Reviewerly/exHarmony huggingface dataset:
Determination of Essential Element and Titanium Contents in Rocks using Flame Atomic and Molecular Absorption Spectrometry

Rocks contain a large number of elements such as Na, K, Ca, Mg, Fe, Mn, and Ti. The determination of those element contents is important in geological and geographical exploration as well as the original identification and classification of rocks. Currently, the analysis of the elemental composition of rocks can be carried out on equipments such as ICP-OES, ICP-MS,... However, in some laboratories with limited conditions, the analysis has many difficulties. In the present study, we analyzed rock samples after being treated by microwave using flame atomic and molecular absorption spectroscopy. The results showed that the content of elements in the range of Na (0.31 – 2.46%), K (1.38 – 3.71%), Ca (0.08 – 0.23%), Mg (0.16 – 0.69%), Fe (2.38 – 5.19) %), Mn(0.08 – 0.1%) and Ti(0.12 – 0.36%). The study provides a simple and cost-effective method for rock analysis.
Binary Classification of Mammograms Using Horizontal Visibility Graph

Breast carcinoma, the most common cancer in women across the world now accounts for almost $$30\%$$ of new malignant tumor cases. Despite the high incidence rate, breast cancer mortality has been maintained under control thanks to recent advances in molecular biology technology and an enhanced level of complete diagnosis and standard therapy. The method strives to overcome the clinical dilemma of undetected and misdiagnosed breast cancer, resulting in a poor clinical prognosis. Early computer-aided detection by mammography is an important aspect of the plan. In most of the diagnostic strategies currently in vogue, undue importance has been given to one of the performance metrics instead of a more balanced result. In our present study, we aim to resolve this dogma by first converting the mammograms into their equivalent graphical representation and then finding the network similarity between two such generated graphs. Subsequently, we will also elaborate on the use of horizontal visibility graph (HVG) representation to classify images and use Hamming-Ipsen-Mikhailov (HIM) network similarity (distance) metric to develop novel triage mammograms according to the severity of the disease. Our HVG-HIM metric-based classification of mammograms had an accuracy of $$88.37\%$$ , specificity of $$92\%$$ , and sensitivity of $$83.33\%$$ . We also clearly highlight the trade off between performance and processing time.
Design and Development of a Mobile Application for the Control and Prevention of Viral Gastroenteritis as a Public Health Problem

OBJECTIVE: This study aims to design and implement a mobile application, "Viral Gastroenteritis", and assess mobile users' satisfaction with this application.METHODOLOGY: This study was conducted in February 2021 in two stages: designing and constructing a viral gastroenteritis disease application based on the Android system and investigating mobile users' satisfaction with this application.RESULTS: Six hundred individuals responded to the questionnaire in this study; 36.5 % were males, 63.5 % were females, 35% were between 20-30 years old, and 43.83% had a bachelor's degree.The study results showed that more than 95% and 90% of the participants had no trouble installing the app.The images adequately conveyed the meaning of viral gastroenteritis, respectively.CONCLUSION: According to the inquiry outcomes, this didactic software can increase users' awareness when facing viral gastroenteritis.
BUILDING A SYSTEM FOR CULTIVATING EXCELLENCE IN PHYSICS TEACHER TRAINING BASED ON UNIVERSITY-SCHOOL COLLABORATION

-The success of basic education curriculum reform depends on the quality of the teaching staff. The quality of the teaching staff depends on the higher normal colleges and universities. Higher normal universities should actively carry out the reform of teacher education mode, organically combine teachers' pre-service education and post-service education, and improve teachers' comprehensive quality and professional level. Based on the experience of U-S (University-School) cooperation, this paper takes the physics major as an example, summarizes the teaching reform experience of the physics normal major, and builds a training system for outstanding physics normal students.
A Multi-level Features Fusion Model for Network Communication based on Machine Learning

Today's societies couldn't function without elaborate networks of communication. Many problems remain unresolved, but novel approaches to these problems are constantly being offered. Many of the problems plaguing existing works, such as high characteristic design cost, challenging feature selection, poor real-time performance, etc., stem from their focus on a wide range of characteristics. Worse still, the difficulty in training models due to data imbalance results in a poor detection rate for aberrant samples. To achieve a more effective and robust model, we present a multi-level feature fusion (MFFusion) model that utilizes a combination of data temporal, byte, and statistical characteristics to extract relevant information from different angles. Too far, MFFusion has outperformed the state-of-the-art on several real-world network datasets in terms of prediction performance and false alarm rate. We also use MFFusion for anomaly detection in an IoT network, using the most recent IoT malicious traffic information. The experimental results demonstrate the adaptability of MFFusion and its suitability for identifying network anomalies in an IoT context with system performance.
Object detection algorithm based on improved YOLOv5

One of the research directions of target detection-based computer vision, in which small target detection is the key and difficult research direction in target detection. Traditional target detection algorithms include Faster RCNN, YOLO, SSD, etc., and there is a problem that indicators such as detection accuracy, false detection rate, and missed detection rate are not ideal for small target detection tasks. In order to improve the above problems, this paper proposes an improved target detection algorithm based on YOLOv5. First, the CBAM attention mechanism is introduced in the Backbone part to strengthen the important feature channels; then a detection layer is added to the network according to the characteristics of the data set to strengthen the extraction ability. Experiments show that the improved YOLOv5s_CS algorithm has a mAP value of 75.1% on the test set, which is 3.9% higher than the original YOLOv5s network.
Hierarchical Semantic Regularization of Latent Spaces in StyleGANs

Progress in GANs has enabled the generation of high-resolution photorealistic images of astonishing quality. StyleGANs allow for compelling attribute modification on such images via mathematical operations on the latent style vectors in the $$\mathcal {W}/\mathcal {W+}$$ space that effectively modulate the rich hierarchical representations of the generator. Such operations have recently been generalized beyond mere attribute swapping in the original StyleGAN paper to include interpolations. In spite of many significant improvements in StyleGANs, they are still seen to generate unnatural images. The quality of the generated images is predicated on two assumptions; (a) The richness of the hierarchical representations learnt by the generator, and, (b) The linearity and smoothness of the style spaces. In this work, we propose a Hierarchical Semantic Regularizer (HSR) (Project Page and code: https://sites.google.com/view/hsr-eccv22 ) which aligns the hierarchical representations learnt by the generator to corresponding powerful features learnt by pretrained networks on large amounts of data. HSR is shown to not only improve generator representations but also the linearity and smoothness of the latent style spaces, leading to the generation of more natural-looking style-edited images. To demonstrate improved linearity, we propose a novel metric - Attribute Linearity Score (ALS). A significant reduction in the generation of unnatural images is corroborated by improvement in the Perceptual Path Length (PPL) metric by $$16.19\%$$ averaged across different standard datasets while simultaneously improving the linearity of attribute-change in the attribute editing tasks.
Machine Learning Information System Data Collection and Prediction of Daily Garbage Weight Using Linear Regression

The observations that the authors have made at the final garbage disposal in Surakarta show that the recording of garbage that goes to the final garbage disposal is still using paper or notebooks. This recording helps officers and employees find garbage recapitulation data in the long term as a performance development analysis report. Departing from these problems, the author aims to develop a web-based garbage recording and reporting information system that can assist final garbage disposal in managing garbage. This information system uses PHP programming language and MySQL as the database. The data collection methods used in this system are interviews, observation, documentation, and literature and software development methods using the Waterfall method. And then, for the test method, use the Black-Box test. In this research, the application also uses the daily prediction amount of garbage prediction with machine learning linear regression.
Big Data Grave Register Information Management System Outside Cemeteries Under Internet of Things

This article mainly aims at the cemetery outside the tomb positioning, MuZhuRen information collection difficult, compensation difficult problem, the government imposed using unmanned aerial vehicle (UAV) advanced positioning technology, control technology and the key technology of Internet of things – information collection and remote transmission technology and information technology such as collecting cemetery outside the tomb of the digital information, and statistical data to construct cemetery outside the tomb letter grave interest rate management system. So as to build a tomb information management system outside public cemeteries with statistical data. The research findings of this paper can construct a refined three-dimensional model for the high-resolution tomb images taken by UAV aerial photography technology and the specific data obtained by UAV aerial photogrammetry technology. The innovation of this paper is that the information collected by UAVs is transmitted to the mobile phone in real time, which is convenient for the government, cemetery managers and their family members to master the cemetery information. In this paper, through the literature analysis and data analysis of the research methods to build a tomb outside public cemeteries information management system.
Robot Confrontation Based on Policy-Space Response Oracles

Solving Nash equilibrium solutions in game has always been a challenging problem, especially for the robot confrontation because of the differential constraint and the complex rewards. In this paper, a novel and stable policy-space response oracles(PSRO) method is proposed which integrates <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha$</tex> -Rank as the meta-strategy solver and covariance matrix evolutionary preference-based policy search(CMA-EPPS) as the oracles solver. Specifically, the robot confrontation task is formulated as a Zero-sum Game problem. Then a novel policy-space response oracles method is developed to solve the Nash equilibrium. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha$</tex> -Rank is used to solve the meta-strategy and to improve the efficiency of the oracles solver, CMA-EPPS is adopted. Finally, the proposed method is utilized to solve the robot confrontation and derives a more stable and better solution than iterated best response, fictitious play and PSRO based on <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha$</tex> -Rank and Best Response.
A Distributed Privacy-Preserving Framework for Deep Learning with Edge-Cloud Computing

Industrial Internet of Things (IIoT) systems can leverage deep learning (DL) models to provide intelligent applications. However, the training process of such DL models tends to leak privacy when the training data contain sensitive operational and management information. Most studies about privacy-preserving DL require a trusted data collector and thus are not suitable in an untrusted environment. In this paper, we propose a distributed privacy-preserving framework for DL with edge-cloud computing, where direct privacy leakage and indirect privacy leakage of training data are both considered. First, a pre-trained convolutional neural network (CNN) is partitioned into two parts for limiting direct privacy leakage of raw data, namely the feature module and the classification module. The feature module consisting of the lower layers of the CNN is deployed on an edge server, which is used to attract the feature of raw data. The feature data is fed to the classification module consisting of the higher layers of the CNN, which is deployed on the cloud server to compute image classification tasks. Second, a perturbation module between the feature module and the classification module is designed for limiting indirect privacy leakage during feature data transmission. The perturbation module uses local differential privacy (LDP) to produce noise in the feature data. Third, to further improve the accuracy, we retrain the classification module with the randomized feature data from edge servers. Experimental results show that the proposed framework achieves high accuracy under low privacy budgets.
Block Storage

This chapter covers 10% of the Certified OpenStack Administrator exam requirements. Block storage is an essential topic for data reliability in the private cloud. As you will learn in this chapter, you cannot build a highly available static solution without using block storage.
Educational Texts as a Factor in Managing the Digital Transformation of the University

Digital transformation is a global driving force for the development of modern society, including the education sector. Responding to the social needs of the state and society, modern universities are becoming actors in the digital transformation of education. At the same time, the success of digital transformation management at the university level is determined not only by the development of digital infrastructure and the teachers mastery of digital technologies. Our research has shown that the digital transformation of education is associated with the creation and dissemination of educational content, namely developing educational texts (printed and digital), which is difficult to implement without a good knowledge of the theory and practice of constructing educational texts, choosing educational texts by a teacher, managing educational activities with their help and building an individual educational experience of students. In this regard, the textual competence of future and current teachers is highly significant. The aim is to identify the management factors of the digital transformation of universities, in particular, the role of educational texts and textual competence for creating content that allows students to organize self–management of educational activities. Research methods: study and analysis of philosophical, psychological, pedagogical and methodological literature on the problem of research and analysis of the practice of content design. Expert evaluation of various types of educational texts in terms of their ability to self-organize educational activities.The article shows the influence of educational texts on the methodology of professional activity of a teacher, his professional values. It was revealed that different educational texts create different ways of managing educational activities, working with these texts affects the educational experience of students. Recommendations for pedagogical universities are formulated.
Food Ordering Systems' Newness

The justification for the Web-based Food Requesting System is to repurpose the ongoing conventional structure with the aid of digital resources and unquestionable PC frameworks, accomplishing their fundamentals so their vital data and information can be effectively managed for an extended timeframe with concise admittance to and control of something almost identical. The typical programming and equipment are fairly accessible and simple to deal with. The Web-based Food Requesting Structure, as illustrated above, might generate a genuinely free, confidential, robust, and rapid organisational technique. It may help the customer focus on their complex projects rather than just the bookkeeping. Moreover, it will support the partnership with improved resource utilisation. The association may remain conscious of mechanised records without unnecessary portions. That says that one need not be connected with information that isn't important while having the option to show up at the data. The point is to streamline and automate its ongoing conventional architecture with the assistance of digital kinds of stuff as well as certain PC programming, satisfying their prerequisites so their significant data or information can be taken care of for a more extensive duration with the streamlined overseeing of something practically the same. Basically, the attempt demonstrates how to supervise for superior execution and greater client organisation.
Historical Examination of the Possibility of Japanese Comprehensive Learning Curriculum as Technical Education

Technical education has the property of including human knowledge, body, and skills. In this article, the author focused on comprehensive learning on the theme of “technology, industry, manufacturing”. Learning technical education as general education includes various themes related to human life. And the perceptions and skills of children and students evolve from a sense of life and play to advanced knowledge and skills. This paper focuses on the commonalities between technical education and integrated learning, and attempts to trace the practical history of integrated learning in Japan from the perspective of technical education.
Causes and Effects of Cyber-Crime Victimization among Educated Youth: A Study of BZU, Multan

As criminal activities are increasing in Pakistan, there are different types of crime that come into the mind.Cyber-crime is the most common among internet users, especially students who are connected online most of the time for different purposes.This study examined the nature of cyber-crime, causes of cyber-crime its and effects on victims.The sample for the study consists of 201 students from department of social sciences Bahauddin Zakirya University, Multan.The researcher used multi stage sampling and questionnaires as a tool for data collection.The results were analyzed by using SPSS.The results revealed that 86.5% of the respondents used internet every day, while 95(47.2%) of respondents used internet for study, other 30(14.9%)heard songs by using internet, 45(22.3%)youth played games and 41(20.3%)watched movies.University students become victimized due to fake online job 124(61.6%),while monetary reward 37(18.6%),chat in open room 20(9.9%) and downloaded games 20(9.95%).There are different effects like 80(39.8%)psychological losses which include mental effect of private information losing and risk of re-victimization.While 55(27.3%)respondents are harassed, on the other hand 21(10.4%)respondents lost their financial data and 45(22.3%)students lost their personal information data.Future researches will be needed to let the youth be fully aware about cyber-crime effects.
Cross-domain Sentiment Classification in Spanish

Sentiment Classification is a fundamental task in the field of Natural Language Processing, and has very important academic and commercial applications. It aims to automatically predict the degree of sentiment present in a text that contains opinions and subjectivity at some level, like product and movie reviews, or tweets. This can be really difficult to accomplish, in part, because different domains of text contains different words and expressions. In addition, this difficulty increases when text is written in a non-English language due to the lack of databases and resources. As a consequence, several cross-domain and cross-language techniques are often applied to this task in order to improve the results. In this work we perform a study on the ability of a classification system trained with a large database of product reviews to generalize to different Spanish domains. Reviews were collected from the MercadoLibre website from seven Latin American countries, allowing the creation of a large and balanced dataset. Results suggest that generalization across domains is feasible though very challenging when trained with these product reviews, and can be improved by pre-training and fine-tuning the classification model.
On the separability of aggregated multilayer networks

Studies are being conducted on analysis methods for multilayer networks. If each layer network of the original multilayer network can be reconstructed from the single-layer network obtained by aggregating the multilayer network, a more detailed analysis of the multilayer network will become possible by using conventional network analysis methods. In this study, we investigate the extent to which the original multilayer network can be reconstructed from the single-layer network with the aggregated multilayer network. Our findings include that semisupervised learning of graph neural networks can reconstruct more than 50% of an original multilayer network from the single-layer network with the aggregated multilayer network.
Application of Network Platform in English Education in the Information Age

At present, information age has penetrated into every corner of life, English teaching (ET), whether in vocabulary or grammar, has inevitably been greatly affected. However, ET under the background of information age has not been paid enough attention to, and has not been fully studied. The new idea and goal of educational reform in the information age is to realize the perfect integration of information technology (IT) and ET, to realize a new mode of teaching, that is, to learn from the essence of traditional ET. At the same time, with the help of multimedia teaching and digital teaching resources, a new ET model is formed to improve ET efficiency. This paper uses the literature research method and the questionnaire method, through the literature inquiry related material to carry on the careful reading, through the other researcher research result carries on the thorough analysis, and unifies the university ET actual situation to carry on the characteristic solution, expounds the university ET present situation and the characteristic, analyzes the network learning system to the English basic teaching influence, through the questionnaire carries on the data contrast (the survey altogether distributes 450 questionnaires, recovers 440, removes the unqualified questionnaire 25, the valid questionnaire 415, accounts for the total distribution questionnaire 92%), thus summarizes in the information age, the practical experience and successful cases of ET reform in colleges and universities network platform.