#1 Galina Rybina, Yuri Blokhin and Sergey Parondzhanov Intelligent planning methods and features of their usage for development automation of dynamic integrated expert systems Keywords: artificial intelligence, integrated expert systems, problem-oriented methodology, model of intellectual environment, AT-TECHNOLOGY workbench, intellectual planning The problems of intellectualization in the development process of dynamic integrated expert systems basing on the the problem-oriented methodology and the AT-TECHNOLOGY workbench are considered. The experience from carrying out intellectual planning for the synthesis of architectural layouts of prototypes in integrated expert systems, the intelligent planner usage, reusable components, typical project procedures, and other components of the intellectual software environment in the AT-TECHNOLOGY complex is described.
#2 Galina Rybina and Elena Sergienko Ontological approach for the organization of intelligent tutoring on the basis of tutoring integrated expert systems Keywords: problem-oriented methodology, tutoring integrated expert systems, student model, ontology model, tutoring model, reusable components, AT-TECHNOLOGY workbench Analysis of the experience of developing and using tutoring integrated expert system in the educational process and creating a single ontological space in the context of solving basic intelligent tutoring problems are discussed. Ontological model is described the methods of courses/disciplines ontologies integration with other components of the architecture of the tutoring integrated expert system, like an individual network student model and adaptive tutoring model are briefly shown.
#3 Soichiro Arai and Junichi Takeno Discussion on explicit consciousness, sub-consciousness, and self-awareness in a conscious system Keywords: unconscious, sub-consciousness, self-awareness, conscious system, the self What is ‘self-awareness” How can explicit consciousness and sub-consciousness be mapped in relation to each other’ How are they related to the self’ How can these entities be represented in an artificial conscious system’ These questions are the focus of this article. People are aware of only the behavior that they are focusing on; they cannot be directly aware of routine behavior such as walking and breathing. The latter is generally called unconscious behavior, and here we call it sub-conscious behavior. To understand self-awareness, therefore, firstly it is important to map explicit consciousness and sub-consciousness, which is where the self is deeply involved. We consider that if there is no self that refers to itself, no one can be aware of what he himself is doing. In this study we map explicit consciousness and sub-consciousness using an artificial conscious system, and then make a new proposal about the relationship between self-awareness and the self.
#4 Naoya Arakawa Simulating the Usage Acquisition of Two-Word Sentences with a First- or Second-Person Subject and Verb Keywords: Language acquisition, Simulation, Personal pronouns This article examines a minimalist mechanism in a simple language game to acquire the use of two-word sentences consisting of a first- or second-person pronoun as the subject and a verb as the predicate. In an experiment, a learner agent with minimalist architecture learned to select the subject and verb while interacting with a caretaker agent. The assumptions and implications of the experiment are discussed.
#5 Kensuke Arai and Junichi Takeno Discussion on the Rise of the Self in a Conscious System Keywords: the self, conscious system, subjective feeling, pain, conflict, body What is human self’ Some argue that there is no such thing as self. However, the subjective feeling that ‘I am writing these words’ makes it hard to deny the existence of the self. We assume that as long as there is the term ‘self,’ there must be some collection of neural networks that represents the concept of the term. Although the whole picture is still a mystery, we have taken a step forward to unraveling the mystery by introducing the idea that ‘the emergence of a new behavior that prioritizes the body underlies the rise of the self.’ When performing imitation behavior, a person can encounter a situation in which he feels pain and tries to avoid it. In this instance, the person engages in two types of behavior almost simultaneously, which are in conflict with each other. Also in this instance, it is assumed that the person gives priority to the safety of his own body and reflexively chooses to respond with avoidance behavior. However, as the imitation behavior continues, the process of imitation and avoidance is repeated many times, making it increasingly difficult to ensure the safety of the body. To address this scenario, we have come up with an idea that enables a conscious system to generate a new rational behavior’that is, voluntarily stop the imitation behavior. We consider that the generation of this new behavior is a significant process that can explain the first step for the development of the self.
#6 Daiki Matsumoto, Hanwen Xu and Junichi Takeno Simulation of the Cognitive Process in Looking at Rubin’s Vase Keywords: cognitive process, Rubin’s Vase, ambiguous image, conflict of concept, conscious program, internal representation system We have successfully simulated the cognitive process in looking at Rubin’s Vase. Rubin’s Vase is an ambiguous image developed by the Danish psychologist Edgar Rubin. The ambiguous image allows the viewer to interpret it in more than one way. The Rubin’s Vase image used in this study depicts a vase in the center in a way that its contour matches the human profile, allowing the viewer to interpret the image as either ‘a vase in the center’ or ‘two faces looking at each other.’ The program we have developed is designed to enable the internal representation systems to change their responses from moment to moment according to changes in input data and to internal knowledge.
#7 Tomoya Sumioka and Junichi Takeno Discussion of Stalking Behavior Using a Conscious System Keywords: conscious system, stalking behavior, rejected type, conflict of concepts, association subsystem Stalking behavior, characterized by specific and dreadful acts such as persistent following, repeated sending of unwanted gifts, lack of sympathy, and even willingness to kill the victim, has become a serious concern in modern society. However, since stalking behavior has something partly in common with any criminal behavior, further discussion of this topic is considered helpful in determining factors behind various criminal behavior. Thus, there is an urgent need to investigate how this mysterious and dangerous behavior arises. There are three main types of stalking behavior: ‘rejected,’ ‘resentful,’ and ‘other (intimacy seeker and incompetent suitor).’ First, we focused on a conscious model of the rejected type, as it is considered the most typical. The artificial conscious model is built to represent a process in which a conflict of concepts arising in the Reason Subsystem is progressively reconciled by the Association Subsystem.
#8 Vladimir Redko and Zarema Sokhova Model of Collective Behavior of Investors and Producers in the Decentralized Economic System Keywords: investors, producers, decentralized system, competition, light agents-messengers, collective behavior The paper presents the interaction mechanism between investors and producers in a decentralized economic system. The main element of the interaction is the iterative process. In this process, each investor takes into account the contributions of other investors into producers. The model is investigated by means of the computer simulation, which demonstrates the effectiveness of the considered mechanism.
#9 Yuichi Takayama and Junichi Takeno A conscious robot that can venture into an unknown environment in search of pleasure Keywords: conscious robot, venture, unknown environment, curiosity, pleasure A ‘conscious system that can venture into an unknown environment’ has been proposed. This study models the process of consciousness of a person who is going into an unknown environment. First, we assumed that to go into an unknown environment, the person needs to be curious about that environment and assured of its safety. Curiosity is a tendency to become interested in unknown phenomena and draw information from them. We consider that to acquire the behavior of going into an unknown environment (curiosity behavior), firstly the person needs in some way to go through many experiences of pleasure in unknown environments and increase curiosity and interest in such environments. To enter an unknown environment the person must also be assured that the environment is safe. We have developed a conscious system that can venture into an unknown environment and tested whether a robot can voluntarily enter an unknown environment.
#10 Vladimir Red’Ko Model of Interaction between Learning and Evolution Keywords: interaction between learning and evolution, genetic assimilation, hiding effect, learning load The lecture characterizes the following main properties of interaction be-tween learning and evolution: 1) the mechanism of the genetic assimilation, 2) the hiding effect, 3) the role of the learning load at investigated processes of learning and evolution
#11 Alexandr Petukhov The theory of information images: basics of model Keywords: communication field, information image, the information images space, virtual particles This paper presents the basic principles of the Information Image Theory.
Methods and approaches suggested in the current article allow us to compare different levels of the described processes depending on the chosen architecture of the model; they are also able to simulate the processes of distortion and generation information images during information and communication social interaction.
Expansion and addition of information images theory in terms of information transmission among individuals enables us to speak about the space of individual information images.
The existence of such a space and creation of correct formalized model help us to explain a number of characteristic effects of human thinking processes. As a result of the current research, the authors introduce an equation that describes the spatial and structural evolution of individual information images. The mathematical model is developed on the basis of this theory. This model can illustrate transmission of information between two and more individuals.
#12 Pavel Gusev and Georgii Borzunov The analysis of modern methods for video authentication Keywords: video authentication, authentication, video sequence, authentication algorithms, non-malicious modifications This report is dedicated to the review of existing methods for video authentication. The study includes analysis and classification of existing methods by using algorithms and by problems which are solved by video authentication. The report presents the definition of video authentication, the typical authentication scheme is described and known algorithms are classified. The existing methods are analyzed in which the most promising directions are revealed and recommendations for further evolution of this subject.
#13 Masahiro Miyata and Takashi Omori Modeling emotion and inference as a value calculation system Keywords: Emotion, Decision Making, Value Calculation System, Model, Probabilistic Inference There were many modeling studies on the emotion. But most of them were phenomenological and don’t approach to the brain or cognitive mechanism. In this study, we discuss on a possibility of its computational modeling based on an idea that an emotion in a wider sense is a value calculation system for an action decision. First, we show a possible brain architecture that the emotion system has a tight interaction with the cortical intelligent system through an interaction between the brain information processing areas of neocortex and limbic system. Then, in this study, we focused on a mechanism of value based intuitive inference on a path finding task in a partially known world. In a computer simulation, we tried to make a model of brainstem that is one of the brain emotion system parts. Though the brainstem looks simple, the model includes an essence of conflict resolving between multiple values.
#14 Andrey Trukhachev and Natalia Ivanova Extracting of High-level Structural Representation from VLSI Circuit Description Using Tangled Logic Structures Keywords: VLSI, Genetic Algorithm, Tangled Logic, Functional Circuit Analysis This paper proposes a method of automatic VLSI circuit analysis. On the first step transistors are grouped by their structure. Groups with irregular structure are highly interconnected to each other. Detecting Tangled Logic Structures (TLS) with a GTL-depended linear ordering and genetic algorithm divides the circuit due to its functional structure and forms the gate-level VLSI circuit. High-level functional blocks in circuit description consist of gate-level cells groups, which are also highly interconnected. After TLS-blocks extracting, it is possible to describe their function. TLS-blocks are smaller, represent a cell of high-level circuit, and are thus more suitable for further functional circuit analysis than a gate-level VLSI circuit.
#15 Anton Yakovenko and Galina Malykhina Bio-inspired Approach for Automatic Speaker Clustering Using Auditory Modeling and Self-Organizing Maps Keywords: cluster analysis, speaker clustering, auditory perception, neural activity, self-organization, text-independence, speech mining, MAP, SOM This paper presents a biologically inspired approach to the problem of the voice biometrics. Aim of this study is to examine capacity of the automatic system, based on a physiologically appropriate computational model of the auditory perception and self-organizing neural networks, to discriminate voices of different speakers, through the analysis of the provided text-independent speech samples. The idea stems from the human ability to successfully extract a various information from speech in the process of verbal communication in a various acoustic conditions, including recognizing the identity of a familiar person by his voice. In the proposed method, speech signal is processed through the model of the auditory periphery, which simulates neural responses of auditory-nerve fibers. From the obtained new signal representation feature vectors are formed, by which neural network will be trained to generate voice-clusters of different speakers. Based on the obtained results, one can conclude that the proposed method has demonstrated high quality of unsupervised classification of speakers by their voices.
#16 Haruo Mizutani, Michihiko Ueno, Naoya Arakawa and Hiroshi Yamakawa Whole brain connectomic architecture to develop general artificial intelligence Keywords: connectome, general artificial intelligence, whole brain architecture, empirical neural circuits, efficient engineering Whole Brain Connectomic Architecture (WBCA) is defined as a software architecture of the artificial intelligence (AI) computing platform which consists of empirical neural circuit information in the entire brain. It is constructed with the aim of developing a general-purpose biologically plausible AI to exert brain-like multiple cognitive functions and behaviors in a computational system. We have developed and implemented several functional machine learning modules, based on open mouse connectomic information, which correspond to specific brain regions. WBCA can accelerate efficient engineering development of the intelligent machines built on the architecture of the biological nervous system.
#17 Timofei Voznenko, Alexander Dyumin, Evgeniya Aksenova, Alexander Gridnev and Vladislav Delov The Experimental Study of ‘Unwanted Music’ Noise Pollution Influence on Command Recognition by Brain-Computer Interface Keywords: brain-computer interface, music influence, command recognition, robotics Nowadays, the alternative methods of human-computer interactions that can drastically improve usability of cyber-physical systems and devices, as mobile robots, especially for disabled persons are in development. These methods includes usage of brain-computer interfaces (BCI). Unfortunately, BCI’s aren’t reliable enough to handle critical devices outside lab environments, since the quality of command recognition can be influenced by external conditions, as noise pollution that can distract the user of BCI. In this paper, we are presenting the experimental study results of the influence of noise pollution in form of unwanted music on the quality of control through BCI.
#18 Alexander Gridnev, Timofei Voznenko and Eugene Chepin The Decision-Making System for a Multi-Channel Robotic Device Control Keywords: decision-making, control channel, command, robotics Nowadays, there is a trend of significant robotic devices functionality increase. Accordingly, the robotic devices can perform more operations and have more new ways to control them. Therefore, in some cases there is a situation when the robotic device receives control commands, i.e. instructions for performing different operations from different alternative control channels. It is a problem of a command selection for execution when different control channels have contradictory information. If there are multiple control channels, which have fundamentally different principle of operation, and multiple commands, which might require the same resources for execution, we need a system that will take into account the specifics of channels and commands, store information about incoming commands and decide which command to execute. In this paper we will present decision-making system with an internal control conflict resolution mechanism for robotics and the implementation example.
#19 Timofei Voznenko, Eugene Chepin and Gleb Urvanov The Control System Based on Extended BCI for a Robotic Wheelchair Keywords: extended brain-computer interface, robotic wheelchair, control channel, robotics In most cases, the movement of wheelchairs is controlled by disabled people using a joystick or by an accompanying person. Significantly disabled patients need alternative control methods without using the wheelchair joystick, because it is undesirable or impossible for these patients. In this article we present the implementation of a robotic wheelchair based on a powered wheelchair which is controlled not by the joystick but by the on-board computer that receives and processes data from the extended brain-computer interface (extended BCI). Under this term we understand the robotic complex control system with simultaneous independent alternative control channels. In this robotic wheelchair version the BCI works with voice and gesture control channels.
#20 Mikhail Kupriyashin and Georgii Borzunov Algorithmic Foundation for Benchmarking of Computational Platforms Running Asymmetric Cryptosystems Keywords: parallel computation, benchmarking, asymmetric ciphersystems, Knapsack Problem General purpose benchmarks do not yield accurate performance estimates for special tasks. In this paper we consider implementation of exact algorithms for the 0-1 Knapsack Problem in order to determine the performance of parallel computation platforms intended for running or performing analysis on asymmetric ciphersystems. We study some features of exact parallel algorithms for the Knapsack Problem, as well as load balancing techniques for them. We propose an algorithmic foundation for computational platform benchmarking aimed at getting accurate performance estimates for these platforms.
#23 Gayar Salakhutdinov and Irina Grigoryeva Impulse X-ray spectrometer based on the thermoluminescent detectors Keywords: Diagnosing Plasma, Thermoluminescent Detectors, X-ray spectrometer, high current electric discharge devices (Z-pinches), X-ray radiation We have developed the compact spectrometer for hot plasma impulse X-ray diagnostics, based on the analysis of thermoluminescent detectors main characteristics. The main results of X-ray emission research on various plasma devices are represented.
#24 Gayar Salakhutdinov Sources of ion emission in micropinch discharge plasma Keywords: plasma, micropinch, x-ray and ion emission sources, spatial structure, low-inductance vacuum spark 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.
#25 Dmitry Efanov and Pavel Roschin The Port-in-Use Covert Channel Attack Keywords: covert channel, information flow, TCP socket, proof-of-concept exploit, multilevel security, mandatory access control, interprocess communication We propose a 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 transition-based class attack. The main idea is to use the availability of TCP port, which is shared among processes at more than one security level, as the communication medium. The possibility or impossibility of binding a socket to a predefined port is used to transmit a bit of 0 or 1 respectively. We implement proof-of-concept exploit, which was used to check the idea and to evaluate covert channel capacity. Experimental results show that the proposed technique provides high rate covert channel, that means a significant threat of confidentiality in multilevel secure operating systems.
#26 Anna Epishkina and Sergey Zapechnikov A technique of blockchain dispersal for efficient storage Keywords: blockchain, storage, memory usage, information array dispersal Blockchain has become a very popular technique of creating non-editable data registers. Over time, very significant amount of data accumulates in these registers. At the same time access to this data occurs relatively rarely, mainly in order to take reference or resolve conflicts. Traditional technology implies that each participant of peer-to-peer network stores a full copy of the blockchain. We propose to apply algorithms of information array dispersal and reconstruction to drastically reduce the amount of data stored by nodes of blockchain network. We provide evaluation of memory usage efficiency to store the blockchain using these methods.
#27 Anna Epishkina and Sergey Zapechnikov Discovering and clustering hidden time patterns in blockchain ledger Keywords: blockchain, time patterns, cryptocurrency, clustering Currently, non-editable blockchain-based ledgers become important tools for cryptocurrency transactions, auditing, smart contracts, copyright registration and many other applications. In this regard, there is a need to analyze the typical, repetitive actions written to the ledger, for example, to identify suspicious cryptocurrency transactions, a chain of events that led to information security incident, or to predict recurrence of some situation in the future. We propose to use for these purposes the algorithms for T-patterns discovering and to cluster the identified behavioral patterns subsequently. In case of having labeled patterns, clustering might be replaced by classification.
#28 Anna Epishkina and Sergey Zapechnikov On attribute-based encryption for access control to multidimensional data structures Keywords: blockchain, multidimensional data structure, OLAP, attribute-based encryption, attribute management Multidimensional data structures are widely used in modern information technologies. We demonstrate that, in addition to well-known applications for OLAP, the blockchain ledgers can also be interpreted as multidimensional data structures. They sometimes contain private or other sensitive information. We argue that attribute-based encryption is a handy tool to control access to multidimensional data structures, discussing the advantages and disadvantages of ciphertext-policy and key-policy attribute-based encryption for this task. We propose a tentative scheme of key and attribute management for multidimensional data structures.
#29 Anastasia Beresneva, Anna Epishkina, Sergey Babkin, Alexey Kurnev and Vladimir Lermontov Handwritten Signature Verification: the State of The Art Keywords: handwritten signature, verification, neural network, mobile application Nowadays handwritten signature and its verification is utilized in a lot of applications including e-commerce. An analysis of verification algorithms and areas of their practical usage is provided. The focus of the investigation is on verification method based on neural network. This type of verification algorithm is realized as a mobile application and its main characteristics are obtained. The directions of further work are concluded including a modification of an algorithm and its realization in order to remove its disadvantages.
#30 Dmitry Efanov and Pavel Roschin The All-Pervasiveness of the Blockchain Technology Keywords: blockchain, bitcoin, digital currency, digital economy, digital society, smart city, smart contract, digital identity, double-spending attack, 51% attack, Sybil attack Conceptually, the blockchain is a distributed database containing records of transactions that are shared among participating members. Each transaction is confirmed by the consensus of a majority of the members, making fraudulent transactions unable to pass collective confirmation. Once a record is created and accepted by the blockchain, it can never be altered or disappear.
Nowadays the blockchain technology is considered as the most significant invention after the Internet. If the latter connects people to realize on-line business processes, the former could decide the trust problem by peer-to-peer networking and public-key cryptography. The purpose of this paper is to consider on distinct use cases at the all-pervasive impact of the blockchain technology and look at this as an inalienable part of our daily life.
#31 Agnese Augello, Ignazio Infantino, Adriano Manfre’, Giovanni Pilato and Filippo Vella Social signs and reaction processing in a cognitive architecture for an humanoid robot. Keywords: Humanoid robot, Social robot, Cognitive Architecture The capability of recognizing human social sign is a key feature for a social robot: it makes possible a full, natural and effective interaction between human beings and the robot. We present a robot whose behavior depends on a cognitive architecture that considers the sociality demand a fundamental aspect of the human-machine interaction. The proposed approach focuses the attention on few important social signs directly connected with the emotive reactions and emotions of an humanoid robot. Furthermore, the cognitive architecture considers the verbal interaction by natural language processing. The experimentations show the effectiveness of the approach and it allows discussing on interesting future research directions.
#32 Ignazio Infantino, Adriano Manfre’ and Umberto Maniscalco Robot navigation based on an artificial somatosensorial system Keywords: soft sensors, humanoid robot, cognitive architecture An artificial somatosensorial system processes robot’s perceptions by mean of suitable soft sensors. The robot moves in a real and complex environment, and the physical sensing of it causes a positive or negative reaction. A global wellness function drives the robot’s movements and constitutes a basis to compute the motivation of a cognitive architecture. The paper presents extensive experimentations and explains the influence of the parameters on the robot behavior and personality.
#35 Alisa Volkert, Stefanie Mueller and Alexandra Kirsch Human-like prototypes for psychologically inspired knowledge representation Keywords: knowledge representation, prototype theory, kitchen robots, grouping We evaluate human grouping of everyday objects for a psychologically inspired knowledge representation based on prototype theory.
Our goal was to empirically identify items that form a group and the respective prototype.
We conducted a study in which participants had to sort different kitchen objects into a digital kitchen. We chose a kitchen as a use case, since people have to tidy up dishes every day.
Our overall aim is to develop a knowledge representation system that one day could be used by kitchen robots. We calculated an overall similarity matrix (OSM) and identified groups whenever at least half of the participants put two items on the same shelf.
#36 Anna Tikhomirova and Elena Matrosova Algorithms for intelligent automated evaluation of relevance of search queries results Keywords: neural network, semantic analysis, algorithm, search query, teaching model, machine learning This paper is devoted to the problem of automated evaluation of relevance of search queries results. High relevance of search algorithm output is the base of effective large quantities of data processing, which is worked at by users of modern informational systems. Automated and reliable estimate of relevance of search queries results will give the opportunity to lower time expenditures for the best algorithm choice. The usage of improved from this perspective algorithms will allow to raise effectiveness and user satisfaction when dealing with automatic search systems in any activities.
#37 Nikita Ushakov and Vitaliy Ivanenko Copyright protection of video content based on digital watermarks Keywords: digital watermarks, video, embedding information, dew The paper proposes the method of digital watermark usage for video copyright protection, that may be a solution to the piracy of digital content. This paper studies different watermark embedding methods for videos. Modified DEW watermarking algorithm is proposed. This method stands out for its technique – the watermark is embedded exclusively by discarding certain high frequency coefficients. Different attacks on video container were studied. The watermark was exposed to most of the common attacks. Performance factors of this algorithm were calculated, they depend on three parameters: energy difference, cut-off point and the number of DCT blocks. Effective values of the parameters were found. The suggested method may act as an effective option for copyright protection.
#38 Dmitry Efanov and Vassili Leonov Reverse Engineering of Altera FPGA Cyclone Family Bitstream by Differential Power Analysis Keywords: reverse engineering, differential power analysis, FPGA, CPLD, altera, cyclone, bitstream format The bitstream is used to describe the configuration data to be loaded into a complex programmable logic device (CPLD) or a field-programmable gate array (FPGA). Open source implementation of CPLD and FPGA compile, place and route and configuration bitstream assemble tools is hindered by the lack of documented open format. Differential power analysis is a form of power analysis which allows to factor the intermediate values within cryptographic or similar computations by statistically analyzing data collected from multiple operations. This paper presents a methodological approach to FPGA bitstream reversing, based on statistical and differential power analysis, focusing on an in-depth analysis of the Altera Cyclone Family bitstream format. General structure and some specific configuration aspects of bitstream encoding are presented.
#39 Christian Tsvetkov and Ivan Vankov How do deep neural networks represent faces’ Keywords: deep learning, face identification, face representations, cognitive validity Deep learning models of vision have recently achieved human-level of performance in tasks such as object recognition and face identification. However, the extent to which these computational models resemble the way biological vision works remains unclear. We address this question by investigating how a deep learning model of face identification represents faces and by comparing the results to data from behavioural experiments. To this end, we use the Bubbles technique (Gosselin & Schyns, 2001) in order to find the critical regions within an image which are needed for correct face identification. The technique is applied to a pretrained deep convolutional neural network model of face recognition (Amos, Ludwiczuk, & Satyanarayanan, 2016), and the results are related to human data from a replication of Gosselin & Schyns (2001). We also use Bubbles to check how faces are represented in a simple connectionist model and in a deep learning model trained on general object recognition.
#41 Boris Onykiy, Evgeniy Tretyakov, Larisa Pronicheva, Ilya Galin, Kristina Ionkina and Andrey Cherkasskiy Methodology of learning curve analysis for development of incoming material clustering neural network Keywords: neural network, material clustering, learning curve, visual analysis This paper describes the methodology of learning curve analysis for development of incoming material clustering neural network. This methodology helps to understand deeply the learning curve adequate level and to bring learning curve structure to the relevant one of the thematic scope of incoming materials. The methodology is based on visual analysis and comprises the building of directed graphs in order to identify data templates. As the battlefield for material clustering the Nuclear Infrastructure Development Section (NIDS) of the International Atom Energy Agency (IAEA) is selected as the support from NIDS’ experts had been available during the research. Some of the challenges the NIDS faces are data aggregation for Country Nuclear Infrastructure Profiles (CNIP) and data assessment after Nuclear Infrastructure Review Missions (INIR).
#42 Max Talanov, Fail Gafarov, Jordi Vallverd’, Sergey Ostapenko, Marat Gazizov, Alexander Toschev, Alexey Leukhin and Salvatore Distefano Simulation of serotonin mechanisms in NEUCOGAR cognitive architecture Keywords: Serotonin, dopamine, disgust, artificial intelligence, simulation, affective computing, emotion modelling, neuromodulation This work aims at demonstrating that the neuromodulatory mechanisms
that control the emotional states of mammals (specifically rat’s brains) can
be represented and re-implemented in a computational model processed by a
machine. In particular we specifically focus on two neurotransmitters, sero-
tonin and dopamine, starting from their fundamental role in basic cognitive
processes. In our specific implementation, we represent the simulation of the
‘disgust-like’ state based on the three dimensional neuromodulatory model
of affects or emotions, according to the ‘cube of emotions’ where dopamine
controls attention and serotonin is the key for inhibition. These functional
mechanisms can be transferred into an artificial cognitive system: inhibition,
for example, can elicit a blocking behaviour that, depending on its intensity
and duration, can push the system to a general emotional state. Therefore,
the main goal of this paper is to implement such a mechanism in a com-
putational system to make it capable of managing a ‘failure’ scenario in
the complex set of inbound parameters appropriate for social environment
useful for highlighting memories, decision making, resources evaluation, and
other cognitive processes. We have simulated 1000 milliseconds of the sero-
tonin and dopamine systems using NEST Neural Simulation Tool with the
rat brain as the model to artificially reproduce this mechanism on a computational system. The results of the simulation experiments demonstrate the
effectiveness of the proposed approach, pushing towards the completion of
the biomimetic model by adding the third neurotransmitter (noradrenaline)
and combining it with synthetic hormones.
#43 Artem Chernyshov, Anita Balandina, Anastasiya Kostkina and Valentin Klimov Intelligent search system for huge non-structured data storages with domain-based natural language interface Keywords: semantic search, semantic map, non-structured data, natural language, domain-based natural languages Nowadays the number of huge companies and corporations has in their disposition various non-structured texts, documents and other data. The absence of clearly defined structure of the data makes the implementation of searching queries complicated and even impossible depending on the storage size. The other problem connected with staff, which may face the problem with misunderstanding of the special query languages, knowledge of which is necessary for the execution of searching queries. To solve these problems, we propose the semantic search system, the possibilities of which include the searching index construction, for queries execution and the semantic map, which would help to clarify the queries. In this paper we are going to describe our algorithms and the architecture of the system, and also to give a comparison to analogues.
#45 Natalia Miloslavskaya Analysis of SIEM Systems and their Usage in Security Operations and Security Intelligence Centers Keywords: SIEM, Security Operations Center, Security Intelligence Center, information security, information security incident To achieve business objectives, to stay competitive and to operate legally modern organizations of all types (e.g. commercial enterprises, government agencies, not-for profit organizations), different size and sphere of activity need to match a lot of internal and external requirements. They are called compliance regulations and mean conforming to a rule, such as a specification, procedure, policy, standard, law, etc. These organizations need to ensure valuable assets, uninterrupted business operation (processes), reliable data and differentiated quality of service (QoS) to various groups of users. They need to protect their clients and employees not only inside but also outside organization itself in connection with which two new terms were introduced ‘ teleworking or telecommuting. According to Gartner by 2020, 30 % of global enterprises will have been directly compromised by an independent group of cybercriminals or cyberactivists. And in 60 % of network breaches, hackers compromise the network within minutes, says Verizon in the 2015 Data Breach Investigations Report. An integrated system to manage organizations’ intranet security is required as never before. The data collected and analyzed within this system should be evaluated online from a viewpoint of any information security (IS) incident to find its source, consider its type, weight its consequences, visualize its vector, associate all target systems, prioritize countermeasures and offer mitigation solutions with weighted impact relevance. The brief analysis of a concept and evolution of Security Information and Event Management (SIEM) systems and their usage in Security Operations Centers and Security Intelligence Centers for intranet’s IS management are presented.
#46 Alisa Volkert, Stefanie Mueller and Alexandra Kirsch Human-like Prototypes for Psychologically Inspired Knowledge Representation Keywords: knowledge representation, prototype theory, kitchen robots, grouping We evaluate human grouping of everyday objects for a psychologically inspired knowledge representation based on prototype theory. Our overall aim is to develop a knowledge representation system that one day could be used by kitchen robots. We conducted a study in which participants had to sort different kitchen objects into a digital kitchen. We chose a kitchen as a use case, since people have to tidy up dishes every day. We calculated an overall similarity matrix and identified object groups whenever at least half of the participants put two items on the
same shelf. Out of these object categories we calculated the respective prototype. We then tested the similarities of all categories to all prototypes, which turned out to be reasonable.
#47 Viacheslav E. Wolfengagen, Larisa Yu. Ismailova and Sergey V. Kosikov Model of conversion of data objects for defining the object-relation mapping Keywords: data model, computational model, lambda-model, data interpretation, data conversion, conceptual modeling, object-relation mapping, transformable mapping The paper considers the problem of building of the transformable object-relation mapping. It is shown that an essential part of the task is to get the conversion of data objects doing their representation adjusted for the corresponding data model. It is offered to receive the decision by a semantic method in case of which the formal models of object system and relational system are considered and their interpretations are set. The transformation mappings are considered as a kind of mappings saving interpretations of the given form. Creation of model of converting of data objects on the basis of applicative computing systems is offered what allows to build models of both object, and relational systems dipping in applicative structures with the given means of expression, in particular, to use a lambda algebra or a lambda model. On this basis the models can be received allow compositions of means of converting and also determination and check of global restrictions for the changes of data determined by the given set of methods of converting. Achievement of flexibility requires use parameterization of the considered construction, i.e. support of dependence of a set of available methods of interpretation on parameters as which semantic characteristics of processed data appear. The prototypes of constructions of models have been used for informational semantic supporting of implementation of the best available technology (or just BAT) in practice in Russia.
#48 Sergey Yarushev and Alexey Averkin Modular Neural Networks in Time Series Forecasting Keywords: Modular neural networks, deep learning, time series, forecasting, neuro fuzzy The paper presents the basic idea of modular hybrid neural networks and considers in detail the main advantages of modular neural network techniques. Also, we considered some examples of neural networks with modular architectures.
#49 Ricardo Gudwin, Andr’ Luis Paraense, Suelen Mapa de Paula, Eduardo Fr’es, Wandemberg Gibaut, Elisa Castro, Vera Figueiredo and Klaus Raizer The Multipurpose Enhanced Cognitive Architecture (MECA) Keywords: Cognitive Architecture, Dual-process Theory, Dynamic Subsumption, CST In this paper, we present an overview of MECA, the Multipurpose Enhanced Cognitive Architecture, a cognitive architecture developed by our research group and implemented in the Java language. MECA was designed based on many ideas coming from Dual Process Theory, Dynamic Subsumption, Conceptual Spaces and Grounded Cognition, and constructed using CST, a toolkit for the construction of cognitive architectures in Java, also developed by our group. Basically MECA promotes an hybridism of SOAR, used to implement rule-based processing and space-state exploration in System 2 modules, with a Dynamic Subsumption Motivational System performing the role of System 1, using a representational system based on conceptual spaces and grounded cognition. We review the conceptual background used on MECA and further provide a detailed description of the many MECA sub-systems.
#50 Vlada Kugurakova and Denis Ivanov Robot Dream paradigm for Anthropomorphic Social Agent Keywords: artificial social agent, robot dream, cognitive architecture, artificial intelligence In this paper, we discuss the possibilities of integration of the Robot Dream paradigm with Anthropomorphic Social Agent (ASA). Anthropomorphic Social Agent is essentially a user interface specifically designed for simulating the realistic human-to-human interaction. The main idea behind the Robot Dream paradigm is to provide highly realistic social behavior when resources of the machine are limited by outsourcing resource-intensive computation to the cloud. We believe that these two concepts go together nicely.
#51 Denis Kleyko and Evgeny Osipov No two brains are alike: Cloning a hyperdimensional associative memory using cellular automata computations Keywords: Vector Symbolic Architectures, distributed representation, knowledge transfer, hyperdimensional computing, cellular automata, cognitive systems This paper looks beyond of the current focus of research on biologically inspired cognitive
systems and considers the problem of replication of its learned functionality. The considered
challenge is to replicate the learned knowledge such that uniqueness of the internal symbolic
representations is guaranteed. This article takes a neurological argument \no two brains are
alike” and suggests an architecture for mapping a content of the trained associative memory
built using principles of hyperdimensional computing and Vector Symbolic Architectures into
a new and orthogonal basis of atomic symbols. This is done with the help of computations on
cellular automata. The results of this article open a way towards a secure usage of cognitive
architectures in a variety of practical application domains.
#52 Mikhail Ivanov and Andrey Starikovskiy New Life of Old Standard: Transition from One-Dimensional Version to 3D Keywords: Block cipher, 2D transformation, 3D transformation, GOST, DOZEN The trend of recent years has been the advent of 2D and 3D cryptographic transformations. Stand-ards that have appeared in the 21st century, specify algorithms based on the use of 2D and 3D transformations (AES, Kuznechik, Keccak, Stribog). In the article a 3D version of cryptographic transformation specified by GOST 28147-89 is suggested. The 3D GOST algorithm is characterized by the high degree of parallelism at the level of elementary operations. Increasing bit depth of the processed data blocks from 64 to 512 bits al-lows 3D GOST to be used for the synthesis of hash algorithms. Algorithm improvement agenda may be similar to the DOZEN family of algorithms.
#53 Sergey Zhurin Selection of rational set of Methods for Insider’s Identification Keywords: insider identification, evaluation of personnel, Rational Set of Methods A huge range of international regulatory documents state the importance of counteracting insiders. One of the most important aspects of the preventive measures against an insider is personnel checks using different techniques, including interviews, social network analysis, lie detector. Being limited in money, it is necessary to choose a technique check list rationally. Here is present the sequence for detection the rational set of methods in several cases related to personal check.
#54 Jake Hecla, Timur Khabibullin and Anastasia Tolstaya Gamma-Probe for Locating the Source of Ionizing Radiation Keywords: Gamma-Probe, Cancer Detection, Lymph Nodes, Detector Resolution, SiPM, Amplifier, Collimator The radionuclide diagnostics unit, described in the article, detects pathological changes of organs and systems of a person. The device is a portable detector of gamma rays that allows to diagnose superficial malignancies using radiopharmaceuticals injected into the body. The gamma probe uses crystal LaBr3:Ce as a scintillator and silicon photomultiplier SiPM as a photodetector. The focus of this paper is the improvement of the amplifier, which originally produced misshapen pulses unsuitable for energy discrimination. Using LTSPICE, a free circuit-modelling program, we performed extensive simulation of both the SiPM and the amplifier. From this work, we determined that high input impedance and unnecessarily high gain were the source of the distortion. Another amplifier better suited to the SiPM parameters was simulated and then prototyped.
#55 Mikhail Alyushin, Alexander Alyushin and Lyubov Kolobashkina Laboratory Approbation of a New Approach for Contrast Enhancement of Human Face Thermal Image Based on Selective Multifunction Pixel Brightness Conversion Function Keywords: Thermal image of human face, improvement of contrast, histogram modification, informative areas of human face The paper suggests a new approach to improving the contrast of the thermal image of a person’s face in the deep infrared region. The approach involves the analysis and modification of the histogram of the image and is based on the use of a selective multivalued function for encoding the brightness of image pixels. The approach is focused on processing first of all thermal images of the person, containing areas with various informativeness. The implementation of the approach in practice makes it possible to improve the contrast of the face image with preservation of all informative features regardless of the level of brightness of the corresponding pixels of the image. In this case, background areas with high and low levels of exposure are compressed. The effectiveness of the proposed approach was tested in laboratory tests of the developed specialized program. For this purpose, the existing library of thermal images of people of different age and sex was used. The achieved gain in the contrast level averaged 2.5 times for the most informative areas of the face. The proposed approach is focused on the use in remote sensing systems of human bioparameters.
#56 Victor Morozov and Natalia Miloslavskaya DLP Systems as a Modern Information Security Control Keywords: Data Loss Prevention, Information security, insider information security threat Today, information is one of the most critical and valuable assets for success and prosperity of any company. The complexity of modern organizations and the trend to move to the cloud and outsource are increasing. At the same time the wide range of new ever-growing information security (IS) threats, especially those related to new information, communication and network technologies, services and devices, are all around us. For example, the well-publicized attack on the Sony Playstation Network, resulted in the loss of user names, passwords, ad-dresses, birth dates and financial details of 77 million users and Sony’s financial loss around $171 million (including estimates for customer support costs, legal costs and the impact on future profits), left the online gaming network suspended for weeks in 2011. The importance of using modern protection tools against inter-nal IS threats is proved. The advantages of DLP systems over alternative solutions are disclosed. The principles and technologies underlying the operation of DLP systems are discussed. The architecture, application features and analytical capabilities of the SearchInform Information Security Perimeter (SearchInform) DLP system are described in detail.
#57 Mikhail Alyushin, Lyubov Kolobashkina and Alexander Alyushin Laboratory Approbation of the Algorithm for Isolating People’s Faces on a Thermal Infrared Image in the Case of their Quasi-Stationary Arrangement in a Room Keywords: Thermal image of the face, Face selection algorithm, Quasi-stationary arrangement of people Increasing the effectiveness of training and training sessions is possible through the implementation of so-called biological feedback. Such feedback allows the teacher, or the instructor, to continuously monitor the current psycho-emotional and functional state of the students. As a result, it becomes possible to adapt the style, pace, training mode and the volume of the material outlined, depending on the current receptivity and fatigue level of the listeners. The main element of systems that implement biological feedback in practice are remote non-contact technologies. Such technologies allow in a fully automatic mode to register the main most informative human bioparameters. Among them, in the first place are the parameters characterizing the current state of the cardiovascular system of man, his breathing system, as well as his peripheral nervous system. The bulk of information is obtained by processing in real time the thermal infrared image of a person’s face. Unfortunately, existing algorithms for distinguishing a person’s face have a sufficiently high computational complexity and insufficient reliability. A typical example in this regard can be a family of algorithms based on the Viola-Jones approach. The approach proposed in the work is based on taking into account additional information about the most likely location of a person’s face on a thermal image. This approach is most appropriate to use in cases of quasi-stationary location of people in the room. A typical example is the location of students at the tables in the classroom. For such cases it is possible to determine the areas of the most probable location of the trainees’ faces, as well as the possible boundaries of their movement. Laboratory tests of the developed program on the basis of the proposed algorithm have confirmed its high productivity, as well as efficiency in identifying students faces in the classroom.
#58 Cynthia Avila-Contreras, F’lix Ramos, Daniel Madrigal and Juan Luis Del Valle-Padilla A bioinspired model of early visual processing with feature and space based saliency for a cognitive architecture Keywords: visual processing, hue feature salience, lateral inhibition, double opponent We present a computational model that describes the early stages of visual processing and the within selective attention mechanisms to generate feature-based (hue or color) activations of salient localizations based on neurophysiology evidence of selective responses in the visual pathway. The model identifies related brain areas, the feasible computations of each one, and proposes the type of data generated and shared among the components. This work is part of the selective aspect of an attention system designed for a broader cognitive architecture for virtual creatures.
#59 Masahiko Osawa and Michita Imai The Functional Plausibility of Topologically Extended Models of RBMs as Hippocampal Models Keywords: hippocampus, restricted Boltzmann machine, computational neuroscience The hippocampus has distinctive functional and structural properties. In this research, we utilize Restricted Boltzmann Machines (RBMs) based models inspired by distinctive structures found in hippocampus; neurogenesis in the dentate gyrus (DG) and recurrent connections in CA3. We review two types of topologically extended models of RBMs inspired by hippocampus. In one type of models, units are dynamically added during the training phase, and in the other type, connections are partly recursive. We analyzed these models both as separate models and combined model. The two types of the proposed models implement functions that the hippocampus has but the classical RBMs don’t. Furthermore, by combining the two proposed models, memorization of chronologically ordered data and memory reconstruction tasks’ performance improved significantly.
#60 Margarita Zaeva and Andrew Evstifeev Criteria for assessing the results of production activities of automobile gas filling compressor stations Keywords: CNG stations, process of automated monitoring, the efficiency of production processes, the efficiency of design solution Within the framework of this paper, the performance indicators of the automotive gas filling compressor stations for 2014 – 2016 are considered. As a result of the analysis of the indicators of more than two hundred stations owned by PJSC Gazprom and transmitting information in the form of corporate statistical reporting forms criteria of estimation of efficiency of results of industrial activity of stations have been generated. The application of these performance evaluation criteria will allow to provide information to decision-makers about problematic entities of the organization in an automated mode.
#61 Natalia Miloslavskaya and Svetlana Tolstaya Organization’s Business Continuity in Cyberspace Keywords: business continuity, cyberspace, cybersecurity violation risks At present the reliable and efficient infrastructure of any organization plays an important role, contributes to the preservation and strengthening of its financial stability and economic development, and at the same time concentrates various risks. New risks are associated with the formation of a modern life environment called cyberspace. In the last decade, the risks of cybersecurity violation have acquired the status systemic risks due to a significant increase in possible consequences from their implementation. To conduct business in cyberspace, it is extremely important to develop solutions that eliminate a contradiction between the inability to avoid modern cyberattacks and strong requirement to quickly restore organization’s business processes. The measures implemented to date to minimize the recovery time of the activities of organizations after cybersecurity attacks may not be sufficient. The brief description of a business continuity concept application to cyberspace is given.
#62 Aleksandr I. Panov, Konstantin S. Yakovlev and Roman Suvorov Grid Path Planning with Deep Reinforcement Learning: Preliminary Results Keywords: path planning, reinforcement learning, neural networks, Q-learning, convolution networks, Q-network Single-shot grid-based path finding is an important problem with the applications in robotics, video games etc. Typically in AI community heuristic search methods (based on A* and its variations) are used to solve it. In this work we present the results of preliminary studies on how neural networks can be utilized to path planning on square grids, e.g. how well they can cope with path finding tasks by themselves as well as how they can be put into service to the conventional heuristic search algorithms, e.g. A*. Conducted experiments show that the agent robustly learns to achieve the goal and that the adaptive heuristic is capable of reducing breadth of the search tree.
#63 Zulfiqar Ali, Muhammad Imran, Wadood Abdul and Muhammad Shoaib A Zero-Watermarking Algorithm for Privacy Protection in a Voice Disorder Detection System Keywords: privacy protection, zero-watermarking, visual cryptography, local binary pattern, MFCC, SVM Unauthorized access to the health information of an individual may create adverse circumstances in their personal life and/or professional career. Various smart healthcare systems have been suggested for use in smart homes and cities, which receive data through the Internet of Things and transmit over a network. One of the major concerns surrounding such healthcare systems is the privacy of individuals. To avoid such predicaments, a privacy protected healthcare system is developed in this study that protects the identity of an individual as well as detects vocal fold disorders. The privacy of the developed healthcare system is based on the proposed zero-watermarking algorithm, which embeds a watermark in a secret key rather than the signals to avoid the distortion in an audio sample. The identity of an individual is protected by the generation of its secret share through visual cryptography. The generated shares are embedded by finding the patterns into audio with the application of One-Dimensional Local Binary Pattern (1D-LBP). The proposed zero-watermarking algorithm is evaluated by using audio samples taken from the Massachusetts Eye and Ear Infirmary (MEEI) voice disorder database. Experimental results show that the proposed zero-watermarking achieves imperceptibility and is reliable in its extraction of identity. In addition, the proposed algorithm does not affect the results of disorder detection and it is robust against noise attacks of various signal-to-noise ratios (SNR).
#64 Lubov Podladchikova, Anatoly Samarin, Dmitry Shaposhnikov and Mikhail Petrushan Modern views on visual attention mechanisms Keywords: visual attention, eye movements, 2D images, 3D environment, individual peculiarities of viewing, return fixations Modern views on visual attention mechanisms, some results of our last psychophysical experiments with eye movements recording and future steps of studying have been considered. Several groups of finding have been determined, namely: (i) unresolved objectives; (ii) problems for which contradictory data are known; (iii) finding which propose revision of some classic views; (iv) views consistent with data obtained in different research centers. The results of psychophysical tests directed on receiving of quantitative parameters of eye movements at performance of different visual tasks to estimate the contribution of bottom-up and top-down mechanisms of visual attention are presented. It is supposed that obtained experimental results can be formalized to use in realistic mathematical models of visual attention.
#65 Margarita Zaeva, Alexander Akhremenkov and Anatoly Tsirlin Probabilistic assessment of the organization of tournaments and examinations using paired comparisons Keywords: tournament organization, probability, paired comparison In this paper a criteria of comparison different tournament organization systems in sporting contests is offered, the criteria uses a probability of winning the fairly strongest player. Two probabilistic models have been analyzed. Calculating formulas for estimating of that probability and probability density of score points by one or another player were obtained. Gotten results also provide an order of objects presenting to experts in organization of examination by paired comparison. An analytical estimation of probability of tournament results (or pared comparison) was obtained, it allows in many cases to avoid of time-consuming procedure of sorting out of possible variants.
#66 Jonathan Rosales, Myrna S. Zamarripa, Felix Ramos and Marco-Antonio Ramos Automatic reward system for virtual creatures, emergent processes of emotions and physiological motivation Keywords: Cognitive architectures, Emotions, Liking, Wanting, Reguard, Motivations, Behaviour, Virtual Creatures Emotional and motivational evaluations are part of the development of rewards within living beings. Particularly, during the perception of stimuli in the environment, these evaluations collaborate with one another to generate reward values automatically, without the need to involve rational processes. In this paper we propose a conceptual model of automatic reward for virtual creatures inspired by neuroscientific evidence, contemplating the processes of emotions and motivations, as well as the generation and recovery of automatic reward values. According to the evidence, the reward process is divided into two processes: liking, which is oriented toward interpreting information inputs and generating reward values, and wanting, focused on the recovery of the stored reward values and the generation of objectives in the environment. The reward process is implemented as a concurrent and parallel naturally distributed system, allowing virtual creatures to adapt to their environment and generate more credible behaviors. The results of liking and wanting are shown in this article through a case study, in which the performance of both processes is observed when the creature interacts with the environment.
#67 Larisa Ismailova, Sergey Kosikov and Viacheslav Wolfengagen Basic constructions of the computational model of support for access operations to the semantic network in the field of implementation of the best available technologies Keywords: informational objects, semantics, computational model, semantic network, intensional logic, access operation The paper considers the approach to solving the task of storing data in the Web environment using semantic networks (SN). The control over the access to SN is identified as a critical task. An approach to the solution based on the use of the controlling SN is proposed. The rationale for the approach involves developing a computational model for supporting the access operations. The construction of a model based on intensional logic is proposed. The basic logical constructions, necessary for building a model, are considered. The testing of the model’s constructions was performed when building the tools of semantic support for the implementation of the best available technologies (BAT).
#68 Mikhail Egorchev and Yury Tiumentsev Semi-empirical Neural Network Based Approach to Modelling and Simulation of Controlled Dynamical Systems Keywords: nonlinear dynamical system, semi-empirical model, neural network, sequential learning algorithm A modelling and simulation approach is discussed for nonlinear controlled dynamical systems under multiple and diverse uncertainties. The main goal is to demonstrate capabilities for semi-empirical neural network based models combining theoretical domain-specific knowledge with training tools of artificial neural network field. Training of the dynamical neural network model for multi-step ahead prediction is performed in a sequential fashion. Computational experiments are carried out to confirm efficiency of the proposed approach.
#69 Dmitry Kozlov and Yury Tiumentsev Neural network based semi-empirical models for dynamical systems represented by differential-algebraic equations of index 2 Keywords: dynamical system, differential-algebraic equations, semi-empirical model, neural network based simulation A simulation problem is discussed for nonlinear controlled dynamical systems represented by differential-algebraic equations of index 2.The problem is proposed to be solved in the framework of the semi-empirical approach combining theoretical knowledge for the plant with training tools of artificial neural network field. Special form network based semi-empirical models implementing implicit Runge-Kutta method of numerical integration are proposed to use. The training of the semi-empirical model allows to elaborate the models of aerodynamic coefficient implemented as a part it. The results of simulation for elaboration procedure of lift coefficient in respect to reentry hypersonic vehicle are presented.
#70 Vishwanathan Mohan and Ajaz Ahmad Bhat Joint Goal Human Robot collaboration-From Remembering to Inferring Keywords: Human robot collaboration, Robot episodic memory, iCub humanoid, Cumulative learning, Episodic Simulation, Goal directed planning The ability to infer goals, consequences of one’s own and others’ actions is a critical desirable feature for robots to truly become our companions-thereby opening up applications in several domains. This article proposes the viewpoint that the ability to remember our own past experiences based on present context enables us to infer future consequences of both our actions/goals and observed actions/goals of the other (by analogy). In this context, a biomimetic episodic memory architecture to encode diverse learning experiences of iCub humanoid is presented. The critical feature is that partial cues from the present environment like objects perceived or observed actions of a human triggers a recall of context relevant past experiences thereby enabling the robot to infer rewarding future states and engage in cooperative goal-oriented behaviors. An assembly task jointly done by human and the iCub humanoid is used to illustrate the framework. Link between the proposed framework and emerging results from neurosciences related to shared cortical basis for ‘remembering, imagining and perspective taking’ is discussed.
#71 Emanuel Diamant Rethinking BICA’s R&D challenges: Grief revelations of an upset revisionist Keywords: Biological inspiration, Brain Research Programs, cognitive modeling, information duality, cognitive information processing Biologically Inspired Cognitive Architectures (BICA) is a subfield of Artificial Intelligence aimed at creating machines that emulate human cognitive abilities. What distinguish BICA from other AI approaches is that it based on principles drawn from biology and neuroscience. There is a widespread conviction that nature has a solution for almost all problems we are faced with today. We have only to pick up the solution and replicate it in our design. However, Nature does not easily give up her secrets. Especially, when it is about human brain deciphering. For that reason, large Brain Research initiatives have been launched around the world. They will provide us with knowledge about brain workflow activity in neuron assemblies and their interconnections. But what is being ‘flown’ (conveyed) via the interconnections the research programme does not disclose. It is implied that what flows in the interconnections is information. But what is information’ ‘ that remains undefined. Having in mind BICA’s interest in the matters, the paper will try to clarify the issues.
#72 Mikhail Turov, Alexey Fomin, Elena Matrosova and Anna Tikhomirova Medical knowledge-based decision support system Keywords: decision-taking process, theranostic process, artificial neural network, genetic algorithm, automated decision support system, machine learning This paper is devoted to the problem of automated supporting of decision-taking process in healthcare.
The theranostic process is typified as an especial case of administrative process. Correct solutions of problems in medicine is based on metering big data. These data is represented by facts from real-life experience and numerous guidance of evidence-based healthcare. Taking into account of enormous aggregation of data for special isolated case is possible on application of automated decision support system based on technology of artificial neural network or genetic algorithm.
#73 Norifumi Watanabe and Fumihiko Mori Sensory Integration Model of Pedestrian by Vection and Somatosensory Stimulation Keywords: Pedestrian Guidance, Vection, Somatosensory, Sensory Integration, Peripheral Vision Display In this study, we clarify the integration mechanism of sensory information of vision and somatosensory sensation in walking. In this experiment, we evaluated the possibility of affecting walking by attenuating a somatosensory sensation by giving vibration stimulation to the feet, and by presenting optic flow to the peripheral visual field to generate a self – motion sensation of vision superiority. Experimental results confirmed that walking in the direction opposite to the self – motion sensation is presented by presenting the optic flow and vibration stimulation. Based on the results of this experiment, we propose that sensory devices such as vision and somatosensory sensation are not exclusive in walking, but are integrated by superposition.
#74 Liudmila Zhilyakova Model of heterogeneous interactions among complex agents. From a neural to a social network Keywords: discrete dynamics, heterogeneous neural network, social network, activity in networks We describe a heterogeneous neural network where neurons interact by means various neurotransmitters. This feature enables exerting selective influence on neurons. We use this formalism as a basis for modeling interactions between agents in a social network in which two types of opposite activity are spreading. The main properties of agents and principles of activity spreading are defined. The classification of agents according to their parameters is represented.
#75 Zahra Gharaee, Peter G’rdenfors and Magnus Johnsson Online Recognition of Actions Involving Objects Keywords: Hierarchical Models, Self-Organizing Maps, Action Recognition, Object Detection We present an online system for real time recognition of actions involving objects working in online mode. The system merges two streams of information pro- cessing running in parallel. One is carried out by a hierarchical self-organizing map (SOM) system that recognizes the performed actions by analysing the spa- tial trajectories of the agent’s movements. It consists of two layers of SOMs and a custom made supervised neural network. The activation sequences in the first layer SOM represent the sequences of significant postures of the agent during the performance of actions. These activation sequences are subsequently recoded and clustered in the second layer SOM, and then labeled by the ac- tivity in the third layer custom made supervised neural network. The second information processing stream is carried out by a second system that determines which object among several in the agent’s vicinity the action is applied to. This is achieved by applying a proximity measure. The presented method combines the two information processing streams to determine what action the agent per- formed and on what object. The action recognition system has been tested with excellent performance.
#76 Valentin Nepomnyashchikh STRATEGIES OF ANIMALS IN AN UNFAMILIAR ENVIRONMENT Keywords: animal behavior, behavioral strategies, novel environment, artificial agents Animals employ a set of behavioral strategies when face an unfamiliar environment. A strategy is defined as an movement sequence which repeats itself until replaced by some other behavior. Some strategies are common for very different organisms ‘ from Invertebrates to humans. Another behavioral property of different animals is that they alternate among several strategies instead of employing only one particular strategy. The alternation is spontaneous: it is observed even in the absence of any changes of external stimuli. A simplest example of the alternation is a series of leftward turns which alternate with series of rightward ones in moving fish and other animals. In this lecture, I discuss some problems related to the strategies and their alternation:
-What mechanisms generate the strategies’ Many strategies are produced by innate ‘wired-in’ central pattern generators (CPG) in animal’s brain. However, the explanation is not universal. For example, rodents, fish and invertebrate show a variety of distinct strategies when placed in an unfamiliar maze. These strategies depend on size and complexity of a maze. It is hard to believe that there are CPGs pre-adapted for conditions which animals never meet in their natural environments. Rather animals are capable to generate new strategies ‘on line’, while coping with a novel environment.
-Why the strategies alternate in a static, unchanging environment’ A possible explanation is that animals try to predict results of its behavior and improve the predictions. To this end, they generate a certain action sequence (i.e. strategy), predict its results and compare the prediction with actual results. Then the prediction is corrected according actual results and the action sequence is repeated in order to verify the corrected prediction. If, after a number of repetitions and corrections, actual results and corrected predictions fairly coincide, animals switch to another strategy.
-Does a variety of strategies an animal or artificial agent is able to generate show a level of its intelligence’ In the course of generation of new strategies and verification of predictions, an animal/artificial agent learns properties of a novel environment and evaluate to what extent it is able to control it. One may suggest that animals/agents, which generate a greater manifold of strategies, are more autonomous and adaptive, and are able to solve more complex tasks. Thus, they are more intelligent. If so, we obtain a quantitative measure of intelligence.
-If the generation of strategies is important for an artificial autonomous agent, then which architecture would allow them to produce various and not wired-in strategies’ It is the key problem, and it could not be solved until mechanisms of strategies generation in animals are understood.
#77 Evgenii Vityaev and Alexander Demin Cognitive architecture based on the functional systems theory Keywords: adaptive behaviour, control system, animat 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. The computer experiments are presented that compare this control system with the control systems based on the reinforcement learning.
#78 Piotr Boltuc Strong Semantic Computing — a BICA framework Keywords: Engineering Thesis in Machine Consciousness, Strong Semantic Computing, Spinozian phenomenalism, Sanz’s challenge, Chinese Room, computer proofs, phenomenal consciousness, phenomenal maps, machine consciousness, BICA continuity Standard computing will be characterized as a functional extension of syntax. This is based, partly, on Searle’s Chinese Room argument. Using BICA philosophy, in particular the claim of continuity between human-animal-robot cognitive architectures, we will define strong semantics as the ability of a cognitive architectures to consult cognitive maps, in particular phenomenal content map. The goal of strong semantic computing in autonomous robotics should be to ‘know what is going on’ before engaging in detailed logical analysis (it can be called Gestalt computing). Such computing is needed in advanced autonomous robotics, especially robots functioning in human environments. Incidentally, this approach provides a partial solution to Searle’s Chinese room case.
#79 Evgenii Vityaev and Alexander Demin Adaptive control of modular robots Keywords: modular robots, adaptive control, control system, snake-like robot, multiped robot, trunk-like robot This paper proposes a learning control system of modular systems with a large number of degrees of freedom based on joint learning of modules, starting with finding the common control rules for all modules and finishing with their subsequent specification in accordance with the ideas of the semantic probabilistic inference. With an interactive 3D simulator, a number of successful experiments were carried out to train three robot models: snake-like robot, multiped robot and trunk-like robot. Pilot studies have shown that the approach proposed is quite effective and can be used to control the complex modular systems with many degrees of freedom.
#80 Sei Ueno, Masahiko Osawa, Michita Imai, Tsuneo Kato and Hiroshi Yamakawa ‘Re:ROS’: Prototyping of Reinforcement Learning Environment for Asynchronous Cognitive Architecture Keywords: Reinforcement learning, Cognitive architecture, Learning Environment, Robotics, Robot learning Reinforcement learning, which is a field of machine learning, is effective for behavior acquisition in robots. The extension of cognitive architecture with synchronous distributed systems is also effective for behavior acquisition. However, early work on the reinforcement learning software framework does not solve the difference between asynchrony and synchrony, and it cannot apply cognitive architecture with asynchronous distributed systems. Therefore, we applied asynchronous distributed systems to reinforcement learning modules to adapt to asynchrony and to extend cognitive architecture with asynchronous distributed systems. We prototyped a reinforcement learning software framework called ‘Re:ROS.”
#81 Edward Ayunts and Aleksandr I. Panov Task Planning in “Block World” with Deep Reinforcement Learning Keywords: neural network, convolution network, deep learning, Q-learning, reinforcement learning, task planning At the moment reinforcement learning have advanced significantly with discovering new techniques and instruments for training. This paper is devoted to the application convolutional and recurrent neural networks in the task of planning with reinforcement learning problem. The aim of the work is to check whether the neural networks are fit for this problem. During the experiments in a block environment the task was to move blocks to obtain the final arrangement which was the target. Significant part of the problem is connected with the determining on the reward function and how the results are depending in reward’s calculation. The current results show that without modifying the initial problem into more straightforward ones neural networks didn’t demonstrate stable learning process. In the paper a modified reward function with sub-targets and euclidean reward calculation was used for more precise reward determination. Results have shown that none of the tested architectures were not able to achieve goal.
#82 Frank Krueger The neurobiological architecture of trust Keywords: trustworthiness, cooperation, brain Trust pervades nearly every social aspect of our daily lives; it penetrates all human interactions from personal relationships to organizational interactions. A plethora of studies have started to gain a deeper understanding of the inherent nature of trust by combining behavioral paradigms with functional neuroimaging, electroencephalographic, lesion, endocrinological, and genetic methods in both healthy and psychopathological populations. However, an overarching framework that integrates those separate findings from different levels into a conceptual framework characterizing trust behavior is still lacking. In this summer school, I will sketch out an integrative neuroscience framework on the neurobiological underpinnings of trust that provides an explanation on how trust behavior emerges from the interplay of genes, hormones/ neurotransmitter, brain circuits, and cognition. The integration into a unified conceptual framework will guide future investigations of the complex interplay between both biological and environmental factors and facilitate the understanding of humun-human but also human-machine inerpersonal trust.
#83 Maksim Sharaev, Vyacheslav Orlov, Vadim Ushakov and Boris Velichkovsky Information transfer between rich-club structures in the human brain Keywords: effective connectivity, information transfer, rich club, transfer entropy The performance of the human brain depends on how effectively its distinct regions communicate, especially the regions which are more strongly connected to each other than to other regions, or so called ‘rich-clubs’. The aim of the current work is to find a connectivity pattern between the three brain rich-club regions without any a priori assumptions on the underlying network architecture. Rich-clubs for the analysis were previously identified with structural MRI. Functional magnetic resonance imaging (fMRI) data from 25 healthy subjects (1000 time points from each one) was acquired and Transfer Entropy (TE) between fMRI time-series from rich-clubs was calculated. The significant results at the group level were obtained by testing against the surrogate data generated on a novel approach. We found stable causal interactions between rostral Anterior Cingulate Cortex L and Dorsal Anterior Cingulate Cortex L, dorsal Anterior Cingulate Cortex L and Paracentral Lobule R but not vice versa. Our work provides an approach to causal analysis of experimental data and demonstrates the applicability to real fMRI study.
#84 Elizaveta Stepanova and Vladimir Pavlovski Realization of the gesture interface by multifingered robot hand Keywords: neural network, multifingered hand, manipulator, collaborative robotics The paper considers theoretical mechanical model of a multifingered arm with 21 degrees of freedom. The main objective of the work – is the synthesis of finger control schemes in the tasks of collaborative robotics, as well as gesture recognition task with the help of neural network training. As the demonstration we propose to observe the results of three gestures recognition with the help of constructed convolutional network. For three lables (classes) 201 images at different distance and at different angles were created. As a result of neural network training the accuracy of classification is 67 percent.
#85 Vasiliy S. Kireev, Ivan S. Smirnov and Victor S. Tyunyakov Automatic Fuzzy Cognitive Map Building Online System Keywords: association rules mining, fuzzy cognitive maps, cognitive map automated building, web-mining, automated data science Under the present-day global crisis, global economy regionalization and technological areas redistribution there has been seen an unprecedented uncertainty growth along with various risks, which makes it harder to take managing decisions. The scenario approach appears to be one of the most popular when it comes to modelling weakly-structured subject fields and complex problems. Fuzzy cognitive maps have good prospects in the field, as they enable us to describe both the structure and the dynamic of the area under study. This paper goes along the topical automated data science and focuses on developing the OCAM (Online Cognitive Automated Mapper) system that allows to automatically-build cognitive maps without turning to experts. The cognitive map building data source of this system is website logs. The article features the main algorithms, the system architecture and some of the system work results. The current and further research is supported by the NRNU MEPhI development program.
#86 Sergey V. Kosikov, Viacheslav E. Wolfengagen and Larisa Yu. Ismailova The presentation of evolutionary concepts in problems of information support to implement the best available technologies Keywords: information system, semantic network, semantic modeling, semantic stability, data model, computational model, theory of categories The paper considers an approach to solving the problem of supporting the semantic stability of information system (IS) objects. A set of IS objects is addressed as a semantic network consisting of concepts and frames. The interpretation that assigns intensional (meaning) and extensional (value) characteristics to network designs is connected to the constructions of the semantic network. The interpretation in the general case depends on the interpreting subject, time, context, which can be considered as parameters. The possibility to preset a consistent interpretation for a given semantic network is regarded as a semantic integrity, and the possibility to control changes in interpretation when the parameter is changed is regarded as semantic stability. Among the tasks related to supporting semantic stability, the problem of modelling evolutionary concepts (EC) is highlighted. It is proposed to construct a computational model of EC based on the theory of categories with a significant use of the concept of variable domain. The model is constructed as a category of functors, and it is shown that the Cartesian closure of the basic category implies Cartesian closure of the category of models. The structure of the exponential object of the category of models has been studied, and it is shown that its correct construction requires taking into account the evolution of concepts. The testing of the model’s constructions was carried out when lining the means of semantic support for the implementation of the best available technologies (BAT).
#87 Hidemoto Nakada and Yuuji Ichisugi Ichisugi Context-Dependent Robust Text Recognition using Large-scale Restricted Bayesian Network Keywords: Bayesian Network, Machine Leaning, Text Recognition We have been proposing a computational model of the cerebral cor- tex called BESOM, which models the cerebral cortex as restricted Bayesian net- works based on recent findings in the neuroscience area. Since BESOM is based on Bayesian network, it inherently allows bi-directional information flow, meaning that it can naturally merge information extracted from concrete data with highly- abstract prior knowledge. As an example of such kind of tasks, we report robust text recognition task with context information. We show that word spelling knowledge and word n-gram could be represented as a part of the network and actually they contribute the text recognition accuracy with noisy text images. We also show that the computational cost is approximately linear with the number of characters and words.
#88 Alexey Artamonov, Dmitry Kshnyakov, Valeriya Danilova, Ilya Galin and Andrey Cherkasskiy Methodology for the Development of Dictionaries for Automated Classification System Keywords: Information classification, classification code, scientific category, keyword dictionary The paper describes a relevant task for research area specialists to define categories for the incoming information and scientific materials by using automated systems (software) and the methodology of developing of dictionaries for such systems. The methodology is based on studying of existing classification codes and developing dictionaries, which contain the most relevant and frequent keywords.
#89 Thomas Collins and Wei-Min Shen A Robust Cognitive Architecture for Learning from Surprises Keywords: autonomous learning from the environment, bio-inspired learning, learning to predict, Partially-observable Markov Decision Processes, active learning Learning from surprises is a cornerstone for building bio-inspired cognitive architectures that can autonomously learn from interactions with their environments. However, distinguishing true surprises — from which useful information can be extracted to improve an agent’s world model — from environmental noise arising from uncertainty is a fundamental challenge. This paper proposes a new and robust approach for actively learning a predictive model of discrete, stochastic, partially-observable environments based on a concept called the Stochastic Distinguishing Experiment (SDE). SDEs are conditional probability distributions over the next observation given a variable-length sequence of ordered actions and expected observations up to the present that partition the space of possible agent histories, thus forming an approximate predictive representation of state. We derive this SDE-based learning algorithm and present theoretical proofs of its convergence and computational complexity. Theoretical and experimental results in small environments with important theoretical properties demonstrate the algorithm’s ability to build an accurate predictive model from one continuous interaction with its environment without requiring any prior knowledge of the underlying state space, the number of SDEs to use or even a bound on SDE length.
#90 Dmitry Filin and Aleksandr I. Panov Applying a neural network architecture with spatio-temporal connections to the maze exploration Keywords: neural networks, SOM, THSOM, self-organizing map, path planning, hebbian rules We present a model of Reinforcement Learning, which consists of modified
neural-network architecture with spatio-temporal connections, known as Temporal
Hebbian Self-Organizing Map (THSOM). A number of experiments were
conducted to test the model on the maze solving problem. The algorithm demonstrates
sustainable learning, building a near to optimal routes. This work describes
an agents behavior in the mazes of different complexity and also influence of models
parameters at the length of formed paths.
#91 Nikolay Bazenkov, Dmitry Vorontsov, Varvara Dyakonova, Liudmila Zhilyakova, Oleg Kuznetsov and Dmitri Sakharov Discrete Modeling of Multi-Transmitter Neural Networks with Neuron Competition Keywords: Discrete dynamics, Heterochemical neuronal system, Neurotransmitters, Neuromodulation, Central pattern generator We propose a novel discrete model of central pattern generators (CPG), neuronal ensembles generating rhythmic activity. The model emphasizes the role of nonsynaptic interactions and the diversity of neuronal phenotypes in nervous systems. Neurons in the model release different neurotransmitters into the shared extracellular space (ECS) so each neuron with the appropriate set of receptors can receive signals from other neurons. We consider neurons of different membrane phenotypes represented as finite-state machines functioning in discrete time steps. Discrete modeling is aimed to provide a computationally tractable and compact explanation of rhythmic pattern generation in nervous systems. The important feature of the model is the introduced mechanism of neuronal competition which is shown to be responsible for the generation of proper rhythms. The model is illustrated with two examples: a half-center oscillator considered to be a basic mechanism of emerging rhythmic activity and the well-studied feeding network of a pond snail. Future research will focus on the neuromodulatory effects ubiquitous in CPG networks and the whole nervous systems.
#92 Arthur Chubarov and Daniil Azarnov Modeling Behavior o f Virtual Actors : A Limited Turing Test f or Social – Emotional Intelligence Keywords: cognitive modeling, virtual actor, virtual environment This work presents the design, implementation and study of (1) a videogame
-like virtual environment simulator, enabling social interaction of avatars controlled by human participants and by virtual actors; (2) a set of virtual actors with varying forms and degree of social-emotional intelligence,
based on the eBICA cognitive architecture; and (3) a limited Turing test for social-emotional intelligence, involving human participants and virtual actors. The virtual environment simulator allows for various forms of emotionally-laden interaction of actors immersed in it in the form of avatars,
with data collection characterizing their behavior in detail. The
objective here is to compare and evaluate models of social
-emotional reasoning based on the Turing test results and other
objective behavioral measures, also taking into account
subjective judgment of participants. One of the long
-term goals is achieving human-level believability of socially-emotional
virtual actors, such as non-player characters in games, personal assistants, robots, and other intelligent artifacts. Preliminary results indicate importance of social-emotional intelligence for believability, and support assumptions of the eBICA architecture.
#93 Sergey Kosikov, Viacheslav Wolfengagen and Larisa Ismailova The type system to provide compositional thinking about data flows in support tasks for the implementation of best available technologies Keywords: domain dynamic, interacting agent, Web tangling, semantic, data flows, type system, homotopy type theory The paper considers the approach to solving the problem of preventing the information system vulnerability that arises due to the insertion and / or performance of a semantically incorrect script (XSS-vulnerability). The solution of this task suggests modeling the information exchange between active objects (agents), including the information containing code fragments. Modeling is supposed to be carried out within the framework of the basic applicative computing system, in which the code fragments can be modeled by applicative objects. An important part of the task is to check the correctness of the scenarios composition, which requires the tools make compositional thinking about data flows, both when they are processed by scripts and contain them. The correctness is proposed to be provided by supporting the fairly strong typing system that excludes incorrect combinations of scenarios. The type system is assumed to be immersed into the applicative environment. The system is proposed to be built on the basis of the homotopy type theory, which ensures, in particular, the introduction of independent and dependent types, as well as the definition of recursion and induction principles for them. An important feature of the system is the possibility to vary the principles of object identification, which makes it possible to select criteria for matching code fragments. Partial approbation of typing constructions is performed with the example of the problem of semantic support for the implementation of the best available technologies (BAT).
#94 Alexander A. Eidlin and Alexei V. Samsonovich A Roadmap to Emotionally Intelligent Creative Virtual Assistants Keywords: cognitive modeling, virtual actor, emotional intelligence, creative assistant, co-robots Cognitive psychology has accumulated a vast amount of knowledge about human social emotions, emotional appraisals and their usage in decision making. Can an emotional cognitive architecture injected into an artifact make it more “humane”, and therefore, more productive in a variety of creative collaboration paradigms’ Here, we argue that the answer is positive. A large number of research projects in the field of digital art that are currently underway could benefit from integration of an emotional architecture component into them. An example is the project Robodanza (a robotic dancer), the functioning of which is based on a hidden Markov model trained by a genetic algorithm, yet lacking deep emotional intelligence. Speaking generally, today, at the time of explosion in neuro- and information technologies (IT), new knowledge about the brain, plus available at low cost new computational powers, call for a jump to a qualitatively new level in addressing the problem of replicating in a computer all the significant functionality of the human mind: primarily, its emotionality, creativity and social believability. We outline a roadmap to building a variety of useful virtual creative assistants to humans based on an emotionally intelligent cognitive architecture.
#95 Andrey M. Zagrebaev, Rustem N. Ramazanov and Andrey V. Trifonenkov About using of AI to choosing a refueling channel and manipulating control rods in RBMK-type reactor Keywords: nuclear reactor control, artificial intelligence, channel reactor refueling, control rod manipulation Nuclear reactor control is usually the composition of automatic and manual control types. This article deals with manual parts of power control and refueling systems of RBMK-type reactor. There are always such aspects of the reactor operation, which automatic systems do not control. The aim of this research is to determine the set of actual choices made by the operator and create mathematical model of decision-making operator based on a neural network. The further research may include estimations of the automatic control system imperfection, estimations of the quality of decisions made and performance tests for the composite computational and AI decision-making software for nuclear reactors.
#96 Basit Shehzad, Mahwish Mukhtar, Ikramullah Lali, Saqib Nawaz and Kinza Mehr Utilization of Recommender System in quantifying social media’s impact on print media Keywords: Twitter Trends, Print Media, Text Mining, Similarity, Difference Recent advancements in Information and Communication Technologies (ICT) and easy access to Internet have made social media the first choice for information sharing related to any important events or news. On Twitter, trend is a common feature that quantifies the level of popularity of a certain news or event. In this work, we examine the impact of Twitter trends on real world events by hypothesizing that Twitter trends has an influence on print media in Pakistan. For this, Twitter is used as a platform and Twitter trends as a base line. We first collect data from two sources (Twitter trends and print media) in the period May to August 2016. Obtained data from two sources is analyzed and it is observed that social media is significantly influencing the print media and majority of the news printed in newspaper are posted on Twitter earlier.
#98 Abdulgabbar Saif, Nazli Omar and Mohd Juziaddin Ab Aziz Building Sense Tagged Corpus Using Wikipedia for Supervised Word Sense Disambiguation Keywords: Word Sense Disambiguation, Wikipedia, Arabic WordNet, Alignment, Text Processing, Similarity Measures The past decade has witnessed construction of background information resources to overcome several challenges in natural language processing and text mining tasks. For non-English languages with poor knowledge sources such as Arabic, these challenges have become more salient especially for handling natural language processing issues that require human annotations. Building of sense-tagged data is a main challenge for supervised techniques that achieved promising results in word sense disambiguation. The manual building of sense-tagged data is a labor and a time-consuming task because each ambiguous word has to be labeled in collected contexts by linguistic experts. Therefore, this paper proposes a knowledge-based method for building the Arabic sense-tagged corpus from Wikipedia. The method starts with mapping Arabic WordNet and Wikipedia to select the Wikipedia article for the corresponding sense in WordNet. In this mapping step, the cross-lingual method is used to measure the similarity between features of a Wikipedia article and a WordNet sense separately. Then, the incoming-links of Wikipedia articles are exploited to extract instances for the sense of each ambiguous word in WordNet. For handling the lack of instances of some articles in Wikipedia, the multiword-based technique is proposed to increase a number of instances for each concept. Experimental results show that the cross-lingual method outperforms monolingual method that is based on Arabic features only. The sense-tagged corpus is created for 50 ambiguous words yielding 148 senses with 30,961 instances.
#99 Vyacheslav Orlov, Victoria Zinchenko, Vadim Ushakov and Boris Velichkovsky Physiological noise reduction algorithms for fMRI data Keywords: fmri, artifact detection, physiological noise reduction, motor task This article describes various approaches for excluding and recognizing artifacts in functional magnetic resonance data. Similar methods can be applied to intracable artifacts, such as breathing and pulse artifacts, from white matter and cerebrospinal fluid pulsations. In this paper, using real experimental data various algorithms and approaches that can be applied not only to standard echo-planar mri-sequences, but also to ultra fast sequences are shown. Such methods make it possible for researchers to reveal only the actual human brain activity and more accurately talk about zones of functionality for certain cognitive tasks.
#100 Amit Kumar Mishra A DIKW Architecture for Cognitive Engineering Keywords: Cognitive Computing, Machine Learning, Cognitive Architecture, Bio-inspired Though the word \cognitive” has a wide range of meanings we define cognitive engineering as learning from brain to bolster
engineering solutions. However, giving an achievable framework to the
process towards this has been a dificult task. In this work we take the
classic data-information-knowledge-wisdom (DIKW) framework to set
some achievable goals and sub-goals towards cognitive engineering. A
layered framework like DIKW aligns nicely with the layered structure
of prefrontal cortex. And breaking the task into sub-tasks based on the
layers also makes it easier to start developmental endeavors towards
achieving the final goal of a brain-inspired system.
#101 Talfan Evans Probabilistic integration of sensory and path integration estimates in grid cells Keywords: Grid cell, Hipppcampus, Navigation, Place cell Grid cells found in the mammalian medial entorhinal cortex (mEC) are neurons with spatially modulated receptive fields that tile 2D space in a hexagonal pattern. Together with place cells, head direction cells and boundary vector cells, grid cells are thought to constitute a neural system for the encoding and navigation of space.
The spacing between successive grid fields increases in discrete steps along the dorso-ventral axis of mEC. This property, along with the periodic nature of their firing patterns, suggests that grid cells may provide a high capacity encoding of space. Moreover, grid cells are thought to update their firing patterns primarily by path integration (PI), the process of integrating self-motion in order to maintain a continuous estimate of location. However, navigation by path integration alone is impractical since noise is integrated over time, leading to drift away from the correct location. Accordingly, the stability of grid firing patterns are known to degrade slowly in darkness, but remain in the presence of sensory cues, suggesting that these serve to stabilise location estimates arising from path integration.
However, the grid firing pattern can also deviate from its otherwise uniform hexagonal arrangement, becoming more elliptical in large square environments, deforming in trapezoidal environments, and adopting a less periodic arrangement on a 1D track. Grid firing patterns also rescale parametrically in response to the contraction or expansion of a familiar environment and reconfigure into globally consistent firing patterns when the animal is allowed to navigate between two previously unconnected environments.
Existing models of grid cells use sensory input to reset the grid firing pattern, rather than probabilistically integrating this input with the PI estimate. Here, we propose that probabilistic integration combined with plasticity between the grid cells and their sensory inputs can account for many of the deformations described above. We also show that integration of these two cues can produce stable grid patterns where both signals are uncertain when considered independently. These results highlight the role of sensory inputs in shaping our perception if space and challenge the assumption that grid cells provide a universal metric of space.
#102 Ronakben Bhavsar, Yi Sun, Na Helian, Neil Davey, David Mayor and Tony Steffert The Correlation between EEG Signals as Measured in Different Positions on Scalp Varying with Distance Keywords: EEG, Biomedical signal processing, Time Series Data Analysis, Cross-Correlation. Biomedical signals such as electroencephalogram (EEG) are the time varying signal, and different position of electrodes give different time varying signals. There might be a correlation between these signals. It is likely that the correlation is related to the actual position of electrodes. In this paper, we show that correlation is related to the physical distance between electrodes as measured. This finding is independent of participants and brain hemisphere. Our results indicate that the EEG signal is not transmitted via neurons but through white matter in a brain.
#103 Ricardo Gudwin A Hands-on Laboratory Tutorial on Using CST to build a Cognitive Architecture Keywords: tutorial, cognitive architecture, CST, Virtual Environment In this tutorial laboratory, we provide a step-by-step set of programming experiments illustrating the main foundations of the CST Cognitive Systems Toolkit in building a cognitive architecture to work as an artificial mind for controlling an NPC (non-player character) in a 3D virtual environment computer game. We start by understanding the sensors and actuators available in the NPC and how to control it inside the game. Then, we introduce the main foundations of CST: Codelets and Memories, and how they should be used to integrate a cognitive architecture. Then, we start building specific codelets and memories for a simple instance of the CST Reference Cognitive Architcture and start using it to control the NPC. The lab is a hand-on programming lab, using Java and Netbeans as language/tool. The attendant should be proficient in programming in Java language in order to follow the tutorial. The headlines of the tutorial can be seen at: http://cst.fee.unicamp.br/tutorials
#104 Irina Karpova About realization of aggressive behavior model in group robotics Keywords: group robotics, social behavior models, aggressive behavior, territory defense task One of the actively developing approaches of group robotics systems creation is the use of social behavior models. Aggressive behavior is one of the underlying mechanisms forming social behavior. In this paper, the application of aggressive behavior concepts is considered by analogy with animal aggressive behavior that can be used for solving tasks of group robotics. As a role model, an ant ‘ a true social insect ‘ is proposed. It was shown that in aggressive behavior of ants, the numerical factor and imitative behavior play an important role. Agent’s aggressive behavior model depending on accumulated aggression and the number of other nearby agents is proposed. The results of computer experiments for territory defense tasks are presented. The results show that aggression is a stabilizing factor for an approximately equal number of agents in different groups. By an increase in group size, aggression becomes a way of capturing foreign territory.
#105 Mario A. Zarco-L’pez and Tom Froese Self-modeling in Hopfield Neural Networks with Continuous Activation Function Keywords: Self-modeling, Hopfield neural network, Hebbian learning, continuous activation function Hopfield neural network can exhibit many different attractors of which most are local optima. It has been demonstrated that combining states randomization and Hebbian learning enlarges the basin of attraction of globally optimal attractors. However, we are interested in knowing which topological constraints can be relaxed. We apply the self-modeling approach to Hopfield network with continuous states with asymmetric and self-recurrence connections, since in biological networks is unlikely to find symmetricy and not self-recurrency. The best results were obtained in structured problems, those whose connections are not setting in a completely random way.
#106 Oleg Nikitin and Olga Lukyanova Control of an agent in the multi-goal environment with homeostasis-based neural network Keywords: cellular homeostasis, neuronal plasticity, adaptive behavior Here we present the model of bio-inspired neuron, and synaptic plasticity, incorporating cellular homeostasis. Network of such neurons is used for multi-goal oriented control task. It was showed that such a model provides adaptive and robust behavior for the controlled agent.
#107 Valeria Seidita and Antonio Chella Representing Social Intelligence: an Agent-Based Modeling Application Keywords: agent-based modeling, social phenomena, smart city, NetLogo Intelligent systems are composed of autonomous components that interact each others, with and through the environment in order to give intelligent support for reaching specific objectives. In such kind of systems the environment is an active part of the system itself and provide input for runtime changing and adaptation. Modeling and representing systems like this is a hard task. In this paper we propose a biologically inspired approach that combined with the use of Agent-Based Modeling allows to create a means for analyzing emergent needs of the system at runtime and convert them into useful intelligent services to be provided. The experiment proposed for validating and illustrating the approach refers to the construction of smart university campus.
#108 Olga Sarmanova, Sergey Burikov, Sergey Dolenko, Eva von Haartman, Didem Sen Karaman, Igor Isaev, Kirill Laptinskiy, Jessica Rosenholm and Tatiana Dolenko Neural network classification method for solution of the problem of monitoring the removal of the theranostics nanocomposites from an organism Keywords: Artificial Neural Network, Inverse Problem, Fluorescent Spectroscopy, Carbon Nanocomposite, Drug Carrier In this study artificial neural networks were used for elaboration of the new method of monitoring of excreted nanocomposites-drug carriers and their components in human urine by their fluorescence spectra. The problem of classification of nanocomposites consisting of fluorescence carbon dots covered by copolymers and ligands of folic acid in urine was solved. A set of different architectures of neural networks and 4 alternative procedures of the selection of significant input features: by cross-correlation, cross-entropy, standard deviation and by analysis of weights of a neural network were used. The best solution of the problem of classification of nanocomposites and their components in urine provides the perceptron with 8 neurons in a single hidden layer, trained on a set of significant input features selected using cross-correlation.The percentage of correct recognition averaged over all five classes, is 72.3%.
#109 Yury Prostov and Yury Tiumentsev Functional Plasticity in a Recurrent Neurodynamic Model: from Gradual to Trigger Behavior Keywords: neurodynamic model, hysteresis, adaptiveness, competition Dynamic model of a recurrent neuron with sigmoidal activation function is considered. It is shown that the neuron activation characteristic (dependence between an input pattern and an output signal) can have the form of both a smooth sigmoidal function and a step function in the form of a quasi-rectangular hysteresis loop due to the presence of the modulation parameter. We demonstrate that a gradual behaviour in the network can be implemented which means that neurons will have output values proportional to how each of them corresponds to the input pattern. In this case signals in the network will be propagated freely but with attenuation. The output values of the neurons also will be low. At the same time a winner-takes-all behaviour can be implemented which means that neurons becomes similar to threshold units. As a result, there will be limited network activity since only a part of the neurons will have output values close to the maximum value while the remaining neurons will have output values close to the minimum value. Thus the presence of the modulation parameter provides a means to change quickly the behavior strategy of the network which affects the processes of pattern recognition and learning.
#110 Vitaly Vorobiev Inference algorithm for teams of robots using local interaction Keywords: inference, robot swarm, static swarm, local interaction One of the possible approaches to the technical implementation of logical inference in robot groups is considered in paper. The problem is that the usual implementations of the inference mechanism, for example, which are used in expert systems, are difficult to implement to robots that work in a team. It is due to the fragmented knowledge of each robot about the environment where they perform the tasks assigned, the need to exchange data during the inference and monitor this process, etc. In addition, the presence of an inference subsystem may be necessary for emergence of emergent properties in a group of robots. The output subsystem can be used to solve a variety of tasks, for example, choosing the most preferred strategy for the whole collective, building a general picture of the world, planning, etc. In this regard, the paper presents some mechanisms that allow the inference in the logic of predicates of the first order for a team of robots whose interaction with each other is exclusively local in nature. The inference procedure is carried out in the team of robots, which form a special structure, called a static swarm.
#111 Paul Robertson and Robert Laddaga Context-driven Active-sensing for Repair Tasks Keywords: Mixed Initiative, Active Perception, Story Understanding, Planning, Computer Vision, Robotic The CART project aims to enable robotic helpers for repair mission the use models of repair missions, to guide active perception in supporting the understanding of the steps being taken by a human operator and to offer assistance in a timely manner based on story understanding.
#112 Ryutaro Ichise A Cognitive Architecture Consisting of Human Intelligence Factors Keywords: Cognitive Architecture, CHC model, Intelligence Factor, Dialogue While there are many types of cognitive architectures available today, one
thing common to all of them is the need to cover the maximum number of the
human intelligence factors used for solving various tasks. However, the
currently existing cognitive architectures were developed based on a variety
of aspects other than the human intelligence factors. One famous model of
human intelligence factors is the CHC model which is studied in psychology.
When it is used as the basis of a new cognitive architecture,
the architecture will cover all the known human intelligent factors used for
solving various tasks.
In this paper, we propose a new cognitive architecture for dialogue
situations based on the CHC model as the first step towards
the formation of a comprehensive cognitive architecture.
We will also outline the initial architecture using a case study.
#113 Basit Shahzad, Sajida Fayyaz and Ikram Lali Social Media’s Impact on Student’s Cognitive Learning and Academic Performance: A Gender Based Comparison Keywords: Academic performance, Social Networking, Impact of Social Networks, Cognitive learning Social media is a famous and common way of communication in the university students all over the world. Excessive use of social media can raise many questions like whether the use of social media affects the cognitive learning and academic performance or not. Our research explores by conducting a survey among students and find which social network is the most popular amongst students and all the factors besides social media which negatively affect the academic performance. The correlation between social media and student’s grades will decides its polarity. In order to collect the basic data, survey technique was used to calculate impact of social media on student’s grades. The participants chosen were almost 200 from government and private sector. The findings highlight that social media is used for information sharing, communication, entertainment, learning and news. This study explores that students who use social media have more awareness for protecting their social identities. All the social media in which students have more interest becomes their source of knowledge. Furthermore, findings indicate that social media has mainly no impact on student’s grades but their interest on social media have direct relation with their learning.
#114 Amit Kumar Mishra ICABiDAS: Intuition Centred Architecture for Big Data Analysis and Synthesis Keywords: cognitive architecture, bio-inspired, intuition, synthesis, Big-Data Humans are expert in the amount of sensory data they deal with each
moment. Human brain not only ‘analyses’ these data but also starts ‘synthesizing’ new information from the existing data. The current age Big-data systems
are needed not just to analyse data but also to come up new interpretation. We
believe that the pivotal ability in human brain which enables us to do this is what is known as ‘intuition’. Here, we present an intuition based architecture for big data analysis and synthesis.
#115 Andrey Starikovskiy, Leonid Panfilov and Ilya Chugunkov Security Module Protecting the Privacy of Mobile Communication Keywords: Security module, ensuring the confidentiality of mobile communication, secure communication system This article describes a communication system that protects user data against theft or damage. In addition to any standard communications system elements the described communication system includes a security module. The security module is installed on the data transmission bus between the processor and communications. The security module includes a processing unit, capable of handling data according to a particular algorithm: encryption, masking or other. It eliminates the possibility of the transmission of any data to communication modules due to undocumented features of the processor, or any other module of the mobile device. The operating system and the security module only know the processing algorithm. Consequently, no information, other than that sent to the operating system leaves the device. The security module provides security and confidentiality of data; it does not require the production of a special security processor or any other units and can be implemented on the element base of leading world manufacturers.
#116 Anastasia Korosteleva, Olga Mishulina, Vadim Ushakov and Olga Skripko Informative characteristics of brain activity to diagnose functional disorders in people with stuttering Keywords: brain activity features, stuttering people, functional MRI The article presents the results of an experimental study of functional disorders of brain activity in people with stuttering. The experiment was carried out using functional magnetic resonance imaging. The purpose of this study was to identify the characteristics of brain activity in people who stutter and the formation of numerical indicators of the available functional disorders. The results of the comparative analysis of brain activity in the areas of Broca and Wernicke for two participants with stuttering and normal speech.
#117 Anastasia Korosteleva, Vadim Ushakov, Denis Malakhov and Boris Velichkovsky Event-related fMRI analysis based on the eye tracking and the use of ultrafast sequences Keywords: fixation-based event related fMRI, eye movements, natural viewing behavior, haemodynamic response functions, ultrafast fMRI The purpose of the study was to investigate the relationship between human cognitive processes and eye movements during inspection of images using methods of ultrafast functional magnetic resonance imaging (fMRI) and eye tracking. We conducted two series of experiments in which participants saw pictures of faces and houses. Statistical processing of the fMRI data showed that visual fixations on different objects in the context of different tasks lead to different patterns of cortical activation, and reconstructed BOLD signal re-sponses show important information about the task context of individual fixations on viewed objects.
#118 Olga Mishulina, Olga Skripko and Anastasia Korosteleva Some features of eye movements during reading and retelling the text by people with stuttering Keywords: eye movement features, stuttering people, eye tracker The connection between cognitive processes and the movement of the human eye during the reading and retelling of the text is investigated. A series of experiments were performed, in which people with normal speech, people with stuttering and in the treatment stage of stuttering took part. The results of the experiment were fixed by the eye tracker and the functional magnetic resonance tomograph. The statistical processing of the tracking data was performed, which discovered stable differences of fixation duration in groups of participants when performing test tasks.
#119 Kazuteru Miyazaki Exploitation-Oriented Learning with Deep Learning -Comparison with a Deep Q-network- Keywords: reinforcement learning, deep learning, deep reinforcement learning, profit sharing, Q-learning, exploitation-oriented learning, atari 2600 games Deep learning has attracted significant interest currently. The deep Q-network (DQN) combined with Q-learning have demonstrated excellent results for several Atari 2600 games. In this paper, we propose an exploitation-oriented learning (XoL) method that incorporates deep learning to reduce the number of trial-and-error searches. We focus on a profit sharing (PS) method that is an XoL method and combine a DQN and PS. The proposed method DQNwithPS is compared to a DQN in Pong of Atari 2600 games. We demonstrate that the proposed DQNwithPS method can learn stably with fewer trial-and-error searches than only using a DQN.
#120 Aaron Sloman Deep, largely unnoticed, gaps in current AI, and what Alan Turing might have done about them. Keywords: Alan Turing, Meta-morphogenesis, Evolved information processing mechanisms, Mathematical reasoning, Euclid, Emotions, Consciousness, Disembodied cognition Gaps at present include the inability of current AI systems to make discoveries made by Euclid, Archimedesw1 and other ancient mathematicians, including discoveries in geometry and topology, long before the development of modern logic, algebra, formal logic, and proof theory. The information-processing abilities required seem to be closely related to the abilities of pre-verbal human toddlers to make proto-mathematical discoveries including topological discoveries, and also forms of intelligence in nest-building birds, squirrels, elephants, orangutans and other species. Current AI vision systems cannot support the uses of vision in discovery of deep mathematical features of geometry and topology, including discovery of impossibilities and necessary connections (related to but different from perception of positive and negative action-affordances). They also lack the meta-cognitive,
reflective abilities required to organise, communicate and defend such discoveries if challenged — precursors to mathematical proof. Current AI language *learning* mechanisms cannot match the language *creating* mechanisms used by young humans, demonstrated dramatically by deaf children in Nicaragua. (See https://www.youtube.com/watch’v=pjtioIFuNf8) Current AI models of emotion and motivation support only shallow mimicry of affective states: they lack the depth and variety of biological mechanisms involved in passionate interest in mathematics, long term grief, deep patriotism, finding something hilariously funny, and many other short and long term states and processes relating to things cared about. The CogAff project attempts to address some of these issues. (http://www.cs.bham.ac.uk/research/projects/cogaff/) This is very different from, and much more difficult than, producing machines with shallow mimicry of human responses. Moreover, emphasis on “embodied cognition”, “enactivism”, and “situated cognition”, focuses on real but shallow products of evolution, ignoring requirements for increasingly *disembodied* forms of cognition to meet increasingly complex and varied challenges in sophisticated organisms inhabiting complex, extended, multi-faceted terrain. The emphasis on embodiment also ignores requirements to apply meta-cognitive
processes to oneself and to others, and abilities to invent, implement, test, debug, and modify novel and increasingly complex engineering solutions to practical problems. Designers of ancient pyramids could not plan a new creation by physically interacting with the materials tools, labourers and temporary structures used during construction. It is likely that the vast majority of important evolutionary transitions in information processing, and the products in animal brains, have not yet been discovered, and that some of them cannot be detected by current scanning mechanisms (which don’t reveal subneural processes, e.g. the chemistry of a synapse). Inspired by a conjecture about what Turing might have worked on if he had not died two years after publishing his paper on morphogenesis, the tutorial will present the conjectured roles of both the fundamental construction kit provided
by physics and chemistry and multiple derived construction kits produced by evolution, often straddling different species. Without the later construction kits, current species could not exist. Some of the construction-kits are required mainly for physical (physiological) structures. Others are required for construction of new forms of information processing. There may be important examples that human scientists have not yet (re-)discovered.
#121 Sergey Kartashov, Vadim Ushakov, Alexandra Maslennikova, Alexander Sboev, Anton Selivanov, Ivan Moloshnikov and Boris Velichkovsky Human Brain Structural Organization in Healthy Volunteers and Patients With Schizophrenia Keywords: diffusion, dMRI, structural connections, rich-club, graph theory The purpose of this work was to study and to compare the structural features of the human brain in two groups of people: healthy volunteers and patients with schizophrenia. According to the data of diffusion magnetic resonance imaging (dMRT), tractography pathways that describe the direction of fibers growth of the white matter of the human brain were reconstructed. Analysis of these paths made it possible to construct maps of the connectivity of all sections of the prepared brain to each other for each subject. With the help of graph theory, so-called rich-club areas were found for each of two groups, that, according to many papers, are the key centers of the brain in the transmission and exchange of information between all areas of the human brain
#122 Dmitriy Sanatov Artificial intelligence in platform data-driven approach for the state and corporate management systems Keywords: searching russian agenda in artificial intelligence, cognitive architecture, big challenge Nowadays we can observe a widespread platform approach which changes market models and society behavior. Government should respond to this trend. Several countries started huge number of projects and programs – from cloud infrastructure to electronic services. But all of them have a very low efficiency and effectiveness. A lot of data about society and business has unstructured format and needed new approaches for their understanding and analysis. There is a request for bridging the gap between this problem and new nature of describing and analysis, including cognitive architectures and artificial intelligence, which can provide technology basis for solving special social problem and can help open new markets.
#123 Ahmad Albatsha and Michael Ivanov Stochastic Data Transformation Boxes for Information Security Applications Keywords: Stochastic Transformation, R-box, Random Feedback Shift Register (RFSR), Non-linear M-sequence Stochastic methods are commonly referred to as methods which are directly or indirectly based on using a pseudo-random number generator (PRNG). In some cases, stochastic methods are the only possible mechanism of protecting information from an active adversary. In this paper we examine a construction of R-boxes, which are a generalization of S-boxes, classical structural elements of cryptographic primitives of hashing, block and stream encryption. R-boxes are in fact stochastic adders, i.e. adders with an unpredictable operating result, which depends on the key table H. A distinguishing feature of R-boxes is their efficient software and hardware implementation.
#124 Olivier Georgeon Artificial lifelong incremental learning: a gentle start Keywords: Developmental learning, open-ended learning, constructivist learning, intrinsic motivation The challenge of designing robots that can learn incremental sensorimotor skills from their autonomous experience interacting with the world throughout their life has gained increasing interest recently. For example, the European commission just funded the Goal-Robot project to address this challenge. Cognitive theories suggest that such incremental sensorimotor learning could pave the way to higher-level intelligence. However, we are still missing a unified theory of developmental learning to solve this challenge. Indeed, current research on artificial developmental learning mostly focuses on specific developmental steps instead of lifelong developmental principles. I will present a new hierarchical sequence learning algorithm and show how it can help make progress addressing this challenge. The main principle learned is to refer to the robot’s sensor data as feedback from the robot’s actions as opposed to passive perception of a pre-given world.
#125 Allen King Building a Proto Brain with the HaveNWant Schemata Keywords: Computational Locality, Micro-Bidirectionality, Animal Learning, Common Cortical Algorithm, Learning by Observation, Deep Learning, Episodic Learning, Factals, Toy Domains, Reification A Proto-Brain is a network of 1-bit HaveNWant (HNW) elements. It learns models of the world experientially and uses them to plan its actions. It’s ‘outer loop” performs the distributed and hierarchical tasks of world modeling, marking situations with unique factals, processing unknowns to produce incremental growth, grabbing attention, and issuing action contexts. Modules used in the Photo-Brain include the: 1) REENACTMENT SIMULATOR, which learns models of its world in small domains experientially and can also be taken offline to plan (aka think) an act. It uses a variant of Q-Learning. Reflexively, it goes to the good spots and avoids the bad ones. 2) UNIQUIFICATION, which maintains a Unique ID factal to detect and evoke common situations. 3) MAZLOVIAN MULTIPLEXOR, which determines the most important model in the simulator. It gains the attention of the Small Fast Learner. 4) LANGUAGE, which connects the various settings of the Reenactment Simulator to sequences of symbols such as words. 5) DYNAMIC LINKS which link sensations to the models that explain them. They also play a critical role in language processing and verb snippets. Individual HNW Networks can compute algorithms like: a) finding the shortest path through constraints; b) finding the largest bidder and selecting only it; c) performing a sequence of actions; d) building distributed associative memories; e) learning sequences (e.g: Morse Code) and then replay them; f) performing distributed Bayesian probability calculations, g) building a distributed Markov state machine; h) performing “Winner Take All’ or Most; and i) emulating Soar rules.
#126 Artemy Kotov, Nikita Arinkin, Alexander Filatov, Liudmila Zaidelman and Anna Zinina Semantic Comprehension System for F-2 Emotional Robot Keywords: Natural Language Comprehension, Syntactic Parser, Text Analysis Within the project of F-2 personal robot we design a system for automatic text comprehension (parser). It enables the robot to choose ‘relevant’ emotional reactions (output speech and gestures) to an incoming text ‘ currently in Russian. The system executes morphological and syntactic analysis of the text and further constructs its semantic representation. This is a shallow representation where a set of semantic markers (lexical semantics) is distributed between a set of semantic roles ‘ structure of the situation (fact). This representation may be used as (a) fact description ‘ to search for facts with a given structure and (b) basis to invoke emotional reactions (gestures, facial expressions and utterances) to be performed by the personal robot within a dialogue. We argue that the execution of a relevant emotional reaction can be considered as a characteristic of text comprehension by computer systems.
#127 Alexei V. Samsonovich A Continuous-Attractor Model of Flip Cell Phenomena Keywords: attractor neural networks, continuous attractor, navigation, head direction cells, animal cognition This paper is devoted to the problem of understanding mechanisms underlying behavioral correlates of head direction (HD) cells in the mammalian retrosplenial cortex. HD cells become active when an animal, such as rat, is facing a particular direction in its environment. The robustness of this phenomenon is usually attributed to attractor dynamics of the HD cell system. According to the standard view, a ring attractor exists in some abstract space, with HD cells symbolically allocated on the ring, so that any natural state of the system corresponds to a bump of activity on the ring. In apparent contradiction with this standard model are recent discoveries of so-called ‘flip cells’, that constitute a minority of HD cells and can either rotate their directional tuning by 180 degrees when an animal transitions between two environments, or interpolate between discordant cues, or demonstrate a bimodal tuning curve. Here a continuous attractor network model is described that is capable of a qualitative reproduction of these phenomena, while being consistent with the ring attractor hypothesis. The model assumes that there is more than one attractor ring in the HD system. Results of the concept-proof simulation suggest a correction to the standard view of how the internal sense of direction is formed in the rat brain.
#128 Pavel A. Bortnikov and Alexei V. Samsonovich A Simple Virtual Actor Model Supporting Believable Character Reasoning in Virtual Environments Keywords: narrative intelligence, social-emotional intelligence, cognitive architectures, believable virtual characters, BICA Challenge An artifact needs to possess a human-level social-emotional intelligence in order to be accepted as a team member and to be productive in the team. A general theoretical model describing this kind of artificial intelligence is currently missing. This work makes one step toward its development, using a simplistic virtual environment paradigm and an emotional biologically inspired cognitive architecture as the basis. We describe a Virtual Actor model supporting believable character reasoning. The model was implemented and tested in a pilot experiment in a virtual environment involving human participants. Preliminary results indicate that a Virtual Actor of this sort can be believable and socially acceptable in a small heterogeneous group.
#129 Alexandr Petukhov and Sophia Polevaya Dynamics of Information Images in the Mind of an Individual during Simultaneous Interpretation Keywords: Communicative field, information image, the information images space, virtual particles. This article reviews the dynamics of information images in the mind of an individual during the simultaneous interpretation from foreign language into Russian, as well as during a number of additional professional tasks (shadowing).
From the experimental point of view, using the technology of event-related telemetry (ERT) of heart rate, we investigated the features of mobilization of vegetative resources for a record-breaking activity in terms of energy efficiency and stressogenic load – simultaneous interpretation (SI).
The results have been analyzed from the point of view of the Information Images Theory. The main provisions of this theory are given along with overview of the hierarchy of information images in the mind of an individual, which determines his real and virtual activity.
This paper provides a model of the dynamics of information images in the mind of individuals during simultaneous interpretation.
#130 Elizaveta Mikhina and Vsevolod Trifalenkov Text clustering as graph community detection Keywords: text clustering, non-parameter clustering, graph community detection, modularity This article suggests a method of text clustering that does not depend on any user-set parameters. Text documents and connections between them are represented as graph nodes and edges and graph community detection method is thus applied to the text clustering problem. The method was tested against news articles collections and proved effective ‘ manual and automatic clustering of text documents in collections were same or really close.
#131 Larisa Ismailova, Sergey Kosikov, Vicheslav Wolfengagen, Anatoliy Zaytsev and Irina Aleksandrova Semantic Filtering of Exemplar Queries Keywords: information system, semantic network, semantic filtering, informational graph, exemplar query, computational model The paper considers an approach to solving the problem of supporting
the similarity mechanisms of conceptual dependencies for semantic
filtering of exemplar queries. The solution is based on the
informational graphs with their structure and semantical labels. The
presence of variational similarity criteria makes it possible to
carry out semantic filtering of the retrieval of a response. The
set of criteria is given. The criteria can be extended for treating
the similarity of frame representing graphs.
#132 Aleksander Smirnov Testing the capsular endoscopic complex Landish Keywords: capsular endoscopic complex, software testing, hardware testing This paper deals with the tests of software and hardware for the ‘Landish’ capsular endoscopic complex (‘E’). The research is conducted to justify the expediency of manufacturing of this product and its use for the examination of the digestive tract.
#133 Valentina Anikushina and Victor Taratukhin Natural Language Oral Communication in Humans under Stress. Linguistic Cognitive Coping Strategies for Enrichment of Artificial Intelligence Keywords: cognitive linguistic, natural language, AI theory Learning machine understand natural human speech is significantly easier and more effective when understood the cognitive processes in humans. Psycholinguistics, as an interdisciplinary study of language and cognition, is extremely helpful in this respect.
The aim of this study is to understand what linguistic coping strategies people develop under stress due to their physiological and psychological structure. An oral communication in a foreign language, English, of people with different backgrounds is being investigated under an isolation stressor.
It is expected that the physiological and psychological features, adrenaline and noradrenaline value indications and anxiety tendency, influence the linguistic performance in a stressful situation. Subjects who share similar organization of the central nervous system and psychological structure, may also indicate similarities in cognition regarding the language production.
The value of the study for artificial intelligence research is in its applicability on natural language systems, e.g. by helping program a more precise speech recognizing system.
#134 Dharani Punithan and Byoung-Tak Zhang Molecular Associative Memory with Spatial Auto-logistic Model for Pattern Recall Keywords: auto-logistic models, second-order neighborhood, pair-wise cliques, hybridization, mutation, associative memory, pattern recall We propose a molecular associative memory model, by combining auto-logistic specifications which capture statistical dependencies within the local neighborhood systems of the exposed knowledge, with the bio-inspired DNA-based molecular operations which store and evolve the memory. Our model, characterized by only the local dependencies of the spatial binary data, allows to capture only a fewer features. Our memory model stores the exposed patterns and recalls the stored patterns through bio-inspired molecular operations. Our molecular simulation exemplifies the applications of associative memories in pattern storage and retrieval with high recall accuracy, even with lower order memory traces (pair-wise cliques) and thus exhibits brain-like content-addressing cognitive abilities.
#135 Sergey Yarushev and Alexey Averkin Time Series Analysis Based on Modular Architectures of Neural Networks Keywords: Modular neural networks, Time series analysis, Forecasting, Artificial intelligence, SOM, Data mining, Deep learning Paper presents a Modular Approach for Time series analysis area. We consider the most important characteristics of modular architectures of neural networks and their advantages under traditional monolithic neural networks. The main idea of this paper is take answer – why modular neural networks have so high performance in many tasks. Also we present few examples of modular approaches which can be applied for time series analysis problem.
#136 David Kelley and Mark Waser Human-like Emotional Responses in a Simplified Independent Core Observer Model System Keywords: emotion, motivational system, safe AI Most artificial general intelligence (AGI) system developers have been focused upon intelligence (the ability to achieve goals, perform tasks or solve problems) rather than motivation (*why* the system does what it does). As a result, most AGIs have an unhuman-like, and arguably dangerous, top-down hierarchical goal structure as the sole driver of their choices and actions. On the other hand, the independent core observer model (ICOM) was specifically designed to have a human-like ’emotional’ motivational system. We report here on the most recent versions of and experiments upon our latest ICOM-based systems. We have moved from a partial implementation of the abstruse and overly complex Wilcox model of emotions to a more complete implementation of the simpler Plutchik model. We have seen responses that, at first glance, were surprising and seem-ingly illogical ‘ but which mirror human responses and which make total sense when considered more fully in the context of surviving in the real world. For ex-ample, in ‘isolation studies’, we find that any input, even pain, is preferred over having no input at all. We believe that the fact that the system generates such un-expected but ‘humanlike’ behavior to be a very good sign that we are successful-ly capturing the essence of the only known operational motivational system.
#137 Victor Taratukhin and Yulia Yadgarova Towards a socio-inspired multi-agent approach for new generation of product life cycle management Keywords: Industry 4.0., future product life cycle management, intelligent design and manufacturing, socio-inspired methods, multi-agent framework, M2M communication, distributed production control The main goal of this paper is to describe new integrated methodology: Agent-based Reconfigurable Generic Organization. This is an integrated approach which allows designing future product life cycle organizations as a class of learning, self learning or adaptive distributed systems using high-level, sign-based communications across entire product lifecycle environment named Product Related Semiosphere (PRS). The definition of PRS is a fundamental question of developing a new class of dstributed semiotic systems with ability to communicate, identify and manufacture engineering artifacts with prescriptive characteristics.
The paper analyses different types of product life cycle management approaches and suggests overall approach for integration of business and engineering knowledge during the whole product life-cycles. It allows to understand the interrelations of different life-cycle stages for acquiring and manipulating concurrent engineering knowledge. The authors proposed the idea of using socio-inspired framework based on applied semiotics and distributed artificial intelligence.
#138 Irina Knyazeva, Vadim Ushakov, Vyacheslav Orlov, Nikolay Makarenko, Boris Velichkovsky, Alexey Poyda, Vitaliy Verkhlyutov and Kozlov Stanislav Resting state dynamic functional connectivity: network topology analysis Keywords: fMRI, dynamics, functional, connectivity, resting state, complex networks, algebraic topology The construction of biologically inspired cognitive architectures is based on data obtained by studying the mechanisms of the brain’s functional networks operations, the causality of their integration and differentiation into the neurophysiological architectures of the cognitive processes of consciousness. One of the key networks involved in maintaining the basic level of consciousness at the resting state is the default mode network (DMN). The network activation increases with perceived mental states as imagination, internal dialogue, and others and reduces with an external stimulation or behavioral task. A complete loss of consciousness is characterized by the synchronization of DMN with the anticorrelated with DMN network. When the level of consciousness changes in the processes of cognitive activity, there is a complex picture of the combination of positive and negative connections between different networks and regions of the brain. At the same time, the changes in the intrinsic brain organization during the cognitive process and the resting state still open question. This work was aimed at studying the dynamics of the architecture of different brain networks interactions at the resting state, including executive and attention networks, cerebellum, DMN, visual network, auditory network, brainstem, the somatosensory and motor networks, sub cortical network. Three algorithms were used for clustering states in neural network connectivity dynamics: direct clustering of functional network using k-means algorithm, modularity-based clustering, topological based clustering . The obtained results showed that in the dynamics of functional neural network connectors there are three expressed states, determined by different types of interactions between DMN networks, attention and other neural networks.
#139 Eugene Borovikov and Szilard Vajda Looking at faces in the wild Keywords: face detection, face matching, artificial neural network, single image per person Recent advances in the face detection (FD) and recognition (FR) technology make it appear that the problem of face image localization and matching is essentially solved, e.g. via deep learning models using thousands of samples per face for training and validation on the available benchmark data-sets. Human vision system seems to handle face localization and matching problem differently from the modern FR systems, as humans appear to detect faces instantly even in most cluttered environments, which may be evolutionary hard-wired and experience-tuned, and often require a single view of a face to reliably distinguish it from all others. This prompted us to take a biologically inspired look at building a cognitive architecture that extensively uses artificial neural nets at the face detection stage and adapts a single image per person (SIPP) approach for face image matching.
#140 Vladimir Spiridonov Once again about insight Keywords: Problem solving, insight, problem representation, transfer Beginning with the works of gestalt psychologists (K’hler, 1921; Wertheimer, 1959) the existence and the role of insight ‘ the key moment in problem solving, associated with an abrupt reorganization of a problem representation, which leads to its solution and is often accompanied by vivid emotional experiences ‘ was questioned. For all its recognizability (for example, most solvers confidently report that they experienced it when solving certain problems), the existence of insight is by no means universally recognized. Substantiated doubts in the reality of this phenomenon appeared almost immediately after the quoted work of V. Kohler. Thus, already in 1949, D. Hebb described acute long-standing disputes between those who were confident in the existence of insight (configurationists), and those who denied the necessity of attracting this concept (learning theorists) (Hebb, 1949). The situation changed drastically after the introduction of the problem space theory by A. Newell and H. Simon (Newell, Simon, 1972) who proposed a step-by-step approach to the goal state with associated gradual local changes of a problem’s representation instead of a single-step solution discovery.
This report is intended to show the ambiguity of the concept of insight and to characterize the present state of its theoretical and experimental research.
#141 Igor Isaev and Sergey Dolenko Training with Noise Addition in Neural Network Solution of Inverse Problems: Procedures for Selection of the Optimal Network Keywords: artificial neural networks, perceptron, noise resilience, inverse problems Addition of noise to the patterns presented to a neural network during its training is a method to increase noise resilience of the trained neural network. However, the effect depends on the level of noise added. This article reports the first results of the study on elaboration of a procedure to select the optimal network or network subset for a given out-of-sample pattern from a set of networks trained with various noise levels, at the example of a model inverse problem.
#142 Alexander Efitorov, Irina Knyazeva, Yulia Boytsova and Sergey Danko GPU-based high-performance computing of multichannel EEG phase wavelet synchronization Keywords: Graphics processing unit(GPU), wavelet phase coherence, EEG analysis, analysis of non-stationary signals The work is devoted to GPU-based high performance realization of algorithm for wavelet phase synchronization. Wavelet phase coherence was applied for analyzing brain activity in states with different degrees of mental and sensory attention. In the analysis of electroencephalographic correlates of mental states, as a rule the focus is on the analysis of the spectral power of a quasi-stationary EEG or task-related power of time-frequency EEG spectra. The analysis of the wavelet phase coherence provides additional information on the organization of brain activity, but time consuming in estimation. Fast implementation can simplify the use of this
method in practice.
#143 Andrey Starikovskiy, Arseniy Zhgilev and Nadezhda Shevchenko Text Messages Protection System Keywords: protection of text messages, message encoding, key length, unauthorized access, security threats, intruder model This paper deals with the development of text messages protection from unauthorized access and malicious software. The structure of an attacker model and main security threats are provided. The article tells about the requirements for protective systems of this kind, examine the main information security threats. The tools of system development such as protecting messages using the RSA algorithm, using ELGamal algorithm and using an algorithm based on elliptic curves are described. The performance results and effectiveness of the proposed ideas are provided. The implementation can be performed directly on mobile subscribers in the form of a software product, or as additional functional software of a virtual operator. The proposed protection system can significantly enhance security of mobile communication.
#144 Peter Boltuc Workshop: THE ENGINEERING THESIS IN MACHINE CONSCIOUSNESS. Keywords: Machine Consciousness, Non-Reductive Machine Consciousness, Discovery Machines, Dreaming Machines, Non-Reductive Physicalism, Sex Robots, Church-Turing Lovers WHAT SORT OF CONSCIOUSNESS MACHINES CAN HAVE’ This is an application of non-reductive materialism to AI.
I will present new material how to recognize machine consciousness (the epistemic problem) based on the work on analysis of sex robots in my paper “Church-Turing Lovers” (OUP, upcoming in an anthology by Lin et al., 2017) and on quantum effects (based on recent work by Sky Darmos and others); creativity aspect extended based on recent research by Stephen Thaler and Troy Kelley. Early project presented in: (2012) “The Engineering Thesis in Machine Consciousness” Techne: Research in Philosophy and Technology 16(2), 187-207 and (2009) “The Philosophical Issue in Machine Consciousness,” International Journal of Machine Consciousness 1(1), 155-176 PDFs available at: https://sites.google.com/site/peterboltuc/publications
Invited by Alexei Samsonovich
#145 Rosario Sorbello, Salvatore Tramonte, Carmelo Cal’, Marcello Giardina, Shuichi Nishio, Hiroshi Ishiguro and Antonio Chella An Android Architecture for Bio-inspired Honest Signalling in Human-Humanoid Interaction Keywords: Honest Signals, Geminoid Robot, Social Robotics, Human-Humanoid Interaction This paper outlines an augmented robotic architecture to study the conditions of successful Human-Humanoid Interaction (HHI). The architecture is designed as a testable model generator for interaction centred on the ability to emit, display and detect honest signals.
First we overview the biological theory in which the concept of honest signals has been put forward in order to assess its explanatory power. We reconstruct the application of the concept of honest signalling in accounting for interaction in strategic contexts and in laying bare the foundation for an automated social metrics. We describe the modules of the architecture, which is intended to implement the concept of honest signalling in connection with a re’nement provided by delivering the sense of co-presence in a shared environment.
Finally, an analysis of Honest Signals, in term of body postures, exhibited by participants
during the preliminary experiment with the Geminoid Hi-1 is provided.
#146 Anton Kolonin Architecture of Internet Agent with Social Awareness Keywords: cognitive architecture, personal agent, social awareness, social network We describe approach, architecture, implementation and practical applications of personal software agent with social awareness, capable to capture socio-temporal context of its user on the Web and in social networks in the course of interactions of the user with agent itself and user’s Internet environments online.
#147 Alexander G. Trofimov, Sergei L. Shishkin, Bogdan L. Kozyrskiy and Boris M. Velichkovsky A Greedy Feature Selection Algorithm for Brain-Computer Interface Classification Committees Keywords: electroencephalogram, brain-computer interface, feature selection, classification, committee, greedy algorithm We propose an approach to electroencephalogram feature selection and classification problems in brain-computer interfaces based on a committee of weak classifiers. The design of a classification committee is formulated as an optimization problem and the greedy algorithm for its solving is considered. The proposed approach is applicable when the objects to be classified are characterized by a large number of features while a few train samples are available. Classification performance of the committee was evaluated on real data and improvement over traditional classification methods was observed.
#148 Naoya Arakawa and Hiroshi Yamakawa Research collaboration with whole brain architecture Keywords: AGI, cognitive architecture, brain, connectome The WBA approach is an approach to realize artificial general intelligence
by ‘mimicking’ the architecture of the entire brain.
It is argued that referring to the brain architecture could help to attain
a unifying and generally accepted framework for the design, characterization,
and implementation of human-level AGI, for the human brain is, of course,
the organ that realizes human-level intelligence and its architecture can be
used as a shared reference architecture. Having a reference architecture
would facilitate collaboration among researchers and the realization of
The reference is now more realistic than before, as we have more neuroscientific
knowledge of brain architecture (such as connectome) and functions and more
practical and theoretical knowledge on information processing or machine
learning to hypothesize the working of the brain.
In this panel, participants will discuss how a community of researchers could
work together to create a unifying and generally accepted framework by referring
to the brain to realize human-level intelligence. Notably, connectomic
architecture, the modeling of brain organs such as the neocortex, hippocampus,
basal ganglia, amygdala, and cerebellum, and required learning algorithms to be
shared will be discussed. Besides architecture, tools for agent simulation and
for neuroinformatics to be shared for research would be discussed. If time
permits, a roadmap with shared frameworks and tools may be discussed.
Researchers with ideas on this topic are encouraged to be discussants.
The format of the discussion will be round-table.
#149 Alexander Efitorov, Tatiana Dolenko, Sergey Burikov, Kirill Laptinskiy and Sergey Dolenko Solution of Multi-parameter Inverse Problem by Adaptive Methods: Efficiency of Dividing the Problem Space Keywords: inverse problems, artificial neural networks, partial least squares, clustering, Raman spectroscopy The considered multi-parameter inverse problem is determination of concentrations of salts or ions in multi-component water solutions of inorganic salts by Raman spectroscopy with subsequent spectra analysis by a non-linear adaptive method (multilayer perceptron type artificial neural networks (ANN) and by a linear adaptive method (partial least squares (PLS) method based on principal component analysis). Dividing the problem space into parts by data clustering simplifies the problem within each cluster but reduces the number of samples. This study compares efficiency of application of this approach for problems with different complexity (determination of concentrations of five salts, or ten salts, or ten ions) and with various distributions of samples over concentration range of the components. Based on experimental results, limitations and areas of application of the approach are discussed
#150 Adekunle A. Adeyemi, Bankole I. Oladapo, Adeyinka O. M. Adeoye and Ayamolowo Olademeji Design and Experimental Model of Automatic Sorting Conveyor Station with Optical Proximity Sensor for Machine Vision Keywords: Machine vision, PLC, Optical sensor, CAD Manufacturing An automatic bottle and PVC classification system based on machine vision is proposed in this research. The design model and characterization of an automated classification system product using a conveyor belt with the aid of machine vision integrating Free and Open Source Software technology and business equipment. The application of machine vision library digital optical image processing Open CV, for mechanical design of the station and manufacturing CAD SOLIDWORKS’ was used for the design and implementation of automation ISA standards. Also a methodology of engineering projects in automation by integrating a PLC, an inverter, View Panel with a Device Net network and the optical sensor to perform the test. The performance testing and sorting pieces of PVC bottles are done in four types of established operation of the integrated system and the efficiency is evaluated. The processing time machine vision is on average 0.28s for a piece of PVC. A capacity of 204 per minute accessories, bottle processing time of approximately 0.26s was achieved, a capacity of 220 bottles for 7 minutes. A maximum mechanical efficiency of 30 products per minute (1910 products hour) with the conveyor system of 20 cm/s and the 40cm distance between products obtaining an average error 0.75% was obtained.
#151 Alexandr Sboev, Roman Rybka, Alexey Serenko, Danila Vlasov, Nikolay Kudryashov and Vyacheslav Demin To the role of the choice of the neuron model in spiking network learning on base of Spike-Timing-Dependent Plasticity Keywords: spike-timing-dependent plasticity, long-term synaptic plasticity, spiking neural networks, computational neuroscience The goal of this work is to study the influence of the neuron model choice on the results of STDP learning on base of simple toy tasks. As shown, the resulting mean output firing rate after STDP learning with restricted symmetric spike pairing scheme does not depend on the mean input rates for such neuron models as Leaky Integrate-and-Fire, Traub, and static neuron. Then this effect, being used to solve a typical classification task of Fisher’s Iris, demonstrates that the classification accuracy does not depend significantly on the choice of the neuron model. Thus, the independence of learning results on the neuron model gives the possibility to use simpler neuron models in further investigations.
#152 Giovanni Pilato and Ernesto D’Avanzo Data-driven Social Mood Analysis through the Conceptualization of Emotional Fingerprints Keywords: Emotion Detection from Text, Data Driven Conceptual Spaces, Social Sensing A body of knowledge shows the emerging of an evidence according to a better account for the emotion spectrum is achievable by employing a detailed selection of emotion keywords. Basic emotions, such as Ekman’s ones, cannot be considered universal, but are related to with implicit thematic affairs within the corpus under analysis. The paper tracks some preliminary experiments obtained employing a data-driven methodology that captures emotions, relying on domain data that you want to model. The experimentation consists of investigating the corresponding conceptual space based on a set of terms (i.e., keywords) that are representative of the domain and the determination. Furthermore, the conceptual space is exploited as a bridge between the textual content and its sub-symbolic mapping as an ’emotional fingerprint’ into a six dimensional hyperspace.
#153 Joseph F. Kayode, Bankole I. Oladapo, Adeyinka. O. M. Adeoye and Samuel O. Afolabi Finite Element Method of Stress Analysis of Reinforced of A 3D Composite Spur Gears Keywords: Spur gear, Composite material, stress analysis, FEM In this paper, spur gears made from graphite epoxy normal and shear stresses are analyzed. The stresses of spur gear are calculated from outer to interior divided into three zones for different angles of reinforcement fibers. Three-dimensional finite element model (FEM) was used in this study. Eight node three-dimensional isoperimetric elements are chosen as the finite element method. In the study calculation of normal and shear stresses in different fiber reinforcement orientations has been carried out and are plotted in the graphs. From the results of the graphs plotted the force applied to the surface of the first gear orthotropic consisting of the normal stresses of the inner gear is greater by about 30% and the stress values are close to each other.
#154 Antonio Lieto Knowledge Representation in Cognitive Architectures: Current Limitations and Open Challenges Keywords: knowledge representation, cognitive architectures, knowledge level, symbolic representations, subsymbolic representation, conceptual spaces, knowledge heterogeneity In this talk I will provide a focused overview of the representational assumptions developed in different cognitive architectures. In doing so I will outline the main problematic aspects of the current proposals (involving the limited size and the homogeneous typology of the encoded and processed knowledge) and I present a possible way out based on different, but complementary, research agendas.
#155 Natalia Miloslavskaya Remote Attacks Taxonomy and their Verbal Indicators Keywords: Attack Kill Chain Model, Verbal Attack Indicator, Remote Attack, Attacks Taxonomy (Classification) To detect and to timely interrupt increasingly sophisticated attacks against modern networks, their systems, services and resources, it is especially important to understand the scenarios and phases of various possible attacks, specific for these networks. Based on the analysis of tremendous number of sources and generalizing various descriptions, remote attacks taxonomy (classification) and their key verbal indicators are proposed.
#156 Rosario Sorbello, Salvatore Tramonte, Marcello Giardina, Carmelo Cal’, Shuichi Nishio, Hiroshi Ishiguro and Antonio Chella Augmented Embodied Emotions by Geminoid Robot induced by Human Bio-feedback Brain Features in a Musical Experience Keywords: Human Humanoid Interaction (HHI), Brain Computer Interface (BCI), Geminoid, Android Robot, Social Robot, Cognitive Architecture, Emotions This paper presents the conceptual framework for a study of musical experience and the associated architecture centred on Human-Humanoid Interaction (HHI). We discuss the state of the art of the theoretical and the experimental research into the cognitive capacity of music. We overview the results that points to the correspondence between the perceptual structures, the cognitive organization of sounds in music, the motor and affective behaviour. On such grounds we bring in the concepts of musical tensions and functional connections as the constructs that account for such correspondence in music experience. Finally we describe the architecture as a models generator system whose modules can be employed to test this correspondence from which the perceptual, cognitive, affective and motor constituents of musical capacity may emerge.
#157 Olga Chernavskaya On representation of emotions in an artificial cognitive system within the Natural-Constructive Approach Keywords: emotions, cognitive architecture, paradox, neuroprocessor, associations The problem of modeling the emotions in an artificial cognitive system is considered within the Natural-Constructive Approach worked out in our previous works (BICA 2013, 2015, 2016). Especial attention is paid to interpretation and imitation of aesthetic emotions caused by impression of Art, Music, Natural Phenomena (rainbow, sunset, etc.). In contrary to so called pragmatic emotions (associated with certain pragmatic goal), the aesthetic emotions have no rational reasons and appear to be quite individual and often inexplicable, i.e., could not be strictly formulated and explained.
We consider these problems using the Natural-Constructed Cognitive Architecture (NCCA), which represents the complex multi-level hierarchical composition of different-type neural processors (in continual representation based on the original dynamical formal neuron concept). The whole system is divided into two separated (but linked) subsystems in analogy with two cerebral hemispheres. It is shown that emotions, being induced by certain sub-cortical structures (thalamus, amygdale, etc.), provide the tool that should control the ‘dialog’ (mutual interaction) between the subsystems in course of solving certain problems. The aesthetic emotions are shown to be connected with some indirect and unformulated associations provided by the lowest (image) level of hierarchy (fuzzy set). It is shown that the concept of chef-d’oeuvre and corresponding effect of goose bumps could be caused by the paradox of recognition arising when the object seems familiar and unusual simultaneously.
#158 Alexei V. Samsonovich Tests, metrics and challenges for HLAI Keywords: Strong AI, Turing test, Roadmap to human-level intelligence, artificial emotional intelligence This discussion panel will brainstorm for key steps on the roadmap to HLAI in terms of metrics and challenges. Here HLAI stands for “human-level AI”, or rather “human-like artificial intelligence”. In particular, we will focus on the ultimate goal: this topic appears to be largely dismissed in recent AI and AGI research. The BICA Challenge put forward by this community is just not specific enough: one cannot tell precisely whether the goal is reached. In contrast, the best-known AI challenge, the unlimited Turing test, is specific and remains the ultimate goal in the field, despite that it did not work efficiently as a drive in research, and has been harshly criticized numerous times. But most importantly, “passing” it means a negative result: i.e., the null hypothesis cannot be rejected. This does not sound like a proof of a breakthrough at all. Neither it guarantees that the outcome will remain negative in a bigger sample. Therefore, it seems like we need a better ultimate goal, formulated as a positive outcome. And it turns out that the Turing Test paradigm can be slightly modified to satisfy this demand. It can be called “The Overman Challenge”, because the idea is to create an artifact which is more human than an average human, and eventually – an artifact that is more human than any of the humans. Is it possible at all’ How can this outcome can be validated’ These and other questions will be discussed.
#159 Sergey A. Dolenko Solving Inverse Problems by Biologically Inspired Adaptive Machine Learning Algorithms Keywords: biologically inspired methods, machine learning, artificial neural networks, inverse problems, indirect measurements A wide family of machine learning algorithms are often called adaptive or data-driven methods, due to their capability of adaptation to the available set of data, learning by example, requiring no physically grounded analytical or computational model or a priori knowledge of the studied object. Such methods may be often called biologically inspired ‘ as by their origin, as by resemblance of their behaviour to the behaviour of data processing systems in living creatures. The scope of problems that are solved by such methods includes those of prediction, evaluation, classification, clusterization, inverse problems, and other data analysis problems. Examples of such methods are artificial neural networks (ANN) and the method of partial least squares, or projection to latent structures (PLS). From mathematical point of view, these methods are sophisticated approximation methods using adaptively tuned combinations of relatively simple functions of most general type.
Many physical methods are based on indirect measurements, and therefore they imply solution of inverse problems (IP) ‘ determination of the sought-for parameters by the observed values. Such problems are often ill-conditioned or even incorrect. That is why adaptive methods of IP solving based on approximation of the inverse function are demanded and efficiently used.
Attention is driven to the main differences between ANN and PLS, and the main shortcoming of PLS ‘ that it is a linear method (yet it is the best linear method). Even with an adequate non-linear pre-processing of data, PLS is often unable to build an approximation comparable by its quality with that implemented by an ANN. The main advantage of PLS is its low computational cost.
In the lecture, methodological aspects of using ANN are discussed. From the point of view of data processing methods, any IP can have various formulations: as a regression, classification (for discrete-valued IP) or optimization problem. The key differences of ANN as a method of solving IP from alternative methods are discussed.
When solving IP, ANN can be used within one of several methodological approaches: ‘model-based’, ‘experiment-based’, and ‘quasi-model’. The difference among these approaches, their properties and areas of application are described.
A separate question arises if the IP being solved is a multi-parameter one. The possible approaches to the order of determination of parameters are autonomous determination, simultaneous determination of all parameters, group determination (with joining of parameters into groups with simultaneous determination within each group), and stepwise determination (when some of the parameters already determined are used as additional inputs for determination of other parameters).
Other useful additional methods discussed in the lecture are cluster-based approach, when the problem domain is separated into several sub-domains, and the IP is solved separately in each of these sub-domains, and training with adding noise into training data, thus increasing noise resilience of the solution. It is stressed that with increasing complexity of a problem, linear methods begin to fail, and ANN turn out to be more resilient to this increasing complexity.
The general purpose of the lecture is to attract attention of a wide audience of young scientists to the great opportunities opened by use of biologically inspired adaptive methods, and by the latest methodological achievements in IP solution by ANN. The material is illustrated by examples of IP from two areas of physics ‘ optical spectroscopy and electrical prospecting.
#160 Mikhail Alyushin and Lyubov Kolobashkina Development of a Metrological Database with Images of a Human Face in the Infrared Range to Evaluate the Effectiveness of Biometric Algorithms Keywords: Biometrics, Metrology, Efficiency of biometric algorithms The work shows the promise of using biometric algorithms intended for remote determination of current human bio-parameters based on processing of the infrared image of his face. Problems that hinder the widespread use of remote biometric algorithms in practice are highlighted. The urgency of creating a metrological database containing video recordings of the human face image in the infrared range for the evaluation of the biometric algorithms efficiency is substantiated. Presented are the results of an analysis of existing databases containing images of a person’s face. Insufficient development of specialized databases containing infrared images of a person’s face was noted. The main shortcomings of such databases are highlighted. An approach to creating a metrological database based on the acquisition and storage of complex personal data is proposed. The main components of such data are video recordings of the face image in the visible and infrared ranges, time synchronized records of human bio-parameters, as well as information on the complexity of test tasks performed. The structure of the laboratory system for obtaining complex data on the person undergoing testing is considered. A typical data structure for a single test cycle is presented. The application results of the first version of the metrological database for experimental research of the effectiveness of algorithms for remote determination of human bio-parameters are presented.
#161 Mikhail Alyushin, Lyubov Kolobashkina and Victor Alyushin Optimization of the Data Representation Integrated Form in the Viola-Jones Algorithm for a Person’s Face Search Keywords: Integral form, Viola-Jones algorithm, Face recognition It is shown that the task of a person’s face recognition is one of the most popular at the present time. Its effective solution determines the reliability of many modern systems of personal identification, security, access control, and video surveillance at thermal and nuclear power plant stations. The speed of facial recognition algorithms is one of the key factors that determine the possibility of using for solving specific practical problems. It is shown that one of the methods for increasing the speed of facial recognition algorithms is the use of an integral form of data representation. The Viola-Jones algorithm is identified as one of the classic approaches to solving the problem of face recognition. The main limitations for using the classical integral form of data representation are shown. Rotation of the head is identified as the main factor leading to a significant increase in the error in determining the brightness indicators for the most informative areas of the face. An approach based on the use of modified integrated forms for image frame data representation is proposed. These forms are oriented to the fast processing of information in the presence of a significant rotation of the head. The most important range of possible changes in the angle of rotation of the head is identified. The data structure is considered in case of using the modified integral form. Experimentally confirmed the possibility of using a limited number of modified forms of data representation while maintaining the high reliability of the work of the human face recognition algorithm.
#162 Aleksander Boruchinkin, Anastasia Tolstaya and Arseniy Zhgilev Cryptographic Wireless Communication Device Keywords: Crypto headset, hardware encryption, protection from eavesdropping The total increase in the use of mobile devices inevitably leads to an increase in the number of different security threats. The cryptographic device described in this article provides a secure (encrypted) transmission of data. It alerts the user if the crypto headset at the opposite end of the link is not trusted, thus extending the offending model (i.e. a list of the potential opportunities of the offender) to the possibility of to the possibility of substituting one of the encrypting devices. All functions for encryption and decryption are enclosed in a separate unit that connects to the phone via Bluetooth. The crypto headset specifications were compared with the closest analogues and ensures a higher level of security for each group of consumers: the corporate sector, government agencies, and individual users.
#163 Anton Kolonin Aigents – personal social graph analytics – demonstration video Keywords: graph analysis, social network, software agent, structure of communications Intelligent software agent capable to capture social context of its user is presented. It is shown, how to connect the agent online to one or more social networks and let it comprehend nature and structure of communications between user and its peers online – in order to help user to organize and improve these relationships as well as find more information to user based on their preferences.
#164 Larisa Ismailova, Viacheslav Wolfengagen, Sergey Kosikov, Irina Parfenova and Iliya Nikulin Means for ensuring compatibility of heterogeneous data models in an interactive visualization environment Keywords: heterogeneous information, semantic characteristics, data interpretation, interpretation control, multi model, model compatibility, visualizing heterogeneous information, visualization support tools, interactive visualization environment The paper considers the problem of visualizing heterogeneous information relevant to the solution of a particular problem domain. An essential part of the task is to get the conversion of data objects doing their representation adjusted for the corresponding data model. Creation of a model of converting of data objects is offered on the basis of applicative computing systems. Achievement of flexibility requires the parametrization of the considered construction, i.e. support of dependence of a set of available methods of interpretation on parameters as which semantic characteristics of processed data appear. The methods of working with interpretation coordination tools have been partially tested when implementing various applications for informational support for the implementation of the best available technologies (BAT).
#165 Daniil A. Azarnov, Arthur A. Chubarov and Alexei V. Samsonovich A Virtual Actor with Social-Emotional Intelligence Keywords: cognitive modeling, virtual actor, emotional intelligence, virtual environment, BICA Challenge This work continues the effort to design and test a universal cognitive model of emotionally biased behavior control and decision making, with the focus on social emotional relationships. Two key building blocks of the model include (i) dynamics of mutual appraisals of actors, determining the likelihoods of action selection, and (ii) moral schemas, or M-schemas, that establish normal behavior of two actors with respect to each other as well as to other entities, under certain implicit mutual relationships, such as partnership. To test the model, we implement a virtual actor embodied as an avatar in a specifically designed virtual environment, and use several paradigms of social interaction. Virtual environments and associated paradigms can be divided into a hierarchy, on top of which are paradigms with dynamically changing social relationships and roles. Using paradigms of this kind, we show that a virtual actor can be indistinguishable from a human participant, both, in its believability and in social acceptability; the latter being measured by the frequency of obtaining help from others. A model of this sort is expected to have broad applications in various fields in the near future.
#166 Ekaterina Kazimirova The importance of cognitive technologies in the era of the Internet of Things Keywords: Internet of Things, cybersecurity, cognitive technologies, neuromorphic computing As the Internet of Things evolves, the attack surface – the number of vulnerable points – will become greater. The huge number of things connected to the Internet will create opportunities for DDoS attacks, identity theft, money theft, etc. This means that we are on the threshold of a new era, which will usher in new cyberthreats and new challenges for security systems developers. The emergence of sophisticated complexes of things that interact with each other and the need for context-based and behavioral analysis mean that cognitive technologies will play a crucial role in ensuring Internet of Things security. Neuromorphic computing systems are also of great interest: given the right architectural solutions, they can contribute to the development of a world of things that will be immune to attacks thanks to their bioinspired intelligence.
#168 Bankole I. Oladapo, Joseph F. Kayode and Samuel O. A Folabi Reduction in Economic Cost and Production Time for Development of a 3D Printer and its effect on world market Keywords: 3D Printers, Economic Cost, Product development, Reducing Time and Cost The individualization and customization of products is now the generally valuable trends for manufacturing companies. New technologies like 3D-printing are enablers for the additional development of this trend. This work is a relatively up to date notion, representing a paradigm shift in how companies commercialize business intelligence. The central indication of the exposed innovation sculpt is the opening up of the innovation process to the outside world. The more exposed innovation is successfully embraced by firms and organizations the more this will reshape and network existing regional innovation systems. This article presents an approach based on two main concepts, one of them is using a rapid prototyping and the other is the process of product development. The purpose of this article is to prove 3D printers can bring benefits to the chain of product development. The main results are a reduction of time and cost, due to the flexibility of the new technologies allows obtaining prototypes in the initial stages of the project, due to the speed of manufacture, being able to avoid future damage in the process.
#169 Gennady Osipov A sign-based model of the world and planning of collective activity Keywords: sign, sign based world model, behavior modeling, goal of behavior Sign-based formalism is considered. The concept of sign arose in the framework of semiotics. Neurophysiological and psychological researches indicate sign-based structures, which are the basic elements of the world model of a human subject. In this formalism it was possible to formulate and solve some problems of behavior modeling, in particular, generating the goal of behavior and dynamic distribution of roles in coalition of actors.
#170 Roman Batusov, Sergey Dolenko and Irina Myagkova Neural Network Prediction of Daily Relativistic Electrons Fluence in the Outer Radiation Belt of the Earth: Selection of Delay Embedding Method Keywords: delay embedding, time series, prediction, neural network, multi-layer perceptron, relativistic electrons of the outer radiation belt of the Earth Prediction of the time series of relativistic electrons fluence in the outer radiation belt of the Earth encounters problems caused by complexity and non-linearity of the ‘solar wind ‘ the Earth’s magnetosphere’ system. Artificial neural networks are a biologically inspired architecture that is a suitable tool to solve problems of such type. This study considers the dependence of the quality of prediction on the type and depth of delay embedding of input features.
#172 Sergey Smirnov News of Russia Keywords: Media, Accreditation, BICA Head of High Technologies Department
#173 Witali Dunin-Barkowski and Ksenia Solovyeva Pavlov Principle in Neuronic Systems, Updated Keywords: Neural Systems, Artificial Neural Networks, Artificial Intelligence, Pavlov Principle We use the label ‘neuronic systems’ for networks of both real and modeling neurons. Recent successes of artificial neuronal systems in obtaining previously considered to be human-only abilities (complex pattern recognition, text translation from one language to another, generation of figure captions, etc.) forces for formulation of general principles, explaining smartness potential of neuronic systems. Up to the end of 2014 there was a general conviction that in artificial neural systems only error back propagation (known as physiologically impossible) provides obtaining of smart functions. The work of Timothy Lillicrap et al.  has effectively overturned that idea. Following these impressive results we have proposed in [2, 3] a general formula, Pavlov Principle (PP), which summarizes the contemporary knowledge of neuronic systems abilities. In lecture we consider further justification of PP and discuss its consequences for understanding brain mechanisms and for elaboration of new AI systems.
The work is supported by RFBR grant 16-07-01059 and by National Technological Initiative Project ‘Artificial Neural Intelligence iPavlov’.
1. Lillicrap T.P., Cownden D., Tweed D.B., Akerman C.J. Random feedback weights support learning in deep neural networks. – arXiv:1411.0247v1 [q-bio.NC] 2 Nov 2014, 27 p.
2. Solovyeva K.P., Shchukin T.N., Ivashchenko A.A., Dunin-Barkowski W.L. Basic Principles of Neural Processing. III All-Russian Conference with International Participation ‘Hippocampus and Memory: Norm and Pathology’, September 7-11, 2015, Pushchino, Russia, pp. 33-34.
3. Dunin-Barkowski W.L., Solovyeva K.P. Pavlov principle in problems of brain reverse engineering. – XVIII International Conference Neuroinformatika 2016. Proceedings, Part 1. MEPHI, 2016, pp. 11-23.
#174 Agnese Augello Social Practices in Human-Agent Interaction Keywords: Social Practice, Cognitive Architecture, Serious Games, IVA, Humanoid, Social Interaction Coping with the issue of modeling a form of social intelligence in an artificial agent, means properly modeling its knowledge with socio-cultural practices, i.e. routinely behaviors shared and performed in society, that integrate physical and mental activities, competences, knowledge and emotions. A proper Social Practice model allows the agent to interpret the current social situation and the social signs, and, as a consequence, properly planning and carrying on the interaction. Two cognitive architectures will be discussed, aimed respectively at the modeling of a social behavior in IVAs for serious games, and in humanoids.
#175 Umberto Maniscalco A Bio-Ispired Artifcial Somatosensory System for a Humanoid Robot Keywords: Soft Sensors., Somatosensory., Cognitive Robotics The capability of a robot of being aware of its internal status is a step forward to the enhancement of human robot interaction. The possibility of feeling either pleasant or unpleasant sensations is at the basis of the motivation level of a robot. It can modulate the “willingness” of accomplishing a given task. Negative sensations can represent an alarm indicating dangerous situations, while the feeling of a reassuring environment or a ‘well-being’ sensation can be a stimulus in pursuing the task, even in the presence of a painful perception. In this paper, we illustrate a bio-inspired somatosensory system embedded in a cognitive model for a humanoid robot. The system is based on a set of soft sensors that have been designed in order to make it possible the interpretation of the robot physical sensations through a proper classification of the perceived somatosensory signals. This interpretation triggers and modulates the motivation level of the robot as well as its behavior
#176 Alexei V. Samsonovich Biologically Inspired Cognitive Architectures: An introduction Keywords: cognitive architecture, human-level AI, BICA Challenge BICA is a promising, rapidly developing area at the intersection of artificial intelligence (AI), biology and cognitive science. One evidence for this is the growing number of scientific publications, in one way or another connected to BICA. The lecture will provide basic knowledge of cognitive architectures, their building blocks and principles, approaches to their implementation, their study and usage in virtual environments.
#177 Ekaterina Movsumova Virtual Reality: immersive environment effectiveness for soft skills and emotions Keywords: virtual reality, soft skills, emotion learning, education, edutainment Using virtual reality in education is no longer a fantasy, but a reality for numerous companies and educational institutions. Some of the educational formats that VR can enhance are full-time education, distance learning, full-time and distance learning combined; and self-learning. With the appearance of VR we can for the first time talk about passing rather than breaking through the Fourth Wall. The concept of the ‘the fourth wall’ in the theater world refers to an imaginary wall between the actors and the audience. When an actor directly addresses the audience, the fourth wall ‘breaks’. This technique is used to better connect the audience to the events happening on the stage. A similar approach is used in literature, cinema and video games. But after VR head-mounted displays came on the scene, the idea of the fourth wall got an interesting twist. Now, instead of watching what is going in a game on the screen, one is directly involved in the virtual action with immersive interaction. What are the current results of this approach’ The presented case studies & research give an overview and prospectives of How VR technoligy will change our education, skill development, emotion learning and perception of reality in our lives.
#178 Ignazio Infantino Computational Creativity for Cognitive Robots Keywords: Computational Creativity, Cognitive Robotics, Cognitive Architectures Computational Creativity (CC) or Artificial Creativity is an exciting field of research that involves artificial intelligence and cognitive science. The lecture introduces relevant theoretical foundations of CC and summarizes the findings in this area in the recent years by Dr. Ignazio Infantino and its colleagues of the Laboratory of Cognitive Robotics and Social Sensing of ICAR-CNR (Italy). The talk deals with a cognitive architecture suitable for humanoid robots and showing complex cognitive capabilities. Two different working example of computational creative robots are discussed: a digital painter robot and humanoid dancer.