Reviewerly/exHarmony huggingface dataset:
The main categories of NewSQL databases and their features

In the modern world, the problem of working with big data and workloads is becoming more and more acute. For more than forty years, relational databases have been the main leading systems for storing, searching and managing data. However, despite their great popularity, application experience and universality, traditional relational DBMS, due to the growing needs for scalability and performance, often cannot meet modern requirements. This has led to the emergence of new alternative data management systems, including NewSQL systems. NewSQL is a class of modern relational database management systems that provide performance comparable to NoSQL systems while maintaining the data consistency guarantees inherent in traditional database systems. The growing interest in NewSQL technology in recent times has led to an increase number of evaluations and comparisons among competing NewSQL technologies. However, today there is still a certain lack of work devoted to the study of the features of NewSQL solutions and their capabilities in comparison with other technologies. This paper discusses the main features of the most famous NewSQL products of different categories and the identified problems associated with them. To overcome the certain ambiguity in the names and translations of some terms related to the subject area under consideration, which takes place in numerous relevant sources, additional explanations are given. For comparison, the paper presents the values of important characteristics inherent in NewSQL, traditional relational and NoSQL database systems. This paper can help researchers and people from the industry choose the best storage solutions for their needs.
IMPACT OF COVID-19 ON BREAST CANCER SCREENING PROGRAM (BCSP) IN INDIA

In the past three years, covid-19 viruses have spread rapidly worldwide, while low and middle-income countries were affected mostly so far. Emergency limits were imposed due to the rapid infection and significant mortality rates. Only emergency medical treatments are available during these shutdowns and lockdowns in India. All non-emergency treatments, such as Breast Cancer Screening Program (BCSP), have been temporarily halted due to the huge number of deaths caused by coronavirus. However, the ability of BC screening programs to improve survival rates while lowering mortality rates has been well demonstrated. Suspension may result in poorer outcomes for patients with BC. In this regard, early detection and treatment are critical for increased survival and long-term quality of life. Thus, we have taken breast cancer patients' data for the last six years i.e. from 2016 to 2021 in India to properly evaluate and analyze for our research. Assessing recent results for various features from, modeled evaluations can aid pandemic responses. Besides that, we proposed a novel method that implements the EDA technique to graphically represent BC patients' data. This experiment was done using Python programming language on Jupyter 6.4.3 platform. We found the sudden rise of BC patients from lakhs to millions in 2019. This signifies the deadly coronavirus has greatly affected people during the pandemic days when people are more serious about this virus rather than screening their breasts.
沖縄県の学校現場におけるものづくり人材育成について

In this study, we organized the current state of the educational system in elementary school programming education, junior high school technology education, and technical high school in Okinawa prefecture, and conducted a questionnaire survey of educators. As a result, even if there are opportunities to learn about robots and sensors in elementary school programming education with the cooperation of manufacturing companies, about one-third of junior high schools in Okinawa cannot sufficiently learn about measurement and control. In addition, although there are many students who go on to technical high schools to learn manufacturing compared to the rest of the country, the placement of teachers at technical high schools is not sufficient, and it is assumed that the training of human resources for manufacturing in Okinawa is not functioning well.
Practice and Systematization of Energy Environment Education for the Technology Education Course at Junior High School.

In the field of technological problem-solving study at junior high school, classes that introduced system thinking skills were conducted, and the effects were verified. As a result, the iceberg model has been shown to be effective in understanding the mechanism of power generation and issues in the power supply and demand system, and also in learning to look ahead to the future comprehensively, which is crucial in solving issues like global warming. In addition, the expression needs to be modified for students easier to understand, as the more abstract the viewpoint, it is used less frequently. It was found that by using the loop diagram step by step, it is possible to grasp the causal relationship between the elements of the power supply and demand system, to think about the power source composition with a basis, and to increase the breadth and depth of the students' thinking. Based on this result, the contents of three stages of system thinking learning using loop diagrams were devised in the technology field related to materials and their processing, nurturing living things, energy conversion and information processing in the technology course at junior high school.
Writing Effective Alt-text for Online Instructional Materials

Instructional materials are increasingly created and made available within an online space. Engaging materials utilize various types of images to break up walls of text and enhance learning. Describing these images with effective alternative text (alt-text) is subjective and contextually-based, requiring a treatment and knowledge beyond a simple checklist. This lesson is aimed to support educators in becoming more accessibility-literate, and to build in robust alt-text at the moment of creation. This lesson presents five different types of images. Each image type serves a distinct purpose within the context of online instructional materials. During the lesson, learners will work through each image type, with opportunities to practice examining HTML alternative tags and writing appropriate alt-text for each type of image.
The Manuals

Abstract This chapter is intended to help composers write idiomatically for the manuals. Composers commonly assume writing for the manuals is identical to writing for the piano; the ‘Overview’ attempts to dispel that myth, examining the differences between organ and piano writing as well as common organist techniques such as finger substitution and hand redistribution. ‘Idiomatic Writing’ looks at specific keyboard techniques in more detail, separating chordal and non-chordal techniques and providing a basis from which composers can innovate idiomatically. Finally, the ‘Multiple Manuals’ section explains effective ways for using more than one manual simultaneously. All of these sections feature extensive music examples showing these techniques in practice.
A Study on Improvement of SW Structure Understanding through Gyeonggi Dream University

Software education is included in the regular curriculum for a certain period and is thoroughly conducted following the software education operation guidelines. Gyeonggi-do Province operates "Gyeonggi Dream University" for high school students belonging to each local office of education. In this study, we compare and analyze the understanding of software between students who have completed software training courses at "Gyeonggi Dream University" and those who have not, and those who have currently majored in software at the university. After comparing the scores obtained from the pre and post test, the analysis was confirmed to be statistically meaningful through the T-test. As a result, the index value of those who took classes through public education among students in the group with an improved understanding of software was 0.9. However, it was confirmed that the improvement level of the Gyeonggi Dream University students was 2.3, and the index value was 4.8. Therefore, it was confirmed that the 'understanding software through coding' course at 'Gyeonggi Dream University' has a positive effect on understanding the basic structure of software.
Object-free backdoor attack and defense on semantic segmentation

Deep neural networks (DNNs) have proven to be applied in various fields, such as autonomous driving. However, it is compromised to a severe threat, namely a backdoor attack, which maliciously manipulates a model by poisoning a small portion of training data. Previous works mainly focused on backdoor attacks in image classification, however, it is scarcely studied in semantic segmentation widely used in computer vision. In this paper, we proposed a novel backdoor attack on semantic segmentation named object-free backdoor attack (OFBA). This method allows the free selection of object classes to be attacked during the inference, breaking the limitation of previous work only attacking object classes determined before training. In addition, to identify the security of unknown models and mitigate the threat of OFBA, we proposed a defense method called Segment Cleanse, alleviating the dilemma of insufficient computing resources and high detection error rates in Neural Cleanse. The experimental results show that our proposed methods are always effective on state-of-the-art models.
A Calibration Algorithm of 3-D Laser Scanner Based on KMPE Loss Function

Aiming at solving the estimation problem of measurement model parameters of a 3-D laser scanner, a kernel mean <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${p}$ </tex-math></inline-formula> -power error (KMPE) loss function-based calibration algorithm is proposed in this article. The KMPE loss function is robust to measurement noise and outliers. Thus, a KMPE loss function-based nonlinear optimization model of measurement model parameters of a 3-D laser scanner is first established according to the distance constraint between each laser scanning point on the calibration sphere and the center of the calibration sphere. Then, to conduct calibration of the model parameters, the optimization model is solved by combining the success-history-based parameter adaptation for differential evolution (SHADE) algorithm with the Levenberg-Marquardt (LM) algorithm. Experimental results show that the proposed algorithm can achieve high calibration accuracy of the measurement model parameters of the 3-D laser scanner by effectively suppressing the influence of measurement noise and outliers.
Artificial intelligence management in tertiary education in order to improve the quality of knowledge and reduce the risks of learning

The subject of the article is the management of artificial intelligence in the field of higher education , which improves the quality of knowledge and reduces the risks of learning in the context of digitalization . The purpose of the article is to identify the problems hindering the development of artificial intelligence in higher education and to develop recommendations for their solution . The research methodology is based on the use of modern methods , techniques and tools in the study of artificial intelligence , allowing to take into account the impact on the education quality , on the psychology participants in the educational process and on the mechanisms for managing its development . The main results obtained in the article provide answers to ambiguous questions about the feasibility of large-scale use of artificial intelligence in education , its dominance among other methods of knowledge transfer and its application in its pure form , without live communication with teachers . The answers received in the article convince the opponents of this topic that the use of the author’s developments regulating the use of artificial intelligence in education leads to an increase in the quality of education and neutralizing the emerging risks of learning from a new generation . The article reveals ethical shortcomings and shows the advantages of the artificial intelligence technologies use in the practice of tertiary education . The main conclusions made in the article are based on the statement that the artificial intelligence system in the field of tertiary education needs to be regulated and managed by development using the methods of psychological , organizational , regulatory and technological character proposed by the authors .
Sustainable and resilient city: The case study of a hypogeum stadium in a desert area

As cities have grown denser, it seemed only natural to extend skywards.However, the sky is not the limit: Cities need to start looking downwards to adapt to new circumstances and reach the next level of existence.Earth is our only home and that humankind faces enormous challenges.To overcome these challenges we need to focus on how spaces underground can contribute to more sustainable and resilient cities.At the same time we need to break out of our existing paradigms and transition to new ways for space use.The aim of paper is to describe a case study presenting the project of a Hypogeum stadium located into a desert area.The authors, through this project try to reply to above mentioned requirements. INTRODUCTIONRapid urbanization is a global phenomenon.In 2008, for the first time in human history, there were more urban dwellers than rural, and the trends show that this is not going to be reversed.The United Nations estimates that by 2030, over 60% of the global population of will be living in "megacities" (10+ million), large (5-10 million), medium (1-5 million), and smaller cities and periurban communities, increasingly concentrated in Asia, Africa, and Latin America.This fraction could rise to two thirds by 2050.The recent Intergovernmental Panel on Climate Change (IPCC) report on Human Settlements, Infrastructure and Spatial planning states that the expansion of urban areas (urban centres and suburbs) is on average twice as fast as the urban population growth, and that the anticipated growth in the first three decades of the 21st Century will be larger than the cumulative urban expansion in all of human history.(Lunardi et al., 2018).In 1950 about 65% of the population worldwide lived in rural settlements and 35% in cities and this number will be reversed by 2050, where 70% will be urban and 30% rural.Almost 6 billion people will be living in urban areas by 2050. Figure 1 shows share of populations living in urban areas.The cities contribute to approximately 70% of global energy use and greenhouse gas emissions but only occupy 5% of the earth's landmass.This is accompanied by the unprecedented increase in demand need for water, land, building material, food, pollution control measures and waste management from urban areas.Therefore, cities are constantly under pressure to provide better quality services, promote local economic competitiveness, improve service delivery, increase efficiency and reduce costs, increase effectiveness and productivity, address congestion and environmental issues.These pressures are motivating cities to turn to sustainable and resilient solutions and experiment with various structure and infrastructure applications.The objective of such a new "city model" is a complex process involving deep-rooted innovation with regard to: tangible and intangible infrastructures, the lifestyles of citizens; the
Retracted: Meta-Analysis on the Improvement of Symptoms and Prognosis of Gastrointestinal Tumors Based on Medical Care and Exercise Intervention

[This retracts the article DOI: 10.1155/2021/5407664.].
Retracted: Comparison of the Ameliorating Effects of Valsartan and Amlodipine on Vascular Endothelial Dysfunction and Oxidative Stress in Elderly Patients with Type H Hypertension

[This retracts the article DOI: 10.1155/2022/5054511.].
APPLICATION OF SUPPORT VECTOR MACHINE ON DROUGHT CODE CLASSIFICATION IN NORTH SUMATRA INDONESIA

This study aims to classify the Drought Code in North Sumatra. Drought Code is part of Fire Danger Rating System (FDRS) developed in Canada. One of the sub-systems of FDRS is Fire Weather Index (FWI) that aims to evaluate fire hazards from current and past weather conditions. Drought Code is classified by using Support Vector Machine. Support Vector Machine is widely used in the data classification process. One of the advantages of Support Vector Machine methods is it has ability in classifying large amount of data and classifying more than two classes or multi-classes. Weather parameters used in this study are rainfall and temperature in North Sumatra. The data used are from 8 (eight) meteorological observation stations in North Sumatra from 2017 to 2021. Drought Code is carried out with several tests using several kernels contained in SVM.
ICTs in education for Deaf and Hard-Of-Hearing learners

This study is a literature review which focuses on the educational needs for the deaf and DHH and the various environments that are available for them, based on ICTs. Most of the reviewed articles are quantitative researches (Survey Research, Experimental Research Design), focused on the effectiveness and the design of the applications and programs that contribute to the communication, as well as the education of deaf and DHH students. Other articles that were studied are literature reviews addressing the educational needs, ICT based tools, as well as suggesting necessary future improvements for this educational approach.
EfficientNet Algorithm for Classification of Different Types of Cancer

Accurate and efficient classification of different types of cancer is critical for early detection and effective treatment. In this paper, we present the results of our experiments using the EfficientNet algorithm for classification of brain tumor, breast cancer mammography, chest cancer, and skin cancer. We used publicly available datasets and preprocessed the images to ensure consistency and comparability. Our experiments show that the EfficientNet algorithm achieved high accuracy, precision, recall, and F1 scores on each of the cancer datasets, outperforming other state-of-the-art algorithms in the literature. We also discuss the strengths and weaknesses of the EfficientNet algorithm and its potential applications in clinical practice. Our results suggest that the EfficientNet algorithm is well-suited for classification of different types of cancer and can be used to improve the accuracy and efficiency of cancer diagnosis.
A Single Image Derain Method Based on Residue Channel Decomposition in Edge Computing

The numerous photos captured by low-price Internet of Things (IoT) sensors are frequently affected by meteorological factors, especially rainfall. It causes varying sizes of white streaks on the image, destroying the image texture and ruining the performance of the outdoor computer vision system. Existing methods utilise training with pairs of images, which is difficult to cover all scenes and leads to domain gaps. In addition, the network structures adopt deep learning to map rain images to rain-free images, failing to use prior knowledge effectively. To solve these problems, we introduce a single image derain model in edge computing that combines prior knowledge of rain patterns with the learning capability of the neural network. Specifically, the algorithm first uses Residue Channel Prior to filter out the rainfall textural features then it uses the Feature Fusion Module to fuse the original image with the background feature information. This results in a pre-processed image which is fed into Half Instance Net (HINet) to recover a high-quality rain-free image with a clear and accurate structure, and the model does not rely on any rainfall assumptions. Experimental results on synthetic and real-world datasets show that the average peak signal-to-noise ratio of the model decreases by 0.37 dB on the synthetic dataset and increases by 0.43 dB on the real-world dataset, demonstrating that a combined model reduces the gap between synthetic data and natural rain scenes, improves the generalization ability of the derain network, and alleviates the overfitting problem.
Storybook Literacy Method Increasing Language Development in Children with Speech Delay

Children's speaking ability is very important in their development. Stages in a child's language development sometimes experience delays and can become a nuisance. The most common and easily recognizable language disorder is speech delay, which means the child's level of speech development is not appropriate for his age. One of the actions that can be taken for children who have speech delays is the storybook literacy method. This method can help develop and train children's language skills and through stories children are required to be active in developing language. The purpose of the study was to determine the effectiveness of the storybook literacy method on language development in children with speech delay. This study uses the One Group pre-test and post-test design. Sampling technique using non-probability sampling saturated sampling type. The sample is 10 children with mild speech delay. The research instrument was using the Stimulation of Early Intervention Detection of Growth and Development (SDIDTK) instrument through the KPSP form. The analysis used is the T-Test. The results of the T-Test test obtained a p-value of 0.000, which means that there is an influence of the storybook literacy method on children's language development with speech delay. Storybook literacy method is effective to improve language development in children with speech delay.
Modifying Advanced Encryption Standard (AES) Algorithm

This paper presents modifications on AES algorithm to improve the security of Standard AES, the improving has been done by three modifications. The first modification use key-dependent dynamic S-box (10 S-box) instead of static S-box (1 S-box), that used by Standard AES in order to improve "confusion" properties represented by Byte Substitution layer. The second modification use key-dependent variable values for shifting "state-matrix" rows process instead of fixed values that used by Standard AES in order to improve "diffusion" properties represented by ShiftRows layer. The third modification is by using two keys instead of one key that used by Standard AES, both of them used for encryption and decryption process instead of one key that used by Standard AES in order to improve the general structure and key generation algorithm of AES. The Modified AES tested and evaluated by five scales (Basic Five Statistical Tests, NIST Tests Suite, Encryption Run Time, Brute-Force Attack and Cryptanalytic Attack) to prove the functionality of modifying.
Breast Cancer Detection Using Support Vector Machine Algorithm

Abstract: Breast cancer is primary cancer affecting women and ranks second as the leading cause of female mortality. The crucial aspect is identifying the presence of breast cancer and pinpointing the affected area. Medical imaging consistently advances, and early detection of cancer is vital in lowering cancer death rates. The enhancement procedure for mammograms involves filtering and discrete wavelet transforms. Contrast stretching is utilized to boost image contrast. Improved Breast cancer early detection and diagnosis are achieved by segmenting mammogram images. From the segmented breast region, features are retrieved. The proposed system identifies the cancer region and classifies patients as either normal or cancerous. The input mammography image is subjected to pre-processing techniques, and undesirable parts of the image are cropped off. Using morphological techniques, the tumor location is separated from the surrounding tissue and marked on the original mammography image. If the mammogram image is normal, the patient is deemed normal; otherwise, the patient is diagnosed with cancer. The Decision Tree Algorithm is utilized for categorization in this study for reasons of comparison.