Reviewerly/exHarmony huggingface dataset:
Development and verification of boiler inspection robot for incinerators

In recent years, waste incineration facilities (hereinafter referred to as incinerators) to be constructed are equipped with boilers to generate steam from the heat of burning discarded garbage and recover energy to generate electricity. However, if the operation is continued, the boiler in the incinerator will accumulate ash and reduce the energy recovery efficiency, so it is necessary to clean and inspect it regularly. At that time, a worker enters the incinerator and cleans it, and inspects the boiler pipe for any unpolished residue. There is ash containing dioxins in the incinerator, and the cleaning workers take careful measures to prevent dust. In terms of work efficiency and safety, it is an urgent task to perform cleaning and inspection work with a robot. We created a robot that moves between narrow pipes with an emphasis on heat exchange.
The University Recommendation System for Higher Education

Recommendation system has become a requirement in today’s world. This recommendation system is spread over large sectors right from education, entertainment, health, business etc. The University Recommendation System is a system which recommend right university for the students based on their GRE, TOFEL score. Due to rapid increase in data and lack of learning in education filed, students often halt selecting wrong universities. Thus, there is a need of recommendation system which is trained to understand the user’s requirements and suggests them that suitable university which they need. Typically, recommendation mechanisms are used based on user’s historical data. It helps the user to discover information and settle on right choices where they do not have the required learning to judge a specific item. This paper explores the flow, types and uses of recommendation system in education.
Performance Analysis of Different GAN Models: DC-GAN and LS-GAN

Generative Adversarial Networks (GAN) is an excellent method to generate fake images using the training dataset provided. Generative Adversarial Network consists of two sub-networks that is generator and discriminator networks that compete against each to produce the desired output. Regular GAN is not compatible enough for synthesizing images. However, with the use of CNN models for the sub-networks, it is easier to learn the distribution of training images. We demonstrate the performance of CNN based DC-GAN model. However, it can be noticed that sigmoid based cross entropy loss function results in mode collapse and convergence problems. Hence, we further investigated the research using LS-GAN model which uses least square error loss function. Both the models were trained on the same training datasets. To validate the results, generator & discriminator loss and FID scores were calculated. In this paper, fthe different datasets are used to carry out the experiments. Comparison between DC-GAN and LS-GAN models, the impact of changing hyperparameters and the significance of calculated metrics is illustrated.
Assessing The Impact of Empowerment on Achieving Employee Performance Mediating Role of Information Communication Technology

Human resources are seen as one of the most important assets that must be maintained, especially the existence of employees' performance, which is needed for the sustainability of organizational performance.Factors which may affect the employees' performance, are information and communication technology (ICT) and empowerment.Therefore, there is a need for human resources that are able to master technology quickly, adaptively and responsively to changing technologies.Also, it is necessary to empower employees that operate useful information and communication technology to effectively and efficiently complete their work.This study aims to determine the relationship of empowerment toward the organizational performance, to determine the role of ICT as a mediator of empowerment toward the organizational performance.A total of 200 questionnaires were distributed to employees Regional Drinking Water Company East Java Indonesia, and finally 128 questionnaires were collected and used as a sample in this study.The data were examined using smart Partial Least Squares (PLS).The results showed that empowerment has a direct effect on employee performance.In addition, information and communication technology mediated the effect of empowerment on employee performance.
Exact Mapping Between a Laser Network and the Classical XY Hamiltonian

We demonstrate experimentally and validate theoretically an exact mapping between coupled-lasers networks and classical spin Hamiltonians by adjusting the loss rate of the individual lasers.
The Fundamentality and Non-Fundamentality of Ontological Categories

In this chapter, I propose a solution to an almost ignored problem in metaphysics and metametaphysics: what is categorial fundamentality and non-fundamentality? My proposal builds on E. J. Lowe’s view on the issue. By means of the newcomer notion of generic identity, I can give an account of something that Lowe did not explicate: the constitution of formal ontological relations. Formal ontological relations (e.g. instantiation) are internal relations that determine ontological form and category-membership. I argue that categorial fundamentality is having membership fully determined by a simple formal ontological relation or relations jointly in an order, and categorial non-fundamentality is having membership at least partly determined by a derived formal ontological relation in an order. In contrast to Lowe, my proposal does not presuppose non-modal essentialism.
Ranked Hunger Games Search Algorithms with Gaussian Distribution-based Fitness Distance Balance Strategy

Fitness-Distance Balance with Functional Weights (FW) is a new and improved selection method based on Fitness-Distance Balance (FDB). FDB can be used to solve the problem of premature convergence. And FW further improves the performance of FDB selection strategy by changing the weights in FDB from fixed values to randomly generated using distribution functions. Hunger Games Search (HGS) is a meta-heuristic algorithm created inspired by animal hunger behavior. HGS algorithm can get good performance when tested on various problems. But the HGS algorithm still has the problem of premature convergence and easily falls into local search. Therefore, we incorporate the FW selection strategy into the HGS algorithm and propose the FWHGS. and tested on the IEEE CEC2017 benchmark function, which has a great improvement compared to the HGS algorithm.
Text Clustering using K-MEAN

Document clustering allows the user to add similar documents to a group. For many years, it has been a fascinating research topic, developed various methods and techniques. However, the study focuses mostly on English and high-resource languages. About Pakistan national anthems, this research gives an experimental estimation of clustering techniques. Because of its short length, thematically clustering Anthem is a difficult task. This paper extracted various characteristics, including stop-words, stemming, corpus tokenization, noise removal, and TF-IDF features from the anthem, and the clustering was conducted using the K-Means algorithm. The results show that a clustering strategy paired with a K-mean clustering algorithm with TF-IDF features has already been used. The dataset is available on GitHub (https://www.kaggle.com/lucasturtle/national-anthems-of- the-world )
Evaluation of Concrete Durability Based on Cloud Model and D-S Evidence Theory

Abstract The environment in alpine region is complex and harsh, and the durability of concrete is seriously affected by freeze-thaw and salt invasion. In this paper, the relative dynamic modulus of elasticity, chloride permeability coefficient, mass loss rate and carbonation depth are selected as the evaluation indexes of concrete durability in Northeast China. Based on prior knowledge and expert group decision-making, the durability grade is divided and the evaluation standard is established; The evaluation model of concrete durability based on cloud model and D-S evidence theory is established. According to the engineering experimental data, the membership degree of concrete durability evaluation index in different grades is obtained through the correlation measurement of cloud model. The normalized evidence is formed and fused by D-S evidence theory. The results show that the durability grade of concrete is grade I, which is consistent with the actual project. It shows that using cloud model and evidence theory evaluation model to evaluate the durability of concrete is a new and effective method.
Testing as an Effective Method for Assessing the Quality of Students' Training Achievements

Electronic educational resources including testing, which simplify the process of searching and structuring educational information have been used more often in educational organizations recently. They have made learning more accessible and interesting for students of higher and vocational education institutions, especially in the context of a pandemic. The article provides a brief theoretical justification of the advantages of testing students' academic achievements. The emphasis is placed on the importance of testing as a tool for remote control of students' knowledge, the advantages and disadvantages of using tests both in the educational process of higher and secondary vocational education and in order to control the acquired knowledge are revealed. The study was carried out by the authors on the basis of a comprehensive use of theoretical and empirical methods. The leading theoretical methods were: analysis, generalization, concretization, forecasting, modeling. The work used such empirical methods as conversations, pedagogical observation, questionnaires, expert evaluation, testing, analysis of performance, generalization of pedagogical experience, methods of statistical processing of experimental data. The results presented in the article of the study of testing of students of MPSU at the initial (entrance test), current and final stage of measuring the level of educational achievements of students demonstrated an increase in the values of the effectiveness of testing at all stages of its implementation, an increase in the effectiveness of test control at the intermediate and final stages of the study. The results of the study made it possible to formulate promising directions for the development of problems of testing the quality of education of students of higher and professional education organizations: improving the forms and methods of analysis and interpretation of test results based on the invariant application of test models: improving the psychological and pedagogical orientation of the use of the testing algorithm for personalized learning trajectories in the practice of mass education, etc.
Smart Attendance System using Face Recognition and RFID Technology

RFID, Fingerprint identification, and Iris identification techniques are used to register attendance in industries and organizations.Among all these personal identification strategies, face recognition consumes less time and is highly efficient.It has several applications in attendance management systems and security systems.In this work, a system is implemented that takes attendance for students and staff during their entry into classes or campus, employees in industries, etc, using RFID and face recognition technology.A time period can be set for taking attendance using the dashboard application which is developed using android and the database is automatically uploaded into the webserver through internet connectivity.This process is done without any human intervention.In this system, a Raspberry Pi is installed with OpenCV library and a Camera module is connected for facial Detection and Recognition.Also, an RFID reader is connected to read and verify the person's identity.The data is stored in the Firebase Database and can be accessed through Python programming.The attendance gets updated in the database and can be viewed through the developed Android application.
Application of Multimedia Technology in Music Teaching in Junior High School

Under the background of the information age, education has gradually shown a new development journey and a new looks, and has begun to develop in the direction of modernization.In the diversified technical system, multimedia, as an important technical module, plays a significant supporting and supporting role in the whole field of education.The rational application of multimedia to music classes can improve the entire teaching environment and at the same time promote the overall implementation of education work efficiently.This article mainly analyzes the function and application of multimedia as an advanced method in the field of music teaching in junior high school.
Automatic Image Captioning Methods

A language known to humans is a natural language. In computer science it is the most challenging task to make the computers understand the natural languages and generating caption automatically from the given image. While a lot of work has been done, the total solution to this problem has been demonstrated daunting so far. Image captioning is a crucial job involving linguistic image understanding and the ability to generate interpretation of sentences with proper and accurate structure. It requires expertise in Image processing and natural language processing. The publishers suggest in this practice a system using the multilayer Convolutional Neural Network (CNN) to generate language describing the images and Long Short Term Memory (LSTM) to concisely frame relevant phrases using the driven keywords. We aim in this article to provide a brief overview of current methods and algorithms of image captioning using deep learning. We also address datasets and measurement criteria widely used for the same.
Classification of Dermoscopic Image of Skin Cancer Using the GLCM Method and Multi-SVM Algorithm

The development of abnormal skin pigment cells can cause a skin cancer called melanoma. Melanoma can be cured if diagnosed and treated in its early stages. Various studies using various technologies have been developed to conduct early detection of melanoma. This research was conducted to diagnose melanoma skin cancer with digital image processing techniques on the dermoscopic image of skin cancer. The diagnosis is made by classifying dermoscopic images based on the types of Common Nevus, Atypical Nevus or Melanoma. Pre-processing is done by changing the RGB image to grayscale (grayscaling), smoothing image using median filtering, and image segmentation based on binary images of skin lesions. The value of Contrast, Correlation, Energy and Homogeneity obtained from the texture feature extraction of the GLCM method is used in the next step, which is the classification process with the Multi-SVM algorithm. The proposed research method shows high accuracy results in diagnosing skin cancer
Basement rocks around the eastern sector of Baranis-Aswan Road, Egypt: Remote sensing data analysis and petrology

The basement rocks around Baranis-Aswan Road, South Eastern Desert of Egypt encompasses a variety of the oldest Pan-African rock units including medium-grade hornblende schist, a variety of gneisses and migmatite as well as medium- to low-grade metavolcanics. These were intruded by syn- to late-tectonic tonalite, granodiorite, quartz diorite as well as gabbros. The exposed basement rock units in the area were discriminated with the detailed lithological mapping using the processed data of ASTER, Landsat-8 and Sentinel-2. Spectral signatures of these rocks were achieved by ASD TerraSpec Halo mineral identifier device. Petrography and geochemistry data enabled to put constraints on their petrogenesis. Geochemically, the schist and gneisses imply K-poor mafic igneous precursors that were developed in a primitive juvenile oceanic environment. The migmatite pertains to stromatic type, whereas granitic magma (neosome) was injected in hornblende schist (paleosome) through cleavage planes. The syn-tectonic tonalite to quartz diorite are also low-K, non-mature calc-alkaline rocks and pertain to oceanic plagiogranite. They display a continuous fractionation evolution trend from quartz-diorite to tonalite and granodiorite.
SentinelPlus: A Cost-Effective Cyber Security Solution for Healthcare Organizations

Electronic Protected Health Information (ePHI) has proven to be quite lucrative by cybercriminals due to their long shelf life and multiple possible avenues of monetization. These highly sensitive data has become an easy target for cyber attackers due to the poor cyber resiliency strategies exercised by Healthcare Organizations. The reasoning behind the poor cyber security management in the healthcare sector sums to the collective impact of budgetary restriction, lack of cyber security competency and talent in the domain, prioritizing convenience over security, and various work culture malpractices. Further-more, a substantial number of data breaches in the healthcare sector are known to be caused by human errors, security misconfigurations, and information mismanagement. Secondly, the increasing prevalence of ransomware and botnet attacks has hampered the efficiency and availability of healthcare services. As a result, in order to provide a holistic security mechanism, this paper presents "SentinelPlus," a machine learning-based security management suite.
Intelligent Image Saliency Detection Method Based on Convolution Neural Network Combining Global and Local Information

Image saliency object detection can rapidly extract useful information from image scenes and further analyze it. At present, the traditional saliency target detection technology still has the edge of outstanding target that cannot be well preserved. Convolutional neural network (CNN) can extract highly general deep features from the images and effectively express the essential feature information of the images. This paper designs a model which applies CNN in deep saliency object detection tasks. It can efficiently optimize the edges of foreground objects and realize highly efficient image saliency detection through multilayer continuous feature extraction, refinement of layered boundary, and initial saliency feature fusion. The experimental result shows that the proposed method can achieve more robust saliency detection to adjust itself to complex background environment.
Atmospheric Instability Conditions during Rainy Seasons over Tanzania

The amount of rainfall and its distribution in time and space is dependent on the atmospheric instability conditions, and on its moisture content. The aim of this study was to determine the atmospheric instability conditions during January to March (JFM), February to April (FMA), March to May (MAM), and October to December (OND) rainy seasons over local climate zones in Tanzania. Zone area average seasonal Convective Available Potential Energy (CAPE), Convective Inhibition (CIN), Precipitable Water (PW) and Lifted index (Li) were calculated and analyzed. Results showed Li &lt; 0 in JFM and FMA over whole Tanzania. During MAM and OND, Li &lt; 0 over the Lake Zone, Western Highlands Zone and Central Zone only. CAPE ranged from 793 J/kg to 1183 J/kg during JFM, and 700 J/kg to 1080 J/kg during FMA. During MAM, CAPE ranged from 170 J/kg to 921 J/kg and from 173 J/kg to 833 J/kg during OND. Results also showed CAPE &gt; 1000 J/kg over the Lake Zone, Western Highlands Zone, Island Zone, and Central Zone. These results show that the atmosphere was moderately unstable during the JFM and FMA and was weakly unstable during the MAM and OND. Therefore, the atmosphere is likely to be more convective during JFM and FMA seasons.&#x0D; Keywords: Lifted index, Convective inhibition, Precipitable water, Convective available potential energy, Atmospheric instability.
Music Education as Faustian Bargain: Re-Enchanting the World with Thomas Mann’s <i>Doktor Faustus</i>

Abstract This article explores the musical motifs in Thomas Mann’s 1947 novel Doktor Faustus and how they relate to the novel’s particular Bildung character. We claim that the novel allows for a challenging musico-pedagogical reading, which opens up new and highly imaginative perspectives on music and music education. Going beyond interpretations that mainly discuss the element of music in light of the novel’s metaphysical or sociocritical pretentions, we believe that Adrian Leverkühn’s musical Bildung materializes a discourse that is primarily educational and affirmative. Bringing in elements of various “posthumanist” philosophical traditions, our article elaborates a musico-pedagogical concept of technology. On the one hand, this enables a wholly new and more appreciative understanding of what Leverkühn’s Faustian bargain could be about; on the other, it allows us to radically rethink the theoretical narrative(s) of musical science and education. The outcome is the call for a “pedagogy of pure instrumentality.“ By seducing students to take part in an experimental and care-ful study for immanent musical technics (rather than techniques), education allows music to re-invent itself, to keep researching the magical procedures and instruments that link together sound and meaning in diverse musical practices, beyond any teleological appropriation.
Orthonormalized basic of fractal stepped multiwavelets – a new multiwavelet technology for signal and image processing

A new class of fractal step functions with linear and nonlinear changes in values is described, and on their basis a recurrent method for constructing functions of a new class of fractal step multiwavelets (FSMW) of various shapes with linear and nonlinear changes in values is developed. A method and an algorithm for constructing a whole family of basic FSMW systems have been developed. An algorithm for calculating the coefficients of a discrete multiwavelet transform based on a multiwavelet packet without performing convolution and decimated sampling operations, in contrast to the classical method, is presented. A method and algorithm for fast multiwavelet transform of low computational complexity has been developed, which, in comparison with the well-known classical Mall's algorithm, is 70 times less in multiplicative complexity, and 20 times less in additive complexity.
Parameter Training Methods for Convolutional Neural Networks With Adaptive Adjustment Method Based on Borges Difference

This paper proposes a momentum algorithm based on Borges difference and an adaptive momentum (Adam) algorithm based on Borges difference to update parameters, which can adjust the momentum information more flexibly. The Borges difference is proposed from the definition of Borges derivative to be combined with the gradient algorithm in convolutional neural networks. The proposed momentum algorithm based on Borges difference and Adam algorithm based on Borges difference can be adjusted more flexibly in order to speed up the convergence. The parameter optimization algorithm with the Borges difference presents a better performance compared with the integer-order momentum algorithm and integer-order Adam algorithm, with the proposed nonlinear adjustment method for the parameter tuning of convolutional neural networks. By analyzing experimental results of Fashion-MNIST dataset and CIFAR-10 dataset, the optimization algorithms based on Borges difference proposed in this paper gain better effects on the optimization model compared with the corresponding ones based on the integer-order difference, and can speed up the convergence speed and recognition accuracy of the image recognition.