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Opis: Recently, there has been a growing interest in scenarios that employ a few (imbalanced) power traces for deep learning based side-channel analysis (DL-SCA). Finding a lightweight DL-SCA architecture that is more generalizable and faster to learn is of great importance when handling such situations. In this work, an initial analysis is conducted on the capability of learning and the susceptibility to attacks of prevalent DL-SCA models, focusing on cases that culminate in an unsuccessful attack. Subsequently, a meta-learning technique, known as prototypical networks, is delineated for the construction of a lightweight deep learning framework. In contrast to conventional DL-SCA models, the proposed architecture, designated as PN-SCA, does not predict the probability of a power trace belonging to an intermediate value in classification. Instead, it facilitates the learning of an encoder capable of embedding power consumption data within a latent space, while also establishing templates, or prototypes, for diverse categories. Moreover, we developed a metric that is specifically intended for the selection of hyperparameters due to the unique training phase of PN-SCA. Finally, four distinct scenarios are established with few (including one imbalanced) power traces to evaluate the correctness of our architecture. The results clearly illustrate the advantages of our PN-SCA in terms of generalization, reduced training costs (with a decrease in profiling traces by over 50%), and significantly enhanced attack effect (with a reduction of power traces requirements by over 90%), thus demonstrating notable improvements over existing methodologies
Ključne besede: side-channel analysis, deep learning based SCA, prototypical networks, few-shot learning
Objavljeno v RUP: 20.05.2026; Ogledov: 21; Prenosov: 2
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Opis: The article presents a dataset of Uzbek base words extracted from a purposefully prepared corpus using the Synonym Thesaurus Support method. This method identifies base words for each school-grade by analysing a large text corpus comprising 142 textbooks intended for school education in Uzbekistan. The definition of the base word used in this article and in the proposed dataset is a word within a synonymic series that: - is the most widely used. - is distinguished by semantic clarity and stability. - has stylistic neutrality. Based on the proposed approach, school textbooks were analysed by dividing them into Primary (school grades 1 - 4), Basic Secondary (school grades 5 - 9), and Secondary (school grades 10 - 11) blocks. Base words that stand out from the general corpus were identified for each school-grade. This method extracted new base words not found in previous school grades and specific to the observed grade. The main idea of the method is to extract base words from the lemma sset of each school-grade using a corpus of synonyms. This allows analysing the level of lexical complexity and class-specific vocabulary richness of texts intended for schoolchildren. The final results are lists of base words specifically extracted from primary (school-grades 1 - 4), basic secondary (school-grades 5 - 9), and secondary (school-grades 10 - 11) school texts; 17,599,48,203, and 20,491 base words, respectively.
Ključne besede: school corpus, base word, basic vocabulary, Uzbek language
Objavljeno v RUP: 20.05.2026; Ogledov: 25; Prenosov: 4
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Opis: This chapter investigates the feasibility and challenges of using millimetre-wave radar for gesture recognition on deformable objects, such as plush toys or other objects made of flexible materials, which are typically not instrumented with sensors. Unlike vision-based systems, which are limited by occlusion and require clear line of sight, radar sensing can detect gestures through non-conductive materials. The authors compare prior work on gesture recognition performance across mid-air, on-object and on-deformable-object contexts using different radar signal representations and deep learning models. In addition, the authors conduct an experiment demonstrating that object deformations do not negatively impact recognition accuracy. These findings open new possibilities for contactless interaction with soft materials in everyday environments without requiring embedded instrumentation.
Ključne besede: radar, gesture recognition, deformable objects
Objavljeno v RUP: 18.05.2026; Ogledov: 104; Prenosov: 2
Celotno besedilo (41,39 MB)
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Opis: The ability to sense mid-air gestures with miniaturised radars embedded in everyday objects opens up new opportunities for interaction. Applications include integration into wearable devices, automotive dashboards, smart furniture, and other components within smart environments. Despite this potential, the lack of studies on how various occluding materials affect gesture recognition performance hinders progress in this area. Previous studies have primarily focused on evaluating only one type of radar signal representation, despite the fact that several other representations exist and were proved effective. To address this, the chapter presents a comparative evaluation of four radar signal representations: In-phase and Quadrature (IQ) representations in the frequency domain and magnitude and range-angle (including both elevation and azimuth components) and range-Doppler. The goal is to assess their robustness against signal distortions introduced by occluding materials. Preliminary results indicate that recognition performance tends to improve with a higher transmission coefficient. Moreover, range-Doppler and range-angle representations exhibit significantly greater robustness to distortion compared to IQ representations
Ključne besede: comparative testing, radar signal representations, sensing through materials
Objavljeno v RUP: 18.05.2026; Ogledov: 84; Prenosov: 2
Celotno besedilo (41,62 MB)
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Opis: Introduction. The Roma community faces numerous challenges in accessing health services, including language barriers, discrimination, low health literacy, and social exclusion. Aim. The aim of the research was to examine the experiences of health professionals in treating Roma patients, with a focus on communication, cultural differences, and access to health services. Special emphasis was also placed on the presence of antigypsyism in the healthcare system. Methods. The research was based on a qualitativedescriptive design. The sample included 15 healthcare workers with experience in treating Roma patients, primarily from the Dolenjska region. Data were collected through semi-structured individual interviews conducted between November and December 2024. The results were analyzed using thematic analysis. Results. Thematic analysis identified four themes: (1) Cultural characteristics of the Roma community, (2) Healthcare of Roma, (3) Interpersonal relationships between healthcare workers and Roma, (4) Communication with Roma. The results show that language barriers, low health literacy, and cultural differences are the main challenges in the treatment of Roma patients. Healthcare workers highlighted the use of “Roma helpers” – cultural mediators, communication adaptations, and educational workshops as successful strategies. Conclusion. The research highlights the importance of intercultural competences for improving healthcare for the Roma community. Healthcare professionals identified key strategies for addressing antigypsyism, such as patience, building trust, involving Roma cultural mediators, and organising targeted workshops. These strategies align with current guidelines, as they are based on respect, inclusion, and co-design of services with the Roma community. The findings can contribute to the development of tailored programmes that promote inclusion and reduce health inequalities.
Ključne besede: Roma community, cultural differences, language barriers, healthcare workers, health literacy
Objavljeno v RUP: 17.05.2026; Ogledov: 78; Prenosov: 0
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