Reviewerly/exHarmony huggingface dataset:
The effects of multimedia utilization of early childhood teachers’ on teacher- child interactions

본 연구는 교사의 멀티미디어 활용이 교사­유아 상호작용에 미치는 영향에 대해 알아보고자 한다. 이를 위해 서울 및 경기지역의 만 3­5세를 담당하고 있는 유치원 및 어린이집 교사 273명을 대상으로 설문조사를 실시하였다. 수집된 자료는 빈도분석, 기술통계, 상관분석, 다중회귀분석을 실시하였다. 연구결과 첫째, 멀티미디어 활용 시 교사­유아 상호작용은 평균 4.02점으로 높게 나타났다. 둘째, 멀티미디어 활용에 대한 효과 인식은 교사­유아 상호작용과 높은 정적 상관이 나타났고, 교사경력에 따라 1­3년 경력의 교사가 발달을 지원하는 상호작용과 부적 상관이 나타났고, 5년 이상 경력 교사가 교사­유아 상호작용과 정적 상관이 나타났다. 셋째, 멀티미디어 활용이 교사-유아 상호작용에 미치는 효과에서는 교사 경력이 5년 이상인 경우가 1년 미만인 경우에 비해 교사­유아 상호작용이 유의하게 높아지는 것으로 나타났고, 담당유아연령이 만 4세인 경우 만 3세인 경우에 비해 교사­유아 상호작용이 낮아지는 것으로 나타났다. 또한 유아교사가 멀티미디어 활용의 효과를 높게 인식할수록 멀티미디어 활용 시 교사­유아 상호작용이 높아지는 것으로 나타났다. 본 연구를 토대로 멀티미디어 활용 시 교사­유아 상호작용을 높이기 위한 효율적 방안에 대해 논의할 수 있었다.The purposes of this study are to examine the degrees of teacher-child interactions when early childhood teachers use multimedia, and to examine the effects of multimedia utilization on teacher­child interactions. The survey was conducted on 273 kindergarten and daycare center teachers in charge of ages 3 to 5 in Seoul and Gyeonggi-do. The collected data were analyzed by using frequency analysis, descriptive statistics, correlation analysis, and multiple regression analysis according to the research questions. The results of this study are as follows. First, teacher-child interactions was high with an average of 4.02 points using multimedia. Second, the perception of the effects on multimedia use was highly positively correlated with teacher-child interactions, and the teachers’ experiences of 1-3 years showed negative correlation with the interaction that supports development, and the teacher who has more than 5 years showed positive correlation with the interaction of the teacher. Third, in the effects of multimedia use on teacher-child interactions, teacher interaction was significantly increased compared to less than one year when teacher career was more than 5 years, and teacher interaction was lower than that of 3 years when teacher age was 4 years old. In addition, the higher the early childhood teachers perceive the effect of multimedia use, the higher the interaction of early childhood teachers when using multimedia. Based on this study, we could discuss the efficient ways to enhance teacher-child interactions when using multimedia.
OPPORTUNITIES FOR PERSONALIZED EDUCATION SYSTEM IN FORMING OF PROFESSIONAL ORIENTATION FOR SECONDARY GENERAL SCHOOL

Исследуются вопросы проектирования профессионально ориентированной персонифицированной системы воспитания (ПСВ) в условиях основной общеобразовательной школы. Раскрывается тема формирования у старшеклассников профессионального самоопределения. Особое внимание обращается на включение семьи в процесс создания персонифицированной системы воспитания. Представлен опыт педагогического взаимодействия школы и семьи в становлении профессионального выбора у старшеклассников посредством построения профессионально ориентированной персонифицированной воспитательной системы. Проанализированы условия и возможности персонифицированной системы воспитания в профессиональной ориентации старшеклассников основной общеобразовательной школы. The state and social order sets the school the task of forming a competitive, professionally mobile graduate on the labor market. This order is most definitely reflected in the Federal state educational standards of basic General education. The idea of forming an educational system is considered as an effective means of solving this problem. The possibilities of a new type of educational system focused on the development of a person as a person (hereinafter – PSЕ – personalized system of education) are investigated. This direction is implemented by an interregional research team led by E. N. Stepanov. This article deals with issues of constructing professional orientation system personalized education system (PSE) for secondary general school. The author looks into the idea of forming professional goals in upperclassmen. Special attention is given to the role of family in that process. Articles give specific examples of successful collaboration between the school system and the family in forming professional orientation in upperclassmen through the creation of personalized systems. The study was conducted to study the possibility and effectiveness of the use of PSV in the formation of readiness of high school students of the main secondary school for professional self-determination. The conditions and opportunities of the personalized system of education in the professional orientation of classmen of the main secondary school are analyzed.
초등 예비교사들의 ‘배추흰나비 한살이’ 탐구에서 나타난 지식변환 유형 탐색

This study divided pre-service elementary school teachers’ knowledge conversion into four types, socialization, externalization, combination and internalization, based on their inquiry activities on the life cycle of cabbage butterfly. As research subjects, this study collected results from 24 pre-service elementary school teachers who carried out inquires on the life cycle of cabbage butterfly for about 2 months. The type of socialization indicates the conversion of tacit knowledge into tacit knowledge, and this study found out that pre-service elementary school teachers acquired and accumulated tacit knowledge from their fellow pre-service teachers, professors and even acquaintances inside and outside the lab. However, there appeared no process that they shared their tacit knowledge with other pre-service teachers or delivered it to them. The type of externalization indicates the conversion of tacit knowledge into explicit knowledge, and this study discovered some cases that pre-service elementary school teachers expressed their inner tacit knowledge into explicit knowledge. However, there was no case found that they converted the verbally-unexpressed tacit knowledge of their fellow teachers or experts into well-defined explicit knowledge. The type of combination indicates the conversion of explicit knowledge into explicit knowledge, and this study discovered some cases that they collected explicit knowledge and converted it into new explicit knowledge for the acquisition, integration and delivery or distribution of explicit knowledge. However, there were few cases that they creatively devised new explicit knowledge by acquiring new knowledge through direct observation and supplementing the existing explicit knowledge. The type of internalization indicates the conversion of explicit knowledge into tacit knowledge, and this study discovered both explicit knowledge embodied through personal experiences and explicit knowledge embodied through simulations and experiments that formed tacit knowledge in process of examining the existing explicit knowledge.
The Use of Learning Logs Strategy in Improving Students’ Ability in Writing Expository Paragraph

To see the difference in students' ability in writing expository paragraphs, this research uses a comparison between log learning and conventional learning strategies. Using true-experimental research, there were pre-test, treatment, and post-test. The subject of this research was the first-year students of State Senior High School 1 Selatpanjang, Meranti Island Regency. Two classes were deployed; an experimental and a control class with total of 47 students. The researchers used cluster random sampling, based on the group. In collecting data, the researchers used a test and an observation list. In assessing students’ writing, the researchers invited two raters, those who are qualified and considered as experts in teaching writing. The researchers analyzed the data by using t-test formula. The result shows that the mean score of students’ post-test in the control class was 65,5. Meanwhile, the mean score of students’ post-test in experimental class was 73,4. The result of t test sees a significant difference between students’ ability in writing expository paragraphs by using Learning Logs Strategy and those using Conventional Strategy (5.26). This result was higher than t table significant 5% (2,02) and 1% (2,69). H0 was rejected and Ha was accepted. It means that there was a significant difference in students’ ability in writing an expository paragraph using Conventional Strategy and using Learning Logs Strategy.
Predicting the proximity to system-scale rupture using fracture networks

&amp;lt;p&amp;gt;A fundamental challenge in geophysics is predicting the timing of large earthquakes. A key step in addressing this problem is constraining the factors that indicate the timing of the next large rupture. To isolate the factors that help predict the proximity of the next earthquake, we develop machine learning models to predict the stress distance to macroscopic failure in triaxial compression X-ray tomography experiments on rocks at the stress conditions of the upper crust. In these experiments, we apply increasing axial stress in steps, and acquire a 3D X-ray tomogram at each stress step while the rock is under constant load, revealing the 3D density distribution. Segmenting the density fields provide the locations of rock (voxels dominated by solid), and pores and fractures (voxels dominated by air). We train the machine learning models using the geometry and clustering properties of the fracture networks identified in the tomography scans. We develop extreme gradient boosting (XGBoost) models to predict the stress distance to failure. In experiments on Carrara marble, monzonite, and granite, the models predict the stress distance to failure with r&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt; values &amp;gt; 0.7. We examine the feature importance to identify the factors that provide the best predictive power of the distance to failure. Measurements of the fracture network clustering and the shape anisotropy of fractures tend to have the highest importance of the features, providing greater predictive information than the fracture volume, fracture length, fracture aperture, and fracture orientation relative to the maximum compression direction.&amp;lt;/p&amp;gt;
Penghitungan Objek Berdasarkan Berdasarkan Jenis Kendaraan Bermotor pada CCTV Lalu Lintas Berbasis Pengolahan Citra Digital Menggunakan Metode Background Subtraction dan Blob Detection

In the road traffic space which is often encountered by passing traffic type of vehicle. To find out the traffic conditions that are needed to calculate vehicle traffic, such as using counting or recording CCTV video. This continues the long and long process that was completed on the error data and the slow pace of traffic engineering decisions. This method is difficult to do in full because of the limited number of counters. This can be done by involving digital processing and CCTV video to be able to classify and transfer vehicle type objects. There are several methods for sharing object imagery, such as SIFT, edge detection and Monte Carlo. This research tries to use the Background Substraction and Blob Detection methods because of its superiority in determining objects and backgrounds and being able to maintain moving objects as well as analyzing screen area calculations. The results of testing with this method obtained the MSE value at the threshold of 100 and 3x3 kernel filter with a pixel area of motorcycle 34-63 pixel-X, 67-155 pixel-Y and cars 73-200 pixel-X, 79-307 pixel-Y and bus / truck 130-128 pixel-X, 305-376 pixel-Y. On evaluation, use the confusion matrix obtained in the morning with an average total of 92% and at night with a total average of 73%. It can be concluded by using CCTV installation parameters and the method used can yields higher accuracy in the morning than at night with the weakness of compiling objects that can make it easier to make objects and test the night to obtain light from vehicle lights generated as vehicle objects the flight.
Driving Tasks Transfer in Deep Reinforcement Learning for Decision-making of Autonomous Vehicles

Knowledge transfer is a promising concept to achieve real-time decision-making for autonomous vehicles. This paper constructs a transfer deep reinforcement learning framework to transform the driving tasks in inter-section environments. The driving missions at the un-signalized intersection are cast into a left turn, right turn, and running straight for automated vehicles. The goal of the autonomous ego vehicle (AEV) is to drive through the intersection situation efficiently and safely. This objective promotes the studied vehicle to increase its speed and avoid crashing other vehicles. The decision-making pol-icy learned from one driving task is transferred and evaluated in another driving mission. Simulation results reveal that the decision-making strategies related to similar tasks are transferable. It indicates that the presented control framework could reduce the time consumption and realize online implementation.
User-Guided Domain Adaptation for Rapid Annotation from User Interactions: A Study on Pathological Liver Segmentation

Mask-based annotation of medical images, especially for 3D data, is a bottleneck in developing reliable machine learning models. Using minimal-labor user interactions (UIs) to guide the annotation is promising, but challenges remain on best harmonizing the mask prediction with the UIs. To address this, we propose the user-guided domain adaptation (UGDA) framework, which uses prediction-based adversarial domain adaptation (PADA) to model the combined distribution of UIs and mask predictions. The UIs are then used as anchors to guide and align the mask prediction. Importantly, UGDA can both learn from unlabelled data and also model the high-level semantic meaning behind different UIs. We test UGDA on annotating pathological livers using a clinically comprehensive dataset of 927 patient studies. Using only extreme-point UIs, we achieve a mean (worst-case) performance of 96.1%(94.9%), compared to 93.0% (87.0%) for deep extreme points (DEXTR). Furthermore, we also show UGDA can retain this state-of-the-art performance even when only seeing a fraction of available UIs, demonstrating an ability for robust and reliable UI-guided segmentation with extremely minimal labor demands.
Learning Discriminative Projection With Visual Semantic Alignment for Generalized Zero Shot Learning

Zero Shot Learning (ZSL) aims to solve the classification problem with no training sample, and it is realized by transferring knowledge from source classes to target classes through the semantic embeddings bridging. Generalized ZSL (GZSL) enlarges the search scope of ZSL from only the seen classes to all classes. A large number of methods are proposed for these two settings, and achieve competing performance. However, most of them still suffer from the domain shift problem due to the existence of the domain gap between the seen classes and unseen classes. In this article, we propose a novel method to learn discriminative features with visual-semantic alignment for GZSL. We define a latent space, where the visual features and semantic attributes are aligned, and assume that each prototype is the linear combination of others, where the coefficients are constrained to be the same in all three spaces. To make the latent space more discriminative, a linear discriminative analysis strategy is employed to learn the projection matrix from visual space to latent space. Five popular datasets are exploited to evaluate the proposed method, and the results demonstrate the superiority of our approach compared with the state-of-the-art methods. Beside, extensive ablation studies also show the effectiveness of each module in our method.
SIDH Hybrid Schemes with Classical Component Based on the Discrete Logarithm Problem over Finite Field Extension

The concept of a hybrid scheme with connection of SIDH and ECDH is nowadays very popular. In hardware implementations it is convenient to use a classical key exchange algorithm, which is based on the same finite field as SIDH . Most frequently used hybrid scheme is SIDH - ECDH . On the other hand, using the same field as in SIDH , one can construct schemes over $\Fpn$ , like Diffie - Hellman or XTR scheme , whose security is based on the discrete logarithm problem. In this paper, idea of such schemes will be presented . The security of schemes , which are based on the discrete logarithm problem over fields $\Fp, \Fpd, \Fpc, \Fps$ and $\Fpo$ , for primes $p$ used in SIDH , will be analyzed . At the end, the propositions of practical applications of these schemes will be presented .
Metode Analisis Diskriminan dalam Pengelompokan Kabupaten/Kota di Provinsi Sulawesi Selatan Berdasarkan Indikator Indeks Pembangunan Manusia

Abstract. Discriminant analysis is a method in multivariat statistic analysis that related with object which have separated into the defined group defined and see the accuracy of the formed group. In this research, clustera analysis is used for the first grouping, cluster analysis is a statistical analysis which aims to classify some objects based on the characteristics similarity among the object. Data for this study is HDI (Human Development Index) of indicator in south sulawesi in 2016. The result of this research are 1st cluster (lower HDI indicator) which have 21 city/ distric and the 2nd cluster (higher HDI indicator) which have 3 city/distric as the closeness value between the cluster that formed is 0.902 which shows the closeness between the cluster is high . Furthermore, the discriminant function that have formed explains that if the life expectancy increase, the HDI indicator in city/distric in south sulawesi province will decrease but if school expectation duration in school , average of duration in school, and parity of pur hasing power is increasing, the HDI indicator in city/distric in aouth sulawesi will also increase.Keywords: Cluster analysis, Discriminant analysis , Human development index indicator.
Ocean surface wind prediction Based on GA-LIESN On-line Learning Model

The winds that often occur on the ocean surface have a great impact on economic activities such as the development of ports and harbor engineering. The accurate forecast of wind speeds on the ocean surface is of great significance. A Genetic Algorithm-Leaked Integral Echo State Network(GA-LIESN) On-line Learning Model for ocean surface wind prediction is proposed in this paper. First of all, the model uses correlation analysis to select the features which are most relevant to the wind speed label as the input of the forecast model. Then introduce leaky integral neurons into echo state network (ESN) to improve the model's short-term memory for time series. Next, an On-line Learning algorithm is used to correct the model in real time by introducing new samples to make the model adaptive. Finally, in order to overcome the blind selection of network parameters, genetic algorithm (GA) is used to optimize the network parameters. The meteorological observation data which are measured from a station in the Bohai Bay are selected as experimental data. Compared with Auto-Regressive and Moving Average Model (ARMA) and ESN, the GA-LIESN On-line Learning Model shows a higher prediction accuracy.
Using SAT Solvers to Finding Short Cycles in Cryptographic Algorithms

A desirable property of iterated cryptographic algorithms, such as stream ciphers or pseudo-random generators, is the lack of short cycles. Many of the previously mentioned algorithms are based on the use of linear feedback shift registers (LFSR) and nonlinear feedback shift registers (NLFSR) and their combination. It is currently known how to construct LFSR to generate a bit sequence with a maximum period, but there is no such knowledge in the case of NLFSR. The latter would be useful in cryptography application (to have a few taps and relatively low algebraic degree). In this article, we propose a simple method based on the generation of algebraic equations to describe iterated cryptographic algorithms and find their solutions using an SAT solver to exclude short cycles in algorithms such as stream ciphers or nonlinear feedback shift register (NLFSR). Thanks to the use of AIG graphs, it is also possible to fully automate our algorithm, and the results of its operation are comparable to the results obtained by manual generation of equations. We present also the results of experiments in which we successfully found short cycles in the NLFSRs used in KSG, Grain-80, Grain-128 and Grain-128a stream ciphers and also in stream ciphers Bivium and Trivium (without constants used in the initialization step).
Joy of GPU Computing: A Performance Comparison of AES and RSA in GPU and CPU

The impact of social media evolution has led to the development of big data where the two Vs, volume of data handled and velocity of data retrieval, have grown exponentially. The volume of data being handled has reached the level of terabyte’s and even exabyte’s. On other hand, the velocity of data retrieval has also matured through the fourth- and fifth-generation Web. To cope up this fastest-growing generation, the processing units also have been improved. The challenge lies in the secured transmission of data at a high speed irrespective of its size. Encryption and decryption are a time-consuming process in a general-purpose computer. The capability of graphical processing units (GPUs) has been proven for general-purpose computing in many research areas. With the computation capability of GPU, fast encryption and decryption could be achieved. In this paper, GPU-based symmetric and public encryption algorithms are proposed to render high performance. The performance analysis is performed based on the execution time of two security algorithms RSA and AES in normal computing platform, central processing unit (CPU) and GPU platform. The parallel versions of the RSA and AES are written in CUDA C. Results show that the performance of the encryption algorithm has greatly increased by using GPU computing.
Active CSS and JavaScript Components

The very reason for using a CSS framework is to use its integrated user-interface elements, which makes it easier to develop complex websites. In addition to CSS Components, UIkit comes bundled with JavaScript elements to add intricate functionality. We will learn about the following components in this chapter:
LibGuides: Graduate Students: Getting Started @ UVic Libraries: Librarian research assistance

This guide highlights services, resources and tools of interest to graduate students and are available through UVic Libraries
PKM Pelatihan Ekonomi Untuk Guru-Guru Anggota MGMP Ekonomi Kabupaten Tanah Datar

Teachers as professional educators are responsible as educators and instructors for their students. In order to work well, teachers must have appropriate competencies in the field of study they are teaching. Economic subjects in high school include the economics and accounting fields, which must be mastered by the teacher. The development of economics and accounting must be reached by the economics teacher who will transfer knowledge to their students. Besides that, the teacher's educational background will also affect student learning process. Training for high school economics teachers is needed to be able to increase the insight and knowledge of teachers. MGMP is one of the means for the teachers to draw and explore economics in order to be a professional teachers. From the results of the pretest and posttest, it can be concluded that there was an increasing in teacher knowledge generated from this training. It is expected that in the future MGMP will have a role in training and developing the knowledge of economics teachers in Tanah Datar.Keywords: economics, accounting, educational, teachers