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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Andrey Laputenko Tatiana Evtushenko Elena Klochkova Nina Yevtushenko Department of Information Technologies National Research Tomsk State University Tomsk Russian Federation Graduate School of Applied Linguistics and Translation Studies Peter the Great St. Petersburg Polytechnic University Saint Petersburg Russian Federation Department of Software Engineering Ivannikov Institute for System Programming of the Russian Academy of Sciences Moscow Russian Federation

The paper addresses the problem of assessing media text readability depending on the correlation of objective and subjective text complexity. Objective text complexity refers to a set of measurable characteristics such as baseline, morphological, syntactic and lexical characteristics of a media text (a piece of news published on university websites), which is calculated automatically for each text. In total, 34 parameters are considered. Subjective text complexity score is assigned to each text by human experts who assess the clarity, structure, cohesion and coherence of the text. The correlation between subjective and objective text complexity is studied using machine learning models. The readability level is measured using machine learning methods and regression-correlation analysis. Namely, an artificial neural network and regression models are used. It is demonstrated that the use of polynomial features and lasso regularization allows to get a compact regression model of a high quality. The use of the chosen machine learning techniques made it possible to estimate the impact of distinct linguistic features on the readability level.

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Evaluating Length of a Shortest Adaptive Homing Sequence for Weakly Initialized FSMs

Evaluating Length of a Shortest Adaptive Homing Sequence for...

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2020 IEEE East-West Design and Test Symposium, EWDTS 2020

作者： Vinarskii, Evgenii Yevtushenko, Nina Lomonosov Moscow State University Department of Computer Science Moscow Russia Ivannikov Institute for System Programming of Ras Software Engineering Deparment Moscow Russia

ISBN: (数字)9781728198996

ISBN: (纸本)9781728198996

There are many research papers devoted to the state identification problem of finite state machines (FSMs) which are widely used for analysis of discrete event systems. A deterministic complete reduced FSM always has a homing sequence (HS) but a number of non-deterministic FSMs not necessarily have a preset HS;nevertheless, those FSMs can still have an adaptive HS. Moreover, adaptive HS can be shorter than the preset. If each state can be an initial state of a complete FSM, i.e., an FSM is non-initialized, then a shortest adaptive HS (if it exists) has polynomial length in the number of FSM states. We show that it is not the case for weakly-initialized FSMs: such FSMs can have a shortest adaptive HS of exponential length in the number of FSM states/transitions. However, the experimental evaluation shows that the non-polynomial upper bound for a shortest adaptive HS was never reached for randomly generated weakly-initialized two-input FSMs. © 2020 IEEE.

关键词： Adaptive homing sequence Non-deterministic FSM weakly initialized FSM

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Speaker's Emotions Recognition Module Based on the GoogleLeNet Neural Network

Speaker's Emotions Recognition Module Based on the GoogleLeN...

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2022 International Conference on Smart Information Systems and Technologies, SIST 2022

作者： Tereikovskyi, Ihor Tereikovska, Liudmyla Kryvoruchko, Olena Tereikovskyi, Oleh Tyshchenko, Dmytro Franchuk, Tamara National Technical University Of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. Of System Programming And Specialized Computer Systems Kyiv Ukraine Kyiv National University Of Construction And Architecture Dept. Of Information Technology For Design And Applied Mathematics Kyiv Ukraine Kyiv National University Of Trade And Economics Dept. Of Software Engineering And Cyber Security Kyiv Ukraine National Technical University Of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. Of Department Of Computer Engineering Kyiv Ukraine

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

The article is devoted to the development of means for recognition of the emotions of the speaker, based on the neural network analysis of fixed fragments of the voice signal. The possibility of improving recognition tools through the use of a convolutional neural network of the GoogLeNet type is shown. The expediency of training the network on the examples of the TESS database is determined. A procedure for representing a voice signal in the form of a square grayscale image has been developed. Based on the use of the specified database, the modification of the GoogLeNet network was carried out. The architecture and software implementation of the recognition module have been developed. With the help of computer experiments, it has been established that after 150 epochs of training on a fairly limited training sample, the modified GoogLeNet makes it possible to achieve an accuracy of recognition of the speaker's emotions of approximately 0.97 on test examples. The obtained accuracy is comparable to the accuracy of the best modern systems of this purpose and confirms the promise of using neural network models like GoogLeNet for emotion recognition based on voice signal analysis. The need for further research aimed at reducing the resource intensity of the neural network model is shown. © 2022 IEEE.

关键词： Emotion Recognition

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Uvl: Feature Modelling with the Universal Variability Language

SSRN

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SSRN 2024年

作者： Benavides, David Sundermann, Chico Feichtinger, Kevin Galindo, José A. Rabiser, Rick Thüm, Thomas Department of Computer Languages and Systems Universidad de Sevilla Av. Reina Mercedes Seville41012 Spain Institute of Software Engineering and Programming Languages University of Ulm Albert-Einstein-Allee 11 Ulm89069 Germany CRC 1608 KASTEL – Dependability of Software-intensive Systems Karlsruhe Institute of Technology Am Fasanengarten 5 Karlsruhe76131 Germany Christian Doppler Laboratory VaSiCS LIT CPS Lab Johannes Kepler University Linz Altenberger Straße 69 Linz4040 Austria

Feature modelling is a cornerstone of software product line engineering, providing a means to represent software variability through features and their relationships. Since its inception in 1990, feature modelling has evolved through various extensions, and after three decades of development, there is a growing consensus on the need for a standardised feature modelling language. Despite multiple endeavours to standardise variability modelling and the creation of various textual languages, researchers and practitioners continue to use their own approaches, impeding effective model sharing. In 2018, a collaborative initiative was launched by a group of researchers to develop a novel textual language for representing feature models. This paper introduces the outcome of this effort: the Universal Variability Language (\UVL), which is designed to be human-readable and serves as a pivot language for diverse software engineering tools. The development of \UVL drew upon community feedback and leveraged established literature in the field of variability modelling. The language is structured into three levels -Boolean, Arithmetic, and Type- and allows for language extensions to introduce additional constructs enhancing its expressiveness. \UVL is integrated into various existing software tools, such as FeatureIDE and FLAMA, and is maintained by a consortium of institutions. © 2024, The Authors. All rights reserved.

关键词： Modeling languages

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An approach to finding the most optimal route in a dynamic graph

An approach to finding the most optimal route in a dynamic g...

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2021 International Conference on Information Science and Communications Technologies, ICISCT 2021

作者： Nishanov, Akhram Khasanovich Allamov, Oybek Turabaevich Ruzibaev, Ortiq Bakhtiyorovich Abdullaev, Anvar Shukhratbekovich Allamova, Shakhlo Turabaevna Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Systematic and Practical Programming Tashkent Uzbekistan Urgench Branch of Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Software Engineering Khorezm region Urgench Uzbekistan Software Enginering Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Tashkent Uzbekistan Urgench Branch of Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Information Engineering Khorezm region Urgench Uzbekistan

ISBN: (纸本)9781665432580

The article proposes an algorithm for determining the optimal ways for users to travel from one place to another. Traffic jams occur in cities as a result of road repairs and car accidents. As a result, the shortest route planned for the trip can become a useless route. Given these circumstances, the article presents a parallel processing method distributed on a large-scale graph to control traffic. As a result, an algorithm for parallel and distributed processing of large-volume graphs was developed and tested for large-volume data. The test algorithm is illustrated with a wide range of graphs. Experimental experiments of the algorithm were conducted on the basis of data from the city of Urgench, Uzbekistan. © 2021 IEEE.

关键词： Smart city

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Deriving Distinguishing Sequences for Input/Output Automata

Deriving Distinguishing Sequences for Input/Output Automata

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East-West Design & Test Symposium (EWDTS)

作者： Nina Yevtushenko Igor Burdonov Alexandr Kossachev Software Engineering department Ivannikov Institute for System Programming of RAS Moscow Russia Software engineering deparment Ivannikov Institute for System Programming Moscow Russia

ISBN: (数字)9781728198996

ISBN: (纸本)9781728199009

Input/Output (I/O) automata are widely used when deriving high quality tests for (components of) complex discrete systems based on so called distinguishing sequences. For I/O automata, the number of distinguishability relations is much bigger than for classical deterministic Finite State Machines (FSM). In order to avoid submitting the same test sequence several number of times, i.e., avoid the "all weather conditions" assumption, the separability relation can be considered. If two Input/Output automata are separable then there exists an input sequence such that after submitting this sequence and observing produced outputs it can be uniquely concluded which automaton is under testing. In this paper, we modify the discipline of applying input sequences and discuss the derivation of separating sequences for automata with mixed states, i.e., states where transitions both under inputs and under outputs are defined, as well as with cycles labeled by outputs. We also illustrate how an adaptive separating sequence can be derived when both automata are input-enabled.

关键词： Automata Testing software engineering programming Transforms Meteorology Adaptive systems

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Prediction and graph visualization of cyber attacks using graph attention networks

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Computers & Security 2025年 157卷

作者： Mucahit Soylu Resul Das Inonu University Department of Organized Industrial Zone Vocational School Computer Programming Malatya Turkiye Firat University Faculty of Technology Department of Software Engineering 23119 Elazig Turkiye

This study proposes a hybrid approach for visualizing cyberattacks by combining the deep learning-based GAT model with JavaScript-based graph visualization tools. The model processes large, heterogeneous data from the UNSW-NB15 dataset to generate dynamic and meaningful graphs. In the data cleaning phase, missing and erroneous data were removed, unnecessary columns were discarded, and the data was transformed into a format suitable for modeling. Then, the data was converted into homogeneous graphs, and heterogeneous structures were created for analysis using the GAT model. GAT prioritizes relationships between nodes in the graph with an attention mechanism, effectively detecting attack patterns. The analyzed data was then converted into interactive graphs using tools like SigmaJS, with attacks between the same nodes grouped to reduce graph complexity. Users can explore these dynamic graphs in detail, examine attack types, and track events over time. This approach significantly benefits cybersecurity professionals, allowing them to better understand, track, and develop defense strategies against cyberattacks.

关键词： Cyber thread prediction Graph attention networks Graph visualization Heterogeneous graph

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Evaluating the length of distinguishing sequences for nondeterministic Input/Output automata

Evaluating the length of distinguishing sequences for nondet...

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2019 IEEE East-West Design and Test Symposium, EWDTS 2019

作者： Burdonov, Igor Kossachev, Alexandr Yevtushenko, Nina Demakov, Alexey Software Engineering Department Ivannikov Institute for System Programming of RAS Moscow Russia

ISBN: (纸本)9781728110035

Distinguishing sequences are used in model based mutation testing in order to distinguish the specification from its mutants that usually represent critical implementation faults. In this paper, we consider distinguishing sequences for Input/Output automata when a sequence of inputs can be applied before getting any response or a sequence of output responses from an implementation under test. We propose a technique for deriving an r- distinguishing trace, i.e. a distinguishing trace with respect to the trace inclusion (quasi-reduction) relation, and obtain the least and upper bounds on the length of a shortest r- distinguishing trace showing that the exponential upper bound with respect to the number of states of the specification automaton is reachable;the results are then adapted for a proper case of Input/Output automata when each input is followed by an output, i.e., for Finite State Machines. © 2019 IEEE.

关键词： Specifications

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Automated Computation of Therapies Using Failure Mode and Effects Analysis in the Medical Domain

arXiv

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arXiv 2024年

作者： Luttermann, Malte Baake, Edgar Bouchagiar, Juljan Gebel, Benjamin Grüning, Philipp Manikwadura, Dilini Schollemann, Franziska Teifke, Elisa Rostalski, Philipp Möller, Ralf Institute of Information Systems University of Lübeck Germany Institute of Telematics University of Lübeck Germany Institute for Software Engineering and Programming Languages University of Lübeck Germany Department of Infectious Diseases and Microbiology University Hospital Schleswig-Holstein Campus Lübeck Germany Institute for Neuro- and Bioinformatics University of Lübeck Germany Institute for Molecular Medicine University of Lübeck Germany Institute for Electrical Engineering in Medicine University of Lübeck Germany franziska.schollemann Department of Anesthesiology and Intensive Care University Hospital Schleswig-Holstein Campus Lübeck Germany

Failure mode and effects analysis (FMEA) is a systematic approach to identify and analyse potential failures and their effects in a system or process. The FMEA approach, however, requires domain experts to manually analyse the FMEA model to derive risk-reducing actions that should be applied. In this paper, we provide a formal framework to allow for automatic planning and acting in FMEA models. More specifically, we cast the FMEA model into a Markov decision process which can then be solved by existing solvers. We show that the FMEA approach can not only be used to support medical experts during the modelling process but also to automatically derive optimal therapies for the treatment of patients. © 2024, CC BY.

关键词： Patient treatment

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Sentiment Analysis Technology of English Newspapers Quotes Based on Neural Network as Public Opinion Influences Identification Tool

Sentiment Analysis Technology of English Newspapers Quotes B...

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International Computer Sciences and Information Technologies (CSIT)

作者： Serhii Voloshyn Victoria Vysotska Oksana Markiv Ivan Dyyak Ihor Budz Vadim Schuchmann Information Systems and Networks Department Lviv Polytechnic National University Lviv Ukraine Lviv Polytechnic National University Lviv Ukraine Osnabrück University Osnabrück Germany Applied Mathematics Department Ivan Franko National University of Lviv Lviv Ukraine Computational Mathematics and Programming Department Lviv Polytechnic National University Lviv Ukraine Software Engineering Department West Ukrainian National University Ternopil Ukraine

ISBN: (纸本)9798350334326

This article dwells upon research concerning human language processing techniques, namely emotion analysis, conducted to identify emotions of author quotations in English newspaper articles. The publication describes general information about human language processing, brief description of analogues based on machine learning and neural network to analyse emotions in text. The main goal is to develop intelligent system for sentiment analysis of English language quotations and keywords identification that influences public opinion. The article describes sequence of program actions and tools that have been used. Then, statistics of tasks performed by neural networks with various functions of activators have been described. In the publication, product demo version has been described, and primary usage scenario has been depicted. In the end, research results have been summarised.

关键词： Training Sentiment analysis Vocabulary Dictionaries Mood Neural networks Text categorization

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