Topic Modeling in Russia: Current Approaches and Issues in Methodology

Topic modeling as an instrument of probabilistic clustering for text collections has gained particular attention within the computational social science in Russia. This chapter looks at how topic modeling techniques have been developed and employed by the Russian scholars, both for Russian and other languages. We divide the works on topic modeling into methodological, applied, relational, and those dedicated to modeling quality assessment. While the methodological studies are the most developed, the works explaining the substance of the Russian-language discussions cover an important niche in political and social science. However, there is a gap between method-oriented works that treat Russian as “language as such” and the works by computational linguists who focus on Russian but treat topic modeling as a method of secondary importance.

Shoreline Detection and Land Segmentation for Autonomous Surface Vehicle Navigation with the Use of an Optical System

Autonomous surface vehicles (ASVs) are a critical part of recent progressive marine technologies. Their development demands the capability of optical systems to understand and interpret the surrounding landscape. This capability plays an important role in the navigation of coastal areas a safe distance from land, which demands sophisticated image segmentation algorithms. For this purpose, some solutions, based on traditional image processing and neural networks, have been introduced. However, the solution of traditional image processing methods requires a set of parameters before execution, while the solution of a neural network demands a large database of labelled images. Our new solution, which avoids these drawbacks, is based on adaptive filtering and progressive segmentation. The adaptive filtering is deployed to suppress weak edges in the image, which is convenient for shoreline detection. Progressive segmentation is devoted to distinguishing the sky and land areas, using a probabilistic clustering model to improve performance. To verify the effectiveness of the proposed method, a set of images acquired from the vehicle’s operative camera were utilised. The results demonstrate that the proposed method performs with high accuracy regardless of distance from land or weather conditions.