Discovering the stances of media outlets and influential people on current, debatable topics is important for social statisticians and policy makers. Many supervised solutions exist for determining viewpoints, but manually annotating training data is costly. In this paper, we propose a cascaded method that uses unsupervised learning to ascertain the stance of Twitter users with respect to a polarizing topic by leveraging their retweet behavior; then, it uses supervised learning based on user labels to characterize both the general political leaning of online media and of popular Twitter users, as well as their stance with respect to the target polarizing topic. We evaluate the model by comparing its predictions to gold labels from the Media Bias/Fact Check website, achieving 82.6% accuracy.

We investigate the political roles of "Internet trolls" in social media. Political trolls, such as the ones linked to the Russian Internet Research Agency (IRA), have recently gained enormous attention for their ability to sway public opinion and even influence elections. Analysis of the online traces of trolls has shown different behavioral patterns, which target different slices of the population. However, this analysis is manual and laborintensive, thus making it impractical as a firstresponse tool for newly-discovered troll farms. In this paper, we show how to automate this analysis by using machine learning in a realistic setting. In particular, we show how to classify trolls according to their political role -left, news feed, right-by using features extracted from social media, i.e., Twitter, in two scenarios: (i) in a traditional supervised learning scenario, where labels for trolls are available, and (ii) in a distant supervision scenario, where labels for trolls are not available, and we rely on more-commonly-available labels for news outlets mentioned by the trolls. Technically, we leverage the community structure and the text of the messages in the online social network of trolls represented as a graph, from which we extract several types of learned representations, i.e., embeddings, for the trolls. Experiments on the "IRA Russian Troll" dataset show that our methodology improves over the state-of-the-art in the first scenario, while providing a compelling case for the second scenario, which has not been explored in the literature thus far.

Marking and engraving of aluminum was carried out by a CuBr (copper bromide) laser operating at the wavelength of 511 nm with a pulse duration of 30 ns. The reflection was investigated of the laser-ablated aluminum surface depending on the laser beam scanning speed and the distance between the laser lines. The changes of the aluminum surface were observed before and after laser processing by an optical microscope and visually. The laser marking quality was estimated by measuring the reflection of the irradiated aluminum surface by an optical spectrometer. The dependence was analyzed of the contrast on the speed for different steps in laser marking of areas of the surface of aluminum samples. Marking by a CuBr laser shows a potential for good quality marking of barcodes and QR codes on aluminum products.

An investigation has been made of dependence of marking contrast on surface roughness, spacing between hatches (that is the pitch between hatches) and the speed for products made of tool steel. Experiments made concern pulse laser CuBr utilizing МОРА system.

Pronoun resolution is part of coreference resolution, the task of pairing an expression to its referring entity. This is an important task for natural language understanding and a necessary component of machine translation systems, chat bots and assistants. Neural machine learning systems perform far from ideally in this task, reaching as low as 73% F1 scores on modern benchmark datasets. Moreover, they tend to perform better for masculine pronouns than for feminine ones. Thus, the problem is both challenging and important for NLP researchers and practitioners. In this project, we describe our BERT-based approach to solving the problem of gender-balanced pronoun resolution. We are able to reach 92% F1 score and a much lower gender bias on the benchmark dataset shared by Google AI Language team.