articles
| Difficulties in prognosis of human behavior are due to the complexity of our cognition, routinely breaking the boundaries of classical rationality. The paper solves this problem for the simplest kind of such “irrational” behavior in which a single binary decision is made in three related contexts. Subjective meanings of these contexts relative to the basis decision alternative are represented by three qubit states, borrowed from quantum theory. These states are bound together by linear superpositions, which encode semantic composition of the contexts in the subject’s mind. The resulting theory supplements classical probabilistic model with nonlinear interference factor, accounting for the “irrational”, emotionally-semantic side of intelligence. This model is built for different realizations of two classic experiments used to study behavioral irrationality: the prisoner’s dilemma and the two-stage gambling task. In 24 such realizations, the interference phase is shown to fall in a narrow range of values, encoding regularities of semantic composition of contexts. Extrapolation of this regularity to novel experiments allows using the model in prognostic mode. This possibility is tested on the task of probabilistic prediction of target decision based on the same probability in two other contexts. For the prisoner’s dilemma and the two-stage gambling task the such prognosis has relative errors of 9 and 11% respectively. The proposed approach allows for putting other quantum models of cognition and decision to predictive and interpretable use, whereas its principles also apply to modeling of decisions with larger sets of contexts and behavioral options. By formalizing a novel type of semantic regularities behind “irrational” thinking, models of the present type open prospects for empowering the existing means for socio-economical analytics and prediction. Continue... | |
| A review for the new edition of the textbook “Competition in Entrepreneurship” for higher educational institutions, published by the University publishing house “Synergy”, is presented. The following main advantages of the textbook are noted: the presence of the author’s view on such issues as the process of competitiveness, its objects, subjects, actors, results, resources, types and character of competitive interaction, types of competitive actions, methods of operational interaction between competitive parties, competitive stability and competitive ability, competitive positions, competitive status, competitive strategies, tactical competitive operations and combinations, competitive situations, competitive tricks and puzzles, management of competitive actions, competitive analysis. In the context of digital economy and the application of intelligent management technologies, the use of the considered publication in the educational process will allow university students to develop the knowledge and skills necessary to conduct successful business activities in modern economic conditions. The tools described in the textbook can be effectively used at the strategic, operational and situational levels of participation in competition, considering the potential for its implementation as part of the organization corporate information systems. Continue... | |
| The recruitment industry is at an inflection point: the integration of artificial intelligence has already made its impact on traditional recruitment processes and has the potential to revolutionize it. This article presents an approach to classify resumes into job categories, using semantic similarity search to improve the candidate selection mechanism in recruiting. Our method differs from traditional keyword-based systems and is a deep learning framework that understands and processes the complex semantics of work-related documents. The purpose of the study is to develop a method for classifying resume texts with a complex organizational structure. This study solves several problems at once: increasing the accuracy of resume classification and finding the most stable model for solving the problem of resume classification. We compared standard machine learning methods with neural network ones and showed the effectiveness of the latter. The results indicate an improvement over traditional ML models, suggesting an approach that can be used for pre-screening artificial intelligence recruiting that selects suitable candidates from other applicants. Further, we discovered problems with instability of results when retraining large language models, when the model, even with the same values of the hyperparameters, gives different results. To better understand this phenomenon, we conducted a series of experiments with the main BERT models, varying two parameters – learning rate and seed. As a result, we find a significant increase in performance at a certain threshold parameter, and we quantify which of the found models perform better. Continue... | |
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№ 2(110)
26 april 2024 year
Rubric: Models and methods Authors: Karimbaev T., Medvedev S., Zakharova E. |
Nonlinear regression models are an important tool in agricultural research, as many biological processes are theoretically and experimentally described by nonlinear functions. In addition to accurately describing experimental data, nonlinear models have the property of physical interpretability of parameters and are more robust outside the domain of the studied sample. Currently, existing methods for calculating model coefficients – such as Ordinary Least Squares, Weighted Least Squares, and Generalized Least Squares – have several drawbacks. The most advanced Generalized Least Squares method relies on a large number of axioms, which are often not adhered to in real examples, and the theoretical proof is not apodictic. This article introduces a flexible, robust, and accurate method for calculating coefficients for arbitrary single-factor regression models based on the maximum likelihood estimation method. The method is theoretically justified with a minimal number of axioms, and examples of results from the software implementation are provided for the logistic function and the Michaelis function using synthetic test data and experimental samples of dry grass mass production depending on the volume of nitrogen fertilizers. The main advantage of the method lies in the simplicity of theoretical proof and the small number of theoretical constraints on the input parameters of the problem. Unlike Generalized Least Squares, the proposed method deterministically converges to the absolute minimum, thanks to the use of the DIRECT algorithm. It can account for heteroscedasticity and does not require manual tuning of optimization parameters to ensure convergence. Considerations for possible extensions of the method to multifactorial regression analysis and potential improvements for heteroscedasticity estimation are also presented. Continue... |
| Due to the enormous changes that have occurred in the world over the last few years, for many companies the issue of economic efficiency of their activities has become vitally important. The events industry was not an exception. The economic side of mass events has not yet described adequate in the scientific literature, which makes it difficult to create high-quality IT tools. Such a toolkit should be based on a mathematical model, which will be built considering the economic features of the activities, which in turn requires the study of these features. Thus, the purpose of the study is to formulate a target problem of integer programming based on factors influencing economic indicators during in the process of mass event. The work is based on an analysis of the results of “field” observations, internal documentation of organizing companies, materials of large public events that are in the public domain, as well as scientific and popular literature on the organization and conduct of mass events. In the article, based on the characteristics of mass events, factors influencing economic indicators during the event are determined, and on their basis, the target problem of integer programming is formed. The three-level model proposed by the author for optimizing the distribution of resources in the process of holding a mass event, based on the criteria of fulfilling the plan for a mass event, reducing the costs of attracting resources and their redistribution, can be further considered as the basis for creating IT tools for automating the processes of mass events, which will provide the greatest flexibility and rational use of available resources in various situations. Continue... | |
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№ 2(110)
26 april 2024 year
Rubric: Algorithmic efficiency Authors: Boykov A., Nikonorov V., Ilin I., Luchin D. |
At present, the automation of production processes, including the use of computer vision, machine learning and artificial intelligence methods, is of relevance at light industry enterprises due to the fourth industrial revolution. The key role in the production processes is played by the quality of manufactured products – textile fabrics, which is directly affected by the process of defectoscopy. Due to the development of digital technologies and the growth of computing power, it is possible to automate the process of defectoscopy of textile fabrics using computer vision to reduce labor costs and increase the accuracy of defect detection. The purpose of this paper is to conduct experimental studies of the marking and detection of specific classes of textile defects using a hardware-software complex of computer vision and using a neural network approach. To achieve this goal, the paper describes the existing classification of textile web defects, describes the used hardware-software system, and presents the application of the neural network model of the Mask R-CNN architecture to solve the problem of exemplar defect segmentation. As part of the study, a manual partitioning of more than 400 tissue photographs into two classes of defects was performed as an extension of the training sample: “weft crack” and “water damage”, the obtained results of the neural network model were evaluated by IoU metrics: the best result for the class “weft crack” DIoU = 0.2, for the class “water damage” DIoU = 0.87. Based on the results of the experimental studies, conclusions are made about the existing potential of using neural network approach for defectoscopy of similar classes of defects. The presented results can be used for training and retraining of various models of object detection, the gained experience can be applied in other spheres of industry. Continue... |
