14th Conference on DATA ANALYSIS METHODS for Software Systems

DAMSS-2023 is the 14th International Conference on Data Analysis Methods for Software Systems, held in Druskininkai, Lithuania. Every year at the same venue and time. The exception was in 2020, when the world was gripped by the Covid-19 pandemic and the movement of people was severely restricted. After a year’s break, the conference was back on track, and the next conference was successful in achieving its primary goal of lively scientific communication. The conference focuses on live interaction among participants. For better efficiency of communication among participants, most of the presentations are poster presentations. This format has proven to be highly effective. However, we have several oral sections, too. The history of the conference dates back to 2009 when 16 papers were presented. It began as a workshop and has evolved into a well-known conference. The idea of such a workshop originated at the Institute of Mathematics and Informatics, now the Institute of Data Science and Digital Technologies of Vilnius University. The Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea, which gained enthusiastic acceptance from both the Lithuanian and international scientific communities. This year’s conference features 84 presentations, with 137 registered participants from 11 countries. The conference serves as a gathering point for researchers from six Lithuanian universities, making it the main annual meeting for Lithuanian computer scientists. The primary aim of the conference is to showcase research conducted at Lithuanian and foreign universities in the fields of data science and software engineering. The annual organization of the conference facilitates the rapid exchange of new ideas within the scientific community. Seven IT companies supported the conference this year, indicating the relevance of the conference topics to the business sector. In addition, the conference is supported by the Lithuanian Research Council and the National Science and Technology Council (Taiwan, R. O. C.). The conference covers a wide range of topics, including Applied Mathematics, Artificial Intelligence, Big Data, Bioinformatics, Blockchain Technologies, Business Rules, Software Engineering, Cybersecurity, Data Science, Deep Learning, High-Performance Computing, Data Visualization, Machine Learning, Medical Informatics, Modelling Educational Data, Ontological Engineering, Optimization, Quantum Computing, Signal Processing. This book provides an overview of all presentations from the DAMSS-2023 conference

What Does Twitter Say About Self-Regulated Learning? Mapping Tweets From 2011 to 2021

Social network services such as Twitter are important venues that can be used as rich data sources to mine public opinions about various topics. In this study, we used Twitter to collect data on one of the most growing theories in education, namely Self-Regulated Learning (SRL) and carry out further analysis to investigate What Twitter says about SRL? This work uses three main analysis methods, descriptive, topic modeling, and geocoding analysis. The searched and collected dataset consists of a large volume of relevant SRL tweets equal to 54,070 tweets between 2011 and 2021. The descriptive analysis uncovers a growing discussion on SRL on Twitter from 2011 till 2018 and then markedly decreased till the collection day. For topic modeling, the text mining technique of Latent Dirichlet allocation (LDA) was applied and revealed insights on computationally processed topics. Finally, the geocoding analysis uncovers a diverse community from all over the world, yet a higher density representation of users from the Global North was identified. Further implications are discussed in the paper.publishedVersio

Slava Ukraini: a psychobiographical case study of Volodymyr Zelenskyy’s public diplomacy discourse

Volodymyr Zelenskyy\u27s public diplomacy during the Russo-Ukrainian conflict was examined in this dissertation. Zelenskyy’s discourse emphasized his action-oriented traits, Ukrainian identity, and nationalism. The study employed LTA, and LIWC-22, for natural language processing analyses of Zelenskyy\u27s public speeches and diplomatic discourse. Zelenskyy demonstrated agency, adaptability, collaboration, and positive language patterns, suggesting confidence and optimism, according to the data. In addition, the research emphasizes how domestic and international factors influence state behavior, as well as how political demands, cultural, historical, and political factors influence Zelenskyy\u27s decision-making. This dissertation sheds light on a global leader\u27s psychobiographical characteristics, beliefs, and motivations during a crisis, thereby advancing leadership and conflict resolution. By incorporating transformational leadership theory into LTA, researchers can gain a better understanding of effective leadership and how it develops strong connections with followers. LTA, LIWC-22, and qualitative coding were used to identify themes and trends in Zelenskyy\u27s speeches. The findings show Zelenskyy\u27s linguistic and leadership traits in public diplomacy, emphasizing the importance of understanding leaders\u27 traits in foreign policy decision-making. Psychobiographical profiles aid scholars in understanding a leader\u27s political views on conflict, their ability to influence events, and how they accomplish their objectives. As a result, perceptions of the state as an actor, as well as foreign policy decisions, must consider the effect of individual leaders. Conclusions include the Brittain-Hale Foreign Policy Analysis Model, based on a heuristic qualitative coding framework; HISTORICAL

The Palgrave Handbook of Digital Russia Studies

This open access handbook presents a multidisciplinary and multifaceted perspective on how the ‘digital’ is simultaneously changing Russia and the research methods scholars use to study Russia. It provides a critical update on how Russian society, politics, economy, and culture are reconfigured in the context of ubiquitous connectivity and accounts for the political and societal responses to digitalization. In addition, it answers practical and methodological questions in handling Russian data and a wide array of digital methods. The volume makes a timely intervention in our understanding of the changing field of Russian Studies and is an essential guide for scholars, advanced undergraduate and graduate students studying Russia today

Exhibiting the Past

With respect to public issues, history matters. With the worldwide interest for historical issues related with gender, religion, race, nation, and identity, public history is becoming the strongest branch of academic history. This volume brings together the contributions from historians of education about their engagement with public history, ranging from musealisation and alternative ways of exhibiting to new ways of storytelling