Abstract:

The article reveals the essence of the expert competence of the detection of borrowing (DB), provides the definition of the levels of development of the expertise of DB, suggests the developed methodology "Determining the level of development of educational expertise of experts in the detection of borrowing in documents", discloses the principles of development of expert competence.

Abstract:

Translated plagiarism has widely spread in a scientific world and posed a serious problem due to the challenges in its automatic detection. However, in the last five years some progress has been observed in this area. The authors of this paper, as well as foreign research team from several universities independently of each other proposed an approach to detect plagiarism based on citation analysis with search of initial source for analyzed suspected paper with the same or similar references. Developed methods of detection of illegal use of borrowed text successfully passed several tests. The report shows the results that we have obtained in the last four years.

Abstract:

The article investigates the problem of aspect-based sentiment analysis. Such version of analysis is more challenging compared to general task of sentiment detection problem. It implies the solutions to the number of related subtasks such as aspect term extraction, aspect term polarity detection and aspect category polarity detection. The solution of aspect-based sentiment analysis problem significantly extends the capabilities of natural language processing systems.

The article gives the overview of previous works in the field and describes the train and test data from the Russian evaluation workshop SentiRuEval. For the task of aspect term extraction the vector space of distributed representations of words was used. Aspect term detection is based on mutual information method and semantic similarity. The paper contains the number of experimental results. At the end the final conclusions are drawn.

Abstract:

The article is devoted to the development of a scientific and methodological apparatus for improving the effectiveness of protecting digital platforms from cyber threats by creating processing and detection algorithms that take into account the cognitive characteristics of users. A conceptual model of a three-stage protection system is proposed, integrating technical security mechanisms with cognitive decision-making models. A heuristic detection algorithm based on Random Forest machine learning with analysis of 47 features, including technical URL characteristics and cognitive-semantic content characteristics, has been developed. A methodology for dynamic integration of four threat data sources has been created, reducing response time from 12–14 hours to two hours. An algorithm for recursive analysis of redirection chains up to ten levels deep to detect masked threats is proposed. Experimental validation on an empirical base of approximately one million records confirmed detection accuracy of 87% when processing one hundred thousand records per hour. The developed solutions ensure compliance with the requirements of GOST R 57580.1-2017 and Russian legislation in the field of personal data protection.

Abstract:

A comparative analysis of image segmentation methods for fire detection was conducted using thresholding in the HSV color space and the U-Net neural network. The study aimed to evaluate the efficiency of these approaches in terms of execution time and fire detection accuracy based on RMSE, IoU, Dice, and MAPE metrics. Experiments were performed on four different fire images with manually prepared ground truth fire masks. The results showed that the HSV method offers high processing speed (0.0010–0.0020 s) but tends to detect not only fire but also smoke, reducing its accuracy (IoU 0.0863–0.3357, Dice 0.1588–0.5026). The U-Net neural network demonstrates higher fire segmentation accuracy (IoU up to 0.6015, Dice up to 0.7512) due to selective flame detection but requires significantly more time (1.2477–1.3733 s) and may underestimate the total fire area (MAPE up to 78.5840%). Visual assessment confirmed differences in methods' behavior: HSV captures smoke as part of the target area, while U-Net focuses exclusively on fire. The choice between methods depends on task priorities: speed or accuracy. Future research directions were proposed, including U-Net optimization and the development of hybrid approaches.

Abstract:

In recent years, large language models (LLMs) have achieved substantial progress in natural language processing tasks and have become key instruments for addressing a wide range of applied and research problems. However, as their scale and capabilities grow, the issue of hallucinations — i.e., the generation of false, unreliable, or nonexistent information presented in a credible manner—has become increasingly acute. Consequently, analyzing the nature of hallucinations and developing methods for their detection has acquired both scientific and practical significance.

This study examines the phenomenon of hallucinations in large language models, reviews their existing classification, and investigates potential causes. Using the Flan-T5 model, we analyze differences in the model’s internal states when generating hallucinations versus correct responses. Based on these discrepancies, we propose two approaches for hallucination detection: one leveraging attention maps and the other utilizing the model’s hidden states. These methods are evaluated on data from HaluEval and Shroom 2024 benchmarks in tasks such as summarization, question answering, paraphrasing, machine translation, and definition generation. Additionally, we assess the transferability of the trained detectors across different hallucination types, in order to evaluate the robustness of the proposed methods.

Abstract:

The Virtual Reality Trainer for the Prenatal Detection of Congenital Heart Disease and Associated Malformations project addresses the need to improve specialized training in prenatal diagnosis through immersive technologies. Its theoretical module, “My Friend the Lung”, gamifies learning about fetal cardiovascular anatomy through 3D puzzles, timed challenges, and badges, achieving good results in its pilot test, is noteworthy. The practical module simulates ultrasounds with haptic devices, reducing diagnostic errors and enabling the generation of personalized ultrasounds. A database with 1,200 labeled studies was consolidated as a fundamental source of information. The implementation aims to initially reduce clinical errors, with the potential to decrease perinatal mortality. This project fuses technological innovation, medical rigor, and interactive pedagogy, positioning gamification as an essential tool in medical education and laying the groundwork for its expansion to other specialties.

Abstract:

The developed algorithm of the visual perception system for game agents, implemented in the Unity game engine, is presented. The proposed method is based on the comparison of images from two cameras, taking into account complex visual effects (lighting, shadows, camouflage), and supplemented with line-of-sight verification, taking into account the speed of the object, and the mechanics of gradual detection. Testing of the system has shown a significant increase in realistic detection compared to traditional methods, while maintaining performance within a small additional load on the processor. The algorithm was optimized using Unity Job System and dynamic camera activation. The scientific literature on similar solutions has also been analyzed and their strengths and weaknesses have been identified. The results can be applied in video game development to create realistic behavior of non-player characters, especially in games with stealth elements.

Abstract:

Currently, synthetic data is highly relevant in machine learning. Modern synthetic data generation algorithms make it possible to generate data that is very similar in statistical properties to the original data. Synthetic data is used in practice in a wide range of tasks, including those related to data augmentation.

The author of the article proposes a data augmentation method that combines the approaches of increasing the sample size using synthetic data and synthetic anomaly generation. This method has been used to solve an information security problem of anomaly detection in server logs in order to detect attacks.

The model trained for the task shows high results. This demonstrates the effectiveness of using synthetic data to increase sample size and generate anomalies, as well as the ability to use these approaches together with high efficiency.

Abstract: Practice of holding the sociological surveys conducted for the purpose of detection of opinion of users on electronic information resources and the related information services provided by Educational and scientific information center of the Kazan National University of Science and Technology is described. Results of these polls are given. Experience on formation of information culture of users of Educational and scientific information center of the Kazan National University of Science and Technology is presented.

Abstract:

A solution for the problem of testing controllers based on reading information from their screens is described. A hardware and software system has been developed for this purpose, consisting of a camera and software modules implementing the necessary algorithms and methods: an image preprocessing module; a menu type detection module; a font character processing module; a text reading module, including one written in various fonts; and the testing module itself. The system has been developed for a specific type of controller with a monochrome 128x64 pixel display. All methods are implemented in Python using popular libraries. The system has been launched into test operation and currently automates several of the most labor-intensive tests. The test set can be expanded using plugins.

Abstract:

The article describes the development of software aimed at processing images of murals of architectural monuments in order to identify defects. For flaw detection, a histogram approach was used – a comparison of the brightness characteristics of two images of frescoes. This method allows you to track the status of architectural monuments with low costs and minimal human involvement. The developed technology is used as part of the protection of the cultural heritage of the island-town of Sviyazhsk.

Abstract:

An approach to automatically recognizing the movement of people at a pedestrian crossing presented in the article. This approach includes two main procedures, for each of which program code commands are given in the C# programming language using the EMGU computer vision library. In the first procedure, pedestrian detection is carried out using a combination of directional gradient histogram and support vector methods. The second procedure allows you to read frames from a video sequence and process them. This approach allows detecting the movements of people at a pedestrian crossing without using specialized neural networks. At the same time, the method proposed in the article demonstrated sufficient reliability of human movement recognition, which indicates its applicability in real conditions.

Abstract:

Relevance of the issue under study is due to tenuity of methods proposed by other researchers that use simple heuristics or machine learning algorithms. The purpose of the article is to provide better way to extract titles from scientific PDF documents and offer better and more reasonable approach to title selection generally. The leading approach to the study is regard as many cases and problems appeared during extraction as possible and find an approach to solve all of them. The results showed the efficiency of chosen approach in case of having a document set with all of considered problems. The research highlights that deep analysis of current task problem is a perspective to make the best solutions and tools. The article may be useful for all researchers and developers who often encounter the problem of document structural analysis or title detection as secondary task of a main program workflow.

Abstract:

This paper examines the microseismic noise spectra a few hours before moderate and strong seismic events. Forty earthquakes with an energy class of K=9.5–14.5 at epicentral distances of 10 to 120 km were considered. A statistically significant increase in the spectral power density (SPD) was detected in the 0.8–2.4 Hz range. Machine learning methods were used to construct a binary classification model that allows detection of earthquake preparations a few hours before an event based on microseismic SPD values in the specified frequency range.

Abstract:

Recommendation systems in the scientific information space serve as essential tools for search and navigation when working with scientific documents. Software code is currently considered as an object of scientific knowledge and, as a result, an important task is to create software lifecycle support systems, in particular, to find similar software solutions, detect code borrowings, analyze and evaluate code quality.

This paper proposes a content-based recommender system that provides users with a personalized list of code fragments that are functionally equivalent to the input query code presented in one of the programming languages from the established set.

The basic algorithm of the system is based on the representation of the program code in the form of an abstract syntax tree followed by the construction of a vector space of program codes. The semantic similarity of program codes is determined by the distance between code vectors in a multidimensional space.

The personalization of recommendations is achieved through a filtering module that ranks the retrieved fragments taking into account the user's profile. The factors under consideration are the language preferences of the user and his areas of scientific interests, extracted through integration with ORCID.

To ensure the system's operation, a specialized dataset was created based on the CodeNet corpus. The problem of automated language detection from a snippet of the presented code in one of the 19 languages included in the current rating list of programming languages has also been solved.

Abstract:

The paper is devoted to the study of the cognitive function associated with the generation of elliptical sentences in the Russian language. The study is conducted by testing this cognitive ability using a computer system specially developed by the authors for this purpose. Testing of this cognitive ability is proposed and implemented for the first time. The system is an extension of Moodle and is openly hosted in the github repository. Elliptical constructions are limited to verbal and nominal ellipses, which are theoretically possible to be completely reconstructed based on the context of the sentence. The study is conducted with the participation of SPbPU students as respondents. The texts of planimetric tasks are chosen as the subject area. As a result of the analysis of testing data, the following results are obtained: the influence of the respondent’s knowledge of the subject area (planimetry) on the test results is established; a tendency towards self-study of respondents was discovered, which is manifested in a reduction in time and an increase in scores as they pass tests; it is shown that respondents are poorly motivated if they do not see feedback on the answer to the completed task. The paper discusses the problems of further development of the testing system and its use in adapting questionnaires (tasks) to assess the knowledge of SPbPU students in the field of automation of bug detection in programs, as well as for diagnosing the functional state of operator specialists and express diagnosis of dementia. It also seems promising to use the system to improve the processes of syntactic parsing of elliptic sentences and automate the restoration of ellipses in the subject area of planimetry.

Abstract:

The paper considers a method for classifying unmanned aerial vehicles (UAVs) and birds based on radar measurements in various object movement scenarios. The relevance of the problem is explained by the complexity of UAV detection due to its small overall dimensions, high agility, and similar geometry to a bird. To solve the problem, a classification method based on the analysis of micro-Doppler signatures, which reflect the dynamics of object motion, is applied. The scenarios are modelled in the Engee environment, where models of radar system, UAV and birds are developed.

Abstract:

This paper focuses on the use of a linguistics-based method for automatic object-oriented sentiment analyses. The study was conducted as part of SentiRuEval automatic sentiment analysis system testing cycle. The original task was to extract users’ opinions (positive, negative, neutral) about telecom companies, expressed in tweets and news. In this study news was excluded from the dataset because, being formal texts, news significantly differs from informal ones in its structure and vocabulary and therefore demands a different approach. Only linguistic approach based on syntactic and semantic analysis was used. In this approach, a sentiment-bearing word or expression is linked to its target object at either of two stages, which perform successively. The first stage includes usage of semantic templates matching the dependence tree, and the second stage involves heuristics for linking sentiment expressions and their target objects when syntactic relations between them do not exist. No machine learning was used. The method showed a very high quality, which roughly coincides with the best results of machine learning methods and hybrid approaches.

Abstract:

This paper addresses the challenge of developing specialized corpus resources for the automated analysis of political manipulation in Russian-language media discourse. Although semantic text analysis and computational discourse analysis have advanced substantially in recent years, most existing corpora and annotation schemes are designed for English-language data and do not adequately capture the linguistic and discursive characteristics of Russian-language news media. The objective of this study is to construct a specialized corpus of Russian-language news texts and to develop an annotation scheme tailored to the automated analysis of political manipulation, with explicit consideration of the linguistic and discursive features of the Russian-language media environment. The study introduces a corpus of sentence-level fragments extracted from Russian-language news texts published between 2010 and 2019, together with an annotation scheme for manipulative techniques. The scheme is based on an adaptation of established international classifications of manipulative strategies and is reduced to a limited set of interpretable techniques relevant to Russian-language news discourse. The proposed framework covers emotional, argumentative, and contextual forms of manipulative influence. The resulting corpus and annotation scheme provide an empirical foundation for the development and evaluation of automated methods for analyzing political manipulation in Russian-language news media and may also support further research in media and political discourse.

Abstract:

Cognitive abilities cover all higher human intellectual functions, including oral and written speech. The use of ellipses in writing is associated with the ability to clearly formulate a thought, avoiding the redundancy of its expression. Testing the ability to use ellipses can be the basis, on the one hand, for assessing the cognitive operation of generating ellipses in writing and detecting violations of this operation. This can serve as the basis for the early detection of dementia in medicine. On the other hand, such testing can be used to teach speech technologies, including in foreign languages. From a scientific point of view, it is important to determine the influence degree of training on the performance of text tasks. The article describes the development of a computer system for remote on-line testing the operation of converting a complete sentence into a sentence with an ellipsis of a certain type, the principles of evaluating the results of testing the subjects. The concept of the system is discussed. The structure of system and software development environment are described. A feature of the system is the creation of a new type of tasks related to natural language processing. The complexity of the development is determined by the need for automated formation of a bank of sentences’ pairs in natural language without ellipses and with ellipses of a certain type, which entails the inclusion, in the software environment, of text parsing tools and algorithms for automated formation of ellipses and / or restoration of complete sentences from elliptical ones. In conclusion, the prospects for the development and application of the proposed system are discussed.

Abstract:

Human Pose Estimation (HPE) has become one of the most relevant topics in computer vision research. This technology can be applied in various fields such as video surveillance, medical care, and sports motion analysis. Due to the increasing demand for HPE, many libraries for this technology have been developed in the last 20 years. Since 2017, many HPE algorithms based on skeletal model have been published and packaged into libraries for easy use by researchers.

These libraries are important for researchers who want to integrate them into real-world applications for video surveillance, medical care, and sports motion analysis.

This paper investigates the strengths and weaknesses of four popular HPE advanced human pose recognition libraries that can run on mobile devices: Lightweight OpenPose, PoseNet, MoveNet, and Blase Pose.

Abstract: In this paper, we propose a method of machine-generated and non-scientific text detection in a collection of scientific papers. The method is based on lexical and morphological analysis of the document examined with the help of language modeling. This technique enables estimation of probability that the text belongs to the class of scientific documents. Experimental evidence shows feasibility of the approach.

Abstract:

This paper presents the experience of verifying client implementations of the TLS cryptographic protocol version 1.3. TLS is a widely used cryptographic protocol today, designed to create secure data transmission channels. The protocol provides the necessary functionality for its tasks: confidentiality of transmitted data, data integrity, and authentication of the parties. In the new version 1.3 of the TLS architecture was significantly redesigned, eliminating a number of shortcomings of previous versions that were identified both during the development of implementations and during their operation. We used a new test suite for verifying client implementations of the TLS 1.3 for compliance with Internet specifications, developed on the basis of the RFC8446, using UniTESK technology and mutation testing methods. To test implementations for compliance with formal specifications, UniTESK technology is used, which provides testing automation tools based on the use of finite state machines. The states of the system under test define the states of the state machine, and the test effects are the transitions of this machine. When performing a transition, the specified impact is passed to the implementation under test, after which the implementation's reactions are recorded and a verdict is automatically made on the compliance of the observed behavior with the specification. Mutational testing methods are used to detect non-standard behavior of the system under test by transmitting incorrect data. Some changes are made to the protocol exchange flow created in accordance with the specification: either the values of the message fields formed on the basis of the developed protocol model are changed, or the order of messages in the exchange flow is changed. The protocol model allows one to make changes to the data flow at any stage of the network exchange, which allows the test scenario to pass through all the significant states of the protocol and in each such state to test the implementation in accordance with the specified program. The presented approach has proven effective in several of our projects when testing network protocols, providing detection of various deviations from the specification and other errors. The current work is part of the TLS 1.3 protocol verification project and covers TLS client implementations.

Abstract:

This paper presents the experience of verifying server implementations of the TLS cryptographic protocol version 1.3. TLS is a widely used cryptographic protocol designed to create secure data transmission channels and provides the necessary functionality for this: confidentiality of the transmitted data, data integrity, and authentication of the parties. The new version 1.3 of the TLS protocol was introduced in August 2018 and has a number of significant differences compared to the previous version 1.2. A number of TLS developers have already included support for the latest version in their implementations. These circumstances make it relevant to do research in the field of verification and security of the new TLS protocol implementations. We used a new test suite for verifying implementations of the TLS 1.3 for compliance with Internet specifications, developed on the basis of the RFC8446, using UniTESK technology and mutation testing methods. The current work is part of the TLS 1.3 protocol verification project and covers some of the additional functionality and optional protocol extensions. To test implementations for compliance with formal specifications, UniTESK technology is used, which provides testing automation tools based on the use of finite state machines. The states of the system under test define the states of the state machine, and the test effects are the transitions of this machine. When performing a transition, the specified impact is passed to the implementation under test, after which the implementation's reactions are recorded and a verdict is automatically made on the compliance of the observed behavior with the specification. Mutational testing methods are used to detect non-standard behavior of the system under test by transmitting incorrect data. Some changes are made to the protocol exchange flow created in accordance with the specification: either the values of the message fields formed on the basis of the developed protocol model are changed, or the order of messages in the exchange flow is changed. The protocol model allows one to make changes to the data flow at any stage of the network exchange, which allows the test scenario to pass through all the significant states of the protocol and in each such state to test the implementation in accordance with the specified program. So far, several implementations have been found to deviate from the specification. The presented approach has proven effective in several of our projects when testing network protocols, providing detection of various deviations from the specification and other errors.