Reviewerly/exHarmony huggingface dataset:
Medical Record Storage System Analysis On The Effectiveness Of Patient Services

This study aims to determine and analyze the medical record storage system on the effectiveness of patient care at the Cikole Public Health Center. The research method used is quantitative with inferential statistical analysis techniques. Data collection techniques by means of observation, interviews and questionnaires. Cikole Lembang Public Health Center is one of the first level health care facilities. Based on observations, the storage system used is a regional storage system in which each region starts from the first digit. There are 2 officers who work in the medical records section of the Cikole Public Health Center. With the storage system used, there are several shortcomings, namely the occurrence of misfiles and duplication. To find out whether there is an analysis of the medical record storage system on the effectiveness of the service, the author gives a questionnaire to the Cikole Public Health Center officers. The results of the questionnaire also show that the highest percentage level is found in item no.7, namely the decentralized storage system is in accordance with the target of 71%. Then the second is item no. 8, namely a decentralized storage system that allows the provision of medical record files on time by 61%. From the results of interviews with medical records officers, suggestions and input were given: (1). The storage system used was changed to centralization so that the alignment system was not duplicated; (2). The use of bindex as an additional tool to make storage neater and easier.
THE COVID-19 PANDEMIC ERA: THE EFFECTIVENESS OF GOOGLE CLASSROOM MEDIA IN DISCRETE MATHEMATICS LEARNING IN TERMS OF STUDENT LEARNING OUTCOMES

One of the learning media in the network that is often used in addressing various learning problems during the Covid-19 pandemic is the use of google classroom media. The purpose of this study is to find out the effectiveness of google classroom media in discrete mathematics learning in terms of learning outcomes. The type of research conducted is quasi experiment research to see the consequences of a treatment by comparing the learning outcomes of two class groups (independent class) or two time groups with the same class (dependent class). The research design is applied using one group pretest-posttest design. The samples in this study were all the fourth semester class A students who took discrete mathematics courses at the S1 level of the Mathematics Education Study Program of the Faculty of Education, Timor University which consisted of 40 students. Sample data was processed with descriptive analysis and average difference test using paired sample t test and N-Gain Score. The research results showed that learning outcomes by using google classroom media is quite effective based the students’ test results. The students’ learning outcomes after learning by applying google classroom media were higher than students’ learning outcomes before learning by applying google classroom media.
The Virtual is Material: Music Improvisation in Post-Digital Ecologies

Link to performance: https://vimeo.com/640946914/cf858052e1This performance-lecture was originally presented at the DRHA (Digital Research in the Humanities and Arts) conference in 2021 in Berlin. 3BP (Paul Stapleton, Adam Pultz Melbye and John Bowers) presents three views on the creation of an online performance ecology that allows the trio to improvise together, despite living in three separate locations. Rather than trying to overcome the instabilities and artefacts introduced by the fluctuations in data transfer, 3BP describe how such properties become native to the trios understanding of its own practice, affording new areas of creative exploration and consideration. The trio draws on Karen Barad’s use of terms such as diffraction and apparatus to discuss how music-making and improvisation embedded in run-away technologies affords emergent behaviour that transcends reflection to allow for diverse and unstable non-linear performances.
A Systematic Review of Predicting Elections Based on Social Media Data: Research Challenges and Future Directions

Abstract: The way politicians communicate with the electorateand run electoral campaigns was reshaped by the emergence and popularization of contemporary social media (SM), such as Facebook, Twitter, and Instagram social networks (SN). Due to inherent capabilities of SM, such as the large amount of available data accessed in real time, a new research subject has emerged, focusing on using SM data to predict election outcomes. Despite many studies conducted in the last decade, results are very controversial, and many times challenged. In this context, this work aims to investigate and summarize how research on predicting elections based on SM data has evolved since its beginning, to outline the state of both the art and the practice,and to identify research opportunities within this field. In termsof method, we performed a systematic literature review analyzingthe quantity and quality of publications, the electoral context of studies, the main approaches to and characteristics of the successful studies, as well as their main strengths and challenges, and compared our results with previous reviews. We identified and analyzed 83 relevant studies, and the challenges were identified in many areas such as process, sampling, modeling, performance evaluation and scientific rigor. Main findings include the low success of the most-used approach, namely volume and sentiment analysis on Twitter, and the better results with new approaches, such as regression methods trained with traditional polls. Finally, a vision of future research on integrating advances on process definitions, modeling, and evaluation is also discussed, pointing out, among others, the need for better investigating the application of state-of-art machine learning approaches. Index Terms: Elections, Social Media, Social Networks, Machine Learning, Systematic Review
High-Capacity and High-Quality Reversible Data Hiding Method Using Recurrent Round-Trip Embedding Strategy in the Quotient Image

In previous research, scholars always think about how to improve the information hiding algorithm and strive to have the largest embedding capacity and better image quality, restoring the original image. This research mainly proposes a new robust and reversible information hiding method, recurrent robust reversible data hiding (triple-RDH), with a recurrent round-trip embedding strategy. We embed the secret message in a quotient image to increase the image robustness. The pixel value is split into two parts, HiSB and LoSB. A recurrent round-trip embedding strategy (referred to as double R-TES) is designed to adjust the predictor and the recursive parameter values, so the pixel value carrying the secret data bits can be first shifted to the right and then shifted to the left, resulting in pixel invariance, so the embedding capacity can be effectively increased repeatedly. Experimental results show that the proposed triple-RDH method can effectively increase the embedding capacity up to 310,732 bits and maintain a certain level of image quality. Compared with the existing pixel error expansion (PEE) methods, the triple-RDH method not only has a high capacity but also has robustness for image processing against unintentional attacks. It can also be used for capacity and image quality according to the needs of the application, performing adjustable embedding.
Cooperative learning: on the issue of national and international application experience

The article presents a comparative analysis of the method of collective mutual learning and the Singapore educational technology. Organizational, methodological and technological features of learning in replaceable pairs are considered. Examples of horizontal and vertical methods of interaction between students are given. An analysis of Singapore EdTech has been made which has a set of structures that make it possible to organize the classroom in a different way. In the conclusion it is proved that both presented methods with different intensities can be integrated into the traditional teaching system, bringing into it a different structure of interpersonal communication of participants in foreign language classes.
Time Line Correlative Spectral Processing for Stratification of Blood Pressure using Adaptive Signal Conditioning

Stratification of Blood Pressure is essential input in most of the cardiovascular diseases detection and prediction and is also a great aid to medical practitioners in dealing with Hypertension.Denoising based on spectral coding is developed based on frequency spectral decomposition and a spectral correlative approach based on wavelet transform.The existing approaches perform a standard deviation and mean of peak correlation in signal conditioning.The artifact filtrations were developed based on thresholding.Filtration of coefficients has an impact on accuracy of estimation and hence proper signal conditioning is a primal need.Wherein threshold is measured with discrete monitoring, time line observation could improve the accuracy of filtration efficiency under varying interference condition.Dynamic interference due to capturing or processing source results in jitter type noises which are short period deviations with varying frequency component.Hence a timefrequency analysis for filtration is adapted for filtration.This paper presents an approach of spectral correlation approach for signal condition in stratification of blood pressure under cuff less monitoring.This presented approach operates on the spectral distribution of finer resolution bands for monitoring signal in denoising and decision making.Existing approaches lacks the capability of loss-less denoising which is efficiently worked out in this paper.
Deep Reinforcement Learning for Dynamic Things of Interest Recommendation in Intelligent Ambient Environment

Recommender Systems for the IoT (RSIoT) aim for interactive item recommendations. Most existing methods focus on user feedback and have limitations in dealing with dynamic environments. Deep Reinforcement Learning (DRL) can deal with dynamic environments and conduct updates without waiting for user feedback. In this study, we design a Reminder Care System (RCS) to harness the advantages of deep reinforcement learning in addressing two main issues of RSIoT: capturing dynamicity patterns of human activities and system update without a focus on user feedback. The RCS is formulated based on a Deep Q-Network (DQN), which works well with the dynamic nature of human activities. We further consider harvesting the feedback automatically in the back end without requiring users to explicitly label activities. Experiments are conducted on three public datasets and have demonstrated the performance of our proposed system.
The Impact of Electronic Crimes On the Risks of Banking Financial Services in Light of the Increasing Use of Banking Information Technology and Communications

This paper is aims to explain the risks of modern electronic financial and banking services. These services include as credit and debit cards in addition to internet and mobile banking which although had increased Information Communication Technology (ICT henceforth) in finance and banking as well as time, money and effort savings. They created new threats and challenges of cybercrimes including card and email fraud and fishing and hacking. According to the results, cybercrime is reducible in terms of finance by strengthening cybersecurity aspects, improving employee training programs, educating the community, enacting new laws against cybercrime using valid international laws on the basis of relevant information and databases.
LibGuides: Professional Development for Organizational Effectiveness at Foley Library: Free Remote ProvDev Options

This guide was developed by the Organizational Effectiveness Committee to provide ongoing access to professional development opportunities for the Foley Library's staff and faculty members. This page covers free professional online development opportunities you can access from your work-from-home situation.
SMAD

Ad retrieval in sponsored search aims to understand user search intentions (user queries) and retrieves a set of ads inferred as being relevant to the queries. Due to the huge amount of search traffic and multiple views of relevance (such as co-clicking, co-bidding or textual similar), it is highly desirable but remain challenging to achieve a large-scale, multi-view matching between queries and ads, particularly in industrial settings. In this paper, we propose a scalable multi-view ad retrieval engine SMAD that we developed and deployed at Taobao, the largest e-commerce platform in China. We construct a multi-relation query-item-ad graph capturing different views of query-ad relevance, which is of large scale and with complex structure. Since in e-commerce platform, the queries and products are organized into a category tree, to deal with the large scale of the graph, we propose a category constrained graph sampling and partition method to enable distributed parallel offline training. To tackle the complex multi-view structure, we propose a multi-view parallel deep neural network (DNN) model to combine the information from different views in a principled way. According to offline experiments and online A/B tests, our framework significantly outperforms baselines in terms of relevance, coverage, and revenue.
Integrating physics modelling with machine learning for remote sensing

Earth observation through satellite sensors, models and in situ measurements provides a way to monitor our planet with unprecedented spatial and temporal resolution.The amount and diversity of the data which is recorded and made available is ever-increasing. This data allows us to perform crop yield prediction, track land-use change such as deforestation, monitor and respond to natural disasters and predict and mitigate climate change. The last two decades have seen a large increase in the application of machine learning algorithms in Earth observation in order to make efficient use of the growing data-stream. Machine learning algorithms, however, are typically model agnostic and too flexible and so end up not respecting fundamental laws of physics. On the other hand there has, in recent years, been an increase in research attempting to embed physics knowledge in machine learning algorithms in order to obtain interpretable and physically meaningful solutions. The main objective of this thesis is to explore different ways of encoding physical knowledge to provide machine learning methods tailored for specific problems in remote sensing.Ways of expressing expert knowledge about the relevant physical systems in remote sensing abound, ranging from simple relations between reflectance indices and biophysical parameters to complex models that compute the radiative transfer of electromagnetic radiation through our atmosphere, and differential equations that explain the dynamics of key parameters. This thesis focuses on inversion problems, emulation of radiative transfer models, and incorporation of the above-mentioned domain knowledge in machine learning algorithms for remote sensing applications. We explore new methods that can optimally model simulated and in-situ data jointly, incorporate differential equations in machine learning algorithms, handle more complex inversion problems and large-scale data, obtain accurate and computationally efficient emulators that are consistent with physical models, and that efficiently perform approximate Bayesian inversion over radiative transfer models.
ADAPTAÇÕES DO SISTEMA PRESENCIAL AO REMOTO EM FUNÇÃO DA PANDEMIA COVID-19: a experiência de curso superior em turismo em instituição de ensino superior estadual

ADAPTATIONS OF THE TRADITIONAL SYSTEM OF EDUCATION TO THE ON-LINE LEARNING DUE TO THE COVID-19 PANDEMIC: the experience of a college education course in tourism in a state university RESUMO: A pandemia COVID-19 gerou a necessidade de isolamento social, que trouxe mudancas a varios setores, dentre esses, o processo de migracao do modal presencial ao ensino remoto. Metodologicamente, esta pesquisa configura-se como relato de experiencia, e procurou estabelecer acoes apresentando o aporte das atividades on-line e a adaptacao ao modelo remoto desde a concepcao, desenvolvimento das etapas realizadas pela instituicao, analisar o cenario tecnico e as condicoes para a execucao das atividades. Destaca-se o professor nesse contexto de mudancas e o aporte institucional para levantamento do cenario, assim como dos academicos, visando a equidade nas condicoes de ensino em instituicao publica. Realizou-se a avaliacao do ano letivo 2020 permitindo maior maturidade para as atividades do ano letivo 2021 que seguem ainda em sistema remoto. Palavras-chave: Educacao e turismo; adaptacoes do sistema de ensino; ensino remoto; Universidade Estadual de Ponta Grossa; pandemia COVID-19. ABSTRACT: The current scenery of the pandemic of COVID-19 generated the need for social isolation, bringing changes to many sectors, including the migration process from the traditional system of education to remote learning. Methodologically, this research is configured as an experience report, and defining conceptual issues and identifying tools for adjusting online activities and adapting to the remote model from conception, development of the steps implemented by the institution, analysis of the technical scenario and how conditions for carrying out the activities. The professor, in this context of changes and the institutional contribution to survey the scenario, stand out, as well as the equity in the conditions of teaching in a public institution from qualified academics. The 2020 academic year was evaluated, allowing for greater maturity for the activities of the 2021 academic year, which are still in the remote system. Keywords: Education and tourism; adaptations of the education system; remote teaching; State University of Ponta Grossa; pandemic COVID-19.
GEOGRAPHIC INFORMATION SYSTEM PEMETAAN TRAYEK BUS SEKOLAH DAN HALTE DI CENTRAL BUSINESS DISTRICT (CBD) BANGKINANG

Along with the development of increasingly advanced technology, the working world is required to improve it performance One of the jobs that really supports the development in Bangkinang is facilitating students and educators with the transportation bus or school bus. This bus is usually used to transport students and educators to and from school. This bus always operates in according with the routes. however, the lack of information obtained by the community causes many educator and students not to optimize the facilities provided by the Kampar Regency Transportation Office. To support this, a geographic information system is used tu map school bus routes and these stops and use Google Maps to display the map. Based on that, hoped that the research objectives can be achieved, to helping the community and students in obtaining information about school buses, knowing the routes of each school bus, knowing the location of the bus stop, and hope that the community optimizes existing facilities such as bus stops.he designed system is a web-based system as a medium to provide information to the public. The programming languages used are HTML, PHP, and Mysql as databases which are expected to facilitate admins in managing data efficiently and easier for users to access this School Bus Route and Bus Stop Mapping information system.
Servicio social en línea: estrategia emergente aplicada por la Universidad Autónoma de Chiapas ante la contingencia por covid-19

The Universidad Autonoma de Chiapas, in an effort to provide students with a safe option to continue with their education processes, developed the social service project called Program of attention to educational backwardness in the basic and high school levels in the state of Chiapas, to assist students of the entity in the basic and high school levels with educational backwardness through distance counseling. The objective of this article is to visualize the challenges faced by social service providers when interacting with different actors involved in carrying out this activity and the means used to do so. The methodology consisted of a review of the reports (partial and final) submitted by the students and a questionnaire applied to them. 16 students from different undergraduate programs participated in the project, in an attempt to identify what interested them in the project, their expectations, the problems they faced, access to and use of ICTs. The students mentioned that at the beginning the project was interesting, doing everything from home and the idea of supporting the most vulnerable population seemed exceptional and pertinent. However, they faced inconveniences such as the commitment of the beneficiaries, their availability, the relationships established with teachers and authorities in the receiving instances, the advice received to do their social service, their own activities as students, the availability and use of technology, connectivity in remote areas, among others. In spite of the inconveniences, there is learning that students value, such as culminating this process in spite of the multiple adversities. Through the social service program, students showed interest and skills to contribute to vulnerable sectors of the population, either by providing advice to improve learning or training people who lack technological skills, and also allowed us to visualize a number of advantages and limitations of the modality.
Chapter 4. DRM and Libraries

In chapter 4 of Library Technology Reports (vol. 56, no. 1), “Digital Rights Management and Books,” Roncevic examines the role of DRM in libraries.
THE INFLUENCE OF READING INTEREST AND SELF-EFFICACY ON READING COMPREHENSION OF THE TENTH GRADE STUDENTS AT AS-SHOFA ISLAMIC SENIOR HIGH SCHOOL PEKANBARU

This research was aimed to investigate the influence of reading interest and self-efficacy on reading comprehension of the tenth grade students at As-Shofa Islamic Senior High School Pekanbaru. The data were collected through reading interest questionnaire, self-efficacy questionnaire and reading comprehension test. This research was correlational research design using quantitative method. The subject of the research was the tenth grade students of As-Shofa Islamic Senior High School Pekanbaru consisting 5 classes. The research sample was taken by using proportional stratified random sampling method from 5 classes which each class had different number of students. The total population was 150 students and the total sample was 50 students. Pearson’s product moment and multiple regression formula were used to analyze the data. The research findings showed that first, there was no significant influence of reading interest on students’ reading comprehension with score 0.268 &gt; 0.05. Second, there was significant influence of self-efficacy on students’ reading comprehension with score 0.012 &lt; 0.05. Third, there was significant influence of reading interest and self-efficacy on students’ reading comprehension with score 0.034 &lt; 0.05 at As-Shofa Islamic Senior High School.Keyword : Reading Interest, Self-Efficacy, Reading Comprehension