Reviewerly/exHarmony huggingface dataset:
보육교직원의 인성리더십에 관한 주관성 연구

In this study, Q-methodology was applied to examine the recognition, values and attitudes of character leadership of childcare staff. The results of this study are as follows. The directors were shown to exhibit managerial character leadership, rational character leadership, emotional character leadership based on gratitude and love, and relational character leadership. The head teachers were shown to exhibit relation-oriented character leadership, stability-oriented character leadership that considered other teachers, efficiency- oriented character leadership to handle work efficiently, and system-oriented character leadership that led teachers into a community with a sense of responsibility. The general teachers were shown to exhibit belief-oriented character leadership to foster a sincere attitude as a teacher, fellowship- oriented character leadership to value their relationships with fellow teachers, value-oriented character leadership to value their current job as a childcare teacher, and child-oriented character leadership to value their relationship with children.
Decoding Algorithm for Quadruple-Error-Correcting Reed-Solomon Codes and Its Derived Architectures

This brief introduces a new method to compute in parallel the roots of a polynomial locator of degree four in a Reed-Solomon decoder. The novelty of this brief is the introduction of an algorithm that transforms the polynomial locator obtained with the Peterson-Gorenstein-Zierler's algorithm into an equivalent one that allows a direct search for the four roots that indicate the location of the symbols in error. This new solution improves a previous approach proposed in the literature for a quadruple-error-correction decoder (QEC) that requires a cubic aid equation. This previous solution does not work in the cases in which the cubic aid does not have a solution. In contrast, the proposal of this brief works in 100% of cases (with t ≤ 4) as it solves an equivalent polynomial. This new algorithm allows two different implementations: a fully parallel architecture which provides an extremely low latency at an extra area cost, and a fully serial architecture with less area, but a higher latency.
Determining Question-Answer Plausibility in Crowdsourced Datasets Using Multi-Task Learning

Datasets extracted from social networks and online forums are often prone to the pitfalls of natural language, namely the presence of unstructured and noisy data. In this work, we seek to enable the collection of high-quality question-answer datasets from social media by proposing a novel task for automated quality analysis and data cleaning: question-answer (QA) plausibility. Given a machine or user-generated question and a crowd-sourced response from a social media user, we determine if the question and response are valid; if so, we identify the answer within the free-form response. We design BERT-based models to perform the QA plausibility task, and we evaluate the ability of our models to generate a clean, usable question-answer dataset. Our highest-performing approach consists of a single-task model which determines the plausibility of the question, followed by a multi-task model which evaluates the plausibility of the response as well as extracts answers (Question Plausibility AUROC=0.75, Response Plausibility AUROC=0.78, Answer Extraction F1=0.665).
A Hybrid Self-Attention Model for Pedestrians Detection

In recent years, with the research enthusiasm of deep learning, pedestrian detection has made significant progress. However, the performance of state-of-the-art algorithms are still limited due to the high complexity of the detection scene. Therefore, in order to better distinguish between pedestrians and background, we propose a novel hybrid attention module which is capable of obtaining inter-dependencies between features from both channel and spatial dimensions through local convolution and dual-pass pooling, and guiding the network to focus on better pedestrians' feature representation while suppressing background noise. Further, we complement the information of channel attention and spatial attention through an effective fusion mechanism. To validate the effectiveness of the proposed hybrid attention module, we embed it into a representative pedestrian detection framework named Center and Scale Prediction (CSP) based detector. The experimental results on the Caltech Pedestrians Benchmark, one of the largest pedestrian detection datasets, show that the proposed method outperform not only the baseline framework but also several state-of-the-arts.
Translation Quality Assessment of Online Translation Systems in Translating English to Thai on Phuket Tourism

This research aims to compare four English-to-Thai translator applications on translating the comments posted on social media. Four translator applications include Google Translate, Baidu Translate, Yandex.Translate and Microsoft/Bing Translate. The comments in this study are on three main popular attractions in Phuket, Thailand. The attractions include a temple (Wat Chalong), a viewpoint spot (Promthep Cape) and a beach (Patong beach). Fifty comments on each attraction are collected from GoogleReviews and TripAdvisor. The total of 300 comments are translated by four translators making the final translated comments of 1,200 comments. All translated comments are evaluated by three language experts on three categories including the grammar structure, the vocabulary and idiom and the overall translation. The evaluation results show that Google Translate is found to be the most suitable translator for translating English comments to Thai comments in this study at 89.33%. Google Translate receives the average suitable score of 4.20 out of 5.00. However, Google Translate is weak at word choices to generate a beautiful sentence. Baidu Translate is very superb at generating a beautiful sentence similar to human languages. However, Baidu Translate only gets a good result on a specific domain or situation. As a result, Baidu Translate is lost to Google Translate in most cases. Even though this research presents the results on Thai language and specific domains, the techniques used in this study can be applied to any language on any domain.
Research on the Application of Information Technology in Children's Music Education

The rapid development of mobile Internet technology has brought education to a new era. This research takes new media and preschool children’s music education as the research objects. Based on the status quo of children’s music education under the new media environment, it studies the innovative application of information technology in children’s music education under the new media environment, and analyzes the influence of new media on music education for preschool children. The positive impact of the coming has already provided an effective basis for the reform of music education for preschool children.
Research and Practice of “423 Mode” Teaching Based on Scratch Assisted Instruction——Taking the Java Programming Course as an Example

Abstract In the case of a general decline in the quality of students in vocational colleges, how to improve the teaching effectiveness of programming basic courses? This article introduces the use of graphical and Scratch software as teaching aids in the basic courses of higher vocational programming. At this stage, following two lines including Scratch and Java in the course design, and also following “423 model” consists of “pre-class”–“in class”–“after class” in the implementation of the classes to cultivate students’ innovative thinking ability, stimulate students’ learning interest, reduce the course difficulty and improving course teaching effectiveness. This teaching mode has achieved ideal teaching effect in Java teaching of Rizhao Vocational Technical College.
Prestasi Belajar Akuntansi Dasar Ditinjau Dari Lingkungan Keluarga Dan Lingkungan Teman Sebaya Pada Siswa Kelas X Akuntansi SMK Negeri 1 Banyudono

This study aims to determine: (1) the effect of the family environment on learning achievement of basic accounting students in class X Accounting SMK Negeri 1 Banyudono. (2) the effect of the peer environment on learning achievement of basic accounting students in class X Accounting SMK Negeri 1 Banyudono. (3) the effect of the family environment and peer environment on learning achievement of basic accounting students in class X Accounting SMK Negeri 1 Banyudono. This type of research is quantitative with a census design. The population in this study were 71 students of class X Accounting at SMK Negeri 1 Banyudono, with a sample of 71 students using a saturated sampling technique. Data collection techniques in this study used questionnaires and documentation. The validity test uses the Product moment correlation formula and the reliability test uses the Cronbach Alpha formula. The data analysis technique used multiple regression test. The results of the study at the 5% significance level indicate that: (1) There is a positive and significant influence of the Family Environment on Basic Accounting Learning Achievement of Class X Accounting Students of SMK Negeri 1 Banyudono. (2) There is a positive and significant influence of Peer Environment on Basic Accounting Learning Achievement of Class X Accounting Students at SMK Negeri 1 Banyudono. (3) There is a positive and significant effect jointly between the Family and Peer Environment on Basic Accounting Learning Achievement in class X Accounting students of SMK Negeri 1 Banyudono. The effective contribution of the family environment variable was 12.5%, meanwhile, the effective contribution of the peer environment variable was 12.9%. The coefficient of determination (R2) is 0.254 or equivalent to 25.4%, the remaining 74.6% is influenced by other variables not examined in this study.
Agile History

As we begin discussing the practical application of agile, it’s important to understand where agile came from. Contrary to popular belief, agile practices did not appear in 2001 with the Agile Manifesto (more on this later). Contemporary practice of agile traces its roots to Scrum, which in turn looks back to quality processes in manufacturing and evolving methods in product development prior to alignment and adaptation for software development and other projects.
Affixations and Allomorphs in Verbs and Nouns in a Research Abstract: A Morphemic and Morphophonemic Analysis

This study aims to analyze and describe the morphemic and phonemic structures of verbs and nouns in a research abstract as source of data. The morphemic structure analysis focuses on the free-bound morphemic affixations of English verbs and nouns while the morphophonemic analysis focuses on the allomorph variations of the inflectional morphemes in English verbs and nouns. Findings of the study reveal that majority of the verbs used in the research abstract have inflectional type of affixations where a bound morpheme is added to the stem as suffix. These morphemes mark tense and number of verbs. The nouns, on the other hand, generally have derivational affixation that involves appending of suffixes to the verb form to derive the noun form. This study affirms that English verbs and nouns generally have inflectional suffixes to mark grammatical categories such as tense and number. In another note, both of the verbs and nouns with inflectional morphemes undergo phonological modifications in terms of their allomorphic variants. The allomorphs [s], [z] and [iz] of the morpheme{s} are used to mark number in nouns and tense in verbs while the allomorphs [d], [ǝd] and [t] of the morpheme {d} mark the tense in regular verbs with inflectional morphemes. The allomorphs in both verbs and nouns involve voicing assimilation and dissimilation as phonological processes. The implications of the findings of the study would be that second language learners of English need to familiarize the morphemic structure of words as they can be very helpful in understanding the meanings of words. Moreover, they have to familiarize the environments where the allomorphic variants of inflectional morphemes are realized so that they would be able to pronounce the words correctly. Such interaction of morphology and phonology can cause learning difficulty for second language learners of English whose first language, like the Cebuano Visayan, is sounded as spelled and is contrary to English which has allophonic and allomorphic variants occurring in words. The analysis can therefore be helpful to teachers in identifying areas of difficulty in learning a second language.
Research Guides: Basic Library Search: Primo: Logging into Primo

Information and guides about how to effectively use our search service, Primo, to find resources at Moon.
Reggio Emilia 교육에서 아뜰리에리스타의 역할과 과제

The Purpose of this research is to explore the roles and tasks of atelierista in Reggio Emilia approach. The method of this research is Focus group in qualitative approach. The Results of this research are as follows : First, The roles of atelierista in teaching and learning process are supporting the dynamic communications and interactions between children, understanding the thinkings and ideas of children by documentation and sharing the learning experiences and feelings sensitively. Second, The tasks of atelierista are managing the atelier, supporting the various materials to children and tasks as a member in kindergarten. In conclusion, Atelierista should comprehend about children sensitively, participate in the curriculum development on documentation, support the materials to children, manage the atelier and cooperate with teachers.
An improved visual model for tracking by segmentation

As if in response to the increased focus of the field on visual object tracking and video object segmentation, this work features several trackers escalating the associations between the two disciplines. These trackers, in particular, build upon an existing D3S tracker that has the capacity to produce both highly-reliable localization as well as an accurate segmentation of the target. Furthermore, said products are used in future target state inference to inform the process and achieve excellent tracking performance. In recognition of the benefits reaped by involving segmentation in visual object tracking, this work proposes several trackers in an effort to further both the accuracy and robustness of the D3S, as well as to improve its speed of inference. Novel trackers are compounded from existing components of the D3S implementation along with other constituents giving prominence to the latest advancements in the field. Namely, the two backbones of the original implementation are merged into a single backbone, CARAFE modules are instated to replace the bilinear upsampling stages, Octave convolution is introduced to improve the speed of feature extraction and the attention mechanism is implemented to incorporate contextual information into the tracking process. Alongside this, the lack of dataset diversity inspires a synthetic dataset to be constructed and used in pre-training stages of representation learning. Finally, the suitability of proposed tracking architectures is determined through rigorous evaluation.
Search-based extensions

It is time that we tackle the last type of customization we can do on the Teams platform: messaging extensions. Messaging extensions are divided into two categories, search based and action based. In this chapter, we will go deeper into the search-based messaging extensions. A search-based extension provides you with the capability to search for information right from within Teams and then use that information inside of your conversation. Let’s assume, for instance, we have a CRM system, then we could create a search-based extension that would allow our users to search the information in our CRM system. But that is not the added value; the added value will be in a card that we can then provide with the result and what will be added to the chat conversation of the user and therefore enriching this chat with valuable information.
State of compact architecture search for deep neural networks

The design of compact deep neural networks is a crucial task to enable widespread adoption of deep neural networks in the real-world, particularly for edge and mobile scenarios. Due to the time-consuming and challenging nature of manually designing compact deep neural networks, there has been significant recent research interest into algorithms that automatically search for compact network architectures. A particularly interesting class of compact architecture search algorithms are those that are guided by baseline network architectures. Such algorithms have been shown to be significantly more computationally efficient than unguided methods. In this study, we explore the current state of compact architecture search for deep neural networks through both theoretical and empirical analysis of four different state-of-the-art compact architecture search algorithms: i) group lasso regularization, ii) variational dropout, iii) MorphNet, and iv) Generative Synthesis. We examine these methods in detail based on a number of different factors such as efficiency, effectiveness, and scalability. Furthermore, empirical evaluations are conducted to compare the efficacy of these compact architecture search algorithms across three well-known benchmark datasets. While by no means an exhaustive exploration, we hope that this study helps provide insights into the interesting state of this relatively new area of research in terms of diversity and real, tangible gains already achieved in architecture design improvements. Furthermore, the hope is that this study would help in pushing the conversation forward towards a deeper theoretical and empirical understanding where the research community currently stands in the landscape of compact architecture search for deep neural networks, and the practical challenges and considerations in leveraging such approaches for operational usage.
The Study on Improvements of Statistics in the MICE Industry: Focused on『MICE Industry Statistics Survey』

The purpose of this study was to suggest improvements to the ‘MICE Industry Statistics Survey'' which was designed for understanding the current scale of the MICE industry. ''MICE industry statistics survey'' is very significant that it is the only industrial statistic including the whole mice industry. Statistics are the basic data for shaping the policies of a country. Accurate statistical data gives the necessary infrastructure for national management. To achieve the purpose of the study, firstly, ‘MICE Industry Statistics Survey'' reports over the past decade were analyzed. In addition, problems and improvements were derived through in-depth interviews with 20 experts in the related field. Based on these, the ways to improve statistical quality were analyzed for more accurate statistical production such as survey design and survey methods from the perspective of practitioners. The results of the study could lead to high credibility and better use of ’MICE industry statistics Survey’.
CURRICULAR INTEGRATION AS A STRATEGY TO STRENGTHEN THE EDUCATIONAL PROCESS IN PUBLIC INSTITUTIONS IN COVID-19 TIMES

This document intends to inform the reflective process that the teachers at the Jorge Eliecer Gaitan High School in Florencia, Caqueta, have had as a solution to the impact that the COVID-19 pandemic has left on public education. First, it addresses the problem identified in school in terms of applied teaching methodology and the need to change it to improve student motivation and learning in the midst of the pandemic. Then, it shows the methodology proposed and applied by the teachers in the school. Finally, it highlights the effects that the methodology has had on the perceptions of teachers and students on the teaching and learning process
Analysis on the Boltzmann Machine with Random Input Drifts in Activation Function

The Boltzmann machine (BM) model is able to learn the probability distribution of input patterns. However, in analog realization, there are thermal noise and random offset voltages of amplifiers. Those realization issues affect the behaviour of the neurons' activation function and they can be modelled as random input drifts. This paper analyzes the activation function and state distribution of BMs under the input random drift model. Since the state of a neuron is also determined by its activation function, the random input drifts may cause a BM to change the behaviour. We show that the effect of random input drifts is equivalent to raising temperature factor. Hence, from the Kullback–Leibler (KL) divergence perspective, we propose a compensation scheme to reduce the effect of random input drifts. In our derive of compensation scheme, we assume that the input drift follows the Gaussian distribution. Surprisedly, from our simulations, the proposed compensation scheme also works very well for other distributions.
The Smartphone: Digital Mediation Through Fluid Assemblages

There is much exciting work being done at the intersection between media theory and STS. As objects become ‘smart,’ as in the Internet of Things, smart homes, and smart cities, they will continue t ...
Cross-Project Software Defect Prediction Based on Feature Selection and Transfer Learning

Cross-project software defect prediction solves the problem that traditional defect prediction can’t get enough data, but how to apply the model learned from the data of different mechanisms to the target data set is a new problem. At the same time, there is the problem that information redundancy in the training process leads to low accuracy. Based on the difference of projects, this paper uses MIC to filter features to solve the problem of information redundancy. At the same time, combined with the TrAdaboost algorithm, which is based on the idea of aggravating multiple classification error samples, this paper proposes a cross-project software prediction method based on feature selection and migration learning. Experimental results show that the algorithm proposed in this paper has better experimental results on AUC and F1.
Working with digital publications: Problems and solutions

The full-text archive of employee publications at a research center enables to keep record of the publications and to promote them, as well as to ensure openness of research results for the global community. However, digital documents have a number of specific qualities as compared to traditional printed documents. An attempt is made to define the problems of linking and content integrity of digital documents and to find possible solutions.
Development of Low Cost System for Estimating RBC, WBC Count Using Image Processing

Blood-related diseases are one of the most widespread and rampant vector-borne diseases in tropical countries like India. With an ever-increasing population and enormous stress on resources like land and water, new avenues open for insects like mosquitoes to breed and propagate the virus. The traditional lab method for the detection of diseases in a human's anatomy involves extracting the blood and subjecting it to various tests to count and detect the number of blood cells. An abnormally low platelet count would indicate the presence of the virus in the body. The usual method undertaken by labs all over the world is the use of the conventional chemical procedures, which may take a few hours to produce the result. The proposed system for the low cost estimating of RBC and WBC is developed using image processing techniques and canny edge detection algorithm. The obtained results are analysed and compared with the conventional methods, and results are obtained with an accuracy of 91.2.