Hypergraph recovery algorithms from a given vector of vertex degrees

Methods of obtaining certain classes of hypergraphs from a given integer vector of vertex degrees are considered. These classes are as follows: hyperedges with unit weight incident upon k vertices; hyperedges with unit weight incident upon k vertices in the case when the vertices may be non-unique; multiple hyperedges incident upon k vertices; and arbitrary hypergraph in which the edges can contain any set of k vertices. For each of these classes, an algorithm is proposed for constructing the hypergraph from an arbitrary vector. If the construction is impossible, the algorithm determines how much the vector should be reduced so that the hypergraph could be constructed.     

Reconstructing 2-complexes by a nonnegative integer-valued vector

Over the last decade, hypergraphs have seen a lot of use in electrotechnical problems, network design, multiindex transportation problems, computer modeling of complex dynamical systems, representations of complex industrial control systems, and many other applied problems. For some of these problems (e.g., multiindex transportation problems), a better representation is given by 2-complexes, a special class of hypergraphs in which each edge is incident to three vertices. It is known that each hypergraph corresponds to the vector of degrees of its vertices, but the converse does not hold. In this work, we consider the problem of reconstructing (implementing, realizing) a 2-complex from an arbitrary vector.     

Synthesis-Analysis of Derivation Sequences of Tables’ Functional Dependences

Journal of Computer and Systems Sciences International - As all computational programs, derivation sequences of functional dependences are twice partially ordered sets: by following the...     

Cyclic Generative Neural Networks for Improved Face Recognition in Nonstandard Domains

A system of methods for improving the quality of face recognition from infrared images is described. For testing the recognition algorithm in a multidomain environment, a database of ordinary and infrared face images is collected. An algorithm based on cyclic generative neural networks is developed. This algorithm makes it possible to transform images from the color domain into the infrared domain, which significantly increases the size of the training sample. It is shown that fine-tuning the recognition algorithm using the generated infrared images improves the recognition result on the test sample.     

Network optimization and problems with coupling variables

Network optimization problems are considered. Their statements include numerous variables and equipments. Decomposition methods are used for their solution. In specific situations, intermediate problems solved by the algorithms have the form of the knapsack problem. In the case when the constraints have a staircase structure, an efficient algorithm can be constructed. The procedure for the sequential recalculation of the coefficients of the objective function in this algorithm can be applied to other problems with unimodular matrices, in particular, to various transportation problems.     

Neural Network Training System for Marker Encoding

Journal of Computer and Systems Sciences International - In this paper, we propose a training system for visual markers that provides the generation and subsequent recognition (under real-world...     

Intelligent System for Identifying Emotions on Audio Recordings Using Chalk Spectrograms

Journal of Computer and Systems Sciences International - A neural network architecture is proposed to identify human emotions on audio recordings. Emotions are understood as fear, joy, sadness,...