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+++ bibtex [2022/01/07 18:44]
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@@ Line 1: / Line 1: @@
+@CONFERENCE{Sychev2021728,
+author={Sychev, O.},
+title={Combining neural networks and symbolic inference in a hybrid cognitive architecture},
+journal={Procedia Computer Science},
+year={2021},
+volume={190},
+pages={728-734},
+doi={10.1016/j.procs.2021.06.085},
+url={https://www.sciencedirect.com/science/article/pii/S1877050921013405?via%3Dihub},
+affiliation={Volgograd State Technical University, Lenin Ave, 28, Volgograd, 400005, Russian Federation},
+abstract={Recently, there has been a big progress in developing artificial deep-learning neural networks and large-scale knowledge graphs. However, the results in these two research fields have serious drawbacks. The solutions offered by neural networks remain unstable and prone to adversarial attacks: while the percentage of correct answers increases, the incorrect answers often contain glaring errors. Large knowledge graphs contain a lot of facts but little knowledge; they are mostly used for information search and retrieval. The ability to reason conclusions on them is limited, and the majority of modern research turns to approximate methods like neural networks and graph embeddings to draw conclusions and use the accumulated knowledge. In this work, I propose a hybrid cognitive architecture inspired by the observable features of human thinking. Pruning obviously wrong solutions seems to be more natural for human symbolic reasoning than making far-fetched strict logical conclusions, while generating new ideas is often intuitive. So a hybrid cognitive architecture can employ generative neural networks as a sort of "intuition" (generating possible solutions) and symbolic inference as a control contour to verify and filter the generated solutions, weeding out dangerous and wrong ideas. This requires creating knowledge graphs containing negative information: the information of what cannot happen or should not be done and why. The problems of creating negative knowledge graphs are discussed. Hybrid cognitive systems using the proposed architecture will be a lot more trustworthy as they will have a system of human-verifiable rules that ensures avoiding the worst errors which can be used in many fields from decision making to natural-language parsing. © 2020 Elsevier B.V.. All rights reserved.},
+correspondence_address1={Sychev, O.; Volgograd State Technical University, Lenin Ave, 28, Russian Federation; эл. почта: oasychev@gmail.com},
+publisher={Elsevier B.V.},
+issn={18770509},
+language={English},
+abbrev_source_title={Procedia 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}
+}
+@incollection{Sychev2021,
+  doi = {10.1007/978-3-030-86960-1_33},
+  url = {https://doi.org/10.1007/978-3-030-86960-1_33},
+  year = {2021},
+  publisher = {Springer International Publishing},
+  pages = {471--482},
+  author = {Oleg Sychev and Anton Anikin and Mikhail Denisov},
+  title = {Inference Engines Performance in Reasoning Tasks for Intelligent Tutoring Systems},
+  booktitle = {Computational Science and Its Applications {\textendash} {ICCSA} 2021},
+  thanks = {rfbr-20-07-00764}
+}
+@inproceedings{13163,
+    author       = {Oleg Sychev and Dmitrii Sasov and Pavel Chechetkin},
+    title        = {Developing An Understanding Of Variables And Expressions In Introductory Programming Courses},
+    booktitle    = {EpSBS - Volume 102 - NININS 2020},
+    year         = {2021},
+    pages        = {1019-1028},
+    publisher    = {European Publisher},
+    doi          = {10.15405/epsbs.2021.02.02.126},
+    url          = {https://doi.org/10.15405/epsbs.2021.02.02.126},
+    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{13164,
+    author       = {Oleg Sychev and Mikhail Denisov and Anton Anikin},
+    title        = {Ways To Write Algorithms And Their Execution Traces For Teaching Programming},
+    booktitle    = {EpSBS - Volume 102 - NININS 2020},
+    year         = {2021},
+    pages        = {1029-1039},
+    publisher    = {European Publisher},
+    doi          = {10.15405/epsbs.2021.02.02.127},
+    url          = {https://doi.org/10.15405/epsbs.2021.02.02.127},
+    thanks = {rfbr-20-07-00764}
+}
+@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{SYCHEV2020,
+title = "Automatic grading and hinting in open-ended text questions",
+journal = "Cognitive Systems Research",
+volume = "59",
+pages = "264 - 272",
+year = "2020",
+issn = "1389-0417",
+doi = "https://doi.org/10.1016/j.cogsys.2019.09.025",
+url = "http://www.sciencedirect.com/science/article/pii/S1389041719304978",
+author = "Oleg Sychev and Anton Anikin and Artem Prokudin",
+thanks = {rfbr-18-07-00032}
+}
+@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}
+}
+@InProceedings{10.1007/978-3-030-29750-3_33,
+doi="10.1007/978-3-030-29750-3_33",
+author="Kultsova, Marina
+and Potseluico, Anastasiya
+and Dvoryankin, Alexander",
+editor="Kravets, Alla G.
+and Groumpos, Peter P.
+and Shcherbakov, Maxim
+and Kultsova, Marina",
+title="Ontology Based Personalization of Mobile Interfaces for People with Special Needs",
+booktitle="Creativity in Intelligent Technologies and Data Science",
+year="2019",
+publisher="Springer International Publishing",
+address="Cham",
+pages="422--433",
+abstract="The paper is devoted to a problem of interface personification for people with special needs on the base of information about their behavior during interaction with mobile applications. This work evolves our previous researches on the development of adaptive user interfaces. Much attention in this paper was given to the investigation of existing approaches to collecting and analyzing the information about user behavior and interaction context data as well as interface adaptation recommendations. The improved interface adaptation mechanism was developed and described based on the ontological representation of the interface patterns and knowledge about users and their interaction with a mobile application. The set of adaptation rules was developed and implemented in the ontology knowledge base. In the paper, we described the improved ontology model and some examples of ontological representation of interface patterns.",
+isbn="978-3-030-29750-3",
+thanks = {rfbr-18-07-00032}
+}
+@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}
+}
+@incollection{Anikin2019_2,
+  doi = {10.1007/978-3-030-25719-4_4},
+  url = {https://doi.org/10.1007/978-3-030-25719-4_4},
+  year = {2019},
+  month = jul,
+  publisher = {Springer International Publishing},
+  pages = {22--27},
+  author = {Anton Anikin and Oleg Sychev},
+  title = {Ontology-Based Modelling for Learning on Bloom's Taxonomy Comprehension Level},
+  booktitle = {Advances in Intelligent Systems and Computing}
+}
+@incollection{Anikin2019_3,
+  doi = {10.1007/978-3-030-25719-4_67},
+  url = {https://doi.org/10.1007/978-3-030-25719-4_67},
+  year = {2019},
+  month = jul,
+  publisher = {Springer International Publishing},
+  pages = {521--526},
+  author = {Oleg Sychev and Anton Anikin and Artem Prokudin},
+  title = {Methods of Determining Errors in Open-Ended Text Questions},
+  booktitle = {Advances in Intelligent Systems and Computing}
+}
 @article{Anikin_2019_1,
 	doi = {10.1088/1757-899x/483/1/012074},
@@ Line 10: / Line 260: @@
 	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 = {18-07-00032, 18-47-340014},
+	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.}
 }
@@ Line 25: / Line 275: @@
 	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 = {18-07-00032},
+	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.}
 }