No abstract

Today, increased attention is drawn towards network representation learning, a technique that maps nodes of a network into vectors of a low-dimensional embedding space. A network embedding constructed this way aims to preserve nodes similarity and other specific network properties. Embedding vectors can later be used for downstream machine learning problems, such as node classification, link prediction and network visualization. Naturally, some networks have text information associated with them. For instance, in a citation network, each node is a scientific paper associated with its abstract or title; in a social network, all users may be viewed as nodes of a network and posts of each user as textual attributes. In this work, we explore how combining existing methods of text and network embeddings can increase accuracy for downstream tasks and propose modifications to popular architectures to better capture textual information in network embedding and fusion frameworks.

No abstract

No abstract

This paper presents theory behind synthesis of special operating decisions in mobile robotics. The authors have developed and implemented an experimental research methodology to substantiate the theoretical and practical significance of the proposed decision structure for incorporation of quasi-cognitive mechanisms in the process of intelligent data processing in such robotics. This paper presents research and testing of a computer model of the abstract decision-making component that analyzes the movement trajectory of a mobile object in the operational space of a mobile robotic system. This approach towards intelligent decisionmaking can be tested for effectiveness by whether it enables the system to detect change in the parameters of the analyzed dynamic object that are important for autonomous analysis of the environment. One finding is that using this novel operating decision structure to improve autonomy contributes to the emergence of a behavior strategy that bypasses the combinatorial methods configured during the development; this improves the system’s adaptability to change in its environment.