Reviewerly/exHarmony huggingface dataset:
An Analysis of Teacher's Dairy for Improving the classroom skills of Novice Korean language teachers

이 연구의 목적은 초보 단계 한국어 교사들의 수업 일지 내용을 면밀히 살펴봄으로써 한국어 교사 재교육 프로그램에서 초보 교사들의 수업능력 제고를 위해 실질적으로 도움이 될 만한 사항들이 무엇인지 알아보고자 하는 것이다. 교육기관의 일원으로서 교사들이 수행해야 할 여러 가지 일 중에서 교사라는 직무를 고려해 볼 때 가장 중요한 일은 성공적인 교수활동일 것이다. 초보 교사들은 교육적 지식과 경험이 부족한 상태이므로 전문적인 수업 능력을 갖추기까지 오랜 시간이 걸린다. 그러므로 초보 교사에게는 경력교사의 도움이나 교육기관의 재교육 프로그램 등과 같은 교사 교육 활동이 지속적으로 이루어져야 한다. 그간의 교사 교육이 대부분 상향식 교사 교육프로그램이나 교사 평가를 위해 이루어졌기 때문에 초보 교사들이 현장에서 마주하는 어려움을 단시간에 해결하기에는 부족함이 있었다. 초보 교사들의 교사일지에는 수업 구성과 내용을 비롯한 수업 운영 전반에 관한 진솔한 자기성찰이 수반된다. 따라서 교사 일지 분석은 하향식 교사 교육 프로그램을 마련하는 데 지침이 될 것이며 궁극적으로 교사의 수업 능력은 물론 교육 기관 수업 운영의 질을 높이는 데도 도움이 될 것이라고 생각한다.The aim of this study is to closely examine the daily class logs of novice Korean language teachers to identify areas that will practically assist Korean language teacher retraining programs to improve their classroom skills. Being a successful teacher is the most important task that teachers have among the various responsibilities they must carry out as members of education institutions. Novice teachers lack educational knowledge and experience and it takes a long period of time for them to obtain expert-level classroom skills. As such, education activities for teachers, such as assistance from experienced teachers or retraining programs run by educational institutions, must be continually provided to teachers. Most teacher training is conducted through top-down teacher training programs or teacher evaluations. This kind of training has been insufficient to quickly resolve the difficulties novice teachers face in the classroom. The daily class logs recorded by novice teachers include information about how they structure their classes and provide their honest self-reflection on how their classes are run. This means that an analysis of these daily class logs can provide guidelines for the creation of a bottom-up teaching education program. Ultimately, such a program will help in improving the classroom skills of teachers and heightening the quality of how classes are run by educational institutions.
한문과 교육의 새로운 지평

This paper has been written to try to find a new classical Chinese curriculum. The main contents of this paper are summarized as follows. First, because the academic study itself cannot be a subject, classical Chinese education should be converted to deepen in quality rather than quantitative expansion, and reorganized around basic concepts. The next revised curriculum of classical Chinese subject will require a new area to be set up based on these principles and to present appropriate learning contents and achievement criteria.Second, the basic characteristics of the classical Chinse education should be derived from language education itself rather than from the content factors such as the wisdom and understanding of ideas of the predecessors. This is because the independent identity of classical Chinese education can be realized only when the basic characteristics of classical Chinese education is derived in broadening and richly constituting the understanding of our language culture, and then emphasizing the utility of studying classical Chinese.Third, since classical Chinese subject is also a subject that educates the current language, it should play an important role in language education focusing on Sino-Korean vocabulary education in the context of contemporary Korean education.Currently, even if the national curriculum is newly revised, the absolute study amount of secondary school students cannot be reduced, and knowledge education is again emphasized worldwide. Considering this point, basic knowledge should be strengthened in our secondary school curriculum, and people in the classical Chinese education should also prepare a curriculum that reflects improving literacy and enhancing classical humanities skills.
Spatial domain image enhancement techniques for acute myeloid leukemia (M1,M4,M5,M7)

&lt;p class="Abstract"&gt;&lt;span&gt;In this paper, several techniques of image enhancement spatial domain is elucidated and analyzed for the purpose of enhancing Acute Myeloid Leukemia (AML) subtype of M1, M4, M5 and M7. The techniques involved contrast stretching of greyscale images, image subtraction and image sharpening. The three methods compared with one another to achieve the highest PSNR value for the suitability technique of AML subtypes (M1, M4, M5 and M7). Firstly, subtypes images converted into grayscale. Then, each four images tested with contrast stretching techniques followed by image subtraction and image sharpening. The performances were evaluated based on Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). Due to its higher value obtained, image sharpening is a good enhancement techniques for Acute Myeloid Leukemia with 68.2083 dB and the lowest MSE achieved of 0.0103.&lt;/span&gt;&lt;/p&gt;
Analisis Kesesuaian Lahan Untuk Tanaman Jagung di Kecamatan Polokarto Kabupaten Sukoharjo Tahun 2019

Indonesia is an agricultural country but the level of imports of food raw materialsis still high. This is because the production of corn is still volatile. This problemalso occurs in research areas that experience fluctuating corn production to declineso that research is needed in order to find out the factors that inhibit cornproduction using surveys and laboratory analysis, with sampling using purposifesampling based on the unit of land obtained. Data were processed using themethod of comparative analysis or weight factor matching and discrete analysis.The weight factor matching comparison method is used to determine the landsuitability class and the limiting factor based on the assessment of severalparameters used is obtained from the comparison results. Descriptive analysismethod is used to explain land suitability classes and limiting factors to landsuitability. From the research results it is known that the land suitability class formaize in the study area has a S2 class with an area of 1119 Ha or 16,% and a S3class of 5550.8 Ha or 83.3% of the total area of the study area. Land suitabilityclass S2 has a limiting factor for effective soil depth, soil pH, total N, P2O5, K2Oand covers land units. S3 land suitability class has limiting factors for soildrainage, P2O5, effective soil depth, and slopeKeywords: Corn, Land Unit, Land Suitability, Weight factor matching
TEHNIK DATA MINING DALAM MENGELOMPOKKAN KASUS PNEUMONIA PADA BALITA BERDASARKAN PROVINSI DI INDONESIA

Pneumonia is an infectious disease that attacks the lungs, causing air sacs in the lungs to become inflamed and swollen. This health condition is often called a wet lung. The condition of a wet lung can be experienced by anyone caused by bacteria, viruses, and fungi that are easily transmitted through the air in sneezing or coughing conditions. But pneumonia in children can be very dangerous and cause death. This study aims to create a grouping model using the K-Means algorithm. The method used is Datamining Clustering K-Means. By using this algorithm the data that has been generated can be grouped into clusters based on these data. The data is sourced from the Indonesian Ministry of Health in 2017. The number of records used is 34 provinces divided into 2 clusters namely high and low clusters. From the calculation of K-Means, there were 3 provinces as the highest cluster and 31 provinces as a low cluster. The implementation process using the Rapidminer 5.3 application is used to help find accurate values. It is expected that with this research can be used as a reference to the government in tackling pneumonia especially in infants to be able to improve health services, supply drugs, and equipment for treatment and anticipate against pneumonia in provinces in Indonesia.Keywords: Pneumonia, K-Means Algorithm, Data Mining
Big Data Analytics on Child Brain Development

As the volume of data generating is increasing continuously in this internet world, the term Big Data is becoming a very popular jargon in today's market. Big Data is used in various sectors of the digital world. In this paper an exertion is made to exhibit that even the kid mental health are venturing into Big Data pool to take all advantages from its different propelled instruments and advancements. The paper introduces the audit of endeavors made in youngster mental health area utilizing Big Data ideas and procedures. Age of technology we live in, every aspect of our life is almost digitalized, be it physical or virtual aspects. One of the most important phases of everybody’s life is undoubtedly their childhood. The base of entire life is built on childhood. We can shape up an entire generation into almost perfection through just tracking and analyzing the right at the start of life. We use Big Data to preserve information about a child right from the start of their lives, which can lead to different beneficial aspects like healthy, disease free lifestyle of a child and this can help us to recognize and major disease right at the start in a child which can provide facility of early treatment and prevention of related diseases in that child in future.
Privacy Leakage Via De-Anonymization And Aggregation In Heterogeneous Social Networks

Although the approach represents a personal commitment, guidance, and recommendation, user profile accumulation of several social networks would result in significant privacy breaches. In this proposal, we propose a Novel Heterogeneous De-anonymization Scheme (NHDS). The NHDS began using a graphical design of the network to dramatically reduce the size of the set of candidates and then used the information to identify the profile that the user mapping and the degree of belief were high. Evaluation of performance data in real social networks shows that NHDS significantly exceeds the original schedule. Finally, we conduct a scientific study of the loss of integrity of results raised several networks based on four sets of social network data. Our findings show that 39.9% of the information is captured in anonymity and the anonymization ratio is 84%. The second policy leaks the population and the interests of users also under consideration, indicating the potential for leaks is recognized privately..
11. Program Management at DARPA

Chapter 11, by Larry Jackel, a former DARPA program manager, provides a perspective on the role of DARPA program managers in the DARPA model.
POLA PEMBELIAN KONSUMEN DAN MENYUSUN STRATEGI PENJUALAN MENGGUNAKAN ALGORITMA APRIORI BERBASIS WEBSITE (STUDI KASUS : PT. XYZ)

PT. XYZ is a company engaged in the business of selling agrocomplex products including agricultural products, plantations, fisheries, health, and household products. Transaction data continues to increase, companies have difficulty knowing the patterns of consumer purchases accurately. Huge accumulation of data can be used by companies to determine strategies that can support the company's business processes. In this study, the implementation of association rule mining was carried out to help determine consumer purchasing patterns. The technique of combining mining rules used is a priori algorithm that is applied to web-based applications to analyze nasaofficial.com transaction data. To get accurate results added calculation of lift ratio. In this study determined the minimum value of support is 10% and a minimum confidence value of 60%. The result is that there are 3 items of goods, namely viterna, hormonal, nasa. The final results of this study indicate that the mining association rules using a priori algorithms have been successfully applied in applications. The highest association rule produced in the transaction data for the past year is if consumers buy viterna (natural animal vitamins) and hormonic then buy NASA POC with a support value of 23% and 96% confidence value.
A Numerically Exact Method for Dissipative Dynamics of Qubits

We propose a numerical technique suitable for simulating the dynamics of reduced density matrix of a qubit interacting with its environment. Our approach, based on a combination of short-iterative Lanczos method (SIL) and a flexible truncation scheme, allows to include in the physical description multiple-excitation processes, beyond weak coupling and Markov approximations. We perform numerical simulations of two different model Hamiltonians, that are relevant in the field of adiabatic quantum computation (AQC), and we show that our technique is able to recover the correct thermodynamic behavior of the qubit-bath system, from weak to intermediate coupling regime.
PSYCHOLOGICAL ANALYSIS ON THE MAIN CHARACTER PERSONALITY IN THE SHORT STORIES BY ROALD DAHL

This research is aimed to analyze the main character personality short stories by Roald Dahl as Depicted in her Short Stories. In conducting this research, the researcher applies the qualitative descriptive method. The researcher uses documentation as the technique of collecting data. All the necessary data which related to the research are gathered by doing several steps. The first tep is the researcher selects the short stories by Roald Dahl. The second step is the researcher gathers the literary reference that related to psychological approach. From the analysis the researcher finds that anyone who lives in this world will not be separated from its basic needs, psychological needs, and self fulfillment need. Like food, water, rest, intimate relationships, and achieving one’s full potential. Keywords: Personality, Psychology, Motivation
A Hybrid Graph Coloring Algorithm for GPUs

Graph algorithms mainly belong to two categories, topology-driven and data-driven. Data-driven approach maintains a worklist of active nodes, the nodes on which work has to be done. Topology-driven approach sweeps over the entire graph to find active nodes. Hybridization is an optimization technique where in each iteration, the computation is done in a topology-driven or data-driven manner based on worklist size. In hybrid implementations, there is a need to switch between topology-driven and data-driven approaches. Typically, a worklist is maintained just in the data-driven part of the algorithm and discarded in the topology-driven part. We propose a variant of hybridization, wherein a worklist is maintained throughout all iterations of the algorithm and still show it to be faster than both, topology-driven and data-driven approaches. We consider a graph coloring algorithm called IPGC (Iterative Parallel Graph Coloring) and implement a hybrid version for the same in a graph domain specific language called IrGL. We observe a mean speedup of 2.13x over a data-driven implementation of IPGC on a suite of 10 large graphs on a NVIDIA GPU.
Proof Nets for the Lambek Calculus with One Division and a Negative-Polarity Modality for Weakening

In this paper, we introduce a variant of the Lambek calculus allowing empty antecedents. This variant uses two connectives: the left division and a unary modality that occurs only with negative polarity and allows weakening in antecedents of sequents. We define the notion of a proof net for this calculus, which is similar to those for the ordinary Lambek calculus and multiplicative linear logic. We prove that a sequent is derivable in the calculus under consideration if and only if there exists a proof net for it. Thus, we establish a derivability criterion for this calculus in terms of the existence of a graph with certain properties. The size of the graph is bounded by the length of the sequent.
Reinforcement Learning with Convolutional Reservoir Computing

Recently, reinforcement learning models have achieved great success, mastering complex tasks such as Go and other games with higher scores than human players. Many of these models store considerable data on the tasks and achieve high performance by extracting visual and time-series features using convolutional neural networks (CNNs) and recurrent neural networks, respectively. However, these networks have very high computational costs because they need to be trained by repeatedly using the stored data. In this study, we propose a novel practical approach called reinforcement learning with convolutional reservoir computing (RCRC) model. The RCRC model uses a fixed random-weight CNN and a reservoir computing model to extract visual and time-series features. Using these extracted features, it decides actions with an evolution strategy method. Thereby, the RCRC model has several desirable features: (1) there is no need to train the feature extractor, (2) there is no need to store training data, (3) it can take a wide range of actions, and (4) there is only a single task-dependent weight parameter to be trained. Furthermore, we show the RCRC model can solve multiple reinforcement learning tasks with a completely identical feature extractor.
Kvarstår könsstereotyper i reklam på sociala plattformar?-En kvalitativ studie om hur Nelly.com skapar sitt budskap med hjälp av influencers

Companies today choose to abandon traditional marketing methods in favor of new and modern marketing methods. One such method is Influencer Marketing which uses a method called word-of-mouth in which information is spread from person to person. This study is therefore aimed to investigate whether the use of gender stereotypical in advertising still remains despite the more modern marketing methods. A semiotic analysis was thoroughly conducted on the company Nelly.com and their Instagram accounts @nellycom and @nlymancom. The result of this study showed that companies working with Influencer Marketing still use gender stereotypical advertising to influence their intended target group, it further showed that men and women are presented in different ways on Instagram in which men are portrayed as classy while women are portrayed as sex objects. This indicate that the male and female roles in traditional marketing is still relevant today. The study also showed that the way companies convey their message on Instagram is vastly different depending on their intended target group. The study aims to contribute to the research field of strategic communication with focus on market communication. (Less)
next-gen-scraPy: Extracting NFL Tracking Data from Images to Evaluate Quarterbacks and Pass Defenses

The NFL collects detailed tracking data capturing the location of all players and the ball during each play. Although the raw form of this data is not publicly available, the NFL releases a set of aggregated statistics via their Next Gen Stats (NGS) platform. They also provide charts showing the locations of pass attempts and outcomes for individual quarterbacks. Our work aims to partially close the gap between what data is available privately (to NFL teams) and publicly, and our contribution is twofold. First, we introduce an image processing tool designed specifically for extracting the raw data from the NGS pass charts. We extract the pass outcome, coordinates, and other metadata. Second, we analyze the resulting dataset, examining the spatial tendencies and performances of individual quarterbacks and defenses. We use a generalized additive model for completion percentages by field location. We introduce a Naive Bayes approach for estimating the 2-D completion percentage surfaces of individual teams and quarterbacks, and we provide a one-number summary, completion percentage above expectation (CPAE), for evaluating quarterbacks and team defenses. We find that our pass location data closely matches the NFL's tracking data, and that our CPAE metric closely matches the NFL's proprietary CPAE metric.
Adjusting Decision Boundary for Class Imbalanced Learning

Training of deep neural networks heavily depends on the data distribution. In particular, the networks easily suffer from class imbalance. The trained networks would recognize the frequent classes better than the infrequent classes. To resolve this problem, existing approaches typically propose novel loss functions to obtain better feature embedding. In this paper, we argue that drawing a better decision boundary is as important as learning better features. Inspired by observations, we investigate how the class imbalance affects the decision boundary and deteriorates the performance. We also investigate the feature distributional discrepancy between training and test time. As a result, we propose a novel, yet simple method for class imbalanced learning. Despite its simplicity, our method shows outstanding performance. In particular, the experimental results show that we can significantly improve the network by scaling the weight vectors, even without additional training process.
Geographical Information System Mapping Based on Android at Equal Senior High School In Lubuklinggau

The problem in this study is there is no geographic information system for mapping senior high schools so that there is a misunderstanding inlocation delivery at aqual Senior high schools (SMA) in Lubuklinggau. This research uses data collection methods, by making observations at the research site, conducting question and answer directly to the source, and documentation by reading literature books. The results of the study show that the program on the geographic information system mapping on Android based equivalents on Android at Lubuklinggau the geographical information system is expected to be a solution for the community in finding the location of Senior High Schools in Lubuklinggau. This application was created using the C I framework and also the ionic framework supported by api Google Maps.
예비유아교사의 교육봉사가 교사-유아 상호작용에 미치는 영향: 교사효능감 매개효과를 중심으로

The purpose of this study is to identify the appropriate time of educational service and to verify the effect of educational service on teacher efficacy and teacher-child interaction. For this purpose, the following were investigated : 1. difference of teacher efficacy and teacher-child Interaction by educational service time, 2. relationship between educational service, teaching efficacy and teacher-child interaction and 3. mediation effect of teacher efficacy in educational service and teacher-infant interaction. The subjects of this study were 272 students in 3rd grade at 4-year university in Seoul and Kyonggi–do. Through questionnaires, the educational service, teacher efficacy, and teacher-child interaction were investigated. And The data were analyzed statistically. The results of the study are as follows. First, teachers efficacy and teacher-child interaction were significantly different according to the hours of service. Second, there was a statistically significant relationship between educational service hours, teacher efficacy, and teacher-child interaction. Third, teacher efficacy has a partial mediating effect between the educational service hours and the teacher-child interaction. The results of this study are meaningful in that it provides basic data on educational service programs for pre-service early childhood teachers.
BoPF: Mitigating the Burstiness-Fairness Tradeoff in Multi-Resource Clusters

Simultaneously supporting latency- and throughout-sensitive workloads in a shared environment is an increasingly more common challenge in big data clusters. Despite many advances, existing cluster schedulers force the same performance goal - fairness in most cases - on all jobs. Latency-sensitive jobs suffer, while throughput-sensitive ones thrive. Using prioritization does the opposite: it opens up a path for latency-sensitive jobs to dominate. In this paper, we tackle the challenges in supporting both short-term performance and long-term fairness simultaneously with high resource utilization by proposing Bounded Priority Fairness (BoPF). BoPF provides short-term resource guarantees to latency-sensitive jobs and maintains long-term fairness for throughput-sensitive jobs. BoPF is the first scheduler that can provide long-term fairness, burst guarantee, and Pareto efficiency in a strategyproof manner for multi-resource scheduling. Deployments and large-scale simulations show that BoPF closely approximates the performance of Strict Priority as well as the fairness characteristics of DRF. In deployments, BoPF speeds up latency-sensitive jobs by 5.38 times compared to DRF, while still maintaining long-term fairness. In the meantime, BoPF improves the average completion times of throughput-sensitive jobs by up to 3.05 times compared to Strict Priority.