Reviewerly/exHarmony huggingface dataset:
RARE-EARTH ELEMENTS, NOBLE AND RARE METALS IN ORES OF THE ALGAMINSKOE ZIRCONIUM OCCURRENCE (KHABAROVSKY KRAI)

Samples of the ore and gravity concentrate from the Algaminskoe zirconium occurrence were studied. Concentrations of rare-earth elements (REE), rare and noble metals were determined. REE content in the ore is 200–300 g/t, with 400 g/t in the gravity concentrate. REE distribution diagrams clearly show Ce depletion and the predominance of intermediate lanthanides and yttrium. The ore and the concentrate contain elevated Hf, W, U, Ta, Au, and Pt, most of which are present in the form of isomorphic admixture in baddeleytte and zircon. Their source is the ancient weathering crust of peralkalic rocks of the Ingili massif. The suggestion has been made that there should be a buried weathering crust enriched in valuable components.
Federated Learning with Cooperating Devices: A Consensus Approach

Federated learning (FL) is arising as another perspective to prepare AI models (machine learning) in conveyed frameworks. Rather than sharing and uncovering the readiness educational record with the specialist, the model limits (e.g., neural associations tendencies) progress in all things considered by very large masses of interconnections, appearing as neighborhood understudies. FL can be applied to control obligated Internet of Things (IoT) devices with moderate and conflicting affiliations. Also, it need not waste time with data to be exchanged with unusable matters, saving security. Despite these advantages, an essential restriction of existing systems is to merge improvements, which depends upon an expert for total and blend of nearby restricts; this has the weight of a solitary inspiration driving disappointment and scaling issues for developing association size. This article proposes a completely flowed (or serverless) learning approach: the proposed FL calculations sway the premium of gadgets that perform information practices inside the relationship by reiterating nearby assessments and standard relationships through plan-based procedures. The technique places the justification for the trade-off of FL inside 5G and past affiliations portrayed by the decentralized association and planning, with information flow over the end gadgets. The proposed methodology is checked by the exploratory instructive records collected inside an ndustrial IoT (IIoT) condition.
Project-Based Learning: For Teachers and School Students

The authors present an advanced development course for school-teachers to improve their project activities efficiency. Project-based learning is being successfully introduced at universities, however it is a rare educational practice at schools. The most efficient way to involve students into project activities is to start doing projects at the earlier stages of education. Therefore, it is urgent to prepare school-teachers to use project-base technology in their work. The course aims at developing project skills of school-teachers. It provides the trainees with a set of knowledge, skills and competences to prepare them to teach their disciplines using project and research methods. The course includes 4 modules and was implemented as an internship at two schools in the city of Kazan (Tatarstan, Russia).
A Transfer Learning Approach for Descriptive Question Answering System

In this era of data explosion, where data is streaming at an exponential rate, efficient strategies of data processing have become an important aspect. The paper focuses on study of current “tate-of-the-art models” and proposes a Transfer learning approach with BERT model fine-tuned on SQuAD v2.0 dataset and improving the overall results based on speed and scaling factors and extending the reach of Question answering systems from generating one-line answers to generate descriptive answers. It is designed to handle user queries, whether it is web-based queries, reading comprehension tests, or Product related enquiries for satisfying user needs. The proposed model was experimented on a custom-made dataset to compare results with the original BERT model and a human-based evaluation method is proposed to evaluate the correctness of the descriptive answers.
Research and Verification of Real-Time Query Technology

At present, with the rapid development of digital technology, power enterprises need to improve business operation efficiency and management level with the help of digital means. Based on the urgent need for real-time query of electricity business, electricity recovery statistics, electricity bill list, power sales briefing and other businesses, this paper analyzes and designs the technical architecture and data flow diagram of real-time query library, carries out component verification from two aspects of core competence and typical scenarios, and analyzes the characteristics of different technologies of ADB and Hologres, The optimization suggestions and directions are given.
Chemical School for Gifted Children

Modern requirements of global industrial development focus on the creation of new breakthrough technologies. One of the main goals of modern education is to grow generators of new engineering ideas. To solve this problem, it is important to develop professional skills of gifted children with a natural predisposition to mastering natural and technical disciplines. Therefore, the creation of special conditions allowing such children to develop engineering skills is of great importance. The article presents special conditions and features of the educational process at the Boarding Lyceum for Gifted Children. The Lyceum created flexible and variable educational system based on interdisciplinary training courses, partnership with universities and industrial companies, professionally focused training, information resources and access to network of multi-level professional training programs and elective courses, and special professional support of teachers. This educational approach allows students to form their personal educational trajectories for special engineering-oriented development. The efficiency of the created conditions is proved by educational outcomes of students.
The Application of Bayesian Network in Higher Vocational Students' Choice of Professional Direction

The grades of six courses of students in the class of 2020 in Chongqing Tourism Vocational College majoring in big data technology are taken as data samples, and the correlations among the six subjects are obtained by learning through bayesian network structure. Based on the Bayesian angiospectrum structure for parameter learning, the conditional probabilities of each discipline's grades are obtained; finally, the joint tree algorithm using bayesian network inference gives the students' grade prediction. The results show that it is feasible to make major direction selection based on the prediction results, which provides an important basis for departmental decision making and student direction selection.
A Comparative Analysis of Various Blockchain Hardware Platforms Using Ethereum

This paper conducts a performance analysis of popular blockchain platforms, RISC-V, to assess the performance and limitations of these state-of-the-art platforms. This paper performs a performance analysis of popular blockchain platforms, RISC-V, to evaluate the performance and limitations of these state-of-the-art platforms. Blockchain, a decentralized transaction and data management technology, is the technology that will have similar impacts as the Internet had on people's lives. Many industries have become interested in adopting blockchain in their IT systems, but scalability is an often-cited concern of current blockchain technology. Therefore, the goal of this article, to analyze and compare the performances of var-ious hardware platforms available for blockchain. The plat-forms preferred for conducting this research works are X86, ARM, and RISC-V. The computational capabilities have been showcased and elaborated in detail. First, the compatibility of the blockchain has been checked with Ethereum. Second, critical parameters like time and space complexity are considered to find the best suitable platform.
Project Activity in the Formation of Subject Competencies

The project-based learning has long established itself as one of the most stimulating methods of mastering the educational program. However, to study in depth of mathematical disciplines, project tasks must be specifically formulated for the topic being studied. The purpose of this work is to study the methodological relationship between the declared projects and the studied topic of mathematics, as well as a comparative analysis of the results of the development of three mathematical disciplines using the project method and without it. The work on the projects has been carried out for three years at our university and it is aimed at activating the independent cognitive activity of students. In 2018–2019, these were applied and educational projects, the topics for which were selected by teachers. In 2019–2020, students were given the right to independently choose a project topic based on their scientific interests and develop it. In one experimental group, students had the opportunity to consult with the teacher to find a solution to the project problem, in the other such an opportunity was not available. The results of both groups were roughly the same. The conclusion that we came to as a result of this work: project activity increases the horizons of students and allows us to see interdisciplinary connections. Project activity is more effective only if the student works closely with the teacher.
Determination of Static and Dynamic Young's Modulus of A Cantilever Beam using Digital Image Correlation (DIC) Method

The Young's modulus is a fundamental mechanical property that measures the stiffness of a solid material. Destructive material testing is the common method to determine Young's modulus. This paper presents the vibration measurement technique using Digital Image Correlation (DIC) as an alternative method to determine the Young's modulus of materials. The principle is based on the linear transverse vibrations of a Euler-Bernoulli cantilever beam with clamped-free boundary conditions. The dynamic responses of three materials (steel, aluminium and brass) were discussed. For each material, the beam was subjected to an initial static transverse displacement at its free end and then allowed to vibrate freely. The vibrations at the free end were captured using DIC measurements, and the results processed and presented in the form of displacement versus time, following which the natural frequencies of the beam can be obtained using the method of Fast Fourier Transform (FFT). The Young's modulus can then be derived using the first natural frequency and the geometry of the beam. The calculated Young's modulus values obtained using the DIC measurements of the free vibrations are compared with the modulus obtained using the static deflection approach, which shows highly favorable agreement. The use of the vibration method to determine Young's modulus using DIC method can help in the non-contact assessment of the existing structural integrity without subjecting test specimens to destructive testing.
Towards Automating the Human Resource Recruiting Process

Companies often receive numerous resumes for each job vacancy, and sometimes the resumes are not classified or even relevant to the job. Consequently, it is a time-consuming task for Human Resources (HR) to shortlist the candidates. In this work, following business process re-engineering and replacing Artificial Intelligence (AI)-driven approaches with organizational processes, we aim at technology disruption by proposing a holistic approach for resume recommendation in recruitment systems. This is done by harnessing the power of novel Machine Learning (ML) algorithms to address the candidate ranking problem. The proposed system starts with a preprocessing phase to extract a set of information from PDF files. Next, it applies ML techniques to compute the similarity between the submitted resumes and the target job description. Finally, it ranks the job-seekers and recommends the best candidates to the human resource. To the best of our knowledge, this is the first work that focuses on the Persian language enabling HR to identify the resumes that are closest to the provided job description.
Future Research Directions

Heterogeneous graph (HG) representation has made great progress in recent years, which clearly shows that it is a powerful and promising graph analysis paradigm. However, it is still a young and promising research field. In this chapter, we first make a summarization of this book and then illustrate some advanced topics, including challenging research issues, and explore a series of possible future research directions. One major potential direction is exploring fundamental ways to keep intrinsic structures or properties in HG. And another direction is to integrate the techniques widely used or newly emerged in machine learning to further enhance the applicability of HG on more key fields. We will illustrate more fine-grained potential works along with these two directions.
Modern Trends in Soft Skills Development for «International Transport Policy» Students

The article discusses modern trends in soft skills development for the “International Transport Policy” students with the help of distance learning technologies. Nowadays taking into account changes of the digital world there is a need of education transformation. Soft skills development provides personnel leadership in a digital era. The value of new technologies is that they allow to live and to study better in a quickly moving world, in various directions and disciplines and to understand flows of modern information. Assimilation of knowledge and abilities development act as a result of vigorous independent students’ activity on conflicts solving, improve professional knowledge, skills, abilities and critical thinking. The objective of our research is identification of the conceptual model “soft skills” opportunities in technical students training. Moreover, the demand for these skills in modern labor market is accented. The term “soft skills” is one of the new tracks now. The article includes the content-analysis of the term «soft skills» providing the integrity of the term perception and its contents, applicability level in communication, and also the ways of soft skills development for “International Transport Policy” students in their foreign language practical training.
Credit card fraudulence detection using Salient Feature Extraction Technique with Adaptive Synthetic Oversampling Models

Credit card fraudulence is a federal offense that takes place frequently in recent times. The phenomenon where an imposter or a scammer tries to make an illegal purchase or transfer of money from one account to another using a credit card that does not belong to him/her, is coined as Credit Card Fraudulence. In modern world, credit card fraud or any type of payment card fraud is a very common but serious crime that occurs both offline and online. But with the help of machine learning algorithms and Salient Feature Extraction Technique (SFET) we can easily detect such offense and help in further investigations. From time to time many data scientists, data analysts, machine learning engineers and other researchers have designed many algorithms to detect credit card frauds. By extracting the most relevant and important features of a transaction, it is quite possible to detect credit card fraud very quickly & efficiently. In this paper, we have shown such an improved way by using Adaptive Synthetic oversampling (ADASYN) model with five notable supervised machine learning models namely Random Forest, Support Vector Machine, Naive Bayes, Logistics Regression and K-Nearest Neighbour. Out of these five machine learning models, K-Nearest Neighbour has shown the best precision, recall, specificity & accuracy. The performance accuracy of Random Forest, Logistic Regression, K-Nearest Neighbour, Naive Bayes & Support Vector Machines are 96.04%, 81.31%, 96.22%, 79.22% & 50.06% respectively.
Modular Commands Recognition Based on Semantic Understanding

Recent commands recognition methods based on deep neural networks (DNNs) have achieved remarkable results, but there are still generalization problems in real scenes. Most commands recognition models can only recognize the command in a fixed format, which is not convenient. In this research, we propose a modular model that separates speech recognition model which can make full use of advanced end-to-end ASR and semantic recognition model which is designed for semantic understanding. We use the tokens recognized by speech recognition model as the input of the semantic recognition model. We tested the performance of the model by using both fixed form commands and commands in the daily pragmatic environment. For this reason, we specially build a command dataset in the daily pragmatic environment. Experimental results show that the model proposed in this paper not only performs well in the task of fixed-format commands recognition but can also realize efficient and accurate command recognition in daily pragmatic environment.
On Robustness of the Normalized Random Block Coordinate Method for Non-Convex Optimization

Large-scale optimization problems are usually characterized not only by large amounts of data points but points living in a high-dimensional space. Block coordinate methods allow for efficient implementations where steps can be made (block) coordinate-wise. Many existing algorithms rely on trustworthy gradient information and may fail to converge when such information becomes corrupted by possibly adversarial agents. We study the setting where the partial gradient with respect to each coordinate block is arbitrarily corrupted with some probability. We analyze the robustness properties of the normalized random block coordinate method (NRBCM) for non-convex optimization problems. We prove that NRBCM finds an $\mathcal{O}(1/\sqrt T )$-stationary point after T iterations if the corruption probabilities of partial gradients with respect to each block are below 1/2. With the additional assumption of gradient domination, faster rates are shown. Numerical evidence on a logistic classification problem supports our results.
Solar vibe

My yoga teacher is hosting a gong bath where the gong is supposedly tuned to the frequency of the solar system. Does the solar system have a frequency? If so, is it possible to tune a gong to it?
Multi-criteria Dual-View Attention Network for Rating Prediction

There have always been recommender systems the majority of the researches predicting uses single ratings. Where on the other hand, Multi-criteria predictions are more accurate. In recommender systems, collaborative filtering is a well-known strategy. Deep learning has produced excellent results in various fields, including computer vision, voice, and text. Deep learning at recommender systems recently gained wide attention, and many recommendation models have been published. However, based on our knowledge, no research has been conducted to collect multi-criteria recommendations and collaborative filtering with a dual view attention network. This paper proposes a multi-criteria Dual-View Attention Network (MDVAN) to predict the multi-criteria ratings for items as they progress from coarse to multi-criteria fine attention level. We explore the dual-view model to fully use the User View and Item View advantages hidden in historical rating behaviors with the multi-criteria problem. Then create a ReLU universal pair-wise channel unit to model the relation of two multi-criteria domains. We implement an aggregation function to incorporate the multi-criteria items in the dual view model. It was shown through experimentation on a real-world dataset that we had exceeded all of the current approaches. And that proof dual-view attention network in multi-criteria recommendation systems achieved great success.
Forecasting Inflation in Indonesia using Long Short Term Memory

The problem of inflation has always been a macro problem that occurs in developing countries, especially Indonesia. Observation of the inflation rate cannot be done by observing only certain years because the problem of inflation is a long-term problem. The case of inflation in Indonesia has become quite important to note since Indonesia adopted the inflation target system. Modeling and forecasting the inflation rate is needed and considered important because it is related to poverty alleviation where people with low incomes or still are required to be able to meet their needs with high prices of goods. One of the forecasting models that can be used is the Long Short Term Memory (LSTM). This model is a development of the previous model Recurrent Neural Network (RNN). The results showed that the best model is the model with 5 nodes in hidden layer, Adam optimizer and 0.01 learning rate. Forecasting results show that until September 2022 Indonesia's inflation rate condition will increase but not significantly increase so that it remains stable below 2% until September 2022 and still classified as mild inflation.
DESIGN AND MODELING OF ANFIS REGULATOR TO COMPENSATE DEVIATIONS OF MOBILE PLATFORM OF PARALLEL CABLE-DRIVEN ROBOT

The article deals with a description of design and modeling of neuro-fuzzy regulator (ANFIS) which provides a compensation of deviations of mobile platform of parallel cable-driven robot. The regulator uses adjusting of lengths of unwounded cables. Evaluation of the control system has been made according to the quality criterion as an elongation of each cable comparatively to given values.
Contextual Quality Assessment of the Newspaper Articles Based on Keyword Extraction

It is often seen that the same news or stories are portrayed in different newspapers from a separate perspective. It always confuses the readers to determine which newspaper publishes better quality news with the rich content in a specialized domain. Thus, contextual quality analysis of any document or newspaper report is always a research concern. In this work, we have proposed a novel graph theoretic method to decide the quality of the newspaper articles written on any specific subject. Different node centrality measuring metrics of the complex graph like degree centrality, betweenness centrality, and clustering coefficient are considered for judging the association among the keywords in any concerned text piece. The amount of necessary and contextual words are presented in an article related to the specific subject represents the quality of the document. The method can be operated to rank the published news articles based solely on contextual quality. Our approach performs without any semantics and prerequisite knowledge. As a case study, we analyze some match reports of FIFA men’s football world cup, 2018 published in three popular newspapers circulated in India. The analytical results reveal the newspaper that published the best quality news reports about the tournament. Our model depicts the unbiased answer than the human perspective and can be used as an alternative procedure in interdisciplinary research areas.
A Survey on AI based Network Slicing Standards

Artificial Intelligence (AI) based Network Slicing (NS) has spurred the interest of both academia and industry. Thus, several Standards Development Organizations (SDOs) have established working groups to produce specifications or standards on AI-assisted Network Slicing. This paper surveys the standardization activities on AI -based Network Slicing. It then introduces a classification in 3 categories, namely use cases, requirements and security, and discusses issues related to each category.