Reviewerly/exHarmony huggingface dataset:
Availability of Cultural Heritage Structured Metadata in the World Wide Web

In the World Wide Web, a very large number of resources is made available through digital libraries. The existence of many individual digital libraries, maintained by different organizations, brings challenges to the discoverability, sharing and reuse of the resources. A widely-used approach is metadata aggregation, where centralized efforts like Europeana facilitate the discoverability and use of the resources by collecting their associated metadata. The cultural heritage domain embraced the aggregation approach while, at the same time, the technological landscape kept evolving. Nowadays, cultural heritage institutions are increasingly applying technologies designed for the wider interoperability on the Web. This paper presents a study of the current application by cultural heritage data providers of technological solutions in use for making structured metadata available for re-use in the Internet. We investigated the use of both linked data and technologies related with indexing of resources by Internet search engines. We have conducted a harvesting experiment of the landing pages from websites of digital libraries that participate in Europeana and collected statistics about the usage of these technologies. These technologies allow for representing structured data within HTML, or for structured data to be referred to by links within HTML or through HTTP headers capabilities. We conclude with a discussion of future work for establishing a solution for cultural heritage aggregation based on the current situation and the available technologies.
Multiple Regression

Regression models allow you to include as many independent variables as you want. In traditional regression analysis, there are good reasons to limit the number of variables. The spirit of exploratory data mining, however, encourages examining a large number of independent variables. Here we are presenting very small models for demonstration purposes. In data mining applications, the assumption is that you have very many observations, so that there is no technical limit on the number of independent variables.
Cost minimisation technique in geo-distributed data centres

Significant growth of Big Data leads to a great opportunity for data analysis. Data centres are continuously becoming more popular. At the same time data centres' cost are increasing as the amount of data is growing. Simply as Big Data is significantly increasing, data centres are facing new challenges. Hence the idea of geo-distributed data centre is introduced. This project investigates on the main challenges that data centres face and presents an enhanced technique for cost optimisation in geographical distributed data centres. Parameters involved such as task assignment, task placement, big data processing and quality of service are analysed. Analytical evaluation results show that joint parameters technique proposed outperformed separate parameter techniques in some cases even with 20% enhancement. Academic Gurobi solver is used for the evaluation.
Python Files

So far, we have worked primarily within one file; that is, all of our code has been saved in a single .py file and run from that same file. However, in the real world, a lot of our programs will be stored in multiple files. What’s more, we are likely to save some of our favorite code snippets and functions in files for later use. It is just the way we programmers – which includes you now – work.
Information Desk

The Yale ReviewVolume 107, Issue 4 p. 148-164 POETRY Information Desk Robyn Schiff, Robyn Schiff Co-editor of Canarium Books, and a professor at Emory University, in Atlanta.Search for more papers by this author Robyn Schiff, Robyn Schiff Co-editor of Canarium Books, and a professor at Emory University, in Atlanta.Search for more papers by this author First published: 20 January 2020 https://doi.org/10.1111/yrev.13586AboutPDF ToolsExport 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 onFacebookTwitterLinkedInRedditWechat Volume107, Issue4October 2019Pages 148-164 RelatedInformation
Geetha Peethambaran, Chandrakant Naikodi, Suresh Lakshmi Narasimha Setty

The Digital era marked by the unrivalled growth of Internet and its services with day-to-day technological advancements has paved way for a data driven society. This digital explosion offers opportunities for extracting valuable information from collected data, which are used by organizations and research establishments for synergistic advantage. However, privacy of online divulged data is an issue that gets overlooked as a consequence of such large-scale analytics. Although, privacy and security practices conjointly determine the ethics of data collection and its use, personal data of individuals is largely at risk of disclosure. Considerable research has gone into privacy preserving analytics, in the light of Big Data and IoT boom, but scalable and efficient techniques, that do not compromise the usefulness of privacy constrained data, continues to be a challenging arena for research. The proposed work makes use of a distance-based perturbation method to group data and further randomizes data. The efficacy of perturbed data is evaluated for classification task that gives results on par with the non-perturbed counterpart. The relative performance of the algorithm is also evaluated on the parallel computing platform Spark. Results show that the technique does not hinder the use of data for holistic analysis while privacy is subjectively maintained.
Studium: Your Personal Mobile Study On-The-Go

The landscape of studying is changing. There are now increasingly more mobile devices that allow people to learn content in numerous ways. This means that mobile devices play a large role in how a whole new generation of children, adolescents, teenagers and young adults understand information. Studium is a mobile application prototype that I have created to demonstrate how mobile devices can be used as a learning tool to enhance academic performance among postsecondary students. The objective of Studium is to illustrate how artificial intelligence can be incorporated into mobile learning applications to improve one’s studying by generating instant practice tests based off notes from lectures or readings. Studium will also demonstrate how traditional learning theories and strategies such as the spacing effect and the lag effect can be implemented into learning applications using brief mindfulness breaks, which incorporates an element of mindfulness that mobile learning applications often lack.
Task Based Virtual Machine Placement in Cloud

Cloud computing offers many advantages by optimizing various parameters to meet the complex requirements.Some of the problems of cloud computing are utilization of resources and less energy consumption. More research and resources heterogeneity complicates the consolidation problem inside cloud architecture. VM placement refers to an ideal mapping of a task to virtual machines (VM) and virtual machines to physical machines (PM). The task-based VM placement algorithm is introduced in this research work. Here tasks are divided in accordance with their requirements, and then search for appropriate VM, again searching for appropriate PM, where selected VM could be sent. The algorithm decreases the use of resources by devaluation of the number of dynamic PMs while further decreases the rate of dismissal of make span and assignment. CloudSim test System is used to evaluate our algorithm in this research work. The outcomes of this implementation show the effectiveness of some current algorithms such as Round robin and Shortest Job First (SJF) algorithms.
Examination of Temperature Variability over Lahore (Pakistan) and Dhaka (Bangladesh): A Comparative Study

Lahore and Dhaka are rapid expanding and over populated cities of South Asia located in Pakistan andBangladesh respectively. The present study focuses on the evaluation of temperature variability in comparison of bothcities. This study primarily aims at the assessment and examination of temperature variations in both mega cities ofSouth Asia which are seasonal as well as the annual. The time series data were analysed by using statistical techniquesAutoregressive Moving Average Model (ARMA) and Autoregressive Integrated Average Model (ARIMA). The resultsreveal that the minimum temperature is increasing much faster than that of the maximum temperature of both cities.However, the temperature rise(in maximum and minimum) has been observed highest during the spring seasons in bothcities.
Type and Strategy of Code Switching Used by English Teachers in The Process of Teaching and Learning English

This research is analyzes about the struggle of Malala’s in He Named Me Malala film. The The purpose of this research were: (1) to describe type of using code switching and (2) to describe the strategy behind the use code switching made by English teachers in English language eaching and learning at SMAN 1 Padang Sago. This research used descriptive qualitative design. The population were all English teachers in the school who taught English subject for 10th, 11th and 12 th graders in the 2019 academic year . The purposive sampling technique was employed in the research in which the total sampling were all 5 English teachers. Instrumens to collect the data were observation chceklist and interview. In the observation checklist, all teachers’ utterancess were categorized according to the types and strategies of the code switching belong to. To determine the frequency of the type and strategy of the code switching emerging in observation checklist, the numberof the type and strategy that emerged were count manulay. Then, the percentage formula was used. Then, the interview was used to validate the result gained from the observation checklist. Finally, among 110 utterances , it was found there was 4%, of tag switching used by the tecaher, inter-sentential switching was 36%, and intra-sentential switching was 60%,. On the other hand, among 10 strategies of code switching proposed by Hoffman (1991), it was found that the teacher preferred to use only 4 strategies only of which are for scosiisted of expressing solidarity 48%, interjection7%, repetition 28%, and clarification19%).
&amp;quot;Statistical Hypothesis Testing in Wavelet Analysis: Theoretical Developments and Applications to India Rainfall” by Justin A. Schulte et al.

The paper highlights (and provides the solution to) an important aspect of the application of wavelet analysis which is relevant to the broad field of geophysics and beyond, where an understanding of non-linear and complex processes is required.Correlation among wavelet coefficients is a very well-known issue which has been discussed in the context of forecasting applications using wavelet-based models in many previous studies.However, it is rather rare to see the accommodation of this aspect in wavelet power spectrum and coherence based studies which the author has successfully demonstrated in this paper.The author provides a novel method of estimating significant periodicities while tacking
Preface

The International Conference on Technics, Technologies and Education ICTTE 2020, Yambol, Bulgaria was held on November 4-6, 2020 online. ICTTE 2020 was organized by the Faculty of Technics and Technologies of Yambol, Trakia University of Stara Zagora, Bulgaria. Co-organizers of the Conference were Yambol Branch of the Union of Scientists in Bulgaria (USB), Balkan Society of Textile Engineers (BASTE), and Academic Society of Computer Systems and Information Technologies of Bulgaria (AOKSIT). ICTTE 2020 brought together researchers, students, and professionals in the industry from different geographical areas who shared results of their scientific work and exchanged knowledge, information and ideas in the fields of engineering: Technics and Technologies in Mechanical, Agriculture and Transport Engineering; Fashion and Textile Design and Technologies; Electrical Engineering, Electronics and Automatics; Computer Technology and Applied Mathematics; Energetics, Energy Efficiency, Architecture; Food Science and Technologies; and Technologies in Engineering Education. List of Editors and Scientific Committee Members, Organizing Committee Members are available in the pdf
Susan L. Beck Receives 2020 Walter H. Bucher Medal

Susan L. Beck was awarded the 2020 Walter H. Bucher Medal at the virtual AGU Fall Meeting in December. The medal is for “original contributions to the basic knowledge of the crust and lithosphere.”
Data Mining Models and Enterprise Risk Management

The advent of big data has led to an environment where billions of records are possible. Data mining is demonstrated on a financial risk set of data using R (Rattle) computations for the basic classification algorithms in data mining. We have not demonstrated that scope by any means, but have demonstrated small-scale application of the basic algorithms. The intent is to make data mining less of a black-box exercise, thus hopefully enabling users to be more intelligent in their application of data mining. We demonstrate an open source software product. R is a very useful software, widely used in industry and has all of the benefits of open source software (many eyes are monitoring it, leading to fewer bugs; it is free; it is scalable). Further, the R system enables widespread data manipulation and management.
Firmware Resiliency: Detection

Detection is the second element in firmware resiliency. Even if we use multiple ways to protect the firmware, a vulnerability in software or hardware may still cause the firmware component to be modified or corrupted. As such, we need to have a way to inform the Root-of-Trust for detection (RTD) to perform the integrity detection in the boot flow. The detection may happen for the mutable code and data components or just the configurable data. Similar to protection, the main threat is from both the software attacker and the hardware attacker.
HMM Based Kannada Speech Synthesis using Festvox

The process which involves generation of human like voice by a machine is called speech synthe- sis. The developments in the fteld of speech synthesis is vast in international languages, but it is limited in Indian languages like Kannada. This work aims at de- velopment of such a system for Kannada language using Festival and Festvox. It is based on parametric analysis and models of speech features, particular to a language and speaker. The system is memoryless and dynamic, wherein only extracted features are stored but not recorded audio. The training process involves speech data acquisition, pre-processing, labelling using Baum- Welch Iteration, whereas testing process involves text analysis, text segmentation, speech synthesis and qual- ity enhancement using acoustic HMM model develop- ment. The quality of synthesis is 3.52 dB to 5.02 dB as measured by Mel-Cepstral Distortion (MCD) score.
Swindling Shonky Anatomization of Credit Card Transactions using Machine Learning

With the fast moving technological advancement, the internet usage has been increased rapidly in all the fields. The money transactions for all the applications like online shopping, banking transactions, bill settlement in any industries, online ticket booking for travel and hotels, Fees payment for educational organization, Payment for treatment to hospitals, Payment for super market and variety of applications are using online credit card transactions. This leads to the fraud usage of other accounts and transaction that result in the loss of service and profit to the institution. With this background, this paper focuses on predicting the fraudulent credit card transaction. The Credit Card Transaction dataset from KAGGLE machine learning Repository is used for prediction analysis. The analysis of fraudulent credit card transaction is achieved in four ways. Firstly, the relationship between the variables of the dataset is identified and represented by the graphical notations. Secondly, the feature importance of the dataset is identified using Random Forest, Ada boost, Logistic Regression, Decision Tree, Extra Tree, Gradient Boosting and Naive Bayes classifiers. Thirdly, the extracted feature importance if the credit card transaction dataset is fitted to Random Forest classifier, Ada boost classifier, Logistic Regression classifier, Decision Tree classifier, Extra Tree classifier, Gradient Boosting classifier and Naive Bayes classifier. Fourth, the Performance Analysis is done by analyzing the performance metrics like Accuracy, FScore, AUC Score, Precision and Recall. The implementation is done by python in Anaconda Spyder Navigator Integrated Development Environment. Experimental Results shows that the Decision Tree classifier have achieved the effective prediction with the precision of 1.0, recall of 1.0, FScore of 1.0 , AUC Score of 89.09 and Accuracy of 99.92%.
The Clocks

The Clocks Roland Leach (bio) I won't have a clock in the house now,prefer to wait in the night not knowingif there are minutes or hours before sunrise. I sometimes hear the luxury car next door startin the dark, like a great cat ready to hunt,and know it is almost time. In summer there are raucous birdsbut winter keeps its darkness closeand I won't keep a clock reminding me of the small leavingsand great grievings that timedrags behind in deep freeze. The healing of time they like to say,the incessant grounding and poundingof the heel on your throat they keep quiet. There are no wars, no declarations,not even rocks thrown, just walls slowlyappearing, leaving a space fitting an anchorite. [End Page 140] Roland Leach Roland Leach published Obliquity, a collection of poems, with Ginninderra Press in 2019. He is a past winner of the Newcastle Poetry Prize, the Josephine Ulrick Award, and the Tom Collins Poetry Prize (twice). His work has also been featured on ABC Radio National's Poetica. He lives in Western Australia, where he teaches literature at a secondary school and manages Sunline Press. Copyright © 2021 Wayne State University Press
CÔTE D'IVOIRE: ATI Debt Finance

Africa Research Bulletin: Economic, Financial and Technical SeriesVolume 56, Issue 1 p. 22435C-22436A Policy and Practice CÔTE D'IVOIRE: ATI Debt Finance First published: 06 March 2019 https://doi.org/10.1111/j.1467-6346.2019.08751.xRead 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 onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume56, Issue1March 2019Pages 22435C-22436A RelatedInformation
Book Reviews

This time, the Book Reviews section has a large portion of books devoted to system dynamics and applications. The section starts with the featured review by Jie Sun on the book Synchronization: From Coupled Systems to Complex Networks by Stefano Boccaletti, Alexander N. Pisarchik, Charo I. Del Genio, and Andreas Amann. The review informs in detail on the content of the book and its relation to the current literature on the subject. One can learn that such a thing as spontaneous symmetry breaking is a mathematical possibility in coupled identical systems, which from many perspectives arouses great interest in this book. System dynamics is also the topic of the book Hidden Dynamics: The Mathematics of Switches, Decisions and Other Discontinuous Behaviour by Mike R. Jeffrey, which is reviewed by Christian Kuehn. Here nonsmooth dynamics are of central interest and discussed in a very inspiring nonstandard manner, as Christian concludes. Biological system dynamics are considered in the volume Understanding Complex Biological Systems with Mathematics, edited by Ami Radunskaya, Rebecca Segal, and Blerta Shtylla. This book is reviewed by Anita Layton, who applauds the editors of the book for organizing the related workshop with a special project-style format and for putting together this volume of research papers. We have four more insightful reviews on methodological and application aspects. Here I would like to put particular emphasis on a young voice in this section. Gennadij Heidel, who finished his Ph.D. this year on a closely related topic, reviews the book Tensor Numerical Methods in Scientific Computing by Boris N. Khoromskij. In the era of data science, well-founded expositions like this should be kept “handy as a reference for tensor methods,” as Gennadij recommends.
Reviewer Acknowledgements

Applied Economics and Finance (AEF) would like to acknowledge the following reviewers for their assistance with peer review of manuscripts for this issue. Many authors, regardless of whether AEF publishes their work, appreciate the helpful feedback provided by the reviewers. Their comments and suggestions were of great help to the authors in improving the quality of their papers. Each of the reviewers listed below returned at least one review for this issue.Reviewers for Volume 7, Number 2 Ali Massoud, Sohag University, EgyptAndrey Kudryavtsev, The Max Stern Yezreel Valley Academic College, IsraelAyoub Taha Sidahmed, SIU, SudanBasanta K. Pradhan, University of Delhi Enclave, IndiaDjebali Nesrine, University of Jendouba, TunisiaGetamesay Bekele Meshesha, Debre Berhan University, EthiopiaHe Nie, Jinan University, ChinaIgor Matyushenko, School of Foreign Economic Relations and Touristic Business, UkraineKembo Bwana, College of Business Education, TanzaniaMarco Muscettola, Independent Researcher-Credit Risk Manager, ItalyMarwa Biltagy, Cairo University, Faculty of Economics and Political Science, EgyptMurad Harasheh, University of Milan-Bicocca, ItalyNicolas Afflatet, University of the Federal Armed Forces, GermanyOltiana Muharremi Pelari, University of Vlora, AlbaniaOlubukunola Ranti Uwuigbe, Covenant University, NigeriaPatrycja Kowalczyk-Rolczynska, Wroclaw University of Economics, PolandPayal Chadha, University of Wales Prifysgol Cymru, KuwaitRajeev Rana, APB Govt. P.G. College, IndiaRamona Orastean, Lucian Blaga University of Sibiu, RomaniaRichard Nguyen, Alliant International University, USARomeo Victor Ionescu, Dunarea de Jos University, RomaniaVictoria Cociug, Academy of Sciences of Moldova, Moldova Nikki GibbsEditorial AssistantOn behalf of,The Editorial Board of Applied Economics and FinanceRedfame Publishing9450 SW Gemini Dr. #99416Beaverton, OR 97008, USAURL: http://aef.redfame.com
Applying Kolmogorov’s Proofs to the Evaluation of Instruction: Generalized and Particular (PART TWO)

PART ONE incorporated probability theory into the theory of true narrative representations (TNR-theory).The proofs show that interactional successes in a course of study must trend toward 100% shared information.Failures converge on zero.PART TWO compares robotic responses to forced-choice test items (an independent series of events) against university students whose responses are dependent on what they learned.The proofs are also applied to experimental measures of what Haertel in 2013 referred to as "value-added" by instruction.When all factors except the course presentation can be held constant, gains in test scores can be taken as measures of value-added by instruction.Such gains as predicted by the proofs are confirmed (1) for radically diverse methods of testing the same subject-matter across time, and (2) for seven iterations of a course, tested by nearly identical test items aimed at the same subject-matter, but with improved alignment/agreement across the critical components of subject-matter, methods of presentation, and procedures of assessment.