Reviewerly/exHarmony huggingface dataset:
PERBEDAAN TINGKAT PENGETAHUAN TENTANG CARA PENYIMPANAN OBAT SEBELUM DAN SESUDAH PEMBERIAN VIDEO PADA SISWA SMAN 1 BEBER CIREBON

The drug storage stage is part of drug management which is very important in maintaining the quality of medicines. This study aims to determine the difference in the level of knowledge about how to store drugs before and after video administration.This research was conducted by comparing the results of the pre-test and post-test of knowledge levels between classes and between genders before and after the video was given. Requirements; The questions that will be used are tested first using the validity and reliability test, the data is analyzed using the median test.The analysis of the median test data showed that there was no difference in the level of knowledge between the results of the pre-test and post-test but although there was no difference, the post-test mean value was > from the pre-test mean. The results of the pre-test for the IPA class were better than the results for the post-test, but for the IPS class the results for the pre-test were smaller than the results for the post-test. And between the two classes the average value of the IPA class is > that of the IPS class. the results of the male pre-test were smaller than the post-test results, and the female pre-test results were smaller than the post-test results. And between the two sexes the average value of male students is < than female students.
A Mixture of Experts in Associative Generalization

Author(s): Lee, Jessica; Lovibond, Peter; Hayes, Brett; Lewandowsky, Stephan | Abstract: After learning that one stimulus predicts an outcome (e.g., an aqua-colored rectangle leads to shock) and a very similar stimulus predicts no outcome (e.g., a slightly greener rectangle leads to no shock), some participants generalize the predictive relationship on the basis of physical similarity to the predictive stimulus, while others generalize on the basis of the relational difference between the two stimuli (e.g., “higher likelihood of shock for bluer stimuli”). To date, these individual differences in generalization rules have remained unexplored in associative learning. Here, we present evidence that a given individual simultaneously entertains belief in both “similarity” and “relational” rules, and generalizes using a mixture of these strategies. Using a “mixture of experts” modelling framework constrained by participants self-reported rule beliefs, we show that considering multiple rules predicts generalization gradients better than a single rule, and that generalization behavior is better described as switching between, rather than averaging over, different rules.
ENSINO REMOTO E SUAS INOVAÇÕES NA PANDEMIA DA COVID-19

The COVID-19 pandemic will always be remembered as one of the greatest challenges facing humanity.In addition to the aspects related to illness and loss of life and the negative impact on the world economy, it brought the need for rapid adjustments to the educational model of school organizations in order to allow the minimum normality of school years and maintenance of teaching and learning.This article presents the experience of the Miguel Pereira Faculty (FAMIPE), which contingently migrated its proposed model of face-to-face training to Law and Public Management courses, to the remote classes model, adopting digital mediation through the Zoom Platform.Developed from bibliographic research, and from a field research, through a survey that evaluated the students' perception of the Zoom Platform, adopted as electronic mediation for remote classes during the pandemic.At the end, the suitability for the proposed use and good usability by the students were concluded.
CREATING INCLUSIVE CLASSROOMS: INTERGROUP DIALOGUE IN THE AGE OF COVID-19

Appears in: EDULEARN21 Proceedings Publication year: 2021Page: 2644 (abstract only)ISBN: 978-84-09-31267-2ISSN: 2340-1117doi: 10.21125/edulearn.2021.0576Conference name: 13th International Conference on Education and New Learning TechnologiesDates: 5-6 July, 2021Location: Online Conference
Developing a Multicampus Model for REU Sites

Her research and teaching interests revolve primarily around microorganisms in engineered environmental systems, including biological wastewater treatment
한국어 시간경과 표현의 인지 시점 연구 - 현저성을 중심으로 -

This study aims to explain the difference in how time is calculated for the lapse between an expression and its response. This study clarifies that the time lapse can be calculated according to the start time, the end time, or both, depending on the verb used in the same sentence. This is due to salience in cognitive linguistics, which is first recognized when the object is visible and specific. Either the starting or ending point is recognized from the meaning of the verb: if the starting point is identified, then the time is calculated around that point; if the telicity is identified, then the time is calculated around the ending point.
Movie Success Prediction Using Recommendation Techniques

A film advice is vital in our social lifestyles due to its electricity in presenting more suitable leisure. One of these gadget can recommend a hard and fast of films to customers founded totally on their hobby, or the popularities of the films. Even though, a set of movie advice systems were proposed most of these both can’t suggest a film to the existing customers effectively or to a new consumer. On this paper we advocate a film advice network that has capability to suggest films to a recent consumer also the others. It mines film databases to that have to gather all of the important information, which include reputation and attractiveness, required for recommendation. It generates film swarms no longer handiest handy for film producer to plot a new brand film but also useful for movie recommendation Experimental research on the real statistics displays the performance and effectiveness of the proposed machine.
Design and Application of Detection Damage Hardware to Your Computer with Certainty Factor

In this study, an expert system is used to detect symptoms of damage to computer hardware. With the application of certainty factor models, it is expected to be able to detect symptoms of damage to computer hardware starting with the symptoms of damage from the computer along with solutions for handling the damage. This study aims to produce a fault diagnosis application on web-based computer hardware. This application is expected to make it easier for users to find solutions regarding experienced hardware problems. This expert system application is made using the certainty factor method so that the resulting diagnosis will display symptoms that have more certainty factors. This application was designed using UML and the PHP programming language with black-box testing. The study was conducted by collecting data through literature study, browsing, and interviews. The research method used is the waterfall method. The time of the research is from September 2019 to February 2020. This research is in an application to diagnose damage to computer hardware. Computer Hardware Damage Detection Application can be a guide for computer users in overcoming the damage to hardware with certainty factor methods and can help users in caring for computer hardware so it is not easily damaged.
Optimasi K-Means dengan Algoritma Genetika untuk Target Pemamfaat Air Bersih Provinsi Riau

Clean water is an important thing in human life. Several actions have been taken by the government to meet the clean water needs of the Riau province. One of them is the Community Based Drinking Water and Sanitation Provision program. Before carrying out activities related to the provision of clean water to the community, the targets to be achieved for the provision of clean water in the future will be determined. This study aims to klaster clean water beneficiary targets using the k-means algorithm with an optimization of the centroid value using a genetic algorithm. Average silhouette number is used to get the optimal number of klasters, which is two klasters. The results of klaster validity were measured using the Davies Bouldin Index (DBI) method where klasterization without optimization resulted in a DBI of 2.164763 and the results of klasterization by carrying out genetic optimization on the centroid value resulted in a DBI value of 2.06894.
Peer Review Jurnal Nasional Terakreditasi Sinta 2, Engaging to Nurturing

The use of English language for very young learners in kindergarten has become fundamental in recent global teaching development, including in Aceh, Indonesia. Therefore, this qualitative research is aimed to discover the English language teaching strategies applied for very young Muslim learners’ at Kiddos English School (KES) Kindergarten, Banda Aceh. The research participants were selected by using non-probability sampling method. Furthermore, semi-structured interview towards four English teachers and participant observation was conducted in two classes of KES. The result of this study showed that KES teachers implemented at least seven strategies to teach English for their students from engaging learners in daily oral language activity, to establishing a nurturing environment, playing games, storytelling, audio-visual, singing, and teaching face to face. Furthermore, the teachers faced four constraints in applying those strategies, which are: students’ misbehavior, inconsistency of students’ attendance, lack of parental involvement, and insufficient number of teacher.
Research Guides: Visualize your Data: Making an Effective Data Visualization

Learn how to create effective data visualizations. Examples of good and bad data viz, and how you can make it effective
Strong subalgebras and the Constraint Satisfaction Problem.

In 2007 it was conjectured that the Constraint Satisfaction Problem (CSP) over a constraint language $\Gamma$ is tractable if and only if $\Gamma$ is preserved by a weak near-unanimity (WNU) operation. After many efforts and partial results, this conjecture was independently proved by Andrei Bulatov and the author in 2017. In this paper we consider one of two main ingredients of my proof, that is, strong subalgebras that allow us to reduce domains of the variables iteratively. To explain how this idea works we show the algebraic properties of strong subalgebras and provide self-contained proof of two important facts about the complexity of the CSP. First, we prove that if a constraint language is not preserved by a WNU operation then the corresponding CSP is NP-hard. Second, we characterize all constraint languages that can be solved by local consistency checking. Additionally, we characterize all idempotent algebras not having a WNU term of a concrete arity $n$, not having a WNU term, having WNU terms of all arities greater than 2. Most of the results presented in the paper are not new, but I believe this paper can help to understand my approach to CSP and the new self-contained proof of known facts will be also useful.
Deep Learning for Analyzing the Data on Object Detection and Recognition

The new era ushered in a steep rise in the domain of computer vision science. Computer vision faced a rudimentary problem of detection and recognition of objects in visual recognition. This has been thoroughly analyzed for the past nearly 10 years. The stated object detection and recognition is the process to classify as well as localize objects for detecting such objects again. The goal of Visual Object Detection (VOD) is to precisely find objects in such target classes with a position in the provided image, as well as to attach an appropriate class mark to each instance of the object. Due to the enormous achievements in the Deep Learning processing of images and detection of objects, Deep Learning object recognition approaches are recently required in an active way for a few years. The commonly used technologies for object detection and recognition are human-computer interaction, video monitoring, satellite imaging, transport system, and movement recognition. Considering the larger family of architectural setups of Deep Learning, a Convolutional Neural Network (CNN), composed of a series of layers within the network of neurons, is utilized for visual imagery. Architectural setups of CNNs of the Deep Learning type produce promising outcomes for the identification of digital image artifacts. Deep Learning, especially Deep CNNs, in recent times has received growing interest in object detection and recognition. Deep Learning stretched the feasible boundaries in the field of Digital Image Processing. This chapter provides a comprehensive overview of recently occurring advances in object detection and object recognition using CNNs. This examines the object detection’s characteristics and recognition models, the available benchmark datasets, and the research that took place on the implementation of object detection and recognition models for different applications.
Biting the Social Media Bullet: Lessons from the COVID19 Pandemic in Social Media Marketing for Librarians

Presentation presented online via Zoom at the event titled The future of Librarians amidst Covid-19: aligning librarianship with virtual spaces organized by the University of Fort Hare.
LibGuides: Business Management: Useful websites

This Libguide is designed to share resources that can be used by School of Economics and Management Sciences students.
Implementasi Data Mining Clustering Tingkat Kepuasan Konsumen Terhadap Pelayanan Go-Jek

Increasingly high demands for mobility in today's society, activities are also increasingly crowded, especially parents, employees, and even students so it is increasingly difficult to find free time to meet the needs of daily life. So that people need something that can answer and be a solution to the complaint without having to drain time and energy with results that do not disappoint. Gojek is a solution to the complaints of people who do not have much free time and want to relax while waiting for their needs to be met, Gojek is an online application that can be downloaded via a smartphone, has more than six services provided therein but the author only takes some of the services to be standard the level of community satisfaction with Gojek services. The purpose of this study was to determine the level of community satisfaction with Gojek services. One method contained in Data Mining used in this study is the Clustering method. To find out the level of community satisfaction done with interviews / questionnaires 120 people in the city of Pematangsiantar. The benefits are to make it easier for Gojek companies to know how the quality of services provided to the community is based on the level of community satisfaction and improve the quality of services provided to the community.
Research Guides: Text and Data Mining: Getting started

A brief guide to tools and resources (including datasets) for getting started with computational approaches to textual analysis.
EFEKTIVITAS PELAKSANAAN PROGRAM GERAKAN LITERASI SEKOLAH MELALUI READING CHALLENGE DI SMA PLUS AL-GHIFARI BANDUNG

This study aims to describe the effectiveness of the implementation of the School Literacy Movement program through the Reading Challenge by using general effectiveness theory according to Campbell JP (1989) which consists of program success, target success, program satisfaction, input and output levels, and goal achievement. thorough. In this study, the approach used by researchers is a qualitative approach. Data collection techniques are collected through interviews and literature studies. The results of this study show that the implementation of the School Literacy Movement (GLS) program at Al-Ghifari Plus High School is sufficient to achieve program success with the participation of students. In this program, although in general, not all students take part in this activity because it is not required, the obstacles faced in implementing the Reading Challenge program at Al-Ghifari High School are students having difficulties in describing it in a fishbone diagram because of the large number of school subject assignments.
A Task-Oriented Dialogue Architecture via Transformer Neural Language Models and Symbolic Injection

Recently, transformer language models have been applied to build both task- and non-task-oriented dialogue systems. Although transformers perform well on most of the NLP tasks, they perform poorly on context retrieval and symbolic reasoning. Our work aims to address this limitation by embedding the model in an operational loop that blends both natural language generation and symbolic injection. We evaluated our system on the multi-domain DSTC8 data set and reported joint goal accuracy of 75.8% (ranked among the first half positions), intent accuracy of 97.4% (which is higher than the reported literature), and a 15% improvement for success rate compared to a baseline with no symbolic injection. These promising results suggest that transformer language models can not only generate proper system responses but also symbolic representations that can further be used to enhance the overall quality of the dialogue management as well as serving as scaffolding for complex conversational reasoning.
Water Security Issues in Inhabited Islands: A Survey on Domestic Water Resources Management in the Sebatik Island, Sabah (Malaysia)

Malaysia is a country rich in natural water resources. The average rainfall annually is high at 2940 mm. High rainfall is essential for surface water (rivers, lakes, ponds) and groundwater supply systems in enhancing the ecological system and natural resources. However, the abundant supply of natural water resources cannot guarantee the existence of water security issues, especially in inhabited island areas such as in the Sebatik island. The definition of water security itself is the ability for each individual to access an adequate supply of clean water at an affordable price to live a productive, clean and healthy life without neglecting the wellbeing of the inhabitants and safeguard the environment. Ironically, it is unachievable if the domestic water supply management in one area is in inadequate control systems and lacks its domestic supply system.  In other words, it is still inefficient, unsystematic and not holistic. This will then disrupt the stability of water supply resources in terms of quantity, quality, and accessibility. Therefore, this study aims to examine the potential sources of water supply systems in Pulau Sebatik and discuss how they are managed and controlled for domestic use. Therefore, to answer the objectives outlined, observation, evaluation and interview methods were carried out accordingly. It is found that three main sources of domestic water supply can potentially be developed and should be improved in terms of quality, quantity, and accessibility, namely groundwater, rainwater and surface water. In essence, the water security issue that exists in Pulau Sebatik is not entirely due to the lack of natural water supply resources but instead, due to unsystematic, inefficient, and incomprehensive management systems, methods, and structures.
Intrusion Detection Algorithm Based on SDA-ELM

A deep learning intrusion detection algorithm based on stacked denoising autoencoder and extreme learning machine (SDA-ELM) is proposed to solve the problem that the traditional machine learning algorithm can’t cope with the classification of multisource heterogeneous network intrusion data. The algorithm use Dropout regularization to improve the SDA deep learning model, and the integration features of low dimensionality and high robustness are extracted. Then the model use ELM to carry out a supervised learning for the low dimension data to recognize the network attack. The algorithm combines the abstract feature extraction capability of SDA and the fast learning ability of ELM. The experimental results show that the SDA-ELM algorithm improves the accuracy of classification and the detection rate of small sample attacks, reduces the false alarm rate.
LibGuides: Resource Sharing/ILL at University of Richmond Libraries : Home

This guide summarizes uses, updates, and guidance for Resource Sharing/ILL at Boatwright Library and Parsons Music Library at the University of Richmond.
Environmental Consumption, Waste Recycling, and Academic Performance among Selected College Students during the COVID-19 Pandemic

The environment is suffering so much, and yet humanity is still adamant about saving it. This study determines the awareness of environmental consumption, waste recycling, and its relationship to the academic performance of selected college students during the pandemic period of COVID-19. Using a descriptive research design, the researcher used an online survey method to collect the data for the study. With the use of the convenience sampling technique, 192 college students from a local city college responded to the online survey. As for the instrument, the study adopted and modified an existing questionnaire and subjected it to reliability and validity tests, which yielded an acceptable result. Using SPSS 22, results show that college students “often” observe different environmental consumption and waste recycling schemes in school. Their academic performance also is good based on their grade point average from their previous semester. Also, the study showed a low positive relationship between environmental consumption, waste recycling, and academic performance. The study concluded that college students still practice appropriate environmental consumption and waste recycling, even during the COVID-19 pandemic. Based on the results, the researcher provided some implications for the learning system.
Ethics, Diversity and Consciousness in AI [President's Message]

Ethics in Artificial Intelligence (AI) is discussed everywhere, in governmental circles as well as scientific forums. It is very often associated with the two widely used concepts of diversity and eXplainable Artificial Intelligence (XAI). The latter was promoted by DARPA, and it is expected to propose methods preserving the rights of users to understand how the AI systems work and why decisions are made. Computational Intelligence methods have much to contribute to this effort to ensure that AI systems are ethical. In July 2020, the European Union has published an Assessment List for Trustworthy Artificial Intelligence (ALTAI) covering a wide range of ethical issues. The Computational Intelligence society (CIS) is at the heart of all these aspects of ethics in AI. They represent fascinating challenges for researchers and they can highlight the power of all Computational Intelligence methods: neural networks, learning methods, fuzzy systems and evolutionary computation. Efforts are already done through the IEEE CIS Task Force on Ethical and Social Implications of Computational Intelligence. Several other CIS Technical Committee task forces and special issues of CIS publications are also focused on XAI. In particular, a special issue of the IEEE Computational Intelligence Magazine dedicated to Explainable and Trustworthy Artificial Intelligence will be published in 2022. The IEEE CIS Cognitive and Developmental Systems Technical Committee and the IEEE Transactions on Cognitive and Developmental Systems also address some of these concerns on a cognitive basis. Moreover, the IEEE CIS is the oversight committee for the IEEE Consortium On The Landscape of AI Safety (CLAIS) and, on a more specific level, it participates in the IEEE Brain Community. I am convinced that the IEEE CIS needs to pay even more attention to these crucial issues.
LibGuides: Using EndNote: Organizing References

This guide will help you become familiar with the reference management software EndNote Web