Reviewerly/exHarmony huggingface dataset:
Technical and policy underpinnings of FAIR data principles

ABSTRACT At the trajectory of exponential data growth and FAIR (Findable, Accessible, Interoperable and Re‐usable) requirements, organizations face the data management and publishing challenges in support of effective data discovery and publishing activities. This panel brings researchers and practitioners who have researched and practiced in managing datasets and providing discovery services to share their experience and expertise in addressing the challenges and proactively responding to trends in research data management, curation, sharing, and reuse.
Examining the Role of Facebook in the Dissemination of Hate Messages over the Agitation for Biafra in Ikeja, Lagos

In Nigeria, during the agitation for Biafra by the Nnamdi Kanu-led Indigenous People of Biafra between 2013 and 2017, the role of Facebook in the dissemination of hate messages by the protagonists and those in opposition to the agitation raised concern about the role of social media as a tool for the spread of hate messages. It is against this background that this research was designed to evaluate the role of Facebook in the spread of hate messages over the agitation for the separate state of Biafra. The study adopted the exploratory design and the mix method approach; both quantitative and qualitative methods were employed. For the quantitative data, 400 questionnaires were administered on purposively sampled respondents. The surveys were analyzed using simple percentages and frequency distribution. Also, content analysis of some purposively selected Facebook messages was carried out. In the end, the research found that hate messages were propagated through Facebook using six major channels during the agitation for Biafra between 2013 and 2017. They include; Facebook Personal Profiles, Status Updates and Wall Postings, Facebook Group Chats, Facebook Video Uploads, Individual Comments and Likes, Video Shares and Reposts, and sharing of articles and links to other social media platforms. In line with its findings, the research recommended that Facebook should review its community standards and policies on postings of hate messages through its medium and also strengthen its regulatory mechanisms to ensure that it does not provide a platform anymore for propagators of hate messages in Nigeria and around the world.
The effects of ownership and home country institutions on the internationalization of African new ventures

This study examines the internationalization of African new ventures. In particular, integrating insights from agency theory and institutional theory, it examines how the knowledge, resources, and influence of firms’ owners shape their willingness and ability to internationalize and, also, how the informal and formal institutions in the firms’ home countries shape the influence of these owners. Supporting our theory, the results suggest that both state ownership and foreign ownership increase African new ventures’ internationalization. Importantly, however, the fractionalization of informal institutions (i.e., variation in ethnicities, languages, and religions) weakens the effect of foreign ownership on internationalization, and the development of formal institutions (i.e., reliable and effective governments) weakens the effect of state ownership on internationalization. We discuss the implications of the study for agency theory, for institutional theory, and for understanding and promoting African new ventures’ internationalization.
Release Candidate Creation

You have previously learned how to code, build, and validate a build of software. In order for that build to become a release candidate, you must package the build in a format that is suitable for a production release. In addition, in order to make our continuous integration “Commit Stage” more robust, you will have to package the build for release to a TDD (test-driven development) environment. This environment was covered in previous chapters as a deployed environment dedicated solely to the execution of automated tests. The type of tests executed in this environment is acceptance tests. For a web application, you would commonly use Selenium for tests through the user interface. You can also create other test suites that would require a fully deployed instance of the software system to be deployed. This chapter will focus on the essential elements you will design and configure in order to convert a build to a versioned release candidate suitable for deployment to downstream environments. The chapter will cover the principles involved, the model and relationships of packages to the software architecture, and the process for storing the packages and using them.
Research Impact & the Library

This invited presentation was the first in the Open Scholarship Series hosted by Wayne State University Library in October 2019. The session was described as follows: Rebecca will present her Research Impact Challenge, a ten-day slate of activities geared to help academics “better understand and manage their online scholarly presence, as well as the impact and reach of their research.” Librarians and faculty will find this informal workshop helpful, both for supporting researchers in the changing scholarly environment, and for finding their own footing as metrics, outlets and tenure and promotion practices evolve. The presentation is an updated version of the Research Impact Challenge Report Out I presented within the U-M Library in March 2019.
Multimedia Interaktif Aksara Jawa Untuk Sekolah Dasar

Abstract: The purpose of the research and development that is producing a eligible interactive multimedia Javanese alphabet products with valid criteria, practical, interesting, and effective. This research and development used Lee & Owens model. They used some following steps; (1) need analysis, (2) designing, (3) development, (4) implementation, and (5) evaluation. The results showed that interactive multimedia  were very valid with an average percentage of 86,93%, very practical with an average percentage of 89,83%, very interesting with an average of 88,97%. Interactive multimedia is also very effective, as evidenced by the results of the post test in the amount of 91.66% of students who have received complete marks.  Abstrak : Tujuan penelitian dan pengembangan ini yaitu untuk menghasilkan multimedia interaktif aksara Jawa yang layak dengan memenuhi kriteria valid, praktis, menarik, dan efektif. Penelitian dan pengembangan ini menggunakan model dari Lee & Owens. Tahapan pengembangan yaitu (1) analisis: analisis kebutuhan dan analisis awal-akhir, (2) desain, (3) pengembangan, (4) implementasi, (5) eveluasi. Hasil penelitian didapatkan bahwa multimedia interaktif sangat valid dengan rata-rata presentase 86,93%, sangat praktis dengan rata-rata presentase 89,83%, sangat menarik dengan rata-rata presentase 88,97%. Multimedia interaktif ini juga sangat efektif, dibuktikan dengan hasil pos tes sebesar 91,66% siswa telah mendapatkan nilai tuntas.
Analyzing and Analyzing the Trends of Community Social Security Council

본 연구의 목적은 지역사회보장협의체 관련 선행연구들을 고찰하여 지역사회보장협의체의 연구동향을 분석하고 지역사회보장협의체의 나가야 할 방향에 대해 모색하고자 하는것이다.이를 위해 지역사회보장협의체를 핵심어로 2003년부터 2018년까지 26개 학술지에 게재된 총46편 논문에 대해 연구경향분석방법을 실시하였다. 각 시기별로 주제어 분석, 연구내용, 연구방법, 연구분야에 대해 어떠한 변화가 있는가를 분석하였다. 이중 연구내용은 이론 및 쟁점 , 변화양상과 대응 , 대안 및 정책제시 , 비판 성찰 , 지역통합 , 참여자 및 환경분석 , 다각적인 접근 7개요소에 대해 분석하였다. 연구방법은 문헌연구 22편, 양적연구 16편, 혼합연구 5편,질적연구는 3편으로 나타났다. 분석결과를 토대로 지역사회보장협의체 관련 연구가 질적으로 더 충실해지기 위해서는 다학문적 접근등을 통한 실제적인 연구 필요성에 대해 제언하였다.The purpose of this study is to analyze the research status of community social security councils and examine the direction of community social security councils by examining previous studies related to community social security councils. For this purpose,we conducted a research trend analysis method for 46 paper published in 26 journals from 2003 to 2018 using the community security council as a key word. We analyzed the change in subject analysis, research methods,and research fields by each period. The contents of the research are seven elemeents of theory and issues , change patterns and responses . alternative and policy presentation , critical reflection , regional integration , participant and environment analysis Respectively. The research methods were 22 articles, 16 quantitative studies, 5 mixed studies and 3 qualitative researchs. Based on the results of the analysis, we suggested the need for practical research through multidisciplinary approach in order to improve the qualitative research on the community security council.
Uso de la gamificación, el juego para mejorar competencias en los aprendices

This article aims to show the experience of the use of elements and design of the games in the Systems Technician competences, focused on the apprentices of the 1621530 tab in a non-playful context. The experiences discussed here aim to give an approach to the use of new pedagogical, didactic strategies where the apprentice’s taste for games is taken into account, teaching happens in a playful way and the instructor feels satisfaction for the achievement of meaningful learning when using real and productive contexts.
Deep Learning techniques for classification of data with missing values

Two deep learning techniques for classification on corrupt data are investigated and compared by performance. A simple imputation before classification is compared to imputation using a Variational Autoencoder (VAE). Both single and multiple imputation using the VAE are considered and compared in classification performance for different types and levels of corruption, and for different sample sizes for the multiple imputation. Two main corruption methods are implemented, designed to test the classifiers for the cases of data missing at random or data missing not at random. The MNIST data set is used for evaluating performance of the different techniques. It is shown that a Multilayer Perceptron (MLP) trained on VAE imputations outperform a MLP using a simple imputation for all tested levels of corruption. A Convolutional Neural Network (CNN) classifier trained with the simple imputation outperforms both the MLP and the VAE classifier on MNIST. This is expected since it is designed to perform well on data sets with high local correlation, like image data sets, whereas the MLP and the VAE classifiers can generalize to other types of data. The reasons for the observed performance of the different techniques and possible implications are discussed. (Less)
User Assigned ISO 3166-1 Alpha-2 Codes and the DNS Root Zone

The ISO 3166 standard is used for the definition of eligibledesignations for country code Top Level Domains. This standard ismaintained by the ISO 3166 Maintenance Agency. The ISO 3166 standardcontains a set of User-Assigned code elements. This document describesthat these values can be used as top level domains for private DNSresolution.
CNN-based text multi-classifier using filters initialised by N-gram vector

Text classification based on convolutional neural networks (CNN) has got more attention recently. This paper presents an improved CNN-based text multi-classifier. First, word vector training is performed on the corpus to be classified. Then, the most important N-grams for a particular category are selected and clustered into different groups. Finally the centroid vectors of different groups are used to initialise the centre weights of filters. Initialisation weights enable CNN to extract N-gram features more effectively and ultimately improve text classification results. Multi-classification experiments using multiple advanced models were performed on different data sets. Experiments show that the proposed model is more accurate and stable than other baseline models.
Role of Duplex Ultrasound for the Assessment of Malignancy in Breast Tumors

Background: Breast cancer is the commonest form of cancer in females which may be curable if detected early.Breast ultrasound (US) is a non invasive, primary screening tool which can evaluate breast tumors in women even with dense breast.By adding color Doppler signal with B-mode US namely Duplex ultrasound better characterization of breast tumors is possible especially for the indeterminate tumors (BIRADS category 3 and 4) on B-Mode US.Objective: The purpose of this study is to assess the efficacy of Duplex Sonography (DS) for assessment of malignancy in breast tumors compared to histopathology.Materials and methods: This cross-sectional study was carried out at National Institute of Nuclear Medicine & Allied Science (NINMAS) from July 2017 to June 2018.A total of 57 female patients having breast tumors (age mean 42.09 years with SD ± 15.85) were included in this study.After clinical assessment, all study patients underwent Duplex Ultrasound and was then subjected to the excisional biopsy and histopathology for confirmation of the diagnosis.Diagnostic performance test of DS was done for diagnosis of breast malignancy compared to histopathology.Receiver-operator characteristic (ROC) curve was also constructed to determine the optimal cut-off point for each spectral Doppler indices.Results: On Color Doppler Sonography (CDS), hypervascularity was more common in malignant tumors than benign lesions (86.7%Vs 18.5%; p=0.001).Spectral Doppler indices including resistive index (RI) and pulsatility index (PI) were significantly higher in malignant tumors (p=0.001).All of these indices had high diagnostic accuracy for differentiating malignant from benign tumors with area under the curve (AUC) which is equals to 0.90, 0.92 for RI and PI respectively.The optimal cut-off points to differentiate benign and malignant tumors from ROC curve for RI was 0.75 (sensitivity: 89.0%, specificity: 85.0%) and that of PI was 1.2 (sensitivity: 96.6%, specificity: 80.0%).All obtained results of sensitivity, specificity and over all accuracy of DS (considering the vessel number on CDS & BIRADS criteria on B-mode US) were 93.3%, 81.5% and 87.7% respectively.Conversely, conventional B-Mode US alone had a sensitivity, specificity and over all accuracy of 100%, 55.6%, and 78.9% respectively.Addition of CDS to B-mode US in the same session increased specificity from 55.6% to 81.5% and accuracy from 78.9% to 87.7%.Conclusion: Duplex ultrasound has a significant role in the evaluation of breast tumors and adds value to B-mode US for distinguishing malignant tumors from benign lesions.
Insurtech: Digitizing Head, Health, Hands and Heart

The following sections are included:IntroductionWhy This Massive Shift is Happening: Millennials are a Different Species Than BoomersMillennials: Individual, Diversed, ToughenedSummary Comparison: Homo Digitalis (Millennial) vs Homo Papyrus (Boomer)Implications for Insurance ProductsHow This Massive Shift is Happening: He Who is Flexible and Controls the Data Will WinFive Ways to Understand the Customer in the New World of DataHow Insurance Companies Can Restructure Their Process by Analysis of Business LinesThe New Foundation of Data: Blockchain and the Challenge to InsuranceA Significant Change in Products and Processes is Needed NowProducts Will Bleed into Each Other
Perceptual Evaluation of Modal Synthesis for Impact-Based Sounds

The use of real-time sound synthesis for sound effects can improve the sound design of interactive experiences such as video games. However, synthesized sound effects can be often perceived as synt ...
Extractive Summarization Based on Quadratic Check

Aiming to solve the problems of insufficient semantic understanding and low statement accuracy in the automatic text summarization of natural language processing, in this paper we present a novel neural network method for extractive summarization by Quadratic Check. The sentence extractor extracts the primary sentences by a scoring and selecting module, among which it selects the final sentences based on impact factor computed by the transformer model and builds the output summary by the quadratic check. The transformer model based on attention mechanisms is trained and generates the probability impact factor of each sentence used for further predicting the relative importance of the sentences. Meanwhile word frequency and position information are used in the process of extractive summarization. Finally, the effectiveness of the proposed method is verified by the experiments on CNN/Daily Mail and DUC2002 datasets.
Wondrous Goggles: An empathy experience device

We are all different, with different abilities, experiences and perceptions of the world around us. Many of the environments that we encounter on a daily basis have barriers for inclusion, whether they are physical barriers or socio-cultural barriers. Through empathy experience devices, we can begin to understand the perceptions and experiences of others to try to eliminate these barriers in order to create environments that are more inclusive. Museums and galleries are a great starting point to create inclusion, as they have historically been exclusionary spaces. In collaboration with the QUT Art Museum, we began to create a culture of inclusion through the exploration of the Wondrous Goggles—a research tool developed by the QUT Design Lab...
An image recognition method using parallel deep CNN

Based on the characteristics of the image, the traditional methods are improved based on the theory of convolutional networks and the research results at home and abroad. Firstly, the training algorithm of convolutional network is studied. By analysing the algorithm, debugging and finding the optimal initialization parameters and the most suitable network structure configuration. Then, for the calculation of the classification result, a multi-region test method is used, and the accuracy of image recognition can be improved by calculating a plurality of regions of the image during the test. Finally, a general dataset input interface is designed for the system, and the experimental results show that the improved convolutional neural network structure is more conducive to obtaining the diversity characteristics of the image. Under the same experimental conditions, the recognition rate is higher than that of the traditional network.
Colour Image Enhancement using Perceptual Saturation and Vividness

A perceptual study was conducted to enhance colour image quality in terms of naturalness and preference using perceptual scales of saturation and vividness. Saturation scale has been extensively used for this purpose while vividness has been little used. We used perceptual scales of a recently developed colour appearance model based on Jzazbz uniform colour space. A two-fold aim of the study was (i) to test performance of recently developed perceptual scales of saturation and vividness compared with previously used hypothetical models and (ii) to compare performance and chose one of saturation and vividness scales for colour image enhancement in future. Test images were first transformed to Jzazbz colour space and their saturation and vividness were then decreased or increased to obtain 6 different variants of the image. Categorical judgment method was used to judge preference and naturalness of different variants of the test images and results are reported.
A Novel Host Readiness Factor for Energy-Efficient VM Consolidation in Cloud Data Centers

Energy efficiency has become a major concern in modern data centers design due to financial and environmental considerations. In existing large data centers, dynamic virtual machine (VM) provisioning between these servers is the most efficient method to improve servers' utilization and reduce energy. These methods use monitoring, historical data, prediction, and virtual machine placement algorithms to achieve their goals. This paper proposed a real-time framework to optimize the process of VM consolidation base energy consumption. It introduces a novel index named Machine Condition Index (MCI) of a server to measure the extent by which physical machine (PM) is suitable to handle the new (consolidated) VM in large scale heterogeneous data centers. This index represents a group of of weighted components including data center network, physical machines, storage, power system and facilities, used in any cloud data center. It is an easily used tool for comparing server energy consumption and can lead to increased efficiency and manageability of data center resources.
Designing a deep-learning network for traffic density and volume prediction

It is relatively easy to know the day to day traffic flow on a highway without taking into account on which lane the cars are driving on. It is more difficult to understand how traffic over time evolves while looking at the lanes, or more specifically, by looking at traffic volume on the specific lanes on a highway, can we have a good guess for where the vehicles should be on a different lane of the same highway in the future? And could it also be possible to predict future congestion in one juncture by looking at incoming traffic from the past at another or multiple junctions somewhere else? This project serves as a precursor for possible future projects by looking at how traffic volume on a given road with 5 lanes evolves over time. The idea is to use past data of traffic volume and then make a model to predict how traffic volume looks in the future specifically for these lanes at certain hours. The historical traffic data was fed into something called an artificial neural network. The process of how an artificial neural network is operating is inspired from how neurons in the brain are operating. A signal is sent from one neuron to another, if this signal is strong enough it will be passed on to the next neuron. However, if the signal is too weak, it will not be passed to the next neuron. These neurons can also receive multiple signals from different neurons. Signals sent between neurons will strengthen the connection between these two. The more signals being sent, the stronger the connections. An artificial neural network works in a similar manner. Between each artificial neuron there is a connection, also known as weight. These weights must be adjusted in such a way so that the network will give good enough results. The traffic data is split up in three parts, one part is the traffic volume at different times, the other part is the corresponding future traffic volume on the same lane and the final part is the test data. Feeding the network the data and adjusting the weights with respect to this information, is called supervised learning. Supervised learning is when the input to the network and its corresponding output are both known. What is unknown, however, are the weights. When training has been completed, the third part of the data is used to test the accuracy of the network. (Less)
An awareness of formulaic clusters in conclusion moves of Applied Linguistics research articles

Writing academic research article is one of the most important endeavors through which the writer can contribute his own knowledge and create a voice among the members of disciplinary community. Exploring the linguistic features which are used to identify the rhetorical structures of academic research articles is of value to have a clearer view of this widely practiced genre. The present research investigates the use of formulaic clusters as a type of linguistic feature in writing research article conclusions by non-native writers. A corpus of 200 research article conclusions written by Iranian L2 users in the field of applied linguistics was compiled and the most frequent four-word formulaic clusters were identified and categorized structurally in the corpus. Then, the identified bundles were classified based on the communicative purpose they serve in each move and step of the conclusions following Yang and Allison’s (2003) move organization of research article conclusions. Findings demonstrated that L2 writers relied heavily on the use of specific formulaic bundles to perform the communicative functions associated with the moves and steps. The largest number of clusters was used to show Move 1, Summarizing the study and the least bundles occurred in Move 2, Evaluating the study.