Reviewerly/exHarmony huggingface dataset:
CHANGING THE NARRATIVE BY CONNECTING EARTH SCIENCE RESEARCH TO THE UNITED NATIONS SUSTAINABLE DEVELOPMENT GOALS

A geoscientist’s understanding of the earth and its complex processes can ensure that we are smarter, wiser, and more informed stewards of the earth. Yet, geoscientists are underrepresented in sustainability science and often only associated with oil and gas and mining. According to the American Geosciences Institute (AGI), geoscience degrees awarded between 2019 and 2020 have declined and fluctuations in enrollment can be linked to the boom-and-bust cycles of these industries. In addition, funding data in the earth and planetary sciences has also decreased. Connecting the geosciences and sustainability sciences and consistent communicating this connection is critical for sustaining the next generation of geoscientists and for solving the challenges threatening our future. The United Nations’ 17 Sustainable Development Goals (SDGs) defined by members of the United Nations are call for action over the next 10 years to eradicate global poverty, end unsustainable consumption patterns, facilitate sustained and inclusive economic growth, social development and environmental protection. This paper presents the results of a data mapping project undertaken by GeoScienceWorld in 2022 to demonstrate how earth science research contributes to the SDGs can change the narrative. Leveraging SDG indexing data from SciVal, GSW analyzed 22K articles published in 46 journals (2016-21); 23% of the 22K mapped to 16 of the 17 SDGs. Majority mapped to 7 SDGs: Sustainable Cities and Communities, Life Below Water, Climate Action, Clean Water and Sanitation, Life on Land, Affordable and Clean Energy, and Responsible Consumption and Production. For comparison, analysis of 168K publications from the top ten earth and planetary science publications indicates that 65% of the research mapped to SDGs and 9 out of 10 mapped to 15 SDGs. Like the GSW corpus, the Scopus Sources mapped to all SDGs, but was more evenly distributed. The GSW SDG Mapping Project is significant because demonstrates how earth and planetary science research contributes to the UN SDGs. The project highlights the value of categorizing earth science research in terms of sustainability and how the data can help shape the narrative. This will enable us to attract and retain students, receive more funding, and more effectively address the challenges of the 21st century.
DIGITALIZATION OF EDUCATION: RISKS AND PROSPECTS

The digital stage of the development of modern civilization still remains a controversial process in general and education in particular. On the one hand, it is assessed as a necessary and progressive step in the life of society, on the other hand, critics of digitalization argue about the loss of freedom by a person, manipulation of his consciousness and dehumanization of public institutions. According to the authors, the digitalization of education in the philosophical and ideological context is a continuation of the scientist line that reinforces the technocracy and pragmatism of the educational environment. The article presents an analysis of the processes of digital transformation of education. In education, one of the serious contradictions is the active use of digital technologies using administrative resources and the emotional and psychological resistance of the parent and pedagogical community to the increased influence of digitalization on the child’s consciousness. Some researchers have argued about the manifestations of “digital dementia” in children. The article provides recommendations on the formation of optimal digital behavior of the student and the development of “rules of digital activity”.
Super-Resolution Reconstruction of Brain MR Images Using Pseudo-3D Convolutional Network

The medical image super-resolution (SR) reconstruction algorithm based on deep learning with standard 3D convolution has too many network parameters. This leads to high computational complexity and low network training efficiency. To address these problems, a lightweight densely-connected residual 3D-CNN (P3DSRNet) combining dense residual connection with pseudo-3D convolution is proposed. Firstly, the dense residual block is used to widen the channel of the convolution layer. Hence, more feature information is transmitted to the activation function, so that the low-layer image features are spread to the higher layers to improve medical image super-resolution. Then, the pseudo-3D separable convolution strategy is employed to train the network, where the standard 3D convolution kernel is separated into multiple convolution kernels. The network is trained stage by stage, so network training converges faster. It effectively solved the problem that the number of parameters increase sharply with the added network training difficulty due to the widened dimension of standard 3D convolution. The experimental results show that the medical images reconstructed by the P3DSRNet model have clearer texture details and better visual effects, compared with those by the traditional interpolation and LRTV super-resolution algorithms. The P3DSRNet model has fewer network parameters, compared with the SRCNN3D and ReCNN super-resolution algorithms. In addition, the SR image PSNRs increase by 1.88 dB, 0.30 dB, and the SSIMs increase by 0.009 6, 0.001 1, respectively, compared with SRCNN3D and ReCNN. The P3DSRNet not only reduces the number of parameters and computational complexity, but also improves the super-resolution performances of medical images.
BLENDED LEARNING IN THE RUSSIAN FEDERATION

The paper discusses the methods of blended learning, which for a number of objective reasons is one of the priorities of modern education. The most significant are prohibitions and restrictions aimed at preventing the spread of the COVID-19 coronavirus in educational institutions, reducing school hours for teaching a foreign language and the development of special information technologies. The introduction of blended learning methods in higher, secondary, additional professional and corporate training makes it possible to compensate for the shortcomings in the case of using only one type of training. The presented table 1 lists the disadvantages of both full-time and e-learning. The paper describes some of the most popular basic models of blended learning: an inverted classroom, a change of work areas, an autonomous group, station rotation, extracurricular activities. The author presents the advantages of blended learning, which combines the technologies of traditional classroom and e-learning, which provides an optimal ratio of teacher-student interaction. Thanks to the use of this technique, optimal management of the educational process is accomplished and the saliency of the student increases, since they are responsible for the results of training and the choice of the method of academic work. The study considers the possibility of integrating the case study method into blended learning.
Intonation for Cello in the Music of J. S. Bach

In this article I describe my journey into the world of intonation and temperament. Intonation stems from the desire to play or sing "in tune," which, over the course of centuries, gradually shifted its focus from pure intervals to an emphasis primarily on melody. In other words, a shift from a vertical to a horizontal, linear focus. The major stumbling block we encounter in our quest for a universal system in which all harmonies are pure is this little "imperfection" that we discover as soon as we try to tune a keyboard instrument: it is mathematically impossible to make all the possible intervals pure. We have the "comma." Many different temperaments have been developed to arrive at a solution that could satisfy musicians from a particular period, from Pythagoras to our current equal temperament.
Performance Analysis and Comparison of Cerebral Stroke Prediction Models on Imbalanced Datasets

A cerebral stroke is an interrupt blood flow to the brain leading cause of death. A number of risk factors increase the risk of stroke occurence because of lifestyle. Machine learning is effective techniques can be applied in prediction of stroke. The different kind of algorithms give the various accuracy and performance in the prediction. This study has proposed the four machine learning algorithm for classifiers to predict of cerebral stroke. The proposed model with various classifier has considered the risk factors such as age, hypertension, heart disease, average glucose level, BMI, and smoking status as feature attributes to predict cerebral stroke. This study conducted on two stroke datasets, and improve the imbalanced of between classes by using SMOTE. The result shows that XGBoost provided the highest accuracy of around 98.08% and 96.73% by comparing to the other machine learning algorithms. In addition, this study evaluates the models by analyzing the statistical parameters include accuracy, precision, sensitivity, F1 score, and AUC. The evaluation reveals that the XGBoost, Random Forest, AdaBoost and KNN classifier achieved the average AUC value of 0.851, 0.868, 0.670 and 0.851, respectively. All models provided the high confidence values, whereas the model with XGBoost classifier gave the highest performance.
Cloud computing &amp; pandemic: Open-source cloud platform reliability for SME system development

The wave of the Covid-19 pandemic has devastated business development, especially traditional SMEs in Indonesia. As the largest contributor to assets in developing countries, SMEs are forced to digitize every business sector they are engaged in. As a form of business adaptation to the development of the 4.0 industrial revolution which is mostly cloud-based, SMEs need to build a cloud system that can be useful for the future of SME’ business development. The need for SMEs in IT infrastructure and databases to move quickly while increasing their competitive advantage is often constrained by high costs and relatively slow returns. The implementation of an open-source cloud computing system is needed to overcome these problems which can provide a good and reliable service information system, as evidenced by stress testing on the Cloud Platform as a Service (PaaS) system using the reliability characteristics of ISO 25010: 2011. In this study, Proxmox was selected as the IT infrastructure and virtualization machine for the SME open-source cloud system to be tested directly.
On interdisciplinary character of bibliology: From bibliologos to neurobibliology

The modern approach to the concept of bibliology is examined and evaluated. The importance of the book studies on the intersection of neurosciences and the humanities knowledge is emphasized. The concept of neurobibliology is substantiated as an interdisciplinary scientific area at the interface of bibliology and theory of cognitive processes. He also suggests to establish an analytical center for bibliology.
Covid-19 Contact Tracing and Vaccine Validation Using Blockchain Technology

The fourth industrial revolution (Industry 4.0) has prompted new and innovative solutions that are reliant on Artificial Intelligence (AI) and contemporary technological advancements. Secure, intelligent, and on-demand healthcare services for patients is one of the core pillars of Industry 4.0. Patient medical data security and privacy is a crucial part of electronic healthcare systems. Disease diagnosis and treatment are highly dependent on the authenticity and security of patient data, both when stored and communicated. Blockchain technology plays a vital role in transaction authentication and secure decentralized immutable data storage. With that said, in this paper, we present an interactive healthcare information system that enables COVID-19 contact tracing and vaccine certificate validation for users. The solution uses a blockchain technique to validate the certificates. The implementation and evaluation details of the system are presented together with result findings.
Rules-Based Distracted Driving Detection System Using Facial Keypoints

Feasible distracted driving detection systems must be intuitive and non-invasive. Computer vision, a subset of deep learning, provides methods for computer systems to mimic humans in perceiving data from digital imaging. Previous work in distracted driving detection with computer vision has primarily focused on the classification of the entire image, which allows for detection based on body positions and objects in the frame. However, this does not fully isolate the human subject from the background and has possibilities for false positives in certain situations. Keypoint detection is a type of computer vision model capable of plotting points on prominent features of the human body using only a digital camera image. In this work, a rules-based algorithm with Euclidean distance normalization between facial keypoints was developed to determine if driver focus deviates from looking forward while driving. This algorithm also incorporates the steering angle to eliminate false positive detections when looking left and right in acceptable turning situations. This algorithm resulted in 100% accuracy in detecting distracted driving within the testing parameters used. However, future work will incorporate additional vehicle data, different camera types, new visual perception forms, and more practical testing scenarios for increased robustness.
Publication: DIY Accessories Making With Nyonya Batik Style

The Nyonya Batik is a sarong that complement the Nyonya’s delicate kebaya. Through this awareness campaign, the main purpose is to create a book that has many benefits for Malaysian and the Baba and Nyonya Community; by spreading knowledges and increasing interest of the new generation to sustain a precious tradition. This book is designed to educate the public about Nyonya Batik and provide guidance to make accessories on their own self easily. Furthermore, this book will enhance the public’s creativity and rejuvenate their mind on art and crafts. Every page gives knowledge that will help the public to learn something new and beneficial for them and the future of Baba and Nyonya community. Therefore, the findings of this study are to develop and design an attractive book to attract and getting the audiences to know more about the Nyonya Batik and making accessories on their own.
Bridging Feature Gaps to Improve Multi-Organ Segmentation on Abdominal Magnetic Resonance Image

Accurate segmentation of abdominal organs on MRI is crucial for computer-aided surgery and computer-aided diagnosis. Most state-of-the-art methods for MRI segmentation employ an encoder-decoder structure, with skip connections concatenating shallow features from the encoder and deep features from the decoder. In this work, we noticed that simply concatenating shallow and deep features was insufficient for segmentation due to the feature gap between shallow features and deep features. To mitigate this problem, we quantified the feature gap from spatial and semantic aspects and proposed a spatial loss and a semantic loss to bridge the feature gap. The spatial loss enhanced spatial details in deep features, and the semantic loss introduced semantic information into shallow features. The proposed method successfully aggregated the complementary information between shallow and deep features by formulating and bridging the feature gap. Experiments on two abdominal MRI datasets demonstrated the effectiveness of the proposed method, which improved the segmentation performance over a baseline with nearly zero additional parameters. Particularly, the proposed method has advantages for segmenting organs with blurred boundaries or in a small scale, achieving superior performance than state-of-the-art methods.
Promoting Your Content—Email, Social Media, and Beyond

Email, social media, and other channels are excellent marketing tools in their own right. You can amplify their power – and the effectiveness of your website and your marketing overall – if you use them not only as platforms on which to publish content but also as tools to promote other content.
Learning Motivation of Children: Viewed from the Socio-Economic Aspect

The poverty rate of Pekanbaru city increases every year. As a result, the dropout rate is increasing. Students experience a decrease in the value of learning outcomes due to helping parents at work. This results in students being less motivated to go to school due to the socio-economic demands of their peers at school. The tendency of students who come from poor families to have a low assessment of their happiness in life (subjective welfare). This study aims to describe the learning motivation of students who come from poor families and how the implications of counseling services in increasing the learning motivation of children who come from poor families. This study uses a descriptive quantitative approach. The population of the study was students from children who received assistance from poor students in 2015 with a sample of 152 students, who were selected by using multistage random sampling technique. The instrument used is a scale of student motivation from poor and reliable families with Cronbach's Alpha value of 0.923. Data were analyzed by descriptive statistics. The research findings show that students' learning motivation is in the high category. Based on the research findings, there are several implications for guidance and counseling services in schools, namely: Information Services, Content Mastery Services, Individual Counseling Services, Group Guidance Services and Group Counseling Services.
Sentiment Research based on Neural Network in University Public Opinion

Online public opinion in colleges and universities, as a key and important part of social online public opinion, will directly affect the overall generation and development of social public opinion. CNN, RNN, LSTM and attention mechanism are often used in analyzing text sentiment in public opinion; the sentiment analysis for public opinion based on image and text fusion has become a current hotspot. By means of decision-layer fusion, the data information of text and image is better combined.
Basics of Programming: Control Structures

This chapter extends the previous chapter to move from simple statements to more complex statements, specifically conditional statements, also known as if/then/else statements, and loop statements, which in Python can take two forms: for loops and while loops. It also extends the ideas of parsing to understanding how the computer reads instructions that extend beyond a single line of code.KeywordsConditional statementFor loopWhile loopControl statement
Custom Convolutional Neural Network Model Implementation for Mask Detection on Atlas Hilens Device

Deep learning is one of the models used in many classifications because it can obtain many characteristics of objects. Health protocols are highly emphasized in this pandemic era. One of the issues that can arise at an event is the public's failure to wear masks. This study developed a system to detect people who are not wearing masks. Transfer learning from several existing deep learning models is used to build the model. The contribution of this research is trying to make a proper and straightforward model that could be implemented in "Atlas Hilens" to get portability. The MobilNetV2 or InceptionV3 model cannot be implemented in "Atlas Hilens" because a kind of FusionBatchNormalizationv3 layer is not supported. The CNN model built on "Atlas Hilens" will be compared to the MobileNetV2 or InceptionV3 model built with Tensorflow on a PC. "Atlas Hilens" can adopt the Tensorflow model to implement, although it needs adjustments from the existing detection model. The efficiency and accuracy of these two models will be compared. This model was trained with a dataset from many data sources on Github with a real-time scenario and several students. The success of this model is that it can detect people who are wearing or not wearing masks with sure accuracy. The accuracy obtained for real-time detection on "Atlas Hilens" is 77.27%, and the accuracy obtained for image detection on PC is 87.35%, with an average accuracy of 82.31%.
Bitcoin’s Global Reach

Not only have individuals gravitated to cryptocurrency as if some magical elixir, but nations have considered it too.
Slogan as Marketing Strategies Used in Makassar Cafes: A Semantic Analysis

This research is conducted to investigate the use of language expressions which were prescribed in the form of slogans which receives less attention in some previous studies. This research employed descriptive qualitative method. This research used field study and contextual theory from J.R Firth (1957). In collecting data, researchers conducted interview sessions between cafe owners and visitors. Results of data analysis show that the meaning of the slogan in each cafe varies based on the concept and situation, but at least among the 20 slogans, there are some slogan that has the same intention. There are 6 slogans lead more to the introduction of their place, 7 slogans lead to the situation that they provide, 2 slogans are more directed in motivating visitors to remain comfortable in the cafe. The function of the slogan that they posted also different from one to another, such as making the cafe more unique and as an identity. The linkage between the slogan and the marketing strategy can also be proven by the existence of interconnected opinions between the slogan’s user and visitors. The presence of contextual theory is also expected to be able to expand the horizons of people who do not only understand lexical language but also contextually.
Strengthening Database Security with Capture the Flag Exercises

Database security has a key role when protecting websites and other IT services. Attacks against databases could result in the leak of confidential data or integrity related security issues. One of the best options to protect services that utilize databases is to simulate real attacks against these. This paper focuses on capture the flag (CTF) style attack simulation and compare existing CTF challenges in the field of database security. The aim was to map the existing databases weaknesses and vulnerabilities into CTF style problems and help penetration testers to understand different attack scenarios against services with database connections. We identified several database related vulnerabilities that are usually not covered by CTF style challenges and a discussion is made how these missing fields could be added to the most well-known offensive security exercises.
Semantic Segmentation of Dental Point Cloud Based on Pointnet ++

Using point cloud to store three-dimensional dental model and realize automatic segmentation of tooth boundary will significantly improve the measurement efficiency of arch length, which is of great significance for the measurement of dentition crowding and the formulation of subsequent corresponding correction plans.With the help of the laser scanning model of dental gypsum, it is transformed into point cloud data, and the deep learning network of local fine features and global features is carried out for the dental model to realize the accurate segmentation and extraction of each tooth boundary, so as to assist in measuring the due length of dental arch and the existing length of dental arch.This paper builds a model based on pointnet ++ network structure and tests it with the data set constructed in this paper.The eval point accuracy is 0.719, which has high accuracy and effectively realizes the accurate segmentation of teeth.
THE ACTIVITY OF THE TRAINING CENTER IN THE ORGANIZATION OF THE EDUCATIONAL PROCESS AT THE UNIVERSITY

Тhis article discusses the practical experience of organizing the work of a student training center on the basis of the South Ural State Humanitarian Pedagogical University. The creation of a student training center is one of the technologies of organizing the educational process at the university.