Symbolic consumers of fine art in the art market and their motivations

The purpose of the article. Research and analysis of algorithms of marketing technologies in the art market by studying the motivations of symbolic consumers of fine arts, namely, individuals who consume products of fine arts through symbolic appropriation. The research methodology is to apply comparative, empirical, and theoretical methods. This methodological approach allows us to analyze the motivations of symbolic consumers of works of art with the subsequent use of research results in the marketing processes of promoting works of art in the art market. The scientific novelty lies in the expansion of ideas about the motivations of symbolic consumers of the fine arts market and in the study of further marketing processes in it. The article analyzes the algorithms of marketing technologies in the analysis of motivations of symbolic consumers of the fine arts market. The article establishes that in the marketing of fine arts it is relevant and necessary to study the behavior of symbolic consumers of works of art as Individuals through the analysis of types of symbolic Consumers, as well as through the analysis of situations of symbolic consumption of works of art as a symbolically appropriated Product at the art market while viewing works of art in exhibition halls. Based on research in the field of art market marketing, we can say that artistic creativity is an area of activity of non-profit public and private museums, gallery and exhibition institutions, as well as large and small businesses, which should professionally analyze consumer motivations, which in turn makes it possible to highly segment consumers of works of fine art in accordance with their motivations. Conclusions. The article defines and analyzes the model of consumption of fine arts by symbolic appropriation, and, accordingly, describes the group (segment) of consumers. We are talking about symbolic consumption, which is associated with the possibility of obtaining aesthetic pleasure through the symbolic appropriation of the work without material possession. It is also proved that consumer behavior in art marketing is determined by three mandatory components: Individual - Product - Situation. It is on these components that the types of motivation are formed, on which the model of consumption and consumer behavior is built. Thus, we have studied individuals who consume works of art, which in turn are products presented in exhibition halls, and we have studied the situations of consumption of works of art that are consumed by symbolic appropriation in exhibitions. Key words: art market, marketing research, motivation of consumers of works of art, symbolic consumption of works of art, behavior of consumers of art market.

Increasing the Prediction Power of Moodle Machine Learning Models with Self-defined Indicators

Starting with version 3.4 of Moodle, it has been possible to build educational ML models using predefined indicators in the Analytics API. These models can be used primarily to identify students at risk of failure. Our research shows that the goodness and predictability of models built using predefined core indicators in the API lags far behind the generally acceptable level. Moodle is an open-source system, which on the one hand allows the analysis of algorithms, and on the oth-er hand its modification and further development. Utilizing the openness of the system, we examined the calculation algorithm of the core indicators, and then, based on the experience, we built new models with our own indicators. Our re-sults show that the goodness of models built on a given course can be significant-ly improved. In the article, we discuss the development process in detail and pre-sent the results achieved.

Analysis of Algorithms for Searching Objects in Images Using Convolutional Neural Network

The problem of finding objects in images using modern computer vision algorithms has been considered. The description of the main types of algorithms and methods for finding objects based on the use of convolutional neural networks has been given. A comparative analysis and modeling of neural network algorithms to solve the problem of finding objects in images has been conducted. The results of testing neural network models with different architectures on data sets VOC2012 and COCO have been presented. The results of the study of the accuracy of recognition depending on different hyperparameters of learning have been analyzed. The change in the value of the time of determining the location of the object depending on the different architectures of the neural network has been investigated.

A Detailed Item Response Theory Analysis of Algorithms and Programming Concepts in App Inventor Projects

Teaching computing in K-12 is often introduced focusing on algorithms and programming concepts using block-based programming environments, such as App Inventor. Yet, learning programming is a complex process and novices struggle with several difficulties. Thus, to be effective, instructional units need to be designed regarding not only the content but also its sequencing taking into consideration difficulties related to the concepts and the idiosyncrasies of programming environments. Such systematic sequencing can be based on large-scale project analyses by regarding the volition, incentive, and opportunity of students to apply the relevant program constructs as latent psychometric constructs using Item Response Theory to obtain quantitative ‘difficulty’ estimates for each concept. Therefore, this article presents the results of a large-scale data-driven analysis of the demonstrated use in practice of algorithms and programming concepts in App Inventor. Based on a dataset of more than 88,000 App Inventor projects assessed automatically with the CodeMaster rubric, we perform an analysis using Item Response Theory. The results demonstrate that the easiness of some concepts can be explained by their inherent characteristics, but also due to the characteristics of App Inventor as a programming environment. These results can help teachers, instructional and curriculum designers in the sequencing, scaffolding, and assessment design of programming education in K-12.

Russian-English dataset and comparative analysis of algorithms for cross-language embedding-based entity alignment

The problem of data fusion from data bases and knowledge graphs in different languages is becoming increasingly important. The main step of such a fusion is the identification of equivalent entities in different knowledge graphs and merging their descriptions. This problem is known as the identity resolution, or entity alignment problem. Recently, a large group of new entity alignment methods has emerged. They look for the so called “embeddings” of entities and establish the equivalence of entities by comparing their embeddings. This paper presents experiments with embedding-based entity alignment algorithms on a Russian-English dataset. The purpose of this work is to identify language-specific features of the entity alignment algorithms. Also, future directions of research are outlined.

Review on Lane Detection and Tracking Algorithms of Advanced Driver Assistance System

Autonomous vehicles and advanced driver assistance systems are predicted to provide higher safety and reduce fuel and energy consumption and road traffic emissions. Lane detection and tracking are the advanced key features of the advanced driver assistance system. Lane detection is the process of detecting white lines on the roads. Lane tracking is the process of assisting the vehicle to remain in the desired path, and it controls the motion model by using previously detected lane markers. There are limited studies in the literature that provide state-of-art findings in this area. This study reviews previous studies on lane detection and tracking algorithms by performing a comparative qualitative analysis of algorithms to identify gaps in knowledge. It also summarizes some of the key data sets used for testing algorithms and metrics used to evaluate the algorithms. It is found that complex road geometries such as clothoid roads are less investigated, with many studies focused on straight roads. The complexity of lane detection and tracking is compounded by the challenging weather conditions, vision (camera) quality, unclear line-markings and unpaved roads. Further, occlusion due to overtaking vehicles, high-speed and high illumination effects also pose a challenge. The majority of the studies have used custom based data sets for model testing. As this field continues to grow, especially with the development of fully autonomous vehicles in the near future, it is expected that in future, more reliable and robust lane detection and tracking algorithms will be developed and tested with real-time data sets.

DESIGNING A FLEXIBLE MANUFACTURİNG MODULE FOR RECONSTRUCTION OF A SECOND-HAND CAR

Based on a comparative analysis of algorithms and models for the design of flexible manufacturing systems (FMS) for the reconstruction and repair of second-hand cars, the purpose and main issues of the research article were determined. The proposed method provides for the development and reconstruction of vehicles that are physically and morally worn out and need to be maintained and redesigned. The aim of the article is to develop a flexible manufacturing module (FMM) for the automated reconstruction of various second-hand car models using an integrated computer-aided design and production system (CAD / CAM). The proposed solution prevents a large amount of the client's (car owner's) time and ensures that the work will be done within the assigned time frame. It also determines the cost at the initial stage, avoiding subsequent additional costs to the client. To implement the tasks at the initial stage, the layout scheme of the FMM was proposed and its mechatronic elements were selected. Based on CAD / CAM technology, the process of automating the design of innovative coatings and assembly of a second-hand car was carried out. Keywords: CAD / CAM technology, flexible manufacturing module, automation of design and assembly, second-hand car, reconstruction.

Complexity analysis of algorithms: A case study on bioinformatics tools

The data volume produced by the omic sciences nowadays was driven by the adoption of new generation sequencing platforms, popularly called NGS (Next Generation Sequencing). Among the analysis performed with this data, we can mention: mapping, genome assembly, genome annotation, pangenomic analysis, quality control, redundancy removal, among others. When it comes to redundancy removal analysis, it is worth noting the existence of several tools that perform this task, with proven accuracy through their scientific publications, but they lack criteria related to algorithmic complexity. Thus, this work aims to perform an algorithmic complexity analysis in computational tools for removing redundancy of raw reads from the DNA sequencing process, through empirical analysis. The analysis was performed with sixteen raw reads datasets. The datasets were processed with the following tools: MarDRe, NGSReadsTreatment, ParDRe, FastUniq, and BioSeqZip, and analyzed using the R statistical platform, through the GuessCompx package. The results demonstrate that the BioSeqZip and ParDRe tools present less complexity in this analysis