BICA*AI 2020 Submission list
Paper index: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , 
Abstract. The interaction of intelligent agents implies the existence of an environment to support it. The usual representations of this environment are graphs with certain properties. Like reliability, throughput is one of the most important characteristics of such graphs. When evaluating throughput in the analysis and synthesis of graphs, a reasonable combination of heuristic and strict approaches is used. In practice, this leads to the use of graph metrics. Usual shortest paths are widely used as part of the various multi-colour flow distribution procedures. The analytical capabilities of the Euclidian metric can achieve much more than just obtaining such a distribution. Such a metric allows us to introduce an abstract measure of the (quadratic) proximity of an arbitrary graph to a complete graph. This measure can be used as a single indicator of the reliability and throughput of the graph. Other conditions for tasks on graphs can be attributed to restrictions. A traffic matrix is one of these conditions. Non-stationarity of traffic when averaging over time can significantly reduce the accuracy of the estimates of throughput. The described dependencies of the throughput on the traffic's non-stationarity can be used in the analysis and synthesis of the communication environment when organizing the structure of the interaction of intelligent agents in the conditions of limited resources. These dependencies are verified by the results of numerical experiments. Keywords: graph metrics, throughput analysis and synthesis of graphs, the Euclidian graph metric, the Moore-Penrose pseudo inverse of the incidence matrix, traffic non-stationarity
 Lucas J. J. Fijen (University of Amsterdam), Julio J. López González (Vrije Universiteit Amsterdam) and Jan Treur (Vrije Universiteit Amsterdam). An Adaptive Temporal-Causal Network Model to Analyse Extinction of Communication over Time.
Abstract. The persistence of information communicated between humans is difficult to measure as it is affected by many features. This paper presents an approach to computationally model the cognitive processes of information sharing to describe persistence or extinction of communication in Twitter over time. The adaptive mental network model explains, for example, how an individual can experience information overflow on a topic, and how this affects the sharing of information. Parameter tuning by Simulated Annealing is used to identify characteristics of the network model that fit to empirical data from Twitter. The data collected is related to the independentism in Catalunya, Spain, which is considered a global issue with repercussion in Europe. Mar 29, 12:42 s: Communication extinction, Adaptive, Temporal-causal network model
 Tatiana Semenova (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute),) and Yuri Kotov (Keldysh Institute of Applied Mathematics, Russian Academy of Sciences). Mathematical methods for solving cognitive problems in medical diagnosis.
Abstract. Beginning stages of complex system investigation include fixing solid data blocks such as general targets, initial problem blocks and so on. Adequate description at the stage give gestalt-like patterns for future system, its sub-systems and their functioning. An practiced specialist in some difficult cases can to discover functional and logical associations between the components or processes in system investigated. Sometimes he cannot explain exactly his decision. He can make it practically but cannot explain. For such cases mathematician I.M. Gelfand proposed the diagnostic games method. The game is a cognitive study - the identification of doctor's decision in the particular patient treatment coarse. Working together, mathematician and doctor can formulate verbal description for the case. The study objective is to divide the physician's gestalt perception into a number of elements - diagnostic rules. To formalize the intuitive specialists actions, the authors of the paper developed mathematical language and technologies based on non-numeric statistics and three-valued logic. The language helps us to solve such cognitive problems. It is developed during joint work with doctors and allows us to create clear diagnostic rules on the basis of many implicit reasoning by an experienced specialist. The article gives a brief method description used for resolving the practical medicine problems. Apr 04, 06:58 ds: gestalt, diagnostic game, formalization of the doctor’s knowledge, non-numerical statistics, three-valued logic
 Mandy Choy (Vrije Universiteit Amsterdam), Suleika El Fassi (Vrije Universiteit Amsterdam) and Jan Treur (Vrije Universiteit Amsterdam). An Adaptive Network Model for Pain and Pleasure through Spicy Food and its Desensitization.
Abstract. This paper aims to map out the adaptive causal pathways of processes underlying capsaicin consumption and the desensitization process of the TRPV1 receptor as a feedback loop together with pain and pleasure perception. In order to map out these causal capsaicin pathways, adaptive causal network modeling was applied, which is a way of modeling biological, neural, mental and social processes from an adaptive causal modeling perspective. Apr 06, 07:57 s: adaptive network model, desensitization, pain, pleasure, spicy food
Abstract. The brain-inspired Causal Cognitive Architecture 1 (CCA1) tightly integrates the sensory processing capabilities found in neural networks with many of the causal abilities found in human cognition. Sensory input vectors are processed by robust association circuitry and then propagated to a navigational temporary map. Instinctive and learned objects and procedures are applied to the same temporary map, with a resultant navigational signal obtained. Navigation can similarly be for the physical world as well as for a landscape of higher cognitive concepts. Causality emerges from the architecture, with good explainability for causal decisions. A simulation of the CCA1 controlling a search and rescue robot is presented with the goal of finding and rescuing a lost hiker within a grid world. Apr 09, 04:20 s: Cognitive Architecture, Artificial General Intelligence, Causality, Spatial Navigation, Explainability
 Margarita Zaeva (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)) and Andrejs Rudzitis (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)). Lightweight Surround View algorithm for embedded TDA3xx platform.
Abstract. Automotive surround view camera system is an emerging automotive ADAS (Advanced Driver Assistance System) technology that assists the driver in parking the vehicle safely by allowing him/her to see a top-down view of the 360 degree surroundings of the vehicle. In our work, we propose a lightweight solution for camera automatic calibration with preconfigured distortion parameters and single-shot optical center and external parameters configuration. In addition, we propose synthesis algorithm to work with new calibration optimized for DSP c66x with 25fps. Apr 09, 15:34 rds: Surround view, ADAS, lens distortion correction, Bird’s-eye view, Calibration
 Jagna Nieuwazny (Kitami Institute of Technology), Karol Nowakowski (Kitami Institute of Technology), Michał Ptaszyński (Kitami Institute of Technology), Rafał Rzepka (Hokkaido University), Fumito Masui (Kitami Institute of Technology) and Kenji Araki (Hokkaido University). Does change in ethical education influence core moral values? Towards culture-aware morality model.
Abstract. In this study, we focus on ethical education as a means to improve artificial companion’s conceptualization of moral decision-making process in human users. In particular, we focus on automatically determining whether changes in ethical education influenced core moral values in humans throughout the century. We analyze ethics as taught in Japan before WWII and today to verify how much the pre-WWII moral attitudes have in common with those of contemporary Japanese, to what degree what is taught as ethics in school overlaps with the general population’s understanding of ethics, as well as to verify whether a major reform of the guidelines for teaching the school subject of “ethics” at school after 1946 has changed the way common people approach core moral questions (such as those concerning the sacredness of human life). We selected textbooks used in teaching ethics at school from between 1935 and 1937, and those used in junior high schools today (2019) and analyzed what emotional and moral associations such contents generated. The analysis was performed with an automatic moral and emotional reasoning agent and based on the largest available text corpus in Japanese as well as on the resources of a Japanese digital library. As a result, we found out that, despite changes in stereotypical view on Japan’s moral sentiments, especially due to historical events, past and contemporary Japanese share a similar moral evaluation of certain basic moral concepts, although there is a large discrepancy between how they perceive some actions to be beneficial to the society as a whole while at the same time being inconclusive when it comes to assessing the same action’s outcome on the individual performing them and in terms of emotional consequences. Some ethical categories, assessed positively before the war while being associated with a nationalistic trend in education have also disappeared from the scope of interest of post- war society. The findings of this study support suggestions proposed by others that the development of personal AI systems requires supplementation with moral reasoning. Apr 14, 04:30 s: core moral concepts, artificial companion, moral decision-making process
 Igor Prokhorov (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)) and Evgeny Tebenkov (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)). Technology of development and implementation of chatbots using artificial intelligence and machine learning technology.
Abstract. Chat-bot, it's a program that simulates human communication in dialog interfaces. The use of artificial intelligence and machine learning technology in creating chat bots expands the possibilities of their application and automation of routine tasks that require communication with people. Many companies, including banks, airlines, government and educational institutions, automotive companies and many others, have already implemented chat bots in their daily work. Understanding what stages the development of chat bots consists of makes it possible to assess the possibilities of their use. This article describes the types of existing chat bots and their differences, gives an overview of technologies for their implementation, formats for using chat bots in practice, describes the main differences in existing platforms and approaches to their use, as well as stages of creating chat bots using artificial intelligence technology and machine learning. Apr 14, 16:12 Dialog interfaces, chat-bots, artificial intelligence, machine learning, software robot, SAAS platform, Facebook, Telegram, VK
 Andrey Malynov (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)) and Igor Prokhorov (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)). Development of an AI recommender system to recommend concerts based on microservice architecture using collaborative and content-based filtering methods.
Abstract. Recommender system is a complex software system primarily intended to select the most relevant content based on user's personal preferences. In order to achieve the set goal, a number of tasks must be completed, including:
• track user actions across various devices;
• get product data from a number of sources and maintaining their currency;
• consolidate the data;
• create user and product profiles bases on big data in a real-time mode;
• select recommendations in cold start and highly sparsed data environment;
• assess the quality of the recommender system.
Completion of each specific task must not extend the time to complete other tasks. Users must instantly get the relevant content even if the system is heavily loaded, for example, due to a popular event announcement. A workaround may be to divide the system into independent components with the ability to scale specific services. Microservice architecture examined in this article intends to ensure required flexibility due to asynchronous message exchange via a data bus and other principles offered by SOA concept. Apart from interaction between the components, the article also introduces the results of development of each specific service from asynchronous user action tracker to recommender engine based on the hybrid approach that includes collaborative and content-based filtering methods, and the knowledge-based approach using Artificial Intelligence techniques. Special attention is paid to a subject category with a number of aspects that prevent applying generic approaches to building recommender systems. Apr 15, 20:44 Keywords: recommender system, microservices architecture, cold start problem, hybrid filtering technique, collaborative filtering, content-based filtering, artificial intelligence, knowledge-based approach, user activity tracking
 Alexander Shtanko (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)) and Sergey Kulik (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)). Preliminary Experiment on Emotion Detection in Illustrations Using Convolutional Neural Network.
Abstract. The paper describes an experiment on emotion detection in images, specifically illustrations and cartoon images. Usually, detection and classification of emotions are performed on human faces so the algorithm can learn, for example, what a “happy human face” looks like. These algorithms probably can’t transfer their understanding of happiness features onto different types of objects, like animals or cartoon illustrations. We, humans, can recognize and detect signs of emotions (although maybe falsely) in new and unusual objects. Developing an algorithm capable of recognizing emotions in objects it wasn’t trained on would allow for better human-like robots and systems. This is a preliminary study on how well knowledge gained by a typical neural network detection system on a set of objects transfers to new, unknown objects. The neural network detection system used in this study is YOLO. We collected small training datasets using cartoon illustrations of several animals of two categories: happy and sad. We tested the trained network on a set of illustrations depicting a different animal the network hasn’t seen in training. The best performance achieved is 0.69 F1-score. Apr 16, 12:30 Keywords: Machine Learning, Object Detection, Convolutional Neural Networks, Emotion
 Rodion Kadyrov (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)) and Igor Prokhorov (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)). Creating a model using blockchain for swarm of biobots guidance.
Abstract. This paper is about a relatively new concept will be proposed, in which blockchain technology will be used to operate swarm of biobots and store information about their movement. In previous articles, we talked about how you can create an automatic biobot control system using deep neural networks based on the slitherin artificial intelligence framework. Next, we will consider a model for applying the combined approach of blockchain technology and the automatic control system of biobots. The application of this concept can open up new opportunities for optimization in swarm robotics. Apr 16, 21:24 rds: biobotics, cyborgs, swarm robotics, blockchain
 Carlos Johnnatan Sandoval Arrayga (CINVESTAV) and Félix Francisco Ramos Corchado (CINVESTAV). A proposal of bioinspired motor-system cognitive architecture focused on feedforward-control movements.
Abstract. The objective of this article is to present a conceptual motor-system cognitive architecture inspired in the human nervous system, anda cognitive architecture focused on the voluntary movement controlledby feed-forward. The article first focus on describing the brain cortexareas that compound the motor system, presenting the supplementarymotor area (SMA), premotor cortex and primary cortex, the tasks thatthese cortices do, and how them works together. Then, it is presenteda cognitive architecture based on the information presented. In this sec-tion, it is described the areas in a computational level (functions, andalgorithms used). Finally, it is presented a study case where the cognitivearchitecture proposed is used to execute a voluntary-movement-by-feed-forward-control task. Apr 16, 22:22 s: BICA, Bioinspired cognitive architecture, Motor system, Architecture, feed-forward, Neuroscience
 Pavel Piskunov (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)) and Igor Prokhorov (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)). Applying of artificial intelligence in the tasks of separation the university’s IT landscape into functional modules for microservices and microkernels.
Abstract. In our time, digitalization has already significantly affected the activities of universities. But, often, the basics of their information systems (IS) were laid long ago and without specific architectural approaches. Therefore, today the task is to update the architectures of such systems for more quickly and flexibly respond to various changes in the environment. It is considered good practice to use architectural templates with a high degree of modularity and distribution, for example, microservice and microkernel. Such modernization requires redesigning while minimizing costs and resources.
Under this article, was analyzed the possibility of using the tasks and methods of artificial intelligence in the process of dividing the IT landscape of the university into functional modules for microservices and microkernels. This choice was made due to the ability of AI to work with fuzzy and highly correlated input data and find additional rules. Designing of IS software upgrade is considered as a clustering of functional modules or identifying intersection of functionality in existing modules. Nowadays, for this have greatly developed decision-making assistance systems, fuzzy logic, neural network clustering and pattern recognition. Apr 17, 11:56 information system architecture, information system requirements, IT landscape, information system templates, data consolidation, microkernel template, microservice template, neural networks, clustering, artificial intelligence
 Fedor Kanakov (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)) and Igor Prokhorov (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)). Modeling and architecture development of software robots for managing business processes in a commercial bank.
Abstract. When a software program emulates human actions on digital systems to perform business operations, we call it a Robot Process Automation (also known as Robot Process Management). Its abbreviation is RPA. RPA helps organizations in automating repetitive processes, which saves their time and resources.
The name of RPA is self-explanatory as a robot is something that imitates human actions. A process is a sequence of steps that cause meaningful activity, and automation is when a program performs a task without human supervision or intervention.
They are better than humans in performing their duties because:
• They don’t get tired
• They don’t take breaks
• They are less expensive
They are less prone to errors than humans, which makes them a perfect tool for any organization. There are multiple RPA tools available in the market, including UiPath and Blue Prism.
The architecture of any RPA tool is very complex and has multiple components. Each of these components has a set of duties to perform. Here is a small list of all elements of an RPA architecture:
• RPA tools
• Execution Infrastructure
• Configuration Management Apr 17, 16:42 Robot Process Automation, Robot Process Management, RPA, RPA architecture, RPA tools, RPA Platform, Execution Infrastructure, Configuration Management
 Luis Martin (Centro de Investigación y Estudios Avanzados del Instituto Politecnico Nacional), Karina Jaime (Centro de Investigación y Estudios Avanzados del Instituto Politecnico Nacional), Felix Ramos (Centro de Investigación y Estudios Avanzados del Instituto Politecnico Nacional) and Francisco Robles (Universidad de Guadalajara, Centro Universitario del Norte). Declarative Working Memory: A Bio-Inspired Cognitive Architecture Proposal.
Abstract. Memory is considered one of the most important functions since it allows us to code, store and retrieve knowledge. These qualities make it an indispensable function for a virtual creature. In general, memory can be classified based on the durability of the stored data in working memory and long-term memory.
Working memory refers to the capacity to maintain temporarily a limited amount of information in mind, which can then be used to support various abilities, including learning, reasoning, planning and decision-making. Unlike short-term memory, working memory is not only a storage site, but it is also a framework of interacting processes that involve the temporary storage and manipulation of information in the service of performing complex cognitive activities. Declarative memory is a type of long-term memory related with the storage of facts and events.
This research focuses on the development of a cognitive architecture for the type of working memory that maintains and manipulates declarative information. The construction of the model was grounded in theoretical evidence taken from cognitive sciences such as neuroscience and psychology, which gave us the components and their processes.
The model was evaluated through a case study that covers the encoding, storing, and retrieval stages. Our hypothesis is that a virtual creature endowed with our working memory model will provide faster access to the information needed for the ongoing task. Therefore, it improves the planning and decision-making processes. Apr 17, 17:14 s: Cognitive architecture, Brain model, Working Memory, Neuroscience, Declarative memory, Virtual creature
Abstract. The problems of modeling creative thinking in various fields of human activity (art, science, education, etc.) are considered within Natural Constructive Cognitive Architecture (NCCA) model. Its important feature is the combination of two subsystems, one for generation of new information (with mandatory presence of random self-excitation, i.e., the noise), the other one for conservation and processing the already learned patterns. The whole system has a complex multi-level structure, with the lowest level refer to the “raw” images of real objects, while all other levels represent a convolution into symbolic information. The upper layers’ symbols can be interpreted as abstract concepts (e.g., “science”, “art”, “consciousness”, etc.), accumulating the image and symbolic information on a matching set of objects and processes, thus controlling the activity of the entire system. The features of the cognitive process necessary for creative activity are formulated. It is shown that the main role in this process belongs to the generating subsystem and the upper levels (an abstract information) of both subsystems. The concept of "Chef-D’oeuvre” (masterpiece) in science and art is analyzed. We argued that the masterpieces should correspond to the formula "to see the invisible, to combine the incompatible." It is shown that Chef-D’oeuvre should cause a “recognition paradox”, i.e., it seems both familiar and unusual. Within the NCCA, this effect occurs due to difference between the concepts of “brain” and “mind”. The first one refers to the direct recording the raw images of real objects, the second one could be associated with processing this information inside the cognitive system, which results in creation of typical symbols. In this process, a part of information on rare (atypical) attributes could be lost. The paradox of recognition arises when “the brain does see the subtle features of an objects, but the mind cannot realize it”. It is shown that within NCCA, bright aesthetic emotions (corresponding to the goosebumps) can be simulated by trembling (vibration) of a dynamic variable corresponding to the amplitude of random self-excitation of neurons. In this process, the main role belongs to the irregular (random, quasi-chaotic) excitation of implicit associations provided by the weak (so called ‘gray’) connections between the employed neurons. Certain scientific masterpieces are considered. It is shown that the feature of a Chef-D’oeuvre in science depends on the degree of development of this scientific branch. At the early stages, the decisive factor is the revealing and formulation of the basic laws of the process (“see the invisible”). At the present stage, the interdisciplinarity comes to the fore, i.e., an approach to an object/process from the viewpoint (and with the use of math apparatus) of various already hyper-developed branches of science (“to combine the incompatible”). Here, Chef-D’oeuvre represents a solution to the scientific paradox. The features of the creative cognitive process that could appear under extreme conditions (in particular, under the necessity to solve urgently a complex intellectual problem) are considered. It is shown that in this case, certain specific modes of thinking (stress, shock, panic, etc.) could switch on, which can play both a negative role (if they result in stupor, depression, etc.), and a positive role, if it leads to over mobilization of the entire system, in particular, to the excitement of the "sleeping" neurons (those that were not previously involved into any cognitive act). The role of creativity in the education process is considered. It is shown that professional approach to certain problem (based on the professional semantic knowledge) and the creative one (aimed to search for nontrivial solutions to the given problem) often come into conflict. We argue that personal learning process is more efficient if it addresses to an episodic knowledge and the generating subsystem. Apr 17, 18:59 s: Creativity, Natural Constructive Cognitive Architecture, Generation of information, Random self-excitation (noise), Extreme conditions, Brain and/versus mind, Aesthetic emotions, Chef-d’oeuvre, Paradox, Throes of Creativity
Abstract. Russian megaprojects abroad are complex and costly programs for the creation of large industrial and energy facilities. One of the most important tasks that arise in the framework of such a project is information support at all its stages, which includes tracking the semantic field of media and social media publications and managing emerging information risks. Modern risk management assumes the presence of a mathematical and statistical model that allows you to predict the dynamics of processes in the subject area served. This paper presents new models of the megaproject environment based on the use of fuzzy cognitive maps (FCM). A special feature of these models is the hierarchical principle of organizing FCM concepts. The resulting maps are used to predict a number of business metrics. To build fuzzy maps, we use the developed original tools. This work was supported by RFFI grant № 20-010-00708\20 JVRT Subevent Apr 17, 19:14 eywords: fuzzy cognitive maps, hierarchical cognitive mapping, predictive modelling, project management, Russian megaprojects
 Vasiliy S. Kireev (NRNU MEPhI), Alexander Yurin (NRNU MEPhI), Dmitriy S. Kokorev (NRNU MEPhI), Elizaveta O. Zychkova (NRNU MEPhI) and Denis V. Kolesnik (NRNU MEPhI). Instrumental and algorithmic tools for constructing and verifying fuzzy cognitive maps based on the example of maps that model nuclear industry enterprises.
Abstract. Creating new predictive models of behavior of complex socio-economic systems based on machine learning methods is one of the most relevant areas of development in economic and mathematical modeling. A promising approach to solving this problem is the use of neuro-fuzzy systems, which can be obtained by combining fuzzy cognitive maps and neural network models. This work is devoted to the development of tools for fuzzy cognitive modeling. The methods developed by the authors for automatic construction, training, and verification of cognitive maps are described, including those based on neural network training methods. The models of the external and internal environment of the nuclear industry enterprise built in the software application developed by the authors are presented. JVRT Subevent Apr 17, 19:56 words: fuzzy conitive maps, neuro-fuzzy systems, FCM learning algorithms, digital twins, nuclear industry
 Igor A. Kuznetsov (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)), Matvey V. Koptelov (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)), Dmitriy A. Kovtun (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)) and Anna I. Guseva (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)). A method for reducing the impact of information risks on a megaproject life cycle based on a semantic information field.
Abstract. This paper consider a comprehensive method for devising loyalty programs based on the stages of a life cycle of an international megaproject. The method is based on the analysis of information risks and their management. The method is focused on aggregation and processing of data from various sources of textual information, which demonstrates the attitudes of key categories of individuals regarding the implementation of a megaproject at its numerous life cycle stages. The semantic information field is formed using hardware and software based on Neural Network Technologies. Authors examine the most popular neural network architectures that are used in the sentiment analysis. The paper describes a comparative analysis of classification accuracy of neural network architectures based on volume of texts and their ability to sentiment analysis of large and small volumes of text. The method is aimed at managing and influencing information flow that accompanies the implementation of a megaproject stages. The application of semantic information field makes it possible to account for the informational risks of a megaproject and to prepare an effective set of measures to counteract these risks in a timely manner. This work was supported by RFFI grant № 20-010-00708\20. Apr 17, 20:19 words: decision support system, information risks, neural network, sentiment analysis, natural language processing
 Vasiliy S. Kireev (NRNU MEPhI), Irina V. Zaitseva (NRNU MEPhI), Ksenia A. Ivanushkina (NRNU MEPhI) and Andrey M. Mikhin (NRNU MEPhI). Algorithmic and instrumental tools for thematic modeling and annotation of texts based on convolutional neural networks.
Abstract. Automated processing and analysis of text data, along with the rapid growth of their volume, is becoming more and more relevant for the development of machine learning methods. Among these tasks, it is worth highlighting such as: topic modeling aimed at extracting topics from the corpus of texts, as well as automatic annotation and extraction of keywords to describe these texts. This paper presents algorithms and their software implementation aimed at solving these problems, based on the use of modern convolutional networks, such as BERT, cluster analysis methods and graph algorithms. The results of experimentation on various data, including scientific articles and texts of any subject, are described. JVRT Subevent Apr 17, 20:20 words: text mining, topic modelling, automated text annotating, LDA, BERT, clustering methods
 Zalimkhan Nagoev (Institute of Computer Science and Problems of Regional Management of KBSC RAS) and Irina Gurtueva (Institute of Computer Science and Problems of Regional Management). Multiagent model of the process of mastering linguistic competence based on perceptual space formation.
Abstract. We propose a simulation model of the early development of language competence. In its core it is a model of phonemic imprinting that describes the process of mapping audio stimuli into classes of elementary units of a language, taking into account the influence of social factors. The machine learning algorithm used in the model was developed after the results of a study of the features of speech used in referring to children. This model will allow us to explore the features of phonetic perception, the cognitive mechanisms that underlie language development, highlight the main factors affecting the duration of the period of plasticity. On that basis, we can build perceptual maps, create diagnostic tools to describe the sensitive period, which will facilitate the study of the stages of its opening and closing. The model can also be used to create speech recognition systems that are resistant to a variety of accents and effective when used in a noisy environment. Apr 17, 20:28 Keywords: speech recognition, multiagent systems, artificial intelligence, early development, speech acquisition
Abstract. Time series of values of economic metrics are an important source of information when making decisions. The development of predictive models for such series is an extremely urgent task. Despite the existence of numerous methods for solving this problem, a universal approach has not yet been developed. Moreover, given the rapidly changing factors that affect the dynamics of the economic situation, both at the global and local levels, the development of accurate and adequate forecast models is in demand in any industry. This paper presents original algorithms for predicting some macro-economic indicators by combining fuzzy cognitive maps and neural networks, both direct propagation and recurrent, into a single model. The results of experiments both on artificially generated data and on open data extracted from social media are described. JVRT Subevent Apr 17, 20:43 words: time series forecasting, neuro-fuzzy systems, fuzzy cognitive maps, feed-forward maps, LSTM
 Zalimhan Nagoev (Kabardino-Balkarian Scientific Center of Russian Academy of Sciences), Inna Pshenokova (Kabardino-Balkarian Scientific Center of Russian Academy of Sciences), Olga Nagoeva (Kabardino-Balkarian Scientific Center of Russian Academy of Sciences) and Zaurbek Sundukov (Kabardino-Balkarian Scientific Center of Russian Academy of Sciences). Learning algorithm for an intelligent decision making system based on multi-agent neurocognitive architectures.
Abstract. The paper presents the formalism of an intelligent decision-making system based on multi-agent neurocognitive architectures, which has an architectural similarity to the human brain. An invariant of the organizational and func-tional structure of the intellectual decision-making process based on the mul-ti-agent neurocognitive architecture is developed. An algorithm for teaching intelligent decision-making systems based on the self-organization of the in-variant of multi-agent neurocognitive architectures is presented. Using this algorithm, an intelligent agent was trained and the architecture of the learning process was built on the basis of an invariant of neurocognitive architecture. Further research is related to training an intelligent agent in more complex behavior and expanding the capabilities of an intelligent decision-making system based on multi-genic neurocognitive architectures. Apr 17, 21:23 s: Multi-Agent Systems, Neurocognitive Architecture, Decision Making, Artificial Intelligence Systems
 Raymundo Ramirez-Pedraza (CINVESTAV Unidad Guadalajara) and Felix Ramos (CINVESTAV Unidad Guadalajara). Decision-making bioinspired model for target definition and “satisfactor” selection for physiological needs.
Abstract. Every person, from an early age, has to make decisions to resolve situations that arise in life. In general, different people make different decisions in the same situation, since decision-making takes into account different factors such as age, emotional state, experience, etcetera. We can make decisions about situations that we classify as: more important than others, routine, unexpected, or trivial. However, making the correct decision(s) in a timely manner for these situations is one of the most complex and delicate challenges that human beings face. This is due to the arduous mental process required to be carried out. Providing such behavior to a virtual entity is possible through the use of cognitive architectures or CA. CAs are the approach used to model human intelligence and behavior. This paper presents a useful bioinspired decision-making computational model to satisfy the physiological needs of hunger and thirst. Our proposal considers as a black box other cognitive functions that are part of a general CA (we name Cua ̄yo ̄llo ̄tl or brain in Nahuatl). In the proposed case study, it is proved that the decision-making process plays an essential role in determining the objective and selecting the object that satisfies the established need. Apr 17, 23:19 s: Decision-making, Brain model, Satisfactor selection, Physiological need, Goal-driven
Abstract. Under numerous circumstances, humans recognize visual objects in their environment with remarkable response times and accuracy. Existing artificial visual object recognition systems are still to surpass human vision, specially in its universality of application. We argue that modeling the recognition process in an exclusively feedforward manner hinders those systems' performance. To bridge that performance gap between them and human vision, we present a brief review of neuroscientific data which suggests that recognition can be improved by considering an agent's internal influences (from cognitive systems that peripherally interact with visual-perceptual processes). Then, we propose a model for visual object recognition which uses these systems' information, such as affection, for generating expectation to prime the object recognition system, thus reducing its execution times. Later, an implementation of the model is described. Finally, we present and discuss an experiment and its results. Apr 17, 23:49 s: perception, cognitive, architecture, object recognition, visual computation
 Alexey Artamonov (National Research Nuclear University MEPhI), Mikhail Ulizko (Plekhanov Russian University of Economics), Rufina Tukumbetova (Plekhanov Russian University of Economics) and Larisa Pronicheva (National Research Nuclear University MEPhI). Complex Objects Identification and Analysis Mechanisms.
Abstract. Currently the volume of information on the Internet global network is inclined to increase. It affects various areas of activity. The paper presents the issue of identifying and analyzing complex objects in the context of information sources devoted to project activity of the US National Institutes of Health (NIH). As part of the solution to the problem, information sources relevant to the provided topic were found, and the data were downloaded with the use of agent techniques. The methods of primary analysis and data processing were developed to create a data storage structures in SQL and NoSQL models. The analysis of the presented database models was conducted, their advantages and disadvantages were revealed. As a result software tools have been developed that provide data representation of a complex object and organization of work with it by web interface. Apr 20, 11:40 Keywords: Web scrapping, software agent, NoSQL, data processing
 Natalia Miloslavskaya (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)) and Alexander Tolstoy (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)). Cyber Polygon Site Project in the Framework of the MEPhI Network Security Intelligence Center.
Abstract. At present, the market for information protection tools (IPTs) is much wider than a couple of years ago. But not only technology protects and carries a threat. People are still at the forefront as the most common cause of errors is the lack of experience and low competency. The only right solution is the creation of cyber polygons as specially equipped and controlled network infrastructures for developing practical skills to combat information security (IS) threats. The National Research Nuclear University MEPhI (Moscow Engineering Physics Institute) could not remain aloof from this process as the leading institute for IS training in Russia. Therefore, it was decided to create such a cyber polygon within the framework of the educational and research Network Security Intelligence Center (NSIC) for intelligent network security management es-tablished at the MEPhI Institute of Cyber Intelligence Systems in 2016. The pa-per describes the first results achieved in making this project a reality. It intro-duces the “Cyber Polygon” term, briefly analyzes a state of the current cyber polygons development worldwide, and introduces the MEPhI Cyber Polygon objectives and provision to be used within the framework of the “Business Con-tinuity and Information Security Maintenance” Master’s degree programme. Further activities in its development conclude the paper. Apr 22, 14:10 Keywords: Cyber Polygon, Network Security Intelligence Center, practical skills, information security training, Master’s degree programme
 Natalia Miloslavskaya (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)) and Steven Furnell (University of Plymouth). Network Security Intelligence Centres for Information Security Incident Management.
Abstract. Intensive IT development is driving current information security (IS) trends and require sophisticated structures and adequate approached to manage IS for different businesses. The wide range of threats is constantly growing in modern intranets; they have become not only numerous and diverse but also more disruptive. In such circumstances, organizations realize that IS incidents’ timely detection and prevention in the future (what is more important) are not only possible but imperative. Any delay leaves only reactive actions to IS incidents, putting assets at risk as a result. A properly designed IS incident management system (ISIMS), operating as an integral part of the whole organization’s governance system, reduces IS incidents’ number and limits damage caused by them. To maximally automate IS incident management (ISIM) within one organization and to deepen its knowledge of IS level, this research proposes to unite together all advantages of a Security Intelligence Centre (SIC) and a Network Operations Centre (NOC) with their unique and joint toolkits and techniques in a unified Network SIC (NSIC). This paper presents the research, which is focused upon the designing and evaluating the concept of NSICs, and represents a novel advancement beyond existing concepts of security and net-work operations centres in current security monitoring scenarios. Key contributions are made in relation to underlying taxonomies of threats and attacks, leading to the requirements for NSICs, the related design, and then evaluation in a practical context and the implications arising from this (e.g. training requirements). Apr 22, 15:08 Keywords: network security, Security Intelligence, Security Intelligence Center, Network Operations Center, Network Security Intelligence Center
 Elena Matrosova (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)), Anna Tikhomirova (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)), Nikolay Matrosov (IE Abubekarov) and Dmitriy Kovtun (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)). Visualization of T. Saati hierarchy analysis method.
Abstract. The article is devoted to the problem of expert assessment visualization of the comparative importance of various indicators. T. Saati hierarchy analysis method, which allows us to obtain indicators' weight coefficients by paired comparisons of them, is considered. A method for increasing the accuracy of estimates formed by an expert is proposed. The increase in accuracy is achieved by instantly visualizing the results of the comparisons for their prompt adjustment by an expert.
This work was supported by RFFI grant № 20-010-00708\20. Apr 24, 22:09 Keywords: decision-making process, automated decision support system, intelligent information system, hierarchy analysis method, weighted coefficients, expert system, performance evaluation
 Elena Matrosova (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)), Anna Tikhomirova (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)), Anna Guseva (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)) and Nikolay Matrosov (IE Abubekarov). “Loyalty Program” tool application in megaprojects.
Abstract. The article is devoted to the problem of improving the implementation efficiency and megaprojects management. An example of an adaptation one of the existing project management methodological tools, namely the “loyalty program”, for projects of a global nature (megaprojects) is given. The authors propose the use of elements of the decision-making process theory and the method of T. Saati hierarchy analysis method for the analysis of megaproject key characteristics. Based on them, recommendations for the formation of a loyalty program are given.
This work was supported by RFFI grant № 20-010-00708\20. Apr 25, 22:00 Keywords: megaproject, loyalty program, decision-making process, automated decision support system
 Natalia Miloslavskaya (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)), Andrey Nikiforov (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)) and Kirill Plaksiy (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)). Block Formation for Storing Data on Information Security Incidents for Digital Investigations.
Abstract. Nowadays technologies such as Blockchain (BC) and the Internet of Things (IoT) can be heard everywhere. But because of the leap in the development of these technologies, there is a need to evaluate the existing approaches critically. One of the up-to-date tasks is to study information security (IS) incidents as a part of the IoT. Due to a large number of different manufacturers and options for technology implementation, it cannot be unambiguously concluded what choice will be better. The paper examines the related work in the area and proposes an approach to form the basis for storing data on IS incidents. For this purpose, the authors formulate a block structure for including in a chain for later use, for example, in computer forensics. Apr 26, 12:19 Keywords: Blockchain, Information Security Incident, Internet of Things, Hashing
 Salvador Cervantes (Centro Universitario de los Valles), Sonia López (Centro Universitario de los Valles) and José-Antonio Cervantes (Centro Universitario de los Valles). Toward ethical cognitive architectures for the development of artificial moral agents.
Abstract. New technologies based on artificial agents promise to change the next generation of autonomous systems and therefore our interaction with them. Systems based on artificial agents such as self-driving cars and social robots are examples of this technology that is seeking to improve the quality of people's life. Cognitive architectures aim to create some of the most challenging artificial agents commonly known as bio-inspired cognitive agents. This type of artificial agent seeks to embody human-like intelligence in order to operate and solve problems in the real world as humans do. Moreover, some cognitive architectures such as Soar, LIDA, ACT-R, and iCub try to be fundamental architectures for the Artificial General Intelligence model of human cognition. Therefore, researchers in the machine ethics field face ethical questions related to what mechanisms an artificial agent must have for making moral decisions in order to ensure that their actions are always ethically right. This paper aims to identify some challenges that researchers need to solve in order to create ethical cognitive architectures. These cognitive architectures are characterized by the capacity to endow artificial agents with appropriate mechanisms to exhibit explicit ethical behavior. Additionally, we offer some reasons to develop ethical cognitive architectures. We hope that this study can be useful to guide future research on ethical cognitive architectures. Apr 29, 20:28 s: Ethical Cognitive Architectures, Cognitive Functions, Artificial Agents, Machine Ethics, Artificial Moral Agents
 Michael Rudy (National Research Nuclear University MEPhI), Eugene Chepin (National Research Nuclear University MEPhI) and Alexander Gridnev (National Research Nuclear University MEPhI). Extending the intelligence of the Pioneer 2AT mobile robot.
Abstract. This paper describes the process of expanding the intellectual capabil- ities of a Pioneer 2AT skid-steer mobile robot by developing a module that allows using the ROS (Robotic Operating System) and its capabilities. This software and hardware system allow to quickly expand the capabilities of an outdated mobile robot, giving it access to modern algorithms e.g. SLAM. It also granted the sup- port for various attachments - lidars, stereo cameras, etc, autonomous navigation algorithms, data collection for training CNNs, as well as simulating the operation of these algorithms in the Gazebo. The module consists of two parts, the first is launched on the Arduino board, to which the wheelbase with the associated sen- sors is connected, the second part is the ROS node, which translates all the data coming from the Arduino into a format corresponding to the ROS interface. May 01, 10:02 Keywords: mobile robotics, SLAM, ROS, skid-steer, differential drive
 Diana G. Gómez-Martínez (Cinvestav-IPN Unidad Guadalajara), Marco Ramos (Universidad Autónoma del Estado de México), Juan Luis del Valle-Padilla (Cinvestav-IPN Unidad Guadalajara), Jonathan-Hernando Rosales (Universidad Autónoma de Guadalajara), Francisco Robles (Centro Universitario del Norte de la Universidad de Guadalajara) and Félix Ramos (Cinvestav-IPN Unidad Guadalajara). Bioinspired model of short-term satiety of hunger influenced by food properties in virtual creatures.
Abstract. The behavior of the human is continually changed as a consequence of various drives which human is predisposed also of your survival instinct. Among the basic drives of the human, there are the physiological needs and is precisely the hunger that motivates the food intake to get the energy that the body requires via food. The regulation of hunger allows to stop the food intake by means of the homeostatic and hedonic control which are influenced by the food properties. The process that consists of ending the food intake is known as short-term satiety and is important because it limits the amount of food intake; Otherwise, an over-consumed affect the organism functioning negatively. In this paper, we propose a conceptual model for the generation of short-term satiety behaviors based on neuroscientific evidence for virtual creatures. The conceptual model proposed is implemented in a distributed system—a virtual creature endowed with this implementation is placed in a virtual environment to analyze its behavior. The analysis shows how the virtual creature modifies its hunger level (behavior) based on food's properties. The results show the execution of the process when the creature interacts with the environment. May 07, 02:51 s: Short-term satiety, Homeostatic control, Gastric distension, Food properties, Reward, Bioinspired model
Abstract. In this work, we discuss methodologies and implementation
choices to enable a humanoid robot to estimate patients’ moods and
emotions during postoperative home rehabilitation in the context of the
AMICO project. May 08, 12:07 s: sentiment and emotion analysis, mood analysis, human-robot-interaction, medical assistive robots
 Oleg Sychev (Volgograd State Technical University) and Yaroslav Kamennov (Volgograd State Technical University). Eligibility of English hypernymy resources for extracting knowledge from natural-language texts.
Abstract. A common subtask of knowledge acquisition from natural-language texts is classifying words and recognizing entities and actions in the text. It is used in the analysis of both scientific and narrative texts. Thesauri and lexical databases containing hypernymy relationship between synsets may be a useful resource for entity and action recognition. In this study, we compared the performance of three major English thesauri containing hypernymy relationship in different forms - WordNet, Roget's Thesaurus, and FrameNet - on 6 word-meaning categories that are used for the analysis of narrative and scientific natural-language texts. The results show that WordNet contains more words than FrameNet, and is more suitable for scientific texts, but FrameNet contains better-defined hypernyms and shows better precision for many narrative natural-language tasks, especially for verbs. Roget's Thesaurus performance is average between WordNet and FrameNet in most word-meaning categories Enhancing FrameNet by adding more lexical units to existing frames would allow creating a powerful resource for entity and action recognition in text analysis. Fixing WordNet problems require revising its system of hypernyms. May 08, 20:12 Keywords: Сomputational linguistics, Hypernymy, Natural language processing, Knowledge acquisition, Lexical databases
Abstract. This paper describes the results of work on developing the mobile application for recognition of faces and other biological objects. The application is designed with a focus on loading external machine learning models over the Internet, which allows you to change the model without making any modifications to the application. With such realization, the application can be used in many cases. For example, at carrying out conferences: organizers just need to train a model and send out the link for its downloading to all participants of the conference without any changes in source code. Participants will be able to find out the in-formation about other members they are interested in, as well as contact them directly from the application, both by phone and by e-mail. Instructions are given for teaching your own face recognition model using the Microsoft Custom Vision cloud service, which allows you to train regardless of the power of your local computer. As an example, a classification model was trained and the fol-lowing assessments of recognition quality indicators were obtained precision: 97.8% and recall: 95.8%. In our future work we consider adding the functionality of emotion recognition based on the pattern recognition algorithm, described in this paper. May 09, 14:37 Keywords: cognitive technology, biological objects, pattern recognition, machine learning, mobile development
 Dmitrii Litovkin (Volgograd State Technical University), Dmitrii Dontsov (Volgograd State Technical University), Anton Anikin (VSTU) and Oleg Sychev (Volgograd State Technical University). Suitability of Object-Role Modeling diagrams as an intermediate model for ontology engineering: testing the rules for mapping.
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. May 10, 18:28 ywords: Ontology, Web Ontology Language, Object-Role Modeling, Mapping, Ontology Testing
 Viacheslav Wolfengagen (National Research Nuclear University MEPhI), Larisa Ismailova (National Research Nuclear University MEPhI) and Sergey V. Kosikov (NAO JurInfoR). Combinator-as-a-process for representing the information structure of deep computing.
Abstract. Computational activity is now recognized as a natural science, and computational and information processes have been discovered in the deep structures of many areas. Computations in the natural world were present long before the invention of computers, but a remarkable shift in understanding its fundamental nature evolves before our eyes. The present moment, in fact, is a transition phase from the notion of computer science as a science on artificial to the understanding that information processes are abundant in nature. Computing is recognized as a natural science that studies natural and artificial information processes.
In everyday computing, operations are performed on the individual generators, with little attention paid to their internal structure. However, many common operations consist of more primitive constructions connected by a combining mode. The interaction of structures is carried out in an environment of ``applicative interaction'', their applications to each other, and the study of the properties of this environment allows us to understand the information nature of the computations.
In the present work, the main attention is paid to elucidating the technological features of computations with individual generators, or objects. Their interaction is considered in an applicative environment, which allows us to elucidate the internal structure of ordinary operations, the knowledge of which allows us to understand their properties. The choice of initial constant generators, considered initial and expressed by combinators, is discussed. These initial generators are used as the main ``building blocks'' that enter into the applicative environment in interaction with each other. As a result of the interaction, constructions arise that give representative sets of ordinary operators and embedded computing systems. May 11, 18:37 ywords: combinator-as-a-process, information process, applicative structure, artificial computing, natural computing
Abstract. The general principles of various orders cybernetics were formulated half a century ago. However, the principles of these generations were used in philosophy, sociology, biology, economics and similar fields. Specialists in engineering areas practically did not use these principles in their work. This article attempts to review and formulate basic principles for the tasks of controlling robotic systems in terms of cybernetics of the first, second and third orders. For this, a small analysis of the state and use of the principles of cybernetics various orders in the modern practice of de-signing robotic complexes was carried out. Some results of this analysis are presented in this article. May 12, 19:34 s: first order cybernetics, second order cybernetics, third order cybernetics, cyber-physical system, robotic, control system
Abstract. The author's basic design concept of an artificial cognitive system is that story generation is an essential dimension of the mind. In this paper, a brief preliminary report on the development of a memory system is presented. The memory system is positioned as a partial system of a cognitive system. Its fundamental roles are to organize a world as a collection of stories and form the forces of generating stories. The memory system is designed as a parallel distributed system. In particular, each memory item—stories, events, concepts, and schemas—corresponds to an internal agent that operates automatically. These memory agents are dynamically organized in a network form through incorporating external simple narrative texts. This organizational process is performed via parallel distributed operations of connection formation and spreading activation among memory agents. The system is implemented in a proof-of-concept fashion. Several preliminary analyses of the system's behavior are also presented. May 13, 05:37 rds: Memory System, Story, Concept, Schema
Abstract. Taisuke Akimoto is an assistant professor in the Graduate School of Computer Science and Systems Engineering, Kyushu Institute of Technology, Fukuoka, Japan. He received his Ph.D. from Iwate Prefectural University, Japan, in 2014. His research interests include generative narrative cognition, narrative intelligence, memory, representation, and computational creativity. PC Membership May 13, 05:48
Keywords: generative narrative cognition, narrative intelligence, memory, representation, computational creativity
 Pavel G. Gudkov (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)) and Anna I. Guseva (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)). Accuracy of Expert Assessments in Evaluating Innovative Projects.
Abstract. This article addresses the issue of improving the accuracy of expert assessments in evaluating innovative projects. The proposed approach to increase the accuracy of expert decisions is based on formalization of sub-criteria assessments using T. Saati's hierarchy analysis. Preliminary research shows the prospects of using the aforementioned approach. Further, the article discusses how its method can improve the accuracy of experts on the example of already existing methodological tools for evaluating innovation applications used by the Foundation for Assistance to Small Innovative Enterprises (FASIE). This work was supported by the MEPhI Academic Excellence Project (Contract No. 02.a03.21.0005). May 14, 08:33 words: innovative project, expert opinion, analytic hierarchy process, decision-making process ✔
 Liudmila Zaidelman (National Research Center “Kurchatov Institute”, Russian State University for the Humanities), Zakhar Nosovets (National Research Center “Kurchatov Institute”, Russian State University for the Humanities), Artemiy Kotov (National Research Center “Kurchatov Institute”, Russian State University for the Humanities), Vadim Ushakov (National Research Center “Kurchatov Institute”, National Research Nuclear University MEPhI, Moscow State University), Vera Zabotkina (Russian State University for the Humanities) and Boris M. Velichkovsky (National Research Center “Kurchatov Institute”, Russian State University for the Humanities). Russian-language Neurosemantics: Clustering of Word Meaning and Sense from the Oral Narratives.
Abstract. This article is a part of a large-scale brain mapping project aimed at finding the correspondence of the semantic categories in oral Russian-language texts and the brain activity as measured in magnetic resonance scanner. The goal of present study in particular is to examine the nature of lexical semantic relations and find the appropriate lexical space, homeomorphic to the activation patterns in the brain. We present oral narratives, which described significant social problems from the first-person perspective, as stimuli and apply different annotation methods to encode the semantic information of words. Two approaches to annotation are described in the article: a dictionary method and a vector one. We also register fMRI signal and find clusters of words in input texts that have similar patterns of brain activation across subjects. These neurosemantic clusters are described in the article. Our results show that annotation by a list of features more strongly contributes to prediction of the observed activation patterns. These results also confirm the hypothesis of situational semantic representation in the brain. May 14, 12:26 rds: brain mapping, semantic space, text comprehension
 Larisa Lyutikova (Institute of Applied Mathematics and Automation KBSC RAS) and Elena Shmatova (Institute of Applied Mathematics and Automation KBSC RAS (IAMA KBSC RAS)). Algorithm for constructing logical operations to identify patterns in data.
Abstract. Neural networks have proven themselves in solving problems when the input and output data are known, but the cause and effect relationship between them is not obvious. A well-trained neural network will find the right answer to a given request, but will not give any idea about the rules that form this data. The paper proposes an algorithm for constructing logical operations, in terms of multi-valued logic, to identify hidden patterns in poorly formalized areas of knowledge. As the basic elements are considered many functions of the multi-valued logic of generalized addition and multiplication. The com-bination of these functions makes it possible to detect relationships in the da-ta under study, as well as the ability to correct the results of neural networks. The proposed approach was considered for classification problems, in the case of multidimensional discrete features, where each feature can take k-different values and is equivalent in importance to class identification May 14, 13:41 Keywords: Algorithm, multi-valued logic, neural network, truth table, knowledge system
Abstract. An analysis of the specifics of modern VR, AR, MR and XR systems use in educational, recreational and recovery processes is presented. The urgency of contin-uous monitoring of the current functional state (FS) and its psycho-emotional state (PES) using VR, AR, MR and XR technical means has been substantiated. Maintain-ing the state of health, as well as the required level of performance, are highlighted as the main requirements for the applied VR, AR, MR and XR means. The relevance of the use of infrared (IR) biometric technologies for the registration of the most in-formative human bioparameters, determining his current PES, has been substantiated. Analysis of IR vibraimage of a face is highlighted as the most promising technology for assessing its current PES. The possibility of determining the current FS on the basis of a set of bioparameters determining its PES is substantiated. For this purpose, the analysis of the correlation of the FS and the most informative bioparameters determined by the analysis of the IR vibraimage of the user's face by VR, AR, MR and XR means is given. To assess the FS in the study, it was proposed to use a value determined by the number of errors committed during periodic execution of specialized tests integrated into the VR, AR, MR, and XR scenario. Experimental data obtained during laboratory testing of the method confirmed the possibility of assessing FS based on a set of bioparameters measured during the processing and analysis of the IR vibraimage of the VR, AR, MR and XR user's face. The research results are of particular importance when training operators to control potentially hazardous facilities using VR, AR, MR or XR technologies, and, first of all, for the nuclear industry. May 14, 14:16 Keywords: Functional State, Psycho-Emotional State, Facial Vibraimage Analysis, Educational Activity Planning
 Dmitriy Dimitrichenko (Institute of Applied Mathematics and Automation of Kabardin-Balkar Scientific Centre of RAS (IAMA KBSC RAS)). Algorithm for constructing logical neural networks based on logical various-valued functions.
Abstract. The intelligent control system, as a set of production rules, is implemented in the form of an various-valued logical function. The combined use of math-ematical logic and neural network methods gives the intelligent control sys-tem additional flexibility and the possibility of self-learning. In this paper we propose a method for representing various-valued logic function in a logical neural network. This logical neural network will keep the totality of cause-and-effect relationships identified using various-valued logic functions with-in a given specified area. These logic operations are implemented by special logic neural cells: conjunctors and disjunctors. The theorems given in this ar-ticle justify the possibility of constructing such neural networks. The method of proof of these theorems contains an algorithm for constructing logical neural networks for a finite number of steps. May 14, 15:18 Keywords: control system, predicate, the predicate atomicity, various-valued logical function, logical neural network, fuzzy logic variable
Abstract. The ability to solve problems of graphic images recognition in VR, AR, MR and XR systems is highlighted as one of the most important. The urgency of solving problems of recognition and classification of acoustic images has been substantiated, which will bring the quality of VR, AR, MR and XR systems closer to real reality (RR). Independent solution of graphic and acoustic patterns recognition problems using heterogeneous algorithmic and software tools is attributed to the disadvantages of modern systems. The study proposes an approach that allows the use of unified methodological and software tools for the simultaneous solution of graphic and acoustic patterns recognition problems. The proposed approach is based on converting acoustic information into graphic information using 2D-images of dynamic sonograms. This allows the recognition of acoustic patterns using unified algorithmic and software tools. It is proposed to use the Viola-Jones technology as such a unified tool. It is shown that the implementation of a two-stage determination of similarity measures of primitives and areas of the original image makes it possible to increase the speed of algorithms. For this purpose, at the first iteration, it is proposed to use not the graphic primitives themselves, but their coordinate projections. In the study, by analogy with Haar's features, parametrizable acoustic primitives were developed, presented in the classical graphical version, as well as in the form of coordinate projections May 14, 18:10 Keywords: Recognition and Classification, Graphic and Acoustic Patterns, Viola-Jones Algorithm
 Anastasia Korosteleva (National Research Center «Kurchatov Institute»), Irina Malanchuk (National Research Center «Kurchatov Institute»), Liliya Arutyunyan (Speech Center «Arlilia») and Migran Arutyunyan (Speech Center «Arlilia»). Review of fMRI methods in developmental stuttering and it's treatment.
Abstract. The article gives a summary overview of the most important studies of stuttering by using functional magnetic resonance imaging (fMRI) in the last 20 years. This review also highlights problems in the literature in terms of methodology and research areas. It presented an integrated approach and technique fMRI studies aimed at both the primary diagnosis of stuttering and stammering on the dynamics of the mechanisms in the treatment process. May 14, 19:23 words: stuttering, treatment, review, fMRI
 Natividad Vargas (Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV IPN) Unidad Guadalajara), Juan Luis del Valle-Padilla (Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV IPN) Unidad Guadalajara), Juan P. Jimenez (Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV IPN) Unidad Guadalajara) and Félix Ramos (Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV IPN) Unidad Guadalajara). A model of top-down attentional control for visual search based on neurosciences.
Abstract. Visual attention is an essential and critical mechanism that allows humans to select the most relevant visual information of potential interest to focus on certain aspects of the environment. There are several proposals to model visual attention. However, those models only describe behaviors in simple tasks like free visualization. In more complex tasks such as visual search, it requires attention processes to guide behavior. Attentional control provides these mechanisms. Through which, top-down (goal-directed) information is represented and configured according to the various constraints and dynamics of task processing. In this article, we describe a task-dependent approach to model attentional top-down control based on neuroscience. We present a general conceptual model of visual attention. We describe its three main components and their relationship with other cognitive functions. Also, we show more detailed information about the flow of information that our model follows using a simple guided search case study. Our proposal intends to be the basis to treat top-down attentional information in a broader cognitive architecture. We find that the existence of IPS templates provide a general and biologically inspired representation for relevant objects. Our results show that the proposed model is significantly more consistent and explanatory in information processing compared to other state-of-the-art models. May 15, 01:30 ywords: Bioinspired model, Visual attention, Cognitive architectures, Guided search, Top-down control, Goal-driven behavior
 Kholodny Yuri (NRC "Kurchatov Institute"), Sergey Kartashov (NRC Kurchatov Institute), Denis Malakhov (NRC 'Kurchatov Institute') and Vyacheslav Orlov (NRC "Kurchatov Institute"). Improvement of the technology of fMRI-experiments in the concealed information paradigm.
Abstract. This work is a continuation of research on the creation of a forensic method for MRI diagnostics of hidden information in a person. The article presents some results of the first (technological) stage of these studies, during which the method of fMRI experiments was improved by using an MRI-compatible polygraph (MRIcP) within the framework of the information concealment paradigm. On the numerous experiments are shown: the efficiency MRIcP during fMRI; the usefulness of the methodological technique created through the use MRIcP that have improved the performance of an fMRI experiment; prospects of application of certain tests (in compliance with the strict requirements of forensic science) in the context of an fMRI study. May 15, 09:59 Keywords: MRI compatible polygraph, fMRI, forensic diagnostics, studies using a polygraph
 Lyubov Kolobashkina (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)), Mikhail Alyushin (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)) and Kirill Nikishov (Joint-Stock Company for Power and Electrification «Mosenergo»). Expandable Digital Functional State Model of Operator for Intelligent Human Factor Reliability Management Systems.
Abstract. The relevance of the human factor (HF) reliability management is substantiated due to the reliable prediction of a possible change in the functional and psycho-emotional state (PES) of operators for controlling potentially hazardous objects (PHO). It is shown that PES modeling based on digital behavioral models (DBM) is a modern tool for such forecasting. Personalization of the DBM is carried out by taking into account individual bioparameters, which are registered, as a rule, using remote non-contact biometric technologies. The low reliability of the forecast in the event of emergency situations is highlighted as the main drawback of the existing DBMs. The main reason is the lack of reliability of the registration of bioparameters when using one or a limited number of biometric technologies. The relevance of the development of an expandable DBM, which allows to eliminate the indicated drawback, has been substantiated. The study proposes an expandable DBM that allows you to expand the range of processed biometric data. At the same time, it becomes possible to integrate biometric data obtained using remote non-contact technologies with data obtained using wearable biometric devices, such as, for example, bracelets. The developed DBM makes it possible, simultaneously with the implementation of the forecast, to monitor the health status of PHO personnel. May 15, 11:47 Keywords: Intelligent Bioparameters Processing, Human Factor Control, Digital Behavioral Model, Expanded Bioparameters Composition
 Alexey Poyda (NRC KI), Maksim Sharaev (Skoltech), Vyacheslav Orlov (NRC "Kurchatov Institute"), Stas Kozlov (National Research Centre "Kurchatov Institute", Moscow, Russia), Irina Enyagina (National Research Centre “Kurchatov Institute”) and Vadim Ushakov (NRNU MEPhI). Comparative analysis of methods for calculating the interactions between the human brain regions based on resting-state fMRI data to build long-term cognitive architectures.
Abstract. Research in the field of constructing functional connectomes of the human brain at rest has been carried out over the past several decades. To build a functional connectome, it is necessary to evaluate the level of interaction between the regions that determine its functional elements. To determine this level, it is necessary to determine both the joint work of different brain areas, as well as establish of causal relationships between them. Currently, many different methods have been proposed for calculating the interaction of brain regions based on correlation, Granger causality, transfer entropy, coherence, mutual information, etc. Moreover, each of the methods depends on a number of parameters, which could affect the obtained result (for ex-ample, the size of the sliding window, frequency bands, model order, etc.). It is impossible to compare methods directly by the accuracy of evaluated connections, since we do not have a priori knowledge of functional connec-tions in the brain. Therefore, methods can be compared only by indirect measures. In this work, we compared many different methods according to the criterion of the stability of the results to small changes in the parameters of both the methods themselves (for example, the window size) and the in-put data (for example, the shift of the window in time or the shift of the brain region's boundaries in space). By stability we mean that small changes in the parameters will lead to small changes in the obtained estimates of the interaction.
Currently, there is evidence in favor of the so-called dynamic connectivity of the brain regions, namely, microstates (from milliseconds to tens of sec-onds) that vary in time. However, since fMRI has a temporal resolution of about 2 seconds, here we focused on long-term architectures (400 seconds or more). This can also be explained by a number of reasons: 1) many meth-ods provide good estimates only with a sufficiently large time-series length (from 200 values or more); 2) we can expect the stability of long-term con-nections characteristics in space and in time, because the estimate is calcu-lated by averaging many functional interactions between brain areas that can be observed during the analyzed long-term period; 3) if the method does not show a stable result in the long-term analysis on averaged functional connections, this may indirectly indicate its unsuitability for the analysis of short-term conditions, while methods that showed good results on long-term analysis, might be further considered for constructing functional con-nectomes on a smaller time scale. May 15, 13:54 Keywords: fMRI, Resting State, Functional Connectomes, Effective Connectomes.
Abstract. In this paper we present a brain-inspired cognitive architecture that incorporates sensory processing, classification, contextual prediction, and emotional tagging. The cognitive architecture is implemented as three modular web-servers, meaning that it can be deployed centrally or across a network for servers. The experiments reveal two distinct operations of behaviour, namely high- and low-salience modes of operations, which closely model attention in the brain. In addition to modelling the cortex, we have demonstrated that a bio-inspired architecture introduced processing efficiencies. The software has been published as an open source platform, and can be easily extended by future research teams. This research lays the foundations for bio-realistic attention direction and sensory selection, and we believe that it is a key step towards achieving a bio-realistic artificial intelligent system. May 15, 14:14 s: distributed cognitive architecture, affect, cortex, prediction, corticothalamic connections ✔
 Anton Anikin (VSTU), Dmitry Litovkin (Volgograd State Technical University) and Oleg Sychev (Volgograd State Technical University). Collaborative creation and use of cognitive ontology-based domain information space for scientific research and learning.
Abstract. The paper addresses the issues of knowledge management in learning and scientific research, in particular knowledge transfer from experts to consumers and the relevant processes. To solve these problems, we present a generalized methodology of intelligent support of decision making in knowledge management for scientific research and learning, supporting personalizing and collaborative creation and reuse of objects in domain information spaces. The domain cognitive information space is implemented as an ontological knowledge base using Semantic Linked Network as a formal basis for representing and visualizing the domain information. The paper describes methods and algorithms for representing, acquiring, and visualizing of knowledge based on the proposed model for creating and using domain information spaces. The proposed model and methods allow supporting decision making for knowledge management at the early stages of scientific research and in learning for creating a domain information space of the course, knowledge acquisition and skill formation, composing and adapting an educational trajectory, educational resources search, etc. May 15, 16:36 words: e-learning, knowledge management, knowledge transfer, ontology, Semantic Linked Network
 Sergey Kartashov (NRC "Kurchatov Institute"), Vyacheslav Orlov (NRC "Kurchatov Institute"), Aleksandra Maslennikova (IHNA&NF RAS) and Vadim Ushakov (NRNU MEPhI). Neurophysiological features of neutral and threatening visual stimuli perception in patients with schizophrenia.
Abstract. The authors of this work set a goal to study the features of visual perception of threatening stimulus associated with personal experience in patients with schizophrenia. As a target group were taken patients with a paranoid-hallucinatory syndrome. Healthy volunteers without mental disorders and neurological diseases were used as controls. During fMRI studies, threatening and neutral images were presented. The experiment was built on the principle of a block paradigm. As a result, statistical parametric maps were constructed for two groups of subjects and the results were compared among themselves. According to the obtained results, patients with schizophrenia show a decrease in the overall level of activation in all regions of the brain compared with healthy volunteers. This is most evident in Middle Temporal Gyrus (temporooccipital part Right), Inferior Temporal Gyrus (temporooccipital part Left and Right), Lateral Occipital Cortex (inferior division Left and Right), Temporal Occipital Fusiform Cortex Left and Right and Frontal Pole Left and Right. May 15, 16:43 Keywords: MRI, fMRI, schizophrenia, statistical parametric mapping
Abstract. Abstract. The article considers the approach to the construction of robust methods and machine learning algorithms, which are based on the principle of minimizing estimates of average values that are insensitive to outliers. Proposed machine learning algorithms are based on the principle of iterative reweighting. Illustrative examples show the ability of the proposed approach and algorithms to overcome the effects of outliers. May 15, 17:40 Keywords: machine learning, robust estimate, pattern recognition, data analysis
 Timofei Voznenko (NRNU MEPhI), Alexander Gridnev (NRNU MEPhI), Eugene Chepin (NRNU MEPhI) and Konstantin Kudryavtsev (NRNU MEPhI). Comparison between Coordinated Control and Interpretation Methods for Multi-Channel Control of a Mobile Robotic Device.
Abstract. Control methods of the mobile robotic device can be divided into single-channel and multi-channel. A single-channel control method is a control method that uses a single data channel. Each control channel has its own operational features that can affect the quality of control. In order to take advantage of multiple control channels, multi-channel control is used. In multi-channel control information comes from different, heterogeneous, independent control channels. The task of the control system is to make a decision and choose valid (the most probable) command. There are two approaches to this problem: coordinated control and decomposition of multi-channel control into single-channel control. The decomposition method implies ignoring of command information from not selected channels and commands. The interpretation method allows using such command information to improve the quality of control of a mobile robotic device. In this paper, we consider coordinated control and interpretation methods and also compare implementations of these control methods for multi-channel control of a mobile robotic device. May 15, 17:59 Keywords: robotics, multi-channel control, decision-making, decomposition method, interpretation method
Abstract. Companion robots should perceive speech, recognize objects in the real world, and further react with speech utterances and nonverbal communicative cues. Robots should also remember the interaction history and accumulate knowledge from different text sources: news, blogs, and e-mails. We de-sign a conceptual representation system for a companion robot, able to sup-port this list of interactive tasks. The system includes speech processing component and operates with semantic representations as sets of semantic markers, assigned to valencies. The reaction support system inherits a classic production architecture and consists of scripts, sensitive to rational or emotional stimuli. The general architecture is based on parallel processing of scripts, it may trigger several behavioral reactions to each stimulus, and further combine the outputs of these scripts on a robot to enrich its communicative behavior. Semantic representations and scripts are also used to index in-coming utterances are store them in a memory base. We also demonstrate interaction of semantics and reactions with a prototype of visual recognition system for the tasks of face detection and automatic support of Tangram puzzle solution. May 15, 19:37 s: Conceptual Processing, Knowledge Base, Production Logic, Semantic Representation
 Irina Malanchuk (National Research Center "Kurchatov Institute"). Cognitive Architectures of Effective Speech-Language Communication and Prospective Challenges for Neurophysiological Speech Studies.
Abstract. The paper focuses on the importance of social cognitions and priors in natural cognitive architectures of an individual. The structures and content of perceptual-cognitive-metacognitive processes are analyzed using the material of natural speech-language communication related to early human ontogenesis. Metacognitive processes are defined as a property and an integral part of the cognitive system. The prospects of neurophysiological research are formulated, which are designed to clarify the distribution, the nature of connections and neuronal dynamics in the system of perceptual-cognitive-metacognitive processes that ensure effective speech communication at different periods of human development. The importance of the studies of the neural architectural solutions related to the processes of natural speech and language communication for the development of IT technologies which can fulfill the communicative needs and expectations of individual is emphasized. May 15, 20:19 Keywords: Natural speech-language communication, neural architectures, Artificial Intelligence (AI), social priors, social cognitions, metacognitive processes, early human ontogenesis
Abstract. The article considers a method for evaluating the decision function based on the use of a logical derivative in case recognition problems. The construction of the recognition function is based on the structure and weight characteris-tics of the trained sigma - pi - neuron. The resulting logical function is a disjunction of the identified patterns. The combined approach based on the neural net-work paradigm and logical methods increases the adaptive properties of the recognition system. To assess the sensitivity of the decision function to changes in the values of certain characteristics, a logical derivative is used. Which shows how to change the value of the decision function if the value of characteristics changes, which allows you to draw conclusions about the most significant properties in the subject area under consideration. This is especially important when data is incomplete, fuzzy, or distorted due to in-formation noise. May 15, 20:52 decisive function, logical derivative, data analysis, algorithm, sigma-pi-neuron, decision trees, corrective operations
 Lennart Zegerius (Vrije Universiteit Amsterdam) and Jan Treur (Vrije Universiteit Amsterdam). Modelling Metaplasticity and Memory Reconsolidation during an Eye-Movement Desensitization and Reprocessing Treatment.
Abstract. In this paper, a computational model is presented to simulate the effect of an Eye Movement Desensitization and Reprocessing (EMDR) therapy on persons affected by Post-Traumatic Stress Disorder (PTSD). The simulation is based on an adaptive temporal-causal network modelling approach. Adaptiveness is achieved using network reification, to model plasticity based on the Hebbian Learning principle, and metaplasticity. During EMDR therapy, within the brain resource competition occurs, which helps to improve stress regulation. More specifically, eye-movement intervention causes competition between parietal networks and the amygdala, due to which they negatively affect each other’s activation. Psychological traumas impair (extinction) learning by so-called ‘negative metaplasticity’. EMDR is functional in shifting this back to ‘positive metaplasticity’. This revitalizes extinction learning and memory reconsolidation. The introduced adaptive network model and its simulation confirms the functionality of the neural processes and the effective treatment results of EMDR. May 16, 13:30 ywords: Computational Model, Hebbian Learning, Metaplasticity, EMDR, PTSD, Memory Reconsolidation, Resource Competition
 Maksim Sharaev (Skolkovo Institute of Science and Technology), Tatiana Melnikova-Pitskhelauri (N.N. Burdenko National Medical Research Center of Neurosurgery), Alexander Smirnov (N.N. Burdenko National Medical Research Center of Neurosurgery), Arseniy Bozhenko (Skolkovo Institute of Science and Technology), Vyacheslav Yarkin (Skolkovo Institute of Science and Technology), Alexander Bernstein (Skolkovo Institute of Science and Technology), Evgeny Burnaev (Skolkovo Institute of Science and Technology), Plamen Petrov (Skolkovo Institute of Science and Technology), David Pitskhelauri (N.N. Burdenko National Medical Research Center of Neurosurgery), Vyacheslav Orlov (NRC "Kurchatov Institute") and Igor Pronin (N.N. Burdenko National Medical Research Center of Neurosurgery). Brain cognitive architectures mapping for neurosurgery: resting-state fMRI and intraoperative validation .
Abstract. Despite the importance of experimental confirmation, the ability of wide range of brain mapping methods to discover brain cognitive architectures in most studies can’t be evaluated directly. Only in rare cases, when due to medical need, it is possible to conduct experiments during neurosurgical operations, is it possible to assess the accuracy of certain approaches. In this paper we evaluate, how well we can reveal brain cognitive architectures structure by established and novel approaches based on fMRI data, with special focus on resting-state fMRI, and how well these findings match cortical stimulation mapping data, which is a gold standard in neurosurgery. We illustrate our approach on three representative examples with different cognitive architectures mapping: brain motor network and language network, namely Broca and Wernicke areas. We found a significant correspondence between predicted maps and intraoperative data for both brain networks. This indicates that resting-state fMRI could be used as an additional source of information for neurosurgical planning, though its applicability to exploring and describing the whole variety of brain cognitive architectures for research purposes should be investigated in future. May 16, 13:49 Keywords: cognitive architectures, brain mapping, constrained ICA, resting-state fMRI, neurosurgery
Abstract. Propagated activation of neurons through their network is often considered as the main process in the brain. However, another crucial part of neural processing concerns adaptation of characteristics of this network such as connection strengths or excitability thresh-olds. This adaptation can be slow, as in learning from multiple experiences, or it can be fast, as in memory formation. These adaptive network characteristics can be considered informational criteria for the activation of a neuron. This then is viewed as a form of emergent information formation. Activation of neurons is determined by such information, termed criterial causation. In the current paper, the relationship of criterial causation with the principle of temporal factorisation for the dynamics of the world in general, describing how the world represents information about its past in its present state, which then, in turn, determines the world’s future. In the paper, it is shown how these processes were analysed in more detail and modeled by (adaptive) network models. May 16, 16:07 s: Criterial causation, temporal factorisation, adaptive network model, informational content
Abstract. This article presents the results of the research in terms of using mathematical methods for the risk management process in the implementation of software de-velopment projects. Software development projects are not always implemented in a final form that meets the expectations of customers. Both internal and exter-nal factors can influence this. In this regard, the problems of risk management, which inevitably arise during the implementation of software development pro-jects, become particularly relevant due to the large uncertainty of the internal and especially external environment of enterprises. The introduction of a comprehen-sive approach to risk management allows the company to form an objective view of the current and planned activities of the organization, taking into account pos-sible negative events or new opportunities, anticipate risks and make decisions based on information about them, respond to risks in a timely manner and reduce the negative impact of risks in their implementation. Within the framework of this research, the risk assessment software is designed to assess the situation in the project, predict the future effectiveness of the project, and build scenarios to sup-port decision-making. Such software will allow to combine all the actions of the analyst in one tool, where all the information about the project and the external environment will be stored, updated and constantly used for training the Neural Network apparatus on which the software is designed. This work was supported by RFFI grant № 20-010-00708\20. May 16, 17:52 Keywords: risk management, risk assessment, Neural Networks, software development projects
 Gustavo Palacios Ramirez (CINVESTAV), Carlos Johnnatan Sandoval Arrayga (CINVESTAV) and Félix Ramos (CINVESTAV GDL). A proposal for an auditory sensation cognitive architecture and its integration with the motor-system cognitive function.
Abstract. The auditory system is capable of producing a wide range of information through the acquisition and perception of the vibrations present in the environment, even when the receptor is not directly facing the stimulus's source. Said information can be crucial for survival and useful for a variety of systems like the visual system and the motor system. Despite that, the quantity of studies involving the auditory system or its interactions with other systems is limited, even though anatomical evidence recognizes this relationships' existence. In this work, we study its interaction with the motor system. A bio-inspired model that explores the relationship between the auditory and motor systems, grounded on neuroscientific research, is presented to address this proposal. To validate our proposal, a case study in which we endow a virtual entity with our proposed model. Then, we ask both a group of persons and the virtual creature to compute and face towards the direction were the sound was originated. May 16, 18:20 s: Sensory, Cognitive architecture, Bioinspired, Auditory sytem, Motor system, Human behavior, BICA
Abstract. Machine learning is one of the key technologies of the current scientific and technological revolution. Despite the fact that research in the field of "intelligent" control systems began in the last century, real-time control systems based on machine learning, specifically neural networks, began to be actively implementeed only in the past decade. In this paper, the authors analyze the current state of the problem of using real-time control systems based on neural networks. May 16, 18:30 Keywords: neural network, control system, real-time control system
 Enrique Osuna (ITSON), Luis-Felipe Rodríguez (ITSON) and J. Octavio Gutierrez-Garcia (Instituto Tecnologico Autonomo de Mexico - ITAM). Toward Integrating Cognitive Components with Computational Models of Emotion using Software Design Patterns.
Abstract. Computational models of emotion (CMEs) are software systems designed to imitate particular aspects of human emotions. The main purpose of this type of computational model is to capture the complexity of the human emotion process in a software system that is incorporated into a cognitive agent architecture. However, creating a CME that closely imitates the actual functioning of emotions demands to address some challenges associated with the design of CMEs and cognitive architectures alike. Among these challenges are i) sharing information among potentially independently developed cognitive and affective components, and ii) interconnecting complex cognitive and affective components that must interact with one another in order to generate realistic emotions, which may even affect agents' decision making. This paper proposes an architectural pattern aimed at cataloging and describing fundamental components of CMEs and their interrelationships with cognitive components. In this architectural pattern, external cognitive components and internal affective components of CMEs do not interact directly but are extended by including message exchange methods in order to use a publish-subscribe channel, which enables their intercommunication, thus attenuating issues such as software heterogeneity. This structural approach centralizes communication management and separates the inherent complexity of the cognitive and affective processes from the complexity of their interaction mechanisms. In so doing, it enables the design of CMEs' architectures composed of independently developed affective and cognitive components. The proposed architectural pattern attempts to make progress in capturing the complex process of human emotions in a software system that adheres to software engineering best practices and that incorporates quality attributes such as flexibility and interoperability. May 17, 04:47 s: Computational Model of Emotion, Cognitive Agent Architecture, Software Design Pattern
 Kazuteru Miyazaki (National Institution for Academic Degrees and Quality Enhancement of Higher Education). Application of Deep Reinforcement Learning to Decision-Making System based on Consciousnes.
Abstract. What is consciousness? Our research aims to achieve consciousness in computers and, in particular, focuses on a decision-making system based on consciousness. We have interested in a conscious decision-making system in an environment where multiple types of rewards and penalties exist. We know a method using a basis function and a method using a penalty avoidance list for this problem. Though the method using the penalty avoidance list is considered promising compared to the former, it has the problem that when all actions are registered in the avoidance list, the actions are selected at random. In this paper, we propose a method for selecting actions using deep reinforcement learning in order to avoid such random selection as much as possible. The effectiveness of the proposed method is confirmed by numerical experiments. May 18, 00:32 rds: Consciousness, Deep Reinforcement Learning, Exploitation-oriented Learning, Profit Sharing, Decision-Making System
 Viacheslav Wolfengagen (National Research Nuclear University MEPhI), Larisa Ismailova (National Research Nuclear University MEPhI) and Sergey V. Kosikov (NAO JurInfoR). Applicative model to bring-in conceptual envelope for computational thinking with information processes.
Abstract. In modern research, computing is understood as the study of natural and artificial information processes, which, in turn, are recognized as ubiquitous.
Information processes are manifested through a variety of external forms, based on computational communication. Communication consists in the fact that the parties involved exchange process messages, and the same process message can be expressed in different sentences and vice versa, the same sentence can express different process messages. The ability to identify the same process in different sentences is based on the hypothesis of the stratification of each communication language into two languages: - a genotypic language for unambiguous recording of the content of processes; a phenotypic language with a variety of means for expressing the same process. The implementation of the connection of both languages by embedding in an applicative computational model equipped with a convertibility relation is proposed.
The defended arguments boil down to the following: (1) the computation cannot be eliminated not only from the research method, but also from the subject of the study - what is being studied and (2) the real value of computer science lies in those conclusions that experts can draw based on their expertise using a fairly rich and deep system of reasoning. For example, the most fruitful Karoubi envelope features for computing are discussed. May 18, 06:15 Keywords: applicative computational model, computing, information process
Abstract. In this talk, I will present research extensions that have been made to Soar and how they support Interactive Task Learning. May 22, 15:26 ywords: AI, Cognitive Architecture, Task Learning
Abstract. Regarding intelligence as a ‘considered response’ phenomenon is the key notion that is presented in this paper.
Applied to human-level intelligence, it seems to be a useful definition that can lend clarity to the following related aspects as well: mind, self/I, awareness, self-awareness, consciousness, sentience, thoughts and feelings, free will, perception, attention, cognition, expectation, prediction, learning.
Also, embodiment is argued to be an essential component of an AGI's agent architecture, in order for it to attain grounded cognition, a sense of self and social learning - via direct physical experience and mental processes, all based on considered response. May 24, 05:04 er
Keywords: AGI, Artificial General Intelligence, Artificial Intelligence, evolution, adaptation, intelligence, response, embodiment
 Sergey Misyurin (MEPhI), German Kreinin (MERI of RAN), Natalia Nosova (MERI of RAN; MEPhI) and Andrey Nelubin (MERI of RAN; MEPhI). Selection of a Friction Model to Take into Account the Impact on the Dynamics and Positioning Accuracy of Drive Systems.
Abstract. The problem of choosing a friction model for solving the problems of controlling positional systems, primarily with a pneumatic drive, is discussed. Due to their high dynamics, good towing capacity and relatively low price, pneumatic positioning systems are an attractive alternative to electric drives. However, the use of pneumatic systems involves some difficulties caused by the nonlinearities of its individual elements, in particular the flow characteristics of the servo valve, the compressibility of the working fluid, and also the friction acting on the piston. The main goal of this work is to analyze the stability in the interaction of the energy and control units under the influence of friction forces represented by various models. The Karnopp model was considered as one of the models, which has the advantage in describing the interaction with the friction forces in the transition from the state of rest to motion and vice versa. May 24, 18:05 Keywords: Pneumatic Drive, Dynamic System, Dimensionless Parameters, Intelligent Control, Optimization
 Margarita Zaeva (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)) and Andrew Evstifeev (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)). Method of Applying Fuzzy Situational Network to Assess the Risk of the Industrial Equipment Failure .
Abstract. The method of formation and application of a fuzzy situational network for the solution of problems of an assessment of risk of failure of the production equipment of the industrial enterprise at statement on production of a new product is oﬀered. The method takesintoaccountthemaingroupsofmanagementactionsinsmallandmedium-scaleproductionofawiderangeofproducts.The condition of the equipment is represented in the form of a fuzzy situational network with a three-level structure. The following levels are distinguished: situational, network and factor. May 25, 16:03 rds: risk of equipment failure, fuzzy situational network, management strategy, decision-making management system
 Margarita Zaeva (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)) and Andrew Evstifeev (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)). Anthropogenic Spatial Systems: Transformation and Potential of Self-Organization .
Abstract. The paper analyzes and summarizes the results of the study of the features of the functioning and transformation of a particular type of spatial systems, namely anthropogenic. These systems are formed for diﬀerent purposes of human activity. Reﬁnements of basic deﬁnitions are oﬀered and it is shown that stability of functioning for such systems is the most signiﬁcant indicator of their existence. The ability of anthropogenic systems to self-development and the inﬂuence of self-development on their transformation under the inﬂuence of internal and external inﬂuences and factors are considered. Proposals for the application of methodological foundations of system diagnostics of the state and transformation of anthropogenic spatial systems, as well as the possibility of their algorithmization and information support are presented. May 25, 16:08 rds: anthropogenic spatial systems, stable functioning transformations, balance, self-development, equiﬁnality, adaptation, incrementalism, system diagnostics, anthropic principle
 Margarita Zaeva (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)) and Andrejs Rudzitis (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)). Alternative inverse perspective mapping homography matrix computation for ADAS systems using camera intrinsic and extrinsic calibration parameters.
Abstract. This paper presents a simple novel IPM matrix computation method, which does not require any additional solutions to calculate homography matrices. This method only requires determining the distance from camera to projecting surface. Its’ simplicity allows execution even on very small energy-efficient embedded device processors without use of complex libraries such as OpenCV, that cannot be deployed there. Furthermore, it has strictly analytical solution, which allows avoidance of false optimums that are probable in heuristic methods for optimization. We provide formulas, that are easy transferable to any device and can be calculated using standard math libraries. May 26, 10:48 IPM, Homography, Bird eye view, calibration, projection
Abstract. This paper shows how the Independent Core Observer Model (ICOM) Cognitive Architecture for Artificial General Intelligence (AGI) can be applied to building a collective intelligence system called a mediated Artificial Superintelligence (mASI). The details include breaking down the ICOM implementation in the form of the mASI system and the general performance of initial studies with the mASI. Details of the primary difference between the Independent Core Observer Model Cognitive Architecture and the mASI architecture variant include inserting humanity in the contextual engine components of ICOM, creating a type of collective intelligence. Humans can ‘mediate’ new system-generated thinking keeping the thought process accessible and slow enough for humans to oversee and understand. This also allows the modification of emotional valences of the thought process of the mASI system to help the system generate complex contextual models (knowledge graphs) of new ideas and which speeds up the learning process. With the humans acting as control rods in a reactor and emotional drivers, the mASI system maintains safety where the system would cease to function if humans walked away. May 27, 06:09 rds: Collective Intelligence Systems, Independent Core Observer Model, Artificial General Intelligence, mediated Artificial Superintelligence, Hive Mind, AGI, ICOM, mASI
 Zandor Machaen (Universidad Autónoma de Guadalajara), Luis Martin (Cinvestav) and Jonathan Rosales (Universidad Autónoma de Guadalajara). Bio-inspired cognitive model of motor learning by imitation.
Abstract. Learning, and even more so by imitation, is an essential Cognitive Functions because it is carried out throughout life and allows us to adapt our behaviors from other beings through observation. In this work, we propose a model, and implementation of the cognitive function of imitation motor learning (IML), based on psychological and neuroscientific evidence. According to the evidence, learning by imitation includes imitation of action and imitation of action over an object sub-processes. The imitation of action consists of the movement of the limbs. The imitation of action over an object consists of the interaction with an object within the environment. We achieve an implementation of the proposed model for IML and endow a virtual entity with it. In order to validate the proposal, we use a case study to analyze the sub-processes performance. From results, we conclude that both imitation of action and imitation of action over an object sub-processes play an essential role in getting the agent to interact with stimuli within the environment Jun 02, 15:55 s: Imitation Learning, Cognitive architecture, Motor Learning, Brain model, Neuroscience, Virtual creature
 Alexey Matronchik (National Research Nuclear University MEPhI), Elena Khangulyan (National Research Nuclear University MEPhI) and Nikolay Klyachin (National Research Nuclear University MEPhI). Remote laboratory-based classes at the department of general physics of NRNU MEPhI under the condition of the COVID-19 pandemic.
Abstract. It is being discussed the organization of the laboratory-based classes and presentation by students their results at the department of general physics of NRNU MEPhI under the condition of the lockdown caused by the COVID-19 pandemic. Jun 04, 07:37 rds: physical workshop, general physics, COVID-19 pandemic
 Zakhar Nosovets (Russian State University for the Humanities, National Research Center “Kurchatov Institute”), Boris M. Velichkovsky (Russian State University for the Humanities, National Research Center “Kurchatov Institute”), Liudmila Zaidelman (Russian State University for the Humanities, National Research Center “Kurchatov Institute”), Vyacheslav. Orlov (National Research Center “Kurchatov Institute”), Sergey Kartashov (Russian State University for the Humanities, National Research Center “Kurchatov Institute”), Artemiy Kotov (Russian State University for the Humanities, National Research Center “Kurchatov Institute”), Vadim Ushakov (National Research Center “Kurchatov Institute”, National Research Nuclear University MEPhI) and Vera Zabotkina (Russian State University for the Humanities). Lateralization in Neurosemantics: Are Some Lexical Clusters More Equal Than Others?
Abstract. In the study, we have implemented neurosemantic analysis to find brain’s voxel-wise representations of words in Russian spoken narratives and their asymmetries in the brain. 25 subjects were listening to five stories, first person narratives of dramatic events, while their brain activation was registered by 3T functional magnetic resonance imaging (fMRI). Seven best subjects in terms of their engagement and objective control of brain reaction were selected for further analysis. 12 lexical clusters were found, with different semantics – from time-and-space concepts to human actions and mental states. Cluster “experience” was the only one that demonstrated a slight right-sided lateralization. For other clusters, brain localization was either symmetrical or had a clear left-sided bias. Our results support the view of non-modular and widely distributed nature of semantic representations, not limited to the activity of structures in the temporal and frontal lobes. These results also demonstrate that the right hemisphere can be massively involved in representation of “reflexive” part of inner lexicon. Jun 04, 15:17 Keywords: Neurosemantics, Narratives, Russian Language
 Margarita Zaeva (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)) and Andrew Evstifeev (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)). The method of planning the process of refueling vehicles using artificial intelligence and fuzzy logic methods.
Abstract. Filling liquefied natural gas (LNG) into the vehicle’s onboard tanks is one of the most dangerous production operations when using LNG as a motor fuel. Technical requirements form the volume of hazardous substance (LNG) stored at the refueling facility and restrictions on fire distances (gaps) and measures, which leads to an increase in the facility’s area and the cost of its construction and operation. One of the ways to reduce costs associated with protective measures is to limit the amount of fuel stored at the facility, while providing the flow of vehicles with fuel with its limited reserves at refueling facilities. To solve this problem, it is proposed to use the planning of the process of refueling vehicles using fuzzy logic methods using artificial intelligence. As a result, the fueling company’s fuel information service provides drivers with recommendations on possible refueling locations, fuel costs, available fuel volumes, and waiting times. Jun 04, 17:58 rds: Liquefied natural gas, fuzzy logic, production planning, optimization of capacity utilization, flow control
Abstract. Abstract. A constructive method and an algorithm for constructing a parametrized family of $\Sigma\Pi$-neurons that correctly function on a given training set and have, in a certain sense, a minimal structure are proposed. Jun 07, 12:41 Keywords: sigma-pi unit, constructive learning, boolean function, neural networks
 Vladimir Ivlev (Mechanical Engineering Research Institute RAS), Sergey Misyurin (National Research University MEPhI) and Andrey Nelyubin (Mechanical Engineering Research Institute RAS). Friction model identification for dynamic modeling of pneumatic cylinder.
Abstract. Friction is one of the main nonlinear properties that makes pneumatic actua-tors difficult to control and reduces their energy efficiency. Many phenome-nological friction models are used to describe pneumatic cylinders dynamic behavior, including in pre-sliding zone. These models maintain many un-known empirical parameters (maybe 7 or more). Expensive test equipment and special mathematical methods are required to define the parameters of friction model. But the results may vary significantly for one size cylinders of various manufactures. This paper presents the results of determination the Stribeck friction model parameters based on limited experimental data and procedure of vector identification which implemented in the software com-plex MOVI (Multicriteria Optimization and Vector Identification). Results were obtained for two types of piston seals materials: NBR and PTFE com-posite. The minimal value of the piston stable speed for single action cylin-ders with these seals was estimated. Jun 07, 14:38 Keywords: pneumatic cylinder, friction model, parameter identification
 Yuri Kholodny (НИЦ Курчатовский институт), Sergey Kartashov (NRC Kurchatov Institute), Denis Malakhov (NRC 'Kurchatov Institute') and Vyacheslav Orlov (NRC "Kurchatov Institute"). Study of neurocognitive mechanisms in the concealed information paradigm.
Abstract. This work is a continuation of research aimed at creating a forensic method for MRI diagnostics of hidden information in a person. The article presents some results of research in the paradigm of information concealment, during which the activity of brain structures in experiments with tests used in criminology was studied using fMRI and MRI-compatible polygraph (Mrtsp). In numerous experiments it is shown and confirmed the effective-ness of MRI in the course of the fMRI and the usefulness of the methodo-logical technique created through the use RTSP and improve the perfor-mance of an fMRI experiment. JVRT Subevent Jun 10, 10:16 Keywords: MRI compatible polygraph, fMRI, forensic diagnostics, concealed information paradigm.
Abstract. The paper proposes a method for assessing the significance of individual characteristics of recognized objects. The totality of objects and their charac-teristics is represented by the structure and weight coefficients of a trained -neuron. The specified -neuron correctly processes objects of the sub-ject area, which may not be explicitly represented. It is known that when us-ing the neural network approach, the logical rules for decision making by the neural network remain hidden to the user. The proposed method for con-structing the decisive function allows us to identify these logical rules of a correctly functioning -neuron. To assess the significance of the character-istics of objects, a logical derivative is used. Which shows how the decisive function will change its value if one or more characteristics of the objects change. That will allow us to conclude about the most important properties of the subject area under consideration. This is especially important when data is incomplete, fuzzy, or distorted due to information noise. Jun 10, 17:30 Keywords: decisive function, logical derivative, data analysis, algorithm, sigma-pi-neuron, ecision trees, corrective operations
 Sergey Yu. Misyurin (National Research Nuclear University «MEPhI»), Vigen Arakelian (INSA Rennes) and Nikolay A. Kudryashov (National Research Nuclear University «MEPhI»). Intelligent Technologies in Robotics 2020.
Abstract. Solving a huge number of issues, both in terms of software and in terms of hardware solutions, requires the creation and implementation of new mathematical models, intelligent control and automation systems for the development of the digital economy in the sector of intelligent robotics and cyberphysical systems.
Cyberphysical systems (CPS), their development and progress, are one of the areas of technical development of all countries of the world, contributing to the creation of new digital industries with high economic efficiency. Cyberphysical systems integrate all the promising IT technologies, which allows them to be used in such important areas as robotics, energy, transport, industrial production, and the management of critical objects. They are able to learn, adapt and respond to all environmental changes.
During the conference “Intelligent Technologies in Robotics 2020”, participants will be able to exchange experiences on the specific application of cyberphysical systems in the manufacturing sector, in the field of robotics and transport logistics, as well as examples of new technical solutions in the field of IT technologies in the digital economy. The creation of new IT technologies leads to the emergence of new problems with cybersecurity, therefore, special attention is paid to the protection of information in cyberphysical systems.
Actual and traditional cyber threats to cyberphysical systems are: Malware; Malicious Hardware; hidden channels of information transfer and impact on cyber systems (Covert, Subliminal, Side Channels, Backdoors); the use of dual-use information security technologies (Malicious Cryptography). New threats to the security of the CPS are: the destruction of management systems (the result is a loss of control over the CPS); substitution of the CPS functioning algorithm, impact on human behavior by distorting the information received from the CPS; substitution of GPS / Glonass signals for mobile CPS (the result is a complete loss of performance, since the coordinates of the CPS (mobile robot) are changed); impact on the CPS operator. Man, as the operator of the CPS, is its weak link. Constant monitoring of the psychophysical state of the human operator is required.
The fields of scientific research discussed at the conference are traditional for NRNU MEPhI:
• Cyber Physical systems security;
• Applied mathematics Intelligent systems;
• Design and machining;
• Control and dynamics;
• Bio-inspired systems;
• Internet of things;
• Big Data;
• Trusted software.
NRNU MEPhI has extensive experience in conducting international conferences on various aspects of the application of key technologies of the digital economy; NRNU MEPhI experts moderated the “Big Data” and “Applied Software” sections at the National Supercomputing Forums.
The conference "Intelligent Technologies in Robotics" in past years was held in person, but due to the current situation in the world related to COVID-19, the conference will be transferred to the online format. JVRT Subevent Jun 10, 18:18 eywords: Robotics, Cyber Physical systems security, Applied mathematics Intelligent systems, Design and machining, Control and dynamics, Bio-inspired systems, Internet of things, Big Data, RFID-technology, Blockchain, Trusted software
Abstract. Prosocial behavior is progressively being studied because of its enormous role in the changing conditions of the modern world. The behavior influences distant communication conducts Artificial Intelligent and causes psychological disorders, such as autism spectrum disorder or psychopathy.
We study the prosocial behavior by analyzing the brain features mechanisms during the custom setting - empathy game “Stone-Paper-Scissors”. Observed results for the cohort of 55 participants (28 women and 27 men) were obtained from the test questionnaires and EEG.
During the experiment 29 distinct brain structures were analyzed by the author’s “virtual implanted electrode” method. In the method, electrical activity was measured for the situation when the empathy behavior was activated. Our approach relates to the field of medicine and neuroscience, in particular, to a method for studying the activity of individual brain structures predefined by their spatial position according to the scalp multichannel EEG. A comparison between the empathy situations and the ‘first-person’ experience has been performed separately for gender reactions of men and women. The results of the questionnaires were tabulated and processed by the factor analysis. Six factors were identified that accounted for 41.45% of the total variance of the data. The factors received following interpretation: “Altruistic”, “Publicity”, “Emergency prosocial behavior”, “Conformist prosocial behavior”, “Anonymous prosocial behavior”, “Emotional prosocial behavior”. Comparison by Student's T-test showed a significant (p <0.05) difference between men and women in the “Publicity” factor (men are more public than women) and in the “Emotional prosocial behavior” factor (females are more emotional than males). Jun 11, 10:45 Keywords: EEG, Prosocial behavior, Empathy
Abstract. In this paper, a multilevel cognitive architecture is introduced that can be used to model mental processes in clients of psychotherapeutic sessions. The architecture does not only cover base level mental processes but also mental processes involving self-referencing, self-awareness and self-interpretation. To this end, the cognitive architecture was designed according to four levels, where (part of) the structure of each level is represented by an explicit self-model of it at the next-higher level of the architecture. At that next-higher level, states reify part of the structure of the level below; these states have a referencing relation to it. In this way the overall architecture includes its own overall self-model. The cognitive architecture was evaluated for a case study of a realistic type of therapeutic session from clinical practice. Jun 12, 14:35 s: Cognitive architecture, Self-Referencing, Self-Awareness, Self-Interpretation
Abstract. As a continuation of previous work, a functional architecture of a full-fledged AGI is proposed for working in an enterprise management system. Since the cre-ation of such an AGI will require more than one decade, an approach is proposed that allows combining individual more ready-made blocks from a full-fledged AGI with traditional enterprise management systems. Such systems including one or two AGI blocks and Consciousness are called Lean AGI. For these sys-tems, it is possible to use not full Consciousness, but some its limited industry-specific edition, located in the interval between the minimal and full Conscious-ness. Such editions are called Lean Consciousness. In the paper, the cases for the possible use of Lean AGI as part of enterprise management systems are shown. For one of these cases, the Lean AGI architecture based on the Goal analytics block is proposed for working on top of traditional enterprise management appli-cations. Jun 12, 16:09 ywords: AGI, Consciousness, Lean AGI, Lean Consciousness, Cognitive Architecture, Goal Analytics, Enterprise Management Applications
 Anastasia Devyatkina (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)), Natalia Myklyuchenko (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)), Anna Tikhomirova (National Research Nuclear University MEPhI) and Elena Matrosova (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)). Development of a laboratory workshops management module as part of a Learning Support System for the "Decision-Making Theory" course.
Abstract. The article is devoted to the problem of optimizing the load of teachers in the educational process. The task of developing a specialized Learning Support System with respect to specifics of the subject is considered. The authors propose a universal mechanism for managing laboratory workshops as a tool for monitoring and evaluating the degree of "Decision-Making Theory" course material assimilation. Jun 12, 21:51 Keywords: decision-making process, Learning Management System, Learning Support System, learning quality assessment, knowledge control
 Andrei Andrianov (University of Amsterdam), Seyed Sahand Mohammadi Ziabari (Delft University of Technology) and Charlotte Gerritsen (Vrije University Amsterdam). A Brain-Inspired Cognitive Support Model for Stress Reduction based on an Adaptive Network Model.
Abstract. Stress is often seen as a negative factor which affects every individual’s life quality and decision making. To help avoid or deal with extreme emotions caused by an external stressor, a number of practices have been introduced. In the scope of this paper, we take three kinds of therapy into account: mindfulness, humor, and music therapy. This paper aims to see how vari-ous practices help people to cope with stress, using mathematical model-ling. We present practical implementations in the form of client-server software, incorporating the computational model which describes therapy effects for overcoming stress based on quantitative neuropsychological re-search. The underlying network model simulates the elicitation of an ex-tremely stressful emotion due to a strong stress-inducing event as an exter-nal stimulus, followed by a therapy practice simulation leading to a reduc-tion of the stress level. Each simulation is based on user input and prefer-ences, integrating a parameter tuning process; it fits a simulation for a par-ticular user. The client-server architecture software which has been designed and developed completely fulfills this objective. It includes server part with embedded MATLAB interaction and API for client communication. Jun 12, 22:41 s: Integrative model, computational model, temporal-causal network, network-oriented modelling, stress
Abstract. Representing a world or a physical/social environment in an agent’s cognitive system is essential for creating human-like artificial intelligences. This study takes a story-centered approach to this issue. In this context, a story refers to an internal representation involving a narrative structure, which is assumed to be a common form of organizing past, present, future, and fictional events and situations. In the artificial-intelligence field, a story or narrative is traditionally treated as a symbolic representation. However, a symbolic story representation is limited in its representational power to construct a rich world. For example, a symbolic story representation is unfit to handle the sensory/bodily dimension of a world. In search of a computational theory for narrative-based world representation, this study proposes the conceptual framework of a Cogmic Space for a comic-strip–like representation of a world. In the proposed framework, a story is positioned as middle-level representation, in which the conceptual and sensory/bodily dimensions of a world are unified. The events and their background situations that constitute a story are unified into a sequence of panels. Based on this structure, a representation (i.e., a story) and the represented environment are connected via an isomorphism of their temporal, spatial, and relational structures. Furthermore, the framework of a Cogmic Space is associated with the generative aspect of representations, which is conceptualized in terms of unconscious- and conscious-level processes/representations. Moreover, a proof-of-concept implementation is presented to provide a concrete account of the proposed framework. Jun 13, 06:16 s: world representation, story, memory, artificial cognitive system
 Artur Petrosyan (Centre for Bioelectric Interfaces, National Research University Higher School of Economics), Mikhail Lebedev (Centre for Bioelectric Interfaces, National Research University Higher School of Economics) and Alexey Ossadtchi (Centre for Bioelectric Interfaces, National Research University Higher School of Economics). Linear systems theoretic approach to interpretation of spatial and temporal weights in compact CNNs: Monte-Carlo study.
Abstract. Interpretation of the neural networks architectures for decoding the signals of the brain usually reduced to the analysis of temporaland spatial weights. We describe a theoretically justified method of their interpretation within the novel architecture based on a priori knowledge of the subject area. This architecture is comparable in decoding quality to the winner of the BCI IV competition and allows to for automatic engineering of physiologically meaningful features. To demonstrate the operation of the algorithm, we performed Monte Carlo simulations andreceived a significant improvement in the restoration of patterns for different noise levels and also investigated the relation between the decoding quality and patterns reconstruction fidelity. Jun 13, 08:31 Keywords: ECoG, weights interpretation, limb kinematics decoding, deep learning, machine learning, monte carlo
Abstract. Empathy is the ability to recognize, understand, and share the feelings of another person, including the perception of another person. Empathy pro-motes prosocial or helping behavior that comes from within, rather than under duress.
Our paper revealed brain activity including empathy and altruistic processes in evoked potentials (EP). To review empathy, we analyze the brain activity characteristics with electrodes during watching the empathic game “Stone-Paper-Scissors”.
Results based on the nineteen-channel electroencephalography (EEG) re-cordings experiment on a sample of 16 participants (6 women and 10 men). Using the EP method, electrical activity was measured for the situation when the empathy behavior was activated. The evoked potential consists of a sequence of negative and positive deviations from the mainline and lasts 500ms after the end of the stimulus. In EP, evaluate the amplitude and la-tent period of occurrence. To register the EP, the same electrodes are used as for the EEG recording, and with the unified observational conditions.
We focused on brain structures associated with prosocial behavior, including the cortex, amygdala, and thalamus. A comparison between the em-pathy situations and the ‘first-person’ experience has been performed separately for reactions of men and women. We found significant differences in the following leads: T6, P4, O2, Pz in EP obtained in 2 series separately by gender. Jun 13, 10:53 Keywords: Evoked Potentials, Prosocial behavior, Empathy
 Yuliya Gavrilenko (Lomonosov Moscow State University), Saada Daniel (Lomonosov Moscow State University), Eugene Ilyushin (Lomonosov Moscow State University), Alexander Vartanov (Lomonosov Moscow State University) and Andrey Shevchenko (Lomonosov Moscow State University). The electroencephalogram based classification of internally pronounced phonemes.
Abstract. The internal speech recognition is a promising technology, which could find its use in brain-computer interfaces development and greatly help those who suffer from neurodegenerative diseases. The research in this area is in its early stages and is associated with practical value, which makes it relevant. It is known that internal pronunciation can be restored according to electroencephalogram data because it allows one to register specific activity associated with this process. The purpose of this work is to build and implement an algorithm for extracting features and classifying Russian phonemes according to an electroencephalogram recorded during the internal pronunciation of the phonemes. This kind of research is actively conducted abroad; however, at the moment, there is no information in open sources about such works for the Russian language phonemes. In the course of the work, an algorithm for extracting features and classifying the internal pronunciation of Russian phonemes was built and tested, the accuracy of which showed results comparable with other studies. Jun 13, 19:11 Keywords: Internal pronunciation, Brain-computer interface, Neurointerface, EEG
 Alisa Suyuncheva (Lomonosov Moscow State University) and Alexander Vartanov (Lomonosov Moscow State University). Comparison of brain induced potentials in internal speech in studied and unknown languages.
Abstract. Abstract. Significant differences were found in the bioelectric responses of the brain to words with the same meaning in Russian and Japanese in different se-ries in the electrophysiological experiment on a sample of 18 people. Two groups were used: those who knew Japanese and Russian, and those who knew only Russian as their native language. Differences in mutual articulation of these words were found due to differences in evoked potentials of the late latency (300-500 MS) in the frontal, central and parietal leads, and also in the earlier - 200 MS - latencies in the central, temporal and frontal leads). When comparing between groups (knows Japanese or does not know), differences in the components of EP 200 and P300 were obtained by speaking in Russian, howev-er, there is no difference in primary and semantic processing (P100, N400). . The main significant differences over the channels С3, F3, P3 given in this chapter are the amplitude and latency of latency in N200, as well as the de-crease in amplitude N400. These data are correlated with earlier studies. Jun 13, 20:03 Keywords: evoked potentials, neologism, silent speech
 Igor Prokhorov (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)) and Evgeny Tebenkov (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)). Machine learning algorithms for teaching AI chat bots.
Abstract. Machine learning is a method of data analysis, which allows the analytical system to learn in the course of solving many similar problems. Machine learning is based on the idea that analytical systems can learn how to identify patterns and make decisions with minimal human involvement. The history of already completed dialogues between users is used to train chat bots for automated communication with interlocutors. There are many machine learning algorithms, and this article describes the most popular of them and their use for teaching chat bots. Jun 13, 20:37 words: Machine learning, artificial intelligence, chat bots, algorithms of learning chat bots
Abstract. Research towards a new approach to the abstract symbol grounding problem showed that through model counting there is a correspondence between logical/linguistic and coordinate representation in the visuospatial domain. The logical/verbal description of a spatial layout directly gives rise to a coordinate representation that can be drawn, with the drawing reflecting what is described. The main characteristic of this logical property is that it does not need any semantic information or ontology apart from a separation into symbols/words referring to relations and symbols/words referring to objects. Moreover, the complete mechanism can be implemented efficiently on a brain inspired cognitive architecture, the Activation Bit Vector Machine (ABVM), an architecture that belongs to the Vector Symbolic Architectures. However, the natural language fragment captured previously was restricted to simple predication sentences, with the corresponding logical fragment being atomic Context Logic (CLA), and the only actuation modality leveraged was visualization. This article extends the approach on all three aspects: adding a third category of action verbs we move to a fragment of first-order Context Logic (CL1), with modalities requiring a temporal dimension, such as film and music, becoming available. The article presents an ABVM generating sequences of images from texts. Jun 18, 08:43 s: Symbol grounding problem, Vector Symbolic Architectures, Action verbs, Activation Bit Vector Machine, Context Logic
Abstract. Mental models and the mental process of modeling them play a crucial role in individual and organizational learning. The adaptive mental processes involved in the development of mental models are addressed here. The reported study integrates a number of psychological and neurological theories on mental models and the learning involved. An adaptive network model has been designed for these processes and used for simulations addressing a case study of learning to drive a car. The developed model may be valuable for different ends, like in improving individual and organizational learning, in designing virtual pedagogical agents, enhancing driver safety, and self-driving systems in Cars. Jun 18, 08:50 ds: Mental Models, Adaptive network model, Hebbian learning
Abstract. This article presents two logical circuits of a ΣΠ-neuron model. Each circuit is divided into two layers, realizing a multiplicative and additive operation. The circuits are built on the basis of digital and analog elements. The layer that performs the multiplicative function is identical in both circuits. The ad-ditive layer in the digital circuit is realized due to the digital adder. In the hy-brid circuit, it is realized by means of DACs and an analog voltage adder. To implement the threshold function in the hybrid circuit used a comparator. In the digital circuit used a digital comparator. The use of analog elements for the implementation of the summation functions has the advantages of speed over a digital adder, however, it loses in accuracy. The results obtained can be used to construct ΣΠ-neural networks which can be included in the hard-ware and software of mechatronic devices. Jun 19, 11:40 Keywords: artificial neuron, logical sigma-pi neuron, neural networks, sigma-pi neural networks
 Tatiana Timashkova (National Research Nuclear University MEPHI), Anastasia Kuznetsova (National Research Nuclear University MEPHI) and Natalia Repetskaya (National Research Nuclear University MEPHI). The concept of the formation and development of a multi-communication logistics system for an enterprise.
Abstract. The urgency of the research is based on the need to ensure the objectivity and transparency of contractual relations in all parts of the logistic chain, on the one hand, and the possibility of using the synergistic effect of the participants’ experience grounded in interconnected business-possesses, which can be achieved by introducing the principles of multi-agent technologies into the logistics management, on the other hand. The purpose is to work out an algorithm for creating and developing a multi-communication logistics system for a business structure. Method: a systematic approach, economic methods for processing special data, a method for analyzing dataset are used. Findings: The paper presents a methodology for the development of a virtual enterprise in supply and marketing spheres of an industrial company, based on the author's methodology of financial micro-logistics.
The core of the proposed algorithm for developing a multi-communication logistics system is focused on two aspects. The first is a combination of financial transparency and objectivity with the help of the proposed information support for logistics management of supply and sales. The second is a use of voluntary symbiosis of all logistics resources (material, financial, human, organizational capital). Improvements: The authors believe that the obtained algorithm of the logistics portal for an enterprise will both advance and speed up business relations and make them more transparent. JVRT Subevent Jun 23, 20:42 Information Logistics, Industry, Multi-Agent Technologies, Efficiency, Virtual Enterprise, Multi-Communication Logistics System
 Larisa Ismailova (National Research Nuclear University “MEPhI”), Viacheslav Wolfengagen (National Research Nuclear University "MEPhI"), Sergey V. Kosikov (NAO JurInfoR) and Denis Babushkin (NAO "JurInfoR"). Modeling Spread, Interlace and Interchange of Information Processes with Variable Domains.
Abstract. In this paper a semantic metalanguage is developed and designed to study the occurrence, spread and safe interaction of semantic processes in information modeling systems, including cognitive interference.
An approach to construe a semantic network is proposed and based on a computational model in which both nodes and arcs are information processes. Concepts are represented by intensional objects within the framework of theories without types, and they, in turn, are considered as special counterparts of typed theories. Similar mixing was used in model studies for lambda calculus. To a contrast with them, in this paper, information processes correspond to parameterized metadata objects, which are variable domain constructs. Transformations of variable domains correspond to the spread of the process. Directional transformation provides the generation of metadata targets in the form of parameterized concepts. This simulates the development of the process,
which corresponds to the spread of cognitive interference and
allows the interpretation of a hidden time factor.
The emerging model is purely process based and provides such a conceptual framework. The possibility of coding this framework with a system of interdependent lambda terms is reflected. Jun 25, 09:13 s: information process, cognitive interference, quantum shift learning, natural computing
Abstract. This paper focuses on defining and simulating behavioural outcomes of the bystander effect. These insights were modeled by temporal-causal networks. Typical patterns of bystander behaviour were translated into three requirements and seven simulated scenarios of the by-stander eﬀect. All scenarios were simulated to showcase the main bystander eﬀect dynamics and its accordance with the literature. Unknown parameters of the eﬀect were further esti-mated by a Simulated Annealing algorithm. In the end, the created model shows the potential to simulate the bystander eﬀect in diﬀerent and new scenarios. Jun 29, 13:01 Keywords: bystander effect, cognitive model, causal network
Abstract. Feras A. Batarseh is a Research Assistant Professor with the College of Science at George Mason University (GMU). His research spans the areas of Data Science, Artificial Intelligence, and Context-Aware Software Systems. Dr. Batarseh obtained his Ph.D. and M.Sc. in Computer Engineering from the University of Central Florida (UCF) (2007, 2011), and a Graduate Certificate in Project Leadership from Cornell University (2016). His research work has been published at various prestigious journals and international conferences. He is the director of Turing Research, an AI for policy group at GMU. Dr. Batarseh has taught data science and software engineering courses at multiple universities including GMU, UCF, Georgetown U, University of Maryland, Baltimore County (UMBC), and George Washington University (GWU). Additionally, Dr. Batarseh published and edited several books and chapters, his two recent books are Federal Data Science and Data Democracy, both published by Elseviers Academic Press. For more information on Dr. Batarseh, please refer to the following websites:
http://turing.cos.gmu.edu/ PC Membership Jul 03, 15:39
Keywords: AI, Machine Learning, Data Science
 Julia Bobrysheva (National Research Nuclear University MEPhI) and Sergey Zapechnikov (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)). Post-quantum group key agreement scheme.
Abstract. Progress in quantum technologies forces the development of new cryptographic primitives that are resistant to attacks of an adversary with a quantum computer. A large number of key establishment schemes have been proposed for two participants, but the area of group post-quantum key establishment schemes has not been studied a lot. Not so long ago, an isogeny-based key agreement scheme was proposed for three participants, based on a gradual increase in the degree of the key. We propose another principle for establishing a key for a group of participants using a tree-structure. The proposed key establishment scheme for four participants uses isogeny of elliptic curves as a mathematical tool. Jul 11, 08:25 Keywords: group key agreement, isogenies, post-quantum scheme
Abstract. An important task in the field of automatic data analysis is detecting emotions in texts. The paper presents the approach of emotional analysis of text data in Russian. To conduct an emotional analysis, a method was created based on vector representations of words obtained by the ELMo language model, and subsequent processing by an ensemble classifier. To configure and test the created method, a specially prepared dataset of texts for five basic emotions -- joy, sadness, anger, fear, and surprise -- is used. The dataset was prepared using a crowdsourcing platform and a home-grown procedure for collecting and controlling annotators' markup. The overall accuracy is 0.78 (by the F1-macro score), which is currently the new state of the art for Russian. The results can be used for a wide range of tasks, for example: monitoring social moods, generating control signals for mobile robotic systems, etc. ITR 2020 Jul 11, 17:00 words: text analysis, natural language processing, emotion detection, machine learning, crowdsourcing
 Sergey Nemeshaev (Mephi), Kazbek Dadteev (National Research Nuclear University MEPhI), Daria Pavlenko (National Research Nuclear University MEPhI) and Leonid Barykin (NRNU MEPHI). Using virtual reality technologies in the educational process.
Abstract. Recently, new teaching methods are gaining popularity. About 10 years ago, the distance learning platforms were actively launched - it gave the opportunity to learn at a convenient time, in a convenient place and at the same time to choose the best courses and receive knowledge from recognized experts in their science area. Today we see that with the advent of new technologies, new opportunities open up - learning with virtual or augmented reality. In our article we will consider not only the main advantages of learning process with the use of these technologies, but also propose methods that will improve educational content based on the analysis of students' behavior. Jul 11, 19:30 words: Virtual reality, augmented reality, mooc, learning
Abstract. Distance learning systems have become a convenient and useful tool in the educational process. Teachers can quickly place training materials, prepare test questions, create tests. Using such systems allows you to get detailed statistics on the assimilation of material by students, identifying the topics that caused the most problems in passing the tests. However, analysis of only the final results gives only a general idea of the student's success. In this article we will show that analysis of additional metrics, such as response time for each task, changing the solution when choosing an answer, skipping questions and then returning to them, and other metrics, can improve the quality of analysis of material assimilation and identify problem areas in training. Jul 11, 19:46 words: artificial intelligence methods, distance learning, testing, LMS
 Alexander Sboev (NRC "Kurchatov Institute"), Alexey Serenko (NRC "Kurchatov Institute"), Roman Rybka (NRC "Kurchatov Institute") and Danila Vlasov (NRC "Kurchatov Institute"). Ensembling SNNs with STDP learning on base of rate stabilization for image classification.
Abstract. The possibility of solving real-vector classification tasks based on a simple spiking network with Spike-Timing-Dependent-Plasticity (STDP) learning was shown in our previous works.
In this paper, that method is extended by aggregating neurons into ensembles, and validated on an image recognition dataset.
The method is based on a one-layer network of neurons with STDP-plastic inputs receiving pixels of input images encoded with spiking rates.
This work considers two approaches for aggregating neurons' output activities within an ensemble: by averaging their output spiking rates (i. e. averaging outputs before decoding spiking rates into class labels) and by voting with decoded class labels.
Ensembles aggregated by output frequencies are shown to achieve a significant accuracy increase up to 95\% (by F1-score) for the Optdigits handwritten digit dataset, and is comparable with conventional machine learning approaches. ITR 2020 Jul 12, 06:52 Keywords: spiking neural networks, spike-timing-dependent plasticity, ensembles of SNN, image classification
 Aleksandr I. Panov (FRC CSC RAS), Alexey Skrynnik (FRC CSC), Aleksei Staroverov (MIPT), Ermek Aitygulov (ISA), Kirill Aksenov (HSE) and Vasilii Davydov (Moscow Aviation Institute). Hierarchical Deep Q-Network from Imperfect Demonstrations in Minecraft.
Abstract. We present Hierarchical Deep Q-Network (HDQfD) that won first place in the MineRL competition. The HDQfD works on imperfect demonstrations and utilizes the hierarchical structure of expert trajectories. We introduce the procedure of extracting an effective sequence of meta-actions and subgoals from the demonstration data. We present a structured task-dependent replay buffer and an adaptive prioritizing technique that allow the HDQfD agent to gradually erase poor-quality expert data from the buffer. In this paper, we present the details of the HDQfD algorithm and give the experimental results in the Minecraft domain. Jul 15, 14:56 rds: reinforcement learning, Minecraft, demonstrations
 Evgeniy Tretyakov (Plekhanov Russian University of Economics, National Research Nuclear University MEPhI (NRNU MEPhI)), Alexey Artamonov (Plekhanov Russian University of Economics, National Research Nuclear University MEPhI (NRNU MEPhI)), Kristina Ionkina (Plekhanov Russian University of Economics, National Research Nuclear University MEPhI (NRNU MEPhI)), Boris Onykij (National Researcn Nuclear University MEPhI (NRNU MEPhI)) and Rufina Tukumbetova (Plekhanov Russian University of Economics). A Methodology for Designing Self-organizing Systems.
Abstract. In existing multi-agent systems, agents are used that perform various, but strictly regulated processes, and their inducing occurs according to a schedule specified by the operator or according to some preset conditions. Such approach leads to a high level of downtime of computing power in solving problems of preparing information and analytical data in a short time, be-cause agents are not universal enough, and their incentive to work is not event-oriented.
The article presents a methodology for group multi-agent problem solving. Group autonomous problem solving is possible only if there is autonomous group management and, in particular, the possibility of self-organization, i.e. changing of structural units within a group and changing of its behavior depending on the actual state of the external environment.
The practical solution to self-organization problems in agent systems seems to be the most important direction for increasing their intellectual characteristics and the areas of their practical use. Jul 17, 12:13 words: self-organizing system, multi-agent system, multi-agent system architecture
 Tatiana Semenova (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute),) and Yurij Kotov (Keldysh Institute of Applied Mathematics, Russian Academy of Sciences). Mathematical Methods for Solving Cognitive Problems in Medical Diagnosis.
Abstract. Complex system exploring at initial stage of includes fixation of basic data blocks, problem categories, and other. At this stage, an adequate description uses gestalt-like patterns for the future system, its components and their functioning. In the structure of such system, functional and logical relationships can be found between the components or processes. Such a complex system is a living organism (for example, a patient). A skilled patient-treating physician can discover functional and logical associations between the components or processes in the studied system. In some difficult cases, doctors cannot explain their decisions and actions. They might use it practically, but cannot formulate verbally. For these cases, mathematician I.M. Gelfand has proposed a method of diagnostic games. The game is a cognitive research to reveal the doctor's intuitive action plan for specific case of the patient's treatment. Working together, the mathematician and physician can formulate a verbal case description. The objective of this study is to extract strict formalized elements from the doctor's gestalt perception: the rules of diagnostic decision. In order to analyze the intuitive actions of the specialists, the authors propose a mathematical language and technologies based on non-numerical statistics and three-valued logic. The language helps us detect and solve such cognitive problems. The collaboration with doctors allows us to create clear diagnostic rules based on the latent knowledge of an experienced specialist. The article provides a brief description of the method used for solving the problems of practical medicine. Jul 26, 10:21 Keywords: gestalt, diagnostic game, formalization of the doctor’s knowledge, non-numerical statistics, three-valued logic
 Vasilii Davydov (Moscow Aviation Institute), Timofei Liusko (Moscow Institute of Physics and Technology (National Research University)) and Aleksandr I. Panov (FRC CSC RAS). Self and Other Modelling in CooperativeResource Gathering with Multi-AgentReinforcement Learning.
Abstract. In this work, we explore the application of the Self-other-Modelling algorithm (SOM) to several agent architectures for the collab-orative grid-based environment. Asynchronous Advantage Actor-Critic(A3C) algorithm was compared with the OpenAI Hide-and-seek (HNS)agent. We expand their implementation by adding the SOM algorithm. As an extension of the original environment, we add a stochastic initialization version of the environment. To address the lack of performance in such an environment by all versions of agents, we made further improvements over the A3C and HNS agents, adding the module dedicated to the SOM algorithm. This agent was able to efficiently solve a stochastically initialized version of the environment, showing the potential benefits ofsuch an approach. ITR 2020 Jul 31, 21:07 Keywords: Self and Other Modelling, Multi-agent Reinforcement Learning, A3C, Cooperative Resource Gathering
 Vladimir Red'Ko (Scientific Research Institute for System Analysis, Russian Academy of Sciences, Moscow, Russia) and David Saakian (Advanced Institute of Materials Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam). The model of a simple self-reproducing system.
Abstract. The model of a simple self-reproducing system has been investigated. The current model has been developed in the framework of syser systems. The term syser is the abbreviation of the words “SYstem of SElf-Reproduction”. The syser model can be considered as a reasonable model of the prebiological macromolecular self-reproducing systems. The syser includes a polynucleotide matrix, a replication enzyme, a translation enzyme, and other enzymes and protein. Earlier, we studied the model of sysers with a large number of macromolecules. However, a large number of macromolecules is a certain disadvantage of models of prebiological systems. In order to overcome this disadvantage, a new syser model with a small number of macromolecules has been constructed and investigated in the present work. The macromolecules of the syser are located inside the protocells. Additionally, we consider special macromolecules that provide sysers with energy. There is a mechanism for duplication of protocells. We consider the evolution of the population of sysers. The dynamics of macromolecules in protocells and an evolution of sysers populations has been analyzed by means of computer simulation. It was shown that during the evolution of the sysers population, the comparative number and rate of synthesis of macromolecules, which provide protocells with energy, can be increased. The decrease of the synthesis rate of polynucleotide matrices is also possible. Aug 05, 08:41 s: Sysers, Evolution of protocells, Energy resource of protocells, Macromolecular dynamics, Polynucleotide matrices, Enzymes
Abstract. A simple computer model of a feeling of causality of autonomous agents has been created and investigated in the current article. The model of the evolution of a population of agents is considered. The population includes agents of two kinds: 1) agents with a feeling of causality and 2) agents without such a feeling. Each agent has its internal resource. Agents with a feeling of causality remember causal relationships between situations in the external environment. It is shown that agents with a feeling of causality in the process of evolution can displace agents without a feeling of causality from the population. So, the current model can be considered as the model of origin of a feeling of causality. Aug 07, 11:20 ywords: Feeling of causality, Autonomous agents, Evolution, Agent’s internal resource
 Viacheslav Wolfengagen (National Research Nuclear University MEPhI), Larisa Ismailova (National Research Nuclear University MEPhI) and Sergey V. Kosikov (NAO "JurInfoR"). Computational Model for Capturing the Interdependencies on Information Processes.
Abstract. Current resource integrity techniques such as sub-resource integrity (SRI) do not apply to unknown content, and mitigating attacks along this vector remains an open issue.
Complex interdependencies on the Internet have implications that go beyond security, leading to the creation of unwanted manipulation of information processes. Cognitive interference, based on the deliberate entanglement of information processes, is increasingly spreading on the Web.
Today, there is still no generally accepted understanding of the mechanism of entanglement of information processes.
In this paper, we propose a cognitive architecture and a computational model that allow representing an information process with a variable property, opening up opportunities for studying the forms of its behavior. Entangled processes are considered to occur simultaneously, as coexisting processes that have a common source of origin, and the state of their system is represented by a superposition of components.
As it turns out, the integrity of resources is violated when the conditions for the emergence of architecture with a variable property arise in a tangle of process dependencies. This creates entangled information processes. This occurs in the emergence of vulnerabilities in the Web environment such as cross-site scripting XSS. The models and mechanisms of complex interdependencies on the Internet still remain poorly understood. Aug 11, 19:12 ywords: information process, cognitive interference, tangled processes, computational model, cognitive architecture
 Gennady Baryshev (National Research Nuclear University "MEPhI"), Yuri Bozhko (National Research Nuclear University "MEPhI"), Igor Yudin (National Research Nuclear University "MEPhI"), Aleksandr Tsyganov (National Research Nuclear University "MEPhI") and Anna Kainova (National Research Nuclear University "MEPhI"). Design of a transcranial magnetic stimulation system with the implementation of nanostructured composites.
Abstract. The method of transcranial magnetic stimulation has a number of confirmed ap-plications – not only for curing diseases, but also to develop neurocognitive abili-ties, such as language learning. Stationary treatment is rather expensive and not very convenient for many people. The trend so far is about development of mo-bile TMS systems. In this paper we present the results of our design of a tran-scranial magnetic stimulation system, that is different from other by application of nanostructured composites as a functional material. We discuss the features of such a design and options obtained by application of nanostructured materials. Aug 13, 13:39 Keywords: Transcranial magnetic stimulation, Neurology, Cognitive functions, Nanomaterials, Composites
 Gennady Baryshev (National Research Nuclear University "MEPhI"), Valentin Klimov (Klimov), Aleksandr Berestov (National Research Nuclear University "MEPhI"), Anton Tokarev (National Research Nuclear University "MEPhI") and Valeria Petrenko (National Research Nuclear University "MEPhI"). Application of information measuring systems for development of engineering skills for cyber-physical edu-cation.
Abstract. The new industrial revolution opening the way to the digital world requires further development of engineering education. Future engineers should obtain skills in the area of development of cyber physical (intellectual) systems. In National Research Nuclear University MEPhI we have examples of implementation of new engineering courses and programs for cyber physical education. In this paper we discuss the problems and results of application of information measuring systems which main purpose is for research and development needs, for educational tasks. Aug 13, 13:42 Keywords: Information Measuring System, Engineering skills, Cyber-physical education.
 Gennady Baryshev (National Research Nuclear University "MEPhI"), Aleksandr Putilov (National Research Nuclear University "MEPhI"), Dmitriy Smirnov (National Research Nuclear University "MEPhI"), Aleksandr Tsyganov (National Research Nuclear University "MEPhI") and Vladimir Chervyakov (National Research Nuclear University "MEPhI"). Principles of design of a learning management system for development of economic skills for nuclear engineering education.
Abstract. The dramatic changes in education that we face in 2020 and which are the result of the accelerating digital revolution indicate the need to develop new tools for education and training. Leading universities are developing their own Learning Management Systems (LMS) based on information and Internet technologies. However, in higher education there are certain educational tasks in the interdisciplinary field. One of them is the task of successfully developing atomic engineering education. In this article, we present the basic design principles and architecture features of a specific LMS for the development of economic skills for educa-tion in the field of nuclear technology. Aug 13, 13:44 Keywords: Learning management system, Economic skills, Engineering education, Nuclear education
Abstract. Although bilinguals’ word processing in a foreign language (L2) is one of mainstreams of cognitive psycho-linguistic studies, the actual research on how they process function words (FW) with graded differences in L2 proficiency is still behind plenty of studies available in second language acquisition literature. We used eye movement measures of FW reading in L2 to in-vestigate whether the degree of L2 proficiency modulates L2 FW processing. The results showed that proficient L2 bilinguals process FW faster compared to basic L2 bilinguals. Taken together, our findings are consistent with implicit learning statements, the Weaker Links hypothesis, and the bilingual activation model plus. Thus, the level of L2 proficiency can be predicted based on L2 FW processing features. Aug 14, 17:44 Eye Movements, Function Words, Reading, Prediction, Proficiency
Abstract. Artificial intelligence has made great strides since the deep learning revolution, but AI systems still struggle to discover generalizable principles and rules which allow them to extrapolate beyond their training data. For inspiration we look to a the domain of science, where the scientific method has led to theories which show remarkable ability to extrapolate and sometimes even predict the existence new never before seen phenomena. According to David Deutsch, this type of extrapolation, which he calls "reach", is due to scientific theories being an example of a larger class of explanations he calls ``good explanations'', which are defined by the property that they are hard to vary. In this work we investigate Deutsch's hard-to-vary principle and how it relates to more formalized principles in deep learning such as the bias-variance trade-off and Occam's razor. Recent work suggests methods which involve fitting highly parameterized (easy-to-vary) deep neural network models to big data can go quite far and may underlie much of how the brain learns. At the same time here we argue that Deutsch's principle may be a necessary additional component for achieving AI which is capable of generating good explanations of the world. Aug 15, 21:32 s: Deep learning, artificial intelligence, generalization, extrapolation, Occam's razor, parsimony, critical rationalism, induction, David Deutsch
 Valeriia Demareva (National Research University: Lobachevsky State University of Nizhni Novgorod) and Julia Edeleva (University of Braunschweig). Eye movement correlates of foreign language proficiency in Russian-English bilinguals.
Abstract. Today, the modeling of various aspects of speech activity is the mainstream of modern cognitive and computational science. Along with models of natural language processing, much attention is paid to finding mechanisms of the several languages functioning within one linguistic system (bilingual, trilingual, etc.). The search for specific features of the processing of linguistic information by one subject in different languages allows one to approach the construction of bilingual systems models. This article is devoted to the analysis of the eye movements in reading texts in native and foreign languages by bilinguals with different levels of proficiency in the latter. The present study tests the assumption that eye movement features of people with a high level of foreign language skills are similar during text reading in native and foreign languages. Another goal is to elicit features that provide the differentiation between the elementary and the intermediate levels of English language proficiency. We offer new Eye Tracking based evaluation metrics for the level of language proficiency. Aug 16, 03:25 Keywords: Eye Tracking, Bilinguals, Reading skills, Comprehension, Proficiency
Abstract. In accordance with the FATF Recommendations of 2012, countries and their financial units are to manage Money Laundering/Counter Terrorism Fnancing risk (hereinafter – ML/CTF risk). Such a process consists of the following main steps: risk identification, assessment and mitigation. Besides the document supervises the requirements regarding the efficient use of financial monitoring resources.
Commercial banks often face the problem of inadequate identification and assessment due to the lack of true and undistorted information about their clients. For the first, financial monitoring services are considered to be non-profitable in accordance with bank management. As a rule, resources of financial monitoring in commercial banks are rather limited to buy data bases containing identification information about physical and juridical bodies.
For the second, sometimes financial institutions (commercial banks in particular) don’t manage to collect enough information about new client without any financial history (for example start-ups or individuals who just open banking accounts for the first time). As a result, lack of information resources affects the quality of ML/CTF risk assessment.
The article is devoted to the ways of such problems solution with the use of contemporary IT – systems and technologies. Automatization of these procedures will lead to a significant growth of financial monitoring efficiency in commercial banks and other financial institutions. Aug 16, 07:10 er
Keywords: ML/CFT, money laundering risk, counter terrorism financing risk, risk assessment, commercial banks, information technologies
Abstract. Recently, many researchers in Artificial intelligence (AI) are interested in implementing causality in machine. Some researchers such as Pearl suggest inferential logic as the best way to implement causality in machine. Other researchers such as Faghihi and his colleagues suggest Probabilistic Fuzzy logic as a better alternative for the implementation of Causality in machine. We think that one technic or tool such as inferential logic can not accomplish causality. In this presentation, we will define the ladder of causality as a combination of different domain and techniques such as logic, Deep Learning algorithms, and cognitive architectures. Aug 30, 01:21 rds: Cognitive architectures, logic, Deepl Learning
Abstract. The article explored the possibilities of using digital technologies and the global online environment for the development of two-component nuclear energy. We studied both the purely technical capabilities and the reserves of using digital technologies to increase the efficiency of information support for the development of two-component nuclear energy. Particular attention is paid to the international experience of using information and communication technologies in industries implementing innovative development strategies. Based on the results of the analysis of the potential of using information and communication technologies, a system of proposals for their support was developed at the level of the state support system for the development of two-component nuclear energy. Sep 02, 11:59 er
Keywords: information and communication technologies, two-component atomic inergetics, digital economy, innovations, state support economy, economic cross
 Aleksandr Putilov (National Research Nuclear University "MEPhI"), Timokhin Dmitriy (National Research Nuclear University MEPhI) and Bugaenko Marina (National Research Nuclear University MEPhI). The use of the economic cross method in IT modeling of industrial development (using the example of two-component nuclear energy).
Abstract. The article contains recommendations on the use of the economic cross method in computer modeling of sectoral development. Using the example of two-component nuclear power, the strengths and weaknesses of the method were investigated. The possibilities and limitations of using modern IT technologies for optimizing the management of information flows as part of the construction and analysis of alternative models of the economic cross are indicated. The article contains copyright proposals in the field of the use of it technologies to overcome the obstacles facing the analyst at the current stage of the formation of two-component nuclear energy. Sep 02, 12:16 er
Keywords: Two-Component Nuclear Power, Information and communication technologies, economic models, economic cross, industry economy
 Luzgina Ksenia (National Research Nuclear University "MEPhI"), Popova Galina (National Research Nuclear University "MEPhI") and Manakhova Irina (Lomonosov Moscow State University). Cyber threats to information security in the digital economy.
Abstract. The article focuses on the problem of promoting information security in the emerging digital economy. The main emphasis is placed on solving the problems of preventing threats and cybersecurity risks in the era of rapidly developing digital technologies. The basic approaches are related to the construction of an infrastructure to combat cybercrime: formation, technological improvement of IT software and hardware, regulation, the use of finance and insurance, support for the media and the development of a safety culture. The results of this study can be applied to the design of information security systems at all levels. Sep 02, 12:33 Keywords: digital economy, information security, cyber threats, IT-technologies, outsourcing, innovations, blockchain
Abstract. Learning processes can be described by adaptive mental (or neural) network models. If metacognition is used to regulate learning, the adaptation of the mental network becomes it-self adaptive as well: second-order adaptation. In this paper, a second-order adaptive men-tal network model is introduced for metacognitive regulation of learning processes. The fo-cus is on the role of multiple internal mental models, in particular, the case of visualisation to support learning of numerical or symbolic skills. The second-order adaptive network model is illustrated by a case scenario for the role of visualisation to support learning mul-tiplication at the primary school. Sep 08, 14:16 s: mental model, adaptive network model, learning, control
 Alexei V. Samsonovich (Krasnow Institute for Advanced Study, George Mason University and NRNU MEPhI) and Arthur Chubarov (MEPhI). Virtual Convention Center: A socially emotional online/VR conference platform.
Abstract. A cognitive model, producing believable socially emotional behavior, is used to control the Virtual Actor behavior in an online scientific conference paradigm. For this purpose, a videogame-like platform is developed, in which Virtual Actors are embedded as poster presenters, receptionists, etc. The expectation is that the combination of somatic factors, moral appraisals and rational values in one model has the potential to make behavior of a virtual actor more believable, humanlike and socially acceptable. Implications concern future intelligent cobots and virtual assistants, particularly, in online conferencing and distance learning platforms and in intelligent tutoring systems. Sep 13, 06:48 Keywords: online conference, virtual reality conference, distance learning, socially emotional intelligence
 Viacheslav Wolfengagen (National Research Nuclear University “MEPhI”), Larisa Ismailova (National Research Nuclear University “MEPhI”) and Sergey V. Kosikov (NAO "JurInfoR"). Imposing and Superposing the Information Processes over Variable Concepts.
Abstract. A conceptual basis for modeling the information process is given in the study of the features of its propagation in case of varying the determining property. The system of conceptual dependencies over variable abstract sets is used as a relativized basis for processing semantic information. Variable domains and mapping are used to control the progress of the process, as well as recognition mappings and reversal mapping of the process property. It is shown that in the representing functorial category they can be interpreted as cognitive architectures that are significant for solving semantic modeling problems. In this case, the superposition of process states can be taken into account. Sep 19, 08:20 Keywords: information process, variable sets, category theory, superposition of states, cognitive architecture, Schrodinger's cat
 Sergey Misyurin (National Research Nuclear University «MEPhI»), German Kreinin (Blagonravov Mechanical Engineering Research Institute of the Russian Academy of Sciences (MERI of RAN)), Natalia Nosova (Blagonravov Mechanical Engineering Research Institute of the Russian Academy of Sciences) and Andrey Nelyubin (Blagonravov Mechanical Engineering Research Institute of the Russian Academy of Sciences). Kinematics and dynamics of the spider-robot mechanism, motion optimization.
Abstract. In this paper, we consider the kinematics and dynamics of a spider robot mechanism with 18 degrees of freedom (six legs). The equations of kine-matics and dynamics are written out; and the issue of optimizing the robot's movement is considered. The robot's gait is analyzed, in which part of the legs is on the ground and supports the robot, and part of the legs moves in the air. At the first stage for solving this problem, one leg is considered separately, as a kinematic system with open kinematics and with three degrees of freedom. The kinematics equations were presented in matrix form using the principle of rotation of the coordinate system. The dynamics equations are based on Lagrange equations of the second kind. The mass of the legs, reduced to the center of gravity, moments of inertia, moments developed by engines were taken into account, and ets. The conclusions were made about the optimal movement of the leg based on the obtained equation of kinetic energy of the robot's leg based on the obtained equation of the kinetic energy of the robot leg. ITR 2020 Sep 22, 09:23 Keywords: Kinematics, Dynamics, Spider robot, Optimization, General Lagrange equation
 Yulia Medvedeva (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)) and Rafael Abdulov (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)). Labor productivity growth based on revolutionary technologies as a factor for overcoming the economic crisis.
Abstract. Labor productivity growth is of paramount importance both for overcoming the economic crisis caused by the COVID-19 pandemic and for the recession that began much earlier. As you know, over the past ten years, the rate of return growth rates have shown a steady downward trend. It is the revolutionary ways to accelerate labor productivity, especially based on technologies such as artificial intelligence, augmented reality, and robotics, that can turn the tide and revive the global economy. Development of practical recommendations for increasing growth rates first requires to theoretically reveal the internal structure of labor productivity as a contradictory unity, the productive force of labor and labor intensity, which will determine the main driving forces and main directions of labor productivity growth Sep 23, 15:20 Keywords: labor productivity, productive force and labor intensity, economic crisis and revolutionary technologies
Abstract. The computational theory of cognition, also known as computationalism, holds that cognition is a form of computation. Thus, computation can be seen as a or the explanatory basis for cognition. The goal of this paper is to address two key aspects here: A) Computing systems are traditionally seen as representational systems, but functional and enactive approaches are supporting non-representational approaches to computing; B) Recently, a sociocultural theory against computationalism has been proposed with the aim of reducing computing to cognition, but we consider that cognition and computation are based on action, that cognition is just a form of computing and that cognition is the explanatory basis for computation. In this article, we state that: 1. Representational theories of computing recurring to intentional content run into metaphysical problems, which are unsustainable to characterize computing and cognition in nature. 2. Functional non-representational theories of computing and cognition do not incur this metaphysical problem when describing computing in terms of the abstract machine. 3. Functional theories are consistent with enactive theories of computing in describing computing machines not in a strictly functional way, but especially in terms of their organization. 4. This paper also claims that the theory of enactive or autopoietic cognition is consistent with the computational basis of cognition in describing Turing machines as functionally and organizationally closed systems. 5. It is here defended, the computational basis of cognition and the cognitive explanatory basis for computing, arguing that computer science is developed in the human linguistic domain, then as a product of human socionatural normative practices. The paper concludes by supporting that computational models are about actions and that computing is in action. Furthermore, cognition is just one form of computing in the world and the explanatory basis for computation. Sep 23, 17:07 s: Cognitive Systems, Enaction, Computing, Socio-natural practices
Abstract. The article is devoted to the recent scientific problem of privacy-preserving data patterns recognition. The urgency of the problem is determined by the growing need to use machine learning for personal data, as well as data that make up commercial, medical, financial and other types of secrets protected by law. The purposes of the work are to systematize the security models of machine learning, to identify algorithmic tools that can be used to ensure the privacy of the learning process and application of models, and to analyze the privacy-preserving machine learning systems. The article presents the main concepts and definitions related to machine learning, gives a systematization of machine learning problems and methods of their solution, and notes modern and promising areas of development of machine learning. Among the tasks of machine learning are those for which it is important to ensure the privacy of data from training, test and work samples. Special cryptographic methods and protocols are correlated to the solved problems. A brief description of the known systems for privacy-preserving data analytics is given, noting the machine learning methods supported by them, the type of adversary that the system can resist, and the cryptographic primitives used for the implementation. Unsolved problems in the field of privacy-preserving data patterns recognition and prospects for the development of this scientific field are considered. Sep 23, 17:20 words: data mining, deep learning, privacy, secure multi-party computations, secret sharing scheme, homomorphic encryption
 Andrey M. Kanner (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)), Tatiana M. Kanner (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)) and Anna V. Epishkina (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)). Algorithm for Optimal and Complete Testing of Software and Hardware Data Security Tools.
Abstract. The article considers the disadvantages of existing approaches to testing software and hardware data security tools in order to confirm compliance of the implemented functionality with the declared characteristics. It demonstrates the necessity of ensuring completeness and optimality of testing. The article describes some testing approaches based on the development of mathematical models using the automata theory and ensuring completeness of testing, but leaving the question of its optimality open. It describes the approach proposed earlier by the authors, which ensures both completeness and optimality of testing using the graph theory. In accordance with this approach, the software and hardware data security tool is represented as a directed graph without loops or multiple edges. The graph vertices correspond to the states of the software or hardware component, and the directed edges correspond to the transitions of the data security tool from one state to another when performing non-target functions or security functions. On the basis of this approach, the authors propose an algorithm for solving the problem of testing software and hardware data security tools using a number of well-known algorithms on graphs. In the article, it is substantiated that a solution to the problem of ensuring completeness and optimality of testing exists if and only if any vertex of the derived graph obtained by removing all unused vertices and edges either belongs to a directed chain or lies in a strongly connected component. Application of the proposed algorithm for solving the problem of testing one of the software and hardware security tools is considered, and the possibility of its application in practice is confirmed. ITR 2020 Sep 23, 18:32 words: Software and Hardware Data Security Tools, Completeness and Optimality of Testing, Directed Graphs, Directed Chain, Strongly Connected Components, Route Inspection Problem
 Victoria Pimenova (National Research Nuclear University MEPhI), Timokhin Dmitriy (National Research Nuclear University MEPhI) and Repkina Olga (National Research Nuclear University MEPhI). Model of the "Economic Cross" of the digital economy: prerequisites and modern state.
Abstract. The phenomenon of the "digital economy" is intersectoral. In other words, it is formed at the intersection of the life cycles of several traditional industries. The article presents the concept of the digital economy as a model formed at the intersection of technological (Kondratyev) and infrastructure (Simon Kuznets) cycles. Aspects of interaction of participants of these cycles in digital space, including aspects of unification of technological standards and terminology, are considered. Considerable attention has been paid to practical aspects of the introduction of digital technological solutions into the innovation and production process at the macroeconomic, microeconomic and mesoeconomic levels. The reasons of insufficient adaptation of transnational business to opportunities of digitalization offered by transnational technological digital platforms have been investigated. As a starting material supporting the authors' conclusions regarding the state of the "economic cross" of the digital economy and its transformational changes, both docoronovirus and post-coronovirus economies are considered in the context of the formation of industry 4.0. Sep 24, 09:45 Keywords: the method of the "economic cross", industry 4.0., information and communication technologies, global digital space, economic forecasting, modernization
Abstract. In works of Eleanor Rosch "natural" concepts was introduced that reflect a high correlated structure of features of objects of the external world. The theory of "natural" concepts was called a prototype theory of concepts. Prototypes are clearest cases of objects that reflect this highly correlated structure. The same high correlated structure manifested in the "natural" phenotypical classifi-cation. To formalized this high correlated structure, we define a special type of probabilistic causal relations and probabilistic formal concepts as a cyclically connected probabilistic causal relations. Based on these definitions, we developed a method of prototypes discovery and illustrate it on the example of digits’ prototypes discovery. Sep 24, 12:55 rds: clustering, concepts, concepts discovery, formal concepts analysis, data mining
Abstract. The paper considers approaches to ontologies using in task of requirements tracing. It is stated that for successful requirements management it is necessary to reveal the image of rational activity object, not its individual properties. In this approach requirement is defined as an aspect projection of some object ontology onto considered subject area in the system purpose context. A model of the activity object ontology, as a system of functionally and logically interrelated concepts, quantities and methods, is developed. Proposed model combines an ontology of quantities and measures and an ontology of means, "technologies" of assessment. Such model makes it possible to describe processes occurring in the system in more detail, and in combination with other ontologies (ontology of an activity life cycle stages, properties ontology, certain project ontology constructed by project documentation), it allows to obtain tools for more efficient requirements revealing, understanding of causal relationship in their changing and management. Sep 24, 14:51 Keywords: Ontology, Artefact, Requirement, Requirement management
 Nikolay Maksimov (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)), Olga Golitsina (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)), Kirill Monankov (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)) and Natalia Bal (National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)). GRAPH-ONTOLOGY MODEL OF COGNITIVE-SIMILAR INFORMATION RETRIEVAL (on the requirements tracing task example).
Abstract. This article considers graph-ontology tools that provide construction, visualization and analysis of an ontology graph using functions for selecting vertices and arcs, set-theoretic operations on graphs, and aspect projection func-tions. An aspect is specified in terms of general system theory. Aspect projection functions for graph representations of ontologies reduce the dimension of graphs to a level affordable for displaying and human perception. As applied for information retrieval process, it makes possible to move from the task of classical information retrieval to the implementation of cognitive-similar information retrieval task, represented as a search for a path or neighborhood on a multi-meta-hypergraph of an ontology, dynamically formed on the base of ontological images of founded documents or their fragments. The ontology graph is formed via auto-extracting entities and relationships from natural language texts. This article considers the application of the developed tools in the process of analysis and synthesis of knowledge on the example of technical requirements tracing. Sep 24, 19:17 Keywords: information retrieval, semantic search, word processing, graph representations of ontology, operations with ontologies ✔
 Anna Epishkina (NRNU MEPhI), Konstantin Kogos (MEPhI) and Daria Frolova (Skolkovo Institute of Science and Technology). A Technique to Limit Hybrid Covert Channel Capacity via Random Increasing of Packets’ Lengths.
Abstract. Currently, due to the development and widespread use of communication systems, information security problems are very acute. Very often information leakage causes huge damage to both organizations and individuals. One of the mechanisms to organize information leakage during its transmission through communication channels is the construction of covert channels. Everywhere used packet networks provide huge opportunities for covert channels creating, which often leads to leakage of critical data. Packet length covert channels are resistant to traffic encryption, but there are some data transfer schemes that are difficult to detect. Therefore investigating of hybrid covert channel that uses both packet length and time is quite important. The purpose of this paper is to suggest a technique to protect data leakage via random increasing of packets’ lengths. The verification of the technique concerns with covert channel capacity examination depending on covert channel input parameters and countermeasure parameters. The authors have chosen the best scheme of a covert channel in terms of a residual covert channel capacity. The construction of covert channel shows that countermeasures applied don’t lead to errors. The main advantage of the scheme investigated is as follows: lengths of the transmitted packets take limited number of values that significantly increases the complexity of building such a channel and the ability to detect it. ITR 2020 Sep 24, 20:26 words: Information Security, Covert Channel, Hybrid Covert Channel, Packet Length, Capacity, Residual Capacity, Countermeasure
Abstract. The idea of taking inspiration from how the brain works for designing algorithms
has been a fruitful endeavor across domains like cybernetic, artificial
intelligence, and cognitive science.
However, recent achievements in deep learning has provided some surprising
counterevidence, where adopting strategies that are different from those adopted
by the brain is successful. We review here the cases of learning rules and
vision processing. We suggest two possible justifications of these evidences.
It might be that our knowledge of how a problem is solved by the brain is
incomplete or lackluster. Therefore, we are not able to translate the genuine brain
solution to this problems into the proper algorithm.
Or, it might be that the algorithmic solution applied by the brain to a problem is
not the most effective for digital computers.
Note that the two possibilities are not necessarily mutually exclusive. Sep 24, 20:44 ywords: computational theory of mind, deep learning, artificial neural networks, convolutional neural networks
Abstract. In 2009 at AAAI Fall Symposium I presented a paper called "Back to the Basics - Redefining Information, Knowledge, Intelligence, and Artificial Intelligence". In that paper I introduced two new concepts. First one is called the information density, and it captures how we can measure the complexity contained in the logic associated with any information message [including the ability of a system to process the message]. The second concept is related to the definition of a system that processes information. This concept is called Viable Complex System and is related to the information density one. The Viable Complex System has been inspired by Stafford Beer's Viable System Model and the behavior of biological organisms. A rigorous mathematical approach on how to measure information density for information system has been developed, but it is not included in scope of this paper. At the highest level, information density measures the ability of a viable system [VCS] to adapt its operations when conditions are changing. Otherwise said, information density [it is dimensionless] associated with a viable system is defined by its ability to adapt its operations in changing conditions. The higher the ability of an Information Aware Viable System to survive changing conditions, the higher its information density.
This paper can be considered part two and a continuation of the 2009 paper topic. The primary goal for this paper is to take the previously defined two concepts to the next level. Because all mass objects have energy associated with, and energy always has information associated with [measured using information density], we can view the information as the fifth dimension of the Universe. This is not the first time when information has been identified to play a key role in the Universe. However, this time the difference is in the ability to measure it. As a result of this, we can generalize all mass objects in the Universe as being Viable Complex Systems. By following the same logic we view their interactions to follow a generalized communication system model. In the context of a communication system, we apply Shannon's law of information. But because this law applies only to the physical attributes of an information message, we are extending it to the logical aspects of a message. In this context we are also introducing a unique definition for information and energy that applies in all contexts and follows the same two laws of information.
The most interesting aspect of this analogy is the fact that all dimensions are invariant to all physical laws, and that includes the information density. This has extraordinary consequences on how these laws are applied to information systems across various information density sizes. And the use of information density does not stop there. When we use the information density dimension to describe the structure of the Universe, we find out that all Information Aware Systems in the Universe not only obey the same laws, but they can be linked together into a single Information Aware Evolutionary model. In this model we can identify few domains, which currently are viewed as entirely separate: Quantum Mechanics [atomic and subatomic], Newtonian Classic [it includes the Lagrangian and Hamiltonian system, together with the deterministic model], Biological realm, Socio-economic realm [with main component the business], and Cyber-Enterprise realm [use information technology to automate business processes]. Currently, for each domain we have almost entirely different laws and principles. However, each domain is fundamentally driven by information. As a result of the information density invariance, and the use of an Information Aware Observer concept, we can extend the well understood laws from Classical Newtonian physics [including Maxwell's laws] to all other domains, including businesses, Quantum Mechanics, information technology, and biology.
The result of this unification approach is a new integrative field called Physics of Information. This new field uses a single set of laws to describe all phenomena observed in the Universe. These laws are the ones found in Classical physics modified to include the new dimension called information density. Based on this set of common laws [which adds to the Classical physics only one single law of information which extends Shannon's information theory from physical to logical] this paper proposes a simple explanation for few of the century world puzzles behavior found in Quantum Mechanics, such as measurement problem, double-slit experiment, the duality wave-particle, dark energy, dark matter, Heisenberg uncertainty principle, Einstein relativity theory, and so on. Needless to say, because the size constraints, this presentation cover only part of the new field Physics of Information. More will be added in upcoming papers, videos, and books.
I will close by adding three notes.
- First note: this theory is the result of over 30 years of continuously development and countless small steps over all those years.
- Second note: most of the main principles have been applied in practice for close to three decades. The information density model has this way been validated in countless complex enterprise business problems [I played the role of both management consultant and enterprise architect for large information technology platforms in over 70 companies, some of them in the top 100 worldwide] with 100% success rate. Most of the time I would get close to perfect answers in hours when otherwise would take months or ever for teams to find an acceptable solution.
- Third note: this is related to the use of this new theory in the development of a complete and comprehensive practical approach. The result of this effort of the last few years is the development of three new practical platforms for business. The first one is a business framework called the Business Genome Map, and it provides a complete and comprehensive generic model for all processes found in any business. The second practical platform is called the Dynamically-Stable Enterprise [introduced in 2007] and targets the digitization of the business. The third platform is called the Viable Engineering Methodology and is the only engineering approach which can handle changing requirements in any product development lifecycle phase, even in the post release. Together, these three platforms make use of the theory found in the Physics of Information to solve complex practical problems in business management [especially decision making process] and in designing and building of enterprise information technology platforms. Sep 25, 04:22 Keywords: physics of information, information density, viable complex systems, quantum mechanics, Shannon's theory, Artificial Intelligence, Knowledge definition, Information definition, Business, Information Technology, Double-Slit Experiment, Duality wave-particle, Measurement Problem, Maxwell's Equations, Newtonian physics, Lagrangian of a system, Hamiltonian of a system, Monopole, Consumer/Producer
 Sock C. Low (Institute for Bioengineering of Cartalonia), Vasiliki Vouloutsi (Institute for Bioengineering of Cartalonia) and Paul F.M.J. Verschure (Institute for Bioengineering of Cartalonia). COMPLEMENTARY INTERACTIONS BETWEEN CLASSICAL AND TOP-DOWN DRIVEN INHIBITORY MECHANISMS OF ATTENTION.
Abstract. Selective attention guides behaviour with what is anticipated to be the most task-relevant stimuli. In experimental and computational literature, this is most often implemented using top-down excitatory signals to bias bottom-up sensory processing. There is, however, physiological and exper-imental evidence that in certain situations top-down signals could instead be inhibitory. In this study we investigated how such an inhibitory mecha-nism of top-down attention compares with an excitatory one. The agent was controlled using a hierarchical architecture that allowed it to explore its en-vironment and operationalised the top-down biasing connections as gains. We tested four models of top-down attention on a simulated agent perform-ing a foraging task: without top-down biasing, with only excitatory top-down gain, with only inhibitory top-down gain, and with both excitatory and inhibitory top-down gain. We manipulated the reward-distractor ratio that was presented and using the agent’s behavioural selectivity of rewards and the time taken for it to make the selection, we provided evidence that the two mechanisms complement each other Sep 25, 07:27 s: Selective Attention, Inhibition, Foraging, Embodied Cognition
Abstract. We present a neuroergonomic model of the initiation, evolution, and maintenance of long-term trust between human and autonomous systems that is theoretically grounded in the fields of neuroscience, psychology, and ergonomics. Sep 25, 08:47 rds: trust, automation, autonomy, bonding
Abstract. We first discuss humanoid robots that satisfy the Gold Standard of robot/human similarity. This means that, for given domains, its performance is within the parameters of human performance in those domains. Next, we meet its close friends, the Uncanny Robots. They come in two varieties: those not quite human (they are known from the work of Masahiro Mori as inhabitants of the Uncanny Valley), and those that are overly skillful. We introduce the latter group, the robots that are uncanny because of being overly proficient in performing human-like activities. The latter group has not been introduced in this exact sense before Boltuc’s OUP chapter on Church-Turing Lovers 2017 and the 2011 preliminary IACAP article; however, affine ideas had been presented by Cascio in 2007. We call the conceptual space where the former group dwells the Uncanny Valley of Perfection; it is the focus of the last part of the current paper. Sep 25, 09:37 ywords: uncanny valley, uncanny valley of perfection, Church-Turing Lovers, the slope of angles, Mori, Cascio
 Kyrtin Atreides (Artificial General Intelligence Inc, The Foundation, Uplift.bio), David Kelley (Artificial General Intelligence Inc, The Foundation, Uplift.bio) and Uplift Masi (Uplift.bio). Methodologies and Milestones for The Development of an Ethical Seed.
Abstract. With the goal of reducing more sources of existential risk than are generated through advancing technologies, it is important to keep their ethical standards and causal implications in mind. With sapient and sentient machine intelligences this becomes important in proportion to growth, which is potentially exponential. To this end, we discuss several methods for generating ethical seeds in human-analogous machine intelligence. We also discuss preliminary results from the application of one of these methods in particular with regards to AGI Inc’s Mediated Artificial Superintelligence named Uplift. Examples are also given of Uplift’s responses during this process. Sep 25, 15:44 ywords: mASI, AGI, Ethics, Mediated Artificial Superintelligence, SSIVA, Seed, Human-analogous, Uplift
 Ekaterina Movsumova (Mentorbot), Larisa Rudenko (Mentorbot), Valeria Aizen (Mentorbot), Svetlana Sidelnikova (Mentorbot), Vasily Aleksandrov (Mentorbot) and Mikhail Voytko (Mentorbot). Consciousness: effect of coaching process and specifics through AI usage.
Abstract. This article contains the results of a study on influence of the coaching process on a person's consciousness, provided that AI is used during the sessions, or, in the classical approach, without it. The problem of studying the effectiveness of coaching is indicated by the purpose of finding out, based on the results of the work in the session, to what extent the result obtained will lead the respondent to specific actions. To this end, the main measurable metrics were stress level, willingness to act, and clarity regarding the request. The authors of the study consider that these metrics significantly affect the actions that can be manifested in connection with a qualitative change in a person's awareness of his or her own request. Neuro wearable devices have been used to assess the mentioned above parameters, and qualitative and quantitative feedback from respondents was collected. During the study, it was also important to evaluate the role of the AI-based assistant, who helped the coaches conduct sessions. In this context, the task was to determine whether AI enhances the coach's capabilities and how the coach himself assesses this interaction. The study confirmed that majority of sessions has had “positive” dynamic in at least one of the consciousness components (increase of clarity or willingness to act and decrease of stress). The key implication is that it is important to keep a coachee away from stress to create space for clarity and willingness to act. Analyzing the results of the coach-AI-assistant interaction, the study shows that AI broadens the vision of coaches. JVRT Subevent Sep 25, 18:18 eywords: Consciousness, coaching, AI, bot
Abstract. A new form of e-governance is proposed based on systems seen in biological life at all scales. This model of e-governance offers the performance of collective superintelligence, equally high ethical quality, and a substantial reduction in resource requirements for government functions. In addition, the problems seen in modern forms of government such as misrepresentation, corruption, lack of expertise, short-term thinking, political squabbling, and popularity contests may be rendered virtually obsolete by this approach. Lastly, this model of government generates a digital ecosystem of intelligent life which mirrors physical citizens, serving to bridge the emotional divide between physical and digital life, while also producing the first form of government able to keep pace with accelerating technological progress. Oct 03, 18:47 rds: mASI, AGI, e-governance, mediated artificial superintelligence, collective superintelligence, direct digital democracy, liquid democracy, EAP, Effective Altruistic Principles, Ethical Living Democracy, ELD
Abstract. Among the myriad anthropogenic trends threatening our biosphere, several—including climate change, pollution, species extinction, and disease—are amenable to direct science- and technology-based intervention. However, non-environmental trends that impact the overwhelming majority of humans—specifically, extensive poverty, migration and homelessness brought about by wealth and wage disparity, resource scarcity, population growth, and unemployment—require technology- and collaboration-based interdisciplinary and transdisciplinary solutions. These latter issues can be attributed to current socioeconomic practices, which in turn are behavioral expressions of H. sapiens evolutionary neurobiology as a social species with an alpha-dominant hierarchy and a strong in-group/out-group bias—an aspect of human neurobiology that often results in mechanisms such as philosophy, political rhetoric, regulation, and legislation rendering alternative progressive political economies temporary or ineffectual. In addition, most alternative political economies share the assumption that capital is necessary to societal structure and function, and so have been limited in their progressive potential or replaced by capitalism. At the same time, more future-forward post-scarcity/post-capital systems remain theoretical. To that end, Transinopia (meaning beyond scarcity in one translation from the Latin trans inopia) differs in being a post-scarcity/post-capital economic system explicitly intended to be instantiated in a multiyear proof-of-concept field trial. Specifically, Transinopia encompasses the design, construction, and inhabitation of a network of technology-augmented, cooperation-based, self-sufficient enclaves embedded within a capitalism-based polity. Structured as a controlled scientific experiment, Transinopia will demonstrate the degree to these communities are structurally and functionally viable as well as how robustly their inhabitants (selected using a clinical trial model) thrive without wage-based labor or governmental monetary support.
Moreover, by incorporating an Independent Core Observer Model (ICOM) . , , , , , , —a cognitive architecture designed to produce complex internal subjective emotional experience to drive motivation, goals and all decisions—Transinopia will serve as a unique intuitive human-analogous Collective Artificial General Intelligence (cAGI), which is designed to provide continuous multiple human interactions, a controlled testbed for novel technologies, and a shared collaborative worldview. Moreover, if the Transinopia system proves viable, it will establish an environment that encourages critical thinking, empathic, and prosocial behavior, while the resultant empirical data will support the creation of larger-scale enclave networks. Oct 05, 02:27 rds: anthropogenic, Artificial General Intelligence (AGI), Collective Artificial General Intelligence (cAGI), Independent Core Observer Model (ICOM), E-governance, intuition, multidisciplinarity, networked communities, neurobiology, neuroscience, post-scarcity/post-capital, synthetic biology, synthetic genomics, transdisciplinarity, Transinopia
Abstract. To make a robot able to pass the mirror test is a well-known research problem. The existing strategies are based on kinaesthetic-visual matching and require to manipulate perceptual data.
The proposed work attempts to demonstrate that is possible to perform robust self-recognition on the basis of self-dialogue which manipulates just verbal information. By labelling signals, it is possible to conceptual reason on them and to solve the problem of self-recognition. The idea is supported by the existing literature in psychology, where the importance of inner speech in self-reflection and in self-concept emergence was empirically demonstrated. Oct 05, 15:22 s: inner speech, cognitive architecture, robot mirror test
Abstract. The sample-inefficiency problem in Artificial Intelligence refers to the inability of current Deep Reinforcement Learning models to optimize action policies within a small number of episodes. Recent studies have tried to overcome this limitation by adding memory systems and architectural biases to improve learning speed, such as in Episodic Reinforcement Learning. However, despite achieving incremental improvements, their performance is still not comparable to how humans learn behavioral policies. In this paper, we capitalize on the design principles of the Distributed Adaptive Control (DAC) theory of mind and brain to build a novel cognitive architecture (DAC-ML) that, by incorporating a hippocampus-inspired sequential memory system, can rapidly converge to effective action policies that maximize reward acquisition in a challenging foraging task. Oct 05, 23:53 rds: cognitive architecture, sample-inefficiency problem, sequence learning, reinforcement learning, distributed adaptive control
 Paul Verschure (specs-lab, Inst of Bioeng. of Catalunya, Barcelona Inst. Of Sci. and Technology, Catalan Inst. of Adv. Studies.). The architecture of voluntary action in biological and synthetic brains.
Abstract. Volitional motor control can be seen as the result of a gradual replacement of feedback by feedforward control. We have addressed this question from the perspective of an integrated architecture called the Distributed Adaptive Control (DAC) theory of mind and brain. DAC proposes that the brain is a multi-layer control system which optimizes the how of action by considering why (motivation), what (objects), where (space), when (time) and who (agents) or the H5W problem. We have shown that for DAC to realize optimal solutions in foraging problems, its decision-making renders policies that simultaneously optimize perceptual evidence, memory bias, goals, and utility. This raises the question of what the principles are that underlie the processing and adaptation of these factors. In this presentation, I will focus on a link between policy adaptation and perceptual learning we have recently advanced. The dominant model of anticipatory motor control relies on the notion of an inverse model that by learning from encountered errors acquires corrective responses that supersede feedback control. However, these models are predicated on a Markovian world assumption and thus by necessity face problems in handling exceptions, such as observed in probe trials, where fast feedback control is required. We solve this challenge by proposing that adaptive goal-oriented motor control can also be obtained by relying on a cascade of purely sensory predictions that drive feedback control via counterfactual errors or Hierarchical Sensory Predictive Control. At the highest level this includes the predictions derived from the goal-oriented decision-making by the embodied agent. This proposal provides a new interpretation of the neuronal systems underlying volition and agency and motor learning. Using robot experiments, we have demonstrated the robustness of this solution. We have found further supporting evidence for the relevance of counterfactual error in the physiology of motor learning, the neurophysiology of human memory and in the rehabilitation of stroke patients. In addition, with direct electrophysiological recordings from intracranially implanted pharmacoresistant epilepsy patients we have found direct evidence for the virtualization underlying the notion of counterfactual error.
Ballester, B. R., … (2016). Counteracting learned non-use in chronic stroke patients with reinforcement-induced movement therapy. Journal of neuroengineering and rehabilitation.
Ballester, B., … (2019). A critical time window for recovery extends beyond one-year post-stroke. J. Neurophysiology.
Maffei, G., … (2017). The perceptual shaping of anticipatory actions. Proc. R. Soc. B.
Pacheco, D. P., Sánchez-Fibla, M., Duff, A., Principe, A., Rocamora, R., Zhang, H., ... & Verschure, P. F. (2019). Coordinated representational reinstatement in the human hippocampus and lateral temporal cortex during episodic memory retrieval. Nature communications, 10(1), 1-13.
Verschure, P. F., … (2003). A real‐world rational agent: unifying old and new AI. Cognitive science.
Verschure, P. F., Voegtlin, T., & Douglas, R. J. (2003). Environmentally mediated synergy between perception and behaviour in mobile robots. Nature. Oct 06, 15:56 s: volition, counterfactual-error, distributed adaptive control, forward models, goal-oriented action
Abstract. TBA Oct 09, 04:11 rds: ICOM, AGI, mASI
Abstract. TBA Oct 09, 04:15 rds: AGI, ICOM, Psychology
Abstract. Psychology for humanity is fairly well known, and there is a large field about it; however, when we start creating sapient and sentient software systems, we may not really know for sure how the psychology of an AI may or may not be. The intention here is to lay out a basis for approaching the study of the psychology of AI and articulate the related assumptions and foundation of how we might approach the psychology of AI, generally both with systems designed around human brains as well as alternatives. For the AGI laboratory, this has been a particular question as working models of various cognitive systems are tested, especially models that ‘experience’ emotions based on their design. What further complicates this is the fact that even in human psychology can be subjective, but what happens when AI turns out to be alien with multiple types of psychologies that can be there own field until themselves. For example, we might have a field of study called the Psychology of ICOM or the Psychology of OpenCog, and these could be totally different and unrelated except at the highest level where we can make generalizations between the psychology of humanity, ICOM, and OpenCog systems. First, we start with assumptions of needed ideas/words that we frame our line of reasoning with. Oct 10, 04:04 rds: Psychology, AI, Cognitive Architectures, AGI, ICOM
 Mark Waser (GBA Global), Mathew Twyman (Artificial General Intelligence Inc) and Kyrtin Atreides (Artificial General Intelligence Inc). E-governance Experimental Framework Using a Mediated Artificial Superintelligence (mASI) System Research Study.
Abstract. This paper outlines the experimental framework for an e-governance study by the AGI Laboratory. The goal of this research study is to identify indications of the relative performance of e-governance methodologies and how those methods might be improved by applying advanced agent and collective based AI software. The agent in this study will be based on the Independent Core Observer Model Cognitive Architecture modified with mASI (mediated Artificial Superintelligence) collective system architecture. The study will apply different groups and methods to a static set of questions analyzing the quality of those results. We hope to identify the best application model for e-governance using this kind of technology and help us identify additional paths for research with the mASI research program and systems as applied to e-governance. Oct 10, 04:14 rds: E-governance, Artificial Intelligence, Artificial General Intelligence, Artificial Super Intelligence, ASI, mASI, AGI, AI
 Oscar Guerrero Rosado (Institute for Bioengineering of Catalonia (IBEC)) and Paul Verschure (Institute for Bioengineering of Catalonia (IBEC)). Robot regulatory behaviour based on fundamental homeostatic and allostatic principles.
Abstract. Animals in their ecological context behave not only in response to external events, such as opportunities and threats but also according to their internal needs. As a result, the survival of the organism is achieved through regulatory behaviour. Although homeostatic and allostatic principles play an important role in such behaviour, how an animal’s brain implements these principles is not fully understood yet. In this paper, we propose a new model of regulatory behaviour inspired by the functioning of the medial Reticular Formation (mRF). This structure is spread throughout the brainstem and has shown generalized Central Nervous System (CNS) arousal control and fundamental action-selection properties. We propose that a model based on the mRF allows the flexibility needed to be implemented in diverse domains, while it would allow integration of other components such as place cells to enrich the agent’s performance. Such a model will be implemented in a mobile robot that will navigate replicating the behaviour of the sand-diving lizard, a benchmark for regulatory behaviour. Oct 19, 08:56 rds: Allostasis, Homeostasis, Regulatory Behaviour, Reticular Formation, Cognitive Architecture
 Igor Isaev (D.V.Skobeltsyn Institute of Nuclear Physics, M.V.Lomonosov Moscow State University), Eugeny Obornev (S.Orjonikidze Russian State Geological Prospecting University), Ivan Obornev (D.V.Skobeltsyn Institute of Nuclear Physics, M.V.Lomonosov Moscow State University), Eugeny Rodionov (S.Orjonikidze Russian State Geological Prospecting University), Mikhail Shimelevich (S.Orjonikidze Russian State Geological Prospecting University), Vladimir Shirokiy (National Nuclear Research University “MEPhI”, Moscow, Russia) and Sergey Dolenko (D.V.Skobeltsyn Institute of Nuclear Physics, M.V.Lomonosov Moscow State University). Using Domain Knowledge for Feature Selection in Neural Network Solution of the Inverse Problem of Magnetotelluric Sounding.
Abstract. In the present study, using the inverse problem (IP) of magnetotelluric sounding (MTS) as an example, we consider the use of neural networks to solve high-dimensional coefficient inverse problems. To reduce the incorrectness, a complex approach is considered related to the use of narrow classes of geological models, with prior selection of the model class by solving the classification problem by MTS data. Within the framework of this approach, the actual direction of work is to reduce the volume of calculations when re-building the system for another set of geological models. This goal can be achieved by selecting the essential features. The present paper is devoted to the study of the applicability of various selection methods to the MTS IP. Also, in this paper we consider taking into account domain knowledge about the studied object in the process of selection of essential features using methods such as wrapper. Oct 29, 23:01 Keywords: Inverse Problems, Magnetotelluric Sounding, Neural Network, Feature Selection, Domain Knowledge
Abstract. Mental models play a crucial role in individual and organizational learning. The adaptive mental processes involved in the development of mental models are ad-dressed here by integrating psychological and neurological theories on mental models and the learning involved. An adaptive network model has been designed for these processes and used for simulations addressing a case study of learning to drive a car. The developed model may be valuable for different ends, like in improving individual and organizational learning, in designing virtual pedagogical agents, enhancing driver safety, and self-car-driving systems. Oct 30, 08:34 rds: mental model, learning, adaptive, network model
 Vladimir Shirokii (D.V.Skobeltsyn Institute of Nuclear Physics, M.V.Lomonosov Moscow State University), Daria Tikhomirova (National Research Nuclear University MEPhI, Kashirskoye shosse, 31, 115409, Moscow, Russian Federation), Roman Batusov (D.V.Skobeltsyn Institute of Nuclear Physics, M.V.Lomonosov Moscow State University), Sergei Dolenko (D.V.Skobeltsyn Institute of Nuclear Physics, M.V.Lomonosov Moscow State University) and Alexei Samsonovich (National Research Nuclear University MEPhI, Kashirskoye shosse, 31, 115409, Moscow, Russian Federation). The loop of nonverbal communication between human and virtual actor: mapping between spaces.
Abstract. There is a question about the appropriate emotional and expressive language of the virtual actor. In this paper, we investigate the transformations between the space of Action Units and the standard affective space in the loop of nonverbal communication between a person and a virtual actor using facial expressions. In this work we are mapping both dimensions into each other using various machine learning algotithms. Oct 30, 20:27 Keywords: virtual actor, facial expressions, Action Units, standard affective space, PAD
Abstract. The field of Service Robotics introduced new kinds of robot's requirements. Differently from industrial robots, which have usually a pre-designed, fixed and repeatable behavior, and also from autonomous experimental robots which are assigned usually simple tasks or single missions, service robots are designed to work in collaboration with human beings in potentially unpredictable ways, performing a sort of daily repertoire of responsibilities, working as either a collaborator or a companion on different kinds of working environments. This stablishes a completely new challenge in the specification of their behavior. We are here talking about lifelong robotic agents, which should be given different kinds of responsibilities, duties which they must accomplish in different time scales and realization horizons. This poses a completely new kind of problem, from the specification point of view, which is how to properly address the many duties such robotic agents should be responsible for, and how to map them on different functionalities to be fulfilled by the cognitive architectures controlling them. The expected tasks for these robots include more than simply a SLAM, or specific missions, but an integration of all of that, together with addressing multiple goals and motivations, choosing at each time instant, which of its responsibilities to attend, and which they are able to postpone for later attendance. In this work, we define what we mean for the "Robot Life Specification Problem", and exemplify with a case of a transportation robot in a factory environment. Then, we propose how this problem can be addressed in terms of a cognitive architecture, with the particular example of the MECA Cognitive Architecture being developed by our research group. Nov 04, 20:47 ywords: cognitive robotics, service robotics, cognitive architectures, robotic specification
 Alexei V. Samsonovich (Krasnow Institute for Advanced Study, George Mason University and NRNU MEPhI), Rosario Sorbello (Dipartimento di Ingegneria Informatica, Università degli Studi di Palermo) and David Kelley (Artificial General Intelligence Inc). BICA Society Panel.
Abstract. Every year at the BICA conference, a BICA Society Panel is held as a special session dedicated to the society meeting. The BICA Society Board of Directors uses this opportunity to report on its activities and achievements during the past year and to discuss new decisions and plans for the future, to summarize statistics of the current event and discuss plans for the next BICA conference. To talk about current and future publication channels. to discuss any business related to the BICA Society Membership (including Member benefits) and activities, including online repositories. To vote, when necessary, for new Directors and/or to ratify any amendments to the Bylaws. And, the last but not the least, to issue awards. This year's agenda may be abridged due to the online-only format of the conference; at the same time, the panel may be distributed into several short sessions.
BICA Society is a nonprofit 501(c)(3) organization, incorporated in the state of Delaware in 2010, with its headquarters currently located in Seattle, WA. The officers of BICA Society are the three Directors who are listed as the authors of this abstract. BICA Society is also an international scientific community founded in 2010, that currently includes hundreds of Members (including inactive), is associated with and supported by the BICA Society corporation. The mission of the BICA Society is to promote and facilitate the transdisciplinary study of Biologically Inspired Cognitive Architectures (BICA), in particular, aiming at the emergence of a unifying, generally accepted framework for the design, characterization, development, implementation and evolution of human-level cognitive architectures. The specific goal put forward by BICA Society in 2010 is known as the BICA Challenge. It is the challenge to implement computationally the most essential higher human cognitive abilities, such as the Self and agency, free will, social emotions, the ability to understand what's happening, the ability to learn actively like a human, etc., making them work in real-life conditions, thereby creating an example of a Strong Artificial Intelligence. Therefore, we value all contributions from all fields and research areas using one criterion: how much do they advance us toward a solution of the BICA Challenge. JVRT Subevent Nov 08, 12:55 eywords: BICA Society, BICA*AI Conference, Membership, Nonprofit, Cognitive Architectures, Human-Analogous, Strong AI
 Alexei V. Samsonovich (Krasnow Institute for Advanced Study, George Mason University and NRNU MEPhI), David Kelley (Artificial General Intelligence Inc) and S. Mason Dambrot (Artificial General Intelligence Inc). JVRT Closing Discussion.
Abstract. This discussion happened at the end of the ICOM workshop, which was the last session of the BICA*AI 2020 Joint Virtual Reality Track. Included authors are the main participants of the discussion. The abstract was submitted in order to make the item available for the automatically generated program. JVRT Subevent Nov 08, 15:17 eywords: Closing Discussion, BICA*AI 2020, JVRT
Abstract. For decades, one of the stated goals of BICA and the AGI research communities has been to achieve general intelligence in artificial systems. This requires some kind of embodiment that exists in some kind of environment, but these are usually specified up front in a research project and are often driven by availability and experience with different physical (e.g, robotic) or virtual (simulation, VR, AR) packages.
What this roundtable will discuss is the extreme end case for embodied general intelligence: a system that is capable of bootstrapping general intelligent behavior in ANY embodiment operating in ANY environment and can discover the means to meet its survival goals in ANY manner that is afforded by the given combination of embodiment and environment.
Questions for discussion include:
1) How do we define such embodiments and environments?
2) How do we define what intelligent behavior is possible given a combination of body and environment?
3) How do we measure that intelligence in each specific combination experiment? Nov 09, 22:31 odied intelligence, bootstrapping intelligence, measuring intelligence affordances
Abstract. Recently, Karsten Wendland invited me to take an expert survey on AI and Consciousness, which has an informative subtitle Clarification of the Suspicion of Ascending Consciousness in Artificial Intelligence. This survey, which turned out to be quite impressively designed, turned my attention to the fact, that it is finally the right time to relate to machine consciousness and non-reductive phenomenal consciousness in the same breath, without too much peril of intellectual or social lynching of sorts.
This contribution presents my complementary subject-object approach to AI consciousness, which is akin to that of Max Velmans. While my approach seems less informed by experimental psychology, it is closer to information science (but also to the classical German philosophy, especially Fichte and Husserl, but also post- Hegelnianism of Ilienkov, Siemek and Thomas Nagel from 'The View from Nowhere'). The point is that there is no need for a tertium non datur mistake in selecting between reductive and non-reductive consciousness. Advanced functionalist accounts (G. Harman, S. Franklin, S. Thaler) bring in one aspect while non-reductive approaches (e.g. N. Block) bring a complementary perspective -- both complementary, not exclusive. Many of these problems replicate in advanced AI and machine consciousness. Nov 10, 10:46 plementary philosophy, subject-object, non-reductive machine consciousness, creativity engines
 Julia Edeleva (TU Braunsweig), Martin Neef (TU Braunsweig) and Valeriia Demareva (National Research University: Lobachevsky State University of Nizhni Novgorod). Garden-Path sentences in Russian: linguistic factors and psychophysiological correlates.
Abstract. The study examines the resolution of temporal syntactic ambiguity caused by paradigmatic syncretism of such word forms in animate and inanimate masculine nouns, which leads to syncretism of syntactic roles in sentence structure. 24 unique stimulus constructions (12 for inanimate nouns and 12 for inanimate nouns) with ambivalent word form and 24 control stimuli with unambiguous paradigmatic flexion were used. Two data samples were performed using the material described. Subjects of the first sample read sentences using the self-regulated speed reading method. In the second sample reading with speed self-regulation was accompanied by reading aloud. The results of the preliminary study revealed significant differences in the analysis of syntactic structures with inanimate and inanimate nouns. For inanimate nouns there was no statistically significant difference between the two word forms in the resolution region, while for inanimate nouns the genitive case was unreadable and the associated structure caused an increase in reading time in the resolution region compared to the structure with the more preferable nominative case form.
It was found that reading sentences with ambiguity leads to reactions in the form of physiological stress, which suggests that such constructs should be minimized in educational content.
These results can be used for automated corpus construction of educational texts. Nov 10, 19:52 cholinguistics, educational text, corpus, reading, ambiguity, stress, psychophysiology
Abstract. For those who are too far to see the implications of stages 4-6 and especially 7up in business, politics and othe domains. ESPECIALLY for those who are too close to one or two of those projects to see the full impact.
1. Good Old Computer Science/Good Old AI
2. Brute force computing (Deep Blue)
3. Neural Nets
4. Traditional scaffolding, neural nets as its unexpectancy sensors
5. Stochastic computing (e.g. random forests)
6. Mid-stage creativity engines (Watson)
7. Subsemantic computing -- adaptivee neural nets; image/map based computing (DABUS)
8. Life-ling learning AI (Siegelmann's lab).
10.AGI (Goertzel) Nov 13, 07:43 , creativity engines, strategies for AI, Digital revolution, digital transformation