Reviewerly/exHarmony huggingface dataset:
Research on Teaching Mode of College Subject—Based on Intelligent Interaction

The development of educational informatization has brought about great changes in educational form and learning mode.Through the deep integration of subject teaching and information technology, colleges and universities can realize the goal of core literacy education.Intelligent teaching based on intelligent interactive technology can promote the transformation of educational ideas, improve TPACK teaching ability, and realize the reform and innovation of teaching mode, so as to guide students to learn independently, practice and process evaluation and reflection.Teachers promote the implicit development of students' core literacy by constructing intelligent interactive individualized learning environment.
The ASU-DCU International Research and Workforce Development Program on Sensors and Machine Learning

The Arizona State University (ASU) - Dublin City University (DCU) International Research Experiences for Students (IRES) project is a summer workforce development program that embeds students in machine learning and sensor research. This collaborative IRES program, funded by the National Science Foundation (NSF), engages faculty mentors from the Sensor, Signal, Information Processing (SenSIP) Center at ASU, and the Insight SFI Research Centre for Data Analytics and Biodesign Europe at DCU to train students in sensor design, analytics, and machine learning algorithm development. Sensor and machine learning research addresses engineering and computing problems in health care and other related applications. IRES participants are tasked with studying hardware, algorithms and software for various tasks including activity detection, gait modeling, imaging, hemochromatosis prediction, and health care analytics. Crosscutting efforts include training in international research presentations, research documentation and building awareness of international policies and standards. The program includes weekly research updates by the students, participation in workshops and continued engagement after the summer experience. This paper describes the various components of IRES sensor and machine learning research through ongoing center projects at ASU and DCU.
Peningkatan Keterampilan Guru Dalam Pembuatan Media Pembelajaran Berbasis Canva Melalui Kegiatan In House Training

School Action Research aims to improve teacher skills in making Canva-based learning media by implementing iHT activities. The type of research is School Action Research. The research subjects were 12 teachers. Data collection techniques using observation and documentation techniques. Data validation with triangulation technique. The data analysis method used qualitative data analysis. The increase in the results of the assessment of the skills of teachers at SDN 1 Babaktulung in making Canva-based learning media in the initial conditions an average value of 44.68 in the Less (K) category, increased in the first cycle with an average value of 69.54 and in the Enough category. (C) and in the second cycle the average score was 89.66 in the Good category (B). The improvement of the skills of teachers at SDN 1 Babaktulung in making individual Canva-based learning media in the initial conditions there were no teachers declared complete, in the first cycle 6 teachers or 50% were declared complete and in the second cycle 12 teachers or 100% were declared complete.
Out of the Maze: Investigating Fluid Intelligence and Numeracy as Predictive Factors of Planning Skills Using Video Games

The aim of this study was to test whether an online video game can be used to investigate planning ability and whether fluid intelligence, objective numeracy, and subjective numeracy are predictive of game performance. Our results demonstrate that fluid intelligence is particularly important, which is in line with previous non-game-based studies that show a relationship between classical planning tests and fluid intelligence. Video games have been previously used for research into cognitive processes and taking them online facilitates data collection on a larger scale. Online video games also afford data collection without the expense and stress of a laboratory environment. For these reasons, using online video games to investigate human cognition is a promising alternative to the classic cognitive paradigms used in laboratories.
Discourse-aware text classification for argument mining

We show that using the rhetorical structure automatically generated by the discourse parser is beneficial for paragraph-level argument mining in Russian. First, we improve the structure awareness of the current RST discourse parser for Russian by employing the recent top-down approach for unlabeled tree construction on a paragraph level. Then we demonstrate the utility of this parser in two classification argument mining subtasks of the RuARG-2022 shared task. Our approach leverages a structured LSTM module to compute a text representation that reflects the composition of discourse units in the rhetorical structure. We show that: (i) the inclusion of discourse analysis improves paragraph-level text classification; (ii) a novel TreeLSTM-based approach performs well for the computation of the complex text hidden representation using both a language model and an end-to-end RST parser; (iii) structures predicted by the proposed RST parser reflect the argumentative structures in texts in Russian.
Polarized Images-Based Dehazing From the Viewpoint of Self-Guided Multi-Image Features Fusion

Different from single image dehazing, polarized images can record richer scene information, and thus polarized images-based dehazing has attracted tremendous attention recently. Existing polarized images-based dehazing methods usually propose some priors or assumptions to calculate the degree of polarization or angle of polarization for dehazing. Although these methods have made significant progress, the priors or assumptions are not always reliable in the real scene, which limits their performance. In addition, most existing methods only consider pixel-level difference information and ignore extracting more effective information from themselves. Based on the above analysis, we propose a novel framework that transforms the polarized images-based dehazing problem into a multi-image fusion problem without any assumption based on the fact that the source of the polarized image is from the same scene but contains different scene information. Specifically, we first pre-train a generic dehazing physical model to obtain an intermediate result that serves as a reference image due to a clearer structure. Then the reference image is used to guide the features fusion to extract more effective features from the input polarized images themselves. Extensive experimental results show that the proposed method achieves superior performance against state-of-the-art methods on both synthetic and real data.
On Music Education Connecting China’s Local Family Communities and Cultures from Nepal, UK and Ireland - A Hybrid and Non-Formal Home Learning Approach

Preliminary research indicates that three-dimensional (3D) printing machines afford a fun way of learning STEM concepts, particularly in primary classes. Educators recommend that 3D machines should be introduced to the students at young age to teach STEM concepts, develop creativity and engage in team work - essential skills for 21st century work force. This presentation […]
In the tradition of Silver Age. In memory of Olga Kostina (1947–2020)

The article is dedicated to the memory of art historian Olga Kostina. In the course of the past three decades professional periodicals “Russian Gallery”, “Russian Art”, “Art Studies Magazine”, almanac “Panorama of Arts” have been published thanks to her valiant work and selfless attitude.
Improving Human Image Synthesis with Residual Fast Fourier Transformation and Wasserstein Distance

With the rapid development of the Metaverse, virtual humans have emerged, and human image synthesis and editing techniques, such as pose transfer, have recently become popular. Most of the existing techniques rely on GANs, which can generate good human images even with large variants and occlusions. But from our best knowledge, the existing state-of-the-art method still has the following problems: the first is that the rendering effect of the synthetic image is not realistic, such as poor rendering of some regions. And the second is that the training of GAN is unstable and slow to converge, such as model collapse. Based on the above two problems, we propose several methods to solve them. To improve the rendering effect, we use the Residual Fast Fourier Transform Block to replace the traditional Residual Block. Then, spectral normalization and Wasserstein distance are used to improve the speed and stability of GAN training. Experiments demonstrate that the methods we offer are effective at solving the problems listed above, and we get state-of-the-art scores in LPIPS and PSNR.
An efficient publicly verifiable and proactive secret sharing scheme

A verifiable proactive secret sharing (VPSS) scheme is a verifiable secret sharing scheme with the property that the shareholders can renew their shares without reconstructing the secret and interacting with the dealer. In this paper, we propose a new VPSS scheme using homogeneous linear recursions and prove its security in a standard model. Our scheme is more efficient than the previous VPSS schemes and is secure against mobile and active adversaries. Furthermore, anyone, not only the shareholders of the scheme, can verify the correctness of the produced shares and sub-shares without observing them and interacting with the dealer and shareholders.
Artifact Management in the Cloud Native Environment

The development history of computer technology can be attributed to the history of the continuous pursuit and improvement of computing efficiency by human. Efficiency is reflected in two aspects: a shorter time to complete computing and fewer resources to complete computing. Since the birth of general-purpose computers, the architecture of computers includes two parts: hardware and software. A computing task is executed by unchangeable general-purpose hardware and changeable software. Hardware and software are two main lines of development that complement and promote each other.
IMPLEMENTATION OF THE COMPETENCE APPROACH IN THE FIELD OF CHEMICAL EDUCATION

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The Application of Three-Dimensional Tomography and Chemical Mapping by Soft X-Ray Microscopy to Fungi Spore on Timbers from the "Nanhai No. 1" Shipwreck

Microorganisms will induce biodeterioration and destruction to ancient wooden culture heritages. The "Nanhai No.1" shipwreck sank off during the Song dynasty (~1160 AD) and was salvaged to land in December 2007. After the wreck was salvaged from the seawater and stored in the museum, it was found that fungal hyphae appeared on the hull in certain places. Further examination revealed that the fungal disease tended to appear in areas enriched with sulfur and iron compounds. Therefore, analyzing the fungi in waterlogged archaeological wood and whether it is related to inorganic compounds is a question that needs to be addressed in the conservation of marine shipwrecks. Here, based on Synchrotron radiation technology, integrating X-ray absorption fine structures with full-field transmission X-ray microscopy to analyze the iron component and map its distribution in a single spore, which probably traces the interaction between inorganic elements with cell and subcellular constituents. In conclusion, the results show examples of soft X-ray tomographic generating 3-D reconstructions associated with chemical mapping of whole spore and demonstrate the power and potential ability of synchrotron radiation-based techniques to study cultural heritage and its conservation application.
The problem of managing the learning behavior of insufficiently motivated students at the stage of mastering research competencies

Mastering the educational program within the framework of the «subject study – testing and exams» strategy is not a problem for the bigger part of students. Another thing is their further development, when the share of independent individual work and the search for creative solutions increases at the stage where they are supposed to acquire research competencies. This is a complex process, and not all students have enough motivation to successfully complete it. The article discusses the ideas of stimulating such students to study.
Effect of Random Splitting and Cross Validation for Indonesian Opinion Mining using Machine Learning Approach

Opinion mining has been a prominent topic of research in Indonesia, however there are still many unanswered questions.The majority of past research has been on machine learning methods and models.A comparison of the effects of random splitting and cross-validation on processing performance is required.Text data is in Indonesian.The goal of this project is to use a machine learning model to conduct opinion mining on Indonesian text data using a random splitting and cross validation approach.This research consists of five stages: data collection, pre-processing, feature extraction, training & testing, and evaluation.Based on the experimental results, the TF-IDF feature is better than the Count-Vectorizer (CV) for Indonesian text.The best accuracy results are obtained by using TF-IDF as a feature and Support Vector Machine (SVM) as a classifier with cross validation implementation.The best accuracy reaches 81%.From the experimental results, it can also be seen that the implementation of cross validation can improve accuracy compared to the implementation of random splitting.
Prediksi Pola Penjualan Barang pada UMKM XYZ dengan Metode Algoritma Apriori

Through the development of information technology today, the need for clear and accurate information is needed in everyday life, so that information will become an important thing in society. Sometimes high information needs are not accompanied by the presentation of adequate information, often information through the mining process is expected to provide information that was previously hidden in the data warehouse so that it becomes important and valuable information [1]. Utilization of existing data in the information system to support decision-making activities, it is not enough to just rely on operational data, a data analysis is needed to explore the potential of existing information. Decision makers try to take advantage of existing data warehouses to explore useful information to help make decisions, this encourages the emergence of new branches of science to overcome the problem of extracting important or interesting information or patterns from large amounts of data, which is called data mining. 2]. MSME XYZ is one of the leading MSMEs in Lubuklinggau City where this MSME sells various kinds of durian products such as tempoyak, lempok durian, peeled durian, durian pancakes, durian ice cream, durian coffee, durian seed chips etc. Every day MSME XYZ carries out activities such as sales transactions, providing product stock and so on, from the existing sales data so far XYZ has not been able to provide information about the pattern of customer spending habits so that transaction data cannot help leaders in making decisions from data collected. there is. Association analysis or association rule mining is a data mining technique to find the rules of a combination of items. One of the stages of association analysis that has attracted the attention of many researchers to produce efficient algorithms is high frequency pattern analysis (frequent pattern mining). The output of data mining can be used to improve decision making in the future.
Lifecycle Management

After Harbor is deployed in the production environment, it continuously provides users the registry service. At this time, Harbor enters the operation and maintenance phase. In the production environment, the user needs to be familiar with the lifecycle management knowledge of Harbor to prevent and control risks.
Development of information culture among teaching staff of general secondary education institutions

Information culture is an integral component of the labor efficiency of specialists in a wide range of fields of professional employment. Information and communication technologies have undergone rapid changes in the main methods and types of human activity in the social environment, which encourage potential users to constantly master them and adapt to new types of professional activity. Professional development programs for teaching staff are created taking into account the current trends in the development of the information technology market and the possibilities of their use in the educational process by teaching staff of general secondary education institutions.&#x0D; The article presents the results of research on the development of information competence of pedagogical workers. A theoretical analysis of the problem of formation of information culture in the conditions of modern education was carried out. The functions of information competence in the structure of teacher professionalism are defined. Within the framework of the scientific project of NASU “Civil Service Competency Framework” (2018), we have developed a method of studying invariants of professional activity of civil servants. The ability to handle a great deal of information, that is viewed by us as an evolutionarily appropriate necessity, is the basis of personal and professional success in present-day information society. Based on the results of our research, we found that only a third of the subjects have developed the competencies that form the basis of information culture. It has been established that the socio-psychological level of information culture is developed at a low level among all teaching staff, regardless of length of service. The technological level, which is related to the ability to use modern ICT and search and accumulate information, is formed in a larger number of researched subjects, compared to the socio-psychological level.
Password Strength Analysis: A Survey

This paper outlines research based on password strength measurement analysis using various machine learning approaches. In the current world, a password is the most indispensable authentication mechanism concerning any system security. Although there are multiple authentication methods available nowadays which provide improved security such as biometrics and smart cards still the password authentication mechanism is auspicious to everyone for enhanced system security. Generally, humans prefer to set a password related to their birthplace, date of birth, keyboard patterns, dictionary words, phone number, etc. Online or offline attackers and intruders can guess these passwords easily and intrude the system security. Privacy and data protection have become a need today. Cyber security is growing in its importance in literally every domain in recent years. Every person must safeguard their data and have a basic understanding of the technical developments taking place in many businesses. Key Words: Machine learning, password, Multi-Layer Perceptron
Pervasive Healthcare: Privacy and Security in Data Annotation

Novel pervasive healthcare solutions often require extensive labeled data, both to train activity, behavior, and symptom detection systems, and to monitor the reliability of the system. The annotation process can be intrusive for participants and raise significant privacy and security issues. We discuss these challenges and identify mitigations.
LOMBOK PEARL QUALITY CLASSIFICATION USING A COMBINATION OF FEATURE EXTRACTION AND ARTIFICIAL NEURAL NETWORKS BASED ON SHAPE

Lombok is attracted to the Moto GP event, which is held annually. Various tourism brands are owned by the island of Lombok, one of which is Mutiara. The ideal Pearl is perfectly round and smooth, but there are a variety of other shapes as well. One method that can be used to process Pearl's image is Computer Vision. For that, it is necessary to have a way to classify the quality of a Pearl based on its shape. The purpose of this study is to propose a system for pearl image classification by combining feature extraction with artificial neural networks. The method used in this study is GLCM feature extraction and Neural Networks. The proposed system can provide good classification results by combining the GLCM method and the Neural Network. This study uses Epochs 5, 10, 15, 30, 50, 100, 200, 300, and 500 with a learning rate of 0.5. The results of this study indicate that Epoch 100 gives the highest accuracy, 91.66%.
IMPLEMENTATION OF SUPPORT VECTOR REGRESSION IN THE PREDICTION OF THE NUMBER OF TOURIST VISITS TO THE PROVINCE WEST NUSA TENGGARA (NTB)

Abstract — Indonesia has a variety of interesting tourist destinations to visit in each region. One area that is used as a favorite tourist destination is the Province of West Nusa Tenggara (NTB). Data The number of tourists visiting the NTB province from 2014 to 2020 tends to change based on data obtained from the Website of the NTB Provincial Tourism Office. The data on the number of visitors will continue to change, even if there is a possibility that it will increase. This can lead to the unpreparedness of the government and other tourism actors in providing the facilities and infrastructure needed by visitors when there is an increase in the number of tourist visits coming to NTB. Therefore, it is necessary to predict the number of tourist visits to NTB with accurate results. In this study, predictions of the number of tourist visits to the Province of NTB were made using the support vector regression method. This research resulted in an application to predict the number of tourist visits to NTB based on Event, Month, and Year. so that it can provide predictive results that are close to the actual value under normal conditions. The data used in this study is data on the number of tourist visits in 2017-2021 and events held in 2017-2021.
A multi-feature fusion based confidence estimation model for Chinese end-to-end speech recognition

Speech recognition has shown excellent performance in recent years, but as we known, this kind of AI task is still supervised and needs labelled data to feed models so that it can achieve a better effect and to inference to get model’s metrics. In actual application scenarios, it always needs many people to check speech recognition’s correctness because of lacking labelled data, it is not realistic to label massive online audio. In addition, this method can’t guarantee comprehensiveness of checking, so confidence estimation algorithm is proposed, which can evaluate speech recognition model’s results and predict error transcriptions automatically. This paper proposed a confidence estimation model based on a multi-feature fusion mechanism and it mainly focus on Chinese end-to-end speech recognition tasks in complex application scenarios. Experiments in Aishell-1 dataset and China telecom’s internal dataset have proved that this model can obtain a good performance.
Design and implementation of community express delivery system

The development of a community express delivery system in the context of COVID-19 reduces the risk of virus infection for people in the community who go out to pick up their deliveries, which highlights the benefits of “contactless delivery”. The system is developed using a separate development method for the front-end and back-end. The mobile end in the front end uses the WeChat applet, and the Web end uses the Vue framework for development. The back-end server is developed in Java and designed with a MySQL database. This system contributes to the community residents’ courier delivery challenges.