Reviewerly/exHarmony huggingface dataset:
Bayesian Modelling in Practice: Using Uncertainty to Improve Trustworthiness in Medical Applications

The Intensive Care Unit (ICU) is a hospital department where machine learning has the potential to provide valuable assistance in clinical decision making. Classical machine learning models usually only provide point-estimates and no uncertainty of predictions. In practice, uncertain predictions should be presented to doctors with extra care in order to prevent potentially catastrophic treatment decisions. In this work we show how Bayesian modelling and the predictive uncertainty that it provides can be used to mitigate risk of misguided prediction and to detect out-of-domain examples in a medical setting. We derive analytically a bound on the prediction loss with respect to predictive uncertainty. The bound shows that uncertainty can mitigate loss. Furthermore, we apply a Bayesian Neural Network to the MIMIC-III dataset, predicting risk of mortality of ICU patients. Our empirical results show that uncertainty can indeed prevent potential errors and reliably identifies out-of-domain patients. These results suggest that Bayesian predictive uncertainty can greatly improve trustworthiness of machine learning models in high-risk settings such as the ICU.
LibGuides: Cloud and Networking Infrastructure Management: Home

This guide is for the NCU specialization Cloud and Networking Infrastructure Management. It contains resources available in the NCU Library.
Development of Basic Concept of ICT Platforms Deployment Strategy for Social Media Marketing Considering Tectonic Theory

This paper presents authors analytical view on social impacts as targeted advertisement into the network environment using Omori tectonic theory for description the processes of audience response evolution. This could be extremely important and useful in the modern world to realize desirable e-Gov informational policy in the circumstances of hybrid treats emergence that is especially relevant for the informational space and reaching a cyber-supremacy. Some mathematical and algorithmic basics were contributed for narrative description of information and communications technologies (ICT) architectural deployment could be used for outer regulation of audience response character by Social Media Marketing (SMM) principles. That could be performed by controlled distribution of specified digital content that contains respective key phrases, for example social advertisements and analyzing respective feed-backs. Some results of the empiric study of live audience response dependence on controlled impacts are discussed. Election processes data and recent media recordings for preliminary proof of the contributed concept feasibility have been analyzed. There were shown using gathered empiric data sets, that the extent of impacts to targeted audience response intensity could be the subject of outer regulation. The index has been contributed for assessment the efficiency of the impact’s propagation inside the audience by calculation of row correlation of keyword occurrence and audience response intensity. The approaches suggested in the article can be useful both for building effective interactive systems of state-society interaction and for detecting manipulative traits when influencing a specific audience.
Expressing Modalization in EFL College Learners Writing: From Modality System of Systemic Functional Grammar

This study examined the language choice for modality patterns to express the degree of probability specifically in email texts based on Systemic...
Sentiment Analysis on Twitter Data using Big Data

With the increasing number of users and data on the Internet, especially social media sites, sentiment analysis topic became one of the important and essential fields for most. Collection of people's feelings and sentiment and classifying the data attracted most businesses and companies. Recently, twitter sentiment analysis has attracted much attention, because of Twitter's growth and popularity. The solution for handling enormous amounts of data from social media is a new term called Big data. Big data is not just for having a large amount of data, but also the importance of processing and the usage of the data.
Icelandic Gigaword Corpus 1 (IGC1) - version 20.05

The Icelandic Gigaword corpus (IGC) is a tagged and lemmatized corpus. The 20.05 version consists of approximately 1550 million running words of text. Each running word is accompanied by a morphosyntactic tag and lemma and each text is accompanied by bibliographic information. The corpus is intended for linguistic research and for use in Language Technology projects. The latest texts in this version are from 2019.To enable the use of the corpus in Language Technology projects it is considered important to secure copyright clearance for the texts to be used. Originally the idea was to secure permission from copyright owners to give access to the texts with Creative Commons licenses . All copyright holders could not agree to those terms. The corpus is therefore divided into two parts, IGC1 and IGC2. IGC1 contains texts that can be used with a special license developed for the Tagged Icelandic Corpus (MIM). IGC2 contains official texts and texts that can be used with a CC BY license. All copyright holders have agreed that their material may be used free of licensing charges. Copyright owners that did not accept the CC BY license signed a special declaration developed for the Tagged Icelandic Corpus with necessary amendments for the IGC1. Size of IGC1 is: 843,127,951 (Custom license) Size of IGC2 is: 711,958,926 (CC BY 4.0------Risamalheildin er morkuð og lemmuð malheild. I utgafu 20.05 eru um 1550 milljonir lesmalsorða (greinarmerki ekki meðtalin) i textum sem eru geymdir i stoðluðu sniði i rafraenu formi. Orð i textunum eru greind malfraeðilega og hverjum texta fylgja bokfraeðilegar upplýsingar um verkið sem textinn er ur. Malheildin er aetluð fyrir malrannsoknir og til notkunar i maltaekniverkefnum. Nýjustu textarnir i þessari utgafu eru fra arinu 2019.Til þess að unnt se að nota malheildina i maltaekniverkefnum er logð ahersla a að afla leyfa fra retthofum fyrir notkun textanna. Upphaflega var miðað við að afla leyfa fra retthofum til þess að veita aðgang að textunum með leyfi sem samtokin Creative Commons hafa skilgreint. Ekki gatu allir retthafar samþykkt þa skilmala. Textum malheildarinnar er þvi skipt i tvo hluta, RMH1 og RMH2. I RMH1 eru textar sem veittur er aðgangur að með serstoku leyfi sem byggist a leyfi sem var utbuið fyrir texta Markaðrar islenskrar malheildar (MIM). I RMH2 eru opinberir textar og textar sem leyfður er aðgangur að með CC BY leyfi . Allir retthafar hafa samþykkt að veita afnotarett af textum sinum an þess að greiðsla komi fyrir. Þegar Morkuð islensk malheild var gerð var utbuin serstok samþykkisyfirlýsing með aðstoð logfraeðings sem var notuð með nauðsynlegum breytingum við gerð RMH og retthafar sem samþykktu ekki CC BY leyfið undirrituðu. Staerð RMH 1: 843,127,951 (MIM leyfi) Staerð RMH 2: 711,958,926 (CC BY 4.0)
The Effect of Six Paramitas Based on Philosophical Inquiry Activity on Young Children’s Self-Recognition and Peer-Interaction

이 연구의 목적은 육바라밀(六波羅蜜) 기반의 철학적 탐구활동이 유아의 자기인식 및 또래상호작용에 미치는 영향을 알아보는 데 있다. 이를 위해 G시의 유아교육기관 2곳에 재원 중인 만 5세 유아 40명(실험집단 20명, 비교집단 20명)을 연구대상으로 선정하였다. 실험집단에는 육바라밀의 여섯 가지 주제를 중심으로 불전설화를 활용한 철학적 탐구활동이 18회기 처치되었다. 비교집단에는 누리과정에 근거한 주제중심의 통합교육활동이 주간교육계획에 따라 실행되었다. 수집된 자료는 SPSS/PC(win 28.0) 프로그램을 사용하여 공변량분석(ANCOVA)을 하였다. 분석 결과 비교집단에 비해 실험집단 유아들의 자기인식 및 또래상호작용에서 각각 유의미한 차이가 있는 것으로 나타났다. 이는 이 연구의 육바라밀기반의 철학적 탐구활동이 유아의 자기인식 및 또래상호작용에 긍정적인 영향을 미쳤음을 의미한다. 뿐만 아니라 불전설화를 활용한 육바라밀기반의 철학적 탐구활동이 유아교육현장에 적용 가능한 교수·학습 방법임을 시사하고 있다.This study examined the effect of Six Paramitas based philosophical inquiry activity on young children s self-recognition and peer-interaction. A group of 40 5-year-old children (each 20 for the experiment group and for the comparison group) were selected from 2 young children education institutions located in the capital “G”. Six Paramitas based (and Buddhist fairy tale adapted) philosophical exploration was applied 18 times to the experiment group, whereas Nuri-curriculum based (and topic focused) integrated education was applied to the comparison group. As a result, children from the experiment group showed a significant difference in self-recognition and peer-interaction. This shows not only Six Paramitas based philosophical inquiry activity affects young children s self-recognition and peer-interaction positively, but also using Buddhist fairy tale with it is the applicable teaching-learning method in the early-childhood education.
Optinformatics Within a Single Problem Domain

This chapter introduces specific algorithm designs of optinformatics in evolutionary learning and optimization within a single problem domain. In particular, the first algorithm considers the knowledge learning and reuse in the form of local search which is integrated in a global search, such as genetic algorithm, with the aim of improving the search efficiency and effectiveness of global optimization. The well-known feature selection is used as the practical problem to evaluate the performance of the built optinformatics method. Further, towards general knowledge learning and transfer in evolutionary search, a paradigm that integrates evolutionary search with transfer learning is presented. Taking vehicle routing and arc routing problem as the case study, knowledge is defined beyond the form of local search. Based on the knowledge definition, the learning, selection, variation, and imitation of knowledge in evolutionary search for routing are discussed. To validate the performance of the optinformatic algorithm, comprehensive empirical studies on commonly used vehicle routing and arc routing problems are conducted.
Design a Blockchain based Electronic Payment with Users’ Anonymity

Security of electronic payment systems have become important, by increasing demand. Protecting against double spending, tracing malicious users, users' anonymity and privacy-preserving are important secure goals of any electronic payment systems. To achieve these goals, blockchain technology is useful, blockchain technology can solve many issues, such as bottlenecks, delays and operational risks that exist in the financial industry, but blockchain-based electronic payment systems cannot punish or trace malicious users without using trusted third party. In this article, we present a blockchain-based electronic payment scheme to protecting the anonymity and privacy of honest users, and also trace and punish malicious users with no need to TTP. We have used a fair blind digital signature scheme and a secret sharing scheme for this purpose. Users also use pseudonyms to maintain anonymity. users' pseudonyms are generated by pre-computations, therefore, the proposed scheme have a good performance.
A MODEL OF PHYSICAL SPACE-TIME AS A TRAJECTORY OF A RANDOM PROCESS IN EXTERNAL PARAMETRIC TIME

A model of physical space-time in the form of a trace from a random walk on the support of some finitely generated algebra is considered. The generating set of elements is considered as a set of initial events of the physical world, and all points generated by wandering are interpreted as their consequences. The special properties of such a model allow us to obtain interesting conclusions about the possible causes of the pseudo-Euclidean metric of our world and its 3 + 1 dimension. Such effects as the expansion of the Universe with low acceleration, gravitational deformation of the metric and the superposition of the states of quantum particles get their explanation. There is an effect of duality of the description of physical interactions as long-range or short-range interaction.
U-Net-Based Approach for Segmentation of Tables from Scanned Pages

The purpose of Table Segmentation, which is a part of document layout analysis (DLA), is to identify and segment the region of interest from the document while ignoring the rest of the page. We present a deep learning approach for segmentation of tables from a rich variety of documents and explain the effect of preprocessing, distortion correction on the output of deep learning model. Our method is based on the U-net architecture wherein convolution operations extract features from the image and de-convolution operation creates a new image with the desired segmentation. The robustness of our model is verified by testing the proposed system on the ICDAR 2013, ICDAR 2019, Marmot datasets and some randomly clicked images. Our model outperforms all the other methods presented in ICDAR 2019 table segmentation competition with an F score of 0.9694.
Analisis pemanfaatan aplikasi online pada pembelajaran daring (dalam jaringan) di sekolah dasar

During the Covid-19 pandemic, all levels of education in Indonesia carried out learning activities from home which were carried out through online learning and continued until the situation was declared better. The purpose of this study was to obtain planning implementation, and evaluation of the using of online applications in online learning (online) at SDN Mangkubumen Lor No 15. The research method used was descriptive qualitative with a case study approach. The research subjects were teachers and students in high class. The data collection technique was carried out by interviews, questionnaires and document analysis with data validity testing using technical triangulation and source triangulation. Data analysis used interactive techniques Miles & Huberman. The results showed that: (1) online learning to plan has made use of the online application and its features according to the needs, making lesson plans and preparing teaching materials; (2) the implementation of online learning was used for providing materials and assignments through online applications, (3) evaluating online learning was difficult for giving reflection to students. Therefore, teachers and students needed to develop a more varied use of online applications so that online learning can be carried out better.
AN EFFORTS TO IMPROVE DIGITAL LITERACY TEACHERS THROUGH THE CREATION OF ELECTRONIC BOOK

The purpose of this devotion program is for teachers at SMK YPPM Boja to have the knowledge and ability to design more creative, innovative, and exciting learning media. Besides, it can realise the reduction of paperless use in supporting environmental conservation. It is high time the learning media switched from paper book to  digital book, considering paper-based books are impractical and heavy to carry, easily damaged and lost. E-book can be the right solution for practical, secure, and accessible learning media. E-book is also a great way to reduce paper use and reduce the flight of trees as paper raw materials, making it more environmentally friendly. This method of community service takes the form of lectures,  discussions,  and practices. The materials that are delivered include the definition of learning media, types of learning media, the introduction of Electronic Book as a  digital learning medium, the excellence of e-book, the techniques and stages of the preparation of e-book, and the implementation of e-book as a learning medium. The result of this community service is the increasing understanding of SMK YPPM Boja teachers in the creation of exciting learning media as well as improving the professional competencies of teachers.
Ensemble Based Graph Convolutional Network for Semi Supervised Learning

In this paper, an Ensemble based Graph Convolutional Network (EGCN) is proposed for relational datasets. EGCN encodes the pairwise similarity between data points for semi supervised learning and feature representation. The GCN model with Laplacian filter is not able to completely explore the local geometry of the data distribution as the null space of Laplacian remains constant along the manifold. This leads to poor extrapolating ability of the GCN model. In order to improve this GCN model, Hessian filter is introduced. The Hessian filter is more efficient and has richer null space. It can encode the relationship among the data points better by fusing structural properties with original features. In our EGCN, two parallel GCNs are executed, one with Laplacian filter and other with Hessian filter. In EGCN model Laplacian, filter accumulate the information from a data point to all its neighbors and Hessian filter gathers the information between adjacent data points that lie in the neighborhood of a data point. Extensive experiment conducted on various relational datasets indicates that EGCN out performs both Laplacian and Hessian filter based GCN models by a significant margin on various relational datasets.
Present an Initial Estimation Method for Logical Transaction-based Software Projects

The first and most basic requirement for successful entry of a project, is have a realistic and reasonable estimation. In this paper, in order to increase accuracy of software projects estimation and reduce complexity of estimation process, we introduce a method called the Logical Transaction Point (LTP). Our method is most appropriate for transactional software. By use of this method can estimate the total size of use-case's and size of the whole software. In this paper we will prove the more accurate the same technique as UCP method and Due to more transparency and simplicity, its deployment is easier. We provide the main basis for this method, the degree of functional point analysis (FPA) and estimation the degree of use case point (UCP).
Automating Implementation of Business Logic of Multi Subject-Domain IS on the Base of Machine Learning, Data Programming and Ontology-Based Generation of Labeling Functions

The article analyzes the possibility of using machine learning (ML) models built with the use of a data programming approach for the implementation of the business logic of multi subject-domain information systems (MSIS). MSIS characterized by the heterogeneity and variability of users, data and functions. ML considered as a promising approach to the implementation of business logic in conditions of variability and heterogeneity of MSIS. To effectively use ML models within MSIS the model-centric architecture on the base of data programming approach is proposed. To quickly obtain high-quality sets of labelled training data, it is proposed to use the data programming approach which based on simple code snippets named labelling functions. To ensure the reuse of knowledge and accelerate the implementation of business logic based on ML models, a technology for automated generation of labelling functions based on domain ontologies has been proposed. The performed experiments confirmed the efficiency and potential effectiveness of the proposed technology.
Effects of Directed Reading Lessons on Second Graders’ Content Area Reading Comprehension

Content area reading supports young children’s literacy development. This quasi-experimental research determined the effects of Directed Reading Lessons (DRLs) on second graders’ content area reading comprehension. A one group pretest-posttest design was administered to 25 second graders from a laboratory school in Quezon City. Research tools included a comprehension pretest and posttest, and independent practice activities. Results show that DRLs helped improve the comprehension of expository texts. Furthermore, it revealed that the students improved better in text-based compared to inference comprehension questions. With these results, it is concluded that DRLs are helpful in teaching primary-school-age-children. Although content area reading is new to the students, DRLs helped them improve in their comprehension of expository texts. The independent practice activities were pilot tested and validated by experts but it was observed that some second graders had difficulty communicating their ideas in written Filipino form. Thus, checklists, observations, focus group discussions, and interviews may also be effective instruments in gathering data for these variables. Further research may examine the use of English expository texts, other levels of comprehension, and other content areas. Subsequent studies may use different populations to examine broader generalizability. Keywords: primary-school-age-children, reading comprehension, content area reading, directed reading lessons
Potential Research Directions

Although optinformatics in evolutionary learning and optimization has made remarkable progress in recent years, there are still a number of potential research directions of optinformatics that we believe will be beneficial to the field of evolutionary computation, which are remained to be explored. In this chapter, the possible research directions of optinformatics in evolutionary learning and optimization are discussed.
Syntax Interference on Translation of Indonesia-English Text

This study aims to describe the type of syntactic interference in the translation of Indonesian text into English, where students are given a text containing 300 words to be translated into English. The method used in this research is descriptive with distributional and equivalent studies. The data is taken from the translation of the text which is then analyzed for errors. The results showed that there was a syntactic problem that emerged from the translation results of students. Some of the syntactic interference that was found included reversed English phrase arrangement, parallelization, absence of articles, absence of to be, and lack of passive sentence structure.
LibGuides: Computing: Other information

A guide for computing students bringing together information about all the books, journals and e-resources for your subject area
A Study on the Operating Status of Elective Subject According to the 2015 Revised High School Mathematics Curriculum

The 2015 revised high school curriculum has been designed to support students’ selection of subjects considering the student's career path, and the mathematics department also organized general/ career elective courses. This study was conducted to derive implications for ensuring students' choice of subjects ahead of the implementation of the high school credit system. First of all, focusing on subject options, I looked at the changes in Korean curriculum, composition of foreign mathematics subjects, and college entrance subjects, and the current status of ‘selective- oriented curriculum' in 1,709 general high schools nationwide and status of co-curriculum operation by education board of city or province. Several implications have been derived based on the findings. It was suggested that elective subjects and career tracks should be prepared so that students can choose their courses according to their career path, and that universities should designate essential subjects so that students can learn what they need and go to college. In addition, it was proposed that improvements in the system to prevent grades from being disadvantaged by the selection of mathematics subjects, and improvements in the completion path and subject composition should be made.
LibGuides: Classroom Management: Specialized Databases

This guide is for the subject of Classroom Management. It contains resources available in the NCU Library.
Breast Cancer Classification Using Artificial Neural Network and Feature Selection

Breast cancer is still one of the leading causes of death in the world. Prevention can be done if the cancer can be recognized early on whether the cancer is malignant or benign. In this study, a comparison of malignant and benign cancer classifications was performed using two artificial neural network methods, which are the Feed-Forward Backpropagation method and the Elman Recurrent Neural Network method, before and after the feature selection of the data. The result of the study produced that Feed-Forward Backpropagation method using 2 hidden layers is better after the feature selection was performed on the data with an accuracy value of 99,26%.