The paper compares performance of various methods of automatic implicit aspect detection in publicism sentences in Russian. The task of implicit aspect detection is an auxiliary task in the aspect-oriented sentiment analysis. The experiments were conducted on a corpus of sentences extracted from political campaign materials. The best results, with F1-measure reaching 0.84, were obtained using the Navec embeddings and classifiers based on the support vector machine method. Fairly high results, with F1-measure reaching 0.77, were obtained using the bag-of-words model and the naive Bayesian classifier. Other methods showed lower performance. It was also revealed during the experiments that the detection quality can differ significantly between the aspects. The detection quality is the highest for the aspects associated with characteristic marker words, for example, “health car” and “holding elections”. More general aspects, such as “quality of governance”, are detected with the worst quality.

The article continues the series of publications on the development and verification of control programs based on LTL-specifications of a special type. Earlier, a declarative LTL-specification was proposed to describe the strictly deterministic behavior of programs, ways of its verification and translation were worked out: for verification, the model checking tool nuXmv is used, and the translation is carried out into an imperative programming language ST for programmable logic controllers.

When verifying the declarative LTL-specification of the behavior of programs, there may be a need to simulate the behavior of its environment. In general, it is required to ensure the possibility of constructing closed-loop systems “program-environment”. In this work, an LTL-specification of constraintly non\-de\-ter\-mi\-nis\-tic behavior of a Boolean variable is proposed to describe the behavior of the environment of logical control programs. This specification allows defining the behavior of Boolean feedback signals, as well as fairness conditions to exclude unrealistic scenarios of behavior.
The article proposes an approach to the development and verification of logical control programs, within which the behavior model of the program environment is described in the form of constraints on the behavior of its input signals, what allows avoiding a separate detailed representation of the processes of the environment operation. As a result, the obtained behavior model of the closed-loop system “program-environment” provides a number of advantages: simplification of the modeling process, reduction of the state space of the verified model, and reduction of verification time. If it is impossible to reduce the behavior of the environment to the behavior of existing input signals, this approach suggests using “imaginary” sensors — additional Boolean variables that are used as an auxiliary means for describing the behavior of input signals. The purpose of introducing imaginary sensors is to compensate for missing sensors to track the specific behavior of some elements of the environment that needs to be taken into account when defining realistic behavior of the inputs of a logical control program.
The proposed approach to the development and verification of programs taking into account the behavior of the environment (a control object) is demonstrated by the example of an industrial plastic molding plant.

The paper presents a method for semantic data analysis by means of complex-valued matrix decomposition. The method is based on the quantum model of contextual decision-making, according to which observable probabilities are generated by qubit states, representing subjective meaning of the contexts relative to the basis decision. In the simplest three-context case, one of these qubits is decomposed to superposition of the remaining two, mathematically encoding semantic relations between the three contexts. For use in data analysis this model is translated to the matrix form, in which rows and columns correspond to the contexts and instances of experiment. The observable real-valued data then emerge from a complex-valued amplitude matrix, decomposed to a product of a real basis matrix and complex-valued matrix of superposition coefficients. This decomposition reveals stable process-semantic relations between the considered contexts, not captured by other methods of analysis. As a result, the data are approximated with higher precision and fewer parameters than singular and non-negative matrix decompositions, truncated to the same dimension. The model is experimentally approved in descriptive and prognostic regimes. The result opens prospects for development of nature-like computational architectures on novel logical grounds.

Process mining is a field of computer science that deals with the discovery and analysis of process models based on automatically generated event logs. Currently, many companies are using this technology to optimize and improve their business processes. However, a discovered process model may be too detailed, sophisticated, and difficult for experts to understand. In this paper, we consider a problem of discovering the hierarchical business process model from a low-level event log, i. e., the problem of the automatic synthesis of more readable and understandable process models based on the data stored in the event logs of information systems.

The discovery of better-structured and more readable process models is extensively studied in the framework of process mining research from different perspectives. In this paper, we present an algorithm for discovering hierarchical process models represented as two-level workflow Petri nets. The algorithm is based on predefined event partitioning so that this partitioning defines a sub-process corresponding to a high-level transition at the top level of a two-level net. In contrast to existing solutions, our algorithm does not impose restrictions on the process control flow and allows for concurrency and iterations.

We give some estimates for the minimum projector norm under linear interpolation on a compact set in ${\mathbb R}^n$. Let $\Pi_1({\mathbb R}^n)$ be the space of polynomials in $n$ variables of degree at most $1$, $\Omega$ is a compactum in ${\mathbb R}^n$, $K={\rm conv}(\Omega)$. We will assume that ${\rm vol}(K)>0$. Let the points $x^{(j)}\in \Omega$, $1\leq j\leq n+1,$ be the vertices of an $n$-dimensional nondegenerate simplex. The interpolation projector $P:C(\Omega)\to \Pi_1({\mathbb R}^n)$ with the nodes $x^{(j)}$ is defined by the equations $Pf\left(x^{(j)}\right)=f\left(x^{(j)}\right)$. By $\|P\|_\Omega$ we mean the norm of $P$ as an operator from $C(\Omega)$ to $C(\Omega)$. By $\theta_n(\Omega)$ we denote the minimal norm $\|P\|_\Omega$ of all operators $P$ with nodes belonging to $\Omega$. By ${\rm simp}(E)$ we denote the maximal volume of the simplex with vertices in $E$. We establish the inequalities $\chi_n^{-1}\left(\frac{{\rm vol}(K)}{{\rm simp}(\Omega)}\right)\leq \theta_n(\Omega)\leq n+1.$ Here $\chi_n$ is the standardized Legendre polynomial of degree $n$. The lower estimate is proved using the obtained characterization of Legendre polynomials through the volumes of convex polyhedra. More specifically, we show that for every $\gamma\ge 1$ the volume of the set $\left\{x=(x_1,...,x_n)\in{\mathbb R}^n : \sum |x_j| +\left|1- \sum x_j\right|\le\gamma\right\}$ is equal to ${\chi_n(\gamma)}/{n!}$. In the case when $\Omega$ is an $n$-dimensional cube or an $n$-dimensional ball, the lower estimate gives the possibility to obtain the inequalities of the form $\theta_n(\Omega)\geqslant c\sqrt{n}$. Also we formulate some open questions.

In this paper, we study undirected multiple graphs of any natural multiplicity $k>1$. There are edges of three types: ordinary edges, multiple edges and multi-edges. Each edge of the last two types is a union of $k$ linked edges, which connect 2 or $(k+1)$ vertices, correspondingly. The linked edges should be used simultaneously. If a vertex is incident to a multiple edge, it can be also incident to other multiple edges and it can be the common end of $k$ linked edges of some multi-edge. If a vertex is the common end of some multi-edge, it cannot be the common end of another multi-edge.

We study the problem of finding the Eulerian walk (the cycle or the trail) in a multiple graph, which generalizes the classical problem for an ordinary graph. The multiple Eulerian walk problem is NP-hard.
We prove the polynomiality of two subclasses of the multiple Eulerian walk problem and elaborate the polynomial algorithms. In the first subclass, we set a constraint on the ordinary edges reachability sets, which are the subsets of vertices joined by ordinary edges only. In the second subclass, we set a constraint on the quasi-vertices degrees in the graph with quasi-vertices. The structure of this ordinary graph reflects the structure of the multiple graph, and each quasi-vertex is determined by $k$ indices of the ordinary edges reachability sets, which are incident to some multi-edge.