Reviewerly/exHarmony huggingface dataset:
Competitiveness of medium-sized white line service center companies in the COVID-19 pandemic

The situation of the COVID-19 pandemic has generated new obstacles and challenges for the competitiveness of various organizations. This article will analyze the impact it has had on white line service center companies. These companies, as well as various SMEs, have found it necessary to implement strategies or actions that increase competitiveness in the workplace. The objective of this study is to observe the performance of competitiveness in white line service centers through the environment of the COVID-19 pandemic. The tool that was used was an observation of medium-sized companies in the implementation of the visit to the white line service center, for the collection of different observations of teamwork. It is important to mention that a positive response was obtained, showing a specific analysis of competitiveness as a result of the new strategies that medium-sized white line companies applied to maintain competitiveness with the new COVID-19 environment.
Bitcoin Cryptocurrency Address Grouping Methods

Nowadays, Bitcoin cryptocurrency is an alternative means of payment for purchases in many areas of our lives. However, fraudsters trying to seize cryptocurrency funds and often attack Bitcoin users. In this regard, methods are being developed to determine reliability of the transfer and prevent the loss of funds by the user. Data on all transactions in the Bitcoin network is publicly available, but does not contain any information about the user, except for the cryptocurrency wallet address, and the user is able to create a new transfer address for each transaction. To check reliability of a potential transfer recipient, algorithms for classifying the Bitcoin users are being developed. For classification, it is necessary to join Bitcoin addresses into groups related to the same user. As a rule, address grouping methods are based on the heuristic of combining transaction inputs and the heuristic of determining the recipient address in the transaction. However, such methods provide inaccurate and incomplete results, which leads to development of new approaches having their own advantages and disadvantages, which limits their scope. Data structure of the Bitcoin cryptocurrency blockchain is considered, as well as comparison of existing approaches to address grouping is provided
The ontological argument as the methodological basis of the considered problem of cosmism

The article is devoted to the content of the theory of cosmism and its conceptual and methodological foundations. The analysis of the content of the works of foreign cosmists and their role in the world, philosophical, cultural and pedagogical science is given. The evolution of mankind has faced many crisis phenomena and negative consequences of their existence. Their impact on the world should be studied not only on the scale of the planet, but also in outer space. The balance between the negative and positive vector of the existence and development of our humanity remains quite fragile, which makes it inevitable to turn to the basic concept of "cosmism". This study reveals the content of the above concept, including the concept of "Russian cosmism", characteristics of the cosmic worldview. The main difference between foreign cosmism and Russian is shown. The concept of "human transformation" in the context of the philosophical and pedagogical direction, the concept of space pedagogy (pedagogy of cosmism, cosmopedagogy), methodological principles of the anthropocosmic concept of education are analyzed in their connection with the modern education system. An important methodological advantage of the pedagogy of cosmism is the consistency of the tasks set, the methodological educational principles included in it. The key and most promising methodological principles of cosmism for the modern education system are identified, which are reflected in cosmopedagogy and are its obvious advantages. The development of the child's cosmic self-consciousness, corresponding to feeling and intuition, must be taken into account in the content of modern education.
Comprehending Object Detection by Deep Learning Methods and Algorithms

In the real world, computer vision is used for more challenging tasks like detecting of objects in an image or video. There are multiple applications of object detection in various domains like animation, autonomous driving, monitoring of traffic, communicating through video. With the development of new emerging technologies in deep learning, finding accuracy of objects by performing classification and detection became possible. When compared to traditional object detection methods deep learning methods has an ability of feature learning and rendering. This paper is mainly focused on the working procedure of convolutional neural networks in detecting objects that are present in the environment of an image. CNN, R-CNN, and Faster R-CNN are the main models of deep learning which are considered for comparative-based study. Comparison between these models is made by identifying their accuracies, limitations, and speed. Among the three models, Faster R-CNN is identified as ideal one as it has higher accuracy and less expensive in nature when compared with R-CNN whereas CNN model can be only used for image classification (Tripathi in Journal of Innovative Image Processing (JIIP) 3:100–117, 2021), but it cannot localize the objects.
Business Account Management and Statistical Report Analysis System Based on Front-end and Back-end Separation Framework

By integrating the needs of each team, combining the previous development experience and the current mature development framework mode, this paper proposes to build a business account management and statistical report analysis system based on the front-end and back-end separation framework, which can be used by each team to improve work efficiency and enhance the reusability and practicality of various marketing data. This paper introduces the web directory structure of the whole system and the contents and related operations displayed on the corresponding management pages. For the needs and problems raised by users, the system adds corresponding functions and pages to solve them. The back-end of the marketing data management system is based on J2EE framework and developed by springboot+mybatis. The front-end is developed by react + ant design + node + echarts+ less.
勉略宁矿集区铜厂铜-铁矿床成因：黄铜矿Re-Os定年和硫化物原位硫同位素制约

摘要: 铜厂铜-铁矿床在成矿时代、成矿物质来源及矿床成因等方面存在较大争议，限制了其成矿模式的建立以及进一步的找矿实践. 利用黄铜矿Re-Os同位素对该矿床进行定年，并利用LA-MC-ICP-MS技术对黄铜矿、黄铁矿及磁黄铁矿等硫化物开展原位硫同位素研究. 分析结果显示，5件黄铜矿Re-Os同位素等时线年龄为484±34 Ma（MSWD=8.7），表明铜厂铜-铁矿床形成于早古生代加里东期. 铜厂铜-铁矿床上部铜矿床中黄铜矿（+9.75‰~+13.1‰）和黄铁矿（+9.22‰~+13.9‰）的δ34S值略高于下部铁矿床中黄铜矿（+8.66‰~+10.9‰）、黄铁矿（+8.85‰~+11.0‰）和磁黄铁矿（+7.93‰~+9.28‰）. 计算得到早期成矿热液的δ34S∑S值约为+10.6‰，晚期成矿热液的δ34S∑S值约为+12.3‰，说明矿床硫是地幔硫混染海水硫形成的，热化学还原在海水硫酸盐还原过程中起到关键作用. 铜厂铜-铁矿床的形成可分为两期：新元古代晋宁期，Rodinia超大陆裂解导致勉略宁地区发生海底火山喷发形成富含Fe、Cu的初始矿源层；早古生代加里东期，大陆边缘持续的裂解和裂陷形成勉略海槽并导致强烈的岩浆活动，富含挥发分及硫的岩浆热液混合海水硫，并从细碧岩中萃取Fe、Cu等成矿物质，早期成矿热液在铜厂地区深部形成铁矿床，随着磁铁矿和硫化物的沉淀，成矿热液演化到晚期阶段并沿断裂构造带向上运移，在铜厂地区浅部形成铜矿床. 关键词: 勉略宁 / 铜厂铜-铁矿床 / Re-Os同位素定年 / LA-MC-ICP-MS / 原位硫同位素 / 成矿模式 / 矿床学
Learning-based absolute 3D shape measurement based on single fringe phase retrieval and speckle correlation

In this work, we propose a learning-based absolute 3D shape measurement based on single fringe phase retrieval and speckle correlation. Our method combines the advantages of Fourier transform profilometry (FTP) techniques for high-resolution phase retrieval and speckle correlation approaches for robust unambiguous depth measurement. The proposed deep learning framework comprises two paths: one is a U-net-structured network, which is used to extract the wrapped phase maps from a single fringe pattern with high accuracy (but with depth ambiguities). The other stereo matching network produces the initial absolute (but with low resolution) disparity map from an additional speckle pattern. The initial disparity map is refined by exploiting the wrapped phase maps as an additional constraint, and finally, a high-accuracy high-resolution disparity map for absolute 3D measurement can be obtained. Experimental results demonstrated that the proposed deep-learning-based method could realize high-precision absolute 3D measurement for measuring objects with complex surfaces.
Correction to: Effective Vietnamese Sentiment Analysis Model Using Sentiment Word Embedding and Transfer Learning

In the originally published chapter 3 the reference to source [18] was erroneously omitted. The first sentence in section 4.2 has been revised by adding the reference [18], and the source has been added in the References section.
On one representation of generalized holomorphic vector via derivatives of harmonic functions

The paper is devoted to investigation of the theory of first-order elliptic systems and generalized holomorphic vectors. By using the Moisil–Teodorescu system the representation of the generalized holomorphic vector is constructed via the derivatives of two harmonic functions of three independent variables. Cite as: J. A. Tokibetov, G. E. Abduakhitova, R. M. Kaparova , “On one representation of generalized holomorphic vector via derivatives of harmonic functions,” Mat. Met. Fiz. Mekh. Polya , 63 , No. 2, 29–35 (2020) (in Russian), https://doi.org/10.15407/mmpmf2020.63.2.29-35
A Blockchain IoT (BIoT) Integrated into Futuristic Networking for Industry

Blockchain innovation is the most confided in across the board cryptosystem that gives a structure to getting exchanges over networks because of its irreversibility and permanence attributes. Blockchain organization, as a decentralized framework, has drawn the consideration of different new companies, managers, and designers. Web of Things (IoT) has been the primary empowering agent of the transformation. Hence, blockchain can fill in as a decent establishment for applications dependent on exchanges and cooperation. IoT executions and purpose are by describe disseminated. This implies blockchain can assist with settling a large portion of the defense weaknesses and detectability worries of IoTs by utilizing blockchain as a record that can monitor how gadgets cooperate, in which state they are and how they execute with other IoT gadgets. IoT relevance has been primarily carried out with advancements in Container Deployment Method (Docker). Blockchain IoT (BIoT) applications are additionally investigated, and a few important answers for work on the adaptability and throughput of such applications are proposed. blockchain and IoT must address for them to effectively cooperate. We have distinguished the central issues where blockchain innovation can help further develop IoT applications. An assessment has likewise been given to demonstrate the attainability of utilizing blockchain hubs on IoT gadgets. This paper section furnishes mixed plans surveys and observes structures for BIoT applications. Blockchain can provide a decent establishment for operations dependent on exchanges and collaborations. IoT executions and functions are by definition circulated. This implies blockchain can assist with tackling the vast majority of the care, weaknesses, and discernibility worries of IoTs by utilizing blockchain as a record that can monitor how gadgets collaborate, and in which way to execute with other IoT gadgets. IoT applications have been fundamentally executed with advances, for example, cloud and haze registering. That means how we implement BIoT in a few futuristic networks. BIoT coordination in Docker Deploy Container, Food Sector (Supply Chain Management), Industrial Cloud, Fog Computing, and Hybrid Computing.
Readiness of teacher the interact with students of different nosologies

The readiness of a university teacher to interact with students of different nosologies has a modular basis, represented by personal, gnostic and procedural components. The article analyzes the current level of knowledge of teachers of the Samara State Institute of Culture and Samara University about the specific capabilities of students with various nosologies. This knowledge relates to the mental development of students; abilities to receive, process, store and use information; difficulties in verbal mediation of information storage and use; technical possibilities of organizing the learning process; readiness for collaborative learning. The preparation of university teachers for interaction with students of various nosologies is built on the basis of a system-holistic approach, its main principles: consistency, integrity, structurality, controllability, transformation.
Formation of digital competence of teachers of computer science

Current socio-cultural situation requires the teacher to understand the general strategy of the digital transformation of education, knowledge of the regulatory framework regulating it, the specifics of conditions for the implementation of pedagogical process in the digital environment, and the readiness to integrate digital technologies into pedagogical activities. Using the potential of the digital educational environment requires solving the problem of forming the digital competence of an informatics teacher. The authors use the ideas of competency-based and activity-based approaches in selecting the content of advanced training programs for computer science teachers. The authors use the principles of a systematic approach in determining the structure of digital competence. The authors describe scenarios of interaction between a teacher and students in a digital educational environment. The authors conclude that it is necessary to additionally prepare the teacher to create high-quality digital educational content, manage the cognitive activity of schoolchildren when teaching using digital learning environments; create a comfortable and safe educational space, and productive communication. The programs are united by a logical line of content in the formation of digital competencies from their application in everyday activities to creative use in pedagogical activities.
The Reality of Knowledge Management in Organizations: Greater Amman Municipality Experience

The modernity of knowledge management as one of the hour topics, imposed the necessity to address this research and to give him enough attention, by taking cognizance of its conceptual framework and trying to address the most important processes and strategies of knowledge management, in addition to explaining the motives that led the Greater Amman Municipality to adopt this concept, highlighting the practical steps taken by this municipality in establishing knowledge management as one of the criteria of excellence that it has set in its mission.&#x0D; The results showed that the Greater Amman Municipality followed a set of practical stages in order to integrate knowledge management in the daily operations of the organization, from the awareness stage to ensure commitment to the planning and implementation stage, disseminate knowledge, and then ensure effective communication between the parties involved in this experiment. The gradual integration of knowledge is essential in order to exploit the implicit and explicit knowledge found in the organization, which enhances its competitiveness and excellence.
Photonic Reservoir Computing: Exploiting Complex Photonics for Information Processing

Reservoir computing (RC), which is a computational paradigm that uses dynamical systems, is a powerful tool for solving highly complex and abstract computational tasks. RC has recently been implemented in a variety of physical systems, including photonic systems. This article introduces its fundamental concepts and recent advances in its optical implementation. As relevant topics, optical implementations of extreme learning machine and deep learning are also discussed.
Assessment of Recruitment Records using Machine Learning

In the era of the fourth industrial revolution (Industry 4.0), the applications of Information Technology (IT) have been widely used in various aspects of life. As the result, analyzing and predicting the result for the application of candidates as well as employers are also growing significantly. Jobseekers and employers want to have accurate information and prediction results in order to have suitable job proposals for themselves and candidates. This research is conducted based on using Machine Learning to meet the requirements of jobseekers and employers in the recruitment evaluation process. We propose to use 4 machine learning methods – Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and Recurrent Neural Network (RNN) to predict job applications. The data set is collected from the Job Center of Binh Duong province. On the basis of the best results method, we build a job application review and visualize the results.
Abstraction-Based Text Summarization using Python Libraries

As there is an increase in the usage of digital applications, the availability of data generated has increased to a tremendous scale. Data is an important component in almost every domain where research and analysis are required to solve the problems. It is available in a structured or unstructured format. Therefore, in order to get corresponding data as per the application's purpose, easily and quickly from different sources of data on the internet, an online content summarizer is desired. Summarizers makes it easier for users to understand the content without reading it completely. Abstractive Text Summarizer helps in defining the content by considering the important words and helps in creating summaries that are in a human-readable format. The main aim is to make summaries in such a way that it should not lose its context. Various Neural Network models are employed along with other machine translation models to bring about a concise summary generation.
Time-Optimal Path Planning of a Hybrid Autonomous Underwater Vehicle Based on Ocean Current Neural Point Grid

Path planning is the precondition for Hybrid Autonomous Underwater Vehicles (HAUV) to enter the submerged area to undertake a mission. The influence of ocean currents on HAUV should be further investigated to obtain a time-optimal path. The improved A* algorithm and the neural network model are employed in this paper to plan a time-optimal path for the vehicle. The HAUV in glider mode is capable of traveling forward mainly through the zigzag motion in vertical plane. Since the vehicle can only receive the command orders when it surfaces from the water, the path is expected to include a series of discrete waypoints in the water surface. At the same time, the presence of submerged riverbeds is also taken into account to avoid hazards for HAUVs when it navigates in the water. It can be demonstrated that ocean currents can be used to decrease the operating time. The comparison results of the two methods verify that the size of the map affects the calculation time. In addition, the neural node represented method surpasses the modified A* method, especially when the map is too large.
Method to Improve English Writing Skills: Virtual Reality

With the increase in the number of English speakers, English education has taken an important place in every country and all grade levels. English writing is a critical part of English teaching. By summarizing the traditional writing teaching method, we believe that the drawback is that it is difficult to balance the relationship between acquiring basic knowledge and improving motivation, so new teaching methods need to be explored. With the development of technology, more technological tools will be available in the future teaching, and virtual reality technology is one of them. This paper argues that the factors causing English writing problems include both vocabulary and learning motivation, and also uses the literature review method to conclude that virtual reality technology can be used in teaching English writing through constructivism, Experiential Learning Theory, Situated Cognition to solve basic vocabulary problems and improve learners' motivation. Through virtual reality education, a new student-centered approach to teaching writing that facilitates students' creative thinking and observation skills can be established.
Research on Product Sales Forecast Based on Test Sales Comment Data

This study predicts product sales based on product sales during the trial period and text mining of online reviews of products. This study uses the BERT model to judge the sentiment tendency of reviews and identify product attributes, build a sales forecast model based on the extracted review features and the sales and product features during the trial sales period, and evaluate the effect of the forecast model. Among the prediction models constructed in the study, the multiple linear regression model has the worst effect, the prediction effect of random forest and BP neural network is close, and the prediction effect of XGBoost model is the best.
Analysis of the influencing factors of online classes satisfaction based on text mining Take MOOC platform art education online classes as an example

The normalization of the epidemic and the convenience of online classes have led to the rapid rise of online education courses. The MOOC teaching platform represents the online course field and is popular in various disciplines. Unlike disciplines such as natural science and engineering technology, art education courses require real-time emotional expression and transmission. However, the existing technical framework is difficult to support the interactive needs of art teaching mode, and the learning effect of art students is not ideal, and the user The learning effect of art students is not ideal, and the phenomenon of low user satisfaction and low willingness to continue learning is more serious. Based on the above background, this paper uses Python to crawl the review texts of art online courses and summarizes the factors influencing students' satisfaction with art education online courses through sentiment analysis and LDA model to provide valuable references for the quality of art online education.
Way Back Home - An Auxiliary Teaching Tools in Biology Education in Colleges

In undergraduate biology education, especially in cell biology, it has been a challenge for students to integrate images well to memorize the complex and scattered expertise. In traditional biology teaching, the teacher's way of explaining with images is not good enough to show the connection between cell structure and function directly. Biology students need to spend long extra time to memorize these very essential points, which not only reduces students' interest in learning, but also is a very painful process for students with poor spatial imagination. However, the newly emerged VR game technology, which is visual, playful, and interactive, can be a good solution to the problem of students' interest in learning and their lack of spatial imagination. According to this situation, this paper designs a VR game as a supplementary teaching tool for cell biology and examines it from seven aspects: game mechanics, visual aesthetics, narrative, motivation, music score, learning objectives, and relevant. With using this VR game, biological related specialized student can directly see the changes in structure and remember them with pleasure, and teachers with similar teaching needs could also use them to improve learning effects. Finally, the advantages and limitations of this teaching aid are evaluated, aiming at broadening the current means of teaching undergraduate biology and providing new ideas for future higher education teaching of cell biology.