Reviewerly/exHarmony huggingface dataset:
Digital Competencies in the Context of Preschool Music Education

Given the digitalization of modern society and the way a child acquires musical experiences, practitioners are expected to apply various media in everyday educational praxis. The theoretical framework of technological pedagogical content knowledge (TPACK) is an approach that connects the knowledge of educators with Information and Communications Technology (ICT), concurrently developing technology-supported pedagogical knowledge and integration of technology into education. The paper aims to investigate educators’ self-assessment of their digital competencies and their implementation in musical activities with preschool children. The respondents are full-time and part-time graduate students of the Early Childhood and Preschool Education study program from faculties of teacher education in Croatia. The obtained results indicate that 91,1 % of respondents didn’t have any formal ICT education while 59,5 % didn’t have any additional type of music education except during their studies. Nevertheless, the greatest knowledge participants have shown on the competencies for using digital tools (CFUDT) scale succeeded by a technological knowledge (TK) scale of the adapted TPACK-TCCMA survey, thus the respondents rated their digital and ICT competencies exceedingly high unlike music and pedagogical competencies. Considering the results obtained, it can be concluded that additional attention should be given to the development of digital, music, and pedagogical competencies during studies that will be applicable in both music and other areas of education.
Corporate Governance by South African State-Owned Entities (SOEs) – Contributing to Achieving Agenda 2063

Following centuries of institutional colonization, Africa remains in dire need of development and has failed to realize its potential to drive sustainable economic growth and prosperity. Despite the residual contribution of Africa’s colonial legacy to this malaise, it is suggested that African states should stop blaming coloniality for their failure to develop and accept responsibility for their own socioeconomic development. To arrest the downward spiral and provide the platform necessary to drive sustainable development across the African continent, the African Union launched Agenda 2063. To claim its rightful place in the global economy with a renewed focus on accountability, African states must change their development trajectory and address the economic, environmental, social, and governance interests of their legitimate stakeholders as they strive for sustainable development and their beneficiaries with added value. African states can no longer afford to remain passive participants in the process, providing raw materials for beneficiation by the Global North. The findings of this paper are based on the assertion that effectively governed state-owned entities (SOEs) represent vehicles available to states to leverage their drive for socioeconomic development in their respective countries, thereby contributing to achieving the Agenda 2063 goals. Despite relying on quantitative data to inform a corporate governance conformance matrix, purposively developed from the OECD Guidelines, World Bank Toolkit, and Agenda 2063, the study adopts an interpretative approach to thematically analyze the content of the published annual reports of South African SOEs. While the relatively high conformance scores achieved by the South African SOEs appear to suggest conformance with strong corporate governance practices. It belies the fact that several of these highly compliant SOEs are currently under investigation in relation to numerous instances of serious fraud and corruption. The incongruence of these public disclosures with the de facto situation implies that they do not represent a meaningful attempt by SOEs to discharge their governance obligations effectively. Instead, they may simply be an attempt to placate stakeholders that these SOEs were being effectively governed rather than to account to stakeholders meaningfully. The study concludes that merely imposing a regulatory, corporate governance framework will not be sufficient to ensure the effectiveness of governance in SOEs, but rather that effective processes must be established to monitor and enforce compliance with these frameworks, together with effective consequence management for non-compliance.
Learning Literature in English in the 21st Century: Turning Digital World Challenges into Grabbable Opportunities

The 21st century unravels how education needs to provide learners with transformative skills, knowledge, attitudes and competencies to help them maximize opportunities while coping with challenges related with the digital world. Teaching Literature nowadays must target not only literary competence development but also other subjects especially in a world driven by Information Communication Technology (ICT) in all sectors of life. In this study, the researcher delves into the merits and demerits of learning literature in a second language in a digital world. Using questionnaire administered to students in literary combinations, information collected revealed lack of access to some important online materials, few or lack of ICT gadgets, oral tradition dwindling, the copy paste culture, students laziness and little interest in reading among other challenges. Opportunities were identified such as accessible various online sources, fresh materials, easy material storage, research and innovation facilities, English language learning through literature and integration of ICT in literature among others. To produce graduates fitting the needs of the current century, it is pertinent to address the reported challenges and grab the identified opportunities with two hands.
CNN VE LSTM TABANLI HİBRİT BİR DERİN ÖĞRENME MODELİ İLE ÇOK ETİKETLİ METİN ANALİZİ

In this article, it is aimed to categorize meaningful content from uncontrolled growing written social sharing data using natural language processing. Uncategorized data can disturb social sharing users with an increasing user network due to deprecating and negative content. For the stated reason, a hybrid model based on CNN and LSTM has been proposed to automatically classify all written social sharing content, both positive and negative, into defined target tags. With the proposed hybrid model, it is aimed at automatically classifying the content of the social sharing system into different categories by using the simplest embedding layer, keras. As a result of the experimental studies carried out, a better result was obtained than in the different studies in the literature using the same data set with the proposed method. The obtained performance results show that the proposed method can be applied to different multilabel text analysis problems.
Analysis of the Impact of the Development Level of Aerobics Movement on the Public Health of the Whole Population Based on Artificial Intelligence Technology

With the enhancement of China's comprehensive national power and the improvement of people's living standards, health has become the goal that people pursue. While people are thirsty for extensive knowledge and a healthy body, they also pay more attention to the cultivation of elegant temperament and the enjoyment of beauty, and aerobics has become a hot spot for national fitness with its advantages of coordinated and beautiful movements, bright and cheerful rhythm and obvious fitness effects. Aerobics is a new popular fitness sports, from the beginning of development by most fitness enthusiasts, especially it is a women's favorite. To this end, the characteristics, value, status, and role of aerobics in the public health of all people are discussed, and the problems of poor recognition effect in the existing aerobics difficulty aerobics action recognition methods are proposed to apply the graph convolutional neural network to the aerobics difficulty aerobics action recognition. The video of aerobics is divided into several images, and the background of the aerobics difficult aerobics action image is eliminated, and the gray scale co-generation matrix is set to estimate the local area blur kernel of the difficult action image to correct the visual error of the difficult action image. "change to" The aerobics action is divided into several difficult action images, and the gray-scale symbiosis matrix is set to estimate the local area fuzzy core of the difficult action image, and correct the visual error of the difficult action image. On this basis, the graph convolutional neural network is pre-trained to construct a human-directed spatial-temporal skeleton map, and the human-directed spatial-temporal map representation is modeled with temporal dynamic information to achieve aerobics difficult aerobics action recognition. The experimental results show that the recognition time of the difficult aerobics movements based on the graph convolutional neural network is shorter and the number of false recognitions is less in complex and simple backgrounds, which proves that the proposed method improves the recognition of difficult aerobics movements to achieve the goal of promoting the development level of aerobics and improving the public health of all people.
Climate Variation Assessment Based on Rainfall and Temperature in Ilorin Kwara State Nigeria

In recent times and across the globe, the concept of climate has gained much relevance because of its dynamic and complex nature and the significant influence it has on various aspects of the environment, including the increasing threat of global climate change. This study focuses on the assessment of climatic variation in Ilorin based on the variations in rainfall and temperature within the period 1980-2015. Time Series Analysis was used to determine the trend of rainfall and temperature within the period 1980-2015. The 5-Year Moving Average was used to smoothen the time series and to eliminate unwanted fluctuations. Linear Regression was used to estimate the value of variable Y (total rainfall or average minimum temperature), corresponding to a given value of variable X (time), while MAKESEN software was used to determine the temporal trend of rainfall indices within the study period. Furthermore, the study focused on the spatial variations of total rainfall and average minimum temperature in the study area. The result revealed that there is downward trend of 0.814 mm per year in the annual mean rainfall and also there is an upward trend of 0.029 per year in average annual minimum temperature and total annual maximum temperature has an upward trend of 0.01 mm per year. Thus, the rainfall is decreasing and temperature tends to increase. Key word: Climate variability, Rainfall, Temperature, Period, Temporal trend, Kwara state
Machine Learning Facemask Detection Models for COVID-19

With the breakout of the global pandemic known as COVID-19 it has forever changed ways of doing everyday things. Moreover, the discovery of new variants it has compelled regulatory authorities make the use of face mask in public places mandatory. Public places such as the public transport, shopping mall and universities where crowds of people come into contact with one another. It further exacerbates the issue by confining the masses in an indoor premise. As part of the enforcing the mandatory sop protocol work force or manpower is allocated that serve as gatekeepers to ensure the use of face mask. Due to the number of people at public places it increases the probability of human error. The solution is to incorporate the use of Artificial Intelligence that would use effective machine learning models to train and develop an effective and accurate facemask detection system. This study takes note of the existing system and develops one using the open-source library called TensorFlow to provide it with different variations of datasets that would simulate real world scenarios. With the implementation of the face mask detection system, it aims to replace manpower and allow artificial intelligence to conduct unsupervised operation that would be more efficient and effective.
Mineralogical and Petrographic Characteristics of the Jurassic Kaban Dacite in the Eastern Sakarya Zone (Olur/Erzurum)

The volcanic rocks of the Kaban dacite, which is the subject of the study, are located in the Olur (Erzurum) region of the eastern Sakarya zone. This zone is divided into four tectonic slices as Hopa-Borçka zone, Artvin Yusufeli zone, Olur Tortum zone and Erzurum Kars Ophiolite zone from north to south, east of the Eastern pontides, and the investigated rocks are located within the Olur-Tortum zone. These rocks are represented by dacites and rhyolites in the study area. Petrographically, it is observed in fine grained grayish, greenish, earthy colors. The main mineral contents are plagioclase, quartz and K-feldspars. Plagioclase, quartz and K-feldspars occur as phenocrysts, but are also observed in microlithic pastes. Plagioclases are characteristic with albite and polysynthetic twinning. Quartz is in the form of anhedral crystals and is distinguished from K-feldspars by its transparent colors in a single nicol.
World robot challenge 2020 – partner robot: a data-driven approach for room tidying with mobile manipulator

Tidying up a household environment using a mobile manipulator poses various challenges in robotics, such as adaptation to large real-world environmental variations, and safe and robust deployment in the presence of humans. The Partner Robot Challenge in World Robot Challenge (WRC) 2020, a global competition held in September 2021, benchmarked tidying tasks in real home environments, and, importantly, tested for full system performances. For this challenge, we developed an entire household service robot system, which leverages a data-driven approach to adapt to numerous edge cases that occur during the execution, instead of classical manual pre-programmed solutions. In this paper, we describe the core ingredients of the proposed robot system, including visual recognition, object manipulation, and motion planning. Our robot system won the second prize, verifying the effectiveness and potential of data-driven robot systems for mobile manipulation in home environments.
Blockchain-based Attendance Management and Payroll System using Hyperledger Composer Framework

Data storage for attendance at a company or agency is usually stored in a local or cloud database. This type of storage has several issues, such as privacy and data integrity, because several parties will fully regulate privacy and data integrity. Therefore, a data storage system is needed that can provide privacy, security, and data integrity to maintain the authenticity of sensitive data. This study wants to develop to implement blockchain technology to store employee attendance data from the HR department in a company. Employee attendance data is taken from the Angular web application integrated with a permissioned blockchain framework called Hyperledger Composer. This study chose Hyperledger Composer because this type of blockchain has a fast validation time. Hyperledger Composer has a Representational State Transfer Application Programming Interface (REST API) named composer-rest-server, which allows Hyperledger blockchain to interact with other components. The implementation results show that Hyperledger Composer can be functionally used as data storage for the attendance management and Payroll system. In addition, Hyperledger Composer performance is measured by evaluating block transaction times. The evaluation of the Hyperledger Composer is done in three ways: directly within the Hyperledger Composer, using the Angular web application through the REST API, and using JMeter through the REST API. As a result, testing for making transaction blocks varies from 1 - 17ms on an experiment directly in Hyperledger Composer, 5 - 296ms through the REST API using JMeter tools, and 1 - 4270ms through the Angular Web Application. The result shows that the performance of the REST API produced by the composer-rest-server is recorded faster than Ethereum. With these results, composer-rest-server can handle systems that require fast transaction times, such as voting systems, health monitoring, and the Internet of Things (IoT) application, because the average time to conduct transactions is still under one second. Thus, Hyperledger as attendance data storage can provide privacy and data integrity.
Sending Emails Using Graph API

Modern applications need to send emails to the stakeholders, customers, or developers or anyone related to the application. There can be a scenario where the customer is placing an order in an e-commerce application. And once the order gets placed, it intimates the customer with the order and the dispatch status. If the order fails, it should notify the operations team to look into it. You can configure your SMTP server for mailing needs on a virtual machine on Azure. However, you will end up maintaining the virtual machine and spend cost and effort to keep the SMTP server up and running. To address this issue, Microsoft provides Graph API deployed on Azure and helps you interact with Microsoft 365 Core services. Using the Graph API, you can send emails.
TERMS: Task management policies to achieve high performance for mixed workloads using surplus resources

Resource contentions and performance interferences can lead to workload performance degradation in mixed-workload deployment clusters. Previous work guarantees the resource requirements of latency-sensitive tasks and reduces performance losses to batch jobs by reclaiming surplus resources from over-provisioned tasks. While the fragmentation of resources leads to a mismatch between provisioned resources and task requirements, resulting in high operation overheads and losses of task fairness. This paper proposes TERMS, the task management policies based on task relevance, resource distribution, and task fairness to achieve efficient and low-cost task management. TERMS mainly includes three types of management policies. The task scheduling policy can schedule new tasks according to task relevance. Task selection strategies select tasks for resource provisioning and task resumption based on resource requirements and task fairness. If necessary, the node selection strategy can be used to choose befitting target nodes based on task relevance and node resource information for task migration when eliminating straggler tasks. Evaluation results show that TERMS can further improve the performance of latency-sensitive services and batch jobs, reduce management overheads, and avoid operation failures.
Server placement method with conditional generative adversarial networks for designing edge computing systems

This paper proposes a server placement method with conditional generative adversarial networks (CGAN) for edge computing system design. The proposed method predicts appropriate location of edge servers without solving optimization problems by using network images for training CGAN. Through numerical evaluation, we show the effectiveness of the proposed method.
Road Semantic Segmentation and Traffic Object Detection Model Based on Encoder-Decoder CNN Architecture

Nowadays, there are lots of deep learning models being used, in the case of limited computing resources, the speed of performing object detection and semantic segmentation at the same time may encounter the problem of slowing down. To tackle this issue, we propose a multi-tasking learning model based on the Encoder-decoder CNN architecture, which merges the object detection and semantic segmentation models into one, thus could be trained with semantic segmentation task and object detection task at the same time, and applied on road and traffic object recognition in Taiwan's unique driving environment. Comparing to executing semantic segmentation and object detection models simultaneously, our proposed model has faster recognition speed and higher accuracy on Cityscapes dataset. The result shows that our proposed method can achieve faster recognition speed and maintain accuracy rate on an embedded platform of Nvidia Jetson TX2 with fewer computing resources.
Modification of the new conjugate gradient algorithm to solve nonlinear fuzzy equations

The conjugate gradient approach is a powerful tool that is used in a variety of areas to solve problems involving large-scale reduction. In this paper, we propose a new parameter in nonlinear conjugate gradient algorithms to solve nonlinear fuzzy equations based on Polak and Ribiere (PRP) method, where we prove the descent and global convergence properties of the proposed algorithm. In terms of numerical results, the new method has been compared with the methods of Fletcher (CD), Fletcher and Reeves (FR), and Polak and Ribiere (PRP). The proposed algorithm has outperformed the rest of the algorithms in the number of iterations and in finding the best value for the function and the best value for the variables.
Real-time Machine Learning Control for Robotic Manipulator by LNB: Lion Naïve Bayes Algorithm

A real-time Machine Learning Control (MLC) of articulated robotic manipulators is presented in this work by exploiting the Lion Optimization Algorithm (LOA) which is combined with the Naïve Bayes (LNB).Here, the proposed model is used for the real-time MLC of robotic manipulators by exploiting the "Fractional Order Proportional-Integral-Derivative (FOPID)" control scheme.Using the proposed LNB model MLC control gain parameters are tuned.By considering the dynamics of the actuator, to convene significant timing constraints, a "Real-Time Operating System (RTOS)" on a microprocessor collaborates with the LNB-MLC.At last, the Mechatronic design and investigational setup of a 6-degree of freedom (DOF) expressed robotic manipulator are modeled.The performance analysis is presented to show the advantage of the adopted models.While compared with the existing control models, the adopted optimization model has practice and theoretical consequences regarding online parameter tuning, real-time ability, and convergent behavior.In both industry and academia, the adopted MLC approaches are appropriate to design the real-time modern controllers.
Marginal structural models using calibrated weights with SuperLearner: application to longitudinal diabetes cohort.

Although machine learning has permeated many disciplines, the convergence of causal methods and machine learning remains sparse in the existing literature. Our aim was to formulate a marginal structural model in which we envisioned hypothetical (i.e. counterfactual) dynamic treatment regimes using a combination of drug therapies to manage diabetes: metformin, sulfonylurea and SGLT-2. We were interested in estimating “diabetes care provision” in next calendar year using a composite measure of chronic disease prevention and screening elements. We demonstrated the application of dynamic treatment regimes using the National Diabetes Action Canada Repository in which we applied a collection of mainstream statistical learning algorithms. We generated an ensemble of statistical learning algorithms using the SuperLearner based on the following base learners: (i) least absolute shrinkage and selection operator, (ii) ridge regression, (iii) elastic net, (iv) random forest, (v) gradient boosting machines, (vi) neural network. Each statistical learning algorithm was fitted using the pseudo-population with respect to the marginalization of the time-dependent confounding process. The covariate balance was assessed using the longitudinal (i.e. cumulative-time product) stabilized weights with calibrated restrictions. Our results indicated that the treatment drop-in cohorts (with respect to metformin, sulfonylurea and SGLT-2) may improve diabetes care provision in relation to treatment naïve cohort. As a clinical utility, we hope that this article will facilitate discussions around the prevention of adverse chronic outcomes associated with diabetes through the improvement of diabetes care provisions in primary care.