Reviewerly/exHarmony huggingface dataset:
Upaya Meningkatan Prestasi Belajar Siswa dengan Metode Make A Match pada Mata Pelajaran Ilmu Pengetahuan Sosial di Kelas IX-C SMP Negeri 4 Praya

The purpose of this research was to improve the students learning achievement of Social Sciences (IPS) in class IX-C of SMP Negeri 4 Praya by using Make A-Match learning method. This research used classroom action research methods and was carried out at the fourth class of IX-C in the even semester of the academic year 2018/2019 with 39 students, in which 16 male students and 23 female students. The sequence of research activities included: (1) Planning, (2) Implementation of actions, (3) Observation, (4) Reflection. The result of the study showed that Make-a Match method was proven to improve the students learning achievement as seen in the result of recapitulation of each cycle from the first cycle, in which 26 students or 65% were said to be complete and 13 students or 33% were not complete, and in the second cycle 37 students or 92.31% were said to be complete and 3 students or 7.69% had not been complete. From the recapitulation result of each cycle above, it was seen that cycle I to cycle II increased by 4.57%. Thus, it can be concluded that the use of Make-a Match learning method can improve the students learning achievement of Social Sciences (IPS) in class IX-C of SMP Negeri 4 Praya in the year of 2018/2019.
LibGuides: Research: Basic Research Process

Starting research can be difficult. These resources are specifically built and provided to help you become a better researcher and find what you need.
An Efficient Action Detection Model Using Deep Belief Networks

Computer Vision and image processing have gained an enormous advance in the field of machine learning techniques. Some of the major research areas within machine learning are Action detection and Pattern Recognition. Action recognition is a new advancement of pattern recognition approaches where the actions performed by any action or living being is tracked and monitored. Action recognition still encounters some challenges that needs to be looked upon and perform recognize the actions is a very minimal time. Networks like SVM and Neural Networks are used to train the network in such a way they are able to detect a pattern of an action when a new frame is given. In this paper, we have proposed a model which detects patterns of actions from a video or an image. Bounding boxes are used to detect the actions and localize it. Deep Belief Network is used to train the model where numerous images having actions are given as the training set. The performance evaluation was done on the model and it is observed that it detects the actions very accurately when a new image is given to the network.
Validation of Graph Based K Nearest Neighbor for Summarizing News Articles

This research proposes the text summarization tool based on a machine learning algorithm which is the modified KNN version which classifies a graph into summary or non-summary. The motivations of this research are the three facts: one fact is that a graph is a visualize representation of data items, another fact is that various similarity metrics among graphs are defined and the other is that the text summarization is able to be viewed into a classification task which a machine algorithm is applicable. The proposed system partitions a text into paragraphs, encode them into graphs in each of which vertices are words and edges are semantic relations between words, and applies the modified KNN version to the text summarization. The proposed approach is empirically validated as the better one, in summarizing news articles domain by domain. We need to consider the domain granularity and pre-classification of each full text into a domain for implementing the text summarization systems.
Subject Guides & Tutorials: Digital Library: About cloudLibrary

The Cloudlibrary contains thousands of ebooks and digital audiobooks and is available to all Wyoming library card holders.
A Linear-time Algorithm for Optimal Tree Completion

A phylogenetic tree, a tree representing evolution among species, is a general tool for bioinformatics. It is increasingly common to have more than one trees for an interested set of species. When there is a one important tree t with missing species, one would like to complete it with a reference tree T that contains all species while ensuring that the resulting tree maintains the structure of t. Christensen, Molloy, Vachaspati and Warnow [Algorithms for Molecular Biology, 2018] addressed this issue by formulating the Optimal Tree Completion Problem. They presented an optimal algorithm called OCTAL that works in O (n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) time where n is the number of species in T. They also proved that OCTAL minimizes the Robinson-Foulds distance between T' and T. We show that this can be done more efficiently by presenting an O(n)-time implementation of their algorithm.
Life In Between Phase : Understanding and Manipulating Microphone Relationships With Visualisation Tools

Phase relationships are a complex and mystifying phenomenon for early stage recording engineers and university students. In this paper, we take the analytical capabilities of time shifting plugin Auto-Align and use it to develop new methods of understanding phase interaction. We utilise the visualisations and time shifting features to assist in recording a multi-miked drum kit and in the post-production soundstage for a thirty-two piece big band. We explore new methodologies for phase interaction and microphone manipulation by running these two experiments and documenting the process using a combination of text, audio, and video.
Performance Impact of Memory Channels on Sparse and Irregular Algorithms

Graph processing is typically considered to be a memory-bound rather than compute-bound problem. One common line of thought is that more available memory bandwidth corresponds to better graph processing performance. However, in this work we demonstrate that the key factor in the utilization of the memory system for graph algorithms is not necessarily the raw bandwidth or even the latency of memory requests. Instead, we show that performance is proportional to the number of memory channels available to handle small data transfers with limited spatial locality. Using several widely used graph frameworks, including Gunrock (on the GPU) and GAPBS \& Ligra (for CPUs), we evaluate key graph analytics kernels using two unique memory hierarchies, DDR-based and HBM/MCDRAM. Our results show that the differences in the peak bandwidths of several Pascal-generation GPU memory subsystems aren't reflected in the performance of various analytics. Furthermore, our experiments on CPU and Xeon Phi systems demonstrate that the number of memory channels utilized can be a decisive factor in performance across several different applications. For CPU systems with smaller thread counts, the memory channels can be underutilized while systems with high thread counts can oversaturate the memory subsystem, which leads to limited performance. Finally, we model the potential performance improvements of adding more memory channels with narrower access widths than are found in current platforms, and we analyze performance trade-offs for the two most prominent types of memory accesses found in graph algorithms, streaming and random accesses.
Gene-Promoter-Sequence Recognition – an Interpretable and Accurate Fuzzy-Genetic Approach

Different methods applied to gene promoter recognition share, in general, the same drawback, i.e., they are non-interpretable black-box-type techniques. The main objective of this paper is the application of our fuzzy rule-based classification approach characterized by genetically optimized accuracy-interpretability trade off (using multi-objective evolutionary optimization algorithms (M-OEOAs)) to gene promoter recognition. Two publicly accessible bacterial DNA benchmark data sets, i.e., Molecular Biology (Promoter Gene Sequences) and iPromoter-FSEn benchmark data sets are considered. For comparison purposes, two M-OEOAs are used in our experiments, i.e., the well-known Strength Pareto Evolutionary Algorithm 2 (SPEA2) and our generalization of SPEA2 (referred to as SPEA3) characterized by a higher spread and better-balanced distribution of solutions. Our results for both considered molecular biology data sets are compared with the results of 16 alternative methods (including several state-of-the-art ones) demonstrating the advantages - in terms of system's accuracy-interpretability trade-off optimization - of our approach.
Sentiment Detection from ASR Output

Emotion and sentiment detection from text have been one of the first text analysis applications. Practical use includes human-computer interaction, media content discovery and applications for monitoring the quality of customer service calls. In this paper we perform a review of established and novel features for text analysis, combine them with the latest deep learning algorithms and evaluate the proposed models for the needs of sentiment detection for monitoring of the customer satisfaction from support calls. The issues we address are robustness to the low ASR recognition rate, the variable length of the text queries, and the case of highly imbalanced data sets. The proposed approaches are shown to significantly outperform the accuracy of the baseline algorithms.
Electronic Propinquity Scale

The theory of electronic propinquity, originally theorized by F. Korzenny, is a phenomenon of mediated communication where interactants experience a psychological feeling of nearness, which is thought to vary based on the communication channel. The measure of electronic propinquity demonstrates clear face validity; the adjectives clearly represent feelings of closeness experienced in interactions. However, these adjective choices are not specific to mediated interactions, as these varying degrees of closeness could be experienced in face-to-face contexts as well. Some studies have also used the measure of electronic propinquity to operationalize the construct of social presence, which is related but can also be a distinct construct. Social presence has been described as “the perception of non-mediation”, which does not exactly match with a feeling of “psychological closeness” described in the original construct of propinquity. Correlating social presence and electronic propinquity together would provide evidence for convergent validity, but could also be a precarious choice.
Hubungan Pola Asuh Orang Tua dan Motivasi Belajar dengan Hasil Belajar

The purpose of this study was to determine the positive and significant relationship between parenting and learning motivation with student learning outcomes. The type of research was ex-postfacto correlation. The techniques of collecting data were observation, questionnaire, and documentation study. The data collection instruments was questionnaires with likert scale, which previously tested validity and reliability. The data analysis used  product moment correlation and multiple correlation. The results of  the study showed that there was a positive and significant correlation between parenting and learning motivation with student learning outcomes by correlation coefficient of 0.640 which is considered as high. Tujuan penelitian ini yaitu untuk mengetahui hubungan yang positif dan signifikan antara pola asuh orang tua dan motivasi belajar dengan hasil belajarpeserta didik.Jenis penelitian yaitu ex-postfacto korelasi.Teknik pengumpulan data yang dilakukan yaitu: observasi, kuesioner, dan studi dokumentasi. Instrumen pengumpul data berupa angket dengan skala likert, yang sebelumnya diuji validitas dan reliabilitas. Analisis data yang digunakan adalah korelasi product moment dan multiple correlation. Hasil penelitian, diperoleh bahwa terdapat hubungan yang positif dan signifikan antara pola asuh orang tua dan motivasi belajar dengan hasil belajar peserta didik ditunjukkan dengan koefisien korelasi sebesar 0,640 yang berada pada taraf “tinggi”. Kata kunci: hasil belajar, motivasi belajar, pola asuh orang tua.
The relationship between differences in adult learners' self-directed learning competencies and lifelong learning

본 연구는 크게 두 가지 목표를 설정하였다. 첫째, 성인학습자들의 자기주도적 학습역량과 평생교육, 특별히 직업관련 형식교육과 직업관련 비형식교육, 참여에서 나타난 격차의 정도를 탐색하고자 하였다(연구문제 1). 둘째, 자기주도적 학습역량의 격차와 평생교육 참여와의 관련성을 실증적으로 분석하고자 하였다(연구문제 2). 이를 위하여 OECD 국제 성인역량 조사(PIAAC) 자료를 활용하여 기술통계와 이분형 로지스틱 회귀분석을 각각 시행하였다. 특별히 비교 가능한 변수들의 가용 여부를 토대로 선별된 OECD 16개 회원국을 대상으로 분석하였고, 우리나라를 중심으로 분석결과를 설명하였다. 분석결과, 정도의 차이가 존재하지만, 자기주도적 학습역량과 직업관련 형식 및 비형식교육 참여에서격차가 모든 국가에서 나타났다. 또한, 대부분 국가에서 자기주도적 학습역량이 높아질수록 평생교육, 특별히 직업관련 형식교육의 참여 가능성이 상대적으로 높은 것으로 나타났다. 우리나라의 경우, 직업관련 형식교육 참여는 개인 배경이, 직업관련 비형식교육 참여는 노동시장 관련 변수들 즉 직무의 숙련수준, 일자리 학습 참여의 빈번도, 복잡문제 해결스킬 사용 빈번도 등이 각각 결정적인 역할을 하는것으로 나타났다.The purpose of this article is two-fold. First, this article aimed to investigate the degree of differences in adult learners’ self-directed learning competencies and participation in lifelong learning(LLL), in particular job-related formal education(JRFE) and job-related non-formal education (JRNFE). Second, this article analyzed the relationship between differences in self-directed learning competencies and participation in LLL. For these purposes, this article conducted descriptive statistics and binary logistic regression analysis using OECD PIAAC data. Especially, this article analyzed 16 OECD member countries that were selected on the basis of the availability of comparable variables and compared them with one another with a focus on Korea. To summarize the findings, although there were differences in the extent of differences in self-directed learning competencies and participation in JRFE and JRNFE among individual adult learners, the differences were identified in all the target countries. In addition, in almost all countries, the greater self-directed learning competencies, the greater the likelihood of participation in LLL, in particular JRFE, appeared to become. For Korea, individual background characteristics appeared to play a key role in determining JRFE participation, while labor market-related factors, i.e., occupation by skill levels, the frequency in workplace learning participation, and the frequency of use of complex problem-solving skill, do the same for JRNFE participation.
Eyenet: Attention based Convolutional Encoder-Decoder Network for Eye Region Segmentation

With the immersive development in the field of augmented and virtual reality, accurate and speedy eye-tracking is required. Facebook Research has organized a challenge, named OpenEDS Semantic Segmentation challenge for per-pixel segmentation of the key eye regions: the sclera, the iris, the pupil, and everything else (background). There are two constraints set for the participants viz MIOU and the computational complexity of the model. More recently, researchers have achieved quite a good result using the convolutional neural networks (CNN) in segmenting eyeregions. However, the environmental challenges involved in this task such as low resolution, blur, unusual glint and, illumination, off-angles, off-axis, use of glasses and different color of iris region hinder the accuracy of segmentation. To address the challenges in eye segmentation, the present work proposes a robust and computationally efficient attention-based convolutional encoder-decoder network for segmenting all the eye regions. Our model, named EyeNet, includes modified residual units as the backbone, two types of attention blocks and multi-scale supervision for segmenting the aforesaid four eye regions. Our proposed model achieved a total score of 0.974(EDS Evaluation metric) on test data, which demonstrates superior results compared to the baseline methods.
The longitudinal relationship of adolescents' reading activity, self-efficacy and academic achievement

본 연구는 청소년의 성장 발달과정에서 독서활동, 자기효능감, 국어, 수학 학업성취도의 변화양상을 살펴보고, 다변량 잠재성장모형을 활용하여 독서활동이 자기효능감을 매개변인으로 국어, 수학 학업성취도에 미치는 종단적 매개효과를 상대적으로 비교 분석하는 데 목적이 있다. 이를 위해 경기도교육연구원이 수집한 경기교육종단연구(GEPS)의 초4 패널 데이터 3차년도(초6)부터 5차년도(중2)까지 3년 동안의 자료를 사용하여 다변량 잠재성장모형 분석을 실시하였다. 연구 결과, 독서활동과 자기효능감은 시간이 지날수록 감소하였고 국어, 수학 학업성취도는 학년이 올라감에 따라 증가하였으며, 초기값과 변화율에 개인차가 있었다. 다변량 잠재성장모형의 경로계수를 살펴본 결과, 자기효능감 초기값과 변화율이 국어, 수학 학업성취도의 초기값과 변화율에 긍정적인 영향을 주었으며 국어와비교하여 수학 학업성취도에 더 긍정적인 영향을 주었다. 또한 시간의 흐름에 따라 청소년의 독서활동이 자기효능감을 매개로 국어, 수학 학업성취도에 미치는 영향을 종단적으로 검증한 결과, 초6 시기인 초기값 간의 매개효과와 초6부터 중2 기간 동안의 변화율 간의 매개효과가 모두 통계적으로 유의하게 나타났으며, 국어와 비교하여 수학 학업성취도의 초기값과 변화율에 더 정적인 영향을 주는결과를 보였다. 마지막으로 본 연구의 의의와 시사점을 논의하였다.The purpose of this study is to investigate the longitudinal relationship of adolescents’ reading activity, self-efficacy, and Korean·Mathematics academic achievement. We further examine the structural relationship among those longitudinal factors of three latent variables. Data used in this study were from the Gyunggi Education Panel Study (GEPS; 2014-2016), Wave Ⅲ (6th grade) toⅤ (8th grade), collecting responses of 3,163 Studuents. Multivariate Latent Growth Modeling (M-LGM) was employed to analyze longitudinal changes of reading activity, their self-efficacy, and academic achievement in Korean and math subjects in three time-points from 6th grade to 8th grade. Results showed that mostly of the growth factors (i.e. initial status and growth rate) in three variables statistically significant, meaning that those variables linearly increased or decreased over time. Specifically, reading activity and their self-efficacy were decreased and academic achievement both in Korean and math was increased. Moreover, students who showed higher self-efficacy in growth factors revealed better academic achievement. Finally, students’ self-efficacy partially mediated between reading activity and academic achievement both in Korean subject, and in math. Theoretical and practical implications were discussed.
MAMS-A*: Multi-Agent Multi-Scale A*.

We present a multi-scale forward search algorithm for distributed agents to solve single-query shortest path planning problems. Each agent first builds a representation of its own search space of the common environment as a multi-resolution graph, it communicates with the other agents the result of its local search, and it uses received information from other agents to refine its own graph and update the local inconsistency conditions. As a result, all agents attain a common subgraph that includes a provably optimal path in the most informative graph available among all agents, if one exists, without necessarily communicating the entire graph. We prove the completeness and optimality of the proposed algorithm, and present numerical results supporting the advantages of the proposed approach.
Virtual Reality as an Urban Tourism Destination Marketing Tool

Although there is a breadth of research demonstrating the effectiveness of virtual reality (VR) as a destination marketing tool, there are limited exploratory studies providing empirical evidence from the perspective of tourism marketers in this specific context. The technological, organisational, environmental (TOE) framework has confirmed explanatory power for studies exploring organisational adoption of various technologies; however, the framework's association with VR research is limited. Responding to this dual gap, this exploratory study explores both internal and external factors associated with VR adoption from tourism marketers' perspectives by employing TOE framework as a theoretical base. Interviews were conducted with seven tourism marketers from three urban tourism destinations in the UK. The preliminary study findings indicate that environmental (perceived competitive pressures and global market appeal) factors are positively associated with tourism marketers' VR adoption, while other TOE factors are not. Finally, theoretical contribution, practical implications and avenues for further research are provided.
Tema, idea principal e ideas secundarias

The present research focused on the elaboration of a learning session whose thematic axis is the topic, the main idea and the secondary ideas. In addition, with two supports, by one hand a pedagogical support and the other hand a theoretical support.Within this learning session, competencies, skills and indicators specified, as well as the parties with their respective activities to develop. In the pedagogical support, everything, corresponding to a learning session is specified defining concepts such as society, learning, education, skills, abilities, strategies, evaluation, etc. In addition to the learning theories that it will be used for its execution.Regarding to the theoretical support, it focused on defining the conceptual part of a topic, main idea and secondary ideas, adding each of these concepts, strategies and examples wherewith the students, to whom this study will been applied, could understand better
To the history of the influence of IT-technologies on the formation and development of a modern information society

The human universe in its development has undergone several stages of civilization transformation. Each of these stages is characterized by a set of etiologic features that directly influenced the quality of life of the society, and hence on all aspects of being at both the micro and macro level, if we consider the existence and functioning of social and political organisms as a system. Technological innovations almost always throughout the studied history in any society reflected social values and, in turn, transformed society in varying degrees.&#x0D; The development of machine-based information processing techniques has influenced the rapid transformation of a modern society from industrial to post-industrial information society.&#x0D; The article briefly describes the key moments of the emergence of the fundamental machine methods of information processing and digital technologies that influenced the formation of the modern information society and its functioning in all aspects of life in the general daily accessibility of a large array of the information which is different by quality.
Business TransformationwithBlockchain

This talk focuses on the business adoption and transformation based on blockchain technology. First, some important features of blockchain are reviewed. Then, the blockchain business and monetization models will be presented. Examples of businesses that leverage the benefits of blockchain are given. Then, the presentation gives business case studies of blockchain in supply chain and payments in which different design approaches of the systems and blockchain adoptions are described and compared. The recommendations and major considerations of using the blockchain technology are also highlighted.