Reviewerly/exHarmony huggingface dataset:
Transformation Pathways to Smart Villages

This chapter shifts from an urban biased smart concept linked to cities to a localised rural perspective using a case study of Mulenzhe Village located in the Limpopo Province of South Africa. The chapter reveals how Mulenzhe transformed into a smart village through reorganisation of a social system. This study adopted a qualitative phenomenological case study research design to reveal the motivations behind the transformation pathway adopted as well as the challenges and opportunities encountered. The construction of the Nandoni Dam in 1998 witnessed a relocation of 402 households from 33 villages located on a flood line considered a flood disaster risk zone in the Mulenzhe area. The relocation process and allocation of new homesteads in Mulenzhe Village marked the inception of a smart village concept. Enhanced rural–urban linkages emerge as one of the important opportunities from the smart village concept. Distorted developmental outcomes are visible as some villages that benefited from the relocation exercise are far from being rural. Transformation of rural spaces into smart villages requires local-level visionary leadership; reorganisation of the social structure including dynamics to access land and other property rights and provisioning of adequate basic services and infrastructure to local communities by the local municipalities. The Internet of Things and connectivity are key to the transformation into smart villages. This requires integrated planning that brings together infrastructure and supporting services. There is need for deconstructing the narrative of associating smartness with urban spaces. Transformation pathways to smart villages are important to spatial planning functions in Africa.
Nonprofit HR departments focusing on employee engagement, learning and development

Board & Administrator for Administrators OnlyVolume 38, Issue 8 p. 8-8 Articles Nonprofit HR departments focusing on employee engagement, learning and development First published: 07 March 2022 https://doi.org/10.1002/ban.31368Read 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 onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume38, Issue8April 2022Pages 8-8 RelatedInformation
Artificial Intelligence and Machine Learning

In the present scenario, cancer is regarded as the deadliest virus across the globe. Cancer is medically defined as a malignant neoplasm; it is a large group of diseases implicating unregulated cell growth in the human body. It can be diagnosed by categorizing tumors into two different types: malignant and benign. Benign tumors are less deadly than malignant ones. They represent an unnatural outgrowth but rarely lead to patients’ death, yet some can increase the possibility of developing cancer. Nowadays, in cancer research, the early diagnosis and prognosis of a cancer species have become necessary as they can facilitate the subsequent clinical management. Machine learning (ML) is a branch of artificial intelligence that employs various statistical, probabilistic, and optimization techniques that allow computers to learn from past examples and detect hard-to-discern patterns from large, noisy, or complex datasets. Several types of cancer detection and classification using machine assistance have opened up a new research area for early detection of cancer, which has been shown to reduce manual system impairments. These techniques can be utilized to model the progression and malignancy for cancer treatment. Taking into account all these concerns, in this chapter we aim to discuss various types of cancer and how ML and deep learning algorithms like decision tree, artificial neural networks, deep neural networks (DNN), and support vector machine help to detect and classify cancer. The predictive accuracy of these ML algorithms in the treatment of cancer has also been discussed.
CHALLENGES OF MODERN EDUCATION - ONLINE TEACHING AND SARS-COV-2 VIRUS

S. Kadum 1, H. Koroman2, M. Hmelak 3 1Faculty of Educational Sciences, University Juraj Dobrila of Pula (CROATIA) 2Elementary school „Ivan Goran Kovačić“ (CROATIA) 3University of Maribor, Faculty of Education (SLOVENIA)
EXPLORING THE IMPACT OF AN ONLINE EDUCATIONAL INTERVENTION ON RESIDENTS AND STAFF WITHIN NURSING AND RESIDENTIAL HOMES IN THE UK

Care homes are at the front of social care provision often managing long term and complex conditions. The advent of the global pandemic in 2020 highlighted the challenges that social care staff were facing as they were required to undertake more complex care delivery and advanced decision making as wider services withdrew in response to the Government’s Covid-19 guidelines. At a time when social care is needing additional support, funding increase plans have excluded budgets for workforce education and training presenting a risk to quality of care and accessibility for those most in need. This has led to a recognition of the need to develop new and innovative educational programmes for those working within the social care sector. A pilot programme of education was designed and developed for carers working within elderly residential and nursing care facilities in the North of England providing quality assured education. The aims were to equip carers with the knowledge and skills to improve residents’ outcomes by addressing unwarranted variations in care, promoting safe care and reducing unplanned hospital admissions. Further aims were to explore a model of education that facilities learning providing optimal opportunity and accessibility for a diverse workforce under pressure. The approach used was an asynchronous teaching method, delivered via an online quality assured learning package. Developed by a team of academics the interactive online packages were designed to engage students in a journey of exploration and learning providing an opportunity to develop core knowledge and clinical skills. A scoping exercise with care home managers was undertaken to identify the educational needs of the carers working within the care homes. In response twelve modules were developed and included within the pilot programme. Evaluation of the education was undertaken using a qualitative approach through the thematic analysis of comments provided in short reflections of learning for each module. A total number of n=47 reflections were analysed, and the key themes identified were those relating to professional hierarchy, professional boundaries and the emotional impact of caring. Overall findings suggest that the impact of using asynchronous digital learning and reflective narrative enables the students to express, in their own words and regardless of their experience the impact of their learning. There was evidence of carers implementing learned knowledge and skills into practice whilst demonstrating their ability to be self-aware.
Virtual Machine Migration in Cloud Computing

Cloud computing provides multiple services to users through the internet and these services include cloud storage, applications, servers, security and large network access. Virtual Machine allows the user to emulate multiple operating systems on a single computer; with the help of virtual machine migration users can transfer operating system instances from one computer to multiple computer machines. In this paper we will be discussing VM migration in cloud and also I will explain the whole procedure of VM migration. The two methods through which we can perform VM migration are Live VM migration and NON-live VM migration.VM migration also helps in managing the loads of the multiple machines and with VM we can save power consumption. People have written about cloud computing and virtual machines in previous studies, but in this research, we'll speak about virtual machine migration in cloud computing, as well as the techniques that are used in the VM migration process. I have used table to show the differences between VM migration techniques.
Analysis of learner’s perceptions about online learning and seeking the education direction

This study understood the perception of non-face-to-face classes(online classes) of Chinese literature majors through a survey and suggested the development direction of non-face-to-face classes. As a result of the survey, the language practice class showed higher lecture satisfaction than the theoretical class in interaction satisfaction and class content satisfaction according to subject type. In the non-face-to-face classes method, the synchronous class showed a higher level of interaction satisfaction than the asynchronous recorded class, but there was no statistically significant difference from the mixed-type class. Also, the difference in satisfaction according to the subject type did not change according to the non-face-to-face teaching method. Considering these results, in the future non-face-to-face class design, it will be necessary to design a class that considers the interaction between the professor and the student in the case of the recorded theoretical class. Quantitative interaction, diversification of methods, and immediate feedback will need to be secured. It seems appropriate to select a synchronous(real-time) class or a mixed-type class with high interaction satisfaction for language practice class as well. Regarding the advantages of non-face-to-face classess, learners cited freedom of time, convenience of place, and possibility of repeat viewing. On the other hand, the problems were the lack of self-directed learning will, system problems, and lack of interaction. In the future, along with the content and technical improvement of non-face-to-face classess, the emotional problems of learners should be considered.
Time-domain source parameter estimation of Mw 3-7 earthquakes in Japan from a large database of moment-rate functions

Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at Geophysical Journal International. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing an older version [v1]Go to new versionTime-domain source parameter estimation of Mw 3-7 earthquakes in Japan from a large database of moment-rate functionsAuthorsKeisukeYoshidaiDHirooKanamoriiDSee all authors Keisuke YoshidaiDCorresponding Authorâ€¢ Submitting AuthorTohoku UniversityiDhttps://orcid.org/0000-0002-1058-9811view email addressThe email was not providedcopy email addressHiroo KanamoriiDCalifornia Institute of TechnologyiDhttps://orcid.org/0000-0001-8219-9428view email addressThe email was not providedcopy email addressPublished Online:Thu, 24 Mar 2022DOI:https://doi.org/10.1002/essoar.10510900.1Download PDFDownload PDFCiteAbout ToolsAdd to FavoritesTrack Citations ShareShare onFacebookTwitterLinked InRedditEmailQR Code Abstract
Transformation of Scientific Activity: Network Direction. Review of the book “Detsentralizovannaya setevaya organizatsiya nauchnogo soobshchestva: perspektivy i problem” by A. V. Oleskin

The changes taking place in the modern world require a revision of the methods for implementing various types of social activities, including scientific ones. Established hierarchical ties among team members, teams, organizations, collaborations, countries cannot always provide an answer to the global challenges facing society. A restart of all communication channels is required. One of the ways to improve the efficiency of interaction among all interested participants in various processes is the formation and development of network forms of organizing activities. The purpose of this work is to analyze the main decentralized network scenarios of research, expert and scientific-commercial activities and identify the most suitable for their implementation. The author of the book defines as one of the options for a decentralized network organization that is effectively used in research activities, “hirama” – a network structure with several partial creative leaders, which allows solving various subtasks without separating the functions of team members: all network members work in parallel on several subproblems of one task. Elements of the alternative variants of network structures considered by the author can also be used in the formation of research teams to solve research problems. Thus, the author of the study reveals the options for the network organization of various types of social activity, including scientific, and determines the boundaries of their application in human activity.
HOW TO ENCOURAGE STUDENTS TO LEARN INDUSTRIAL INFORMATION TECHNOLOGY ON THEIR OWN. ONLINE RESOURCES AND ENTICING EXERCISES

Industrial Information Technology students had to adapt to confinement and space restrictions during the COVID-19 pandemics. During these months, they could not develop many of the firsthand exercises and projects we propose to them. Fortunately, there were positives too, since many of them discovered on their own that there are plenty of tools available for them to learn.Teachers also lived the same situation. How could we encourage our students to keep on learning in such a tough environment? How could they adapt to the new challenges the remote work and isolation brought with them?Additionally, Industrial IT courses are themselves challenging for Industrial Engineering students since it is a fast-evolving area and the tools and techniques change pretty fast. Students with basic programming skills face the challenge of programming functions in the Cloud, programming IoT devices, or developing container-based applications even during their internship periods in nearby enterprises. They should be able to adapt to the new scenarios they meet in their near-future work environment.In this context, the present work has been conducted. A series of challenges and demos have been launched parallel to the regular programme of the course. These challenges are presented in an enticing way using short videos and blog entries, and all of them provide online resources for doing the hands-on exercises proposed. From Cloud computing to learning new programming languages, the students are able to tackle the challenges presented using online tools such as Kaggle or Microsoft Azure. the teachers give the guidelines for achieving the goals, but the proposals have always open exercises that make easier to gain new skills during the learning process.The experience has had positive feedback from the students, both in the official surveys of the course and in the direct feedback we get from them.
COMPETENCIES MODEL OF TRAINING FUTURE PRIMARY SCHOOL TEACHERS TO WORK WITH CHILDREN WITH DISABILITIES IN AN INCLUSIVE ENVIRONMENT

Annotation:The relevance of the study is due to the need to improve the quality of training teachers for professional activities in conditions of inclusion. A significant role in this process is assigned to pedagogical colleges, based on a competency-based approach to teaching students to work with people with disabilities and disabilities. Special attention should be paid to the special organization of correctional and developmental work with children with severe speech pathology. The problem of the research is to determine the conditions for the implementation of the competence approach in the formation of professional competencies among students of a pedagogical college. The purpose of our research was to develop and test a model of practice-oriented training of students of a pedagogical college for working with children with severe speech pathology in an inclusive education based on a competency-based approach.The scientific novelty and theoretical significance of the research is the substantiation and implementation of a set of pedagogical conditions: organizational, contributing to the formation of professional competencies in the field of inclusive education; technological, aimed at designing a practice-oriented model of training teacher college students to work with children with severe speech pathology, based on a competence-based approach; methodological, consisting in the development and selection of diagnostic tools that allow to identify the level of formation of professional competencies presented in various disciplines, modules, courses of additional professional retraining. The study was conducted on the basis of the Shakhty Pedagogical College among 4th year students studying in the specialty 44.02.05 Correctional pedagogy in primary education in the amount of 25 people.Research methods:To assess the level of formation of professional competencies in working with children with severe speech pathology, in the conditions of inclusive education, we used a comprehensive test developed in accordance with the Federal State Educational Standard in the direction of preparation 44.02.05. Correctional pedagogy in primary education. Also, the students were offered the A.A. Reana “Motivation for Success and Fear of Failure”.The priority condition that increases the level of the aforementioned readiness and ensures the development of the competitiveness of the future specialist is the formation of professional competencies of students through the implementation of an integrated interdisciplinary approach to the study of the problem of teaching children with severe speech pathology.The practical significance of the research is the development and testing on the basis of the Shakhty Pedagogical College of a variable program of general professional training "Inclusive Education", the creation of a social and pedagogical workshop for students; compiling the content of teaching materials for students on working with children with severe speech pathology.
THE IMPORTANCE OF DEVELOPING STUDENTS' DIGITAL SKILLS FOR THE DIGITAL TRANSFORMATION OF THE CURRICULUM

L. Luić 1, M. Alić 2 1University North (CROATIA) 2University of Zagreb, Faculty of Humanities and Social Sciences (CROATIA)
A machine-learning approach for the reconstruction of ground shaking fields in real-time

&amp;lt;p&amp;gt;Real-time monitoring is of primary importance for rapid and targeted emergency operations after potentially destructive earthquakes. A key aspect in determining the impact of an earthquake is the reconstruction of the ground shaking field, usually expressed as a peak ground parameter. Traditional algorithms approach this task by computing the ground shaking field from the punctual data at the stations and relying on ground motion prediction equations (GMPEs) computed on estimates of the earthquake location where the instrumental data are missing. The results of such algorithms are then subordinate to the evaluation of location and magnitude which can take several minutes.&amp;lt;br&amp;gt;To fill the gap between the arrival of the data and the (first preliminary) estimation (usually computed in a few minutes), we introduce a new data-driven algorithm that exploits the information from the station data only. Such an algorithm, consisting of an ensemble of convolutional neural networks (CNNs) trained with a database of ground shaking maps produced with traditional algorithms, can provide real-time estimates of the ground shaking field and the associated uncertainty. Since CNNs cannot handle sparse data a Voronoi tessellation of a specific peak ground parameter recorded at the stations is computed and used as an input to the CNNs; site effects and network geometry are accounted for using a (normalized) Vs30 map and a station location map, respectively.&amp;lt;br&amp;gt;The developed method is robust to noise, can handle network geometry changes over time without the need for retraining, and can resolve multiple simultaneous events. Although having a lower resolution, the results obtained are compatible with the ones from traditional methods. A fully-operational version of the algorithm is running on the servers at the Department of Mathematics and Geosciences of the University of Trieste showing real-time capabilities in handling stations from multiple Italian strong-motion networks and outputting results with a resolution of 0.05&amp;amp;#176;x0.05&amp;amp;#176;.&amp;lt;/p&amp;gt;
Improvement of value added of regional waste management through Maggots cultivation

A social community, namely RW 12 at Rancamulya Village, Pamengpeuk District, manages independently their region's household waste in a simple way. However, the community does not separate the organic and non-organic waste. This causes environmental pollution and makes the waste does not have added value. Therefore, this community service program aims at increasing community knowledge and participation in household waste management and practicing Maggot cultivation to provide added value. The program is conducted using the participatory method through waste management training to change the community mindset about the importance of protecting the environment and motivate them to sort waste from their homes. In addition, practicing Maggot cultivation is carried out to increase the added value of organic waste. The results show that knowledge and participation of the community in sorting waste increase, and the economic value of waste also increase due to the sale of cultivated Maggots and inorganic waste.
A noise-resilient online learning algorithm with ramp loss for ordinal regression

Ordinal regression has been widely used in applications, such as credit portfolio management, recommendation systems, and ecology, where the core task is to predict the labels on ordinal scales. Due to its learning efficiency, online ordinal regression using passive aggressive (PA) algorithms has gained a much attention for solving large-scale ranking problems. However, the PA method is sensitive to noise especially in the scenario of streaming data, where the ranking of data samples may change dramatically. In this paper, we propose a noise-resilient online learning algorithm using the Ramp loss function, called PA-RAMP, to improve the performance of PA method for noisy data streams. Also, we validate the order preservation of thresholds of the proposed algorithm. Experiments on real-world data sets demonstrate that the proposed noise-resilient online ordinal regression algorithm is more robust and efficient than state-of-the-art online ordinal regression algorithms.
THE ROLE OF WEBSITE FUNCTIONALITIES FOR STAKEHOLDERS’ COLLABORATION AND FUTURE ENLARGEMENT OF UNIVERSITY-CENTERED MULTI ACTORS' DIGITAL DISTRIBUTED RESEARCH INFRASTRUCTURE

Information about the paper titled "THE ROLE OF WEBSITE FUNCTIONALITIES FOR STAKEHOLDERS’ COLLABORATION AND FUTURE ENLARGEMENT OF UNIVERSITY-CENTERED MULTI ACTORS' DIGITAL DISTRIBUTED RESEARCH INFRASTRUCTURE" at IATED Digital Library
Research on multi-end trust based on blockchain in distribution network business

With the continuous deepening of 5G communication application in the field of power Internet of Things, a large number of terminal devices are widely distributed in the distribution network, and a large amount of data needs to be trusted in a relatively short time.The traditional manual inspection method is inefficient and has insufficient safety protection. This paper first analyzes some common consensus mechanisms, and then proposes an improved consensus mechanism, which inhibits the phenomenon of node dishonesty and reduces the hidden danger of data security by means of proof of reputation (PoR).