems. Some of the successful approaches for this, resort to architectural novelties, such as ensembles, with increased complexities in terms of the number of parameters and training procedures. Whereas some other approaches make use of surrogate samples, which are easy to create and work as proxies for actual out-of-distribution (OOD) samples, to train the networks for OOD detection. In this paper, we propose a very simple approach for enhancing the ability of a pretrained network to detect OOD inputs without even altering the original parameter values. We define a probabilistic trust interval for each weight parameter of the network and optimize its size according to the in-distribution (ID) inputs. It allows the network to sample additional weight values along with the original values at the time of inference and use the observed disagreement among the corresponding outputs for OOD detection. In order to capture the disagreement effectively, we also propose a measure and establish its suitability using empirical evidence. Our approach outperforms the existing state-of-the-art methods on various OOD datasets by considerable margins without using any real or surrogate OOD samples. We also analyze the performance of our approach on adversarial and corrupted inputs such as CIFAR-10-C and demonstrate its ability to clearly distinguish such inputs as well. By using fundamental theorem of calculus on neural networks, we explain why our technique doesn't need to observe OOD samples during training to achieve results better than the previous works.
The Intelligent Based Jigsaw Model (JBCC) as an Effort to Increase Study Motivation, HOTS, and Basic Student Learning Outcomes

This research is research and development education or Research and Development (R&D).The study was conducted in SD Palu Selatan District with a limited sample test conducted in class V at SDN 1 Tatura.The area test was carried out at four elementary schools in South Palu District.Based on the results of the study, it can be concluded that: (1) the design of the JBCC procedure as an effort to increase learning motivation, HOTS, and student learning outcomes, is carried out procedurally through development syntax: origin group discussion -expert group discussion -back to the homegroupquiz game -assessment.Instruments that are declared valid and suitable for use consist of JBCC diagram and matrix design, teacher and student questionnaires/interviews, lesson plans, teacher and student activity observation sheets, learning motivation, HOTS FRISCO and Bloom's Taxonomy.(2) teachers' perceptions of JBCC on average 89.14% with a very high response category and students agreed with an average score of 4.27, and the response presentation reached 85.39% (very high); (3) significant differences between the control class and the experimental class in the limited test, namely: students' learning motivation; In the little difficulty, the difference in the scores of classical learning motivation between the control class and the experimental study is 0.21, and the percentage is 4.21%.The difference in HOTS assessment results is an average of 0.46 and a rate of 9.20%.Learning ability based on Bloom's Taxonomy level is 0.13 on average and 3.03%.Significant differences between the control class and the experimental class in the comprehensive test (five sample schools): learning motivation averaged 0.93, and 1.8% HOTS averaged 0.32 and 6.12% the Bloom Taxonomy level is an average of 0.15 and a percentage difference of 2.89%.From the research results, it can be concluded that the JBCC development model can be used as an effort to increase learning motivation, HOTS, and student learning outcomes.
Learners’ Attitudes Towards English Learning and Online English Learning in a Korean University Context

This study examines how learners’ attitudes vary regarding English language learning in general and online English language learning in particular, and notes how students with different levels of English adapt to the different learning contexts. Ninety-five participants were classified into low-, medium-, and high-proficiency groups based on their English performance. A background questionnaire, along with the Attitudes towards English Language Learning (AELL) scale, and the Attitudes towards Online English Language Learning (AOELL) scale were employed in the study. The findings of the study indicate that the performance of the high- and medium-proficiency groups was significantly greater than that of the low-proficiency group in terms of cognition and learning-effort on the AELL and surplus value of online English language learning on the AOELL. Based on those results, this study suggests pedagogical implications and in-class activities teachers can use to better foster positive learning attitudes in their EFL classes.
Syntactic Analysis Based on Morphological characteristic Features of the Romanian Language

This paper gives complete guidelines for authors submitting papers for the AIRCC Journals. This paper refers to the syntactic analysis of phrases in Romanian, as an important process of natural language processing. We will suggest a real-time solution, based on the idea of using some words or groups of words that indicate grammatical category; and some specific endings of some parts of sentence. Our idea is based on some characteristics of the Romanian language, where some prepositions, adverbs or some specific endings can provide a lot of information about the structure of a complex sentence. Such characteristics can be found in other languages, too, such as French. Using a special grammar, we developed a system (DIASEXP) that can perform a dialogue in natural language with assertive and interogative sentences about a “story” (a set of sentences describing some events from the real life).
Inflation and Cryptocurrencies Revisited: A Time-Scale Analysis

This letter revisits the time-series relation between cryptocurrency prices and forward inflation expectations. Using wavelet time-scale techniques, a positive link between cryptocurrencies and forward inflation rates is identified, focused on a brief period surrounding the onset of the COVID-19 pandemic. This coincides with a rapid and synchronized decrease in cryptocurrency prices and forward inflation expectations, followed by a swift recovery to pre-crisis levels. Outside of the crisis period, we find no clear evidence of any inflation hedging capacity of Bitcoin or Ethereum during times of increasing forward inflation expectations.
Application of Jigsaw Learning Method Using Play Money Learning Media to Improve Mathematics Learning Outcomes

&lt;p&gt;&lt;em&gt;This study&lt;/em&gt;&lt;em&gt; aims to improve mathematics learning outcomes about money in class III semester I SDN 4 Mangin, Karangrayung District, Grobogan Regency, 2017/2018 academic year through toy money media and the application of Jigsaw learning m&lt;/em&gt;&lt;em&gt;ethod&lt;/em&gt;&lt;em&gt;. The research subjects were grade 3 students at SDN 4 Mangin, totaling 14 students. The research design is classroom action research consisting of two cycles. Each cycle consists of 2 meetings. Data collection techniques using evaluation in the form of assignments, worksheets and evaluation questions. While the data analysis includes qualitative data and quantitative data. Learning outcomes from the pre-cycle were obtained, from 14 students, only 5 students (36%) had reached the mastery target with a minimum score of 65. In the first cycle, classical completeness was 71%. While in the second cycle of 100%. These results indicate that the application of the Jigsaw method using toy money media can improve mathematics learning outcomes for grade 3 students at SDN 4 Mangin.&lt;/em&gt;&lt;/p&gt;
Advances in Graph Signal Processing: Fast graph construction a Node-adaptive graph signal reconstruction

This thesis consists of two parts in both data science and signal processing over graphs. In the first part of this thesis, we aim to solve the problem of graph construction in big data scenario, which is critical for practical tasks, like collaborative filtering in recommender systems, spectral embedding or clustering in learning algorithms. We achieve to accelerate the data-driven graph construction algorithms by relying on an approximation technique for large matrix multiplication, diamond sampling. We show its potential in real problems by extensive experiments. In the second part, we improve the performance of the graph signal reconstructions by exploiting the local properties of graph signals. We propose a node-adaptive regularization with an improved degree of freedom, so a more general signal smoothness assumption is allowed. Different regularization weights design methods are proposed to achieve its best performance. By comparing it with Tikhonov regularization, we observe its superiority in graph signal reconstruction and interpolation, also in graph signal sampling.
THE DEDICATED SOCIAL NETWORKING APPLICATION CONCEPT FOR CONTEXTUAL LEARNING

As a result of the changes brought about by COVID-19, students are readjusting to the new learning situation.They are employing various means and methods to accommodate the new constraints.Teachers and learners alike have gained new pedagogical experience as the context for teaching and learning has been changed by the pandemic.Technological advancement has empowered various pedagogical approaches to ease the learning process.In this study, we applied a user-centered design principle to design a dedicated social media application for educational purposes.Social media and other digital channels have become essential mediums for digital native students.These tools, e.g., email, chat, search engines, etc., are often scattered and not embedded in a single platform.Tools such as Microsoft Teams, Zoom and Moodle are appropriate mediums for teaching and learning, but they are not suitable for students on the move to perform their tasks or teamwork activities.The results of our study indicate that students need a social media based learning platform for efficiency and effectiveness in their learning process.The application concept also appealed to the target users.
Lightness Modulated Deep Inverse Tone Mapping

Single-image HDR reconstruction or inverse tone mapping (iTM) is a challenging task. In particular, recovering information in over-exposed regions is extremely difficult because details in such regions are almost completely lost. In this paper, we present a deep learning based iTM method that takes advantage of the feature extraction and mapping power of deep convolutional neural networks (CNNs) and uses a lightness prior to modulate the CNN to better exploit observations in the surrounding areas of the over-exposed regions to enhance the quality of HDR image reconstruction. Specifically, we introduce a Hierarchical Synthesis Network (HiSN) for inferring a HDR image from a LDR input and a Lightness Adpative Modulation Network (LAMN) to incorporate the the lightness prior knowledge in the inferring process. The HiSN hierarchically synthesizes the high-brightness component and the low-brightness component of the HDR image whilst the LAMN uses a lightness adaptive mask that separates detail-less saturated bright pixels from well-exposed lower light pixels to enable HiSN to better infer the missing information, particularly in the difficult over-exposed detail-less areas. We present experimental results to demonstrate the effectiveness of the new technique based on quantitative measures and visual comparisons. In addition, we present ablation studies of HiSN and visualization of the activation maps inside LAMN to help gain a deeper understanding of the internal working of the new iTM algorithm and explain why it can achieve much improved performance over state-of-the-art algorithms.
Modeling Bilingual Conversational Characteristics for Neural Chat Translation

Neural chat translation aims to translate bilingual conversational text, which has a broad application in international exchanges and cooperation. Despite the impressive performance of sentence-level and context-aware Neural Machine Translation (NMT), there still remain challenges to translate bilingual conversational text due to its inherent characteristics such as role preference, dialogue coherence, and translation consistency. In this paper, we aim to promote the translation quality of conversational text by modeling the above properties. Specifically, we design three latent variational modules to learn the distributions of bilingual conversational characteristics. Through sampling from these learned distributions, the latent variables, tailored for role preference, dialogue coherence, and translation consistency, are incorporated into the NMT model for better translation. We evaluate our approach on the benchmark dataset BConTrasT (English-German) and a self-collected bilingual dialogue corpus, named BMELD (English-Chinese). Extensive experiments show that our approach notably boosts the performance over strong baselines by a large margin and significantly surpasses some state-of-the-art context-aware NMT models in terms of BLEU and TER. Additionally, we make the BMELD dataset publicly available for the research community.
DIGITAL PLATFORMS FOR TEACHING EDUCATIONAL RESEARCH. CASE STUDY WITH RESEARCH-BASED LEARNING METHODOLOGY IN UNIVERSITY COURSE

In recent months, and with the context of online education, Research-Based Learning (RBL) emerged as an innovative alternative to promote cognitive and technological abilities in college students. By recognizing that online learning has challenges to face, RBL represents an alternative that compiles competencies development to daily and professional tasks. Many Authors include RBL as a method that has the following advantages: increases information search skills, academic performance, student self-confidence, critical thinking, motivation, and self-learning. Besides, RBL combined with curriculum integration aims to broaden students’ perspectives and make their learning meaningful by producing critical thinking skills. This research uses a case study as the methodology design. The participant population was twenty-four students from the Pedagogy and Psychopedagogy College programs at the senior and junior years. The students took three courses with transversal participation in between them for four months. The participating students received classes using digital platforms as Google Meet and Zoom. Additionally, students learned to use digital applications such as ResearchGate and Mendeley. At the end of the course, professors conducted an online survey with closed and open-ended items. To analyze the information, researchers carried out a multimethod analysis using MAXQDA2020. Considering, quantitative-descriptive results, it was identified that students valued as positive activities for their learning in Research: 66.7% the Interview planning and application activity, 58.3% the Oral presentation activity, 54.2% valued as positive activities for their learning the activity with Digital Scientific Databases (Library). Likewise, 91.7% of the sample value positively the use of digital platforms for research learning, while 8.3% valued it negatively. The Qualitative findings underpin 6 thematic axes: (1) Inquiry skills in specialized Databases, (2) Knowledge application skills, (3) Identification and use of research methodology, (4) Challenges in RBL: Online teaching modality, (5) Academic writing skills, (6) Digital skills for research. It is concluded that the impact of research-based learning is relevant for the formation of competencies such as inquiry in databases and the application of methodologies. Digital platforms and applications are positively valued when used as a tool for learning mediation and focused on the application of the knowledge reviewed in synchronous classes.
PEDAGOGICAL MODEL OF THE FORMATION OF THE MUSIC TEACHER MUSICAL AND PERFORMING CULTURE USING OF ETHNIC SINGING TRADITIONS

PEDAGOGICAL MODEL OF THE FORMATION OF THE MUSIC TEACHER MUSICAL AND PERFORMING CULTURE USING OF ETHNIC SINGING TRADITIONS