Reviewerly/exHarmony huggingface dataset:
Use of Game Images to Improve Results and Learning Activities of The Beautiful Materials of The Equality of Class IV Students in SD Kartika XIX-4 Cimahi

Active and creative attitudes are the main capital for elementary school students in getting optimal learning outcomes, because it is used in understanding the material about the beauty of diversity using a thematic approach.As many as 80% of students in grade IV SD Kartika XIX-4 need a variety of learning to avoid boredom in learning, especially because of thematic material that makes students try harder to understand each learning context.The theory used is the concept of games, the concept of strategies and methods of learning and classroom action research.The method used is classroom action research consisting of 2 cycles with stages of planning, action, observation and reflection.Research Subjects were fourth grade students of SD Kartika XIX-4 totaling 36 students.The results obtained are there is an increase in student learning outcomes by using picture games in the learning process.With the final conclusions of this study is a picture game can be used to improve the results and learning activities in the material Beautifulness of togetherness for grade IV students of SD Kartika XIX-4, Cimahi.
Seismology at School in Nepal: a network and program for education and citizen seismology

&amp;lt;p&amp;gt;Nepal, located above the convergent India-Eurasia plate boundary, has repeatedly experienced devastating earthquakes. During the 2015 magnitude 7.8 Gorkha earthquake, an often-reported experience was that people were not aware of the threatening seismic hazard and have insufficient level of preparedness. An important source of the problem is that earthquake-related topics are not part of the school curriculum. Earthquake education reaching a broad group of the population early in their lives is therefore strongly needed.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We established an initiative in Nepal to introduce seismology in schools, with focus on education and citizen seismology. We have prepared educational materials adapted to the Nepali school system, which we distributed and also share on our program&amp;amp;#8217;s website: . In selected schools, we also installed a low-cost seismometer to record seismicity and to allow learning-by-doing classroom activities. Our approach was very well received and we hope it will help making earthquake-safe communities across Nepal.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The seismic sensor installed in schools is a Raspberry Shake 1D (RS1D), selected based on performance in laboratory tests and adequacy to field conditions. At a test site in Switzerland we were able to record magnitude 1.0 events up to 50 km distance with a RS1D. In Nepal, 22 such seismometers installed in schools create the Nepal School Seismology Network providing online data openly. The seismometer in each school allows students to be informed of earthquakes, visualize the respective waveforms, and estimate distance and magnitude of the event. For significant local and regional events, we provide record sections and network instrumental intensity maps on our program&amp;amp;#8217;s website.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In 6 months of network operation, more than 194 local and teleseismic earthquakes of M&amp;amp;#8805;4 have been recorded. From a local and a global catalogue, complemented with our own visual identifications, we provide an earthquake wave detectability graph in distance&amp;amp;#8212;magnitude space. Based on our observations, we calibrate a new magnitude equation for Nepal, related to the epicentral distance &amp;lt;em&amp;gt;D[km]&amp;lt;/em&amp;gt; and to the observed peak ground velocity &amp;lt;em&amp;gt;PGV[&amp;amp;#181;m/s]&amp;lt;/em&amp;gt;. The calibration is done to best fit local catalogue magnitudes, and we will present the updated parameters at the conference.&amp;lt;/p&amp;gt;
Event Presence Prediction Helps Trigger Detection Across Languages

The task of event detection and classification is central to most information retrieval applications. We show that a Transformer based architecture can effectively model event extraction as a sequence labeling task. We propose a combination of sentence level and token level training objectives that significantly boosts the performance of a BERT based event extraction model. Our approach achieves a new state-of-the-art performance on ACE 2005 data for English and Chinese. We also test our model on ERE Spanish, achieving an average gain of 2 absolute F1 points over prior best performing model.
A Study on the Interconnection between National Disaster Management System and Private Disaster Prevention IT Technology through Application

In order to strengthen the disaster prevention phase and the management of social disasters, we will examine the plan of To-Be disaster management system interconnected by using intelligent information technologies such as IoT, Cloud, Big Data, Mobile and AI. The disaster management system can be upgraded by constructing an intelligent infrastructure based on Big Data analysis of the disaster signals before and after the disasters generated by private mobile and IoT. Big Data of disaster Signals can be customized to users in a timely manner through AI methodologies of supervised and unsupervised learning and reinforcement training. In the long term, it is expected that not only will the capacity of disaster response be improved, but the management ability centering on prevention will be enhanced as well.
Demographic Variables Influencing Selective Mutism Among Secondary School Adolescents In Obio/Akpor Local Government Area Of Rivers State

The study examined demographic variables influencing selective mutism among secondary school adolescents in Obio/Akpor Local Government Area of Rivers State. The study adopted the ex-post-facto research design. Two research questions as well as two corresponding null hypotheses guided the study. The population of the study comprised all 14,784 senior secondary school students (SSS 1, 2 and 3) in the 16 public secondary schools in Obio/Akpor LGA of Rivers State. A sample of 80 secondary school adolescents were randomly and independently selected for the study using the purposive sampling technique. A standardized instrument tagged Skills Deficit Diagnostic Instrument (SDDI), was adopted to collect data for this study. The test re-test method was used to determine reliability and the score obtained was 0.81. Data collected was analyzed using mean, standard deviation and t-test statistics. The finding of the study showed that socioeconomic status significantly influence selective mutism among secondary school adolescents whereas family type did not. Based on the findings of this study, the researcher recommended among others that counseling for adolescents with selective mutism should be encouraged in all schools with the aim of improving adolescents’ mental health and pro-social skills.
L2-Constrained RemNet for Camera Model Identification and Image Manipulation Detection.

Source camera model identification (CMI) and image manipulation detection are of paramount importance in image forensics. In this paper, we propose an L2-constrained Remnant Convolutional Neural Network (L2-constrained RemNet) for performing these two crucial tasks. The proposed network architecture consists of a dynamic preprocessor block and a classification block. An L2 loss is applied to the output of the preprocessor block, and categorical crossentropy loss is calculated based on the output of the classification block. The whole network is trained in an end-to-end manner by minimizing the total loss, which is a combination of the L2 loss and the categorical crossentropy loss. Aided by the L2 loss, the data-adaptive preprocessor learns to suppress the unnecessary image contents and assists the classification block in extracting robust image forensics features. We train and test the network on the Dresden database and achieve an overall accuracy of 98.15%, where all the test images are from devices and scenes not used during training to replicate practical applications. The network also outperforms other state-of-the-art CNNs even when the images are manipulated. Furthermore, we attain an overall accuracy of 99.68% in image manipulation detection, which implies that it can be used as a general-purpose network for image forensic tasks.
LibGuides: WRT 202 Intermediate College Writing: Virtual Library Services for COVID-19

This guide will assist students enrolled in WRT 202 with more advanced research processes for college research papers and assignments. Link to library services during COVID-19 pandemis
Using the YOUTUBE portal to train future studio managers abstract arts and crafts

Currently, the rapid growth of the scientific information flow, the volume of which increases every year, encourages the search for new effective methods, methods and means of training that would allow students to present more information for the same unit of study time and prepare high-level professionals. The paper presents the experience of using YouTube in the process of implementing the system of training future managers of studios of decorative and applied art (DAA) for professional activity in the Samara State Institute of Culture. The purpose of the paper is to consider the main issues related to the use of YouTube capabilities in the educational process when preparing future managers of DAA studios. The main approaches to the study of the problem are an analytical review of theoretical sources on this problem by such authors as A.A. Andreev, I.G. Zakharova, V.A. Kurina, N.V. Lomovtseva, S.A. Nazarov, Yu.P. Preobrazhensky, E.G. Skibitsky, I.R. Stashkevich, L.I. Kholina, G.A. Shangin, as well as the study and generalization of the accumulated pedagogical experience and analysis of the Federal state educational standard-bachelor's degree in the direction of training 51.03.02 Folk Art Culture. The results of the study were: prospects for using YouTube video materials in the process of implementing the system of training future managers of decorative and applied art studios (DAA) for professional activities. The criteria for selecting the material are formulated. The paper presents examples of using YouTube features in lectures and discussions on the discipline "Organization and management of folk art". The theoretical significance of the study is that the results obtained can serve as a basis for new scientific ideas about the use of YouTube in the process of implementing the system of training future managers of studios of decorative and applied art (DAA) for professional activities. The practical significance of the study lies in the focus of its results on improving the system of readiness for professional activity of future managers of DAA studios. The paper is of interest to higher school teachers, postgraduates and undergraduates.
LibGuides: Searching Articles, Books, & More: Course Reserves

This guide gives you basic information about how Summon works to find things owned by University of Rochester Libraries
Effects of in-game advertising on brand awareness in virtual reality game interactions

Advertisers keep seeking new ways to target a wider audience while game developers are trying to monetize their game. This study investigates how Virtual Reality (VR) interactions influence user's ...
유아교사의 민족정체성이 통일교육역량에 미치는 영향 - 민주시민자질·다문화신념의 매개효과 -

This study set national identity, democratic citizen’s virtues, and multicultural belief as influencing factors of an early childhood teacher’s competence in unification education, and intended to verify the mediating effect of democratic citizen’s virtues and multicultural belief with the national identity of early childhood teachers as an independent variable and early childhood teacher’s competence in unification education as a dependent variable. The significance of this study is in how it has suggested the purpose and direction of teacher education for unification education of infants by identifying the correlation between influencing factors on an early childhood teacher’s competence in unification education and prepared basic data for teacher education so as to increase early childhood teacher’s competence in unification education.
Analisis Kebutuhan Bahan Ajar Booklet Bangun Datar dan Sifat-sifatnya untuk Siswa Sekolah Dasar/Madrasah Ibtidaiyah

This study aims to present the results of a needs analysis teaching material booklet flat build and its characteristics in class III B MI Qomarul Hidayah Trenggalek students. This research is descriptive research. The data were obtained from interviews with class III B MI Qomarul Hidayah Trenggalek teachers, the results of the documentation, and the results of a needs analysis questionnaire filling by 15 students of class III B MI Qomarul Hidayah Trenggalek in 2019/2020 academic year. From the results of a needs analysis questionnaire, it was obtained that students in class III B MI Qomarul Hidayah Trenggalek 86,7% needed teaching materials that had practical size, 93,3% needed additional learning resources about flat build and its characteristics more complete material, 93,3% needed practice exercises to measure understanding of the material flat build and its characteristics, 93,3% needed teaching materials that could be used for independent learning, and 100% agreed if booklet flat build and its characteristics were developed. It can be concluded that students of class III B MI Qomarul Hidayah Trenggalek agreed if booklet flat build and its characteristics were developed. The booklet developed was adjusted to the results of interviews with class III B MI Qomarul Hidayah Trenggalek teachers, the results of the documentation, as well as the results of filling the needs analysis questionnaire of students class III B MI Qomarul Hidayah Trenggalek namely: having a practical size, more complete material, there were exercises, and can be used for independent learning
Design of Android-based Application for Utility of Pesticides and Fertilizers

There are lots of QR code scanners and barcode scanners are available right now but they are unable to play the content which is encoded by QR code generator.Right now, they were just decoding that QR is code in the form of web page link.This is the limitation and to overcome this limitation we are developing that particular Android application.Our project is real runtime project, we will print that particular QR code on either bottle or packet of the pesticide and fertilizer and then by scanning that particular code we will go for video play.
Patient Cohort Retrieval using Transformer Language Models

We apply deep learning-based language models to the task of patient cohort retrieval (CR) with the aim to assess their efficacy. The task of CR requires the extraction of relevant documents from the electronic health records (EHRs) on the basis of a given query. Given the recent advancements in the field of document retrieval, we map the task of CR to a document retrieval task and apply various deep neural models implemented for the general domain tasks. In this paper, we propose a framework for retrieving patient cohorts using neural language models without the need of explicit feature engineering and domain expertise. We find that a majority of our models outperform the BM25 baseline method on various evaluation metrics.
Using Bayesian Network for Soil Organic Carbon Prediction despite its Incredible Complexity

&amp;lt;p&amp;gt;In this research, static Bayesian networks (BN) is presented for predicting Soil Organic Carbon (SOC) in the complex and open soil systems. BN is a graphical computational model which provides a simple technique to define the nonlinear dependencies and, therefore, to implement a compact representation of the complex systems. Moreover, the BN is used as a simulation tool for effective processing of the complex system outcomes by probability propagation methods. This permits evaluation and potential intervention in complex soil systems and determines the dependencies between different variables. We use a BN to identify key factors in predicting England and Wales SOC. Then, we explore the relationships between different key factors such as geographical, environmental or climate and their roles individually in predicting SOC, particularly to identify those which have the highest impact. The proposed BN is also used to calculate the effectiveness of these interventions where the uncertainties associated with these casual relationships at the same time. This approach works with data from the variety of sources and handles a mix of subjective and objective data and can incorporate variables which differ across the contexts. The effectiveness of the technique is demonstrated with a case study to predict SOC.&amp;lt;/p&amp;gt;
Formal Verification of the Correctness of Operating System on Assembly Layer in Isabelle/HOL

Formal verification of the operating system refers to modeling the operating system in mathematical language, and proving the system fully implements the functions described in design. Isabelle/HOL is an interactive theorem prover that allows us to use formal expressions to describe mathematical formulas and provides a series of tools to prove these formulas. MINIX 3 is a microkernel-based open source operating system with high security. The context switching algorithm is used to switch the CPU from one process or thread to another, and saving the context is an important step. In this paper, we introduce the model of the related codes of operating system in Isabelle/HOL, and verify that the code performs expected results as an example of formal verification of operating system.
A Class Incremental Temporal-Spatial Model Based on Wireless Sensor Networks for Activity Recognition

With the development of Internet of Things (IoT), all kinds of sensors appear everywhere in our lives. Sensor-based Human Activity recognition (HAR) as the most common application of IoT system, has attracted attentions from both academia and industry due to the popularity of IoT. In most real-life scenarios, HAR is dynamic. For instance, in the process of classifying the activities, a new class is often encountered. To effectively recognize new activities, we propose a three-stage class incremental temporal-spatial model based on wireless sensor networks for activity recognition. In the first stage, when the new class arrives, rather than using the traditional method to train all the old class data, we design a method based on the recognition score to select part of the old class data as a sample for training, which represents the data distribution of the old class data. In the second stage, we analyze the temporal and spatial characteristics of activity data in combination with physical knowledge, the model extract temporal-spatial features, which further enhances the recognition effect. In the third stage, we avoid the phenomenon of ‘catastrophic forgetting’ of a network by finetuning distillation loss and classification loss. To demonstrate the effectiveness of our proposed model, we conducted comparative experiments using different models on different public datasets. The results showed our proposed model can not only recognize new classes, but also maintain the recognition ability of old classes.
That looks interesting! Personalizing Communication and Segmentation with Random Forest Node Embeddings

Communicating effectively with customers is a challenge for many marketers, but especially in a context that is both pivotal to individual long-term financial well-being and difficult to understand: pensions. Around the world, participants are reluctant to consider their pension in advance, it leads to a lack of preparation of their pension retirement [1], [2]. In order to engage participants to obtain information on their expected pension benefits, personalizing the pension providers' email communication is a first and crucial step. We describe a machine learning approach to model email newsletters to fit participants' interests. The data for the modeling and analysis is collected from newsletters sent by a large Dutch pension provider of the Netherlands and is divided into two parts. The first part comprises 2,228,000 customers whereas the second part comprises the data of a pilot study, which took place in July 2018 with 465,711 participants. In both cases, our algorithm extracts features from continuous and categorical data using random forests, and then calculates node embeddings of the decision boundaries of the random forest. We illustrate the algorithm's effectiveness for the classification task, and how it can be used to perform data mining tasks. In order to confirm that the result is valid for more than one data set, we also illustrate the properties of our algorithm in benchmark data sets concerning churning. In the data sets considered, the proposed modeling demonstrates competitive performance with respect to other state of the art approaches based on random forests, achieving the best Area Under the Curve (AUC) in the pension data set (0.948). For the descriptive part, the algorithm can identify customer segmentations that can be used by marketing departments to better target their communication towards their customers.
Implementasi Learning Manegement System (LMS) Berbantukan Edmodo untuk Meningkatkan Hasil Belajar Siswa pada Materi Fluida Dinamis

In the 21st century, emphasizing renewal learning systems using information and communication technology (ICT). One uses a learning management system (LMS). This study aims to determine the effectiveness in implementing the learning Management System (LMS) aided by Edmodo to improve student learning outcomes on dynamic fluid material. This research was conducted at one of the Gamping 1 High Schools with a quasi-experimental research design. As a sample in this study were 11th grade X students, selected by random sampling. The research data collection instrument used to pretest and posttest and the results of the study were tested by N-Gain analysis. The results showed that the learning outcomes of students increased in the high category with very good student responses. This means Edmodo-assisted Learning Management System (LMS) is very effective to improve student learning outcomes on dynamic fluid material.
Implicit Feedback Deep Collaborative Filtering Product Recommendation System

In this paper, several Collaborative Filtering (CF) approaches with latent variable methods were studied using user-item interactions to capture important hidden variations of the sparse customer purchasing behaviours. The latent factors are used to generalize the purchasing pattern of the customers and to provide product recommendations. CF with Neural Collaborative Filtering(NCF) was shown to produce the highest Normalized Discounted Cumulative Gain (NDCG) performance on the real-world proprietary dataset provided by a large parts supply company. Different hyperparameters were tested using Bayesian Optimization (BO) for applicability in the CF framework. External data sources like click-data and metrics like Clickthrough Rate (CTR) were reviewed for potential extensions to the work presented. The work shown in this paper provides techniques the Company can use to provide product recommendations to enhance revenues, attract new customers, and gain advantages over competitors.