Reviewerly/exHarmony huggingface dataset:
Automatic Classification of Alzheimer’s Disease based on CPD Brain Point Cloud Registration for Feature Extraction

In this paper, a new scheme of feature extraction based on the registration of two point clouds that model the brain surface area and a cerebral baseline, is presented. The aim is to design an automatic Random Forest classifier based on brain-shape pattern extraction, in order to discriminate among Control subjects, patients with Mild Cognitive Impairment, and patients with Alzheimer's disease using their MRI data. The features used were rotation and displacement parameters obtained from the rigid-register transformation matrix between each subject's point cloud model and the brain baseline cloud. Additional features consisted of distance metrics performed over the aligned clouds and the baseline; those metrics attempt to estimate the cerebral cortex deformations. The training set utilized 60 MRI grouped into subsets of 20 studies a class; while the test consisted of 30 not seen data (10 per class). The proposed method achieved up to 87.2% accuracy in the final validation stage. These results suggest that our proposed scheme could be useful to discriminate among several pathologies that affect brain gray matter morphology. Moreover, the proposed features might be used as brain deformation quantifiers.
PENGEMBANGAN MEDIA PEMBELAJARAN BERBASIS VISUAL BASIC APPLICATION POWERPOINT PADA MATERI SISTEM PERSAMAAN LINEAR DUA VARIABEL

This research is development research. This study aims to develop VBA Powerpoint based learning media on SPLDV material for class VIII junior high school students. Samples for this research are taken from one of junior high school in Bandung. This study refers to the ADDIE development model which includes five stages, namely: Analysis (Analysis Phase), Design (Design Phase), Development (Development Phase), Implementation (Implementation Phase), and Evaluation (Evaluation Phase). The instrument of this research is in the form of assessment sheets for material experts, media experts, teachers and students. Based on the analysis of learning media through the instruments given to material experts, media experts, teacher and students obtained the following results, material experts stated that the product was very valid with an average value of 3.64 and media experts stated that the product was valid with an average value of 3.06. While the results of teacher responses can be concluded that the product is very valid with an average value of 3.50 and from the results of student responses can be concluded that the product is valid with an average value of 3.18.1
Proposal of a welding skill training system using VR technology

In this paper, we propose a welding skill training system for beginners using VR technology. In this system, a user can do welding in a virtual space in some situations, and a user can also observe welding operation by an expert from any point in a virtual space. Experimental result suggested that this system would be effective for welding skill training for beginners.
Thematic Mapping and Evaluation of Temporary Sequence of Multi-zone Satellite Images

The goal of this paper is to present and compare algorithms for thematic mapping of multispectral satellite images. The paper proposes a nonlinear multi-dimensional filter to combine the results of processing of several multi-temporal multispectral images. Options for implementation of procedures are discussed.
FastEstimator: A Deep Learning Library for Fast Prototyping and Productization

As the complexity of state-of-the-art deep learning models increases by the month, implementation, interpretation, and traceability become ever-more-burdensome challenges for AI practitioners around the world. Several AI frameworks have risen in an effort to stem this tide, but the steady advance of the field has begun to test the bounds of their flexibility, expressiveness, and ease of use. To address these concerns, we introduce a radically flexible high-level open source deep learning framework for both research and industry. We introduce FastEstimator.
Customer Opinions Evaluation: A Case Study on Arabic Tweets

This paper presents an automatic method for extracting, processing, and analysis of customer opinions on Arabic social media.We present a four-step approach for mining of Arabic tweets.First, Natural Language Processing (NLP) with different types of analyses had performed.Second, we present an automatic and expandable lexicon for Arabic adjectives.The initial lexicon is built using 1350 adjectives as seeds from processing of different datasets in Arabic language.The lexicon is automatically expanded by collecting synonyms and morphemes of each word through Arabic resources and google translate.Third, emotional analysis was considered by two different methods; Machine Learning (ML) and rulebased method.Finally, Feature Selection (FS) is also considered to enhance the mining results.The experimental results reveal that the proposed method outperforms counterpart ones with an improvement margin of up to 4% using F-Measure.
Characteristics of a New Competency Education Framework in OECD Education 2030 Project and its Implication to Korea’s 2015 Revised Curriculum

[연구목적] 본 연구는 최근 OECD가 ‘DeSeCo 2.0’이라고 불리는 Education 2030 프로젝트를 통해 새로운 역량교육의 틀을 제시하고 있다는 점에 주목하고, OECD Education 2030에 나타난 역량교육의 주요 특징과 시사점을 2015 개정 교육 과정과의 비교를 통해 살펴보는 데 그 목적이 있다. [연구방법] 이러한 목적을 달성하기 위해 이 연구에서는 문서 분석 방법을 사용하였다. 역량교육의 필요성에 대한 문제의식, 역량의 의미와 구성 요소, 핵심역량의 하위범주와 성격, 역량을 반영한 교육과정 개발 방향 및 원리 등의 네 가지 측면을 분석 준거로 삼았다. [연구결과] OECD Education 2030에 제시된 역량교육은 2015 개정 교육과정과 비교해 보았을 때, 역량기반 교육과정 설계 원리의 측면에서는 큰 차이가 없었다. 그러나 앞서 세 가지 분석 준거와 관련하여서는 2015 개정 교육과정과는 다른 특징이 두드러졌다. [결론] OECD Education 2030이 2015 개정 교육과정을 더 나은 역량기반 교육과정으로 설계하는 데 주는 시사점을 역량교육의 필요성에 대한 관점 변화, 역량 개념의 체계화ㆍ구체화 작업, 학교교육을 둘러싼 사회변화의 면밀한 분석과 관련하여 논의하였다.[Purpose]The purpose of this study was to explore the characteristics of a new framework of competency education in OECD Education 2030 project and its implication to Korea’s 2015 revised curriculum. [Methods] To achieve this purpose this study conducted document analysis with focus on various documents related to OECD Education 2030 and 2015 revised curriculum in Korea. [Results] As a result of this study, the characteristics of the competency education frameworks were examined based on four analysis criteria: 1) perspectives on necessity of competency education, 2) meanings of competency and its elements, 3) categories of key competencies and their nature, and 4) development directions of competency-based curriculum. [Conclusion] Finally, Several implications were suggested in order to redesign the Korea’s 2015 revised curriculum as a better competencybased curriculum in the near future.
Analysis of the possibility of shortening the time of creating a mobile application for Android and iOS systems using Xamarin technology

The article presents a comparison of times needed for creating a mobile application for Android and iOS system using multi-platform Xamarin technology and respective native technologies for both systems. The authors’ application was implemented in three programming environments: VisualStudio (using Xamarin technology), Android Studio and XCode. The results of the conducted research have shown that thecreation of an application in Xamarin technology takes approximately half the time.
PAC-Bayesian Contrastive Unsupervised Representation Learning

Contrastive unsupervised representation learning (CURL) is the state-of-the-art technique to learn representations (as a set of features) from unlabelled data. While CURL has collected several empirical successes recently, theoretical understanding of its performance was still missing. In a recent work, Arora et al. (2019) provide the first generalisation bounds for CURL, relying on a Rademacher complexity. We extend their framework to the flexible PAC-Bayes setting, allowing us to deal with the non-iid setting. We present PAC-Bayesian generalisation bounds for CURL, which are then used to derive a new representation learning algorithm. Numerical experiments on real-life datasets illustrate that our algorithm achieves competitive accuracy, and yields non-vacuous generalisation bounds.
Zero-shot Dependency Parsing with Pre-trained Multilingual Sentence Representations

We investigate whether off-the-shelf deep bidirectional sentence representations trained on a massively multilingual corpus (multilingual BERT) enable the development of an unsupervised universal dependency parser. This approach only leverages a mix of monolingual corpora in many languages and does not require any translation data making it applicable to low-resource languages. In our experiments we outperform the best CoNLL 2018 language-specific systems in all of the shared task's six truly low-resource languages while using a single system. However, we also find that (i) parsing accuracy still varies dramatically when changing the training languages and (ii) in some target languages zero-shot transfer fails under all tested conditions, raising concerns on the 'universality' of the whole approach.
The use of Round Robin technique to improve students' speaking skill

This study aims to find out whether the use of Round Robin Technique can improve the speaking skill of junior high school students. This research was conducted in one class and applied the pre-experimental design that was one group pre-test and post-test design. There were four meetings in this research including pre-test and post-tests. The sample is students in one class that consisted of 32 students, with 15 females and 17 males. The instruments used to obtain the data were pre-test and post-test. The mean of the student speaking score of the pre-test was 43.71 and the mean of the post-test was 79.2. The improvement from pre-test to the post-test was 35.49 points. The average score of the students was 79.2, it was higher than the Criteria Mastery of Learning (or KKM, Kriteria Ketuntasan Minimal) of a junior high school in Banda Aceh, which is 70.00. The result of this study can be shown in the t-test that there was an improvement of students’ speaking skill, with the t-test of 25.3, the degree of freedom is 31 and t-table is 1.696. Based on the result of the test, it can be concluded that the use of Round Robin Technique improved the students’ speaking skills. This technique offered the students’ chances to share ideas and opinion with their peers, speak up and express their argument in class.
Real-time 6D Racket Pose Estimation and Classification for Table Tennis Robots

For table tennis robots, it is a significant challenge to understand the opponent's movements and return the ball accordingly with high performance. One has to cope with various ball speeds and spins resulting from different stroke types. In this paper, we propose a real-time 6D racket pose detection method and classify racket movements into five stroke categories with a neural network. By using two monocular cameras, we can extract the racket's contours and choose some special points as feature points in image coordinates. With the 3D geometrical information of a racket, a wide baseline stereo matching method is proposed to find the corresponding feature points and compute the 3D position and orientation of the racket by triangulation and plane fitting. Then, a Kalman filter is adopted to track the racket pose, and a multilayer perceptron (MLP) neural network is used to classify the pose movements. We conduct two experiments to evaluate the accuracy of racket pose detection and classification, in which the average error in position and orientation is around 7.8 mm and 7.2 by comparing with the ground truth from a KUKA robot. The classification accuracy is 98%, the same as the human pose estimation method with Convolutional Pose Machines (CPMs).
Efficient Soft-Constrained Clustering for Group-Based Labeling

We propose a soft-constrained clustering method for group-based labeling of medical images. Since the idea of group-based labeling is to attach the label to a group of samples at once, we need to have groups (i.e., clusters) with high purity. The proposed method is formulated to achieve high purity even for difficult clustering tasks such as medical image clustering, where image samples of the same class are often very distant in their feature space. In fact, those images degrade the performance of conventional constrained clustering methods. Experiments with an endoscopy image dataset demonstrated that our method outperformed various state-of-the-art methods.
Morphological segmentation for extracting Spanish-Nahuatl bilingual lexicon

The aim of this work is to extract word translation pairs from a small parallel corpus and to measure the impact of dealing with morphology for improving this task. We focus on the language pair Spanish-Nahuatl, both languages are morphologically rich and distant from each other. We generate semi-supervised morphological segementation models and we compare two approaches (estimation, association) for extracting bilingual correspondences. We show that taking into account typological properties of the languages, such as the morphology, helps to counteract the negative effect of working with a low-resource language.
Differentially private anonymized histograms

For a dataset of label-count pairs, an anonymized histogram is the multiset of counts. Anonymized histograms appear in various potentially sensitive contexts such as password-frequency lists, degree distribution in social networks, and estimation of symmetric properties of discrete distributions. Motivated by these applications, we propose the first differentially private mechanism to release anonymized histograms that achieves near-optimal privacy utility trade-off both in terms of number of items and the privacy parameter. Further, if the underlying histogram is given in a compact format, the proposed algorithm runs in time sub-linear in the number of items. For anonymized histograms generated from unknown discrete distributions, we show that the released histogram can be directly used for estimating symmetric properties of the underlying distribution.
THE IMPLEMENTATION OF REWARD AND PUNISHMENT TOWARDS STUDENT PERCEPTION IN ENGLISH LEARNING AT IKIP SILIWANGI

This Research titled THE IMPLEMENTATION OF REWARD AND PUNISHMENT TOWARD STUDENTS’ PERCEPTION IN ENGLISH LEARNING AT IKIP SILIWANGI aimed to investigate the students’perception of reward and punishment and analyze the positive effect to student. This study imployes a qualitative reseach and the strategy used in this reseach is descriptive qualitatiave. This research took place at English Education Department Program IKIP SILIWANGI by involving interview college students at two classes randomly. The finding of this research is students’perception of reward and punishment. the students who proper to get a punishment when the students do not do the assignment, coming late etc. and the students who proper to get the reward when the students can answer the questions and also get additionl score. they can get B score for someone always present in every meetings, but it depend on the individu in order to do not repeat the mistake that they have done so obviously giving a reward can motived the student in learning activity because the students will to compete to get that reward
Strategic University Practices in Student Information Literacy Development

This study aimed to examine the role of university practice in student information literacy development. The study employed qualitative research design and involved 31 lecturers, librarians and students in a public research university. Data was collected using semi-structured interviews and analyzed using qualitative data analysis approach. The study found that student information literacy development is a discursive practice of information searching, organizing, analyzing, and synthesizing embedded in learning orientation that focuses on building, validating and disseminating knowledge and its mediating artefacts. The practice is influenced by multiple practices of communities within the institutions, namely the lecturers, librarians, students, and management. The study implicated student information literacy development could be supported by strategic partnerships between lecturers and librarians at the department level with lecturers holding an extra responsibility to stage the practice in the context of knowledge discipline so that students would develop, validate and disseminate knowledge and its mediating artefacts.
Silhouette Extraction for Shadow Volumes Using Potentially Visible Set

Figure 1: left: Wireframe representation of a given model.middle: We voxelize the space around the model.One voxel on the lowest octree level is selected, based on the light position, and all potentially-silhouette (need to be tested) and silhouette edges (guaranteed to be silhouette) can be collected by ascending the octree hierarchy.right: Red coloured edges are those that are a part of the silhouette after testing the set of potentially silhouette edges (all red and black ones).Only a small subset of model edges need to be tested, which considerably reduces the computational complexity.
Online Realtime Sentiment Analysis Tweets by Utilizing Streaming API Features From Twitter

&lt;p class="JGI-AbstractIsi"&gt;Twitter is one of the social media that has a simple and fast concept, because short messages, news or information on Twitter can be more easily digested. This social media is also widely used as an object for researchers or industry to conduct sentiment analysis in the fields of social, economic, political or other fields. Opinion mining or also commonly called sentiment analysis is the process of analyzing text to get certain information in a sentence in the form of opinion. Sentiment analysis is one of the branches of the science of Text mining where text mining is a natural language processing technique and analytical method that is applied to text data to obtain relevant information. Public opinion or sentiment in social media twitter is very dynamic and fast changing, a real time sentiment analysis system is needed and it is automatically updated continuously so that changes can always be monitored, anytime and anywhere. This research builds a system so that it can analyze sentiment from twitter social media in realtime and automatically continuously. The results of the system trial succeeded in drawing data, conducting sentiment analysis and displaying it in graphical and web-based realtime and updated automatically. Furthermore, this research will be developed with a focus on the accuracy of the algorithms used in conducting the sentiment analysis process.&lt;/p&gt;
Model Compression with Two-stage Multi-teacher Knowledge Distillation for Web Question Answering System

Deep pre-training and fine-tuning models (such as BERT and OpenAI GPT) have demonstrated excellent results in question answering areas. However, due to the sheer amount of model parameters, the inference speed of these models is very slow. How to apply these complex models to real business scenarios becomes a challenging but practical problem. Previous model compression methods usually suffer from information loss during the model compression procedure, leading to inferior models compared with the original one. To tackle this challenge, we propose a Two-stage Multi-teacher Knowledge Distillation (TMKD for short) method for web Question Answering system. We first develop a general Q\&A distillation task for student model pre-training, and further fine-tune this pre-trained student model with multi-teacher knowledge distillation on downstream tasks (like Web Q\&A task, MNLI, SNLI, RTE tasks from GLUE), which effectively reduces the overfitting bias in individual teacher models, and transfers more general knowledge to the student model. The experiment results show that our method can significantly outperform the baseline methods and even achieve comparable results with the original teacher models, along with substantial speedup of model inference.
The Effects of Academy Selection on Career Decision Efficacy and Career Preparation Behavior among Cosmetology Academy Students

This study attempted to investigate the effects of academy selection on career decision self-efficacy and career preparation behavior among cosmetology academy students. For this, a questionnaire survey was conducted among 10-12th graders who were taking skincare, hairdressing, nail-care and makeup classes at cosmetology academies in Seoul and Gyeonggi from December 10, 2018 to January 10, 2019. Among a total of 330 questionnaires distributed, 316 copies excluding 14 poorly answered ones were used for final analysis, and the results found the following: Academy facilities and self-efficacy are critical elements for enhancing students’ career decision self-efficacy and career preparation behavior. It was also found that as career decision self-efficacy increased, career preparation behavior improved.
Resource Utilization Scheme of Idle Virtual Machines for Multiple Large-Scale Jobs Based on OpenStack

Cloud computing services that provide computing resources to users through the Internet also provide computing resources in a virtual machine form based on virtualization techniques. In general, supercomputing and grid computing have mainly been used to process large-scale jobs occurring in scientific, technical, and engineering application domains. However, services that process large-scale jobs in parallel using idle virtual machines are not provided in cloud computing at present. Generally, users do not use virtual machines anymore, or they do not use them for a long period of time, because existing cloud computing assigns all of the use rights of virtual machines to users, resulting in the low use of computing resources. This study proposes a scheme to process large-scale jobs in parallel, using idle virtual machines and increasing the resource utilization of idle virtual machines. Idle virtual machines are basically identified through specific determination criteria out of virtual machines created using OpenStack, and then they are used in computing services. This is called the idle virtual machine–resource utilization (IVM–ReU), which is proposed in this study.
Efficient computation of longest single-arm-gapped palindromes in a string

In this paper, we introduce new types of approximate palindromes called single-arm-gapped palindromes (shortly SAGPs). A SAGP contains a gap in either its left or right arm, which is in the form of either wgucuRwR or wucuRgwR, where w and u are non-empty strings, wR and uR are respectively the reversed strings of w and u, g is a string called a gap, and c is either a single character or the empty string. Here we call wu and uRwR the arm of the SAGP, and |uv| the length of the arm. We classify SAGPs into two groups: those which have ucuR as a maximal palindrome (type-1), and the others (type-2). We propose several algorithms to compute type-1 SAGPs with longest arms occurring in a given string, based on suffix arrays. Then, we propose a linear-time algorithm to compute all type-1 SAGPs with longest arms, based on suffix trees. Also, we show how to compute type-2 SAGPs with longest arms in linear time. We also perform some preliminary experiments to show practical performances of the proposed methods.