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September 11, 2026
How to Assess Students Knowledge in the Age of AI
A researcher at HSE University has proposed a flowchart to help lecturers decide how to assess students who use artificial intelligence. It shows where the use of AI should be restricted and where it can be incorporated into the learning process. The article has been published in IT Professional.
September 9, 2026
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You cannot study spoken dialects from books. Instead, you need to go to a village, seek out its elders, and earn the trust of local residents before you can record hours of spontaneous stories. This is how Natalia Muravleva, Associate Professor at the Faculty of Humanities, conducts her research. Her internship in Serbia continued her long-standing study of dialects spoken by Macedonian settlers. In this interview, she discusses how diaspora cultural centres help researchers reach informants, why native speakers need to be interviewed only in their own language (otherwise, as she puts it, they may 'break'), and how a single field season helped her finalise her monograph. She also shares warm memories of autumn in Belgrade and of colleagues with whom grammar can be discussed in three languages at once.
September 9, 2026
Scientists Train Neural Network to Generate Process Plans from 3D Models
Researchers at the HSE FCS AI and Digital Science Institute have developed CAD2TechSpec, a framework that converts 3D models of mechanical parts into machining process plans—step-by-step instructions for machine tools. The solution aims to reduce the time required for the design and preparation of technical process documentation in mechanical engineering, aircraft manufacturing, and other high-tech industries. The study findings have been published in PeerJ Computer Science.

 

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bXES: A Binary Format for Storing and Transferring Software Event Logs

P. 279–293.
Evgenii V. Stepanov, Mitsyuk A. A.

Modern software produces a lot of events that can be analyzed using process mining techniques. The first step in any process mining pipeline is the collection of event logs. Then, those event logs need to be stored persistently on the disk to be transferred via local and global networks. The problem is that software event logs usually consist of many events, each of which can specify tens of attributes. In such a context, the event log stored in the conventional XML-based XES format consumes a tremendous amount of memory. Moreover, it is not read-friendly, i.e., does not provide tools with any advantage while reading an XES file. In this paper, we present bXES, a binary format for storing and transferring event logs, especially software event logs. We highlight main characteristics of software event logs which are utilized in the format scheme, and next we describe the new format. We developed open-source tools to convert XES logs to bXES and vice versa. Tools are available for C# and Rust ecosystems. Finally, we conduct experiments to demonstrate the bXES compatibility and evaluate it. Based on experimental data, bXES event logs are significantly more compact than conventional XES logs and can compete with logs stored in binary EXI format. This reduces storage volume needs and weakens communication constraints when transferring event logs. The experiments are conducted with data from real-life business processes and software events.

Language: English
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Keywords: Software Process MiningSoftware analysisEvent Logs Storing
Publication based on the results of:
New methods in the study of formal computation models, computer systems and related theoretical computer science problems (2025)

In book

26th International Conference, DAMDID/RCDL 2024, Nizhny Novgorod, Russia, October 23–25, 2024, Revised Selected Papers. Data Analytics and Management in Data Intensive Domains. (CCIS, volume 2641)
Springer, 2026.
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