Reviewerly/exHarmony huggingface dataset:
Application Of Snowball Throwing Learning Model To Improve Learning Outcomes of Writing Simple Dialogues

Based on the initial survey the learning outcomes of the fifth-grade students of SDN Tasikharjo, Jenu District, Tuban Regency in the Indonesian language subject, the material for writing simple dialogues are still low. The purpose of this research is to improve the learning outcomes of fifth-grade students in the Indonesian subject matter of writing simple dialogues by applying the snowball throwing learning model. This research is a classroom action research using quantitative and qualitative approaches. Data were collected through observation sheets, test instruments, and documentation, then analyzed using descriptive quantitative and qualitative descriptive methods with information obtained that the learning outcomes of Indonesian students in writing simple dialogues using the Snowball Throwing learning model in class V SDN Tasikharjo have increased. Quantitatively obtained data from the results of learning cycle I students get an average score of 73.6 then increase to 80 in cycle II. Meanwhile, qualitatively it shows that students are very active and enthusiastic in the learning process using the snowball throwing learning model. This can be seen in the results of the observations made.
Comparative Review of Code Clone Detection Tools and Techniques

Code reuse is a typical movement in programming improvement and is one of the primary reasonsbehind code clones and programming advancement. A codeclone is a part of the source code that is identicalor highly similarto another part (clone) as with reference to structure and semantics. There have been various correlation and assessment concentrates to relate those which gave noteworthy commitments to the clone detectionresearch. These also exposed how challenging it is to compare different tools for certain reasons. There is no examination that comprehends which device or system works better in various sorts of code clones. In this exploration venture, an exhaustive examination is given on the exhibitions of as of now accessible clone detectiontools and techniques.
Application of the Lipirtup Strategy in Literacy Book Writing Training For Elementary School Teachers in Tuban Regency

Writing literacy books for teachers and students is currently a requirement to create  the School Literacy Movement. Beginner writers often think about what to write and how to get started. This training aims to present strategies for starting and implementing literacy book writing activities. This training is a conceptual and practical study that presents strategic concepts to motivate and facilitate elementary school teachers in carrying out literacy book writing activities. As the name implies, the lipirtup strategy. Lipirtup is an acronym for li = see, pi = choose, r = contemplate, tu = write, and p = publish. The Lipirtup strategy is a writing activity step that begins by seeing / reading / observing the object to be written; select the object to be written, contemplate the object to be written; write down the object that has been contemplated; and publish the results of his writing. With this lipirtup strategy, literacy books will be created to realize the Book Writing School Movement.
EPOQAS: Development of an Event Program Organizer with a Question Answering System

With the increase in the various types of communication channels, communicating via online chatting has become common and is now considered an effective means of communication during real-time meetings. Various communication media, for example, traditional tools (such as Facebook Messengers, Twitter, Google Docs) or novel technologies (such as Slido and Miro), can be used as side channels; however, all these media remain separated from the main meeting. Therefore, in this paper, we propose an event program organizer containing a simple chatting function that can be used for questions and answers; this system has been named Event Program Organizer with a Question Answering System (EPOQAS). The system can be easily operated, and it has unique functions that enable the meeting participants to communicate with one another. Also, the source code of the system is published as an open-source software. Hence, implementing an instance of this system in research meetings will accelerate scientific discussions.
Conceptualizing Subject Classification

In this research, we attempt to conceptualize the classification system by which information and knowledge are classified in libraries. This paper also examines the role of subject ontogeny in classification and delineates how subjects are classified following certain schemas and systems. It stresses on the role of ontology and ontogeny in classification schemes and how these tools of knowledge organization help libraries organize information more efficiently for their fast retrieval.
A DATA DRIVEN BREAST CANCER DETECTION METHOD

One of the most common chronic diseases is breast cancer. Breast cancer is not only widespread, but it is also quite complicated. Treating patients with Breast cancer demands doctors to examine enormous amount of data – often too much for human clinicians to analyze on their own. To accelerate the development of better Breast cancer treatments, a big data analytics model can quickly draw meaningful insights and identify cancer-related patterns in the data which radiologists cannot. Big data analytics can help enhance the accuracy of breast cancer screenings, reduce the number of biopsies needed and increase physicians’ confidence in the accuracy of assessments made for screening exams. This article proposes a big data analytics model in breast cancer detection which is capable of processing an enormous amount of data quickly to assist in the early detection of breast cancer with better accuracy. This will augment care delivery, diagnostics and reduce the death rate. In this work, we build a model based on Machine Learning Algorithm to predict breast cancer. The result shows that the machine learning model based on big data analytics can predict better than traditional models, which currently incorporate only a small fraction of patient data.
SetMargin Loss applied to Deep Keystroke Biometrics with Circle Packing Interpretation.

This work presents a new deep learning approach for keystroke biometrics based on a novel Distance Metric Learning method (DML). DML maps input data into a learned representation space that reveals a semantic structure based on distances. In this work, we propose a novel DML method specifically designed to address the challenges associated to free-text keystroke identification where the classes used in learning and inference are disjoint. The proposed SetMargin Loss (SM-L) extends traditional DML approaches with a learning process guided by pairs of sets instead of pairs of samples, as done traditionally. The proposed learning strategy allows to enlarge inter-class distances while maintaining the intra-class structure of keystroke dynamics. We analyze the resulting representation space using the mathematical problem known as Circle Packing, which provides neighbourhood structures with a theoretical maximum inter-class distance. We finally prove experimentally the effectiveness of the proposed approach on a challenging task: keystroke biometric identification over a large set of 78,000 subjects. Our method achieves state-of-the-art accuracy on a comparison performed with the best existing approaches.
Trusted Rating For Sna Using Svd Model

The main aim of the paper is to enhance the performance of a novel recommendation site for hotel by data mining.When we are about to view the rating of a hotel given by other people, we can’t rely accurately on it as there are many fake reviews. So we can overcome the problem by viewing only the ratings given by our friends and friends of friends in our trusted network. TrustSVD therefore created on top of a state of the art recommendation algorithm. By considering both the implicit and explicit influence of ratings the predictions are made. The proposed technique is used to merge with social trust information thus from that we can get the trusted network. Thus by using the trusted network reviews we can avoid the fake reviews. In case of cold start and data sparsity problem the friends of friends list is considered.
Klasterisasi Angka Usia Muda Melek TIK Berdasarkan Algoritma K-Means Menurut jumlah Provinsi Indonesia

Advances in technology that can’t be separated from human life, making information easier to obtain. The results of a survey conducted by APJII on the penetration of internet users in 2018 based on age, stated that the use of the internet was dominated by young people. For this reason, it is hoped that in the future there will be improvements in numbers in the form of equitable use of ICT in all provinces in Indonesia. This research was conducted based on the clustering of young people who are ICT literate. The amount of data used is in accordance with the current number of Indonesian provinces, which are thirty-four provinces using the K-Means algorithm. The dataset in this study was obtained from the official government website https://.www.bps.go.id/ from 2017 to 2019. Clustering was carried out only to group provinces into two types of groups, which can later be used as evaluation material for the government in the framework of the equitable distribution of ICT in each province. The final result of this study is that there are twenty-five provinces that are included in cluster 1 and nine provinces are included in cluster group 2. Thus it is necessary to increase the ICT literacy rate for provincial clusters whose values are still lagging behind other provinces.
LibGuides: General Information: Donating to the University Libraries

Contact and General Information about the Libraries (Staff, History, Mission, Hours, Employment, etc.) Donating to the Thomas Jefferson and Saint Louis Mercantile Libraries
From Metacybernetics to Mindset Agency Theory

Metacybernetics is a general theory of higher order cybernetics that can be used to generate cybernetics theories. Such theories will necessarily adopt the axioms of metacybernetics, but can develop through the use of relevant configurations. The topic of the set of slides explains the exemplar of Mindset Agency Theory - a trait theory of personality psychology that has some connection with Maruyama's Mindscape Theory. Mindset Agency Theory has a pragmatic dimension, delivering a model able to diagnose personality pathologies. The slide summarise the results of an analytic diagnosis of two cases studies, one being of the of late president Trump. The set of slides provides a takeaway summary from the book entitled Configuration Approach to Mindset Agency Theory: A Formative Trait Psychology with Affect, Cognition and Behaviour.
THE APPLICATION OF ROLE PLAYING LEARNING MODEL TO IMPROVE RESPONSIBILITY AND LEARNING ACHIEVEMENTS (Study at Social Science Subject In VIII Grade Students of Junior High School 1 Rejang Lebong)

This research was aimed to describe, first the application of the role playing learning model that can improve the students' responsibility, the second role playing learning models can improve the students' social studies learning achievements, third the effectiveness of applying the role playing learning model to improve students' social studies learning achievements. This research used a classroom action research design and continued with quasi-experiments. The data collection was done by tests and observation. The subject of this classroom action research was the VIII.C grade students of Junior High School 1 Rejang Lebong. The students in class VIII.D was used as the experimental class and the students in class VIII.B was used as the control class. The results of this research showed that the application of the role playing learning model can improve responsibility and learning achievement of social studies students, and the application of role playing learning models can improve social studies learning achievement at Junior High School 1 Rejang Lebong
Self-supervised Recommendation with Cross-channel Matching Representation and Hierarchical Contrastive Learning

Recently, using different channels to model social semantic information, and using self-supervised learning tasks to maintain the characteristics of each channel when fusing the information, which has been proven to be a very promising work. However, how to deeply dig out the relationship between different channels and make full use of it while maintaining the uniqueness of each channel is a problem that has not been well studied and resolved in this field. Under such circumstances, this paper explores and verifies the deficiency of directly constructing contrastive learning tasks on different channels with practical experiments and proposes the scheme of interactive modeling and matching representation across different channels. This is the first attempt in the field of recommender systems, we believe the insight of this paper is inspirational to future self-supervised learning research based on multi-channel information. To solve this problem, we propose a cross-channel matching representation model based on attentive interaction, which realizes efficient modeling of the relationship between cross-channel information. Based on this, we also proposed a hierarchical self-supervised learning model, which realized two levels of self-supervised learning within and between channels and improved the ability of self-supervised tasks to autonomously mine different levels of potential information. We have conducted abundant experiments, and many experimental metrics on multiple public data sets show that the method proposed in this paper has a significant improvement compared with the state-of-the-art methods, no matter in the general or cold-start scenario. And in the experiment of model variant analysis, the benefits of the cross-channel matching representation model and the hierarchical self-supervised model proposed in this paper are also fully verified.
Empirical software product line engineering

The adoption of Software Product Line Engineering (SPLE) is usually only based on its theoretical benefits instead of empirical evidences. In fact, there is no work that synthesizes the empirical studies on SPLE. This makes it difficult for researchers to base their contributions on previous works validated with an empirical strategy. The objective of this work is to discover and summarize the studies that have used empirical evidences in SPLE limited to those ones with the intervention of humans. This will allow evaluating the quality and knowing the scope of these studies over time. Doing so, research opportunities can arise. Analyzing the authors and institutions that investigate SPLE supported by empirical studies will also help to know which institutions have knowledge of the subject, leading to detect and encourage collaboration among researches. A systematic literature review was conducted with the focus on those studies in which there is human intervention and were published between 2000 and 2018 (the systematic literature review was developed in 2019). We considered peer-reviewed papers from journals and top software engineering conferences. Out of a total of 1880 studies in the initial set, a total of 62 primary studies were selected after applying a series of inclusion and exclusion criteria. We found that, approximately 56% of the studies used the empirical case study strategy while the rest used experimental strategies. Around 86% of the case studies were performed in an industrial environment showing the penetration of SPLE in industry while 81% of the experiments were conducted in an academic environment. Around 95.16% of the studies address aspects related to domain engineering while application engineering received less attention. Most of the experiments and case study evaluated showed an acceptable level of quality. The first study found dates from 2005 and since 2008, the interest in the empirical SPLE has increased.
Graph Neural Network Learning in Large Graphs - A Critical Review

Graph Neural Networks have been extensively used to learn non-Euclidian structures like graphs. There have been several attempts to improve the training efficiency and to reduce the learning complexity in modelling of large graph datasets. In this paper we have reviewed the approaches which perform convolutions to model large graphs for classification and prediction. We have critically analysed each of these approaches and veracity of their claims of reduced complexity and have reported their shortcomings. We have further analysed the approaches from graph-dataset perspective.
Neuro-Fuzzy: Artificial Neural Networks & Fuzzy Logic

Neuro Fuzzy is a hybrid system that combines Artificial Neural Networks with Fuzzy Logic. Provides a great deal of freedom when it comes to thinking. This phrase, on the other hand, is frequently used to describe a system that combines both approaches. There are two basic streams of neural network and fuzzy system study. Modelling several elements of the human brain (structure, reasoning, learning, perception, and so on) as well as artificial systems and data: pattern clustering and recognition, function approximation, system parameter estimate, and so on. In general, neural networks and fuzzy logic systems are parameterized nonlinear computing methods for numerical data processing (signals, images, stimuli). These algorithms can be integrated into dedicated hardware or implemented on a general-purpose computer. The network system acquires knowledge through a learning process. Internal parameters are used to store the learned information (weights).
Successful Integration of Virtual Worlds in Learning Environments: A Case Study of a Supportive Learning Ecosystem

Background: Using virtual worlds with students and faculty in Initial Teacher Education (ITE) can provide exciting opportunities to develop innovative pedagogies. This work can begin in the ITE courses and filter into K-12 classrooms. Identifying the best practices is of the utmost importance in facilitating effective teaching and learning. The present case study seeks to highlight the elements that influence the successful integration of virtual worlds in learning environments. Methods: This case study was part of a more extensive participatory action research project (PAR). In that four-year project (2010-2014), data were gathered from over 1500 ITE students and six faculty at a regional university in Australia. The inductive analysis of the data gave rise to several case studies, one of which is presented in this paper. The data sets were obtained from virtual world interventions, surveys and interviews. Results: It was found that virtual worlds can support teachers in developing innovative pedagogical approaches in classrooms. However, resistance from one or more elements in the learning ecosystem can hamper the integration of virtual worlds into the educational landscape. These elements range from learners to technology systems. The inductive analysis identified the variables that influence the adoption of virtual worlds and facilitate innovative pedagogies. Conclusion: This paper delineates various elements of a learning ecosystem and their importance in the adoption of a sustainable virtual world, regardless of the learning application used. A specific case study is discussed here, since it best demonstrates the value of a supportive learning ecosystem and the practical ways of utilizing its elements to support future integration of virtual worlds. Suggestions are also made for future implementations in any faculty across a university.
2020 Strategic priorities

QUT Library's 2020 Strategic Priorities contribute to and align with the QUT Blueprint, the QUT Real World Learning 2020 Vision and the QUT Research and Innovation Strategy.
CCS Library: MFA: Color & Materials Design: Professional Information

This guide is designed to steer Color & Materials Design students to Library resources that pertain specifically to their concentrations.
PEDAGOGICAL SYSTEM OF RESPONSIBILITY FORMATION IN PRIMARY SCHOOL STUDENTS BASED ON NATIONAL STAFF

This paper examines the process of forming responsibility among junior schoolchildren, and is also determined by the increasing level of maladjusted junior schoolchildren who are not adapted to traditional learning in educational organizations, who now come to their first classes in primary school conditions in our modern country.
LibGuides: WSU System Strategic Planning Resources: Strategic Planning

A collection of materials created and curated by the Environmental Scanning subcommittee of the WSU System Strategic Planners. These resources are organized into the six topical areas for the assumed trends outlined in the WSU System Strategic Plan . This collection of resources is designed to XYZ and was created by the Environmental Scanning Work Group.
No Formal System Containing Sets Arithmetic and Relations Between the Rational Numbers is Consistent

We create two contradictory arguments that can be developed in any formal system containing sets, arithmetic, and relations between the rational numbers. Such a formal system is inconsistent.
Editorial September 2021

LiteracyVolume 55, Issue 3 p. 147-148 Editorial Editorial September 2021 Diane R. Collier, Corresponding Author Diane R. Collier dcollier@brocku.ca Corresponding author: Diane R. Collier, Faculty of Education, Brock University, St. Catharines, Canada. email: dcollier@brocku.caSearch for more papers by this authorNatalia Kucirkova, Natalia KucirkovaSearch for more papers by this author Diane R. Collier, Corresponding Author Diane R. Collier dcollier@brocku.ca Corresponding author: Diane R. Collier, Faculty of Education, Brock University, St. Catharines, Canada. email: dcollier@brocku.caSearch for more papers by this authorNatalia Kucirkova, Natalia KucirkovaSearch for more papers by this author First published: 02 September 2021 https://doi.org/10.1111/lit.12260Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume55, Issue3September 2021Pages 147-148 RelatedInformation
Special Issue on Interactive Visual Analytics for Making Explainable and Accountable Decisions

research-article Share on Special Issue on Interactive Visual Analytics for Making Explainable and Accountable Decisions Authors: Cagatay Turkay University of Warwick, Coventry, UK University of Warwick, Coventry, UKView Profile , Tatiana Von Landesberger University of Cologne and University of Rostock, Cologne, Germany University of Cologne and University of Rostock, Cologne, GermanyView Profile , Daniel Archambault Swansea University, Swansea, Wales, UK Swansea University, Swansea, Wales, UKView Profile , Shixia Liu Tsinghua University, Beijing, People’s Republic of China Tsinghua University, Beijing, People’s Republic of ChinaView Profile , Remco Chang Tufts University, Medford, USA Tufts University, Medford, USAView Profile Authors Info & Claims ACM Transactions on Interactive Intelligent SystemsVolume 11Issue 3-4December 2021 Article No.: 17pp 1–4https://doi.org/10.1145/3471903Online:03 September 2021Publication History 0citation187DownloadsMetricsTotal Citations0Total Downloads187Last 12 Months187Last 6 weeks20 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access