Biologically Inspired Cognitive Architectures (BICA) for Young Scientists

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 euclidian reward calculation was used for more precise reward determination. Results have shown that none of the tested architectures were not able to achieve goal.

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 electrical properties 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, differing in their electrical activity, 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 an example of 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.

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.

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.

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 socialemotional 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.

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.

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. Generalizing on this example, we outline a roadmap to building a variety of useful virtual creative assistants to humans based on an emotionally intelligent cognitive architecture.

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.

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 Architecture and start using it to control the NPC. The lab is a hands-on programming lab, using Java and Netbeans as language/tool.

An artificial somatosensory 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 preliminary experimentations and explains the influence of the parameters on the robot behavior and personality. Pepper freely moves in an office environment searching for people to engage. The robot searches for a safe path, avoiding obstacles and aiming to explore a significant part of a known space by an approximative map stored in its long term memory (LTM). The short-term memory (STM) stores somatosensory values related to perceptions considered relevant for the navigation task. The collection of previous navigation experiences allows the robot to memorize on the map places that have positive (or negative) effects on robot’s wellness state. The robot could reach the places labeled as negative, but it needs some positive counter effects to contrast its reluctance.

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.

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.

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.

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.

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 responses show important information about the task context of individual fixations on viewed objects.

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).

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.

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).

The results of our psychophysical tests by eye movement recording and the following stages of research have been considered. Several groups of the known findings in this area were determined, namely: (i) unresolved objectives; (ii) contradictory data; (iii) findings which propose revision of some views; (iv) similar findings obtained in different research centers. The most important results of our psychophysical tests are as follows: (a) bimodal distribution of fixation duration during joint presentation of target objects and distractors; (b) dynamical formation of target images creates conditions for dosed change of perceptual load; (c) decrease of fixation duration and increase of saccade amplitude during the last test stage when volunteer makes the decision about completion of the current visual task; (d) structure of viewing scan path, fixation density and duration, probability of return fixations are specific for each human during viewing of the affective images; (e) return fixations are arranged with reference to areas of interest on image; (f) spatial distribution of fixation duration, velocity and amplitude of saccades are significantly different between tests of viewing of 2D images and navigation in 3D environment. Using of the obtained results in realistic mathematical models of visual attention has been discussed.

The lecture characterizes the following main properties of interaction between 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. During the genetic assimilation, phenotypes of modeled organisms move towards the optimum at learning; after this, genotypes of selected organisms also move towards the optimum. The hiding effect means that strong learning can inhibit the evolutionary search for the optimal genotype. The learning load can lead to a significant acceleration of evolution.

The problems of intellectualization in the development process of dynamic integrated expert systems basing on the problem-oriented methodology and the AT-TECHNOLOGY workbench are considered. The experience from carrying out intellectual planning development plan generating 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 workbench is described.

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.

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° 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.

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%.

The paper considers theoretical mechanical model of a multifingered arm with 21 degrees of freedom. The main objective of the work is the creation of gesture interface. Gesture interface includes the set of gestures, the synthesis of finger control schemes for 26 gestures, as well as gesture recognition task with the help of convolutional neural network training. As the demonstration we propose to observe the results of 26 gestures recognition with the help of constructed convolutional network. For 26 classes 15600 images at different distance and at different angles were created. As a result of convolutional neural network training the accuracy of a test set classification is 76%.

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.

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.

Reinforcement learning (RL), which is a field of machine learning, is effective for behavior acquisition in robots. Asynchronous cognitive architecture, which is a method to model human intelligence, is also effective for behavior acquisition. Accordingly, the combination of RL and asynchronous cognitive architecture is expected to be effective. However, early work on the RL toolkit cannot apply asynchronous cognitive architecture because it cannot solve the difference between the asynchrony, which the asynchronous cognitive architecture has, and the synchrony, which RL modules have. In this study, we propose an RL environment for robots that can apply the asynchronous cognitive architecture by applying asynchronous systems to RL modules. We prototyped the RL environment named “Re:ROS.”

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.

We describe a heterogeneous neural network where neurons interact by means of various neurotransmitters using the common extracellular space. Every neuron is sensitive to a subset of neurotransmitters and, when excited, secretes its specific neurotransmitter. This feature enables establishing the selective connections between neurons according to sets of their receptors and to their outputs. We use a simplification of this formalism as a basis for modeling interactions between agents in a social network, where the two opposite types of activity are spreading. Agents have beliefs of different strength and activation thresholds of different heights (which correspond to neuronal excitation thresholds) and can be more or less sensitive to an external influence (which corresponds to weights of neuron receptors). The main properties of the agents and the principles of activity spreading are defined. The classification of agents according to their parameters is provided.

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.

Health information is critical for the patient and its unauthorized access may have server impact. With the advancement in the healthcare systems especially through the Internet of Things give rises to patient privacy. We developed a healthcare system that protects identity of patients using innovative zero-watermarking algorithm along with vocal fold disorders detection. To avoid audio signal distortion, proposed system embeds watermark in a secret key of identity by visual cryptography rather than audio signal. The secret shares generated through visual cryptography are inserted in the secret watermark key by computing the features of audio signals. The proposed technique is evaluated using audio samples taken from voice disorder database of the Massachusetts Eye and Ear Infirmary (MEEI). Experimental results prove that the proposed technique achieves imperceptibility with reliability to extract identity, unaffected disorder detection result with high robustness. The results are provided in form of Normalized Cross-Correlation (NCR), Bit Error Rate (BER), and Energy Ratio (ENR).

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.

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.

Currently, immutable 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.

Multidimensional data structures are widely used in modern information technologies. They sometimes contain private or other sensitive information. We argue that attribute-based encryption is a handy tool for access control to multidimensional data structures, discussing the advantages and disadvantages of ciphertext-policy and key-policy attribute-based encryption for this task. We propose a scheme of attribute management for multidimensional data structures.

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.

The trend of recent years has been the advent of 2D and 3D cryptographic transformations. Standards 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.

The 0–1 Knapsack Problem is a well known NP-complete problem. It is used as the core primitive in several asymmetric cipher systems. Designing such systems requires a reliable method of computational platform benchmarking. But the existing general-purpose benchmarks are not accurate enough, as they are mostly based on floating-point arithmetics, while the Knapsack Problem relies on large amount of calculations with very long integers. Therefore, a new specialized benchmark is required to get accurate performance estimates. In this paper we study some features of exact parallel algorithms for the Knapsack Problem, as well as load balancing techniques for them. We then choose several algorithms based on their scalability and applicability to the asymmetric cipher system analysis and suggest a new algorithmic foundation for computational platform benchmarking comprised of these algorithms.

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.

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.

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, addresses, 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 internal 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.

The paper presents different approaches to a cognitive visualization of multidimensional data of chirality-dependent carbon nanotubes thermal and electrical properties. It is remarkable that the proposed visual analytics approaches are able to demonstrate hidden relations between features of carbon nanotubes.

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.

This paper proposes a method of automatic VLSI circuit analysis. We propose pattern-free, technology independent method for extracting of functional blocks with irregular structure. 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.The experimental data obtained as a result of the principle electrical circuits of different degree of connectivity analysis confirmed the effectiveness of the proposed method.

This paper is devoted to the problem of automated support of decision-taking process in healthcare. The theranostic process is typified as a special case of an administrative process. Correct solutions of problems in medicine are based on metering big amounts of data. These data are represented by facts from real-life experiences and numerous guidance of evidence-based healthcare. Taking into account an enormous aggregation of data for a special isolated case is possible with application of an automated decision support system based on technology of artificial neural networks or genetic algorithms.

The paper proposes the method of digital watermark usage for video copyright protection, that may be a solution to the piracy of digital content. Digital watermark embedding system should prevent illegal access to the digital watermark and its container. 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, however during the extraction label bit errors can occur. Threshold of these errors was found. The watermark was exposed to most of the common attacks, also on low bit-rate videos. Robustness to these attacks was showed. 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.

Early cognition of possible unloyal behavior is the most important aspect of the preventive measures against a malicious act in a facility. Being limited in money, it is necessary to choose the rational set of methods for forecast of unloyal behaviour. This methodology may be applied for the forecast of the other psychological or biological parameter also.

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.

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.