Reviewerly/exHarmony huggingface dataset:
Potential Applications of Marine Deposits Assessment Using Radiometric and Geochemical Data Integration in Arewa-Jantulu NW, Nigeria

Exploration for uranium, thorium, potash, and manganese at this time can be seen as a magnitude that can settle into the immense economic benefit to the host community and the country at large. These minerals have transformed the economic landscape of many host countries and they can be mined to contribute significantly to the Nigerian economy. In this study In-situ measurements using Radiometric and trace element analysis of intercontinental deposits discovered by the paleomagnetic analysis were carried out in Arewa-Jantulu part of the Gwandu formation northwestern Nigeria. The present study was aim to measure and characterize the concentration of radioactive anomalies using radiometric and geochemical measurements. Uranium (U), Thorium (Th), and Potassium (%K) as well as the total count (TOT) gamma-ray concentration was measured with a portable Gamma-ray spectrometer. The results of the gamma-ray measurements shows spatial distribution plots of different levels of radioactivity over the studied area, which reflect contrasting radioelements contents for exposures to various rock types. The phase identification of marine soil sediment samples was determined from Atomic Absorption Spectrometry (AAS). While the study has shown the correlation plots of various trace elements versus radioelements concentration as well as the total concentration of some profiles of the study area. This study identified the need for the interpretation of geochemical data in this location to confirm the results obtained are meaningful. The study has also revealed the computed mean and standard deviation for trace elements analysis for ease assessment. For radioelements occurrences, the spatial distribution plots of activity of uranium (eU), thorium (eTh), and (K %) give an indication where high concentrations of thorium and uranium occur.
Public Service Ethics for the Fourth Industrial Revolution and Prevention of Corruption

본 연구에서는 4차 산업혁명이 공직자의 공직윤리에 어떠한 영향을 줄 수 있으며 이를 위하여 어떠한 공직자의 윤리가 확립되어야 하는지를 살펴보았다. 4차 산업혁명이 공직에 미치는 영향으로는 첫째, 4차 산업혁명은 공직에서 전문직공직자에 대한 역할이 증가하면서 반복적이며 단순한 일의 경우는 AI가 결정하는 일이 많아지겠지만 중요한 정책은 결국 공무원이 결정해야 할 것이다. 단순한 정책결정에서 AI의 결정은 헌신, 소명감, 사명의식 등이 반영되지 않는 기계적인 결정이 이루어질 가능성이 있어 공직자의 책임성 문제를 유발 할 수 있다. 둘째, 전문기술과 지식을 기반으로 집중된 소수의 결정으로 직권남용 및 횡령 등의 부패가 발생할 가능성도 있다. 셋째, 많은 데이터의 수집을 통한 정책결정 시 개인정보를 침해 할 가능성이 있다. 따라서 공직자들은 4차 산업혁명에 대응하기 위하여 다음가 같은 능력이 요구된다. 첫째, 과학기술에 대한 지식확보, 둘째, 고품질의 데이터, 즉 전문성과 데이터의 결합, 셋째, 시민과의 협업을 위한 투명성과 정보공유, 넷째, 정부, 기업, 시민 간의 다양한 글로벌 네트워크를 통한 성과공유와 위협에 대한 공동대처, 다섯째, 열린 마음, 그리고 규제절차의 지속적 개선을 포함한 민첩한 업무대응이다. 이러한 능력을 기반으로 4차 산업혁명에 필요한 공직자 공직윤리를 구축하기 위하여 다름과 같은 요소가 고려되어야 한다. 첫째, 4차 산업혁명에 적합한 새로운 공직윤리규범의 설정이 필요하다. 둘째, 인간에 대한 이해 즉, 인문적교육(윤리교육)이 강화되어야 한다. 셋째, 업무수행에서 취득하게 된 개인정보의 엄격한 보호 및 프라이버지 침해에 대한 공직윤리의 규제가 필요하다.This study examined how the Fourth Industrial Revolution can affect public officials ethics and what kind of ethics should be established for this purpose. The effect of the Fourth Industrial Revolution on public office is as follows: First, the fourth industrial revolution is repeated as the role of professional public officials increases in public office, and AI will make more decisions in simple cases, but important policies should be decided by public officials. In simple policy decisions, AI decisions may cause the responsibility of public officials because mechanical decisions that do not reflect commitment, sense of calling, sense of mission, etc. Second, there is a possibility that corruption such as abuse of authority and embezzlement may occur due to a small number of decisions focused on professional skills and knowledge. Third, it is possible to infringe personal information when making policy decisions through collecting a lot of data. Therefore, public officials need the following ability to cope with the Fourth Industrial Revolution. First, it is agile work response including securing knowledge of science and technology, second, combining high quality data, that is, expertise and data, third, transparency and information sharing for collaboration with citizens, fourth, joint response to performance sharing and threat through various global networks between government, enterprise and citizens, fifth, open mind, and continuous improvement of regulatory procedures. Based on these abilities, factors such as difference should be considered in order to establish public service ethics necessary for the fourth industrial revolution. First, it is necessary to establish a new public service ethics standard suitable for the fourth industrial revolution. Second, understanding of human beings, that is, humanistic education (ethics education) should be strengthened.Third, it is necessary to regulate public service ethics for strict protection of personal information acquired in business performance and infringement of privacy.
An Application of Decision Support Technique for Global Software Project Monitoring and Rescheduling Based on Risk Analysis

We present an application of a decision support technique for global software monitoring and rescheduling. The technique developed is based on risk analysis. It was initially described in (Marchwicka, 2020). This paper extends (Marchwicka, 2020) by providing a real-world example. It also uses a different set of risk measures and a new ranking method based on Principal Component Analysis (PCA). The technique was applied to a real global software project that was realized by three teams geographically located in two different countries. A post-factum project data was used to illustrate the technique step by step and show how IT project managers can use it to support their decisions. The presented technique is iterative and adaptive, and it allows control of the overall risk level in each control step and allows rescheduling.
Compliance with Brazil's National Petroleum Agency standards with convolutional neural networks to classify petrographic thin sections

PreviousNext No AccessEnergy in Data Conference, Austin, Texas, 20–23 February 2022Compliance with Brazil's National Petroleum Agency standards with convolutional neural networks to classify petrographic thin sectionsAuthors: Vinicius Martins BotelhoDean Pereira de MeloVinicius Martins BotelhoPetróleo Brasileiro S.A.Search for more papers by this author and Dean Pereira de MeloPetróleo Brasileiro S.A.Search for more papers by this authorhttps://doi.org/10.7462/eid2022-05.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract The Brazil's National Petroleum Agency regulations obliges any company that wishes to drill for oil within the nation's borders to regularly send geological data about its subsurface. One data type that it is required to be sent is rock thin sections photos. A thin section of rock is the result of a thicker portion of rock being ground up and polished so it can be studied and analyzed. During a typical exploration campaign, several thousand rock thin sections are generated, photographed and analyzed. Although they are stored in an electronic document management system, there are several manual steps in order to do so. This work adapted a convolutional neural network from a well-known image classification problem to assess the compliance of over 24,000 photos sent semesterly to this national agency. Keywords: convolutional neural networks, thin sections, data quality, compliancePermalink: https://doi.org/10.7462/eid2022-05.1FiguresReferencesRelatedDetails Energy in Data Conference, Austin, Texas, 20–23 February 2022ISSN (online):2159-6832Copyright: 2022 Pages: 33 publication data© 2022 Published in electronic format with permission by Energy in DataPublisher:Energy in DataSociety of Exploration Geophysicists HistoryPublished: 03 May 2022 CITATION INFORMATION Vinicius Martins Botelho and Dean Pereira de Melo, (2022), "Compliance with Brazil's National Petroleum Agency standards with convolutional neural networks to classify petrographic thin sections," SEG Global Meeting Abstracts : 16-18. https://doi.org/10.7462/eid2022-05.1 Plain-Language Summary Keywordsconvolutional neural networksthin sectionsdata qualitycompliancePDF DownloadLoading ...
Fair Trading of Crops in a Trusted and Transparent Manner using Smart Contracts

In the past few years, Indian farmers face problems due to middlemen’s excessive exploitation in several ways. Intermediaries play a vital role as a bridge between farmers and consumers, due to the existence of several layers of commission agents or mediators(middlemen) who are buying crops from farmers at least possible cost and sell those at extravagant prices which monetary abuse both the farmers and consumers. The government has already tried plentiful various schemes, yojanas, and online solutions to clear up the obstacles, occur in the process of buying and selling produce. However, every technique to unfold the root problems was either short term or not up to the mark. Hence, it is necessary to provide a trusted, transparent, and decentralized solution. Therefore, this paper proposed a Blockchain-based framework that eliminated the agriculture sector’s intermediates, specifically for crop trade. Additionally, we integrated Blockchain with the Android application to provide an easy interface and maximize user satisfaction. The proposed strategy makes it easy for farmers to sell their products to nationwide buyers, which provides them a reasonable allowance according to the input cost and the inflation rates, respectively, and it also creates a trusted wide marketplace to sell their crops. Through this, by exhibiting just a single layer in trade, the social and monetary welfare of farmers, consumers and government are targeted. We created several Smart Contracts and deployed to a Blockchain Network of Hyperledger Fabric Platform and then measured the performance using Hyperledger Caliper benchmark.
THE ROLE OF EXERCISES IN THE METHODOLOGICAL AND MATHEMATICAL LITERACY DEVELOPMENT OF FUTURE PRIMARY SCHOOL TEACHERS

The article is devoted to the role of exercises in the methodological and mathematical literacy development of future primary school teachers. An exercise is an action that is repeatedly performed in order to acquire knowledge, form skills or methods of activity. We consider the system of mathematical exercises, the stages of their implementation, the algorithms for performing each type of mathematical exercises are revealed: tasks, examples, equations. The stages of execution are presented: reading and analysis, execution, results of exercises. The essence of special exercises for the development of methodological and mathematical literacy of future primary school teachers (modeling and practice-oriented exercises) is revealed. Examples of tasks from the textbook "Mathematics" for primary classes and the algorithm of their execution are considered. The results of experimental research work on the basis of the Institute of Pedagogy and Psychology with the participation 81 students of 3rd-year of the specialty "Pedagogy and methods of primary education" are presented. The final conclusions are given.
Research on effects of pre-trained language models in medical named entity recognition

Named entity recognition (NER) is a step stone for numerous downstream applications, and medical NER is an important part of NER. Prior studies have applied various pre-trained language models (PLMs) to medical NER, but they ignore to systematically investigate cons and pros of these PLMs. In this paper, we investigate cons and pros of prevalent PLMs in medical NER. To be specific, we first pre-train three PLMs (i.e., word2vec, GloVe and ELMo) from scratch and fine-tune Chinese BERT model with 300k entries of real-world Chinese Electronic Medical Records. Then, we combine above PLMs with BiLSTM-CRF to evaluate effects of these PLMs. Experimental results on CCKS2019 dataset show that context-dependent PLMs (ELMo and BERT) significantly outperform con-text-independent PLMs (word2vec and GloVe) in medical NER, by up to 4.98% absolute F1 gains. Moreover, our best model achieves new state-of-the-art results on this benchmark dataset. Furthermore, we make additional analyses from perspectives of time and space complexity.
Digital Universities: Features and Key Characteristics

The article discusses the phenomenon of “Digital Universityˮ. The goal is to identify the essence of this concept, its content, as well as the criteria for transformation into a digital university. The article outlines the problems of transformation and its ways. Digitalization, like any phenomenon, is multifaceted and can have both positive and negative effects on the educational process and the quality of education. The study conducted by the authors made it possible to describe in detail the key characteristics of a digital university, which has the features of a research organization, an educational organization, an IT company, information banks / knowledge repositories and marketing organizations.
Effects of scale on segmentation of Nissl–stained rat brain tissue images via convolutional neural networks

There are numerous efforts to automate the delineation of rat brain regions from rat brain histology. A leading approach uses convolutional neural networks which model anatomical variability and determine cytoarchitectonic boundaries. Currently, it is not clear what scale of the input tissue images offers the most information for these models to exploit. In this work, we test a fully convolutional architecture, U–Net, with Nissl–stained rat brain tissue images of different scales. We show that the networks obtain a lower precision and higher recall when trained on large-scale images. Conversely, networks trained with small-scale images produce fewer false-positive predictions and more false-negative predictions. Our work provides valuable insight into the optimal scale needed for convolutional neural networks to segment brain regions from Nissl-based images of the brain.
Organization of Developmental Training in Preschool Educational Institutions

Self-realization, self-presentation is one of the most important qualities of the modern man. It is especially important to support this work in the educational process, bringing it to the centre of educators and children’s activities. One important way of doing this is to give tasks that ensure independence of thought. It is possible to ensure and monitor the development of children through classwork. The children demonstrate skills such as understanding, application, analysis, etc. at different stages of cognition. An important condition in the lesson content is the development of a humane position in children, situation selection for children, creative search, problem-solving, communication and cooperation skills in training, a reflection of self-regulation, self-awareness, reflection, selection of methods that allow children to develop cognitive activity. Methods of raising cognitive activity include comparison, analysis, generalization, question method, solution of logical tasks, research, etc.
Textures

Chapter 5 continues the theme of digital image manipulation and considers transformations such as rotation or scaling with required pixel interpolation to create the most accurate final result. The GPU hardware texture units are used for this and their features are discussed. The cx utilities provided with our code include wrappers that significantly simplify the creation of CUDA textures. Curiously, these hardware texture units are rarely discussed in other CUDA tutorial material for scientific applications but we find they can give a 5-fold performance boost. We show how OpenCV can be used to provide a simple GUI interface for viewing the transformed images with very little coding effort. We end the chapter with a fully working 3D image registration program using affine transformations applied to volumetric MRI data sets. The 3D affine transformations are about 1500 times faster on the GPU than on the host CPU and a full registration between two MRI images of size 256 × 256 × 256 takes about one second.
Strategies for improving the use of Jakarta City Tour Bus in post-pandemic Covid-19 using IPA Method

Abstract The Jakarta City Tour Bus is one of the strategies of the DKI Jakarta Provincial Government to support the tourism sector. However, in recent years there has been little interest in running the Jakarta City Tour Bus. The lack of some service attributes of the Jakarta City Tour Bus performance and the new phase of adjustment during the Covid-19 pandemic are factors causing the decrease in the number of Jakarta City Tour Bus users since 2019. This study aims to formulate strategies to improve the use of the Jakarta City Tour Bus in the post-pandemic Covid-19 based on user preferences. The analytical method used in this research study is Importance-Performance Analysis (IPA) to formulate strategies for improving the use of Jakarta City Tour Bus in post-pandemic Covid-19. The results showed that there were 11 service attributes of the Jakarta City Tour Bus which had a low level of performance but had a high level of importance. Then, there are strategies to improve the use of Jakarta City Tour Bus which is divided into 2 (two) categories, including the strategies for improving the use of Jakarta City Tour Bus in normal and new normal situations.
Pre-professional practices in the training of agricultural graduates: the case of Ecuador using fuzzy cognitive maps

Objective This research seeks to identify strategies that promote the sustainability of pre-professional practices in agricultural careers in Ecuador through the perceptions of actors from the academy, public sector, and business.Design/methodology/approach This research uses online surveys, fuzzy cognitive maps, and scenario analysis. The survey collects information on the characterization of pre-professional practices and perceptions on the development of practices related to management, administration, and contribution to the academic training of the agricultural graduate. The information was obtained from 194 surveys.Results The findings highlight three strategic variables: (i) plans, programs, and projects (ii) experience achieved in pre-professional practices, and (iii) management and administration of pre-professional practices. In the simulation of scenarios, the institutional framework of the pre-professional practices is reflected in the administrative management that includes the monitoring of practices and curricular updating. While the management of pre-professional practices responds to the agricultural problem with adequate monitoring and curricular plan.Practical implication In Ecuador, pre-professional practices are part of the regulations imposed by the state towards higher education institutions. The research seeks strategies to connect the needs of the rural productive sector through pre-professional practices.Theoretical implication The research contributes to the link between pre-professional practices and the rural productive sector in the academic training of agricultural graduates.Originality/value Few empirical studies have examined the development of pre-professional practices in Ecuador as a strategy to link agricultural careers with the rural productive sector, where the student can identify job opportunities.
Information Theory Limits of Neuromorphic Energy Efficiency

The fundamental advantage of neuromorphic systems is their low power consumption, which emerges from their event-based computation implemented via spikes. However, we do not have a theory that explores the fundamental limits of the energy consumption that a neuromorphic system can achieve. In this work we present an approach to find those limitations using a mixture of principles from information theory and combinatorial techniques. We obtain a systematic way of finding the number of neurons and spikes per time unit that allow a required representational capacity.
Robust Data Transmission in 5G Networks Without Infrastructure Based on Blockchain Technology

Mobile ad hoc networks are faced with various security challenges emanating from malicious attacks. Their dynamic nature make nodes more vulnerable to attacks from either malicious nodes or intruders since there is no fixed infrastructure resulting in each node acting as router to transmit data. Currently, several solutions have been proposed and implemented in different ways aimed at eliminating or reducing these malicious attacks. This document proposes a model to increase the robustness of data transmission using blockchain technology. As blockchain technology forms the basis of our robust model, the simulation results obtained were analysed in terms of blockchain parameters. The achieved results show that the blockchain technology implemented in the MANET network ensures the elimination of malicious nodes in the network, based on changed or discarded data packets by individual nodes. In addition to these parameters, we analysed the proposed network in terms of the number of generated blocks and the rate of malicious nodes in the network, because these parameters have a great impact on the robustness of data communication.
Academic Cooperation and Mobility: In the Context of the Covid-19 Pandemic

In the article, through the prism of the analysis of the founding documents, policies and practices of the EU for the development of higher education, formulates and argues the author’s position regarding the strategic perspectives and prerequisites for internalization of higher education against the background of the COVID-19 pandemic. Some major challenges have been formulated that hinder the natural connections between universities around the world. Some expected long-term benefits from the development of full internalization in the conditions of digitalization of higher education are substantiated on the example of the activity of Sofia University and the conducted research. In conclusion, the author argues that the mission of universities to stay connected is a priority, even in the pandemic situation.
Data Literacy Scale Development for High School Students

In this study, it was aimed to develop a scale to determine the data literacy level of high school students.The sample of the research consists of the students studying at high school in the province of Afyonkarahisar, Turkey in the 2020-2021 academic year.Exploratory sequential design, one of the mixed methods was employed in the study.The data were analyzed by Exploratory Factor Analysis (EFA) and it resulted in a structure with 21 items and four subdimensions named as data literacy awareness, self-awareness, data access, data literacy self-efficacy.Cronbach's alpha values for the subscales were found to be 0.910, 0.821, 0.722 and 0.648, respectively and 0.918 for the scale.It can be accepted that the data literacy scale for high school students, consisting of 21 items and 4 sub-dimensions, is a reliable scale.
Protecting unknown qubit states from decoherence of qubit channels by weak measurement

Abstract The problem of combating de-coherence by weak measurements has already been studied for the amplitude damping channel and for specific input states. We generalize this to a large four-parameter family of qubit channels and for the average fidelity over all pure states. As a by-product we classify all the qubit channels which have one invariant pure state and show that the parameter manifold of these channels is isomorphic to S 2 × S 1 × S 1 and contains many interesting sub-classes of channels. By tuning the parameter of the weak measurement, we show that it is possible to increase the average input–output fidelity without blocking too many particles to pass through the channel. Quantitative analysis reveals that the increase of average fidelity can be up to 0.3, by blocking less than half of the particles.