Anton Anikin

@article{Litovkin_2021,
	doi = {10.1088/1742-6596/1801/1/012009},
	url = {https://doi.org/10.1088/1742-6596/1801/1/012009},
	year = 2021,
	month = {feb},
	publisher = {{IOP} Publishing},
	volume = {1801},
	number = {1},
	pages = {012009},
	author = {D V Litovkin and A V Anikin and O A Sychev and T Petrova},
	title = {{ORM} Diagram as an Intermediate Model for {OWL} Ontology Engineering: Prot{\'{e}}g{\'{e}} {ORM} Plugin Implementation},
	journal = {Journal of Physics: Conference Series},
	abstract = {OWL2, a widely-used ontology-representation language, is poorly perceived by humans because OWL2 statements have a low level of abstraction. To solve this issue, various OWL2 ontology editors are used, which allow to group statements and represent them using some visual notation. ORM-diagram is a good candidate for an intermediate model for authoring and understanding of OWL2-ontology as Object-Role Modelling notation supports visually distinguishable constructs, has the high expressive capabilities, and implements the node-link paradigm and the attribute-free approach A Protégé plugin allowing to create an ORM2-diagram using the live error checking approach was implemented. The plugin allows us to form a valid object-oriented diagram model in computer memory using a widely known ontology authoring tool Protégé.},
thanks = {rfbr-18-07-00032, rfbr-20-07-00764}
}

@ARTICLE{Denisov2021256,
author={Denisov, M. and Anikin, A. and Sychev, O. and Katyshev, A.},
title={Program execution comprehension modelling for algorithmic languages learning using ontology-based techniques},
journal={Advances in Intelligent Systems and Computing},
year={2021},
volume={1184},
pages={256-269},
doi={10.1007/978-981-15-5859-7_25},
url={https://link.springer.com/chapter/10.1007%2F978-981-15-5859-7_25},
affiliation={Volgograd State Technical University, Volgograd, Russian Federation; Software Engineering School, Volgograd, Russian Federation},
abstract={In this paper, we propose an ontology-based approach to model a program execution comprehension so to be able to explain to the novice programmer the essence of his/her error. We have studied the algorithmic languages model operating with actions and basic control structures (“sequence,” “branching,” and “looping”) and designed the rules to capture any deviation from the permissible. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.},
correspondence_address1={Anikin, A.; Software Engineering SchoolRussian Federation; эл. почта: Anton@Anikin.name},
publisher={Springer Science and Business Media Deutschland GmbH},
issn={21945357},
language={English},
abbrev_source_title={Adv. Intell. Sys. Comput.},
thanks = {rfbr-18-07-00032}
}

@ARTICLE{Litovkin2021188,
author={Litovkin, D. and Dontsov, D. and Anikin, A. and Sychev, O.},
title={Suitability of Object-Role Modeling Diagrams as an Intermediate Model for Ontology Engineering: Testing the Rules for Mapping},
journal={Advances in Intelligent Systems and Computing},
year={2021},
volume={1310},
pages={188-194},
doi={10.1007/978-3-030-65596-9_24},
url={https://link.springer.com/chapter/10.1007%2F978-3-030-65596-9_24},
affiliation={Volgograd State Technical University, Volgograd, Russian Federation},
abstract={Creating and understanding ontologies using OWL2 language is a hard, time-consuming task for both domain experts and consumers of knowledge (for example, teachers and students). Using Object-Role Modeling diagrams as an intermediate model facilitates this process. To achieve this, the method of mapping ORM2 diagrams to OWL2 ontologies and vice versa is necessary. Such methods were proposed in different works, but their suitability and possible errors are in doubt. In this paper, we propose a method of evaluating how well existing rules of mapping follow ORM semantics. Several ontologies were created using mapping rules and tested. During testing, a significant difference between ORM2 and OWL2 basic properties and assumptions were discovered. This difference require updating the mapping rules. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
correspondence_address1={Anikin, A.; Volgograd State Technical UniversityRussian Federation; эл. почта: anton@anikin.name},
editor={Samsonovich A.V., Gudwin R.R., Simoes A.d.},
publisher={Springer Science and Business Media Deutschland GmbH},
issn={21945357},
isbn={9783030655952},
language={English},
abbrev_source_title={Adv. Intell. Sys. Comput.},
thanks = {rfbr-20-07-00764}
}

@ARTICLE{Denisov2021408,
author={Denisov, M. and Anikin, A. and Sychev, O.},
title={Dynamic Flowcharts for Enhancing Learners’ Understanding of the Control Flow During Programming Learning},
journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
year={2021},
volume={12909 LNAI},
pages={408-411},
doi={10.1007/978-3-030-86062-2_42},
url={https://link.springer.com/chapter/10.1007%2F978-3-030-86062-2_42},
affiliation={Volgograd State Technical University, Lenin Ave, 28, Volgograd, 400005, Russian Federation},
abstract={In introductory programming learning, flowcharts are often used to help students comprehend patterns of behavior of control flow statements like alternatives, loops, and switches. The use of flowcharts in assignments along with lectures can increase their learning effect. In some areas, the potential of flowcharts remains largely underestimated. Flowcharts are rare in intelligent learning systems for learning programming, although class, object, and sequence diagrams are quite popular. This poster describes the implementation of a non-editable flowchart widget in our intelligent programming tutor on control flow structures (hereinafter referred to as the exerciser). In normal use, the student chooses the next correct action in the program text (algorithm), i.e., makes one transition through the flowchart. Our exerciser allows making a wrong choice and gives a text hint explaining the reason for incorrectness. The wrong or inexistent transition is also displayed on the flowchart to help localize the error. © 2021, Springer Nature Switzerland AG.},
correspondence_address1={Anikin, A.; Volgograd State Technical University, Lenin Ave, 28, Russian Federation; эл. почта: anton.anikin@vstu.ru},
editor={Basu A., Stapleton G., Linker S., Legg C., Manalo E., Viana P.},
publisher={Springer Science and Business Media Deutschland GmbH},
issn={03029743},
isbn={9783030860615},
language={English},
abbrev_source_title={Lect. Notes Comput. Sci.},
thanks = {rfbr-20-07-00764}
}

@ARTICLE{Anikin2021_3,
author={Anikin, A. and Sychev, O. and Denisov, M.},
title={Ontology reasoning for explanatory feedback generation to teach how algorithms work},
journal={Frontiers in Artificial Intelligence and Applications},
year={2021},
volume={338},
pages={V-VI},
doi={10.3233/FAIA210100},
art_number={239-244},
url={https://ebooks.iospress.nl/doi/10.3233/FAIA210100},
affiliation={Volgograd State Technical University, Russian Federation; Software Engineering School, Volgograd, Russian Federation},
abstract={Developing algorithms using control structures and understanding their building blocks are essential skills in mastering programming. Ontologies and software reasoning is a promising method for developing intelligent tutoring systems in well-defined domains (like programming languages and algorithms); it can be used for many kinds of teaching tasks. In this work, we used a formal model consisting of production rules for Apache Jena reasoner as a basis for developing a constraint-based tutor for introductory programming domain. The tutor can determine fault reasons for any incorrect answer that a student can enter. The problem the student should solve is building an execution trace for the given algorithm. The problem is a closed-ended question that requires arranging given actions in the (unique) correct order; some actions can be used several times, while others can be omitted. Using formal reasoning to check domain constraints allowed us to provide explanatory feedback for all kinds of errors students can make. © 2021 The authors and IOS Press.},
editor={Frasson C., Kabassi K., Voulodimos A.},
publisher={IOS Press BV},
issn={09226389},
isbn={9781643682044},
language={English},
abbrev_source_title={Front. Artif. Intell. Appl.},
thanks = {rfbr-20-07-00764}
}

@CONFERENCE{Sychev2021_2,
author={Sychev, O. and Anikin, A. and Terekhov, G.},
title={Demonstrating Concepts through Visual Simulators: Two Cases in the Programming Domain},
journal={Proceedings of IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC},
year={2021},
volume={2010-October},
doi={10.1109/VL/HCC51201.2021.9576398},
url={https://ieeexplore.ieee.org/document/9576398},
affiliation={Volgograd State Technical University, Software Engineering Department, Volgograd, Russian Federation},
abstract={One of the ways to demonstrate subject-domain concepts is to let the learner play in the domain-related sandbox, while receiving the explanations of the domain laws that were broken when the user makes a wrong move. This can help the learners who understand definitions of the concepts poorly. We present two visual simulators for demonstrating concept properties and domain laws, implemented as web applications. They can be used to study programming, enabling trial-and-error learning, supported by the error messages, explaining why the user's action was wrong. © 2021 IEEE.},
editor={Harms K., Cunha J., Oney S., Kelleher C.},
publisher={IEEE Computer Society},
issn={19436092},
isbn={9781665445924},
language={English},
abbrev_source_title={Proc. of IEEE Symp. Vis. Lang. Hum.-Cent. Comput., VL/HCC},
thanks = {rfbr-20-07-00764}
}

@ARTICLE{Sychev2021mdpi,
author={Oleg Sychev and Nikita Penskoy and Anton Anikin and Mikhail Denisov and Artem Prokudin},
title={Improving comprehension: Intelligent tutoring system explaining the domain rules when students break them},
journal={Education Sciences},
year={2021},
volume={11},
number={11},
doi={10.3390/educsci11110719},
art_number={719},
url={https://www.mdpi.com/2227-7102/11/11/719},
affiliation={Software Engineering Department, Electronics and Computing Machinery Faculty, Volgograd State Technical University, Volgograd, 400005, Russian Federation},
abstract={Intelligent tutoring systems have become increasingly common in assisting students but are often aimed at isolated subject-domain tasks without creating a scaffolding system from lower-to higher-level cognitive skills, with low-level skills often neglected. We designed and developed an intelligent tutoring system, CompPrehension, which aims to improve the comprehension level of Bloom’s taxonomy. The system features plug-in-based architecture, easily adding new subject domains and learning strategies. It uses formal models and software reasoners to solve the problems and judge the answers, and generates explanatory feedback about the broken domain rules and follow-up questions to stimulate the students’ thinking. We developed two subject domain models: an Expressions domain for teaching the expression order of evaluation, and a Control Flow Statements domain for code-tracing tasks. The chief novelty of our research is that the developed models are capable of automatic problem classification, determining the knowledge required to solve them and so the pedagogical conditions to use the problem without human participation. More than 100 undergraduate first-year Computer Science students took part in evaluating the system. The results in both subject domains show medium but statistically significant learning gains after using the system for a few days; students with worse previous knowledge gained more. In the Control Flow Statements domain, the number of completed questions correlates positively with the post-test grades and learning gains. The students’ survey showed a slightly positive perception of the system. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.},
correspondence_address1={Sychev, O.; Software Engineering Department, Russian Federation; эл. почта: o_sychev@vstu.ru; Anikin, A.; Software Engineering Department, Russian Federation; эл. почта: anton.anikin@vstu.ru},
publisher={MDPI},
issn={22277102},
language={English},
abbrev_source_title={Educ. Sci.},
thanks = {rfbr-20-07-00764}
}

@InProceedings{10.1007/978-3-030-80421-3_6,
author="Sychev, Oleg
and Anikin, Anton
and Penskoy, Nikita
and Denisov, Mikhail
and Prokudin, Artem",
editor="Cristea, Alexandra I.
and Troussas, Christos",
title="CompPrehension - Model-Based Intelligent Tutoring System on Comprehension Level",
booktitle="Intelligent Tutoring Systems",
year="2021",
publisher="Springer International Publishing",
address="Cham",
pages="52--59",
abstract="Intelligent tutoring systems become increasingly common in assisting human learners, but they are often aimed at isolated domain tasks without creating a scaffolding system from lower- to higher-level cognitive skills. We designed and implemented an intelligent tutoring system CompPrehension aimed at the comprehension level of Bloom's taxonomy that often gets neglected in favour of the higher levels. The system features plugin-based architecture, easing adding new domains and learning strategies; using formal models and software reasoners to solve the problems and judge the answers; and generating explanatory feedback and follow-up questions to stimulate the learners' thinking. The architecture and workflow are shown. We demonstrate the process of interacting with the system in the Control Flow Statements domain. The advantages and limits of the developed system are discussed.",
isbn="978-3-030-80421-3",
thanks = {rfbr-20-07-00764}
}

@InProceedings{10.1007/978-981-15-0637-6_31,
doi="10.1007/978-981-15-0637-6_31",
author="Litovkin, Dmitry
and Anikin, Anton
and Kultsova, Marina",
editor="Yang, Xin-She
and Sherratt, Simon
and Dey, Nilanjan
and Joshi, Amit",
title="Interactive Visualization of Ontology-Based Conceptual Domain Models in Learning and Scientific Research",
booktitle="Fourth International Congress on Information and Communication Technology",
year="2020",
publisher="Springer Singapore",
address="Singapore",
pages="365--374",
abstract="The paper presents an approach to knowledge transferring and sharing on the base of the semantic link network (SLN) representing expert knowledge in the explicit form. To provide an efficient SLN understanding, it is represented with a geometric graph which can be interactively visualized using a combination of the appropriate visualization methods. The coupling of these methods allows getting a different level of details of the SLN visualization in accordance with the user needs. The proposed approach is planned being implemented in the knowledge management system for learning and scientific research.",
isbn="978-981-15-0637-6",
thanks = {rfbr-18-07-00032, rfbr-18-47-340014}
}

@article{Anikin_2019_1,
	doi = {10.1088/1757-899x/483/1/012074},
	url = {https://doi.org/10.1088%2F1757-899x%2F483%2F1%2F012074},
	year = 2019,
	month = {mar},
	publisher = {{IOP} Publishing},
	volume = {483},
	pages = {012074},
	author = {Anton Anikin and Dmitry Litovkin and Elena Sarkisova and Tatyana Petrova and Marina Kultsova},
	title = {Ontology-based approach to decision-making support of conceptual domain models creating and using in learning and scientific research},
	journal = {{IOP} Conference Series: Materials Science and Engineering},
	thanks = {rfbr-18-07-00032, rfbr-18-47-340014},
	abstract = {The paper presents an approach to knowledge management in learning and scientific research that allows increasing the availability of expert knowledge and reducing semantic noise during knowledge transfer. The availability of expert knowledge is ensured by transforming them from implicit into explicit form (in the form of Semantic Link Network). The reduction of semantic noise is achieved through the integration of knowledge in different forms and their personalization for different groups of knowledge recipients. In the paper, the tasks of decision-making support are formulated which should be performed by the knowledge management system on the base of the proposed approach.}
}

@article{Anikin_2019_2,
	doi = {10.1088/1757-899x/483/1/012073},
	url = {https://doi.org/10.1088%2F1757-899x%2F483%2F1%2F012073},
	year = 2019,
	month = {mar},
	publisher = {{IOP} Publishing},
	volume = {483},
	pages = {012073},
	author = {A Anikin and A Katyshev and M Denisov and V Smirnov and D Litovkin},
	title = {Using online update of distributional semantics models for decision-making support for concepts extraction in the domain ontology learning task},
	journal = {{IOP} Conference Series: Materials Science and Engineering},
	thanks = {rfbr-18-07-00032},
	abstract = {Most of the information processed by computer systems is presented in the form of text corpuses. The number of such texts (as well as the corpus as a whole) only increases with time, and therefore the word processing tasks remain relevant to this day. Ontology allows to describe semantics using domain concepts and relations between them [1, 2]. In the ontology learning task, the ontology is dependent on quality of corpus which may not be readily available. There are different approaches to creating ontologies (including the use of different tools and frameworks). This paper discusses the use of word2vec (group of related models that are used to produce word embeddings) using online vocabulary update and extension of the original data corpus with additional training for the domain concepts extraction to automate the domain ontology creation.}
}

@InProceedings{10.1007/978-3-030-29750-3_7,
doi="10.1007/978-3-030-29750-3_7",
author="Litovkin, Dmitry
and Anikin, Anton
and Kultsova, Marina",
editor="Kravets, Alla G.
and Groumpos, Peter P.
and Shcherbakov, Maxim
and Kultsova, Marina",
title="Semantic Zooming Approach to Semantic Link Network Visualization",
booktitle="Creativity in Intelligent Technologies and Data Science",
year="2019",
publisher="Springer International Publishing",
address="Cham",
pages="81--95",
abstract="In the paper, we described a semantic zooming approach to the visualization of special kind structures - semantic link networks (SLN), represented as a visual graph. The proposed approach allows decreasing semantic noise in SLN overview and navigation and also simplifies the process of understanding the domain studied with SLN by means of semantic zooming. We proposed priori importance levels of SLN items and semantic zooming scale to visualize the SLN with different details level. We designed an interactive SLN visualization process including the following SLN transformations: filtering SLN items, context collapse and expansion for SLN item, and changing the details in the visualized object representation in the geometric SLN graph. The transformation algorithms were developed, and also examples of SNL semantic zooming were described in details in the paper.",
isbn="978-3-030-29750-3",
thanks = {rfbr-18-07-00032, rfbr-18-47-340014}
}

@InProceedings{10.1007/978-3-319-99316-4_1,
author="Anikin, Anton
and Sychev, Oleg
and Gurtovoy, Vladislav",
editor="Samsonovich, Alexei V.",
title="Multi-level Modeling of Structural Elements of Natural Language Texts and Its Applications",
booktitle="Biologically Inspired Cognitive Architectures 2018",
year="2019",
publisher="Springer International Publishing",
address="Cham",
pages="1--8",
abstract="Methods of extracting knowledge in the analysis of large volumes of natural language texts are relevant for solving various problems in the field of analysis and generation of textual information, such as text analysis for extracting data, fact and semantics; presenting extracted information in a convenient for machine processing form (for example, ontology); classification and clustering texts, including thematic modeling; information retrieval (including thematic search, search based on the user model, ontology-based models, document sample based search); texts abstracting and annotating; developing of intelligent question-answering systems; generating texts of different types (fiction, marketing, weather forecasts etc.); as well as rewriting texts, preserving the meaning of the original text for presenting it to different target audiences. In order for such methods to work, it is necessary to construct and use models that adequately describe structural elements of the text on different levels (individual words, sentences, thematic text fragments), their characteristics and semantics, as well as relations between them, allowing to form higher-level structures. Such models should also take into account general characteristics of textual data: genre, purpose, target audience, scientific field and others. In this paper, authors review three main approaches to text modeling (structural, statistical and hybrid), their characteristics, pros and cons and applicability on different stages (knowledge extraction, storage and text generation) of solving problems in the field of analysis and generation of textual information.",
isbn="978-3-319-99316-4",
thanks = "rfbr-18-07-00032"
}

@article{Matyushechkin2018,
title = "Web-service for Translation Pictogrammes Sages into Coherent Text in Russian",
journal = "Известия Волгоградского государственного технического университета",
number="5",
volume = "215",
pages = "30 - 36",
year = "2018",
issn = "1990-5297",
url = "https://elibrary.ru/item.asp?id=34991077",
author = "Dmitrii Matyushechkin, and Marina Kultsova, and Anton Anikin",
abstract="This article examines the implementation of web-service providing translation of pictogram messages into text messages in Russian. Also approaches to the decision of the given task are considered and results of development of methods of translation are presented. The developed web-service for the translation of pictogram messages into text messages can have a wide range of applications in the field of augmentative and alternative communication for people with mental and speech disorders. Also, this web service can be used by third-party software developers for people with disabilities, which will enable their programs to translate pictogram messages.",
thanks="rfbr-18-07-00032"
}

@ARTICLE{Anikin2017133,
author={Anikin, A. and Litovkin, D. and Kultsova, M. and Sarkisova, E. and Petrova, T.},
title={Ontology visualization: Approaches and software tools for visual representation of large ontologies in learning},
journal={Communications in Computer and Information Science},
year={2017},
volume={754},
pages={133-149},
doi={10.1007/978-3-319-65551-2_10},
note={cited By 0; Конференция 2nd Conference on Creativity in Intelligent Technologies and Data Science, CIT and DS 2017 ; Дата конференции: с 12 September 2017 по 14 September 2017;  Код конференции:197189},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029446521&doi=10.1007%2f978-3-319-65551-2_10&partnerID=40&md5=afe223b4bf23023f708f73a0259ee0ae},
affiliation={Volgograd State Technical University, Volgograd, Russian Federation; Volgograd State Socio-Pedagogical University, Volgograd, Russian Federation},
abstract={In this paper, we address the issue of large ontologies visualization for learning. The ontologies can be used to improve the efficiency of the learning when the learner explores a new subject domain and needs its conceptual model. The ontologies can help to overview this new subject domain, to better understand current knowledge, the knowledge that should be got, the subject domain structure, main concepts, relationships between them and relevant information resources. However with increasing the complexity of domain structure, the number of concepts and relationships, it becomes more difficult to overview and understand conceptual model represented with the ontology. We review the approaches to ontology visualization and their implementation in the software tools that can help to resolve this issue. © Springer International Publishing AG 2017.},
author_keywords={Ontology;  Ontology visualization;  Semantic web},
keywords={Computer software;  Semantic Web;  Visualization, Conceptual model;  Domain structure;  Information resource;  Ontology visualizations;  Visual representations, Ontology},
correspondence_address1={Kultsova, M.; Volgograd State Technical UniversityRussian Federation; эл. почта: marina.kultsova@mail.ru},
editor={Groumpos P., Kravets A., Shcherbakov M., Kultsova M.},
sponsors={},
publisher={Springer Verlag},
issn={18650929},
isbn={9783319655505},
language={English},
abbrev_source_title={Commun. Comput. Info. Sci.},
document_type={Conference Paper},
source={Scopus},
}