The Faculty of Humanities was created on December 1, 2014. It trains instructors and researchers in the field of language and literature, as well as specialists in philosophy, history, and modern culture.
The main goal of the faculty is to teach students how to understand and analyse various cultural processes, employ current research strategies, and effectively put their knowledge into practice.
The faculty’s staff are leading Russian academics and practitioners from various cultural fields, as well as invited foreign specialists. Students receive a modern education in the humanities, as well as thorough language preparation, which allows them to find extensive professional opportunities upon graduation. Students are given the opportunity to conduct research and gain practical experience at major private and public establishments.
Our strengths:
1. Interdisciplinary approach
We study the humanities alongside other academic fields so that students can apply their skills in various areas.
2. International cooperation
We maintain active international ties, which allows students to undertake internships and study abroad, as well as broaden their outlook and cultural experiences.
3. Research
We encourage and support student participation in research projects. This gives them an opportunity to apply their knowledge in practice and make a contribution to the development of the humanities.
Our graduates pursue careers in public and commercial organisations and various types of mass media. They also implement their own media, cultural, social, and educational projects.
Publications
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Book
BRICS and Climate Change: Balancing National Interests, National Development Goals and Global Environmental Sustainability
This book delves into the intricate interplay between climate change and the dynamic shifts in global power structures, focusing on the expanded BRICS. Offering a distinctive vantage point by encapsulating the evolving dynamics of Brazil, Russia, India, China, and South Africa, the book through this unique perspective, sheds light on the nuanced relationship between environmental challenges and the geopolitical landscape. It has an interdisciplinary approach, seamlessly weaving insights from political science, economics, development studies, and the natural sciences. This holistic integration of diverse disciplines enhances the reader's understanding, presenting a comprehensive analysis of the multifaceted issues at the nexus of climate change and global politics. It not only maps out the current climate crisis confronting humanity in the twenty-first century, but also extends a helping hand to policymakers. The inclusion of pragmatic policy recommendations adds a pragmatic dimension, providing valuable insights that policymakers may find instrumental in addressing the challenges posed by climate change.
Singapore: Springer, 2024.
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Article
Predicting First-Language and Second-Language Proficiency Using Eye Fixation Data and Demographic Information: Assumptions, Data Representations and Methods
ABSTRACT Studying first-language (L1), second-language (L2) acquisition, and bilingualism using eye
movement data has become a popular topic in psycholinguistic and educational research communities. The
current research uses eye fixation data along with demographic information, to investigate the five research
questions (RQ) as follows. Q1 Is it possible to predict L1 from the eye fixation data using artificial intelligence
(AI) methods? Q2 Is it possible to predict second-language proficiency (L2P) from eye-fixation data using AI
methods? Q3 Which of the six L2P assessment batteries under consideration is more effective in predicting
L2P? Q4 How informative is eye fixation data or its combination with demographic information in predicting
L1 and L2P? Q5 How can eye fixation data be represented for training AI models in predicting L1 and L2P?
We used the MECO L2 data set and scrutinized the performance of three families of AI methods. In respect to
each RQ the results showed that 1) using only eye fixation data, it is possible to predict L1 with a ROC-AUC
equal to 0.755; 2) using only eye fixation data, it is not possible to predict L2P accurately (since a R2-score
equal to 0.216 was obtained); 3) L2 Lexical Skills is the most effective L2P assessment battery; 4) combining
the eye-fixation data with demographic features led to a significant improvement in the performance of the
models, i.e., a ROC-AUC equal to 0.997 in predicting L1 and a R2-score equal to 0.899 in predicting L2P
were obtained, and simultaneously downgraded the impacts of eye-fixation parameters; 5) the 2D-scatter
plot images can be considered an appropriate candidate for training AI models using only eye-fixation data
–at least for predicting L1.IEEE Access. 2024.
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Book chapter
Time Series Generation with GANs for Momentum Effect Simulation on Moscow Stock Exchange
The ability to accurately simulate financial markets is crucial, as it allows researchers and practitioners to rigorously test and refine trading strategies without the high risks associ-ated with real-world experimentation. By leveraging Generative Adversarial Networks (GANs), this research aims to enhance the robustness and effectiveness of trading strategies by providing a controlled environment to assess potential outcomes and strategy resilience under varied market conditions. In this work, we propose the application of GAN s for simu-1ating multidimensional time series in the context of developing and testing trading strategies. We conduct an experimental study to jointly simulate the log-returns of several stocks on Moscow Exchange for Momentum effect evaluation. Compared to traditional methods such as bootstrapping, GANs can better model and interpolate the non-parametric complex nature of the data, providing an increased diverse sample size. This methodology could be beneficial for investors seeking new opportunities to test and tune hyperparameters of other trading strategies.
In bk.: Proceedings of the IEEE/IAFE Computational Intelligence for Financial Engineering (CIFEr-24). IEEE, 2024. P. 1-7.
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Working paper
Exploring the Effectiveness of Methods for Persona Extraction
The paper presents a study of methods for extracting information about dialogue participants and evaluating their performance in Russian. To train models for this task, the Multi-Session Chat dataset was translated into Russian using multiple translation models, resulting in improved data quality. A metric based on the F-score concept is presented to evaluate the effectiveness of the extraction models. The metric uses a trained classifier to identify the dialogue participant to whom the persona belongs. Experiments were conducted on MBart, FRED-T5, Starling-7B, which is based on the Mistral, and Encoder2Encoder models. The results demonstrated that all models exhibited an insufficient level of recall in the persona extraction task. The incorporation of the NCE Loss improved the model's precision at the expense of its recall. Furthermore, increasing the model's size led to enhanced extraction of personas.arxiv.org. Computer Science. Cornell University, 2024