Reviewerly/exHarmony huggingface dataset:
Formation of digital competences and skills of students in the transitional period of digital didactics

The article analyzes the approaches of domestic and foreign researchers to the definition of the concept of "digital didactics". The distinctive features of traditional and digital didactics are presented, the characteristics of the new pedagogical branch are given and the main features of digital didactics are highlighted. The result of the research is the introduction of an author's online course on the discipline "Management of the learning process using DOT" for bachelor students of pedagogical specialties, which allowed the formation of digital competencies of students, expressed in the ability to analyze and establish the qualitative compliance of a digital educational product with the goals and objectives; apply and adapt existing digital educational products to specific goals and objectives the learning process; independently extract and assimilate information, transform it according to the revealed patterns by means of modern technologies in the conditions of digitalization of the educational process. The formed competencies allowed students to take into account the changes taking place in the learning process associated with the transformation of education. The article opens the beginning of a discussion in the field of digital didactics in the system of higher pedagogical education and higher professional education within the framework of the introduction of author's developments aimed at the formation of the digital educational process, the implementation of new forms and methods of teaching. Keywords: digitalization of education, digital didactics, digital learning, digital competencies, skills.
Professional teaching competence: key point of developing pupils’creativity

The article points out the results of the research of a competent professional education specialist activity aimed at developing creative abilities of trainees. In the modern multilingual education system great importance is attached to teaching a foreign language, increasing students’ interest and motivating language learning. Through the use of active teaching methods that develop creativity, it is possible to activate students and create a harmonious learning environment. The authors analyzed the concepts of competence, creativity and conducted a survey on the use of innovative and creative teaching methods by teachers. The survey involved 100 respondents: school teachers, graduate students, teachers of educational centers. More than 77% of respondents believe that it is necessary to use a creative approach in teaching foreign languages; more than 86.3% of respondents consider it is necessary to use critical thinking methods to increase the effectiveness of teaching foreign languages and develop creativity. It is crucial to prepare a creative environment for teaching foreign languages according to 86% of respondents. Respondents stated out methods as the most useful of specific methods: 73.3% chose "picture gallery", of which 57.8% chose "quality line" and 53.3% chose the "mind mapping" option. In the final part of the survey, several methods were proposed to obtain information about the extent to which teachers use critical thinking techniques. Keywords:educational process, development of creative abilities, creativity, competency, pupils &nbsp
PEDAGOGICAL APPROACHES TO THE DEVELOPMENT OF OPTIMISM: EXPERIENCE OF COMPARATIVE ANALYSIS OF MAKARENKO-ROGERS’ IDEAS

Introduction. Finding optimism oriented systems of education and upbringing is one of the urgent problems as there is a need to develop approaches that would guide modern systems of upbringing and education to the joy and new opportunities of those who study, as well as ensure success in educational activities. Materials and Methods. Comparative analysis, comparison, generalization, systematization and pedagogical interpretation of C. Rogers and A. Makarenko’s ideas. Results. The pedagogical principles and conditions for the development of optimism are presented on the basis of comparative characteristics of A. Makarenko-C. Rogers’ humanistic ideas. Discussion. A comparative analysis revealed an amazing similarity between the two approaches to the person by A. Makarenko and C. Rogers. The key in them remains the principle of self-worth of the individual, which takes the form of unconditional acceptance of the child by C. Rogers and demanding love by A. Makarenko. Such principles give rise to a certain system of relations of cooperation between the teacher and the pupil, provide rationale for creating favourable pedagogical conditions in which the optimistic faith of those who teach is confirmed and the possibility of success is provided for those who study. The growth of self-esteem and the new desires and hopes it generates reinforces the person’s optimistic will to live. Conclusion. Pedagogical optimism can be learned; it should be developed and maintained at the first stage of the teacher’s professional development.
Regularization for Unsupervised Learning of Optical Flow

Regularization is an important technique for training deep neural networks. In this paper, we propose a novel shared-weight teacher–student strategy and a content-aware regularization (CAR) module. Based on a tiny, learnable, content-aware mask, CAR is randomly applied to some channels in the convolutional layers during training to be able to guide predictions in a shared-weight teacher–student strategy. CAR prevents motion estimation methods in unsupervised learning from co-adaptation. Extensive experiments on optical flow and scene flow estimation show that our method significantly improves on the performance of the original networks and surpasses other popular regularization methods. The method also surpasses all variants with similar architectures and the supervised PWC-Net on MPI-Sintel and on KITTI. Our method shows strong cross-dataset generalization, i.e., our method solely trained on MPI-Sintel outperforms a similarly trained supervised PWC-Net by 27.9% and 32.9% on KITTI, respectively. Our method uses fewer parameters and less computation, and has faster inference times than the original PWC-Net.
Students of tan given university with the trend of accost internet literature

Internet literature was born as an inevitable result of the information technology age. Students in the 4.0 era can connect to study via social networks, access cloud computing, and exploit big data with mobile phones 24/7. Therefore, accessing internet literature is very easy. Through surveying students of Tan Trao University with internet literature, we initially have assessments and orientations to promote the role of internet literature with students and help authors create online perfect work
Analysis of Neutrosophic Elements in the Determination of Bankruptcies in SMEs Using Machine Learning

Nowadays, Machine Learning techniques stand out, especially in the business sector, in predicting bankruptcies in small and medium-sized enterprises (SMEs). This reduces the probability of making bad investments when creating SMEs. Therefore, a systematic review of Machine Learning for predicting bankruptcies in SMEs was conducted to identify ideal articles. The search was conducted on Taylor Francis Online, IEEE Xplore, ARDI, ScienceDirect, ACM Digital Library, Google Scholar, and ProQuest. As a result, information was collected from 84 definitive studies on determining bankruptcies in SMEs using Machine Learning. Therefore, this study aims to determine the state-of-the-art regarding determining bankruptcies in SMEs using Machine Learning. To obtain the results, the Saaty Neutrosophic AHP method was used to identify the most applied business sector and predict possible bankruptcy due to its broad nature of indeterminacy in that subset. The systematic review results have allowed for determining essential details regarding the state-of-the-art of determining bankruptcies in SMEs using Machine Learning.
Special Issue on Navigation and Control Technologies for Autonomous Mobility

Autonomous mobility, as exemplified by self-driving cars, autonomous mobile robots, drones, etc., is essential to the acceleration and practical application of transportation services and the automation of delivery, guidance, security, and inspection. Therefore, in recent years, expectations have been building for autonomous mobility to grow as a technology that not only improves the convenience and comfort of transportation and the efficiency of logistics but also leads to solutions to various social problems. Various technological elements are required to ensure the safety and quality of autonomous mobility. For example, technology is needed to create environmental maps and automatically determine obstacles based on data acquired by cameras and sensors such as LiDAR. Technologies for planning appropriate routes and controlling robots safely and comfortably are also essential. This special issue highlights 24 exciting papers, including 20 research papers, three letters, and one development report. They are related to “recognition,” “decision and planning,” and “control” technologies for autonomous mobile robots, such as self-driving cars and drones. The papers’ keywords are as follows: • Collision avoidance, path planning, path tracking control • Motion control, attitude control • Measurement, position and posture estimation, modeling • Point cloud processing We would like to express our gratitude to all authors and reviewers, and we hope that this special issue contributes to future research and development in autonomous mobility.
Differential Flatness-Based Parameter Estimation for Suspended Load Drones

The transportation of goods by drones using cable towing has recently attracted considerable attention. When flying a suspended load drone, any discrepancy between the mathematical model and the actual drone deteriorates control performance. However, because some physical parameters are difficult to measure, creating an accurate mathematical model is extremely difficult. Therefore, we propose a parameter estimation method using differential flatness that can be extended for application to suspended load drones. This method overcomes the problem of dealing with higher-order derivatives of flat outputs and enables the estimation of physical parameters. In this study, we experimentally show that the proposed method improves trajectory tracking performance.
Ford v Ferrari to ChatGPT v Bard

The primary drivers of AI advancements right now are not addressing user needs or information access problems; they are, rather, driven by competition.
Classification and identification of communication jamming signals of intelligent connected vehicle based on CNN

With the development of intelligent connected vehicles, especially L3 level automatic driving technology, it is very important to identify and avoid the impact of malicious jamming signals on the communication of intelligent connected vehicles. In this paper, we classify and identify the five kinds of common intelligent connected vehicle communication jamming signals: single-tone jamming signal, multi-tone jamming signal, linear swept-frequency jamming signal, partial band jamming signal and noise frequency modulation jamming signal. Firstly, six kinds of features of communication jamming signals are extracted: frequency domain moment kurtosis coefficient, frequency domain moment skewness coefficient, single frequency encircled energy, average spectral flatness coefficient, frequency domain parameter and time domain moment kurtosis coefficient, and then classified and identified based on convolution neural network (CNN). The simulation results show that when the jamming-to-noise ratio (JNR) is higher than -7dB, the recognition rate is over 92%, which indicates that CNN has good classification and recognition performance for communication jamming signals.
A cross-domain weakly supervised method based on imprecise labels for person RE-ID

With the rise of command city construction, the importance of pedestrian re-identification based on video surveillance has become increasingly prominent. The person re-identification task mainly studies recognizing images of the same person under different cameras. Due to the high cost of labeling data in supervised learning in common methods, and the lack of guidance for labeling data in unsupervised learning, the experimental results are unsatisfactory. In this paper, we propose a new weakly supervised person re-identification problem framework: a human-computer interaction-based sample active push and auxiliary labeling method is designed to quickly obtain a small number of identity-related labels with low labor cost; At the same time, the weak supervision information that is easily obtained in the real scene is introduced into the pseudo-label generation process to improve the clustering method, aiming to solve the problem of insufficient initial labeling data of images in the label estimation stage and weakly supervised labels are not well utilized; Finally, in view of the insufficient utilization of weakly supervised labels in the model training process, a weakly supervised loss function WSP loss is designed and added to the training to optimize the overall loss function. Compared with the current state-of-the-art unsupervised and weakly supervised person re-identification work, this paper shows better performance under the same conditions and even using less labeled information.
Applicant Screening System Using NLP

This research work is mainly focused on selecting or short-listing the eligible candidates from the pool of applicants in a relatively short span of time. As technology is rapidly progressing, manpower requirement also increases exponentially. Thus, to calibrate the cream of the crop, this online application screens the applicants resume for a specific recruitment ad. This is designed in such a way that the applicant as well as the hiring agency can both be benefited, i.e., the applicant can use it to avail the job opportunities, apply for it and improve their abilities if they don't meet the criteria. Hiring agencies can mention the details of the job openings available. This bilateral website allows applicant to upload their resume, the resume uploaded will be compared with the job occupation requirement posted by the hiring agencies by using Natural Language Processing [NLP]. Results are generated using cosine similarity, then the similarity of both the uploaded documents in percentage is displayed. The eligibility of the candidate is decided based on the displayed result. Recent techniques include CNN KNN algorithms which are complex and time consuming, this project uses NLP tools, which simplifies the process, reduces the time consumption and also gives accurate answers.
Image Feature Understanding and Semantic Representation Based on Deep Learning

Image feature understanding and semantic expression is an interdisciplinary subject involving computer vision, natural language processing, machine learning and so on. It has been the focus and difficulty in the field of computer vision and image processing. At the same time, it is also an important part of the intelligence of computer vision, which can serve all fields of life and industry, and has very important research significance. With the development of deep neural network, this paper will do some research on image feature understanding and semantic expression based on deep learning. The image feature understanding and semantic expression uses the depth neural network as a tool to construct a network model that maps from the basic visual features of the image to the high-level semantic description, aiming at the complex target image. get a natural language description based on the semantic content of the image. In this way, the semantic information contained in the image can be translated into natural language text.
Basic Concepts of Probability

A commonly used statistical measure is the measurement of probability. Probability is governed by both additive and multiplicative rules. These rules determine if events are independent of one another or dependent on previous or related outcomes. Conditional probability governs events that are not independent of one another and helps researchers to better make predictions about future datasets.
Penerapan Data Mining Untuk Prediksi Perkiraan Hujan dengan Menggunakan Algoritma K-Nearest Neighbor

Rain is a condition where water droplets fall from clouds to the earth. In life, the presence of rain is highly anticipated, rain can help people who have a profession as farmers. Rain that occurs on a large scale will really provide obstacles for the community, in addition to hampering activities or activities especially those carried out on outdoor rain can also cause disaster for the community in the form of flooding. Estimating rain for the community is very important, knowing whether it will rain or not can make it easier for the community to anticipate the possibilities that may occur due to rain. However, in the process of delivering forecasts, there is often an uneven distribution of information and delays in conveying information to the public regarding whether or not rain will occur. The community should be able to independently predict whether or not rain will occur. Data processing should be done properly and correctly. Data mining is a way that can be done to assist in data processing. In this study, the settlement process will be carried out using the K-Nearest Neighbor (K-NN) algorithm. The results obtained show that the data testing decision is NO. In other words, data mining and the K-Nearest Neighbor algorithm can help the problem solving process
Penerapan Algoritma Decision Tree Untuk Penentuan Pola Penerima Beasiswa KIP Kuliah

The Indonesian Smart College Card (KIP Lecture) is a government program that has been implemented from 2020 until now. KIP Lectures are distributed by the Ministry of Education, Culture, Research and Technology through universities in each region. Where each university gets a different quota - based on the level of progress of the college. The provision of quotas for each university based on the accreditation at each university raises its own problems for these universities. The problem faced is that the number of new prospective students who register to take the KIP Lecture program exceeds the quota set for each university. The provision of KIP Lecture assistance to the wrong person will lead to misuse of assistance and also inappropriate targets. The acceptance of the selection process for new prospective students can be seen from the previous process that has been carried out. Data mining is a technique used to solve problems in large data processing. Decision Tree is an algorithm that is included in the classification technique in data mining. The process in the decision tree aims to group or classify data against their respective classes. The results of the Decision Tree algorithm are in the form of decision trees and rules, the results obtained are in the form of rules that can be used for future decision-making processes
Mask Detection on Motorcyclists Using YOLOv4

The use of mask is a mandatory for everyone in the pandemic regulation to prevent the spread of COVID-19 infection. This becomes a pandemic regulation for everyone, especially in public places like in traffic situation, such as pedestrian and motorcyclists. However, many motorcyclists ignore this rule or do not use the mask properly, let alone they have high risk in being infected by the virus; Thus, a computer vision-based solution is required to help monitoring it. This study aims to built a system to automatically detect the use of mask on motorcyclists. Here, we propose a YOLOv4 model, one of YOLO variants, which is popular in the object detection task and featured with a considerably high speed in real-time situation. This study also implements domain adaptation to discuss the object detection performances. Based on the experimental results in various scenarios, our model obtained average accuracy of 78.3% and IoU of 64.8% for class with_mask, average accuracy of 78.4% and IoU of 56.3% for class without_mask, and average accuracy of 87% and IoU of 55.5% for class incorrect_mask
Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

To grasp an object successfully, we must select appropriate contact regions for our hands on the surface of the object. However, identifying such regions is challenging. This paper describes a workflow to estimate the contact regions from marker-based tracking data. Participants grasp real objects, while we track the 3D position of both the objects and the hand, including the fingers' joints. We first determine the joint Euler angles from a selection of tracked markers positioned on the back of the hand. Then, we use state-of-the-art hand mesh reconstruction algorithms to generate a mesh model of the participant's hand in the current pose and the 3D position. Using objects that were either 3D printed or 3D scanned-and are, thus, available as both real objects and mesh data-allows the hand and object meshes to be co-registered. In turn, this allows the estimation of approximate contact regions by calculating the intersections between the hand mesh and the co-registered 3D object mesh. The method may be used to estimate where and how humans grasp objects under a variety of conditions. Therefore, the method could be of interest to researchers studying visual and haptic perception, motor control, human-computer interaction in virtual and augmented reality, and robotics.
Considering Individual Features of Stamps of Gold Tooling in Attribution of Russian Bindings of the 18th century

Book bindings of the 18th century, like all bindings of the period of hand press, are unique objects of individual labour, they were made to order and could be done much later than the date of printing. Russian bindings of this time are overwhelmingly anonymous, do not have the stamp and signature of the bookbinder or any written evidence of the binding customer, which makes it difficult to attribute them. The Department of Rare Books (Book Museum) of the Russian State Library (RSL) stores books by a major collector of the 18th century P.K. Khlebnikov, bound in the late 1750s — 1780s in one bookbinding workshop. The owner sought to record in super-exlibrises his career growth and changes in social status, and it helps to date the binding work within a frame of few years. The bindings were decorated with gold embossing using different stamps and roles. Over time, the bookbinderʼs tools were enriched, new stamps were used in embossing, and the old ones were put out of use. Linking the use of a particular stamp to a specific time helps to date other similar bindings made in the same workshop. So, impressions of stamps that came into use no earlier than 1775 were identified on the bindings of books from the library of Prince I.A. Vyazemsky. Identical prints are also found on bindings from other collections. The article provides an example of Russian edition of the 1730s in a luxurious morocco binding with a similar embossing. Information about the stamps used in P.K. Khlebnikovʼs bindings assisted to significantly adjust the time of the masterʼs work and date the binding to the second half of the 1770s — 1780s. Thus, it is obvious that the identification value of individual stamps of gold tooling when describing the bindings of the 18th century is high. The detailed description of decorative elements with photofixation of prints, the creation of a kind of stamp bank would make it possible to identify other arrays of bindings of close origin, and biographical knowledge about the owners would help both in the localization of hypothetically reconstructed bookbinding workshops and in more accurate dating of samples.