Yugra State University Bulletin

Subject of research is the criminal law relations that arise when military personnel are held accountable for participating in acts of aggression, the specifics of qualifying their actions as complicity in a crime, as well as the problems of executing criminal orders in the context of military operations.

Purpose of research to conduct a multifaceted examination of the legal framework regulating the imposition of criminal liability upon military personnel involved in aggressive actions. The investigation seeks to delve into the intricate web of legal provisions that govern such responsibilities while simultaneously exploring the profound historical legacy of the Nuremberg trials.

Research methods include formal legal analysis of normative legal acts; comparative legal method in the study of domestic and international legislation; historical and legal analysis of the materials of the Nuremberg trials; a systematic approach in the study of criminal law norms on complicity; the method of legal modeling in the development of proposals for improving legislation.

Objects of research: norms of international and national criminal law governing responsibility for aggression; materials of the Nuremberg trials and modern judicial practice; legal positions of international tribunals on military liability; doctrinal approaches to understanding the institution of complicity in war crimes.

Research findings: in the course of the study, the peculiarities of bringing to justice for complicity in crimes of aggression were analyzed, and the problems of qualifying the actions of military personnel in the execution of criminal orders were identified. The necessity of distinguishing the responsibilities of leaders and perpetrators of war crimes is substantiated. Recommendations are proposed to improve the mechanism of prosecution for war crimes, taking into account the specifics of the participation of military personnel. Criteria for determining the limits of responsibility for the execution of criminal orders in the context of military operations have been developed.

Subject of research: criminal-law qualifiers “public demonstration… in information and telecommunication networks (including the Internet)” and “commission using the Internet” in cases of illegal harvesting and trafficking of especially valuable wildlife and plant objects and their derivatives.

Purpose of research: to determine the content and correlation of the features "public demonstration... in information and telecommunication networks (including the Internet)" and "commission using the Internet," to identify consistent judicial criteria for their establishment and typical grounds for reclassification.

Research methods: doctrinal (black-letter) and system-structural analysis; comparative legal method; interpretation of statutes and case-law reasoning; case study with content analysis of judgments (online inspections, platform responses, expert reports).

Objects of research: Russian court judgments in cases concerning illegal harvesting and trafficking of derivatives of especially valuable wildlife and plant objects (including online postings and digital content), together with related procedural materials (protocols of online inspections, platform data, expert opinions).

Research findings:

1. Courts consistently distinguish between the use of the Internet as an instrumental mode of committing the deal and public demonstration as a content-based display of the object/process/result accessible to an indefinite circle of persons.

2. A three-part test has emerged for public demonstration: openness of access to the publication, identifiability of the object via authentic images of the actual item, and a behavioral linkage “online content → in-person display → (attempted) sale.”

3. Typical grounds for reclassification to the “use of the Internet” qualifier include non-authentic images (borrowed from open sources), lack of proper digital fixation of openness and account/ad posting attribution, and evidence proving only a communication channel without a content display.

Subject of research: the criminogenic influence of the mass media, implemented through the interconnected mechanisms of public consciousness manipulation, disinformation dissemination, and propaganda activities.

Purpose of research: to identify and analyze the specifics and mechanisms of the «manipulation – disinformation – propaganda» triad on the deformation of the society’s value-normative system, the provocation of unlawful behavior, and the creation of systemic threats to law and order in the digital age.

Methods and objects of research: the study is based on methods of theoretical analysis (criminological, sociological, and media studies), as well as the analysis of specific examples from the contemporary media environment. The object of the research is the activity of mass media and digital platforms possessing criminogenic potential.

Research findings: it is established that manipulation, disinformation, and propaganda from an escalating circuit of negative influence, where manipulation creates cognitive dependence of the audience, disinformation acts as a direct catalyst for unlawful actions, and propaganda reinforces deviant models in the public consciousness. It is proven that in the digital era, the destructive potential of this triad is multiplied due to artificial intelligence technologies (deepfakes), the speed, and algorithmic nature of information dissemination, which generates new complex threats requiring adequate countermeasures at the level of legislation and the development of public media literacy.

The subject of the research is dictated by the necessity to develop and utilize highly sensitive quantum, wave technologies, and quantum sensors in data analysis, pattern recognition, automated diagnostics of disease types using modern sensors in the macroworld, medical visualization of human organs [14], presented in the form of images F(x,y) as well as data captured as time series f(t) and images F(x,y) together, using one-dimensional wavelet wave functions of two types a1 and a2, varying within the interval ±π introduced and utilized in this work [2].

Purpose of research: the development of highly efficient algorithms and software packages for joint analysis of interrelated data presented in the form of time series and images obtained by various methods and sensors: X-ray, ultrasound, different types of tomography: computed (CT), magnetic resonance (MRI), positron emission (PET), including methods of photonics, multi-channel electrocardiographs (ECG) and electroencephalographs (EEG), radiothermographs, and other multi-channel devices used in functional diagnostics, image recognition, diagnosis of disease types in human organs in medicine, and much more, displayed by modern sensors based on quantum technologies [11–13] using wave functions of 2 types, – changes in frequency and temporal (spatial) components of observation data by phase, respectively, – a1 and a2 waves, extracted from «quanta of information» f(x) and F(x,y) [1], reflecting the states of human organs in the corresponding.

Research methods: a) transformation of «quanta of information» f(x) and images F(x,y) of sets of studied objects into two types of wave functions: phase-frequency a1=φ(a,b̅) and phase-time (spatial) a2=-φ(a̅,b) characteristics [1; 6], calculated through sequences of single-level discrete two-dimensional wavelet transformation dwt2, – separating the video image F(x,y) into matrices of details [4; 6]: approximations (cA), horizontals (cH), verticals (cV), diagonals (cD) for each of the colors (red (R), green (G), blue (B)), if the image is colored, functions of one-dimensional continuous wavelet transformation cwt(f(t),1:k,'cgau5') and multi-channel averaging operations of wavelet coefficient φ(a,b) matrices from the outputs of wavelet transformations cwt of video image F(x,y) details by columns and rows, i.e. performing operations a1=φ(a,b̅) and a2=-φ(ɑ̅,b); b) selection on the constructed images in Surfer of wavelet coefficients φ(a,b) of variable f(t) multi-frequency sawtooth wave functions of type a2 of 4 types of details AHVD, used for diagnosing types of diseases; c) statistical analysis of the correlations of wave functions of types a1 or a2 of the diagnosed disease with similar characteristics of sets of diseases in known types of organ diseases.

Objects of research: time series f(t) and images F(x,y) of the examined, particularly video images of the eye's fundus for diagnosed types of eye diseases, multichannel phase-frequency a1=φ(a,b̅) and phase-spatial a2=-φ(a̅,b) characteristics – wave functions of the original data and calculated using wavelet transforms dwt2 and cwt and matrix averaging φ(a,b) operations across rows and columns.

Research findings: a) the possibility of transforming «quanta of information»: time series f(t) used in medicine, video images F(x,y) in particular, of eye diseases, into two types of wave functions a1 and a2 using wavelet transformations, corresponding to the corpuscular-wave nature of the micro and macro world has been implemented; b) the possibility of automated diagnosis of types of eye diseases using correlations of the computed wave functions of types a1 and a2 of the diagnosed disease with the wave functions a1 and a2 of the sets of diseases in known types of diseases has been realized; c) it is shown that when correlating the wave functions of a diagnosed disease of a certain type with the wave functions of sets of known disease types, the average values in the columns of the correlation matrix have different values; in the column of correlations for diseases of the same type, the mean value is maximal, while the standard deviation std is not always minimal, depending on the composition of the images in the types, which is an indication of detecting the disease type in the correlation matrix; d) correlational links have been established between types of diseases in the diagnosis of disease types using wavelet functions a1 and a2, characterizing the strength of the links between types of diseases caused by the commonality of properties of the structural tissues of the visual organ, where types of eye diseases develop, as well as the origin of diseases; (e) a reliable method has been found for localizing the type of diagnosed disease in the correlation matrix of characteristics a1 or a2 of the disease with the characteristics of sets of images of diseases in known types of diseases, based on the correlation of the characteristics of the diagnosed disease and the characteristics of the image provided by the researcher, the types of diseases of which match, with the characteristics of sets of diseases in known types of diseases; (f) a reliable method has been found to separate types of diseases with a probability p>0,95 based on maximizing the observed value t_test of the criterion when testing the hypothesis of equality of means of two populations by eliminating the correlation coefficients in two parts of the column; (g) the requirements for the formation of sets of input data, «quanta of information» have been established to obtain reliable diagnoses of disease types; these include: increasing the capacities of input data sets in disease types; increasing the detail in observations, including increasing the orders of calculated a1 and a2 waves in wavelet transformations.

Subject of research: an array of data including socio-economic parameters of transactions in the primary housing market of the Chelyabinsk region.

Purpose of research: to create a neural network model for predicting real estate sales volumes with a time horizon of one year based on retrospective data. The predictive model should be easy to set up and operate, as well as provide for an intuitive user interface.

Research methods: preliminary data processing in accordance with the CRISP-DM standard, correlation analysis, normalization, k-fold cross-validation, construction of a fully connected neural network with two hidden layers and a sigmoidal activation function, the L-BFGS optimization method.

Objects of research: forecasting sales volumes in the regional primary housing construction market.

Research findings: a neural network forecasting model has been created using the Loginom Community analytical platform, which has a modular structure, which ensures its versatility. The model was successfully tested on a validation sample, demonstrating a prediction accuracy of 88.33%.

Subject of research: the time spent by teachers at educational institutions during the verification of handwritten assignments (tests, independent assignments), as well as algorithms for automatic recognition and verification of handwritten text.

Purpose of research: development a software for verifying handwritten assignments of students in general education organizations using optical character recognition (OCR) algorithms, machine learning, and web development tools.

Objects of research: processes and methods for automated verification of handwritten assignments of students in general education organizations using modern text recognition and machine learning technologies.

Research methods: analysis of handwriting recognition methods and techniques during the Unified State Exam, a review of modern transformer-type OCR services and frameworks, and the construction of a client-server web application architecture.

Research findings: the developed architecture of the software package, including modules for model initialization, image preprocessing with alignment, noise reduction, and segmentation functions, text recognition, results aggregation, and a user-friendly web interface for uploading assignments and viewing results.