This document provides several pieces of meta-information about the MLFMF data set collection, as well as some additional details and results from the experiments. For a detailed description of the preprocessing scripts and the script for running the model, please refer to the README in the repository. However, due to space limitations, all the preprocessed data can be found at https://doi.org/10.5281/zenodo.10041075, We obtained the source code of the libraries from their publicly available GitHub repositories. At the time of collection, we retrieved the latest versions of the libraries, which are specified in Table 1.

This document provides several pieces of meta-information about the MLFMF data set collection, as well as some additional details and results from the experiments. For a detailed description of the preprocessing scripts and the script for running the model, please refer to the README in the repository. However, due to space limitations, all the preprocessed data can be found at https://doi.org/10.5281/zenodo.10041075, We obtained the source code of the libraries from their publicly available GitHub repositories. At the time of collection, we retrieved the latest versions of the libraries, which are specified in Table 1.

Providing students with individualized feedback through assignments is a cornerstone of education that supports their learning and development. Studies have shown that timely, high-quality feedback plays a critical role in improving learning outcomes. However, providing personalized feedback on a large scale in classes with large numbers of students is often impractical due to the significant time and effort required. Recent advances in natural language processing and large language models (LLMs) offer a promising solution by enabling the efficient delivery of personalized feedback. These technologies can reduce the workload of course staff while improving student satisfaction and learning outcomes. Their successful implementation, however, requires thorough evaluation and validation in real classrooms. We present the results of a practical evaluation of LLM-based graders for written assignments in the 2024/25 iteration of the Introduction to Bioinformatics course at the University of Ljubljana. Over the course of the semester, more than 100 students answered 36 text-based questions, most of which were automatically graded using LLMs. In a blind study, students received feedback from both LLMs and human teaching assistants without knowing the source, and later rated the quality of the feedback. We conducted a systematic evaluation of six commercial and open-source LLMs and compared their grading performance with human teaching assistants. Our results show that with well-designed prompts, LLMs can achieve grading accuracy and feedback quality comparable to human graders. Our results also suggest that open-source LLMs perform as well as commercial LLMs, allowing schools to implement their own grading systems while maintaining privacy.

The 1st AI4GOV training workshop titled Bias in AI (focused on fundamentals) is the first organized training workshop with others to follow within the scope of the Horizon Europe project AI4GOV- Trusted AI for Transparent Public Governance fostering Democratic Values. High scores have been a primary focus of competitive players, with numerous tournaments and competitions held worldwide.

The pathloss radio maps of the dataset were generated based on the simulations by the ray-tracing software Win-To foster research and facilitate fair comparisons among Prop from Altair [3], on a dataset of urban environments. The recently proposed pathloss radio map prediction methods, city maps were fetched from OpenStreetMap [4] in the cities we have launched the ICASSP 2023 First Pathloss Radio Ankara, Berlin, Glasgow, Ljubljana, London, and Tel Aviv, Map Prediction Challenge.

The European Summer School on Artificial Intelligence (ESSAI) is a direct product of European AI research being increasingly coordinated and scaled up across projects, research organisations and countries. ESSAI's immediate predecessors are the Advanced Course on AI (ACAI), organised since 1985 under the auspices of the European Association for Artificial Intelligence (EurAI), and the TAILOR Summer School on Trustworthy AI organised since 2021 by the European ICT-48 Network of Excellence on Trustworthy AI through Integrating Learning, Optimisation and Reasoning. Last year, these two schools were already co-located in Barcelona with two parallel tracks as well as joint events.

The 1st AI4GOV training workshop titled Bias in AI (focused on fundamentals) is the first organized training workshop with others to follow within the scope of the Horizon Europe project AI4GOV- Trusted AI for Transparent Public Governance fostering Democratic Values. Do you remember the Rick and Morty cartoon? Recently, I learned that this movie even provided inspiration for video game development. A game called elastic man game features Morty as the primary character. This is the official website of the game: https://elastic-man.com

The Kleefstra Syndrome Scientific Conference 2023: "Moving towards the uptake and use of Artificial Intelligence (AI) in research and clinical work" focuses on Kleefstra syndrome, a rare genetic disorder with app. Due to the rarity of the disease, a global perspective is needed to foster new research insights. The event brings together Kleefstra syndrome PAOs, clinicians and researchers from different domains having a common interest: share new research findings related to Kleefstra syndrome that will lead the Kleefstra community to optimize current care and to reach their final goal, the discovering of a life-changing treatment and cure for Kleefstra syndrome. A special focus is given to artificial intelligence (AI), which is generally still something new for the rare disease communities, but it can play a crucial role, especially in shortening the time needed for new research insights.

SMASH is an innovative, intersectoral, career-development training program for outstanding postdoctoral researchers, co-funded by the Marie Skłodowska-Curie Actions COFUND program. SMASH is open to researchers around the world who are interested in developing cutting-edge machine learning applications for science and humanities. During the five years of the SMASH project (2023-28) and over three planned calls, SMASH aims to hire 50 individuals for 2-year full-time postdoctoral contracts with highly attractive conditions. Fellows will be hosted by five leading Slovenian research institutions: the University of Nova Gorica, the University of Ljubljana, the Jožef Stefan Institute, the Institute of Information Science*, and the Slovenian Environment Agency*. Applicants should propose ambitious research projects in one of SMASH's five key research areas, that are centered on the use of cutting-edge machine learning, or more broadly, artificial intelligence techniques, to address some of the world's most challenging questions in: Applicants should choose the SMASH host institution and supervisor with whom they will coordinate the project proposal preparation.

COVID-19, the disease caused by the novel coronavirus (SARS-CoV-2), first emerged in Wuhan, China late in December 2019. Not long after, the virus spread worldwide and was declared a pandemic by the World Health Organization in March 2020. This caused many changes around the world and in the United States, including an educational shift towards online learning. In this paper, we seek to understand how the COVID-19 pandemic and increase in online learning impact college students' emotional wellbeing. We use several machine learning and statistical models to analyze data collected by the Faculty of Public Administration at the University of Ljubljana, Slovenia in conjunction with an international consortium of universities, other higher education institutions, and students' associations. Our results indicate that features related to students' academic life have the largest impact on their emotional wellbeing. Other important factors include students' satisfaction with their university's and government's handling of the pandemic as well as students' financial security.