Reviewerly/exHarmony huggingface dataset:
Using Computational Software Root Solvers: A New Paradigm For Problem Solutions?

Many of the "procedures" for solving engineering problems are formulations to solve an algebraic equation or a system of algebraic equations-to extract roots.Computational software systems, such as Mathcad, Mathematica, Matlab, and EES, make possible "direct" solutions of root-finding problems in which the solution procedure is transparent to the user.These computational systems permit a unified approach, a "new" paradigm, to be used for the solution to many engineering problems.The unified approach consists of three steps: (1) formulate a well-posed system of algebraic equations, (2) use a computational system root solver to do the "arithmetic," and (3) verify the results.This paper explores the use of the unified approach for mechanical engineering problems and investigates the pedagogical inferences of the unified approach using computational software systems for undergraduates in Mechanical Engineering.The unified approach permits the student to focus more on the engineering aspects than the "arithmetic" aspects.With less time spent on arithmetic, more time is available for students to engage is higher-level synthesis and understanding.
MOBILE ASSISTED THIRD SPACE (MATS) – WHAT IS THE POTENTIAL FOR M-LEARNING?

This paper outlines the use of Third Space theory to support teaching and learning in the context of educational technologies such as m-learning.This research forms part of a wider project which seeks to better understand the potential of mobile learning and develop a framework for mobile assisted third spaces (MATS).The literature highlights several features of Third Space learning that can be clearly allied to the pedagogic principles outlined in m-learning pedagogy such as the iPAC Framework.The symbiotic nature of Third Space theory and mobile pedagogy is discussed and the potential of MATS to support mobile learning practices are explored.
Data Science: Prediction and Analysis of Data using Multiple Classifier System

In modern times, with the trending technology, for classification of Big Data it is ubiquitous that Deep Neural network algorithms are used.The experiment was carried out considering relatively smaller data.In this paper, we propose, a model Multiple Classifier System, in which the different classifiers are ensembled.We have ensembled different classifiers like LR, LDA, KNN, CART, NB, and SVM.To check the performance of the Multiple Classifier System, we have used Iris flower dataset.When the neural networks and the Multiple Classifier System was compared with the performance, the MCS has shown graduation increase in the results.
Prototipe Sistem Pendukung Keputusan Pemilihan Karyawan Teladan dengan Menggunakan Metode Profile Matching

The high performance of workers is closely related to the reward system. The award in the form of selecting exemplary employees encourages the creation of self-employed, disciplined, and achieving employees. Improper employee role choices can lead to injustice and impact on employee motivation. Number of employees, the number of criteria in the assessment, the potential of subjective judgment, and the system is still a simple manual of work processes exemplary employees in PT SRI takes a long time. To solve the problems that occur, then prepare a decision support system for the creation of a more effective and efficient decision. With the new system is expected to enter in the search data, calculate in the assessment of selection of exemplary employees, and decision decision. In making this Decision Support System will use the method of Matching Case Study in PT SRI. This study discusses the analysis of the work, skills or skills, work attitude, knowledge (knowledge), Personality and socialization are categorized in the core and secondary factors using GAP methods Analysis in accordance with Profile Matching method. The result of the research is a decision support system capable of providing effective and efficient
The ICT Literacy Skills of Secondary Education Teachers in Greece

This article discusses the ICT literacy of secondary education teachers in Greece. Regardless of their specialty, a representable sample of 700 in-service teachers from 283 secondary education schools throughout Greece, participated in our questionnaire survey that was conducted from December 2017 until June 2018. The teachers were questioned about their familiarity with personal computer and smartphone use, whether they use ICT devices in the classroom, while also reporting any obstacles they face in order to implement ICT in the educational process. The data collected enabled us to review the teachers’ ICT literacy. The ICT literacy definition we endorse is the one offered by ETS (Educational Testing Service) (2007); “ICT literacy is using digital technology, communications tools, and/or networks to access, manage, integrate, evaluate, and create information in order to function in a knowledge society”. The findings of the research showed that the majority of Greek teachers are skilled ICT users, embracing their implementation in their teaching interventions, although ICT related Greek policies are contradictory.
PENGARUH PERSEPSI KOMPETENSI DASAR DOSEN TERHADAP MOTIVASI BELAJAR MAHASISWA

This study aims determine the effect of Lecturer on the Motivation of Student Learning Result of Accounting Education Program Education Faculty at Banten Jaya University. This research uses quantitative approach method. The subjects of the study are students of Accounting Education Program Education Faculty at Banten Jaya University. In this research data technique used is questionnaire or questionnaire to 69 respondentsfrom 82 Accounting Education study program students.The method used in this research is descriptive research method, and practical analysis of data analysis, validity test, reliability test, normality test, homogeneity test, analysis, simple linear regression analysis, significance test (f test, t test, coefficient of determination).From the results of data analysis that has been done to obtain the results that include the category "High Quality Basic Competence Lecturer" with a percentage of 78.63%. Meanwhile, the motivation of student learning outcomes is categorized as "Highly Motivated" with a score of 3.98. The results obtained from the obtained motivation of 0.686 which means that Lecturer Basic Competence (X) to Motivation Student Outcomes (Y) is equal to 68.8% and the rest of 31.2% by other variables not examined. Thus it can be concluded that there is a significant influence between Basic Competence of Lecturers to Motivation Student Results this is because the value of t count&gt; t table (7.711&gt; 1.667) or significance &lt;0.05 (0.000 &lt;0.05) so that Ho is rejected.
Signcryption schemes with insider security in an ideal permutation model

Abstract Signcryption aims to provide both confidentiality and authentication of messages more efficiently than performing encryption and signing independently. The “Commit-then-Sign &amp; Encrypt” (CtS&amp;E) method allows to perform encryption and signing in parallel. Parallel execution of cryptographic algorithms decreases the computation time needed to signcrypt messages. CtS&amp;E uses weaker cryptographic primitives in a generic way to achieve a strong security notion of signcryption. Various message pre-processing schemes, also known as message padding, have been used in signcryption as a commitment scheme in CtS&amp;E. Due to its elegance and versatility, the sponge structure turns out to be a useful tool for designing new padding schemes such as SpAEP [T. K. Bansal, D. Chang and S. K. Sanadhya, Sponge based CCA2 secure asymmetric encryption for arbitrary length message, Information Security and Privacy – ACISP 2015, Lecture Notes in Comput. Sci. 9144, Springer, Berlin 2015, 93–106], while offering further avenues for optimization and parallelism in the context of signcryption. In this work, we design a generic and efficient signcryption scheme featuring parallel encryption and signature on top of a sponge-based message-padding underlying structure. Unlike other existing schemes, the proposed scheme also supports arbitrarily long messages. We prove the construction secure when instantiated from weakly secure asymmetric primitives such as a trapdoor one-way encryption and a universal unforgeable signature. With a careful analysis and simple tweaks, we demonstrate how different combinations of weakly secure probabilistic and deterministic encryption and signature schemes can be used to construct a strongly secure signcryption scheme, further broadening the choices of underlying primitives to cover essentially any combination thereof. To the best of our knowledge, this is the first signcryption scheme based on the sponge structure that also offers strong security using weakly secure underlying asymmetric primitives, even deterministic ones, along with the ability to handle long messages, efficiently.
Mathematical Assessment of Blocks acceptance in Blockchain using Markov Model

Blockchain, introduced in bitcoin system is disrupting the way transactions are done in businesses via charging small transaction fees. It is observed that the acceptance of blocks in the blockchain is based on transaction value. Transmittals through blockchain are favoured due to low transaction cost and grander security. However, attempt of implementing blockchain to revolutionise business processes writhes from problem of waiting time for blocks. In this paper, the transaction-confirmation-time is appraised using queuing theory, where the approval rate of blocks in blockchain is concocted using Markov queue model. Four specific use cases of 1, 3, 6 and 60 confirmations are considered for practical purposes.
WITHDRAWN: Adaptive multi-graph hashing for scalable multimedia retrieval

This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy.
Nuclei Segmentation and Count in Breast Pathology Image with Deep Learning

Nuclei quantitative analysis is significance in pathology image diagnosis such as Hematoxylin and eosin staining (H&amp;E) slides and immunohistochemistry (IHC). In recent years, deep learning, in particular convolutional neural networks (CNN), have rapidly become a methodology of choice for nuclei segmentation. Training model requires datasets of images in which a vast number of nuclei have been annotated, however, there are few annotated IHC images. Here, we proposed a CNN model trained by publicly accessed annotated H&amp;E images, and IHC slides can be tested after color deconvolution and normalization with color distribution of H&amp;E image. Model showed better performance in nuclei segmentation than open source software with testing breast cancer H&amp;E and IHC images from hospital. This result suggests the application potential of our new method for nuclei quantitative analysis, which is objective and efficient.
Grey Literature and Untold Stories

Book Review| May 01 2019 Grey Literature and Untold Stories: United States National Park Service Studies on LGBTQ History, Reconstruction, and the Civil War to Civil Rights Commemoration LGBTQ America: A Theme Study of Lesbian, Gay, Bisexual, Transgender and Queer History edited by Megan E. Springate. Washington, DC: National Park Foundation, 2016. 2 vols. xv + 624 pp. and 638 pp.; illustrations, notes, appendix, index; https://www.nps.gov/subjects/tellingallamericansstories/lgbtqthemestudy.htm.The Era of Reconstruction, 1861-1900: A National Historic Landmarks Theme Study by Gregory P. Downs and Kate Masur. Washington, DC: National Park Service National Historic Landmarks Program, 2017. 157 pp.; illustrations, notes, bibliography, appendices; https://www.nps.gov/orgs/1207/07-28-2017-reconstruction-theme-study.htm.Civil War to Civil Rights Commemoration: Summary Report by Carol Shively. Washington, DC: National Park Service, 2016. 244 pp.; illustrations, appendices; https://www.nps.gov/civilwar/upload/CIVIL-WAR-TO-CIVIL-RIGHTS-SUMMARY-REPORT-1-v2.pdf. Joan M. Zenzen Joan M. Zenzen Joan M. Zenzen writes histories and conducts oral history interviews for the US federal government and non-profit organizations. She has written several administrative histories for the National Park Service, including about two Civil War battlefield parks. She is completing an administrative history of Rock Creek Park, a National Park Service unit in Washington, DC. She is based in the Washington, DC, area. Search for other works by this author on: This Site PubMed Google Scholar The Public Historian (2019) 41 (2): 317–321. https://doi.org/10.1525/tph.2019.41.2.317 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Joan M. Zenzen; Grey Literature and Untold Stories: United States National Park Service Studies on LGBTQ History, Reconstruction, and the Civil War to Civil Rights Commemoration. The Public Historian 1 May 2019; 41 (2): 317–321. doi: https://doi.org/10.1525/tph.2019.41.2.317 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentThe Public Historian Search This content is only available via PDF. © 2019 by The Regents of the University of California and the National Council on Public History2019 Article PDF first page preview Close Modal You do not currently have access to this content.
Booktrailer as a means for forming the design competence of future teachers

Article is devoted to a problem of forming design competence of future teacher. Its relevance is connected with change of specifics of the pedagogical activity demanding continuous search of new training technologies, interaction forms, and pedagogical maintenance and according to causing special requirements to design competence of the teacher. In this regard the problem of search of the most effective remedies of forming design competence of future teacher at a grade level at the higher vocational school is staticized. According to authors, considerable potential in this plan the innovative forms of design based on broad use of information technologies have. In this regard authors of article concentrated the attention on identification of potential opportunities of such modern form of pedagogical design, the main on use of electronic educational resources as booktrailer. In work the theoretical provisions concerning a concept design competence, a method of projects, design abilities are stated. Identification of potential of booktrailer for forming design competence of future teacher and also development of recommendations about the organization of design activities for creation of booktrailer in the course of studying of pedagogical discipline History of pedagogics and education was result of the conducted research. Application of the obtained data will help with a solution of the problem of creating favorable conditions for professional and personal development and self-development of students at a stage of high school training.
Sequential Gaussian Processes for Online Learning of Nonstationary Functions

Many machine learning problems can be framed in the context of estimating functions, and often these are time-dependent functions that are estimated in real-time as observations arrive. Gaussian processes (GPs) are an attractive choice for modeling real-valued nonlinear functions due to their flexibility and uncertainty quantification. However, the typical GP regression model suffers from several drawbacks: 1) Conventional GP inference scales <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {O}(N^{3})$</tex-math></inline-formula> with respect to the number of observations; 2) Updating a GP model sequentially is not trivial; and 3) Covariance kernels typically enforce stationarity constraints on the function, while GPs with non-stationary covariance kernels are often intractable to use in practice. To overcome these issues, we propose a sequential Monte Carlo algorithm to fit infinite mixtures of GPs that capture non-stationary behavior while allowing for online, distributed inference. Our approach empirically improves performance over state-of-the-art methods for online GP estimation in the presence of non-stationarity in time-series data. To demonstrate the utility of our proposed online Gaussian process mixture-of-experts approach in applied settings, we show that we can sucessfully implement an optimization algorithm using online Gaussian process bandits.
Radiometric Surveys for Detection of Uranium in Dhofar Region, Sultanate of Oman

The Proterozoic basement of the Dhofar region of the Sultanate of Oman might be considered as a potential source of Uranium mineralization like other Proterozoic basins of the world, which collectively contribute 30%-40% to the global production. Uranium exploration assumes great importance in this part of the world and therefore geochemical and geophysical exploration for Uranium was initiated. Initial, random sampling of the study area with Gamma ray spectrometry as well as in situ XRF (Xray Florescence) analysis indicated the presence of around 30-40 ppm of Uranium. Since Gamma ray spectrometry results in the calculation of the heat produced during radioactive decay of Potassium (K), Uranium (U) and Thorium (Th) within the rocks in the study area, measurements were acquired along eleven traverses of varying length from 100-250 m at a traverse interval of 20 m and sampling interval of 10 m. Individual plots of the concentrations of radioelements such as U, K and Th have shown favorable anomalous radioactive sources. Besides this, Uranium to Thorium (eU/eTh) and Uranium to Potassium (eU/K) ratio plots have been presented and the alteration zones associated with Uranium mineralization through all traverses have been identified. Further, the generated composite ternary image based on the combination of K, Th and U from Gamma ray spectrometry determines different relative amounts of radioelements and paves the way for deciphering the level of radioactivity in the study area. However, there seems to be no presence of strong Uranium anomalies in the near surface of the limited study area. As the study area is heavily faulted, the depth to concealed subsurface fault structures is estimated to be around 64 m based on Hartley spectral analysis of total magnetic anomaly (line-6).
Role of Organizational Change in Developing Team Performance and Support Positive Work Environment

Current study aimed at examining the influence of organizational change on two of the main aspects of any organization which are team performance and work environment. Study followed the quantitative approach through utilizing the questionnaire as a tool of study. Through applying the questionnaire on a sample of (211) managers from industrial organizations in Jordan; results indicated that there appeared to be a positive influence of organizational change on both team performance and work environment. Through the study, results indicated that technology as a variable of organizational change appeared to be the influential variable on both team performance and work environment which indicated the importance of technology in organizations and how to handle its interference and transition in a positive approach.&amp;nbsp;&#x0D; &#x0D; Based on results, current study recommends enhancing the managerial support within industrial organizations in Jordan as it appeared that there was a lack in the support that management has on managing change. In addition to that, technology appeared to be of high importance, so it is recommended to focus more on technologies and manage the transition from manual to technological approaches within industrial organizations.
Physical and Numerical Modelling of Tsunami Run-up on Seawall at Sloping Beach

Tsunami run-up on land has a large destructive power. Further studies are deemed necessary to understand the process and characteristics of tsunami run-up in coastal areas. Seawall structures can reduce the run-up of a tsunami depending on the height of the seawall crest. Physical modeling shows that seawall may significantly reduce run-up (𝑅) and inundation (𝑋𝑖). The highest reduction up to 55% where the seawall peak height is 7 cm and the water depth is 15 cm. With the same scenario in numerical modeling, the percentage reduction is 67.53%. The highest inundation (Xi) in the scenario without seawall structure is 6.081 m when the initial water depth (d0) equals to 30 cm. The result of the numerical model for the same scenario is 6.970 m. Seawall as tsunami mitigation structure is only effective when the tsunami wave is relatively low compared to the seawall height (H/ sw). Reduction percentage &gt; 25%, with conditions that H/ sw is &lt; 0.856 (physical model) and &lt; 0.802 (numerical model).
Cost-evaluation of Cloud Portfolios: An Empirical Case Study

Today, Amazon is the Cloud service market leader with the EC2 platform. Three predominant marketspaces exist on this platform: spot marketspace, reservation marketspace, and the well-known on-demand marketspace. Also other providers such as Google, Microsoft and VirtuStream run multiple marketspaces. Consumers can purchase their virtual machines from different providers on different marketspace to form Cloud portfolios: a bundle of virtual machines whereby the virtual machines have different technical characteristics and pricing mechanisms. An industry-relevant research challenge is to provide best practices and guidelines for creating cost-efficient Cloud portfolios. In this paper, we used Amazon’s marketspaces and the dataset from the Bitbrains datacenter to analyze the cost-efficiency of heterogeneous Cloud portfolios - portfolios where the virtual machines are purchased from different marketspaces. We found out that heterogeneous portfolios are more cost-efficient than homogeneous portfolios for almost all analyzed situations. Our analysis further revealed that consumers request virtual machines that are over-sized which forms a significant field of cost-optimization. A second dataset from the Bitbrains datacenter - from another domain of application - validates our findings.
Text Summarization and Topic Models

We have come quite a long way in our journey through the world of text analytics and natural language processing. You have seen how to process and annotate textual data for various applications. We also looked at state-of-the-art text representation methods with feature engineering. We also ventured into the world of machine learning and built our own multi-class text classification system by leveraging various feature extraction techniques and supervised machine learning algorithms. In this chapter, we tackle a slightly different problem in the world of text analytics—information summarization.