With the development of technology allowing for a rapid expansion of data science and machine learning in our everyday lives, a significant gap is forming in the global job market where the demand for qualified workers in these fields cannot be properly satisfied. This worrying trend calls for an immediate action in education, where these skills must be taught to students at all levels in an efficient and up-to-date manner. This paper gives an overview of the current state of data science and machine learning education globally and both at the high school and university levels, while outlining some illustrative and positive examples. Special focus is given to vocational education and training (VET), where the teaching of these skills is at its very beginning. Also presented and analysed are survey results concerning VET students in Slovenia, Serbia, and North Macedonia, and their knowledge, interests, and prerequisites regarding data science and machine learning. These results confirm the need for development of efficient and accessible curricula and courses on these subjects in vocational schools.

Advancements in Assistive Technologies (ATs) have significantly improved the quality of life and autonomy for people with intellectual disabilities and autistic individuals, enhancing education, social interaction, employment, and mental health. Nevertheless, several barriers still emerge in the daily implementation of such technologies, highlighting the need to explore them in depth. Using a two-round Delphi method, we conducted a research study with an expert panel of 284 participants from 31 European countries, collecting quantitative and qualitative data. We tested ten future-oriented projections and gathered insights on participants’ backgrounds and attitudes towards ATs for this demographic. To enhance representation, experts from academia, service providers, industry, and disability organizations, as well as individuals with disabilities and their families, were included. Although the findings show strong consensus among experts on the positive impact and desirability of ATs for promoting the social inclusion of people with intellectual disabilities and autistic individuals, they also reveal differing perceptions across expert groups and identify two distinct future scenarios linked to different cultural, social, and economic hindrances. In this sense, our study provides diverse global perspectives, with the potential to help governing bodies understand the expectations, concerns, and needs related to ATs for often underrepresented groups–people with intellectual disabilities and autistic individuals in this case.

In this paper we revisited a database with measurements of the dielectric properties of rat muscles. Measurements were performed both in vivo and ex vivo; the latter were performed in tissues with varying levels of hydration. Dielectric property measurements were performed with an open-ended coaxial probe between the frequencies of 500 MHz and 50 GHz at a room temperature of 25 °C. In vivo dielectric properties are more valuable for creating realistic electromagnetic models of biological tissue, but these are more difficult to measure and scarcer in the literature. In this paper, we used machine learning models to predict the in vivo dielectric properties of rat muscle from ex vivo dielectric property measurements for varying levels of hydration. We observed promising results that suggest that our model can make a fair estimation of in vivo properties from ex vivo properties.

When pitch is explicitly modelled for parametric speech synthesis, microprosodic variations of the fundamental frequency f₀ are usually disregarded by current intonation models. While there are numerous studies dealing with the nature and the origin of microprosody, little research has been done on its audibility and its effect on the naturalness of synthetic speech. In this work, the influence of obstruent-related microprosodic variations on the perceived naturalness of articulatory speech synthesis was studied. A small corpus of 20 German words and sentences was re-synthesized using the state-of-the-art articulatory synthesizer VocalTractLab. The pitch contours of the real utterances were extracted and fitted with the Target-Approximation-Model. After the real microprosodic variations were removed from the obtained pitch contours, synthetic variations were applied based on a microprosody model. Subsequently, multiple stimuli with different microprosody amplitudes were synthesized and evaluated in a listening experiment. The results indicate that microprosodic variations are barely audible, but can lead to a greater perceived naturalness of the synthesized speech in certain cases.

Large Language Models (LLMs) offer advanced text generation capabilities, sometimes surpassing human abilities. However, their use without proper expertise poses significant challenges, particularly in educational contexts. This article explores different facets of natural language generation (NLG) within the educational realm, assessing its advantages and disadvantages, particularly concerning LLMs. It addresses concerns regarding the opacity of LLMs and the potential bias in their generated content, advocating for transparent solutions. Therefore, it examines the feasibility of integrating OpenLogos expert-crafted resources into language generation tools used for paraphrasing and translation. In the context of the Multi3Generation COST Action (CA18231), we have been emphasizing the significance of incorporating OpenLogos into language generation processes, and the need for clear guidelines and ethical standards in generative models involving multilingual, multimodal, and multitasking capabilities. The Multi3Generation initiative strives to progress NLG research for societal welfare, including its educational applications. It promotes inclusive models inspired by the Logos Model, prioritizing transparency, human control, preservation of language principles and meaning, and acknowledgment of the expertise of resource creators. We envision a scenario where OpenLogos can contribute significantly to inclusive AI-supported education. Ethical considerations and limitations related to AI implementation in education are explored, highlighting the importance of maintaining a balanced approach consistent with traditional educational principles. Ultimately, the article advocates for educators to adopt innovative tools and methodologies to foster dynamic learning environments that facilitate linguistic development and growth.