Reviewerly/exHarmony huggingface dataset:
Innovating the Construction and Management of the Teaching Faculty in Colleges and Universities

Colleges and universities are the fundamental grounds for cultivating high-quality and application-oriented talents. They are inseparable from national progress and social development. The construction of the teaching faculty is a huge factor affecting the sustainable development of colleges and universities. College and university teachers play important roles in education, teaching, research, and professional construction. Teaching activities and talent training are inseparable from the support of excellent teachers [1]. In order to meet the challenges of the new round of curriculum reform, strengthening the construction and management of college and university teachers as well as accelerating the development of higher education have become urgent tasks for the development of colleges and universities. Based on this, this paper analyzes the noteworthy predicaments in the construction of the teaching faculty in colleges and universities and proposes corresponding suggestions for them.
Filter-Flow-Based Bit-Depth Expansion

Bit-depth expansion is a technique for recovering a high-bit-depth (HBD) image from a low-bit-depth (LBD) image. Although recent high-performance displays can display each color in 10-bit or 12-bit depth to express more detailed color gradations, most existing image and video sources still have an 8-bit depth. Simple restoration from an LBD image into an HBD image leads to artifacts such as missing high-frequency information and false contours, so high-quality bit-depth expansion approaches have been explored. In recent years, deep convolutional neural network (CNN) techniques have provided excellent performance in many image processing tasks such as super-resolution, classification, deblurring, and denoising. In this paper, we propose a novel bit-depth expansion approach using predictive filter flow (PFF). PFF predicts a spatially variable filter using the CNN and reconstructs the target image by filtering it with the input image. The proposed method trains the PFF network that predicts the filter to transform an LBD image into an HBD image. Experimental results confirm that the proposed method provides better results than state-of-the-art bit-depth expansion algorithms.
Teaching through electronic devices and learning in times of pandemic in Peru [Enseñanza por medio de dispositivos electrónicos y aprendizaje en tiempos de pandemia en el Perú]

At the opening of blended classes there was an increase in electronic devices for learning. Therefore, this research deals with teaching through electronic devices for learning in times of pandemic. The objective was to determine how electronic devices influence learning. It is based on the comparative descriptive and cross-sectional method, for which a representative sample of 40 students was taken, who rotated by group alternately to face-to-face and remote classes in order to avoid getting infected. Questionnaire instruments were used on: Do they have electronic devices for remote classes? Do they have internet connectivity for remote classes? Do they understand the face-to-face teachings of mathematics and communication courses? And do they capture the teachings in a virtual way of mathematics and communication? It was determined that, in week 8, there were 95% of students who have electronic devices, internet service with 90%, understand the teachings in person with 88% and understand the teachings virtually with 80%. It is concluded that, as the weeks passed, there was an increase in electronic devices and internet service, which improved the teachings in the face-to-face class and strengthened what was learned in the remote classes.
Development of digital marketing technologies for promoting educational services of Russian universities in the context of global transformations

The article substantiates the need to use digital marketing tools to promote Russian educational services to the international educational market. The concept of a marketing information campaign to promote educational services in foreign countries is proposed, which provides for the use of channels and means of interaction with target groups to form an attractive image of the higher education system and encourage foreign applicants to enroll in Russian universities. Conclusions are formulated about the inclusion in the list of works on the development of the information component of the brand promotion of Russian universities of measures for search SEO optimization of university sites, more active use of SMM tools, the use of targeted, contextual advertising, positioning and placement of information on foreign Internet portals.
Physical Training Organisation in Educational Institutions of the Ministry of Internal Affairs of Russia

The study aims to substantiate the need to change and improve physical training organisation for cadets - future police officers. The paper considers the issues of physical training organisation in educational institutions of the Ministry of Internal Affairs of Russia, suggests ways to solve them. Scientific novelty of the study lies in identifying pedagogical conditions that make it possible to improve the physical training of police officers, taking into account the tasks of operational and official activities facing them. As a result, it has been shown that physical training implemented in educational organisations of the Ministry of Internal Affairs of Russia should not just be aimed at the formation of a certain set of motor skills outlined in the training programme, but also form the necessary motivation for cadets’ independent physical development, cultivate an internal need for a healthy lifestyle.
Errata to “Parallax Free Registration for Augmented Reality Optical See-Through Displays in the Peripersonal Space” [1] (DOI: 10.1109/TVCG.2020.3021534)

In the original article, there was a mistake in the content of Table 2 page 8 column 1 as published. The values of the mean and standard deviation of the virtual-to-real overlay error in visual angles, which are reported for different checkerboard distances, are to be corrected. Due to a typing error within the data analysis code, we mistakenly considered an erroneous value of the average angular resolution for the eye-replacement camera. This scale factor is used to pass from the original registration errors (expressed in pixel) to the angular registration errors (in arcmin). The value of the average angular resolution is $\approx 2.67$≈2.67 arcmin/pixel. The corrected Table 2 appears below.
An Attention-GRU Based Gas Price Prediction Model for Ethereum Transactions

Each operation on the Ethereum network requires a corresponding gas cost, and the gas price is customized by the user who uses it. Under normal circumstances, the shorter the user’s requirement for the transaction completion time, the higher the gas price should be set. However, users cannot set a reasonable gas price every time, which will cause operation failure or gas waste. Therefore, predicting an affordable gas price is of great significance. We propose a gas price prediction method based on deep learning to find the lowest transaction gas price in the next block and help users save transaction costs. Based on previous research, we obtained influence factors of block gas upper limit, transaction gas upper limit and mining difficulty that cause gas price fluctuations, combined with the attention mechanism to obtain the weight of the influence factor, we use the gated recurrent unit (GRU) neural network to determine the gas price prediction. Extensive experiments based on 2000 actual blocks show that the proposed gas price prediction method has high accuracy and reliable performance, which saves users a large amount of transaction costs; because the appropriate gas price can be predicted, the success rate of users’ transactions increases by 22.9%.
Improving Rule Mining for Entry, Descent, and Landing Simulations Using Knowledge Graphs

Designing planetary entry, descent, and landing (EDL) systems requires analyzing large datasets containing tens of thousands of parameters. These datasets are generally manually analyzed by subject-matter experts trying to find interesting correlations and couplings between parameters that explain the behaviors observed. A popular approach to automate the extraction of explanation rules is association rule mining, in which rules with high statistical strength are mined from the dataset. However, current rule mining algorithms generate too many rules that are redundant, too complex, too obvious, or do not make sense to the user. In this paper, we propose a new approach to improve the comprehensibility, insightfulness, and usefulness of the association rules generated during the analysis of an EDL dataset by leveraging a user-provided knowledge graph. The knowledge graph captures the user knowledge about EDL and the specific problem at hand. We then use a statistical relational learning framework based on probabilistic soft logic to assess the degree of consistency of the rule with the user’s knowledge of the system. The method is validated in a small study with subject-matter experts. The results of the study also show interesting relationships between comprehensibility, usefulness, and insightfulness of the extracted rules.
Automatic Short Answer Grading to Pedagogical Questions Using Knowledge Graphs and Pre-trained Models

To achieve deep-level learning by students in interactive teaching through questions, it is necessary to analyze students’ answers and evaluate them in detail. Automatic Short Answer Grading (ASAG) is a technique for grading short sentences such as students’ answers. In recent years, ASAG has achieved high accuracy by using large-scale language models. However, existing methods tend to rely on the word agreement with the model answer and thus cannot provide detailed evaluation for feedback. In this study, we propose an evaluation method combining the knowledge graph of the textbook and the pre-trained language model to evaluate students’ answers in detail．We constructed a knowledge graph to represent the knowledge in the textbook systematically. When constructing a knowledge graph from a textbook, there are two possible methods: one is to construct a knowledge graph based on the relationships among words in the textbook, and the other is to construct a knowledge graph by retaining the syntactic information of the sentences in the textbook. Therefore, we constructed knowledge graphs using these two methods and verified which knowledge graph was more effective. In addition, the existing ASAG datasets do not include a detailed evaluation of the answers. Therefore, we collected questions and sample answers related to Informatics I, a high school course, and constructed a dataset of Informatics I questions with seven evaluation labels. Using the constructed knowledge graph and the Informatics I question dataset, we verified the effectiveness of the evaluation method for answers based on the knowledge graph in the textbook. As a result, we found that the knowledge graph based on word relationships fluctuates the structural information of answers. On the other hand, knowledge graphs that retain syntactic information of sentences do not change the syntactic information of answers and provide effective information for evaluation, and the Knowledge-aware Answer Grading Model from K-BERT was found to be an effective method for evaluation based on knowledge graphs.
Penggabungan Algoritma Hill Cipher dan ElGamal untuk Mengamankan Pesan teks

Hill Cipher is a one of the symmetric key cryptography algorithm that using an invertible matrix with an order n×n as a key to encrypt and decrypt plaintext. Meanwhile, ElGamal is other asymmetric key cryptography algorithm that use the complexity of discrete logarithms in the encryption and decryption process. In this study, the authors are interest in combine the Hill Cipher and ElGamal algorithms to secure text messages. The author use the matrix as a symmetric key and converts the plaintext in the table of ASCII 256. Then encrypt using the Hill Cipher algorithm which results the ciphertext from messages and ElGamal algorithm results the ciphertext of the symmetric key. In processing decryption using the ElGamal algorithm to determine the symmetric key that will be used as a key in the decryption process with the Hill Cipher algorithm so that the original plaintext is obtained. Then the results obtained are that the combination of the Hill Cipher and ElGamal algorithms to secure text messages can be done it well.
Professionally Oriented Tasks in Mathematics for Future Geography and Ecology Teachers

The study aims to develop didactic means of teaching Mathematics to undergraduate students in the training programme 44.03.05 “Pedagogical Education”, programme specialisation “Geography. Ecological Tourism”. The paper considers the issue of introducing a set of professionally oriented mathematical tasks into the educational process of Pitirim Sorokin Syktyvkar State University and provides methodological recommendations for their use. Scientific novelty of the study lies in identifying the types of mathematical tasks for future teachers, i.e. students in the educational programme specialisation “Geography. Ecological Tourism”, aimed at developing their professional competence. As a result of the study, the researcher has clarified the definition of professional orientation of training, has determined the feasibility of developing and using a set of mathematical tasks in the educational process that takes into account students’ educational programme specialisation, namely “Geography. Ecological Tourism”, and contributes to greater interest in Mathematics, has formulated the criteria for the selection of tasks included in the set.
Why and how to create training sessions in the form of a game at a university?

Based on the analysis of the scientific literature, the paper describes the signs of the game as a form of education at the level of higher education, its advantages and disadvantages, methodological recommendations for development. The results of the theoretical study are taken into account when preparing and conducting training sessions in a game form with university students. The generalization of work experience made it possible to determine the algorithm and methods for creating training sessions in a game form, techniques for providing the necessary pedagogical conditions to overcome the shortcomings of the game form. The algorithm includes: targeting based on the formed competence and type of occupation; Definition of activity content; development of a system of training assignments; identification of the features of the group affecting educational and gaming activities; determining a gaming base and a gaming task; selection of methods of influencing the emotional state of students; preparation of educational means. Popular games, plots of famous films are considered as the basis for creation. Two functions of game tasks are highlighted: to serve as a framework for solving the educational task (the quality of the educational task affects game achievements); provide an advantage when performing a training task. Pedagogical conditions for the organization of classes in the form of a game and techniques for their provision are determined: to prevent the fixation of attention exclusively on the game task, students are offered to predict the goal and upcoming activities on the basis of information about the formed competencies, summarizing the results after the completion of each game stage; to take into account the characteristics of the group the introduction of additional game conditions, modification of the game, organization of educational and gaming activities of each; to ensure compliance with ethical standards adaptation of well-known stories and forms, control by the teacher and the jury; to create a favorable psychological climate turning to popular stories and games, using provocative techniques; to provide an assessment system detailed instruction of jury members, use of various forms of assessment and presentation of work.
BUILDING THE PROFESSIONAL COMPETENCE OF FUTURE PROGRAMMERS USING METHODS AND TOOLS OF FLEXIBLE DEVELOPMENT OF SOFTWARE APPLICATIONS

To support their professional expertise, a modern programmer must constantly follow new technologies, learn new methods of solving problems (best practices), exchange experience, use auxiliary tools that accelerate the development process, should be able to work in a team and develop their knowledge and skills. The task of modern IT education is to meet the demands of the information technology market. Specialists should be provided with proper training, which will give them relevant professional competences. The present paper analyzes modern methodologies of flexible software development and tools, defines professional competences related to software development based on the standard of higher education for the specialists of the Bachelor’s academic level in Software engineering specialty. The novelty of the research lies in the justification of the competency-based approach to the training of future programmers. This approach involves the use of methods and tools for flexible development of software applications in three stages of project tasks of different types and complexity, which are formed in accordance with certain professional competencies. At Phase 1, students studied flexible methodologies and tools for developing software applications in the Software Design academic discipline. In Phase 2, flexible methodologies and software development tools were used during academic and technological practical training, in particular, students performed a group project according to the Scrum methodology, using Kanban approaches. In Phase 3, students worked individually on the Bachelor’s thesis under the guidance of a teacher. The article describes the organization of the work process on the principles of flexible development and flexible learning, presents the results of experimental research, which showed an increase in the level of professional competencies in software development. A statistical analysis of the results of the experiment has been carried out and their significance has been proved.
TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s

This paper presents a novel nearest neighbor search algorithm achieving TPU (Google Tensor Processing Unit) peak performance, outperforming state-of-the-art GPU algorithms with similar level of recall. The design of the proposed algorithm is motivated by an accurate accelerator performance model that takes into account both the memory and instruction bottlenecks. Our algorithm comes with an analytical guarantee of recall in expectation and does not require maintaining sophisticated index data structure or tuning, making it suitable for applications with frequent updates. Our work is available in the open-source package of Jax and Tensorflow on TPU.
Fuzzy Control Method Based on Dynamic Self-optimization

The most commonly used in industrial control is the digital PID control method. For most control objects, the use of digital PID control can achieve satisfactory control results. But in the ball mill controller, the PID control will produce time lag effect, resulting in decision-making mistakes. This paper designs a fuzzy control method based on dynamic self-optimization, which can use the sensitive output of the controlled process as the input of the fuzzy controller in the fuzzy control loop, and the output of the fuzzy controller is the adjustment value of the controlled system. Input. Another input of the fuzzy controller is the set value input. The simulation results show the effectiveness of this method.
Smart Waste Segregation System using Convolutional Neural Networks

As the population increases, waste management has become difficult in today’s world. It is estimated that, until now in India in the year 2022, the total amount of waste generated is 62millon tons, wherein nearly 43 million tons of waste is collected in which about 12million tons is treated and 31million tons is dumped in landfill. Moreover, segregation of waste also is a tough task for the workers. Since the waste may be hazardous and infectious for the human life, the segregation must be carried out without labor. This problem is majorly being faced in developing cities. To overcome this, a smart waste segregation system using CNN is proposed in this paper. Segregation of waste from building itself brings about a large change in waste management. As the waste gets collected in a dumping area, it is identified using Open CV with the help of Pi camera. The captured image of waste is compared with default images using CNN algorithm, and segregation is done using robotic arm. Furthermore, the ultrasonic sensor present within the bin monitors the level of waste; when the waste level reaches the maximum, it alerts the authorized person with the help of GSM module. As the result, this keeps the buildings clean and supports the swatch Bharath mission. Existing waste segregation system use moisture sensor to differentiate between wet and dry waste ,even with small water droplets present on dry or metal waste detects it as wet which is a wrong identification.
Role of Cloud Computing for Improvement in Healthcare Services

Cloud helps in offering on-demand latest technology that helps in deploying, accessing and using network-accessed information along with various applications and resources. Nowadays electronic health records are maintained by many hospitals that want to undergo a change in their legacy system. This type of transformation has helped physicians, nurses and also administrative staff access the desired record whenever needed. They believe that this may change the complete face of health information technology. However, lack of security and privacy are two important concerns that may provide hazards when choosing cloud solutions for various health-related factors. This problem can be avoided to some extent by evaluating cloud technology in an effective manner before its complete adoption. This paper uses four major aspects i.e., technology, security, legal and management for finding different types of challenges of this computing model. When any health services want to migrate from traditional to cloud-based health services then they can do different types of strategic planning for determining strategy, allocated resources and direction for maintaining a cloud environment in their organization.