Reviewerly/exHarmony huggingface dataset:
The Critical Role of the Leader or Supervisor in Building the Employee Trust

Authentic leadership, transparent organizational communication, and employee arrangements are linked to employee trust. Each and every employee has different strengths, and knowing you can count on your team members to fill in your missing gaps helps reduce stress and improve productivity. Trust forms the basis for effective communication, employee retention, and employee motivation and contribution of flexible energy, the extra effort that people voluntarily invest in work. When trust exists in an organization or in a relationship, nearly everything else is easier and more comfortable to achieve. Modern-day employees have a lot of expectations when it comes to their companies. Highly skilled top performers can afford to be selective about the type of company they work for and it has been increasingly shown that employees want to be part of honest, authentic and transparent organisations. Transparency in business is something that has been discussed a lot recently, with a recent Forbes poll revealing that majority of employees think their organizations were held back by a lack of transparency. But it is one thing to be aware of the value of work place transparency and quite another to conscientiously build it into your corporate culture.
Harnessing the Data Revolution: Data Science Corps (NSF)

Federal Grants & ContractsVolume 45, Issue 1 p. 2-2 Grants Alerts Harnessing the Data Revolution: Data Science Corps (NSF) First published: 25 November 2020 https://doi.org/10.1002/fgc.31420 Read 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 onEmailFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume45, Issue13 December 2020Pages 2-2 RelatedInformation
Research on Image Super Resolution Reconstruction Model Based on Deep Learning

Abstract Artificial neural network has made the rapid development of artificial intelligence with its super-learning ability, making artificial neural network become a research hotspot again. At present, deep learning has been widely used in various fields such as computer vision, speech processing, and natural language processing, and has even played a leading role in some fields. The single image super-resolution reconstruction technique aims to reconstruct a low-resolution image through a series of algorithms to reconstruct a corresponding high-resolution image. This paper first briefly introduces the relevant theories of artificial neural networks, then studies the fast super-resolution reconstruction model, and improves the model layer and filter size to establish a new improved model.
Lindsay Anderson’s ideas about authorship in the cinema

This chapter discusses Lindsay Anderson's understanding of authorship as expressed through his critical writings. It analyses how each of his films reveal both his authorship as a practitioner including his reflections on his work.
After postmodernism in educational theory?

Let us not proclaim the death of postmodernism prematurely. It may well be that forms of postmodern expression have had their day, but we still live under Lyotard’s postmodern condition (1984), our public services (for example) run on narrowly performative lines as fractured societies throw up recycled fragments of old ideologies as protest movements: nationalism, religious fundamentalism and unreconstructed Marxism. There is no new grand narrative: old men such as Bernie Sanders and Jeremy Corbyn have taken their turns as the voices of the young. (Is Beyoncé the most influential global voice under 40?) Allied to this is the more arguable contention that postmodernity does not simply follow modernity according to a linear model of time and therefore cannot come to an end. Remember that many sociologists have always fought against the threat the postmodern posed to their avowedly progressive narratives.
GKE Security

In this chapter, you will learn best practices to secure the GKE cluster. The following topics will be discussed:
European Science Editing is in full open access now

I am excited to announce that with this volume European Science Editing (ESE) has shifted from the print to a fully digital open access version. The journal underwent several changes last year. First of all, our publisher, the European Association of Science Editors (EASE) was generously offered – and accepted – a new ARPHA submission system (powered by PenSoft). Together with the EASE president Pippa Smart and EASE Council, we decided to transform ESE into a fully open access online journal. After several months of planning and re-thinking our strategy, a small working group (some members of the EASE Council and of ESE’s associate editors) prepared a proposal, the main idea of which was to divide the journal in two overlapping publications: European Science Editing and EASE Digest. The former will continue to publish original articles, reviews (formerly “essays”), viewpoints, and correspondence using the fully open access ARPHA submission system (flow publishing) but will drop the other sections, namely News notes, The editor’s bookshelf, This site I like, and EASE Forum Digest). These sections, which our readers consider particularly valuable, will now be published in EASE Digest with a few selected articles from ESE. The Digest will be available to EASE members only. As the proposal was accepted by the EASE Council in September 2019, the journal’s transformation is already under way. I wish to thank Silvia Maina (This site I like), Fiona Murphy (Book reviews), Elise Langdon-Neuner (EASE-Forum Digest), Anna Maria Rossi (The Editor’s bookshelf), and James Hartley and Denys Wheatley (members of the International Advisory Board) for the great work they have done and for their cooperation.
Automatic Video Annotation of Human Health Care Action via Clustering

Vision-based activity monitoring provides applications that revolutionized the e-health sector. Considering the potential of crowdsourcing data, to develop large scale applications, the researchers are working on consolidating smart hospital with crowd sourcing data. For creating a meaningful pattern from such huge data, a key challenge is that it needs to be annotated. Especially, the annotation of medical images plays an important role in providing pervasive health services. Although, multiple image annotation methods such as manual and semi-supervised exist. However, high cost and computation time remains a major issue. To overcome the abovementioned issues, a methodology is proposed for automatic annotation of images. The proposed approach is based on three tires namely frame extraction, interest point's generation, and clustering. Since the medical imaging lacks an appropriate dataset for our experimentation. Consequently, we have introduced a new dataset of Human Health care Actions (HHA). The data set comprises of videos related to multiple medical emergencies such as allergy reactions, burn, asthma, brain injury, bleeding, poisoning, heart attack, choking and spinal injury. We have also proposed an evaluation model to assess the effectiveness of the proposed methodology. The promising results of the proposed technique indicate the effectiveness of 78% in terms of Adjusted Rand Index. Furthermore, to investigate the effectiveness of the proposed technique, a comparison is made, by training the neural network classifier with annotated labels generated by proposed methodology and other existing techniques such as semi-supervised and manual methods. The overall precision of the proposed methodology is 0.75 (i.e., 75%) and semi-supervised learning is 0.69 (69%).
System Construction of College Students' Education Management based on Big Data Analysis Technology

The management information of college students is complex and changeable, and it is easy to have the problem that college information is difficult to control. China’s University information system is mainly divided into several major systems: educational administration system, student system, Party mass system, book system and security system. These systems will produce a large amount of data information every day, and also need to invest some human, material and human resources for mining, analysis and decision-making. The use of big data technology in college student management can better improve the authenticity and reliability of student management data, so as to screen the most valuable information from a large number of cumbersome data, speed up the data mining and analysis speed of college management system, and effectively improve the decision-making efficiency of colleges and universities. At the same time, colleges and universities can also design and develop more humanized management and service systems close to students through the advantages of big data technology, promote the informatization of colleges and universities, and provide strong technical guarantee for the development of smart campus.
Effects of Police Recruits' Career Choice and Career Satisfaction on Educational Performance

This study analysed the differences in career choices, career satisfaction and educational performance and the effects of career choices and satisfaction on educational performance among police officer
16. Web semantics

This article presents an overview of web semantics, i.e., the use and study of semantics in the context of the Web. We differentiate between explicit web semantics, building on Semantic Web standards for web-based knowledge representation (ontologies) and reasoning, and implicit web semantics, building on text and link mining from web resources.
FACILITATING SKILLS OF GRADE SCHOOL TEACHERS IN INCREASING LEARNING RETENTION AMONG GRADE SCHOOL PUPILS

&lt;p&gt;Teachers are a facilitator of the learning process. Teachers give pupils an ample time to discover new things on their own. Many challenges faced by the teachers in facilitating the activity of learners such as planning, creating, preparing, and selecting the materials used in the activity. Because of this, the task of the teachers is multi-faceted. Many things to be considered in making learning activities, must suit the capability of the learners, this activity can be adapted to different learning styles, and the activity must stimulate the interest of the learners. But not all activities are fitted for all learners because learners are diverse and unique in nature. The needs of pupils are the major concern of the teachers, to retain the learning of the pupils and especially the learning could apply in their daily lives for meaningful learning. The research dealt with the challenges of the teachers in increasing the learning retention among pupils and possible activities that stimulate the interest of the learners. This research measure the responses measured in getting the frequency, percentage, and weighted mean using formulas to analyze the data gathered. Utilize descriptive design with a survey questionnaire as the research instrument. This research has two sets of respondents the grade school teachers and pupils. &lt;br&gt;&lt;/p&gt;
Create the Right Ambiance for Your Event

Nonprofit Communications ReportVolume 18, Issue 4 p. 2-2 Event Planning Create the Right Ambiance for Your Event First published: 03 March 2020 https://doi.org/10.1002/npcr.31432Read 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 onEmailFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume18, Issue4April 2020Pages 2-2 RelatedInformation
Chaotic Signal Analysis and Processing

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Correction

This article refers to:Ever-shifting roles in building, composing and performing with digital musical instruments
Two Stage MRI De-noising using NLM with Bitonic Filtering

Medical image processing is very wide and most interesting area of research now days. It covers wide application in diagnosis of diseases and analysis of inner body structure. Medical image processing include the study of internal body structure like organs, tissues, etc., which produce better information and help to detect disease using the digitalized data of human organs. In last few decades, so many denoising techniques were proposed like DWT techniques, Maximum likelihood (ML) etc. But all these algorithms have some limitations to preserve edges and fine details. So there is need of compromise between de-noising quality and edge preservation to use images for real time application. The proposed advancement in Non-Local Means filtering with different distant function and analyze them for best results. NLM filter reduce noise without degrading important features like edges and fine details. The analysis is based on the quality and quantity base like MSE, PSNR and SSIM.
System Analysis and Design for Document Classification

The text-mining process starts with a keyword search in text collections. Current text processing technology allows a search technique beyond simple Boolean searches by using natural language queries. Since search engines can recognize any of thousands of keywords and phrases but not the concepts behind the text, it is necessary for researchers to construct an automatic keyword extractor to generate the “Keyword List” for each document. Later, this list can act as the knowledge base to associate unorganized documents to meaningful classes. Failures in identifying the keywords for a certain concept will result in missing values or data for that specific concept.
administrasi peserta didik

students are the main components in the administration of education because students are objects that are developed in the process of education. in this article will explain how the process of implementing good educational administration and as expected.
Privacy-Preserving Anonymity for Periodical Releases of Spontaneous Adverse Drug Event Reporting Data: Algorithm Development and Validation (Preprint)

<sec> <title>BACKGROUND</title> Spontaneous reporting systems (SRSs) have been increasingly established to collect adverse drug events for fostering adverse drug reaction (ADR) detection and analysis research. SRS data contain personal information, and so their publication requires data anonymization to prevent the disclosure of individuals’ privacy. We have previously proposed a privacy model called MS(&lt;i&gt;k&lt;/i&gt;, &lt;i&gt;θ*&lt;/i&gt;)-bounding and the associated MS-Anonymization algorithm to fulfill the anonymization of SRS data. In the real world, the SRS data usually are released periodically (eg, FDA Adverse Event Reporting System [FAERS]) to accommodate newly collected adverse drug events. Different anonymized releases of SRS data available to the attacker may thwart our single-release-focus method, that is, MS(&lt;i&gt;k&lt;/i&gt;, &lt;i&gt;θ*&lt;/i&gt;)-bounding. </sec> <sec> <title>OBJECTIVE</title> We investigate the privacy threat caused by periodical releases of SRS data and propose anonymization methods to prevent the disclosure of personal privacy information while maintaining the utility of published data. </sec> <sec> <title>METHODS</title> We identify potential attacks on periodical releases of SRS data, namely, BFL-attacks, mainly caused by follow-up cases. We present a new privacy model called PPMS(&lt;i&gt;k&lt;/i&gt;, &lt;i&gt;θ*&lt;/i&gt;)-bounding, and propose the associated PPMS-Anonymization algorithm and 2 improvements: PPMS+-Anonymization and PPMS++-Anonymization. Empirical evaluations were performed using 32 selected FAERS quarter data sets from 2004Q1 to 2011Q4. The performance of the proposed versions of PPMS-Anonymization was inspected against MS-Anonymization from some aspects, including data distortion, measured by normalized information loss; privacy risk of anonymized data, measured by dangerous identity ratio and dangerous sensitivity ratio; and data utility, measured by the bias of signal counting and strength (proportional reporting ratio). </sec> <sec> <title>RESULTS</title> The best version of PPMS-Anonymization, PPMS++-Anonymization, achieves nearly the same quality as MS-Anonymization in both privacy protection and data utility. Overall, PPMS++-Anonymization ensures zero privacy risk on record and attribute linkage, and exhibits 51%-78% and 59%-82% improvements on information loss over PPMS+-Anonymization and PPMS-Anonymization, respectively, and significantly reduces the bias of ADR signal. </sec> <sec> <title>CONCLUSIONS</title> The proposed PPMS(&lt;i&gt;k&lt;/i&gt;, &lt;i&gt;θ*&lt;/i&gt;)-bounding model and PPMS-Anonymization algorithm are effective in anonymizing SRS data sets in the periodical data publishing scenario, preventing the series of releases from disclosing personal sensitive information caused by BFL-attacks while maintaining the data utility for ADR signal detection. </sec>
AN INCREMENTAL APPROACH FOR SOLUTION OF TEXT CLUSTERING PROBLEM

Text clustering is a significant study field in data mining. In text clustering, hidden-information is revealed by analysing text datasets with data mining techniques. Text clustering is used in many current application areas such as document recognition, document organisation, indexing, and visualisation. Since it is used in important applications it is in need of efficient algorithms. In this paper, k-means based algorithms in literature are investigated and a novel k-means based text clustering algorithm is defined. Proposed algorithm is compared with existing k-means algorithm in literature on five real-world datasets. Obtained implications show that recommended algorithm is efficient and useful.
PENGARUH MOTIVASI BELAJAR DI SMA NEGERI 2 BURU

This research is motivated by the phenomenon that all parents want their children's education to be good, so they pay attention and optimize for his child's education. The existence of this need then humans are encouraged to fulfill it. This impulse is called motivation. In this case, of course the role of motivation is very important. How teachers make efforts to be able to grow and give motivation so students do good learning activities. The purpose of this study is (1) To find out whether there is an influence of learning motivation on student achievement in SMA 2 BURU. (2) To find out how much influence the motivation of learning on student achievement in SMA 2 BURU. This research uses a quantitative approach with a descriptive type. The population is at BURU 2 Senior High School, totaling 220 students. using purposive random sampling technique. The sample was 79 students. Research methods using questionnaire and documentation. All instruments have met the requirements as a measurement requirement, because they have tested so that it meets the validity test requirements using product correlation and reliability using Cronbach alpha, and has passed the test precondition. Data analysis using T-test and a large percentage of the influence using the coefficient of determination Adjuster R Square. The results showed that (1) there was a significant effect of Learning Motivation on Learning Achievement evidenced from the value of Tcount&amp;gt; Ttable namely 3.731&amp;gt; 1.9915 and Sig &amp;lt;0.05, namely 0.000 &amp;lt;0.05 (with a level of 5%). (2) The magnitude of the influence of the Adjusted R square determination coefficient of 35.9% so that the effect of learning motivation (X), on learning achievement (Y) is weak positive means that if the variable X rises then the variable Y does not rise weakly or together all variables will rise significantly and the remaining 65.1% is explained by other variables outside the variables used.
Network Big Data Analysis and Mining Based on DPI Technology

Abstract In recent years, the rapid development of mobile data network, with 4G commercial wireless network has entered the data era, more and more big data impact on the network of mobile operators, network monitoring and network security need to be strengthened. Depth packet detection technology DPI. The purpose of this paper is to complete network data analysis by in-depth identification of key words in IP packets, so as to meet the current demand of operators for network analysis. In this paper, DPI technology is applied to the actual network, and the DPI data analysis system is constructed by combining the 2G and 3G networks of China mobile. The key technologies in DPI include character recognition, behavior pattern recognition and application layer gateway recognition. At the end of the experiment, the mass signaling data from GB interface and iu-ps interface were obtained to effectively identify various applications and data mining in the network. This paper expounds big data analysis and mining technology and its decision application research from different aspects, hoping to provide theoretical reference for experts and scholars studying the application of big data analysis and mining technology.
Cryptoassets: potential implications for financial stability

The phenomenon of cryptoassets emerged over the past few years and has prompted several national and international institutions to deal with the issue, in particular with its implications for financial stability. In order to assess the impact, a taxonomy is needed. The paper starts with exploring the blockchain, which is the enabling technology, and continues with a discussion on Financial Technology (FinTech) both in general and with a focus on blockchain applications. Further, the ecosystem of cryptoassets is explored and an analysis of cryptocurrencies, which are the most popular application of cryptoassets, is attempted. In each case, a non-technical briefing on the topic is provided, followed by some thoughts on potential financial stability implications. The issue is rather new and largely inconclusive. For the time being, we adhere to international institutions’ general recommendation that the risks are not significant as yet, but the phenomenon is dynamic and vigilant monitoring is warranted.
Questionnaire-Based Prediction of Hypertension Using Machine Learning

Machine learning has proven its ability in health care as an assisting technology for health care providers either by saving precious time or timely alerts or vitals monitoring. However, their application in real world is limited by availability of data. In this paper, we show that simple machine learning algorithms especially neural networks, if designed carefully, are extremely effective even with limited amount of data. Specifically with exhaustive experiments on standard Modified National Institute of Standards and Technology database (MNIST) dataset, we analyse the impact of various parameters for effective performance. Further, on a custom data set collected at a tertiary care hospital for hypertension analysis, we apply these design considerations to achieve better performance as compared to competitive baselines. On a real-world dataset of only a few hundred patients, we show the effectiveness of these design choices and report an accuracy of 75% in determining whether a patient suffers from hypertension.
Research Guides: Getting Started at the Library : Library Services

Learning the basic skills you need to get the most of everything WKU Libraries has to offer! Access library services (Interlibrary Loan, Reference Services, Chat, make an appointment with a librarian, check in & out books and materials.