Truncated Tentative Abstracts

001—Matthew Taylor. The Biological Path Towards AGI.
Today’s wave of AI technology is still being driven by the ANN neuron pioneered decades ago. Hierarchical Temporal Memory (HTM) is a realistic biologically-constrained model of the pyramidal neuron reflecting today’s most recent neocortical research. This talk will describe and visualize core HTM concepts like sparse distributed representations, spatial pooling and temporal memory. AGI is a common goal of many computer scientists. So far, machine learning techniques have created amazing results in narrow fields, but haven’t produced something we could all call “intelligent”. ...

002—Natalia Miloslavskaya. Network Security Intelligence Center as a Combination of SIC and NOC.
In modern networks, information security (IS) incidents have become not only numerous and diverse, but more damaging and disruptive. According to 2017 Cyber Attacks Statistics from hackmaggedon.com, among top attacks are malware, account and DNS hijacking, targeted attacks, DDoS, defacements, malvertising, and SQL injection. Various preventive controls based on IS risk assessment results decrease the majority, but not all IS incidents. Any delay and only reactive actions to IS incidents puts organization’s assets under risk. Therefore, an IS incident management system has become an ...

003—Natalia Miloslavskaya. Developing a Network Security Intelligence Center.
In this paper, continuing our research on designing a Network Security Intelligence Center (NSIC) as a combination of a Security Intelligence Center and a Network Operations Center, we applied the PDCA approach to its development in accordance with the requirements, which we formulated for its operations and self-protection. Following the Plan stage of this approach, we proposed a five-layered NSIC’s infrastructure and its high-level zone security infrastructure. The main area of short-term further work concludes the paper.

004—Puneet Mathur and Ramit Sawhney. A Firefly Algorithm based Wrapper-Penalty Feature Selection method for Cancer Diagnosis.
Advances in cancer diagnosis methods have led to the development of highly accurate, detailed and voluminous data. Unfortunately, high dimensional data often leads to poor accuracy and high processing time. Swarm intelligence based feature selection methods have been highly efficient in the biomedical domain, which motivates the exploration of more adaptive and newer wrapper based methods such as the Firefly algorithm. This paper explores the inclusion of a penalty function to the existing fitness function promoting the Binary Firefly Algorithm to drastically reduce the feature set to an ...

005—Emanuel Diamant. BICA Cognitive Architectures: Brain Inspired or Biologically Inspired?
Artificial Neural Networks were devised as a tool for Artificial Intelligence design implementations. However, it was soon became obvious that they are unable to fulfill their duty because the lack of understanding the human brain's functional complexity. Contemporary science posits that intelligence can be distinguished not only in human brains. Intelligence today is considered as a fundamental property of each and every living being. Therefore, lower simplified forms of natural intelligence are more suitable for investigation and further replication in artificial cognitive architectures.

006—Yuuji Ichisugi and Naoto Takahashi. A Formal Model of the Mechanism of Semantic Analysis in the Brain.
We propose a formal model of the mechanism of semantic analysis in the language areas of the cerebral cortex. The framework of Combinatory Categorial Grammar is modified so that it does not use lambda calculus that represents semantic rules. By disabling some parts of the model, it is possible to reproduce utterances resembling Broca aphasia and Wernicke aphasia. We aim at uniting this model with the Bayesian network model of the cerebral cortex in the future.

007—Anton Anikin, Oleg Sychev and Vladislav Gurtovoy. Multi-level modeling of structural elements of natural language texts and its applications.
Methods of extracting knowledge in the analysis of large volumes of natural language texts are relevant for solving various problems in the field of analysis and generation of textual information, such as text analysis for extracting of data, fact and semantics; presenting extracted information in a convenient for machine processing form (for example, ontology); classification and clustering of texts, including thematic modeling; information retrieval (including thematic search, search based on the user model, ontology-based models, document sample based search); texts abstracting and ...

008—Gayar Salakhutdinov and Irina Grigoryeva. Sources of ion emission in micropinch discharge plasma.
A set of spectrometers for simultaneous research of spatial structure of x-ray and ion emission sources in high current discharge is described. Experimental results of micropinch plasma ion and x-ray emission sources and their origin are discussed. Spectral characteristics of ion emission from micropinch plasma discharge are discussed. Specters of ion emission from micropinch plasma according to the conducted spectral measurements main contribution in image performing on ion pinpoints are made by one-, two-, three- and four-charged iron ions with energies from 5 to 80 keV. To determine the ...

009—Artem Sukhobokov, Ruslan Galimov and Aleksandr Zolotov. A system of strategic management based on Systemic Learning algorithm.
The paper is devoted to the development of strategic management’s systems for companies and organizations to solve two problems that are typical for the current level of these systems: insufficient synchronization of strategic management’s systems with operational management’s systems and insufficient adaptability of strategic management’s systems to changes in the external environment. To solve the first problem, it is proposed to use for each category of objects in the operational management’s system a separate internal perspective in the strategic management’s system. To solve ...

010—Wolfgang Bartelt, Tirtha Ranjeet and Asit Saha. Meta-Engineering: A Methodology to Achieve Autopoiesis in Intelligent Systems.
This paper presents an architecture of autopoietic intelligent systems (AIS) as systems of automated “software production” -like processes based on meta-engineering (ME) theory. A self-producing AIS potentially displays the characteristics of artificial general intelligence (AGI). The architecture de-scribes a meta-engineering system (MES) comprising many subsystems which serve to produce increasingly refined “software-production” -like processes rather than producing a solution for a specific domain. ME-theory involves a whole order of MES and the ME-paradox, expressing the fact ...

011—Paul Horton, Christian Huyck and Xiaochen Wang. Neural 2D Cart and Pole Control and Forward Model.
It is useful to have a robot controller and forward model in simulated or emulated neurons. Such a neural system can both aid understanding of the same task in biological neurons, and provide an efficient mechanism for robot control. This paper describes a controller and forward model for the 2D cart and pole problem. It uses standard control algorithms implemented in simulated neurons. The system runs on a simulated problem, but is a step toward more complete work with real bipedal robots. The next steps include a 3D cart and pole model, a forward model for a fast walking robot (based ...

012—Pentti Haikonen. Visual Priming in a Biologically Inspired Cognitive Architecture.
Each moment the senses produce large quantities of sensory information. Most of this information is not important and remains unnoticed or subconsciously perceived at best, while only the relevant information reaches the focus of attention and becomes consciously perceived. Attention is controlled by various external effects, like change and novelty, stimulus strength, etc. Internal cognitive conditions like context and expectations control attention by priming; the sensitization of sensory perception for the expected entities. Sensory priming can be implemented also in artificial ...

013—Oleg Grigoriev, Boris Kobrinskii, Gennadiy Osipov and Alexey Molodchenkov. Health management system knowledge base for formation and support preventive measures plan.
In the world currently P4 medicine is actively developing. The main aim of this direction is to predict health characteristics of a particular person and to carry out preventive measures before the manifestation of the first symptoms of possible disorders. An early prognosis should serve to reduce the risk of pathology. Accordingly, the lifestyle and the plan of medical and social activities should be aimed at reducing the influence of factors and situations that contribute to the occurrence of diseases. In the intelligent health-saving system, various data, for example, obtained using ...

014—Danila Vlasov, Alexandr Sboev, Roman Rybka and Alexey Serenko. Spiking neural network reinforcement learning method based on temporal coding and STDP.
Spiking single-layer perceptron competitive reinforcement learning method for solving the classification tasks based on encoding the input by patterns of spike times along with Spike-Timing-Dependent Plasticity is proposed. Learning is based on pattern memorising phenomenon and achieved by stimulation of corresponding output neuron with reinforcement signal and winner-take-all competition provided by inhibitory interconnections of output neurons. Gaussian receptive fields is used to encode input data to patterns of spike times. The learning method is tested on Fisher's Iris and Wisconsin ...

015—Anastasiia Gavrilkina, Olga Golitsina and Nikolay Maksimov. Building a well-formalized conceptual semantic network based on scientific and technical texts.
This paper presents a method for automatically constructing a conceptual semantic network (metagraph) based on texts of documents reflecting immanent and situational relations. The proposed methods are based on the idea that language is an instrument of cognition and reflection the results of mental activity. Thus the metagraph can be considered as an "assembly drawing" of the image (result of cognition) of the research object. Verbose constructs, which corresponds to concepts and relationships, are extracted from texts by analyzing the linguistic characteristics of words that reflect the ...

016—Nikolay Maksimov and Kirill Monankov. Focused on Cognitive Tasks Interactive Search Interface.
The components, structure and technologies of the semantic search interactive interface are substantiated, including for information support tasks of large scientific, technical and analytical projects. Semantic search is considered as a basic function of the cognitive process. In the context of the concept of "information search" it can be considered as a process of synthesizing a virtual document (the image of knowledge), formed from fragments (more precisely, their meanings) of documents. Accordingly, the interface is a means of forming a trajectory that provides a synthesis process. The ...

017—Yuichi Watanabe, Takahiro Hoshino and Junichi Takeno. A Consideration of the Pathogenesis of DID from a Robot Science Perspective.
In this study, we propose a consciousness model that simulates the dissociation symptoms and the pathogenesis of dissociative identity disorder (DID) using a conscious system constructed using consciousness modules called MoNADs. We are currently considering the use of a conscious system to reproduce dissociative disorders, one of the representative types of mental disorders, in order to promote an understanding of advanced mental disorders in humans. With the model presented in this paper, we attempt to achieve a "self-dispersing conscious system" and reproduce the dissociation symptoms by ...

018—Kristina Ionkina, Alexey Artamonov, Evgeny Tretyakov and Alexey Timofeev. Electronic document processing operating map development for the implementation of the data management system in a scientific organization.
The implementation of electronic data management system (EDMS) is a pressing issue not only for commercial organizations, but also for scientific organizations and laboratories with a large flow of unstructured information. Competent automation of data processing and analysis allows to significantly reduce labor costs for the entire cycle of incoming materials processing. To implement such system within a large scientific organization engaged in various multidisciplinary research on a wide range of areas, it is necessary to solve the problem of optimizing the processes of classification, ...

019—Aleksandr Vavrenyuk, Viktor Makarov, Viktor Shurygin and Dmitrii Tcibisov. Encrypted Data Transmission over an Open Channel.
This paper discusses methods for transmitting cipher messages over an open communication channel using an additional channel for transmitting a secret key. Set of the possible one-time keys are contained in the table on the sending and receiving side. The key to encrypt and decrypt a message is selected based on a signal that can be transmitted over an additional channel at a specified time or a timestamp in the cipher message itself. The proposed method also allows the use of a truly random number generator to obtain secret keys, which provides an absolutely strong cipher. Two variants of ...

020—Dmitry Saenko and Pavel Shapkin. A Type-Safe Approach to Identifying and Merging Changes in Complex Information Objects.
This article presents a tool for type-safe detection and merging of changes in information objects. This problem usually occurs as part of data migration and integration processes. Data can have a complex structure consisting of multiple properties and nested objects. We propose a tool that simplifies the solution of this problem with the help of a type-safe approach to data merging. This approach helps to reduce the number of errors and the number of situations when merging cannot be applied. It is possible due to reliance on the type theory. Scala programming language is used to ...

022—Roman Guschin, Vladimir Rozaliev, Yulia Orlova, Alla Zaboleeva-Zotova and Vladislav Berdnik. The method for searching emotional content in images based on low-level parameters with using neural networks.
The paper proposes a method to improve the modeling of subjectivity and human understanding based on the annotation of emotional semantics of images using the theory of fuzzy sets. Adaptive acceleration and reverse propagation of the neural network (BP). The annotation method was tested by analyzing images from the solar image database. In addition, the accuracy of the search is based on 85\% of our method. This study is the basis for a more accurate semantic analysis and verification.

023—Özge Nilay Yalçın and Steve Dipaola. A Computational Model of Empathy for Interactive Agents.
Empathy has been defined in the scientific literature as the capacity to relate another's emotional state and assigned to a broad spectrum of cognitive and behavioral abilities. Advances in neuroscience, psychology and ethology made it possible to refine the defined functions of empathy to reach a working definition and a model of empathy. Recently, cognitive science and artificial intelligence communities made attempts to model empathy in artificial agents, which can provide means to test these models and hypotheses. A computational model of empathy not only would help to advance the ...

024—Dmitry Efanov and Pavel Roschin. The challenges of multi-factor user authentication in cloud signing.
This paper is intended to provide a cloud-based digital signature platform with multi-factor authentication, including biometric authentication. A digital signature is an electronic analogue of a written signature; the digital signature can be used to provide assurance that the claimed signatory signed the information. In addition, a digital signature may be used to detect whether or not the information was modified after it was signed (i.e., to detect the integrity of the signed data). These assurances may be obtained whether the data was received in a transmission or retrieved from ...

025—Dmitry Efanov and Pavel Roschin. The Advanced Port-in-Use Covert Channel Attack.
We propose an advanced version of port-is-in-use attack, which is intended for leaking sensitive information in multilevel secure operating systems. Our approach is based on TCP socket mechanism widely used in Linux for interprocess communication. Despite the strong limitations inherent in operating systems with mandatory access control, sockets may not be restricted by the security policy, which makes it possible theoretically to transfer information from one process to another from a high security level to a low one. The proposed attack belongs to the operating system storage ...

026—Marcia A. van der Poel and Jan Treur. An Adaptive Network-Oriented Cognitive Model for Major Depression and its Treatment.
This paper presents an adaptive neurologically inspired cognitive model for the field of Major Depressive Disorder. This cognitive models is based on an (adaptive) temporal-causal network modelling approach incorporating a dynamic perspective on mental states and causal relations. The adaptive temporal-causal network modelling approach is used to address how a Deep Brain Stimulation treatment used for this disorder can work by a Hebbian learning effect. The model has turned out to produce simulation patterns as are expected from the literature. Verification by mathematical analysis has shown ...

027—Zvonimir Dujmić, Emma Machielse and Jan Treur. A Temporal-Causal Modeling Approach to the Dynamics of a Burnout and the Role of Physical Exercise.
In this paper from a Network-Oriented Modeling perspective a temporal-causal network model for burnout is introduced. The model can be the basis for a virtual patient agent model, and offers also possibilities to simulate certain forms of treatment. The model was evaluated by simulation experiments, verification by Mathematical Analysis and validation by Parameter Tuning for given patterns found in the literature.

028—Michael Accetto, Jan Treur and Valentina Villa. An Adaptive Cognitive-Social Model for Mirroring and Social Bonding During Synchronous Joint Action.
This paper presents an adaptive temporal-causal network model of human syn-chronization and bonding during a joint action. Two adaptive modelling princi-ples were adopted: the Hebbian learning principle for the mirroring process under-lying synchronisation, and the Homophily principle representing the social bond-ing. As validation reported experimental conditions were simulated. Mathematical analysis was performed to derive asymptotic properties and correctness.

029—Dmitry Zuev, Alexey Kalistratov and Andrey Zuev. Machine learning in IT service management.
IT Service Management (ITSM) is a variety of activities directed towards maintenance of IT infrastructure. Hence, it is considered to be an important activity for any company, even to one not related to IT. Time of incidents’ resolution is the key performance indicator for ITSM. To reduce resolution time, authors propose infrastructure incident prediction model. Model is based on machine learning technologies. Application of machine learning models in ITSM allows significant improves in customer experience and handling issues more efficiently, decreasing service desk agents’ efforts and ...

030—Vasiliy S. Kireev, Alexander Rogachev and Alexander Yurin. Web-analytics based on Fuzzy Cognitive Maps.
One of the main tasks of web Analytics is to analyze the structure of websites, on the basis of which data is determined by the behavior of visitors to make decisions on the development and expansion of the functionality of the web resource. Cognitive maps are used to model weakly structured subject areas in which, for a number of reasons, it is impossible to formalize the relationship between factors functionally. For construction of fuzzy cognitive maps seek help from the expert of the problem area, which on the basis of their knowledge and experience can reliably identify the factors and ...

031—Dmitry Liman and Alexander Dyumin. Simulation of Distributed Log Collection in Robotic Swarms.
The paper are focused on simulation of robotic swarms and, in particular, various log data collection approaches in such systems. Robotic swarm nodes are usually resource-limited systems that can fail easily (become discharged, etc); moreover, they have a limited communication radius and low data storage capacity. Therefore, the collection of a large amounts of data is required when such systems perform, and it is important to be able to quickly and reliably transmit the collected logs to data storage places in cases of semi-real time monitoring, failures detection, system debugging, ...

032—Andrey Mikryukov and Michail Mazurov. On correlation between information analytical system structures of situation centers and multi-layer selective neural networks.
The present paper considers the issues of application of multi-layer selective neural networks for building up the models in information analytical systems of situation centers. It shows the equivalence of architecture of operation processes in the information analytical systems of situation center to the structure of multi-layer neural network with “deep” learning. Applicability of selective neural networks as a part of situation center information analytical system for solving the tasks of image recognition, situation classification and prediction, etc. has been substantiated here. ...

033—Pavel Nenashev, Vadim Gumeniuk, Alexander Gridnev, Timofei Voznenko, Alexander Dyumin and Eugene Chepin. Intelligent System for Hexapod Locomotion Adaptation to the Environment.
Walking robots have advantages for solving many practical problems compared with wheeled robots. The hexapod can easily pass through a rough terrain with a complex terrain and obstacles. However, six legs give more freedom to choose locomotion mode. There are many known types of locomotions (tripod, wave, etc.) and new ones can be generated. Accordingly, for each type of surface (solid, loose, slippery, stony, etc.) it is more effective to use some specific set of locomotions. In general, the surfaces are characterized by relief, adhesion, etc., which are the main criteria for surface ...

034—Aleksandr Sashin, Mikhael Rudi, Alexander Gridnev, Timofei Voznenko, Alexander Dyumin and Eugene Chepin. Stereo Camera-based Obstacle Detection and Avoidance with Disparity and Optical Flow Analysis for Mobile Robot.
Nowadays mobile robots are more often used in everyday life. One of challenges is obstacle detection and avoidance in dynamic or unknown environments. There are many techniques to detect obstacles using various sensors (lidars, sonars, infrared sensors, cameras, stereo cameras, omnidirectional cameras, etc.). One of the most perspective techniques is usage of stereo camera. Now camera systems are less expensive than few years ago and can be installed on low cost robots. Stereo cameras are used for distance measurements, but disparity and depth computation algorithms are error-prone. Our ...

-35—Alexander Gridnev, Timofei Voznenko, Alexander Dyumin, Eugene Chepin and Konstantin Kudryavcev. Analysis of Commands Recognition Errors Influence on Mobile Robot Control Reliability.
There are a number of techniques to control mobile robots. In our case, we control a robotic wheelchair with extended BCI (brain-computer interface) that includes operator’s EEG data, voice and gesture commands. Every control channel have false negative and false positive errors. We can tune command detection algorithms by specifying threshold parameters, that reduces errors of one type and increases of another. To choose parameters values we need to analyse influence of false negative and false positive errors on wheelchair controllability and safety. Enhanced version of our navigation ...

036—Aliona Petrova, Timofei Voznenko, Alexander Gridnev and Alexander Dyumin. The Method of Emotional State Evaluation Using Brain-Computer Interface.
At present, there are many methods of the individual's emotional state evaluation, such as monitoring the subject’s physiological sensor measurements (heart rate, blood pressure), etc. One of these measurements is electroencephalography (EEG) data, which can be obtained using a brain-computer interface (BCI). In the process of using and training in BCI usage, it would be important to take into account the emotional state of the operator to provide more reliable use of this device as such state may influence on operation quality. By analyzing the EEG data it is possible to obtain ...

037—Anastasiia Cherepanova, Aliona Petrova, Timofei Voznenko, Alexander Gridnev and Alexander Dyumin. The Research of Distracting Factors Influence on Quality of Brain-Computer Interface Usage.
Nowadays, BCI (brain-computer interface) is a perspective method for human-machine interaction with a lot of usage concepts but before it can be used in broad scale many practical aspects should be investigated. For example, one should spent a lot of time on training before he or she can achieve sufficient control accuracy of a system through BCI. An important factor in working with BCI is distraction factor that can have a negative impact on training, and then, subsequently on the quality of control. Such factors can occur in normal conditions of device usage - that's why they are need to ...

038—Konstantin Kudryavcev, Aleksei Cherepanov, Timofei Voznenko, Alexander Gridnev and Alexander Dyumin. The Method of Statistical Estimation of the Minimum Number of Tests for Reliable Evaluation of the Robotic Multi-Channel Control Systems Quality.
There is a growth in adoption of multi-channel (tactile, voice, gesture, brain-computer interface, etc.) control systems for mobile robots that help to improve its reliability. But, a complex control system requires a large number of tests to determine the quality of operation. Hence, multi-channel control systems are subjects of rigorous testing process for estimation of the number of successful command recognitions and the number of errors. The more tests will be conducted, the more accurate evaluation of the control channel quality will be. However, in most cases, carrying out the tests ...

039—Jianyong Xue, Olivier Georgeon and Salima Hassas. Causality Reconstruction by an Autonomous Agent.
Most AI algorithms consider an agent’s input data as "percepts" received from the environment. Constructivist epistemology, however, suggests an alternative approach in which the agent’s input data is considered as a feedback resulting from the agent’s previous action. This paper presents design principles to let a constructivist agent learn regularities of actions and feedback and to reconstruct causality that can explain such regularities. The agent organizes its behaviors to fulfill a form of intentionality defined independently of a specific task. The experiment shows that the ...

040—Anna Gorbenko and Vladimir Popov. Deceptive Actions and Robot Collision Avoidance.
The problem of collision avoidance is extensively studied in contemporary robotics. Efficiency and effectiveness of multirobot task cooperation and safety of human-robot interactions depend critically on the quality of the solution of the problem of collision avoidance. Classical path planning and obstacle avoiding approaches can not guarantee a sufficiently high quality of the solution of the problem of multirobot and human collision avoidance. So, to solve the problem with a sufficiently high quality, some special algorithms and approaches are needed. In this paper, we consider the notion ...

041—Galina Rybina and Dmitry Demidov. Automated acquisition, representation and processing of temporal knowledge in dynamic integrated expert systems.
This paper analyzes the results of automated knowledge base construction for dynamic integrated expert systems on the basis of the so-called combined method of knowledge acquisition with temporal extensions.

042—Anna I. Guseva, Vasiliy S. Kireev, Pyotr V. Bochkaryov, Igor A. Kuznetsov and Stanislav A. Filippov. Association rules mining for predictive analytics in IoT cloud system.
The Internet of Things is one of the fastest growing areas of research currently. A promising area for the introduction of this technology is housing and communal services, for which the reduction of accidents, increasing efficiency, in general, focus on transparency, personalization of services and payments for the end user are relevant. This article is devoted to the development and testing of predictive algorithm for predicting the need for repair of various units within the smart home, such as heating, ventilation and air conditioning. The basis for the algorithm is the association rules ...

043—Steve DiPaola, Liane Gabora and Graeme McCaig. Informing Artificial Intelligence Generative Techniques using Cognitive Theories of Human Creativity.
As AI based Deep Learning generative techniques mature, especially those that use creativity as their guiding direction, can they be better informed by current cognitive theories about the human creative process. We use our updated modifications using ‘Deep Dream’-based convolutional neural network techniques mixed with other cognitive based computational art rendering systems to demonstrate how cognitive concepts of human creativity such as ‘honing theory’. ‘intrinsic motivation’ and ‘seed incident’ can be captured computationally. Conversely, we discuss how these kinds of ...

044—Vishwa Alaparthy, Amar Amouri and Salvatore Morgera. A Study on the Adaptability of Immune models for WSN Security.
Researchers in the field of artificial cognitive systems have persistently tried to adopt mechanisms and paradigms deployed in biological systems. Artificial neural network is one of the early examples adopting brain cell architecture and its computational practices. Similarly, Human Immune System (HIS) can be adopted for intrusion detection systems (IDS). The HIS recognizes and tries to eliminate external entities such as bacteria and viruses and works towards defending it from such threats. Detection and prevention mechanisms from HIS has been studied and adopted for Wireless Sensor ...

045—Valeriy Chernenkiy, Yuriy Gapanyuk, Georgiy Revunkov, Yuriy Kaganov and Yuriy Fedorenko. Metagraph approach as a data model for cognitive architecture.
There is no doubt that cognitive architecture model must be capable of describing very complex hierarchical systems. The basic lower-level "lingua franca" model is required for cognitive architecture description. We propose to use a metagraph model as a basic model. The key element of the metagraph model is metavertex which may include inner vertices and edges. From the general system theory point of view, a metavertex is a special case of the manifestation of the emergence principle, which means that a metavertex with its private attributes and connections become a whole that cannot be ...

046—Noufa Alnajran, Keeley Crockett, David McLean and Annabel Latham. A Heuristic Based Pre-processing Methodology for Short Text Similarity Measures in Microblogs.
Short text similarity measures have lots of applications in online social networks (OSN), as they are being integrated in machine learning algorithms. However, the data quality is a major challenge in most OSNs, particularly Twitter. The sparse, ambiguous, informal, and unstructured nature of the medium impose difficulties to capture the underlying semantics of the text. Therefore, text pre-processing is a crucial phase in similarity identification applications, such as clustering and classification. This is because selecting the appropriate data processing methods contributes to the ...

047—Galina Rybina, Yuri Blokhin and Levon Tarakchyan. Intelligent planning and control of integrated expert systems development process.
The paper is focused on the problems of development of integrated expert systems basing on problem-oriented methodology. Special operational component - intelligent planner controls the development process. The intelligent planner uses automated planning technology techniques in its core. The development of integrated expert system is shown as a planning problem. Some necessary models and technical details of intelligent program environment are described. The other crucial components of intelligent program environment such as reusable components and standard design procedures are briefly ...

048—David Saakian and Vladimir Red’ko. Sysers: the important model of self-reproducing system.
The current work describes the model of sysers. Syser is the abbreviation of SYs-tem of SElf-Reproduction. Syser is the model of rather general and universal system of self-reproduction. The model sysers was initially proposed as macromolecular self-reproducing systems. However, this model is close to a self-reproducing system of biological cells, moreover, sysers characterize general architectures of self-reproducing entities. In particular, the architecture of sysers is very similar to the architectures of self-reproducing automata investigated by John von Neuman. The paper describes the ...

049—Vlada Kugurakova. Neurotransmitters level detection based on human bio-signals, measured in virtual environments.
In this article, we explore the possibility of using the visual and sound stimuli obtained in various incidents when immersed in virtual reality, to detect human emotion by measuring the human bio-signals: heart rate, electroencephalogram (EEG), blood volume pressure, skin temperature and galvanic skin response (GSR) using biosensors. Further classification of signals occurs using a neural network. The received statistical characteristics are used as a contribution to the neural network for classification according to the cube of Löwheim emotions. The resulting algorithm for recognizing ...

050—Gennady Vinogradov. Self-organizing expert communities in educational projects.
The paper considers a problem of forming a knowledge model of a specialist with higher education. It shows that the way to solve this problem is to transfer universities to a design and technological type of organization. The most promising form of education project management is a model of information interaction within the framework of active self-developing network expert communities. An elementary part of such community is a professional expert. Integration in a natural intelligence network structure forms a collective strategic subject, which is a tool of a knowledge and action synergy ...

051—Viacheslav Burlov, Andrey Andreev and Fedor Gomazov. Mathematical model of human decision - a methodological basis for the realization of the human factor in safety management.
Without a methodological basis for solving the problems of human factor in safety management in the form of process conditions, we cannot guarantee the achievement of the goal of the activity. This situation gave rise to a fundamental problem: "The results of activities to address the problems of safety management processes do not meet the expectations of the decision-maker". The decision maker acts on the basis of three categories: system, model and goal. There are two approaches are known for the development of the system. Development of the system on the basis of analysis and development ...

052—Victor Rusakov. Matrices, Shortest Paths, Minimal Cuts and Euclidian Metric for Undirected Graphs.
Abstract. In the framework of the algebraic approach to looking into characteristics of undirected graphs, the sequence of their matrix representations has been established. The specifics of the matrices in such a sequence makes it possible at last to make a supposition about the explicit form of the Moore-Penrose pseudoinverse of the incidence matrix of an undirected graph. The rightness of the hypothesis has been confirmed by checking using the conditions of Penrose. The usual minimal cuts and shortest paths are shown as objects which are generated by norms introduced into the vector ...

053—Thomas Collins and Wei-Min Shen. Surprise-based Learning of State Representations.
There is an ever-increasing need for autonomous robots that are capable of adapting to and operating in challenging partially-observable and stochastic environments. Standard techniques for autonomous learning in such environments are often fundamentally reliant on human-engineered features, one of the most important of which is an a priori specification of the agent's state space. Designing an appropriate state space demands extensive domain knowledge, and even minor changes to the task or the agent might necessitate re-engineering. These limitations have given rise to end-to-end, ...

054—Taisuke Akimoto. Stories as Mental Representations of an Agent’s Subjective World: A Structural Overview.
Narrative is a universal form of human information and communication. The generative cognition of narrative will be a core element of a human-like and human-friendly autonomous agent. In the field of artificial intelligence (AI), the knowledge aspect of narrative, including script knowledge and episodic memory, has been studied from a functional perspective. However, this knowledge aspect is rooted in the close relationship between a narrative and how the human mind organizes subjective experiences into a mental representation. We use the term “story” to refer to the uniform mental ...

055—Adrián Ulises González Casillas, Luis Parra, Luis Martin, Cynthia Ávila-Contreras, Raymundo Ramírez Pedraza, Natividad Vargas and Félix Ramos. Towards a model of visual identification based on neuroscience.
Cognitive sciences and computer vision have proposed diverse models to acquire, transform and interpret visual information, mainly aimed to achieve realistic, yet efficient approaches to those capacities. One of the key aspects of visual processing is identification of objects in the scene, that entails the perceptual association of visual features with semantic information extracted from memory. In this study, we present a model for visual recognition that resembles the way the human's brain interact in order to achieve this process. The model describes the processes in V1, V2 and V4 to ...

056—Avi Pfeffer and Spencer Lynn. Scruff: A Deep Probabilistic Cognitive Architecture for Predictive Processing.
The theory of predictive processing encompasses several elements that make it attractive as the underlying computational approach for a cognitive architecture. We introduce a new cognitive architecture, Scruff, capable of implementing predictive processing models by incorporating key properties of neural networks into the Bayesian probabilistic programming framework. We illustrate the Scruff approach with conditional linear Gaussian (CLG) models, noisy-or models, and a Bayesian variation of the Rao-Ballard linear algebra model of predictive vision.

057—So Negishi, Taku Hayami, Hiroto Tamura, Haruo Mizutani and Hiroshi Yamakawa. Neocortical functional hierarchy estimated from connectomic morphology in the mouse brain.
The mechanisms of information processing in the neocortex underlie brain functions based on the hierarchy of individual cortical areas. This study was focused on the determination of the neocortical hierarchy resulting from a clustering analysis of connectomic morphology with cortical layer resolution. K-means clustering effectively classified connectivities into two groups. And then the description of the neocortical hierarchy was simplified to a wiring diagram to understand how sensory information flows in the biological neural networks. In this initial work, the validity of the resulting ...

058—Kristina Ionkina, Andrey Svistunov, Ilya Galin, Boris Onykiy and Larisa Pronicheva. MIAS database semantic structure.
The paper reviews the principles of designing websites, including the HTML 5 markup and schemes of organizing information on websites; it evaluates the possibility for using Schema.org as a core of the database of the Multiagent Information Analytical System (MIAS) for information of technological and natural science content, developed by the faculty of Department 65 “Analysis of Competitive Systems”, MEPhI.

059—Artem Chernyshov, Anita Balandina and Valentin Klimov. Intelligent processing of natural language search queries using semantic mapping for user intention extracting.
Nowadays the leading world scientists and engineers center their attention to data mining and machine learning algorithms optimization and acceleration rather than inventing new ones. The natural language processing methods and tools are widely in use in production in the area of machine translation. The researches in the area of search engines and semantic search are mostly concentrated on data storage and further analysis. The majority of search engines use the huge amounts of previously accumulated user requests for predicting the search output without taking in attention this user ...

060—Alexander Vartanov and Irine Vartanova. Four-dimensional spherical model of emotions.
The four-dimensional spherical emotional space was obtained by multi-dimensional scaling of subjective differences between the emotional expresions in sound samples (there were used words "Yes" and "No", said in various emotional condition). Euclidean dimensions of the space are interpreted as following neuronal mechanisms: first two dimensions are corresponded to an estimation of a situation by a sign of emotion - dimension 1 (pleasant, is it useful or not, and unpleasant) dimension 2 is - a degree of information definiteness -, dimensions 3 and 4 are conding type of activity: ...

061—Sahand Mohammadi Ziabari and Jan Treur. Computational Analysis of Gender Differences in Coping with Extreme Stressful Emotions.
In this paper a computational analysis is presented of differences between men and women in coping with extreme emotions. This analysis is based on an adap-tive temporal-causal network model. It takes into account the suppression of con-nections between preparation states and sensory representations of action effects due to an extreme stressful emotion. It is shown how this model can be used to represent the difference between males and females facing an extreme emotion, thereby performing their own methods in coping with the extreme emotion, for males fight or flight and for females ...

062—Rosa Schoenmaker, Jan Treur and Boaz Vetter. A Temporal-Causal Network Model for the Effect of Emotional Charge on Information Sharing.
By addressing the underlying cognitive and affective processes in this paper, a temporal-causal network model was designed for sharing behaviour (retweeting) on Twitter. The model represents the integrated cognitive and affective processes and explains how the use of emotions in addition to information can cause an amplification in the diffusion of this information.

063—Félix-Francisco Ramos-Corchado, Diana-Gabriela Gomez-Martinez, Jonathan-Hernando Rosales, Vianney Muñoz-Jimenez and Marco-Antonio Ramos-Corchado. A bio-inspired self-responding emotional behavior system for virtual creatures.
Communication is a fundamental aspect of the interaction among human beings and even between dierent species. In particular, our physical behaviors provide a large part of this communication that expresses our internal states. Most of these behaviors are autonomous and reactive, detonated by the stimuli perceived in the environment. Within this type of self-responding behaviors are the behaviors of basic emotions. In this paper, we propose a conceptual model for the generation of self-responding behaviors for virtual creatures inspired by neuroscientific evidence, focusing on those centered ...

064—Elena Matrosova and Anna Tikhomirova. Intelligent data processing received from radio frequency identification system.
The present article is devoted to the issue of improvement of utilization efficiency of modern information technologies in order to enhance the quality and safety of healthcare delivery. The introduction of Real-time Locating Systems (RTLS), in particular Radio Frequency Identification System (RFID), applied in conjunction with intelligent data processing system can reduce the severity of the consequences of event risks implementation, as well as avoidance of a number of such consequences. The authors of the present article suggest using mathematical tools of fuzzy logic theory for event ...

065—Evgenii Vityaev and Alexander Demin. Cognitive architecture based on the functional systems theory.
In this paper the cognitive architecture based on the Functional Systems Theory (TFS) by P.K.Anokhin is presented. This architecture based on the main notions of this theory: goal, result, anticipation of the result. This theory is described on physiological and informational level. The logical structure of this theory was analyzed and used for the control system of the purposeful behavior development. This control system contains the hierarchy of functional systems that organize the purposeful behavior. The control system was used for the agents modeling that are solving the foraging task. ...

066—Douglas Summers-Stay, Peter Sutor and Dandan Li. Representing Sets as Summed Semantic Vectors.
Representing meaning in the form of high dimensional vectors is a common and powerful tool in biologically inspired architectures. While the meaning of a set of concepts can be summarized by taking a (possibly weighted) sum of their associated vectors, this has generally been treated as a one-way operation. In this paper we show how a technique built to aid sparse vector decomposition allows in many cases the exact recovery of the inputs and weights to such a sum, allowing a single vector to represent an entire set of vectors from a dictionary. We characterize the number of vectors that can ...

067—Alexander Alyushin. Increase a Person's Face Vibrational Image Information Content Due to Compensation of Components Caused by His Speech Activity.
The relevance of the human factor management problem is shown with the aim to minimize the possibility of accidents and catastrophes of technogenic origin. The role of modern remote technologies for monitoring the current functional and psychoemotional state of personnel of hazardous enterprises and industries is substantiated. The technology of analyzing the vibrational image of a person's face is classified as one of the promising information technologies. The problem of occurrence of vibrational distortions is revealed at carrying out of active speech dialogue. An approach is proposed ...

068—Mikhail Alyushin and Lyubov Kolobashkina. Person's Face Thermal Image Vibrational Components Processing in Order to Assess His Current Psycho-Emotional State.
An approach to the processing of the vibrational components of a person's face thermal image is proposed with the aim of assessing its current functional and psycho-emotional state. The essence of the approach is to create and use an individual model of the person's face thermal image. Such a model includes graphic components containing information on the most informative areas from the point of view of the unconscious vibrations and facial expressions of the face manifestation; the face areas with a pronounced manifestation of the modulation effect due to cardiac and respiratory activity of ...

069—Mikhail Alyushin and Lyubov Kolobashkina. Increase of the Person Current Psycho-Emotional State Estimation Results Reliability During its Vibroimage Analysis in Conditions of Illumination Unevenness and Instability.
The approach allowing to increase reliability of the person current psycho-emotional state estimation results at the analysis of its image vibration components (VC) is offered. The essence of the proposed approach is to spatially-temporally compensate the person's face VC image distortion caused by unevenness or temporal instability of illumination. It is proposed to use correction of pixel brightness values to compensate for distortions caused by temporal instability of illumination. Such correction is carried out in proportion to the magnitude of the excess of the light level of its mean ...

070—Max Talanov, Alexey Leukhin, Jordi Vallverdu and Fail Gafarov. Modeling psycho-emotional states via neurosimulation of monoamine neuro-transmitters.
In this paper we present a new computational bio-inspired approach and results of the validation, we use the three-dimensional model of emotions created by Hugo Lövheim "cube of emotions" with 8 basic psycho-emotional states, and validate it via neurosimulation in the NEST (Neural Simulation Tool). As the extension of the model "cube of emotions" we present the computational model that bridges psycho-emotional states with computational processes and validation as neurosimulation results. We re-implemented dopamine, serotonin, noradrenaline pathways of a rat brain to replicate 8 basic ...

071—Norifumi Watanabe and Kota Itoda. Analysis of Gaze Behaviors in Virtual Environments for Cooperative Pattern Modeling.
In a goal type ball game such as soccer and handball, a plurality of players who can pass are searched for, and each player's intention is estimated and a player who can pass is selected. Furthermore, it confirms the position and behavior of enemies that exist around passable players, estimates their intentions, and determines teammate players who pass the pass most successfully. In order to realize instantaneous intention estimation and judgment subject to strong temporal and spatial constraints, cooperative patterns shared within the group are considered to exist. Therefore, in this ...

072—Naoto Takahashi and Yuuji Ichisugi. Toward Human-like Sentence Interpretation - a Syntactic Parser Implemented as a Restricted Quasi Bayesian Network.
Although most sentences expressed in a natural language is ambiguous, human beings straightforwardly understand the intended message even when a computer program generates countless possible interpretations. If we want to create computer programs that understand natural languages in the same way as human beings do, a promising way would be to implement a human-like sentence processing mechanism instead of implementing a ``exhaustive-listing-and-selecting'' method. Human's cerebral cortex plays crucial roles for language processing and seems to work as a kind of Bayesian network. Then it ...

073—Kristina Gaisina, Ruslan Gaisin, Max Talanov, Ekaterina Bobrinskaya, Ilnur Khairullin and Sasha Suhanaeva. Validation of three-dimensional model for emotions based on monoamine neurotransmitters.
Background The classification of human emotional reactions is normally based on the analysis of video data, psychological tests, electroencephalography data, galvanic skin response and other non-invasive methods. Material and methods In this paper we discuss the skin electric potential data registration of a human while she/he is watching specially selected videos that are supposed to activate basic emotions of fear and disgust. We present the selected video materials and data logging process and human emotional reactions model. We have implemented the model via numerical methods. We ...

074—Masahiro Miyata and Takashi Omori. Emergence of symbolic inference based on a value driven intuitive inference by associative memory.
Human has two types of inference, Intuitive one and logical one, and they were studied separately. However, a site for the logical inference has not been found in brain, and a modeling in a form of distributed neural network is desirable as the brain process. In this study, we propose a model of inference in which the intuitive inference is realized as a search process in a continuous and distributed associative memory, and is switched to a symbolic mode in which each step of the association converges to a stable state of self-recollection realizing a step by step logic like behavior. The ...

075—Elena Matrosova, Anna Tikhomirova and Denis Naumov. Decision support system in the sphere of vacancy selection for graduates of technical universities.
In the present article there are considered approaches to problem solving of selection of suitable vacancies for young experts on the basis of correlation of a competency based matrix of the graduate with employer’s requirements. The offered technique involves five stages: correlation of disciplines and competences, prioritization of employer’s requirements, initial selection of compared competences from the competency based matrix of a graduate, correlation of requirements and competences, assessment of the graduate’s competence relevance in relation to the vacancy. Technique ...

076—Ilya Daylidyonok, Anastasiya Frolenkova and Aleksandr Panov. Extended Hierarchical Temporal Memory for Motion Anomaly Detection.
This paper describes the application of hierarchical temporal memory (HTM) to the task of anomaly detection in human motions. An extended version of HTML is proposed, in which feedback on the movement of the sensor's focus on the video frame is added, as well as intermediate processing of the signal transmitted from the lower layers of the hierarchy to the upper ones. By using elements of reinforcement learning and feedback on focus movement, the HTM' temporal pooler includes information about the next position of focus, simulating the human saccadic movements. Processing the output of the ...

077—Dharani Punithan and Byoung-Tak Zhang. Lifelong Learning with the Feedback-loop between Emotions and Actions via Intrinsic Reward.
We propose a cognitive model where an autonomous agent incorporates a cybernetic feedback-loop between emotional states and activity-selection mechanism via intrinsic reward. In our model, emotions play a crucial role in providing motivation for agents to choose other activities and thereby promote continuous learning. We show that lifelong learning emerges as the emotions of the agent, decide the activity-selection via intrinsic reward. We qualitatively analyze the emotional dynamics, exhibited by the proposed cognitive system in a minimalistic environment.

078—Victor Alyushin. Analysis and Visualization of the Psychological Climate in the Team.
The importance of maintaining a normal working psychological climate in the team is underlined. An approach is proposed for assessing and visualizing the psychological climate in a team based on the use of modern information technologies for remote monitoring of the current psycho-emotional state of a person. As a measure of personal conflict, it is proposed to use the magnitude of the nervous tension that occurs when all personal forms of interaction in the collective are realized. As a basis for graphical visualization of all interpersonal relations, it is suggested to use a circular graph ...

079—Ricardo Gudwin, André Luis Paraense, Suelen Mapa de Paula, Eduardo Fróes, Wandemberg Gibaut, Elisa Castro, Vera Figueiredo and Klaus Raizer. An Urban Traffic Controller using the MECA Cognitive Architecture.
In this paper, we present a Cognitive Manager for urban traffic control, built using MECA, the Multipurpose Enhanced Cognitive Architecture, a cognitive architecture developed by our research group and implemented in the Java language. The Cognitive Manager controls a set of traffic lights in a junction of roads based on information collected from sensors installed on the many lanes feeding the junction. We tested our Junction Manager in 4 different test topologies using the SUMO traffic simulator, and with different traffic loads. The junction manager seeks to optimize the average waiting ...

080—Amit Kumar Mishra. WEBCA: Weakly-Electric-Fish Bioinspired Cognitive Architecture.
Neuroethology has been an active field of study for more than a century now. Out of some of the most interesting species that has been studied so far, weakly electric fish is a fascinating one. It seems to be doing communication, echo-location and inter-species detection efficiently with an interesting configuration of sensors, neurons and a simple brain. In this paper we propose a cognitive architecture inspired by the way these fishes handle and process information. We believe that it is easier to understand and mimic the neural architectures of a simpler species than that of human. ...

081—Olga Chernavskaya. On revealing and resolving the scientific paradoxes within the artificial cognitive system.
The concept of scientific paradox and the possibility to reveal and resolve these paradoxes by means of artificial intelligence are discussed. The Natural-Constructed Cognitive Architecture (NCCA) for modeling the cognitive process is presented. This architecture is aimed to interpret and reproduce the human-like cognitive features including uncertainty, individuality, intuitive and logical thinking, and the role of emotions in cognitive process. It is shown that this architecture involves, in particular, the high-level abstract symbolic information that could be associated with the concepts ...

082—Tianbai Yuan, Jianyu Wang and Junichi Takeno. Research for Obsessive-compulsive Disorder based on Consciousness System.
In the future a society where human beings coexist with robots is possible, and the research of robots that can understand human behavior and intention become increasingly important. Due to the high stress of modern society, more and more people get various mental disorders. For this reason, the authors think that a support robot to care for and cure mental disorder patients is necessary. Therefore, this research aims at modeling the human obsessive-compulsive disorder and common treatment methods that based on consciousness system. We believe that this research will be useful for research ...

083—Agnese Augello, Frank Dignum, Manuel Gentile, Ignazio Infantino, Umberto Maniscalco, Giovanni Pilato and Filippo Vella. A Social Practice oriented Signs Detection for Human-Humanoid Interaction.
In this work we propose a cognitive architecture, based on the Social Practice (SP) theory, aimed at the modeling of socially adaptive robots, able to interact with people, recognizing and interpreting the specific social context where it is acting. The proposed social robot is able to recognize and interpret social signs during ongoing social practices. The cognitive architecture is inspired by the well-known Psi model, and it is equipped with a Social Practice Engine that manages the whole conduct of the robot. The use of such an architecture simplifies and makes more natural the ...

084—Agnese Augello, Umberto Maniscalco and Giovanni Pilato. NarRob: A Humanoid Social Storyteller with Emotional Expression Capabilities.
In this paper we propose a model of a robotic storyteller, focusing on its abilities to: 1) choose from time to time a story to narrate as a result of a brief interaction with the user, and 2) select the more appropriate gestures to accompany the story, according also to the specific situation and the typology of interlocutor. The robot is endowed with a repository of stories together with a formalization of the knowledge about them, such as the genre and main elements (characters, moral, the meaning of the used terms). It has also equipped with a repository of gestures, inspired by ...

085—Antonio Lieto. Heterogeneous Proxytypes Extended: Integrating Theory-Theory Representational Assumptions and Mechanisms with Prototypes and Exemplars.
The paper introduces an extension of the notion of agents conceptual representations intended as "heterogeneous proxytypes", firstly introduced in Lieto 2014. Such notion relies on the so-called proxytype theory of concepts and combines it with the heterogeneity approach to concept representations, according to which concepts do not constitute a unitary phenomenon. The main contribution of this paper is in that it details how to reconcile, under a heterogeneous representational perspective, different theories about concept representation and reasoning in cognitive systems and ...

086—Miriam Buonamente, Haris Dindo, Antonio Chella and Magnus Johnsson. Simulating Music with Associative Self-Organizing Maps.
We present an architecture able to recognise pitches and to internally simulate likely continuations of partially heard melodies. Our architecture consists of a novel version of the Associative Self-Organizing Map (A-SOM) with generalized ancillary connections. We tested the performance of our architecture with melodies from a publicly available database containing 370 Bach chorale melodies. The results showed that the architecture could learn to represent and perfectly simulate the remaining 20\% of three different interrupted melodies when using a context length of 8 centres of activity in ...

087—Larisa Ismailova, Sergey Kosikov and Viacheslav Wolfengagen. A computational model for support access policies to semantic web.
The paper discusses a solution to the problem of data storage in a Web environment and providing access to the data based on their semantics. The problem involves restricting access to data in accordance with the description of the access rights for different classes of users. The information is interpreted in different ways according with the semantics assigned to users. Access policies are proposed as the main technical tool for describing access rights. The provided information is consistent with the semantic description of the user accessing the information. The semantic matching of ...

089—Anurag Singh and Jeevanandam J. Hybrid video surveillance systems using P300 based computational cognitive threat signature library.
Comprehensive Integrated Border Management System (CIBMS) is aimed to reduce the infiltration at borders of countries and to overcome risk. It is an integration of manpower, sensor, networks, intelligence, command and control solutions. This research work is intended to create a reference library on threat signature using cognitive technology which will enhance the intelligence for CIBMS to reduce the human effort using cognitive science through Brain Computer Interface (BCI) application. The system proposes to use an Electroencephalogram (EEG) cap to monitor the operators brain signals when ...

090—Ajaz Bhat, John Spencer and Larissa Samuelson. A dynamic neural model of language acquisition and visual dynamics in cross-situational word learning tasks.
Background: While AI models for natural language processing (NLP) tasks have boomed in recent years, their application to reverse-engineer language and cognition remains minimal owing to three key limitations: 1) Models of NLP are not grounded in real psychological processes like attention, working memory etc. that are fundamental to language learning; 2) Automated language models neglect the role of visual dynamics in word learning; 3) Existing language models do not simulate word learning at a moment-to-moment timescale. Furthermore, the neural plausibility of NLP models is widely ...

091—Daisuke Mizuno, Kazuteru Miyazaki and Hiroaki Kobayashi. On Stable Profit Sharing Reinforcement Learning with Expected Failure Probability.
In this paper, Expected Success Probability (ESP) is defined and a reinforcement learning method Stable Profit Sharing with Expected Failure Probability (SPSwithEFP) is proposed. In SPSwithEFP, Expected Failure Probability (EFP) is used in the roulette wheel selection method and ESP is used in the update equation of the weight of a rule. EFP can discard risky actions and ESP can make the distribution of learned results smaller. The effectiveness is shown with simulation experiments for a maze environment with pitfalls.

092—Ronakben Bhavsar, Neil Davey, Na Helian, Yi Sun, Tony Steffert and David Mayor. Time Series Analysis using Embedding Dimension on Heart Rate Variability.
Heart Rate Variability (HRV) is the measurement sequence with one or more visible variables of an underlying dynamic system, whose state changes with time. In practice, it is difficult to know what variables determine the actual dynamic system. In this research, Embedding Dimension (ED) is used to find out the nature of the underlying dynamical system. False Nearest Neighbour (FNN) method of estimating ED has been adapted for analysing and predicting variables responsible for HRV time series. It shows that the ED can provide the evidence of dynamic variables which contribute to the HRV time ...

093—Sergey Kosikov, Larisa Ismailova and Viacheslav Wolfengagen. Network modeling environment for supporting families of displaced concepts.
The paper considers the problem of supporting the semantic stability of the information system in the course of changing its subject area. The representation of changes is performed on the basis of connection of methods of applicative computational technologies and the “functor-as-object” construction by the use of formalism of displaced concepts. The database of the system is represented as a semantic network. Various mechanisms of the displaced concepts arising are considered, including those for formalizing the change in the scope of the concepts of the subject area, their ...

094—Adam Bennett and Anthony White. Synfire Circuits: Constraint Programming Technique for Combining Functional Groupings of Spiking Neurons.
Existing training techniques for spiking neuronal networks tend to be monolithic in nature and scale poorly to larger networks. This paper presents a technique for combining multiple functional neural groupings into a more complex composite network. This is accomplished by ensuring that four axioms hold true for the composite network. The axioms were designed to ensure that incoming signals arrive simultaneously to any component groupings. A number of experiments were conducted in which an algorithm implementing the axioms was used to combine component groupings into more complex networks; ...

095—Igor Rodriguez, Aitzol Astigarraga, Elena Lazkano, José María Martínez-Otzeta and Iñigo Mendialdua. Robots on Stage: a Cognitive Framework for Socially Interacting Robots.
This article is an attempt to characterize the cognitive skills involved in the development of socially interacting robots. We argue that performative arts, such as oral improvised poetry, can serve as a useful testbed for the development and evaluation of robots that interact with humans. The paper presents a speech-based humanoid poet-performer that can (1) listen to human commands and generate poems on demand; (2) perceive audience's feedback and react displaying the corresponding emotional response; and (3) generate natural gesticulation movements enriched with social signals depending ...

096—Takuma Seno, Masahiko Osawa and Michita Imai. An Intrinsically Motivated Robot Explores Non-reward Environments With Output Arbitration.
In real worlds, rewards are easily sparse because the state space is huge. Reinforcement learning agents have to achieve exploration skills to get rewards in such an environment. In that case, curiosity defined as internally generated rewards for state prediction error can encourage agents to explore environments. However, when a robot learns its policy by reinforcement learning, changing outputs of the policy cause jerking because of inertia. Jerking prevents state prediction from convergence, which would make the policy learning unstable. In this paper, we propose Arbitrable Intrinsically ...

097—Dmitry I. Krylov and Alexei V. Samsonovich. Designing an emotionally-intelligent assistant of a virtual dance creator.
Intelligent agents and co-robots, or cobots, become increasingly popular today as creators of digital art, including robotic or virtual dancing. Arguably, the creativity of such tools is linked to their social-emotional intelligence. In this work we question this hypothesis, extending the general paradigm of an emotionally-intelligent creative assistant (Samsonovich, 2018) to virtual dance creation. For this purpose, a semantic map of dance patterns is constructed. Transitions among the patterns are selected among local transitions on the map, following general rules. The outcome is judged ...

098—Tomasz Szandała. Genetically Evolved Extreme Learning Machine for Letter Recognition Dataset.
It is well known that the performance of learning feed forward neural networks is in general far slower than required and it has been a major bottleneck in their applications. Two key obstacles the slow gradient-based learning algorithms which are extensively used to train neural networks. Combining slow training process with even slower evolutional methods appears to be incomprehensible but here comes the Extreme Learning Machine. ELM has randomly chosen hidden nodes and analytically determined only the output weights of network. In theory, this algorithm tends to provide good ...

099—Thaddé Rolon-Mérette, Damiem Rolon-Mérette and Sylvain Chartier. Generating Cognitive Contexts with Feature-Extracting Bidirectional Associative Memory.
Associative learning is a key concept in human cognition that is fairly understood. It con-sists of associating pairs of patterns. However, when faced with similar patterns, challeng-es arise. In contrast, the mechanisms to deal with such challenges are not yet understood. In the field of artificial neural networks, the proposed cognitive contexts (CC) shed some light on the subject by using orthogonal contextual information to distinguish similar patterns. Although this method greatly improved learning and recall performances of simi-lar patterns, it relied on the use of arbitrary ...

100—Anastasiya Kostkina and Denis Bodunkov. Document Categorization Based on Usage of Features Reduction with Synonyms Clustering in Weak Semantic Map.
Nowadays the number of huge companies and corporations has in their disposition various non-structured texts, documents and other data, but the most part of data is still text documents which differ in their subject matter and content. The organization of work with such data is complicated because of their characteristics and requires modern tools for their processing and analysis. The solution to this problem may be to refer to machine learning algorithms and natural language processing methods, improve the existing clustering and classification algorithms. For document classification, we ...

101—Gabriela Sejnova, Michael Tesar and Michal Vavrecka. Real world implementation and testing of neural module networks in a humanoid robot.
Large neural networks trained in an end-to-end fashion usually fail to generalise over novel inputs which were not included in the training data. In contrast, biologically-inspired compositional models offer a more robust solution due to adaptive chaining of logical operations performed by specialised modules. In this paper, we present such cognitive architecture based on the End-to-End Module Networks (N2NMNs) model (Hu et al., 2017), which we implemented in the humanoid robot Pepper. The architecture is focused on the Visual Question Answering task (VQA), in which the robot answers ...

102—Piotr Bołtuć. BICA-Mary.
Not all physical knowledge is easily accessible to human beings as knowledge by description. The so called 'knowledge argument' [Jackson] assumes that, if Mary (a scientist, color-blinded from birth, who has expert knowledge about colors) learns something upon seeing her first colored object, then physicalism is false. We claim that this shows nothing of philosophical value since a simpler account is the following: The initial setting of human cognitive architecture has no link from knowledge by description to the feel of phenomenal qualities. In natural context, such link must be created ...

103—Sergio Castellanos, Luis-Felipe Rodríguez, Luis A. Castro and J. Octavio Gutierrez-Garcia. Computational Model of Emotion Assessment Influenced by Cognition in Autonomous Agents.
In this paper, we design and develop a computer model of emotions based in the context of an Integrative Framework proposed by Rodriguez. In particular we define the mechanisms to 1) assign an emotional value to the events perceived by the agent, using a set of appraisal variables and 2) model the influence of cognition on each appraisal variable modifying the limits of fuzzy membership functions associated to each of them, giving a degree of emotional intelligence to the agent. We also defined a test case, which consisted of three agents, two of them with different personalities (as a ...

104—Joscha Bach. The Cortical Conductor Theory: Towards Addressing Consciousness in AI Models.
AI models of the mind rarely discuss the so called “hard problem” of consciousness. Here, I will sketch a possible functional explanation for phenomenal consciousness: the conductor theory of consciousness (CTC). Unlike IIT, CTC is a functionalist model of consciousness, and accounts for the emergence of a first person perspective reporting about phenomenal experience, with these experiences being the memory of the contents of an attentional protocol.

105—Darsana Josyula, Jesuye David and Christion Banks. Levels of Metacognition and their Applicability to Reinforcement Learning.
Recognizing patterns is an integral aspect of human intelligence; we know that every year, winter will come and with it cold weather and snowfall. We therefore go to the stores to purchase coats and other tools that will ensure our comfort and survival. We are not shocked when the weather changes during a seasonal change, and try to figure out how to cope because it is not new. We instilled this ability into an autonomous agent- Chippy. We considered a dynamic environment, where seasons, called rewards, change at predetermined rates and intervals. Seasonal changes are constructed into ...

106—David Kelley and Mark Waser. Feasibility Study and Practical Applications Using Independent Core Observer Model AGI Systems for Behavioral Modification in Recalcitrant Populations.
This paper articulates the results of a feasibility study and potential impact of the theoretical usage and application of an Independent Core Observer Model (ICOM) based Artificial General Intelligence (AGI) system and demonstrates the basis for why similar systems are well adapted to manage soft behaviors and judgements, in place of human judgement, ensuring compliance in recalcitrant populations. Such ICOM-based systems may prove able to enforce safer standards, ethical behaviors and moral thinking in human populations where behavioral modifications are desired. This preliminary ...

107—Howard Schneider. Meaningful-Based Cognitive Architecture.
An overview is given of the cognitive architecture of the biologically inspired meaningful-based learning system (MBLS), which can inherently perform the sensory processing associated with neural networks and the symbolic logic associated with human cognition, without the use of an external memory. The elementary unit of the MBLS, a reconfigurable Hopfield-like network (HLN) can rapidly connect to varying sets of other HLNs depending on the level of abstraction which yields a practical maximal “meaningfulness” where this is defined as the reciprocal of the Shannon entropy. The MBLS can ...

108—Patrycja Świeczkowska, Rafal Rzepka and Kenji Araki. Analyzing motivating texts for modelling human-like motivation techniques in emotionally intelligent dialogue systems.
In this paper, we present studies on human-like motivational strategies which eventually will allow us to implement motivational support in our general dialogue system. We conducted a study on user comments from a discussion platform Reddit and identified text features that make a comment motivating. We achieved around 0.88 accuracy on classifying comments as motivating or non-motivating using SVM and a shallow neural network. Our research is a first step for identifying compu tational features of a motivating piece of advice, which will subsequently be useful for implementing the ability to ...

109—Sebastian Kahl and Stefan Kopp. Modeling nonverbal communicative signaling in predictive social motorics.
In successful social interaction, we depend on our communication partner's reciprocity to grasp their state of knowledge. Here, we briefly describe our previous computational modeling work of the dynamics within the human social brain. In order to increase communicative efficiency, we propose a model of communicative signaling in a predictive sensorimotor system by using information from interaction reciprocity and a detailed self-other distinction.

110—Valerio Targon. Toward Semiotic Artificial Intelligence.
This paper describes a method for processing natural text and assigning meaning to it without the need of any a priori linguistic knowledge. The semiotic cognitive automaton is driven only by the observations it makes and operates based solely on grounded symbols. An experiment of learning from one book in the English language is detailed, where certain paradigms of words are first formed thanks to side effects occurring in samples of text and then connected to functions and structures part of the internal reality of the automaton. The meaning assigned to these linguistic constructs clearly ...

111—Masahiko Osawa, Takashi Omori, Koichi Takahashi, Naoya Arakawa, Naoyuki Sakai, Michita Imai and Hiroshi Yamakawa. Function Map-Driven Development for AGI.
This paper introduces the function map, a directed graph containing 'function nodes,' each of which consists of the description of a function, tasks, an implementation, and associated brain areas. A function is implemented when the implementation on the node accomplishes the tasks. To construct an AGI system, its overall functions must be decomposed to a large number of sub functions. To facilitate decomposition, we propose a function map-driven development, in which a function map with a hierarchical structure is continuously constructed. This approach requires research and development to ...

112—Oleg Nikitin and Olga Lukyanova. Predictive regulation of neurotransmitter receptor pool for weight correction restriction.
Artificial neural networks lack the adaptive properties of natural neural systems. The main goal of this work was to propose a theoretical model that would integrate different levels of homeostasis. In this paper, we propose a bio-inspired model, including homeostatic synaptic plasticity, limited by neurotransmitter receptors. The use of the echo state network for the regulation of neuron hyperparameters is suggested. The resulting model can be used to construct neural networks that perform adaptive tasks.

113—Olga Lukyanova and Oleg Nikitin. Modeling of extrasynaptic information transfer in neural networks using braid theory.
Current neural network approaches mostly consider the synaptic signal transfer as a basis of interneuronal communication. At the same time, extrasynaptic signaling plays important role in the animal behavior. In the paper processes of information distortion during the extrasynaptic mixing are studied using the braid theory. An approach to the procedural generation of deep neural networks incorporating properties of extrasynaptic information dynamics is proposed.

114—Antonio Chella, Francesco Lanza, Arianna Pipitone and Valeria Seidita. Knowledge Acquisition and Introspection in Human-Robot Cooperation.
When cooperating with a team including humans, robots have to understand and update semantic information concerning the state of the environment. The run-time evaluation and acquisition of new concepts fall in the critical mass learning. It is a cognitive skill that enables the robot to show environmental awareness to complete its tasks successfully. A kind of self-consciousness emerges: the robot activates the introspective mental processes inferring if it owns a domain concept or not, and correctly blends the conceptual meaning of new entities. Many works attempt to simulate human brain ...

115—Anastasia Korosteleva, Olga Mishulina and Vadim Ushakov. Information approach in the problems of data processing and analysis of cognitive experiments.
In this paper, information indicators for solving statistical problems at various stages of analyzing psychological test data are analyzed: in the process of detecting and removing from the sample incorrectly formulated test tasks, for grouping participants according to similarity indicators of their answers in test tasks and highlighting participants with unique characteristics. The proposed methodology is illustrated by the data of a psychological test aimed at identifying a person's ability to spatial imagination, the formation of associative links between objects and the solution of ...

116—Ben Goertzel, Misgana Bayetta, David Hanson and Matthew Ikle. Shifting and Drifting Attention While Reading: A Case Study of Nonlinear-Dynamical Attention Allocation in the OpenCog Cognitive Architecture.
A simple experimental example of the general principle of "cognitive synergy" underlying the OpenCog AGI architecture is explored: An OpenCog system processing a series of articles that shifts from one topic (insects) to another (poisons), and using its nonlinear attention-allocation dynamics (based on the ECAN Economic Attention Networks framework) to spread attention back and forth between the nodes and links within OpenCog's Atomspace knowledge store representing the words in the sentences , and other nodes and links containing related knowledge. With this setup, we study how the ...

117—Zalimhan Nagoev, Irina Gurtueva and Larisa Lyutikova. Model for Authomatic Speech Recognition Using Multi-Agent Recursive Cognitive Architecture.
A concept of a fundamentally new approach to the development of speech recognition systems is proposed, as applications built based of existing approaches are not effective when used in noisy conditions and cocktail-party situations. The architecture of speech recognition system in an environment with several speakers based on multi-agent recursive cognitive models with imitation of the attention mechanism is constructed. The speech recognition system allows to model selectivity of perception in speech peculiarities for a speaker using multi-agent self-organization. Principles for selective ...

118—Lyudmila Skiteva, Vadim Ushakov and Vitaliy Verkhlyutov. Analysis of Resting State according to the data of mag-netoencephalography.
Resting State (RS) is the basic state of human consciousness. Many neural networks of the brain in RS interact with each other. The same activity can be observed with a difficult cognitive load. Thus, RS reflects the whole dynamics of consciousness. Data, recorded through two methods: functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) is generally used to image resting state network (RSN). The effective connection of RSN was visualized by fMRI, but the lack of this method is a poor temporal sensitivity. The MEG method makes it pos-sible to get data with a good ...

119—Marek Otahal and Miroslav Kovar. Designing Motion memory for Hierarchical Temporal Memory.
We present a “MotionPooler”, a layer in Hierarchical Temporal Memory (HTM) responsible for stable representations of complex motion patterns/sequences. Numenta recently started research in sensory-motor learning(SM) and published so called ColumnPooler (CP). This CP first showcased capability to learn sequences and at the same time recognize objects. In this paper, we take this CP, compare its similarities to Capsule networks, another recently emerging, popular theory that operates with location stored within (cortical) columns. Then we discuss known limitations of the existing Column ...

120—Marek Otahal. Specialized HTM layer for neural BCI communication.
We explore the possibility of modern electrode-arrays for a direct neural-BCI (nBCI), in a way that Neuralink is researching. We aim to achieve this via natural semantic representations (sparse, distributed...SDR) as used in Hierarchical Temporal Memory (HTM). First is a review of projects modeling the brain at suitable manner (neural/columnar activity). Then we showcase the way to communicate between such semantic representation and plain text communication (NLP). The work is concluded by research showing how stable abstract sequence learning done by HTM can represent patterns from the ...

121—Marek Otahal. Emerging Creativity: the Role of Anomalies, NLP and semantic representations.
We discuss psychological and neurobiological approaches to defining creativity. We then demonstrate how anomalies (random choice), abstract representations/words (NLP) and semantic representations play crucial role in emergence of novel thoughts & ideas, thus creating a biologically plausible, working model of human creativity.

122—Louise Johannesson, Martin Nilsson and Claes Strannegård. Basic Language Learning in Artificial Animals.
We explore a general architecture for artificial animals, or animats, that develops over time. The architecture combines reinforcement learning, dynamic concept formation, and homeostatic decision-making aimed at need satisfaction. We show that this architecture, which contains no \textit{ad hoc} features for language processing, is capable of basic language learning of three kinds: (i) learning to reproduce phonemes that are perceived in the environment via motor babbling; (ii) learning to reproduce sequences of phonemes corresponding to spoken words perceived in the environment; and ...

124—Marek Otahal. Implementation of Predictive coding into Hierarchical Temporal Memory.
Hierarchical Temporal Memory (HTM) is a biologically plausible neocortical model. One of its features are implicit online learning and generating predictions. We demonstrate a way how the existing theory of Predictive coding (PC) could be implemented into HTM in order to improve learning of complex temporal sequences by providing more stable feedback from the predictive/error coding.

125—Alexey Serenko, Roman Rybka, Alexandr Sboev, Danila Vlasov and Andrey Filchenkov. Adjusting parameters of a spiking neural network with STDP by a genetic algorithm for solving a classification task.
Spiking neural network learning is closely connected to the plasticity mechanism chosen. This work uses a network model based on Leaky Integrade-and-Fire neurons and Spike-Timing-Dependent Plasticity. The aim of the model is to assess the influence of the parameter choice for the network configuration chosen on the accuracy of solving the classification task. The toy task is Fisher's Iris dataset. Parameter adjustment is performed with a genetic algorithm (GA) on base of NeuroEvolution of Augmenting Topologies (NEAT). The results of the GA with different fitness functions are compared to ...

126—Ziying Zhang, Nele Russwinkel and Sabine Prezenski. Modeling Individual Strategies in Dynamic Decision-making with ACT-R: A Task Toward Decision-making Assistance in HCI.
With the development and more breakthroughs in computer science, psychology, or cognitive science, the well-developed artificial intelligence (AI) becomes one of the new main purposes of research. The key point of AI's development is the accurate understanding and prediction of the behavior and decision-making process of the human. For real environments, which are always dynamic and changing, the variation of individual differences in humans makes this even more demanding. Most tasks in human-computer interaction area always involve more than one factor and its decision-making consists of ...

127—Viacheslav Wolfengagen, Sergey Kosikov and Larisa Ismailova. Data enrichment with provision of semantic stability.
The paper considers the problem of data enrichment, which is understood as supplying the data with semantics with further introduction of structuring. It also considers the data collected from heterogeneous sources with their subsequent organization in the form of information graphs. A data model is used in the form of a network, the framework of which is objects and relations between them. What is more, as objects, in turn, the relations can be used, and this potentially leads to higher order structures. The data connections and data dependencies are also taken into account. Providing ...

128—Malte Schilling and Andrew Melnik. An Approach to Hierarchical Deep Reinforcement Learning for a Decentralized Walking Control Architecture.
Locomotion in animals is characterized as a stable, rhythmic behavior which at the same time is flexible and extremely adaptive. Many motor control approaches have taken considerable steps taking insights from biology. As one example, the Walknet approach for six-legged robots realizes a decentralized and modular structure that reflects insights from walking in stick insects. While this approach can deal with a variety of disturbances during locomotion, it is still limited dealing with novel and particular challenging walking situations. This has lead to a cognitive expansion that allows to ...

129—Igor Isaev and Sergey Dolenko. Group Determination of Parameters and Training with Noise Addition: Joint Application to Improve the Resilience of the Neural Network Solution of a Model Inverse Problem to Noise in Data.
Solution of inverse problems is usually sensitive to noise in the input data, as problems of this type are usually ill-posed or ill-conditioned. While neural networks have high noise resilience by themselves, this may be not enough in case of incorrect inverse problems. In their previous studies, the authors have demonstrated that the method of group determination of parameters, as well as noise addition during training of a neural network, can improve the resilience of the solution to noise in the input data. This study is devoted to the investigation of joint application of these methods. ...

130—Tatiana Dolenko, Alexander Efitorov, Olga Sarmanova, Igor Isaev, Kirill Laptinskiy, Sergey Dolenko and Sergey Burikov. Application of Wavelet Neural Networks for Monitoring of Extraction of Carbon Multi-Functional Medical Nano-Agents from the Body.
In this study, a new implementation of optical imaging of luminescent carbon multi-functional medical nano-agents in biomaterial using wavelet neural networks is proposed. It is demonstrated that this approach allows simultaneous detection of graphene oxide based drug carrier nanocomposites and their components against the background of autofluorescence of human urine with a sufficiently low detection threshold by concentration. The results are compared to similar results obtained by the authors using perceptron type artificial neural networks. The solution of this inverse problem of ...

131—Vladimir Shirokii, Roman Batusov, Artur Chubarov, Sergey Dolenko and Alexei Samsonovich. Patterns of cognitive activity in a human vs collaborative robot interactive game.
In order to define different cognitive patterns we analyze sequences of human actions in a game with so-called collaborative robots. Since there are complex win or lose game conditions in this game, in order to win, the player needs to cooperate with one of the bots in game. Each of two game bots may cooperate with the player or not during the game, causing the player to change his strategy. In this study, investigations of change of cognitive patterns in different rounds and during each round are performed. To achieve this goal, time series event analysis and analysis of associative rules ...

132—Alexander Efitorov and Sergey A. Dolenko. Adaptive Neuro-Fuzzy Inference System Used to Classify the Measurements of Chemical Sensors.
Many data processing problems are successfully solved by artificial neural networks (ANN) possessing the property of a universal approximator. However, in case when the number of data patterns available is small, ANN may tend to overtrain and not to generalize well enough. An alternative is use of such a biologically inspired cognitive architecture as fuzzy networks, or Adaptive Neuro-Fuzzy Inference Systems (ANFIS), based on the notions of fuzzy logics and often used in control systems. Like conventional ANN, ANFIS can be also trained by example with error backpropagation algorithm. In this ...

133—Misgana Belachew, Ben Goertzel, Matthew Ikle and David Hanson. Shifting and Drifting Attention While Reading: A Case Study of Nonlinear-Dynamical Attention Allocation in the OpenCog System.
One of the core principles of the OpenCog AGI design, "cognitive synergy", is exemplified by interactions between the dynamics of OpenCog's attention allocation processes (governed by the ECAN Economic Attention Networks framework), and the dynamics of the other cognitive processes on which attention is being focused. In order to explore this interaction in a very simple context, we have been experimenting with cases where ECAN is used in interaction with OpenCog's Natural Language Processing (NLP) pipeline agents. Here we focus on one simple example: An OpenCog system processing a ...

134—Sergey Kartashov, Nikolay Ponomarenko and Vadim Ushakov. The Concept Of Functional Tractography Method For Cognitive Brain Studies.
The aim of this work is to develop method of functional tractography based on the fast MRI sequence (Multi-Band EPI). It is planned to identify active areas of white matter (active tracts) responsible for the realization of motor function and visual perception. The functional MRI method, universally recognized and quite popular in cognitive brain studies, clearly reveals sources of activity in the gray matter of the brain. Proposed method of functional tractography supposed to make it possible to determine the activity in the deep structures of white matter tracts, which gives a number of ...

135—Vadim Ushakov, Denis Malakhov, Vyacheslav Orlov, Sergey Kartashov and Yuri Kholodny. Research of neurocognitive mechanisms of revealing of the information concealing by the person.
This work is related to the creation of an MR-compatible polygraph and the development of methods for detecting the hierarchy of neural networks of the cognitive organization of hidden memory markers. At the moment, there is practically no scientific work on lie detection, in which both the classical polygraph and the MRI scanner were simultaneously used. Combining these methods will help increase the probability of recognizing the facts of hiding important information and carry out an objective assessment of the truthfulness of the reported information. This method can also be used to ...

136—Vladimir Dorokhov, Denis Malakhov, Vyacheslav Orlov and Vadim Ushakov. Experimental model of study of consciousness at the awakening: fMRI, EEG and behavioral methods.
For the study of neuronal correlates of consciousness, a simple and effective model is the comparison of sleeping and waking states. Consciousness turns off during sleep and turns on at waking. The moment of awakening from sleep is a promising model for the study of neurophysiological correlates of consciousness. We developed a psychomotor test, the monotonous performance of which, causes within 60 minutes alternating episodes with the disappearance of consciousness when falling asleep (the "microsleep") and its restoration upon awakening (wakefulness). When performing this test, the ...

137—Larisa Ismailova and Sergey Kosikov. Metamodel of transformations of concepts and object-relational mapping.
The paper considers the problem of the search for the origin of objects and / or their sources necessary to support the object-relational mapping. The solution is presented in the form of a variant of a specialized semantic search that takes into account the possibility of transforming conceptual constructions of the domain description while working with the system. To take into account such transformations a metamodel of transformations of concepts is proposed, it also accounts the transformation of the connections between them. The basic metamodel is a specialized system of an applicative ...

138—Shoya Matsumori, Yuki Abe, Masahiko Osawa and Michita Imai. Investigation of Incremental Learning as Temporal Feature Extraction.
In this paper we discuss an effect of feature extraction using Incremental Learning Restricted Boltzmann Machine (IL-RBM).We trained the model on Moving MNIST and analyzed the obtained representation by visualizing hidden activities and reported some meaningful features obtained in incremental learning, similar to that of obtained in Slow Feature Analysis (SFA).

139—Olivier Georgeon. The telic versus atelic distinction in Artificial Intelligence.
Most Artificially Intelligent agents are designed to seek goals defined by their designer. For example, in their foundational definition of Reinforcement Learning (RL), Sutton & Barto consider that "the agent [...] must have a goal or goals relating to the state of the environment. The formulation [of RL] is intended to include just these three aspects – sensation, action, and goal – in their simplest possible forms without trivializing any of them" (Sutton & Barto 1998, p 4). Some authors in developmental robotic (Weng et al. 2001; Asada et al. 2001), however, criticized this stance and ...

140—Alexei V. Samsonovich and Ksenia Kuznetsova. Semantic-Map-Based Analysis of Insight Problem Solving.
Intelligent agents and co-robots, or cobots, become increasingly popular today as assistants of creators of arts. They can be also expected to become popular in the near future as assistants in other creative work, including research and, in particular, insight problem solving. Arguably, the success of such tools is linked to their social-emotional intelligence. This work continues the previous study of the concept of a cobot-assistant of an insight problem solver (Kuznetsova & Samsonovich, 2018). The concept is based on a semantic map, that represents cognitive and emotional states and ...

141—Olga Altukhova, Pavel Borovikov, Ivan Balashov, Dmitry Trofimov, Aleksandr Garmash, Timofey Komarov and Georgy Lebedev. Method using parallel computations and clustering in the problem of genotyping HLA.
Identification of genes variants (alleles) in organisms can become a time-consuming task due to great diversity. Polymorphism is also a feature of HLA, the main complex of human histocompatibility. Errors in sequencing can also cause incorrect determination of alleles. The authors of the paper propose a method for analyzing high-performance sequencing data, which allows observing high accuracy in the problem of genotyping HLA.

142—Alexander Efitorov, Vyacheslav Orlov, Vadim Ushakov, Vladimir Shirokiy and Sergey Dolenko. Comparison of nonlinear methods of motion correction in fMRI data.
The paper considers the problem of motion correction in fMRI data by various methods existing in standard packages: SPM, C-PAC, BROCCOLI. The method of rigid body correction is too poor for using in real world cases, because registration of a single 3D fMRI snapshot is not instantaneous, but is performed sequentially by slices. The goals of this paper are comparison of various methods in the case of nonlinear and partial shifts and rotations of slices of fMRI records controlled artificially, and elaboration of recommendations for using these methods in different cases.

147—Ronakben Bhavsar, Neil Davey, Na Helian, Yi Sun, Tony Steffert and David Mayor. Efficient Methods for Calculating Sample Entropy in Time Series Data Analysis.
Recently, different algorithms have been suggested to improve Sample Entropy (SE) performance. Although new methods for calculating SE have been proposed, so far improving the efficiency (computational time) of SE calculation methods has not been considered. This research shows such an analysis of calculating a correlation between Electroencephalogram(EEG) and Heart Rate Variability(HRV) based on their SE values. Our results indicate that the parsimonious outcome of SE calculation can be achieved by exploiting a new method of SE implementation. In addition, it is found that the electrical ...

148—Sergey Kosikov, Larisa Ismailova, Viacheslav Wolfengagen and Polina Beliytskaya. Means of search for similar structures of exemplar queries.
The paper considers the problem of enhancing the semantic capabilities of information systems, which improve the quality of the solution of the search problem on demand. It is assumed that the information in the system is presented in the form of an information graph. The paper considers queries specifying an example of the structure of a graph that must be received as a response, or instance queries. The response is supposed to be received to the query using the semantic filtering, based on the ranking of the instance results similar to the query graph. The problem of manipulating similar ...

149—Brett Martensen. The Perception-Action Hierarchy and its Implementation Using Binons (Binary Neurons).
The perception-action hierarchy contains a model of the environment as experienced based on what has been recognized and done. This presentation gives a mechanistic/functional explanation of how binons(binary neurons) are used to represent and implement this hierarchy. Two kinds of temporal binons are used to learn and repeat experiences. They are the Action and Expectation control binons. They are equivalent to command neurons (production rules) and the prediction part of the motor control forward model. Learning takes place in the three stages of babbling, practicing and automaticity. The ...

150—Evgenii Vityaev and Alexander Demin. Adaptive control of multiped robot.
In the paper, a logical-probabilistic method of adaptive control of modular systems is presented. It base on the functional similarity of modules, the logical-probabilistic algorithm of directed search of rules and joint training of control modules. Starting with the search for common rules for all modules of the control system then it upload them with a more specific ones in accordance with the ideas of semantical-probabilistic inference. With the use of an interactive 3D-simulator, successful experiments were conducted with virtual multiped robot mode. Experimental studies have shown that ...

151—Charlotte Commu, Jan Treur, Annemieke Dols and Yolande A.L. Pijnenburg. An Adaptive Network Model for a Possible Therapy for the Effects of a Certain Type of Dementia on Social Functioning.
This paper first describes a temporal-causal network model for recognition of emotions shown by others. The model can show both normal functioning and cases of dysfunctioning, such as can be the case with certain types of dementia. More specifically the focus of the paper is on a specific type of therapy that has been incorporated in the model (thus becoming adaptive) to study the effects and potentials of this therapy to improve the dysfunctional behaviour. Simulations have been performed to test the model. A mathematical analysis was done which gave evidence that the model as implemented ...

152—Magdalena Wiercioch. A Topic Modeling Approach To Understand Human.
In the modern age one may observe the amazing growth of social media content on the Internet. This can be treated as an inspiration to the development of the text analysis software and algorithms to understand and solve real life problems. At the same time, the process of vital information extraction from unstructured texts seems to be one of the main computational and scientific challenges nowadays. Topic models are considered as one of the most popular machine learning method that takes the latent topical structure of a number of documents into consideration. For instance, Latent Dirichlet ...

153—Stevo Bozinovski. Zajonc-Lazarus Cognition-Emotion Primacy Debate and Crossbar Adaptive Array: 1980-1982.
The paper considers the well known cognition-emotion primacy debate between Zajonc and Lazarus in the period 1980-1982 along with a contribution to that debate given in the same time, in 1981, by the Crossbar Adaptive Array (CAA) artificial neural network. CAA was an artificial brain type of neural network, because in addition of computing cognitive actions, it computed emotive evaluations of the consequence of those actions. This paper gives a new view toward cognition-emotion primacy debate considering contributions of Zajonc, Lazarus, and the CAA network. The paper points out that …

154—Irina Parfenova, Larisa Ismailova, Sergey Kosikov and Viacheslav Wolfengagen. Multi Model Visualization Based on Data Models Intergration in Semantic Network Environment.
The paper considers the problem of visualization of information organized in accordance with several data models. A set of visual elements with attached attributes is offered as a final result of visualization, this being an abstraction of the representation of vector graphic information. In the given formulation the problem can be considered as an individual case of the data enrichment problem, consisting of the semantically adequate attachment of attributes describing the visual representation. Coordination of data models for their visualization is performed on the basis of methods of ...

155—Anna Epishkina. A Technique to Test Non-Binary Random Number Generator.
Nowadays random numbers are used almost everywhere, e.g. in statistical tasks, gambling, information security, artificial intelligence. Quality of output numbers determine the successful task solving. However, a lot of information security tools implementations have not got reliable sources of really random numbers and as a result fail. In order to increase a random number generator rate non-binary random number generators are created. Nevertheless all known mechanisms to test output sequence can be applied only to binary numbers. The author propose a technique to obtain properties of ...

156—Anna Epishkina and Sergey Babkin. Authentication Protocols Based on One-Time Passwords: the State of The Art.
Authentication is one of the main tasks of information security and its development will help to apply artificial intelligence statements in practice. When computers will do a lot of tasks for humans in will be quite important to give access to the proper computer and system security will be based on the authentication protocol security. One-time password is a sequence of symbols that is generated ones to be used in authentication process. It is secure because if eavesdropper knows it he can not utilize it again. In modern authentication protocols one-time passwords are generated dynamically ...

157—Margarita Zaeva, Anatoly Tsirlin and Vardges Hovsepyan. The selection of robust controller settings for technological objects with time-lag.
The problem of robust stability and selection of parameters of typical regulators for technological objects with time-lag is considered. The set of possible values of the parameters of the transfer function of the object is assumed to be closed and limited, and the modulus and phase of the amplitude-phase characteristic of the open system are monotonically decreasing with frequency.

158—Margarita Zaeva and Andrew Evstifeev. Analysis of compressor control structures and algorithms for compressor drives for automobile gas filling compressor stations.
Prototypes of structures and control algorithms for compressor drives for automobile gas filling compressor stations are considered. The stations have switching facilities with semiconductor switches on fully managed switches. Examples of realization and technical solutions for controlling compressor drives of different designs, performance and power are given.

159—Margarita Zaeva and Andrew Evstifeev. The structure and algorithm for controlling the shut-off valves of a multi-section mobile car gas refueler.
An embodiment of the structure and control algorithm of the isolation valve of multi-section mobile car refueling. It is shown that this option provides the most energy efficient and rational option for refueling consumers. The calculation of the most rational pressure, number and volume of sections is performed. The algorithm of control is based on the modified algorithm of multicriteria selection Edgeworth-Pareto.

160—Nimat Ullah, Jan Treur and Sander L. Koole. A Computational Model for Context-Dependent Adaptive Emotion Regulation.
Emotions play a vital role in any individual’s life. Sometimes they can be undesirable. In case of undesirable situation, out of a number of emotion regulation strategies a strategy is chosen depending upon the situation. This choice is context-sensitive: it is adapted to the specific context. This paper presents a computational model which models this form of adaptation. Simulation results are reported for two strategies which illustrate the adaptivity for specific contexts. 

161—Irina Barinova, Vera Gur'Eva, Yurij Kotov and Tatiana Semenova. Mathematical methods for structuring and formalization of medical experience.
The article is devoted to the study of medical knowledge structure. In the process of the patient's treatment, the doctor makes a number of important decisions, not all of which are within the theoretical knowledge obtained during training at the University or from professional literature. His personal experience gives much. But practical experience is usually scattered and non-systematic. The doctor produces himself the ordering and justification of the information. Our data processing methods allow us to systematize the new knowledge elements and to select suitable reasons for …

162—Fawad Taj. Computational Model for Reward-Based generation and maintenance of Motivation.
In this paper, a computational model for the motivation process is presented that takes into account the reward pathway for motivation generation and associative learning for maintaining motivation through Hebbian learning approach. The reward prediction error is used to keep motivation maintained. These aspects are backed by recent neuroscientific models and literature. simulation experiments have been performed by creating scenarios for student learning through rewards and changing controlling their motivations through regulation.

163—Irina Suslina and Valeriia Tarasova. Approaches to legal protection of software made by foreign authors in the State of Israel.
In this work we analyzed main approaches to legal protection of software by foreign authors in the State pf Israel. We reviewed Israeli intellectual property law that concerns software. As as result of work done, we found main appropaches of software legal protection and identfied its legal frameworks. 

164—Robert Laddaga. Responding to Surprise.
Any good cognitive architecture should be able to explain how to respond to surprise. This includes surprise discovery, diagnosis, immediate reaction, and learning both with respect to improved expectation and improved response. An example of such a cognitive architecture, active perception, is described ...

165—Paul Robertson. Visual Attention.
In the CART project we are developing a robotic assistant for repair operations. The robot watches a human repair person perform a repair in a situation where there may be many distractions. The robot must follow what is being done and be able to explain what is being done and why. If the repair person makes a mistake, the robot must be able to explain how to correct the error. In this talk, I discuss the role of attention in humans and animals and the abstraction of those ideas used in the model of attention used in CART. Attention plays an important role in learning and is deeply ...

166—Alexei V. Samsonovich, Rosario Sorbello and Antonio Chella. BICA Society Panel.
The panel agenda includes BICA Society Awards, annual report, discussion of the BICA Journal and BICA Conference future plans ...

167—Howard Schneider. Procedia Submission: Meaningful-Based Cognitive Architecture.
An overview is given of the cognitive architecture of the biologically inspired meaningful-based learning system (MBLS). The basic element of the MBLS is a reconfigurable Hopfield-like network (HLN) which can rapidly connect to other HLNs depending on the level of abstraction which yields a practical maximal “meaningfulness,” defined as the reciprocal of the Shannon entropy of the HLNs. Without any external memory the MBLS synergistically processes external data (and internal data – “thoughts”) with sensory processing abilities found in neural networks and some of the symbolic ...

168—Rosario Sorbello. Title: TBA.
Rosario Sorbello is a Professor of Robotics at the University of Palermo and Co-Director of RoboticsLab. He received Laurea degree and Ph.D. in Computer Science in 1998 and 2001. From 2008 he started a collaboration with Prof. Hiroshi Ishiguro, Osaka University, in the field of innovative humanoid robot. Currently, his main research interests are: social long-term human-humanoid interaction, emotional cognitive brain computer architecture for Geminoid and Telenoid, android robot for health-care …