Selected Abridged Tentative Abstracts

Antonio Lieto. Computational Explanation in BICA. In this lecture I will focus on the epistemological role of the computational explanation of biologically inspired systems and architectures. Such problem has impact both on the design phase of computational systems and on the phase concerning the results interpretation. I will provide an overview of the main methodological approaches for the design of BICA and will outline which kind of computational models have an explanatory role and which one cannot be considered explanatory at all. In particular I will show that purely functionalist models and system (i. e. models based on the..


Andrey Filchenkov and Alexey Zabashta. Towards machine learning algorithm comparison within meta-learning framework. I'm a postdoctoral research assistant in Geraint Wiggins' Computational Creativity lab at Queen Mary, University of London. My PhD (Cornell University, 2013) was in Experimental Psychology and Cognitive Science, with a focus on musical learning and memory. I use a variety of methods for my research, including behavioural experiments, EEG studies, and computational modelling of perception and cognition. Currently I examine perception, expectation, memory, and creativity, in both humans and machines. I think biologically inspired cognitive architectures are the future!


Evgeny Osipov. Reflections on BICA challenges in the context of dependable intelligent cyber-physical systems. Sensors and actuators are built into clothes, mobile phones, cars, trains, control loops of factories. When interconnected in networks they turn technologies into Cyber-Physical Systems (CPS). Life in our society is de facto dependent on the reliable and correct functionality of CPS. Traditional design and maintenance methods for CPS are mainly manual, which make them inflexible and vulnerable. European Union demands fully autonomous, intelligent and dependable (reliable, safe, accessible) functionality of CPS. The major problem towards enabling of this vision is the currently low degree..


Artur Azarov, Alexander Tulupyev, Tatiana Tulupyeva and Maxim Abramov. Character Reasoning of the Social Network Users on the Basis of the Content Contained on Their Personal Pages. The present research deals with the possibility of social network users value orientations reconstruction based on the information contained on the their personal pages. In the first part of the research 126 participants were included. They were students with the average age of 22, 39% men and 61% women. During the second part of the research more than 1300 posts published during 15. 09. 2013 and 15. 03. 2014 by the 39 users of social network were analyzed by the experts on the basis of developed classification. 89. 7 % of respondents connect to the social networks more than 1 time a day and..


Artur Azarov, Maxim Abramov, Tatiana Tulupyeva and Alexander Tulupyev. Users’ of information systems protection analysis from malefactor’s social engineering attacks taking into account malefactor’s competence profile. Great attention of specialists information security given to the protection of software and hardware components of the information system, while users of the information system has been neglected and may violate the confidentiality of corporate data. The article considers the addition of the complex "information system — personnel — critical documents" with the competence profile of the attacker.


Artur Azarov, Maxim Abramov and Ekaterina Golovina. Differentiation of groundwater tax rates as an element of improving the economic mechanism in the state groundwater extraction management. Since Russia has rich resources of fresh underground waters, one of the major practical problems in their fund managing is a rational use of its resources and protection of aquifers from contamination and depletion. Economic instrument in the structure of state groundwater extraction management is a system of taxation. Modern system of groundwater extraction taxation is currently imperfect and has definite drawbacks. Among them are: incorrect system of tax rates for underground waters usage, budget deficit, that shifts to other areas of the national economy. The purpose of this article is..


Tarek Richard Besold and Oliver Kutz. Tutorial on Computational Analogy-Making, Concept Blending, and Related Forms of Non-Classical Reasoning. A two-session tutorial on analogy and corresponding computational systems, concept blending and invention, and their relation to cognitive science on the one hand, and theoretical computer science on the other hand.


Galina Rybina and Yuri Blokhin. Automated Planning: Usage for Integrated Expert Systems Construction. The problems of intellectualization in the development process of integrated expert systems basing on the the problem-oriented methodology and the AT-TECHNOLOGY workbench are considered. The experience from carrying out intellectual planning for the synthesis of architec- tural layouts of prototypes in integrated expert systems, the intelligent planner usage, reusable components, typical project procedures, and other components of the intellectual software en- vironment in the AT-TECHNOLOGY complex is described.


Galina Rybina, Elena Sergienko and Iliya Sorokin. Some Aspects of Intellectual Tutoring Based on the Integrated Tutoring Expert Systems Usage. The aim of this work is the analysis and synthesis of experience in the development and usage of tools for intellectual tutoring, functioning as part of AT-TECHNOLOGY workbench in the study process.


Galina Rybina, Dmitriy Demidov and Dmitriy Chekalin. Collaboration of All-purpose Static Solver, Temporal Reasoning and Simulation Modeling Tools in Dynamic Integrated Expert Systems. The paper discusses scientific and technological problems of dynamic integrated expert systems development. Putting various inference tools together with simulation modeling tools gives a cumulative result in temporal knowledge processing.


Victor Rybin, Galina Rybina and Sergey Parondzhanov. Dynamic Intelligent Systems Integration and Evolution of Intelligent Control Systems Architectures. The work deals with the problems of integration and hybridization in today's dynamic intelligent systems. On the example of the individual classes of intelligent control systems (ICS) development experience the evolution of ICS architectures in accordance with the integration paradigm of artificial intelligence with models, methods and tools from other areas (automatic control system, simulation, etc. ) are examined. An example of the integration of complex discrete systems simulation models with of dynamic integrated expert systems separate components developed in MEPhI and based on..


Galina Rybina and Ivan Danyakin. Some aspects of temporal knowledge acquisition and representation in dynamic integrated expert systems. We review the problems of the acquisition of temporal knowledge for the automated construction of knowledge base in dynamic integrated expert systems, the development of which is based on the task-oriented methodology and AT-TECHNOLOGY workbench. Analyze modern approaches of temporal knowledge acquisition from different sources of knowledge. And present features of the extended knowledge representation language and combined knowledge acquisition method, as well as promising directions of its development.


Carlos León. An Architecture of Narrative Memory. Narrative is ubiquitous. According to some models, this is due to the hypothesis that narrative is not only a successful way of communication, but a specific way of structuring knowledge. While most cognitive architectures acknowledge the importance of narrative, they usually do so from a functional point of view and not as a fundamental way of storing material in memory. The presented approach takes one step further towards the inclusion of narrative-aware structures in general cognitive architectures. In particular, the presented architecture studies how episodic memory and procedures in..


Natalia Ivlieva, Julia Chistova and Alexandr Gorkin. Neuronal activity changes in behavioral effectiveness loss. . The ability to successfully solve a situation, when previously effective behavior lost its effectiveness, is one of living being advantages relative to neurocomputer. In this study we investigated the neural basis of behavior of an individual, faced with impossibility to achieve the desired result. We registered the activity of neurons in the posterior cingulate cortex in rats in sessions of instrumental food-acquisition behavior, when experimentor at some moment switched of the effective pedal and its pressing lost the feature of sequential appearence of food in feeder. We have found..


Andrei A. Lapushkin. Application of Hopfield neural network to the N-Queens problem. This paper describes one of the methods to use Hopfield neural network in combinatorial optimization. This subject has many problems and one of them is the N-Queens problem. The algorithm of solving this problem is written in Matlab and the results can be shown as a chessboard NxN with N-Queens.


Olga Lomakina, Lubov Podladchikova and Dmitry Shaposhnikov. Spatial and temporal parameters of eye movements during viewing of affective images. ABSTRACT Experimental results about the human eye movement parameters during free viewing of IAPS` images have been described. For each subject (n=20), the same images (10 positive, 10 negative, and 10 neutral ones) were presented. Each volunteer had similar scanpath during viewing of images with different valence. Coefficient of correlation (r) between number of tests with detected regions of interest at presentation of negative and positive images was equal to 0. 84 (r=0. 80 between the tests with negative and neutral images, r=0. 77 between the tests with positive and neutral images)...


Alexander Efitorov, Irina Myagkova, Natalia Sentemova, Vladimir Shiroky and Sergey Dolenko. Prediction of Relativistic Electrons Flux in the Outer Radiation Belt of the Earth Using Adaptive Methods. Prediction of the time series of relativistic electrons flux in the outer radiation belt of the Earth encounters problems caused by complexity and nonlinearity of the «solar wind – the Earth’s magnetosphere» system. This study considers such prediction by the parameters of solar wind and interplanetary magnetic field and by geomagnetic indexes, using different methods, namely, Artificial Neural Network, Group Method of Data Handling and Projection to Latent Structures (also known as Partial Least Squares). Comparison of quality indexes of predictions with horizon from one to twelve..


Alexander Sboev, Danila Vlasov and Alexey Serenko. Infuences of different neuron models and synaptic plasticity forms on spiking neuron learning. Results of investigations of learnability of different spiking neuron models (Leaky Integrate-and-Fire, Hodgkin-Huxley, Izhikevich, static) in case of complex input signals which encode binary vectors are presented. Role of selection of such common model components as short-term plasticity, spike pairing scheme, parameters of learning protocol is evaluated and discussed.


Nikita Golubtsov, Daniel Galper and Andrey Filchenkov. Active adaptation of expert-based suggestions in ladieswear recommender system LookBooksClub via reinforcement learning. Fashion recommendation is one of the developing fields in e-commerce. Many different types of recommender systems exist with their own disadvantages. In this paper we create a recommender system for ladieswear that utilizes all RS approaches: collaborative filtering, content-based, demographic-based and knowledge-based. Using stylists’ suggestions, we created distant space for items, user clusters and connected item features to users’ characteristics. Stylist initial ratings were used to solve the cold-start problem. We adopted UCB algorithm for active selecting items which should be..


Ivan Smetannikov, Evgeniy Varlamov and Andrey Filchenkov. Swarm MeLiF: Feature Selection with Filter Combination Found via Swarm Intelligence. Combination of algorithms being called ensemble is a widely used machine learning technique. In this paper we propose a new method SwarmMeLiF which aims to find the best combination of basic filters and uses swarm optimization methods for this purpose. In this work we combine filters by combining the measure they use to evaluate feature importance. Thus, the problem of filter ensemble learning is reduced to finding a linear combination of these measures. We applied several swarm optimization method and found that PSO shows the best results and outperforms the original MeLiF.


Marina Umbatova, Oleg Kostritsa, Lyudmila Zinchenko and Vladimir Verstov. Cognitive Infocommunication in Nanoengineering. In the paper, we review features of cognitive technologies in nanoengineering applications.


Vadim Kazakov, Vladimir Verstov, Lyudmila Zinchenko and Vladimir Makarchuk. Visual Analytics Support for Carbon Nanotube Design Automation. In the paper, we present our approach to visual analytics support for carbon nanotube design automation. The nanoworld is invisible for a human eye. As a result, standard human methods for decision making are not applicable. We illustrate our approach for research of thermal properties of single-walled carbon nanotubes. Practical outcomes of our approach are discussed.


Irina Knyazeva, Vyacheslav Orlov, Vadim Ushakov, Nikolay Makarenko and Boris Velichkovsky. On alternative instruments for the fMRI data analysis: General linear model versus Algebraic topology approach. This work aimed at comparing two different approaches (classical general linear model based on the Bayesian approach and the method of algebraic topology) for fMRI data processing in a simple motor task. Subjects imposes block paradigm, consisting of three identical blocks. The duration of each block was 40 seconds (20 seconds of rest and 20 seconds of right hand fingers busting). To obtain statistically significant results were carried out 20 sessions of experiment. The results obtained by both methods were very close to each other, but correspondence between statistically significant..


Vladimir Red'Ko. Models of autonomous cognitive agents. The lecture describes current models of autonomous cognitive agents. The study of these models can be considered as the method of investigations of biologically inspired cognitive architectures (BICA). The main attention is paid to the models that are used at studying of cognitive evolution. Several examples of such models are outlined. Schemes of new models are proposed.


Maksim Sharaev, Vadim Ushakov and Boris Velichkovsky. Causal interactions within the Default Mode Network as revealed by low-frequency brain fluctuations and information Transfer Entropy. The Default Mode Network (DMN) is a brain system that mediates internal modes of cognitive activity, showing higher neural activation when one is at rest. There are several recent studies of causal interactions (i. e. effective connectivity) between the DMN key regions. As a rule, these studies use model-based approaches such as Granger Causality (GC) or different versions of Dynamic Causal Modeling (DCM). The aim of the current work is to find a connectivity pattern between the four DMN key regions without any a priori assumptions on the underlying network architecture. For this purpose..


Vyacheslav Orlov, Sergey Kartashov, Vadim Ushakov, Anastasiya Korosteleva, Anastasia Roik, Boris Velichkovsky and Georgy Ivanitsky. “Cognovisor” for the human brain: Towards mapping of thought processes by a combination of fMRI and eye-tracking. The aim of this work was to describe localization of active brain of different types of thinking – spatial and verbal. The method of functional magnetic resonance imaging (fMRI) was used. Seven right-handed healthy volunteers aged from 19 to 30 participated in the experiment. In the experiment, the subject was brought against 6 types of tasks (about 30 of each type) distributed from the figurative to the semantic thought: 1 – of the four proposed options pick an element of the puzzle, suitable for shape and pattern on it; 2 - of the four proposed options pick an element of the puzzle,..


Viktoriya Zavyalova, Irina Knyazeva, Vadim Ushakov, Alexey Poyda, Nikolay Makarenko, Denis Malakhov and Boris Velichkovsky. Dynamic clustering of connections between fMRI resting state networks: A comparison of two methods of data analysis. In the present paper we describe an approach to the dynamical clustering of fMRI resting state networks and their connections, in which we use two known mathematical methods for data analysis: topological data analysis and k-means method. With these two methods we found about 4 stable states in group analysis. Dynamics of these states is characterized by periods of stability (blocks) with subsequent transition to another state. Topological data analysis method allowed us to find some regularity in subsequent transitions between blocks of states for individuals but it was not shown that the..


Alexander Efitorov, Tatiana Dolenko, Sergey Burikov, Kirill Laptinkiy and Sergey Dolenko. Neural Network Solution of an Inverse Problem in Raman Spectroscopy of Multi-component Solutions of Inorganic Salts. The paper presents a study into several aspects of solution of the in- verse problem on determination of concentrations of components in a multi- component water solution of inorganic salts by processing Raman spectra of the solutions by perceptron type artificial neural networks. The studied aspects are: 1) determination of the optimal architecture of a multi-layer perceptron, 2) influence of the input dimensionality reduction by aggregation of adjacent spectral channels on the error of problem solution. The results are compared for two data arrays including spectra of solutions of:..


Zarema B. Sokhova and Vladimir G Redko. Agent-based model of interactions in the community of investors and producers. This paper presents an agent-based model of a transparent market economic system. The community of investors and producers is considered. The agents-messengers realize the information exchange in the community. The computer simulation demonstrates the natural behavior of the considered economic system.


Helio Perroni Filho and Akihisa Ohya. A Biologically Inspired Architecture for Visual Self-Location. Self-location - recognizing one's surroundings and reliably keeping track of current position relative to a known environment - is a fundamental cognitive treat for entities biological and artificial alike. At a minimum, it requires the ability to match current sensory (mainly visual) input to memories of previously visited places, and to correlate perceptual changes to physical movement. Both tasks are complicated by variations such as lighting conditions, changes to environment composition, and the presence of moving obstacles. This article presents the Difference Image Correspondence..


Alexey Degterev and Mikhail Burtsev. Simulation of learning in neuronal culture. The neuronal cultures in vitro plated on the multi-electrode arrays is an important object of research in modern neurosciences. The protocol of culture stimulation which allows to receive a required response of culture on a selected electrode in response to stimulation is known. Such stimulation protocol can be considered as the elementary form of learning. In this study we create model of neuronal culture in vitro and obtained primary data on ability of such model to learning through stimulation.


Dmitry Volkov and Olga Mishulina. The approach to modeling of synchronized bursting in neuronal culture using a mathematical model of a neuron with autoregulation mechanism. The paper presents mathematical model of spike activity of a neuronal culture which exhibits bursting behavior – synchronized spontaneous packs of population activity. Neuron in the developed neural network model is a modification of Leaky Integrate-and-Fire neuron. The neuron model acquires a new quality due to the introduction of two new neuron state variables – “resource” and “strength”. The new learning mechanism for synaptic weights is proposed. It assumes dependence of weight corrections from the neuron “strength” and the intensity of spike activity of presynaptic..


Yury Telnov and Ivan Savichev. The Competency Management Based on Ontologies: Issues of Using in Organizations. . Learning activities can be considered the final outcome of a complex process inside knowledge intensive organizations. This complex process encompasses a dynamic cycle, a loop in which business or organizational needs trigger the necessity of acquiring or enhancing human resource competencies that are essential to the fulfillment of the organizational objectives. This continuous evolution of organizational knowledge requires the management of records of available and required competencies, and the automation of such competency handling thus becomes a key issue for the effective functioning..


Alexey Chernyshev. Bayesian Optimization of Spiking Neural Network Parameters to Solving the Time Series Classification Task. This work contains the application of spiking neural networks to time series classification task. Because of the lack of mathematical framework for such biologically inspired neural networks, this work tries to solve optimization task of parameters of the network with surrogate models. To define classification task quality metric that measures separability index based on Fisher's discriminant ratio is used.


Olga Chernavskaya. The Cognitive Architecture within Natural-Constructive Approach. We present Natural-Constructive Approach (NCA) to modeling the cognitive process, which is based on the dynamical theory of information, the neurophysiology data, and neural computing (combined with the technique of nonlinear dynamic differential equations). It is shown that the cognitive architecture designed within this approach enables us to interpret and reproduce peculiar features of the human cognitive process, namely – uncertainty, individuality, participation of emotions, intuitive and logical thinking.


Igor Isaev and Sergey Dolenko. Comparative Analysis of Residual Minimization and Artificial Neural Networks as Methods of Solving Inverse Problems: Test on Model Data. This study compares perceptron type neural network and residual minimization for solving inverse problems, at the example of a model inverse problem. Stability of both methods against noise in data was investigated. The conclusion about limited applicability of residual as a criterion of the solution quality has been made.


Ilya Sokolov and Mikhail Burtsev. Patterns of spiking activity of neuronal networks in vitro as memory traces. Neuronal cultures in vitro plated on the multi-electrode arrays are very promising as an experimental model to study basic principles of learning that can later motivate development of new artificial cognitive architectures. But it is still an open question if patterns of spontaneous activity in neuronal cultures can be interpreted as memory traces and if these traces can be modified in a learning-like manner. We studied experimentally in vitro development of spontaneous bursting activity in neuronal cultures as well as how this activity changes after open or closed loop stimulation. Results..


Aleksey Skrynnik, Alexander Petrov and Alexander Panov. Hierarchical temporal memory implementation with explicit states extraction. The new implementation of hierarchical temporal memory is proposed in the paper. The main difference of proposed implementation is a chain extract module, that complements spatial and temporal polling modules of HTM. New module simplify cross level regions connection implementation (e. g. feedback). Also we described an experiment that illustrate how hierarchical temporal memory with explicit states extraction works.


Valentin Klimov, Artem Chernyshov, Anita Balandina and Anastasiya Kostkina. A new approach for semantic cognitive maps creation and evaluation based on affix relations. This paper is devoted to a new method of creating semantic maps by means of affix relations. We show the differences between our approach and already existing ones. We also explain the necessity of our research, as it is unique for Russian language and our approach could be used in further researches and for creating semantic cognitive maps for Russian language. In the end of the paper we present the results of our work and further plans of our research.


Skiteva Lyudmila, Trofimov Aleksandr, Ushakov Vadim, Malakhov Denis and Velichkovsky Boris. MEG data analysis using the Empirical Mode Decomposition method. In the present paper it is proposed to use the Empirical Mode Decomposition method for frequency band analysis of MEG data, and the method is compared with the classical method of narrow band filtering and Hilbert transform. By the example of MEG data recorded during performing volitional sensorimotor actions by volunteers, it is shown that the Empirical Mode Decomposition method can potentially detect useful information inaccessible to classical methods of frequency band analysis.


Olivier Georgeon. Implementing Trace-Based Reasoning in a cognitive architecture with the aim of achieving developmental learning. I will present what Trace Based Reasoning (TBR, e.g., Cordier, Lefevre, Champin, Georgeon, & Mille 2013)—a new technique of Knowledge Engineering—can bring to research on Biologically Inspired Cognitive Architectures. TBR is a sort of Case-Based Reasoning (e.g., Aamodt & Plaza 1994) applied to learning from initially un-segmented and possibly un-interpreted sequences of events of interaction. In particular, TBR techniques proved suited to designing a cognitive architecture that avoids making common assumptions; namely, that the environment is stationary, deterministic, or discrete, or ..





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