Reviewerly/exHarmony huggingface dataset:
Development of a design system for synthesis and analysis the motor wheel based on a valve electric motor of combined excitation for hybrid and electric vehicles

The article describes a project to create an electric transmission for ground vehicles. As a layout, a motor-wheel variant is proposed, in which a traction electric motor, power electronics, control system and brake system are integrated into the wheel disk. This arrangement of the traction motor allows you to free up the space of the car for comfort and placement of the electric power storage. A combined excitation machine is proposed as an electric motor, the magnetic flux in which is created simultaneously by permanent magnets and an excitation winding. Permanent magnets allow you to increase the power in the limited dimensions of the disk, and the excitation winding allows you to adjust the magnetic flux, which significantly expands the range of speeds and torques of the vehicle. The design of the electric motor is innovative and protected by a patent of the Russian Federation. To develop a number of similar motor wheels, a design system has been proposed, which consists of two parts: synthesis and analysis. The synthesis system generates the optimal geometry of the electric motor for a specific technical task of the customer. It contains a multi-level structure that allows you to implement developments for various project situations with and without restrictions. The system contains seven such levels. Optimality criteria can be selected depending on the requirements of the project. The analysis system is based on the principle of a digital double. Its methodological basis is the finite element method. The system allows you to conduct a detailed analysis of the electromagnetic and thermal state, to obtain all the necessary integral characteristics. The performance of the design system has been tested on a digital analogue of a real racetrack, which is divided into sections with specified speed modes. The program outputs 15 parameters of both the car and the electric motor. In conclusion, the article shows the design study of a motor wheel for a racing electric car. The design of the prototype and the results of its tests are presented. The design system for the development of motor wheels based on a valve motor of combined excitation can be used for mass production.
An approach to creating a thinking process in systems empowered with intelligence using 3D environments

The study is aimed at solving the problem of thinking and the sources of building thought processes in systems empowered with intelligence as one of the fundamental steps to the creation of artificial intelligence. Artificial intelligence performs creative functions traditionally considered human prerogative, using computer programs to understand human intelligence and not being limited to biologically plausible methods. In this regard the evolution of a traditional computer program into a system capable of self-creation is being implemented, depending on the conditions of the external and internal events and processes. The article authors present a number of intermediate results of the research in the field of advanced technologies and artificial intelligence achieved on the basis of experiments run on the study of the semantic structures construction – sources aimed at shaping a thinking process in systems empowered with intelligence. The research carried out by the authors of the article contributes to building of basic algorithms as a parametrically polymorphic system. Scheme of one of the main functions of the master algorithm is presented. An array of constructions, semantically related and called by a route determined by a vector, the direction of which is aimed at minimal costs winning, which together act as the fundamental method for creating a master algorithm.
Determining a Trustworthy Application for Medical Data Visualizations through a Knowledge-Based Fuzzy Expert System

Medical data, such as electronic health records, are a repository for a patient's medical records for use in the diagnosis of different diseases. Using medical data for individual patient care raises a number of concerns, including trustworthiness in data management, privacy, and patient data security. The introduction of visual analytics, a computing system that integrates analytics approaches with interactive visualizations, can potentially deal with information overload concerns in medical data. The practice of assessing the trustworthiness of visual analytics tools or applications using factors that affect medical data analysis is known as trustworthiness evaluation for medical data. It has a variety of major issues, such as a lack of important evaluation of medical data, the need to process much of medical data for diagnosis, the need to make trustworthy relationships clear, and the expectation that it will be automated. Decision-making strategies have been utilized in this evaluation process to avoid these concerns and intelligently and automatically analyze the trustworthiness of the visual analytics tool. The literature study found no hybrid decision support system for visual analytics tool trustworthiness in medical data diagnosis. Thus, this research develops a hybrid decision support system to assess and improve the trustworthiness of medical data for visual analytics tools using fuzzy decision systems. This study examined the trustworthiness of decision systems using visual analytics tools for medical data for the diagnosis of diseases. The hybrid multi-criteria decision-making-based decision support model, based on the analytic hierarchy process and sorting preferences by similarity to ideal solutions in a fuzzy environment, was employed in this study. The results were compared to highly correlated accuracy tests. In conclusion, we highlight the benefits of our proposed study, which includes performing a comparison analysis on the recommended models and some existing models in order to demonstrate the applicability of an optimal decision in real-world environments. In addition, we present a graphical interpretation of the proposed endeavor in order to demonstrate the coherence and effectiveness of our methodology. This research will also help medical experts select, evaluate, and rank the best visual analytics tools for medical data.
Research on the Intelligent Construction of UAV Knowledge Graph Based on Attentive Semantic Representation

Accurate target recognition of unmanned aerial vehicles (UAVs) in the intelligent warfare mode relies on a highly standardized UAV knowledge base, and thus it is crucial to construct a knowledge graph suitable for UAV multi-source information fusion. However, due to the lack of domain knowledge and the cumbersome and inefficient construction techniques, the intelligent construction approaches of knowledge graphs for UAVs are relatively backward. To this end, this paper proposes a framework for the construction and application of a standardized knowledge graph from large-scale UAV unstructured data. First, UAV concept classes and relations are defined to form specialized ontology, and UAV knowledge extraction triples are labeled. Then, a two-stage knowledge extraction model based on relational attention-based contextual semantic representation (UASR) is designed based on the characteristics of the UAV knowledge extraction corpus. The contextual semantic representation is then applied to the downstream task as a key feature through the Multilayer Perceptron (MLP) attention method, while the relation attention mechanism-based approach is used to calculate the relational-aware contextual representation in the subject–object entity extraction stage. Extensive experiments were carried out on the final annotated dataset, and the model F1 score reached 70.23%. Based on this, visual presentation is achieved based on the UAV knowledge graph, which lays the foundation for the back-end application of the UAV knowledge graph intelligent construction technology.
Deep Learning Based Cross-View Human Detection System

Abstract Human detection is an important research branch in the field of computer vision, and it is widely used in vehicle-assisted driving, intelligent monitoring, intelligent transportation and other aspects. In order to solve the problems of poor real-time performance, low detection accuracy and inability to detect under occlusion in traditional human detection methods. This paper proposes a deep learning based trainable cross-view human detection system, where the encoder-decoder network focuses on solving the feature association problem after perspective transformation. Then, the system was tested on the WildTrack dataset. The results show that our cross-view human detection system outperforms conventional systems in terms of speed and accuracy across the board, achieving a satisfactory performance rate of 0.71 MODA in the presence of extensive occlusion.
Curriculum Learning for Debiased Recommendation with Explicit and Implicit Feedback

Abstract The recommender system (RS) has played an increasingly important role in Internet applications. Recent literature on RS mainly focused on better fitting the user behavior data. However, user behavior data is observational, not experimental. This makes for a wide range of biases in the data. In this paper, we introduce a novel framework to combine the advantages of both multi-task and curriculum learning for the debiased recommendation. Unlike existing methods that are limited to specific feedback, our method follows multi-task learning to unify both explicit and implicit feedback. And these two feedbacks are learned in a curriculum learning manner by shifting from explicit to implicit. In this way, our method not only makes better use of the available information in user behavior data but also overcomes the task-balancing problem in multi-task learning. Extensive experiments have been conducted on two real-world datasets and prove that our method delivers state-of-the-art performance and significantly improves the debiasing ability of the recommendation model.
Transformation of the aggregate subject of activity in the context of digitalization of education

The criteria for academic success are academic (educational) performance, reflecting the level of educational achievement, as well as interest, motivation, quality and methods of mental work (activity, tension, pace, duration, consistency, the ratio of rational and irrational methods of work, etc.). In our work, we examined how the above factors affect the success of students' learning activities in different forms of organizing the activities of the study group. Two main forms are considered: distance learning and full-time. The study allows us to trace the role of sociopsychological factors affecting the transformation of the aggregate subject of activity in the context of digitalization of education. The study involved two groups of university students. The first group: students of 1-4 courses, who have a distant form of organizing the activities of a group of 60 people. The second group: students of 1-4 courses, who have a full-time form of organizing the activities of a group of 60 people. The total sample size was 120 people. Methods used: Study method motivation learning at the university T.I. Ilyina; Test - questionnaire "Motivation for success and fear of failure" by AA Reana; Methodology studyingI social and psychological self-assessment of the team of O. Nemov; Questionnaire for the diagnosis of the ability to empathy. The research carried out confirms the proposed assumption that the form of organization of the group's activities significantly affects the success of its educational activities. Therefore, we can say that students with a full-time form of organizing activities are more successful in their educational activities than students with a distant system, since they strive to a significant extent to gain knowledge, master a profession, achieve success, and be within the team. to share his interests, to maintain friendly and comradely relations with each other than students with a distant form of organization of activities, who strive to be outside the team, since they may regard their classmates as potential competitors who can hinder the achievement of success in their educational activities.
Optimal photovoltaic plant dimensioning using consumption data

Abstract In the transition away from fossil energy, the use of renewable energy is gaining more importance. Agricultural businesses have a high energy demand as well as space for the installation of renewable energies, such as photovoltaic systems (PV). However, with the growing difference between electricity prices and feed‐in tariffs, the profitability of photovoltaic systems increasingly depends on the share of self‐consumption. Farmers who want to invest in a PV system are facing the challenge which configuration they should buy. To determine the optimal size and orientation of a PV system, an optimization problem can be formulated which maximizes the profit after 20 years. The cost function is composed of the net present value (NPV), which takes into account the investment and maintenance costs, as well as sales profits and electricity purchase costs. To calculate these, the flow of electricity for one year in a hourly resolution is simulated. For this purpose, the individual consumption curve, which is obtained through measurements, and the power generation of the photovoltaic plant is considered. The power generation is based on physical equations and uses the geographical location of the farm. The size and orientation, such as azimuth and tilt of the system, are included as variable parameters in the optimization problem. Due to the annual curve, both the high‐yield summer months and the winter months are considered. Depending on this generation power curve and its overlap with the consumption power curve, differently oriented and sized photovoltaic systems are recommended. In this paper, the influence of the photovoltaic system orientation is analyzed using different artificially generated consumer curves, each with increased consumption at different times of the day.
Digital visual modelling of educational virtual media

The modern educational process exists in an interactive information world. Modelling educational virtual media for mass involvement of learners in the process of formation of relevant competences. Digital visual art approaches form a holistic visual image of a virtual medium to actualize the subject of learning for learners. Examples of the implementation of the approaches in the creation of virtual media by Boxglass (Izhevsk) in educational institutions are described.
Single Camera Person Re-identification with Self-paced Joint Learning

Abstract Existing re-identification (re-ID) methods rely on a large number of cross-camera identity tags for training, and the data annotation process is tedious and time-consuming, resulting in a difficult deployment of real-world re-ID applications. To overcome this problem, we focus on the single camera training (SCT) re-ID setting, where each identity is annotated in a single camera. Since there is no annotation across cameras, it takes much less time in data acquisition, and enables fast deployment in new environments. To address SCT re-ID, we proposed a joint comparison learning framework and split the training data into three parts, single-camera labeled data, pseudo labeled data, and unlabeled instances. In this framework, we iteratively (1) train the network and dynamically update the memory to store the three types of data, (2) assign pseudo-labels to the unlabeled images using a clustering algorithm. In the model training phase, we jointly train the three types of data to update the CNN model, and this joint training method can continuously takes advantages of both labeled, pseudo labeled or unlabeled images. Extensive experiments are conducted on three widely adopted datasets, including Market1501-SCT and MSMT17-SCT, and show the superiority of our method in SCT. Specifically, the mAP of our method significantly outperforms state-of-the-art SCT methods by 42.6% and 30.1%, respectively.
The impact of forensic accounting tools in investigating white-collar crime

This study examines the impact of forensic accounting tools in the investigation of white-collar crime in Malaysia. Models consist of multiple regression has been evaluated. Our results show, the impact Encase software, Computer Aided Audit Tools (CAAT), Forensic Toolkit (FTK), and ProDiscover Forensics have a significant relationship with investigating white-collar crime in Malaysia. Firstly, Encase is widely acknowledged as the world's premier provider of digital forensics, cyber security, and electronic discovery, making it the gold standard for criminal digital forensics. Secondly, CAAT allows a database of thousands of transactions to be centrally audited and finished more efficiently and quickly. Thirdly, FTK's primary purpose is speed and performance, hence it concentrates on pre-indexing files to improve speed. It is compatible with electronic discovery and mobility. Lastly, ProDiscover is a great computer security tool because it enables computer specialists to swiftly locate all data on computer disks and safeguard evidence for use in legal procedures. This study raises awareness of forensic accounting tools to investigate white-collar crime more effectively.
Boosting With Prior for Accurate Classification

Adaptive Boosting (AdaBoost) based meta learning algorithms generate an accurate classifier ensemble using a learning algorithm with only moderate accuracy guarantees. These algorithms have been designed to work in typical supervised learning settings and hence use only labeled training data along with a base learning algorithm to form an ensemble. However, significant knowledge about the solution space might be available along with training data. The accuracy and convergence rate of AdaBoost might be improved using such knowledge. An effective way to incorporate such knowledge into boosting based ensemble learning algorithms is presented in this paper. Using several synthetic and real datasets, empirical evidence is reported to show the effectiveness of proposed method.Significant improvements have been obtained by applying the proposed method for detecting roads in aerial images.
Knowledge sharing and perceived risk in transportation business in terrorist areas

The case study has purpose on the knowledge sharing behavior requires other variables to be involved. The pattern of human resource development and transportation management needs to understand the behavior of employees and consumers that lead to the return of consumption, which in this case means the business process, to formulate the human resource management to organize the specialize knowledge to do transportation job in the risky province. It is evident that the implementation of the predominant behavior is often ambiguous. This is necessary to explore the effect of the transmitted behavior in order to identify the outcome that guild to desirable behavior for transportation business. Policy plans in existing risky areas that adjust people's attitudes should adjust the management in the area to make employees aware of safety, including formulating strategic plans that highlight the quality of service leading to the satisfaction for stakeholders, and enhancing the capability of company personnel who have to regularly interact with consumers. Therefore, the model adopted in this study is considered to be an important part in improving the management in people that facing the risk of terrorism area and epidemic crisis, as well as providing future guidelines to crisis management.
Development of an up-to-date competency profile of the head of the university educational program

The article presents a theoretical model and empirical data of the study of the competency profile of the head of the university educational program. We developed a research strategy to study the importance of professional competencies that allow us to successfully implement and develop university-level educational programs. A brief review of publications in the subject area under study made it possible to describe the theoretical model of the competency profile of the head of the university educational program and to identify empirical indicators for the preparation of expert survey questions. The expert survey was attended by 55 people, representatives of the teaching staff who manage educational programs of higher education. The survey database was processed in the SPSS Statistics program (version 23). Assessment of the importance of relevant professional competencies of educational program managers allows us to identify areas of advanced training and training programs for this category of university employees. The results of the study contribute to the improvement of the university’s work, aimed at the development of relevant educational programs of higher education.
Product Recommendation System Based on Deep Learning

Abstract With the development of Internet big data and e-commerce, the widespread popularity of information, information acquisition and personalized recommendation technologies have attracted extensive attention. The core value of personalized recommendation is to provide more accurate content and services around users. The recommended scenarios are not uniform, and different dimensions need to be considered. For example, we are facing enterprises or individuals, different age groups, different levels of education, social life and other aspects. In this paper, the classic DNN (Deep Neural Networks) double tower recommendation algorithm in the recommendation algorithm is used as the ranking algorithm of the recommendation system. It is divided into user and item for embedding respectively. The network model is built using tensorflow. The data processed by the initial data through feature engineering is sent into the model for training, and the trained DNN double tower model is obtained. Recall adopts collaborative filtering algorithm, and applies tfidf, w2v, etc. to process feature engineering, so as to better improve the accuracy of the system and balance the EE problem of the recommendation system. The recommendation module of this system is divided into data cleaning as a whole. Feature engineering includes the establishment of user portraits, the analysis of multiple recall and sorting algorithms, the adoption of multiple recall mode, and the implementation of a classic recommendation system with in-depth learning. This makes the recommendation system better balance the interests of both the platform and users, and achieve a win-win situation.