Reviewerly/exHarmony huggingface dataset:
Private initiative as a growth factor of Book publishing activity in Russia in the last third of the 18th century

Importance. A landmark event in the history of Russia in the last third of the 18th century was the development of private printing houses, which destroyed the state and church monopoly on printing publications. The work of publishing houses played a decisive role in satisfying the spiritual needs of the broad masses of the population, contributed to the secularization of the national book culture. At the same time, the richness and diversity of fiction, educational and scientific literature, produced both in Russian and in foreign languages, thoroughly influenced the emergence and dissemination of new information and facts, increased the level of education of all segments of the reading population. Despite the existence of extensive research on the book publishing of this period, there is a well-known imbalance in the description of the activities of famous domestic printers and their foreign colleagues. Meanwhile, it was foreign specialists (I.M. Hartung, J.J. Weitbrecht, etc.) who played a key role in opening the first private printing houses in Russia. The study of their activities contributes to the increment of knowledge on the Russian book culture history, allows you to consider unknown pages of Russian book publishing and book trade. Research Methods. The main sources of the study are the materials of the Russian State Archive of Ancient Acts (Fund 17, Fund 248), as well as legislative acts and orders of that time that influenced the book trade. Results and Discussion. Being a characteristic feature of the development of information culture in the first half of the 18th century, censorship could not stop the transformative role of books in the history of Russia. As a result of the intensification of book printing in the country, the emergence of free book trade, the successful operation of new education models, interest in printed products has noticeably increased. Book business has ceased to be only a capital occupation and has become the property of the whole country. By allowing everyone to start printing houses at will, the state equated them with factories, which facilitated the processes of creating and developing their own printing business. Conclusion. The activity of private book publishers in Russia in the second half of the 18th century created opportunities for the formation of the national book market, filling it with various scientific, fiction and educational literature. Thus, the printers contributed to the dissemination of the widest knowledge among the Russian population, increased the variety and quality of the literature sold.
Football referee gesture recognition algorithm based on YOLOv8s

Gesture serves as a crucial means of communication between individuals and between humans and machines. In football matches, referees communicate judgment information through gestures. Due to the diversity and complexity of referees’ gestures and interference factors, such as the players, spectators, and camera angles, automated football referee gesture recognition (FRGR) has become a challenging task. The existing methods based on visual sensors often cannot provide a satisfactory performance. To tackle FRGR problems, we develop a deep learning model based on YOLOv8s. Three improving and optimizing strategies are integrated to solve these problems. First, a Global Attention Mechanism (GAM) is employed to direct the model’s attention to the hand gestures and minimize the background interference. Second, a P2 detection head structure is integrated into the YOLOv8s model to enhance the accuracy of detecting smaller objects at a distance. Third, a new loss function based on the Minimum Point Distance Intersection over Union (MPDIoU) is used to effectively utilize anchor boxes with the same shape, but different sizes. Finally, experiments are executed on a dataset of six hand gestures among 1,200 images. The proposed method was compared with seven different existing models and 10 different optimization models. The proposed method achieves a precision rate of 89.3%, a recall rate of 88.9%, a mAP@0.5 rate of 89.9%, and a mAP@0.5:0.95 rate of 77.3%. These rates are approximately 1.4%, 2.0%, 1.1%, and 5.4% better than those of the newest YOLOv8s, respectively. The proposed method has right prospect in automated gesture recognition for football matches.
Development of a Decommissioning Robot with a Simple Structure Capable of Traversing Steps Using Two Different Drive Systems

In response to the imperative need for robotic involvement in the decommissioning of the Fukushima Daiichi Nuclear Power Plant, a proactive initiative was undertaken. This initiative led to the organization of the “7th Decommissioning Creative Robot Contest,” in which pivotal challenges related to nuclear plant decommissioning were addressed. Recognizing inaccessible areas within a decommissioned plant, the primary objective of this study was to develop a remotely operated robot capable of executing decontamination tasks in such environments. Two distinct locomotion systems were designed: mecanum wheels and crawlers. The mecanum wheels, characterized by their ability to execute approximately 100 mm vertical movements, facilitated the robot to surmount obstacles, particularly by negotiating 100 mm steps by engaging the tracks. Simultaneously, to address decontamination tasks at elevated locations, a threefold expandable ladder truck system was integrated, augmenting the operational range of the robot. Participation in the contest provided a platform for showcasing the developed technologies and validating their practicality. Despite encountering challenges during competition, such as damage to one locomotion system, the other locomotion system can be used to reach the destination. Experience serves as a crucial means of verifying technological advancements. Issues such as malfunctions in the range of motion control due to faulty limit switch contacts and unexpected loads on the tire elevation motors were noted, which hindered the achievement of the anticipated results. Despite these drawbacks, the adopted structural design demonstrates notable advantages, particularly in terms of maneuverability. The combination of mecanum wheels and crawlers along with a ladder truck system demonstrated adaptability to various scenarios. This adaptability holds significant promise for future nuclear power plant decontamination applications. While the competition may not have yielded the desired outcome, the lessons learned and the verified practicality of the developed technologies reinforce their potential utility in addressing the future challenges of nuclear plant decommissioning.
Navigation Path Following Platform for a Greenhouse Shuttle Robot Using the State-flow Method

Localization is an important method for autonomous indoor robots to recognize their positions. Generally, the navigation of a mobile robot is conducted using a camera, Lidar, and global positioning system. However, for an indoor environment, GPS is unavailable. Therefore, a, state-trajectory tracking method is utilized based on a Lidar map. This paper presents the path following of an autonomous indoor mobile robot, that is, a shuttle robot, using a state-flow method via a robot operating system network. MATLAB and Linux high-level computers and an inertial measurement unit sensor are used to obtain the Cartesian coordinate information of a bicycle-type mobile robot. The path following problem can be solved in the state-flow block by setting appropriate time and linear and angular velocity variables. After the predetermined time, the linear and angular velocities are set based on the length of the path and radius of the quarter-circle of the left and right turns in the state-flow block, path planning, which can execute the work effectively, is established using the state-flow algorithm. The state-flow block produces time-series data that are sent to Linux system, which facilitates real-time mobile platform path following scenario. Several cases within the path-following problem of the mobile robot were considered, depending on the linear and angular velocity settings: the mobile robot moved forward and backward, turned in the right and left directions on the circular path. The effectiveness of the method was demonstrated using the desktop-based indoor mobile robot control results. Thus, the paper focuses on the application of the state-flow algorithm to the shuttle robot specifically in the narrow indoor environment.
The role of the motivational aspect in vocal students’ professional development

Importance. The necessity of using the motivational aspect in the professional training of music specialists, which contributes to the effectiveness of the educational process, is determined. A motivated student strives to constantly improve his skills, which allows him to become a professional in his field. The types of motivation that influence the vocal students’ professional development and various aspects such as vocal training, musical abilities development, music theory studying, working on performance technique and much more that contribute the vocalist’s professional development are revealed. Research Methods. Based on a theoretical analysis of specialized literature and systematization of materials from scientific sources, the role of the motivational aspect in vocal students’ professional development is substantiated. Results and Discussion. The possibilities of motivation that contribute to improving the quality of education and developing the personality of a vocal student are considered. The characteristics of the main methods that teachers use to motivate vocal students are given. Recommendations are offered for the use of motivation in the process of vocal students training. Conclusion. It is concluded that motivation contributes to the education quality improving, vocal student’s personality development and his successful adaptation in the modern world.
Deep Learning-Based Vehicle Type and Color Classification to Support Safe Autonomous Driving

This technology can prevent accidents involving large vehicles, such as trucks or buses, by selecting an optimal driving lane for safe autonomous driving. This paper proposes a method for detecting forward-driving vehicles within road images obtained from a vehicle’s DashCam. The proposed method also classifies the types and colors of the detected vehicles. The proposed method uses a YOLO deep learning network for vehicle detection based on a pre-trained ResNet-50 convolutional neural network. Additionally, a Resnet-50 CNN-based object classifier, using transfer learning, was used to classify vehicle types and colors. Vehicle types were classified into four categories based on size whereas vehicle colors were classified into eight categories. During autonomous driving, vehicle types are used to determine driving lanes, whereas vehicle colors are used to distinguish the road infrastructure, such as lanes, vehicles, roads, backgrounds, and buildings. The datasets used for learning consisted of road images acquired in various driving environments. The proposed method achieved a vehicle detection accuracy of 91.5%, vehicle type classification accuracy of 93.9%, and vehicle color classification accuracy of 94.2%. It accurately detected vehicles and classified their types and colors. These can be applied to autonomous and safe driving support systems to enhance the safety of autonomous vehicles.
Zhang neural networks: an introduction to predictive computations for discretized time-varying matrix problems

Abstract This paper wants to increase our understanding and computational know-how for time-varying matrix problems and Zhang Neural Networks. These neural networks were invented for time or single parameter-varying matrix problems around 2001 in China and almost all of their advances have been made in and most still come from its birthplace. Zhang Neural Network methods have become a backbone for solving discretized sensor driven time-varying matrix problems in real-time, in theory and in on-chip applications for robots, in control theory and other engineering applications in China. They have become the method of choice for many time-varying matrix problems that benefit from or require efficient, accurate and predictive real-time computations. A typical discretized Zhang Neural Network algorithm needs seven distinct steps in its initial set-up. The construction of discretized Zhang Neural Network algorithms starts from a model equation with its associated error equation and the stipulation that the error function decrease exponentially fast. The error function differential equation is then mated with a convergent look-ahead finite difference formula to create a distinctly new multi-step style solver that predicts the future state of the system reliably from current and earlier state and solution data. Matlab codes of discretized Zhang Neural Network algorithms for time varying matrix problems typically consist of one linear equations solve and one recursion of already available data per time step. This makes discretized Zhang Neural network based algorithms highly competitive with ordinary differential equation initial value analytic continuation methods for function given data that are designed to work adaptively. Discretized Zhang Neural Network methods have different characteristics and applicabilities than multi-step ordinary differential equations (ODEs) initial value solvers. These new time-varying matrix methods can solve matrix-given problems from sensor data with constant sampling gaps or from functional equations. To illustrate the adaptability of discretized Zhang Neural Networks and further the understanding of this method, this paper details the seven step set-up process for Zhang Neural Networks and twelve separate time-varying matrix models. It supplies new codes for seven of these. Open problems are mentioned as well as detailed references to recent work on discretized Zhang Neural Networks and time-varying matrix computations. Comparisons are given to standard non-predictive multi-step methods that use initial value problems ODE solvers and analytic continuation methods.
Technologies of literary and methodical teacher education

Importance. The purpose of the study is to reflect the search for the most productive ways of teaching literature. To achieve this goal, it is necessary to include modern technologies in the methodology of literary reading in primary and secondary schools to prepare teachers for effective teaching. Research Methods. The technologization methods of the educational process are related to the typical features’ understanding of technology, the classification of technologies in pedagogy and, in particular, in didactics, with the development of educational and training technologies that are available to primary school teachers and teachers of philology. Results and Discussion. The technologies of multilevel perception of the text by the teacher, complex analysis of the work in preparation for the lesson, achievement of expressive reading of the text, technology of co-creative reading, discrete reading, immersion in the artistic text, preparation for retelling, carrying out dictionary work, etc. have been developed and tested in teaching practice. Special attention should be paid to the technology of artistic and speech accompaniment of children’s life activities during the day, which has not only an educational, but also a healthsaving, educating character. Each technology cannot be described in detail within the scope of the study, therefore, the list of references contains sources containing detailed theoretical justifications of technologies and multiple examples of applications. Conclusion. The conclusion of the study on the technologization of the educational process is related to the self-education of the teacher, who is currently provided with a variety of opportunities for this.
Formal Security Analysis of ISA100.11a Standard Protocol Based on Colored Petri Net Tool

This paper presents a formal security analysis of the ISA100.11a standard protocol using the Colored Petri Net (CPN) modeling approach. Firstly, we establish a security threat model for the ISA100.11a protocol and provide a detailed description and analysis of the identified security threats. Secondly, we use the CPN tool to model the protocol formally and conduct model checking and security analysis. Finally, we analyze and discuss the results of the model checking, which demonstrate that the ISA100.11a standard protocol may have vulnerabilities when certain security threats exist, and provide some suggestions to enhance the security of the protocol. This research provides a certain level of security assurance for the ISA100.11a standard protocol and serves as a reference for similar security research on protocols.
Dual-side Adversarial Learning based Fair Recommendation for Sensitive Attribute Filtering

With the development of recommendation algorithms, researchers are paying increasing attention to fairness issues such as user discrimination in recommendations. To address these issues, existing works often filter users’ sensitive information that may cause discrimination during the process of learning user representations. However, these approaches overlook the latent relationship between items’ content attributes and users’ sensitive information. In this paper, we propose a fairness-aware recommendation algorithm (DALFRec) based on user-side and item-side adversarial learning to mitigate the effects of sensitive information in both sides of the recommendation process. Firstly, we conduct a statistical analysis to demonstrate the latent relationship between items’ information and users’ sensitive attributes. Then, we design a dual-side adversarial learning network that simultaneously filters out users’ sensitive information on the user and item side. Additionally, we propose a new evaluation strategy that leverages the latent relationship between items’ content attributes and users’ sensitive attributes to better assess the algorithm’s ability to reduce discrimination. Our experiments on three real datasets demonstrate the superiority of our proposed algorithm over state-of-the-art methods.
Evaluation of Problems While Marketing Clothing on Instagram with the Pythagorean Fuzzy AHP Method

Abstract Due to the fierce international competition in today’s clothing market, businesses have started to pay more attention to marketing activities. One of the most popular of these marketing activities is Instagram marketing. For this reason, the aim of this study was to identify the problems faced by clothing businesses that perform garment marketing on Instagram, to rank them in order of importance and to develop solutions to the most important problems. Within the scope of the research, firstly, the problems encountered by businesses while marketing clothing on Instagram were determined through data obtained from 14 businesses through a designed questionnaire. The problems identified are technical problems, cargo-related problems, difficulty in increasing the number of followers, and various other issues. Secondly, all the problems identified in the research were ranked by the Pythagoras Fuzzy AHP Method according to the degree of importance; increasing the number of followers, technical problems, lack of trust and others. In the last stage of the research, expert opinion from Instagram marketing experts of 14 businesses was used to develop solutions to the three most important problems previously identified. The solution suggestions developed are as follows: to increase the number of followers of businesses, to increase the number and variety of advertisements and to make advertising campaigns continuous; for technical problems - to share purchase links to customers, to keep the programs used constantly updated, to get professional support and to focus more on story ads instead of post ads; and for the problem of lack of trust - honesty, phenomenon support, product photo shoots within the business.
Secure Aggregation Protocol Based on DC-Nets and Secret Sharing for Decentralized Federated Learning

In the era of big data, millions and millions of data are generated every second by different types of devices. Training machine-learning models with these data has become increasingly common. However, the data used for training are often sensitive and may contain information such as medical, banking, or consumer records, for example. These data can cause problems in people’s lives if they are leaked and also incur sanctions for companies that leak personal information for any reason. In this context, Federated Learning emerges as a solution to the privacy of personal data. However, even when only the gradients of the local models are shared with the central server, some attacks can reconstruct user data, allowing a malicious server to violate the FL principle, which is to ensure the privacy of local data. We propose a secure aggregation protocol for Decentralized Federated Learning, which does not require a central server to orchestrate the aggregation process. To achieve this, we combined a Multi-Secret-Sharing scheme with a Dining Cryptographers Network. We validate the proposed protocol in simulations using the MNIST handwritten digits dataset. This protocol achieves results comparable to Federated Learning with the FedAvg protocol while adding a layer of privacy to the models. Furthermore, it obtains a timing performance that does not significantly affect the total training time, unlike protocols that use Homomorphic Encryption.
An adaptive dynamic window approach for UUV obstacle avoidance planning in 3D environments

Abstract Unmanned Underwater Vehicles (UUVs) are essential equipment for Marine development, widely used in Marine scientific research, Marine resource survey, and Marine security. The autonomous navigation planning ability in unknown environments is a critical indicator for UUV intelligence. This paper focuses on the particularity of UUV motion and the complexity of the underwater environment and proposes an adaptive Dynamic Window Approach (DWA) for UUV obstacle avoidance planning. The adaptive DWA introduces novel heading angle evaluation and adaptive dynamic strategies to solve the problems of poor adaptability of traditional DWA to complex environments and unreasonable path selection in encountering dynamic obstacles and approaching targets. Simulations verify the effectiveness and superiority of the proposed method.