The paper presents an algorithm for the worst case response time (WCRT) estimation for multiprocessor systems with fixed-priority preemptive schedulers and the interval uncertainty of tasks execution times. Each task has a unique priority within its processor, a period, an execution time interval [BCET, WCET] and can have data dependency on other tasks. If a decrease in the execution time of the task A can lead to an increase in the response time of the another task B, then task A is called an anomalous task for task B. According to the chosen approach, in order to estimate a task’s WCRT, two steps should be performed. The first one is to construct a set of anomalous tasks using the proposed algorithm for the given task. The paper provides the algorithm and the proof of its correctness. The second one is to find the WCRT estimation using a genetic algorithm. The proposed approach has been implemented software as a program in Python3. A set of experiments have been carried out in order to compare the proposed method in terms of precision and speed with two well-known WCRT estimating methods: the method that does not take into account interval uncertainty (assuming that the execution time of a given task is equal to WCET) and the brute force method. The results of the experiments have shown that, in contrast to the brute force method, the proposed method is applicable to the analysis of the real scale computing systems and also allows to achieve greater precision than the method that does not take into account interval uncertainty.

Software protection from exploitation of possible unknown vulnerabilities can be performed both by searching (for example, using symbolic execution) and subsequent elimination of the vulnerabilities and by using detection and / or intrusion prevention systems. In the latter case, this problem is usually solved by forming a profile of a normal behavior and deviation from normal behavior over a predetermined threshold is regarded as an anomaly or an attack. In this paper, the task is to protect a given software P from exploiting unknown vulnerabilities. For this aim a method is proposed for constructing a profile of the normal execution of the program P, in which, in addition to a set of legal chains of system and library functions, it is proposed to take into account the distances between adjacent function calls. At the same time, a profile is formed for each program. It is assumed that taking into account the distances between function calls will reveal shell code execution using system and / or library function calls. An algorithm and a system for detecting abnormal code execution are proposed. The work carried out experiments in the case when P is the FireFox browser. During the experiments the possibility of applying the developed algorithm to identify abnormal behavior when launching publicly available exploits was investigated.

Spatially inhomogeneous structures of light waves are used as a mechanism of compacting information in optical and fiber-optic communication systems. In this paper, we consider a mathematical model of an optical radiation generator with a nonlinear delayed feedback loop and a stretching (compression) operator of the spatial coordinates of the light wave in a plane orthogonal to the radiation direction. It is shown that the presence of a delay in the feedback loop can lead to the generation of stable periodic spatially inhomogeneous oscillations. In the space of the main parameters of the generator, the spaces of generation of stable spatially non-uniform oscillations are constructed, the mechanism of their occurrence is studied, and approximate asymptotic formulas are constructed.

A statically typed version of the data driven functional parallel computing model is proposed. It enables a representation of dynamically changing parallelism by means of asynchronous serial data flows. We consider the features of the syntax and semantics of the statically typed data driven functional parallel programming language Smile that supports asynchronous sequential flows. Our main idea is to apply the Hoar concept of communicating sequential processes to the computation control on the data readiness. It is assumed that on the data readiness a control signal is emitted to inform the processes about the occurrence of certain events. The special feature of our approach is that the model is extended with the special asynchronous containers that can generate events on their partial filling. These containers are a stream and a swarm, each of which has its own specifics. A stream is used to process data which have identical type. The data comes sequentially and asynchronously at arbitrary time moments. The number of the incoming data elements is initially unknown, so the processing completes on the signal of the end of the stream. A swarm is used to contain independent data of the same type and may be used for the massive parallel operations performing. Unlike a stream, the swarm’s size is fixed and known in advance. General principles of the operations with the asynchronous sequential flows with an arbitrary order of data arrival are described. The use of the streams and the swarms in various situations is considered. We propose the language constructions which allow us to operate the swarms and streams and describe the specifics of their application. We provide the sample functions to illustrate the use of the different approaches to description of the parallelism: recursive processing of the asynchronous flows, processing of the flows in an arbitrary or predefined order of operations, direct access and access by the reference to the elements of the streams and swarms, pipelining of calculations. We give a preliminary parallelism assessment which depends on the ratio of the rates of data arrival and their processing. The proposed methods can be used in the development of the future languages and tool-kits of architecture-independent parallel programming.

Two resources (submarkings) are called similar if in any marking any one of them can be replaced by another one without affecting the observable behavior of the net (regarding marking bisimulation). It is known that resource similarity is undecidable for general labelled Petri nets. In this paper we study the properties of the resource similarity and resource bisimulation (a subset of complete similarity relation closed under transition firing) in Petri nets with invisible transitions (where some transitions may be labelled with an invisible label (τ) that makes their firings unobservable for an external observer). It is shown that for a proper subclass (p-saturated nets) the resource bisimlation can be effectively checked. For a general class of Petri net with invisible transitions it is possible to construct a sequence of so-called (n, m)-equivalences approximating the largest τ-bisimulation of resources.

Network algorithms are often used to analyze and interpret the biological data. One of the widely used approaches is to solve the problem of identifying an active module, where a connected subnetwork of a biological network is selected which best reflects the difference between the two considered biological conditions. In this work this approach is extended to the case of a larger number of biological conditions and the problem of the joint clustering in network and correlation spaces is formulated.

To solve this problem, an iterative method is proposed at takes as the input graph G and matrix X, in which the rows correspond to the vertices of the graph. As the output, the algorithm produces a set of subgraphs of the graph G so that each subgraph is connected and the rows corresponding to its vertices have a high pairwise correlation. The efficiency of the method is confirmed by an experimental study on the simulated data.

Process-Aware Information Systems (PAIS) is a special class of the IS intended for the support the tasks of initialization, end-to-end management and completion of business processes. During the operation such systems accumulate a large number of data that are recorded in the form of the event logs. Event logs are a valuable source of knowledge about the actual behavior of a system. For example, there can be found information about the discrepancy between the real and the prescribed behavior of the system; to identify bottlenecks and performance issues; to detect anti-patterns of building a business system. These problems are studied by the discipline called “Process Mining”.

The practical application of the process mining methods and practices is carried out using the specialized software for data analysts. The subject area of the process analysis involves the work of an analyst with a large number of graphical models. Such work will be more efficient with a convenient graphical modeling tool. The paper discusses the principles of building a graphical tool “VTMine for Visio” for the process modeling, based on the widespread application for business intelligence Microsoft Visio. There are presented features of the architecture design of the software extension for application in the process mining domain and integration with the existing libraries and tools for working with data. The application of the developed tool for solving various types of tasks for modeling and analysis of processes is demonstrated on a set of experimental schemes.