Reviewerly/exHarmony huggingface dataset:
Optimalisasi Layanan Perpustakaan di Era New Normal

Government policy in implementing the New Normal Era is a hope for people to rise from adversity in facing the Covid-19 pandemic. The enthusiasm and hope in welcoming the new life order provides an opportunity for the community to start carrying out activities that were previously restricted or stopped. The application of new normal is welcomed with a positive attitude in various sectors. In the library education sector welcomed positively with the new order of life that began to be applied gradually. How is the preparation done by the library to open services for users in this New Normal Era? The normal concept in library services must be really prepared, both in terms of human resources and collections. Three important things that must be considered to prepare library services are 1. Services must prioritize the health of librarians and users 2. Design services and facilities in the library 3. Make policy access to the library.
Effect of Firm Characteristics on Capital Structure of Deposit Money Banks Listed on Nigeria Stock

This study sought to ascertain the effect of Firm Characteristics on Capital Structure of Deposit Money Banks (DMBs) listed on Nigeria Stock Exchange using a sample of fifteen (15) DMBs from 2008-2017. Ex-Post Facto research design was employed while secondary data were collected and subjected to multiple regression and correlation analysis in order to achieve the study objectives. A firm characteristic, which is the independent variables, was measured by size and asset tangibility, while capital structure, which is the dependent variable, was measured by Debt-Capital ratio, Debt-Asset ratio and Debt-Common Equity ratio. Results of this study suggested that Firm Characteristics have a significant negative effect on Debt-Capital ratio; Firm Characteristics have a significant positive effect on Debt-Asset ratio; Firm Characteristics have a non-significant positive effect on Debt-Common Equity ratio. This implies that, the two variables have a strong positive effect on the capital structure of DMBs. Overall; the implications of the findings of this study support both the trade-off theory and the pecking order theory of capital structure. This study recommended that, management of DMBs need to embrace innovation as a way of increasing the efficiency of the assets. Increased efficiency of assets is critical to maximizing the profitability of the firms, which consequently reduces the negative impact arising from the cost of debt (financing costs). Additionally, finance managers are advised to adopt a residual dividend policy while at the same time, emphasizing on cost-effectiveness.
Measles and Misrepresentation in Minnesota: Can there be Liability for Anti Vaccine Misinformation that Causes Bodily Harm?

Balancing protecting and compensating victims of harmful fake news and protecting freedom of speech and the information flow is both important and challenging. Vaccines are one area where misinformation can directly cause harm. When misrepresentation leads people to refuse vaccines, disease outbreaks can happen, causing harms, even deaths, and imposing costs on the community. The tort of negligent misrepresentation that causes physical harm appears a custom-made remedy for those affected. However, courts – appropriately – narrowed the tort to protect freedom of speech and the flow of information. This article uses an especially egregious example of anti-vaccine misrepresentation to examine the boundaries of the tort. In 2017, a measles outbreak in Minnesota sickened tens of people, mostly young children of the Somali community in Minneapolis, and hospitalized over twenty young children. The outbreak can be clearly linked to efforts by anti-vaccine groups to target the Somali community and convince its members that the measles, mumps, rubella vaccine (MMR) causes autism – a claim countered by extensive evidence. Using this case, the article examines under what circumstances promoters of misinformation can be held liable for negligent misrepresentation, suggesting a distinction between counseling-like situations and purely public speech, and between types of communications.
Post-Stack Data Enhancement and Noise Removal Combining Different Filtering Methods through Statistical Approach

Summary The existing methods for Post-stack noise removal and data enhancement, in some cases do not give the expected results, and the remaining noise may interfere with structural and stratigraphic interpretation, and with reservoir characterization. To address this problem, we propose a statistical approach that evaluates through Principal Components Analysis (PCA) different post-stack filters, splitting the input data into several components, attempting to identify and remove those associated to noise. This workflow was successfully applied to a 3D PSTM full stack seismic volume with highly dipping structures, affected by acquisition footprints and residual diffraction, showing the expected results and separating the input data into its signal and noise components. The advantage of this workflow is that it can be applied to any kind of stack data (e.g., full and angle stacks, AVO attributes, ...) to remove any kind of noise without jeopardizing the seismic information.
Help-Seeking Behaviors in Research Data Management

Investigations on the help-seeking behavior of academic library patrons have to date primarily focused on the undergraduate experience, most often in the context of reference interactions. This study seeks to explore the help-seeking behaviors of a different audience - faculty in the natural and physical sciences at an R2 land-grant university. Eighteen faculty in the natural and physical sciences at the University of Idaho were individually interviewed using an in-depth qualitative interview format and all transcripts were coded and analyzed using an open transcript data visualization tool created at the University of Idaho Library.&#x0D; Responses revealed that faculty are seeking help from colleagues; peers outside the university, via connections formed in graduate school or professional circles; and through DIY solutions like "just googling it," but less often through university resources and programs. Using the results of this project as a starting point, we will explore how libraries might better understand the help-seeking behavior of research faculty, with an eye towards developing services and sources that better meet faculty research needs.
IMPLEMENTASI METODE K-NEAREST NEIGHBORDALAM PERAMALAN PENJUALAN MOBIL BEKAS DI KOTA BATAM

ABSTRACTThe development of information technology begins to enter the most important part in a field of work, information obtained in a job is general and specific. Computers are one of the results of technological developments that can be used to help all types of work, including transactions in business work, with those in the computer software, a user can perform transaction operations quickly. The use of computers is also needed by a marketing. Car sales of PT. Auto Grace is a company engaged in the sale and purchase of used cars, an area domiciled in the city of Batam. Car sales at PT. Auto Grace is currently still unstable and many cars are not sold and are not maintained due to the lack of consumer interest in buying. So that the company does not experience sales problems, the company leadership must be able to find solutions in a way that is able to predict. So this study aims to predict car sales using the K-NN algorithm. The data set used to predict is taken from sales data from 2017 to 2020. After analysis using the K-NN algorithm, with the help of rapidminer software, the accuracy rate in predicting sales is 63.89% with a precision level of 100%. . The question of take that sale car at year 2021 will experience a decline, due to the outbreak of the Covid-19 virus which has resulted in an economic recession.
Consequences of STC 76/2019, May 22 on privacy and apps to monitorize COVID. The case of Radar COVID

This paper focusses on the consequences for the dialectic between preservation of privacy and the opportunity to take advantage of the possibilities that technology offers us to control epidemics such as COVID STC 76/2019, of May 22 rules a set of accurate guarantees to be taken into consideration in the design of applications that control and surveillance, justified by the entity of the epidemiological crisis, but which in no case should serve to limit or restrict constitutionally guaranteed rights This paper will analyze the case of Radar COVID
Minconvnets: a New Class of Multiplication-Less Neural Networks

In this article, MinConvNets where the multiplications in the forward propagation path of CNNs are approximated by minimum comparator operations are introduced. Hardware complexity of minimum operator is of the order of O(N), whereas for multiplication it is O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). Firstly, a methodology to find approximate operations based on statistical correlation is presented. We show that it is possible to replace multipliers by minimum operations in the forward propagation under certain constraints, i.e. given similar mean and variances of the feature and the weight vectors. A modified training method which guarantees the above constraints is proposed. And it is shown that equivalent precision can be achieved during inference with MinConvNets by using transfer learning from well trained exact CNNs.
Classification of News Articles using Supervised Machine Learning Approach

Today the big challenge for NEWS organization to well organize the news and well categorize the news in automatically no need the data entry people to enter and select the category and then based on the category and its sub-category they will be manually selected and enter the details and then after this the analysis will later on used for different aspects. The news is almost every second used in different sources of media in soft and hard. We use the both sources of the Pakistan News in dual languages English and Urdu both and process them and prepare them for machine learning and based on the Machine learning trained data we build a very effective and efficient model that can predict the title category of the news and category of description of the news. We use different machine learning algorithms and different features extraction finally we build the model using the machine learning algorithm with 89% accuracy with logistic regression.
THE USE OF DOMINO CARDS AS TEACHING MEDIA OF DESCRIPTIVE TEXT AT SMP N 6 WONOGIRI

This study explains how the teachers used domino cards in teaching descriptive text at SMP 6 Wonogiri. This type of research is descriptive qualitative. The subjects of this study were teachers and students of SMP 6 Wonogiri with 2 teachers and 6 students.This study uses interview and observation techniques in data collection. The results of this study indicate that the implementation of teaching descriptive text using domino cards improved students’ vocabulary mastery, student enthusiastic in learning process and fluency in writing descriptive text. However, there were several difficulties faced by the students as expressed by as follows: 1) the students had limited vocabulary and a tenses. 2) The students were unfamiliar with and never used domino cards before. 3) The students found it difficult to understand the instructions given. Although experiencing some difficulties, the students felt happy and enthusiastic about the domino cards as teaching media in learning descriptive texts.
Design of an administration software system for academic events

This work presents the design of a software system for the management and administration of academic events in a higher education institution, which allows handling the information of the events in an efficient way, strengthening existing resources and fostering an organizational culture within the university community.
Assessing the Effectiveness of Sulfur Dioxide and Nitrogen Dioxide Pollutants due to Abadan Oil Refinery on Hospital Areas Using CALPUFF Model in 2018: A Descriptive Study

Assessing the Effectiveness of Sulfur Dioxide and Nitrogen Dioxide Pollutants due to Abadan Oil Refinery on Hospital Areas Using CALPUFF Model in 2018: A Descriptive Study
Reducing the Documents Embeddings Dimension Using Principal Component Analysis

In this paper, a number of issues related to the use of word embeddings for solving the problem of classifying text documents were considered. Models of word embeddings have been a popular object of theoretical research since the mid-1970. The development of these models, including the transition from count based models to predictive ones, was stimulated by the need to reduce the large computational costs arising from their practical use. This problem is still relevant today. When solving practical problems, one often has to abandon the independent construction of word embeddings and use ready-made solutions. As a result, the problem of post-processing of the existing word embeddings, for example, reducing its dimension, becomes urgent. Recently, a number of works have appeared in which an unusual approach to using the principal component analysis to reduce the dimension of word embeddings has been investigated. In this approach, projections are removed not on the last, but on the first principal components. It turns out that in relation to the problems of determining the word similarity and word analogies, this approach can increase the accuracy of their solution. Experimental studies, the results of which are presented in this work, show that this effect is not observed in solving the problem of document classification. Removing projections to the first directions leads to a decrease in the classification accuracy. At the same time, the traditional approach, which removes projections to the last principal directions, in most cases gives the best result.
ONLINE WRITELOGUE EFFECTIVENESS IN THE STUDENTS’ MASTERY OF THE REDUCED RELATIVE CLAUSES

In this pandemic Covid-19 situation, all teaching learning activities are done online from the elementary school up to the universities. What the writers would do is actually implementing a blended learning, but Covid19 makes it impossible so that it is done fully online. Thus, this study would find out that the full-online platform writelogue with its module can improve the students’ ability in using the reduced relative clauses. This module in which the students learn the NP pattern ‘HN (Head Noun) + reduced relative clauses’ is one of a number of NP patterns in the writers’ multi-year research. Using the accessibility of the Learning Manangement System, the writelogue, the students did the pre-test, the exercises and the post-test. Besides, they filled out the questionnaire concerning the usability of the writelogue in terms of language, exercises, tutorial video, and technical operation. The results show that the online writelogue course enhances the pre-test scores of the students in their post-testresult, and the students find the benefits of the writelogue course to their learning the reduced relative clause on line.
Strengthening SIKNAS Through Evaluation of Health Information System in Bali Regional Health Office by HMN Framework Method

One of many benefits from the health sector development is to simplify the information exchange that supports decision making process. The availability of information support in health sector at the Bali Regional Health Office that is managed in an integrated manner related to data, information, technological devices, indicators, procedures, human resources that are useful in making the HIS development roadmap that could strengthen the national health system. The poor ability of decision makers in setting priorities in managerial programs reflects the weakness of HIS resources in the health office so there is an evaluation needed as a basis to ensure that HIS has been running as expected. This study aims to determine the description of HIS evaluation with HMN framework on the HIS component in the Bali Regional Health Office. This study uses descriptive analysis of the seven variables studied. The number of respondents is five officers who manage HIS. The results of the HIS evaluation showed that in general the HIS in the Bali Regional Health Office was in the category of “Very Adequate” seen from the components of HIS Management (83%), HIS Indicators (93%), Information Products and Dissemination and Information Use (89.21%) while three other components consisting of HIS Resources (78.75%), HIS Data Sources (77.29%) and HIS Data Management (75%) have an “Adequate”. So it can be concluded that the evaluation of the health information system in the Bali Provincial Health Office has a positive assessment that can be used in strengthening HIS at the national level.
E - Learning And Blended Learning

This chapter book discusses various aspects of management in recent times. The chapters take a critical look at current trends in management and discuss its various perspectives.The esteemed authors discuss the need for e-learning and blended learning, role of training of skill development, effectiveness of ERP, employment situation in MSME, entrepreneurship's business finance, CSR and its practices, performance appraisal methods and supply chain management.
Local bi‐directional funnel network for salient object detection

Existing deep-learning–based saliency detection methods mainly design a sophisticated architecture to integrate multi-level convolutional features, for example, recurrent network or bi-directional message passing model, and achieve gratifying performance. However, the direct transmission of information with large span, for example, the information from the deepest layer and the shallowest layer, may lead to antagonistic problems. To address this problem, we propose a local bi-directional funnel network (LBDFN) to effectively integrate multi-level features for salient object detection. In the proposed local bi-directional funnel network, a local bi-directional feature integration (LBDFI) module is designed to fuse the features with similar properties from adjacent convolution layers, which can solve the problem of mismatch between fusion features well. The extensive experiments on five saliency detection datasets clearly demonstrate that the proposed method outperforms the state-of-the-art approaches.
Learning Representation in Colour Conversion

Colours can be represented in an infinite set of spaces highlighting distinct features. Here, we investigated the impact of colour spaces on the encoding capacity of a visual system that is subject to information compression, specifically variational autoencoders (VAEs) where bottlenecks are imposed. To this end, we propose a novel unsupervised task: colour space conversion (ColourConvNets). We trained several instances of VAEs whose input and output are in different colour spaces, e.g. from RGB to CIE L*a*b* (in total five colour spaces were examined). This allowed us to systematically study the influence of input-output colour spaces on the encoding efficiency and learnt representation. Our evaluations demonstrate that ColourConvNets with decorrelated output colour spaces produce higher quality images, also evident in pixel-wise low-level metrics such as colour difference (ΔE), peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). We also assessed the ColourConvNets' capacity to reconstruct the global content in two downstream tasks: image classification (ImageNet) and scene segmentation (COCO). Our results show a 5-10% performance boost for decorrelating ColourConvNets with respect to the baseline network (whose input and output are RGB). Furthermore, we thoroughly analysed the finite embedding space of Vector Quantised VAEs with three different methods (single feature, hue shift and linear transformation). The interpretations reached with these techniques are in agreement suggesting that (i) luminance and chromatic information are encoded in separate embedding vectors, and (ii) the structure of the network's embedding space is determined by the output colour space.
ANALISIS KESIAPSIAGAAN SISWA DALAM MENGHADAPI RISIKO BENCANA GEMPA BUMI DAN TSUNAMI

This study aimed to determine the preparedness of students in facing the risk of earthquake and tsunami disasters at MTsN 1 Kota Pariaman. This study is motivated by the lack of understanding of students in dealing with disaster risks, so that there are still many students who are not ready. This study is descriptive quantitative. The population in this study were students of class vii and viii MTsN 1 Kota Pariaman in the 2018/2019 academic year, totaling 489 students. Sampling in this study amounted to 220 students with an error rate of 5% and the sampling technique used the Random Sampling Test. The data was collected using a questionnaire with a Guttman scale measurement. The results of the study in general the readiness of students were in the very ready category, namely as many as 118 respondents or 53.6%. This proves that there has been an increase in student preparedness.
The Future of Bitcoin Futures

Although bitcoin emerged as a cryptocurrency relying on new blockchain technology in 2009, it is still unclear how to regulate it. The recent emergence of bitcoin futures poses a new level of risk to the economy, again raising questions of regulation. This Article examines the potential regulatory regime for bitcoin futures.
정치 텍스트 읽기 교육을 위한 선거공보의 특성 분석

This study explored the possibility of political text-reading education as a Korean-language educational response to the fostering of the citizenship of learners, one of the main topics of education in the 21st century. The message communication characteristics of election bulletins were analyzed by dividing them into ''delivery of optimized models for personality'', ''strategic persuasion through projection of expected values'' and ''extending contact situations using intertext related to authority'' with the aim of fostering community citizenship and expanding learners'' interest in political participation into symbolic communication areas. The educational goals derived from the analysis are as follows. First, in the process of reading election bulletins, the truthfulness and sincerity judgment of a candidate''s personal information should be emphasized. Second, the persuasion strategy used in election bulletins should be used as an opportunity to explore the political needs of the community surrounding such persuasion. Third, the understanding of intertexts used in election bulletins should be aimed at expanding the historical sense of the political landscape of the community.
Einstieg in die Python-Programmierung

No AccessMach was mit Python & Raspberry Pi!Jan 2021Einstieg in die Python-ProgrammierungJörn DongesJörn DongesSearch for more papers by this authorhttps://doi.org/10.3139/9783446466005.003SectionsAboutPDF ToolsAdd to FavoritesDownload CitationTrack CitationsCopy LTI LinkPDF key 'share (en)' returned an object instead of string.FacebookTwitterEmailLinkedIn previous chapternext chapter FiguresReferencesRelatedDetails 2020Pages: 33-80Print ISBN: 978-3-446-46150-5eISBN: 978-3-446-46600-5 Copyright & Permissions© 2021 Carl Hanser Verlag GmbH & Co. KGPDF downloadLoading ...