Searching for Nearest Strings with Neural-Like String Embedding

We analyze an approach to a similarity preserving coding of symbol sequences based on neural distributed representations and show that it can be viewed as a metric embedding process.

Approaches to Sequence Similarity Representation

We discuss several approaches to similarity preserving coding of symbol sequences and possible connections of their distributed versions to metric embeddings. Interpreting sequence representation methods with embeddings can help develop an approach to their analysis and may lead to discovering useful properties.

ImageSpace: An Environment for Image Ontology Management

More and more researchers have realized that ontologies will play a critical role in the development of the Semantic Web, the next generation Web in which content is not only consumable by humans, but also by software agents. The development of tools to support ontology management including creation, visualization, annotation, database storage, and retrieval is thus extremely important. We have developed ImageSpace, an image ontology creation and annotation tool that features (1) full support for the standard web ontology language DAML+OIL; (2) image ontology creation, visualization, image annotation and display in one integrated framework; (3) ontology consistency assurance; and (4) storing ontologies and annotations in relational databases. It is expected that the availability of such a tool will greatly facilitate the creation of image repositories as islands of the Semantic Web.

On Handling Replay Attacks in Intrusion Detection Systems

We propose a method for detecting and analyzing the so-called replay attacks in intrusion detection systems, when an intruder contributes a small amount of hostile actions to a recorded session of a legitimate user or process, and replays this session back to the system. The proposed approach can be applied if an automata-based model is used to describe behavior of active entities in a computer system.

The Cascade Orthogonal Neural Network

In the paper new non-conventional growing neural network is proposed. It coincides with the Cascade- Correlation Learning Architecture structurally, but uses ortho-neurons as basic structure units, which can be adjusted using linear tuning procedures. As compared with conventional approximating neural networks proposed approach allows significantly to reduce time required for weight coefficients adjustment and the training dataset size.

Self-Learning Fuzzy Spiking Neural Network as a Nonlinear Pulse-Position Threshold Detection Dynamic System Based on Second-Order Critically Damped Response Units

Architecture and learning algorithm of self-learning spiking neural network in fuzzy clustering task are outlined. Fuzzy receptive neurons for pulse-position transformation of input data are considered. It is proposed to treat a spiking neural network in terms of classical automatic control theory apparatus based on the Laplace transform. It is shown that synapse functioning can be easily modeled by a second order damped response unit. Spiking neuron soma is presented as a threshold detection unit. Thus, the proposed fuzzy spiking neural network is an analog-digital nonlinear pulse-position dynamic system. It is demonstrated how fuzzy probabilistic and possibilistic clustering approaches can be implemented on the base of the presented spiking neural network.

Virtual Reconstruction of the Ancient Russian Fortress Koporye

One of the major strategic tasks in Russia’s northwest of the past centuries was the defense of the country’s territories from enemy incursions coming from the West. In order to solve this task, a robust shield in the form of a system of fortresses was created between the 13th and 15th centuries. The system included such fortresses as Koporye, Yam, Korela, Oreshek, and others. In our age, these monuments have become an essential part of Russia’s historic and cultural heritage and an important element of the tourism cluster “The Silver Ring of Russia.” The Centre of Design and Multimedia at St. Petersburg National Research University of Information Technologies, Mechanics and Optics, jointly with the Department of History and the Faculty of Arts at St. Petersburg State University, working under a three-year grant from the Russian Foundation for Humanities Research (#12-01-12041), is implementing a multimedia information system “Ancient Fortresses of Russia’s Northwest”. Historically accurate virtual reconstruction of several fortresses as they existed during certain historic periods, done in such a way as to allow the future creation of virtual tours of these sites, has become the focus of this project’s research. In the present paper, we describe the main phases and results of virtual reconstruction of the best-preserved fortress, Koporye.

Interactive Multimedia Solutions Developed for the Opening of the New Stage of the Alexandrinsky Theatre

This paper focuses on teamwork by the National Research University of Information Technologies, Mechanics and Optics (NRU ITMO) and the Aleksandrinsky Theatre in preparation of opening of the New Stage of the Alexandrinsky Theatre. The Russian State Pushkin Academy Drama Theatre, also known as the Alexandrinsky Theatre, is the oldest national theatre in Russia. Many famous Russian actors performed on the Alexandrinsky’s stage and many great directors. May 2013 marked the opening of the New Stage of the Alexandrinsky Theatre. The theatre complex comprises three buildings: the new stages building, a media center, and the building housing a center of theatre education. Several plays shown simultaneously on multiple stages within the new complex’s buildings constituted the opening gala of the New Stage of the Alexandrinsky Theatre. The works of Fyodor Dostoyevsky were the plays’ unifying theme. NRU ITMO employees developed several interactive theatre project solutions implemented for the opening of the Alexandrinsky Theatre’s New Stage.