The paper presents the most comprehensive and large-scale global study to date on how higher education students perceived the use of ChatGPT in early 2024. With a sample of 23,218 students from 109 countries and territories, the study reveals that students primarily used ChatGPT for brainstorming, summarizing texts, and finding research articles, with a few using it for professional and creative writing. They found it useful for simplifying complex information and summarizing content, but less reliable for providing information and supporting classroom learning, though some considered its information clearer than that from peers and teachers. Moreover, students agreed on the need for AI regulations at all levels due to concerns about ChatGPT promoting cheating, plagiarism, and social isolation. However, they believed ChatGPT could potentially enhance their access to knowledge and improve their learning experience, study efficiency, and chances of achieving good grades. While ChatGPT was perceived as effective in potentially improving AI literacy, digital communication, and content creation skills, it was less useful for interpersonal communication, decision-making, numeracy, native language proficiency, and the development of critical thinking skills. Students also felt that ChatGPT would boost demand for AI-related skills and facilitate remote work without significantly impacting unemployment. Emotionally, students mostly felt positive using ChatGPT, with curiosity and calmness being the most common emotions. Further examinations reveal variations in students’ perceptions across different socio-demographic and geographic factors, with key factors influencing students’ use of ChatGPT also being identified. Higher education institutions’ managers and teachers may benefit from these findings while formulating the curricula and instructions/regulations for ChatGPT use, as well as when designing the teaching methods and assessment tools. Moreover, policymakers may also consider the findings when formulating strategies for secondary and higher education system development, especially in light of changing labor market needs and related digital skills development.

The application of machine learning models for prediction and prognosis of disease development has become an irrevocable part of cancer studies aimed at improving the subsequent therapy and management of patients. The application of machine learning models for accurate prediction of survival time in breast cancer on the basis of clinical data is the main objective of the presented study. The paper discusses an approach to the problem in which the main factor used to predict survival time is the originally developed tumor-integrated clinical feature, which combines tumor stage, tumor size, and age at diagnosis. Two datasets from corresponding breast cancer studies are united by applying a data integration approach based on horizontal and vertical integration by using proper document-oriented and graph databases which show good performance and no data losses. Aside from data normalization and classification, the applied machine learning methods provide promising results in terms of accuracy of survival time prediction. The analysis of our experiments shows an advantage of the linear Support Vector Regression, Lasso regression, Kernel Ridge regression, K-neighborhood regression, and Decision Tree regression—these models achieve most accurate survival prognosis results. The cross-validation for accuracy demonstrates best performance of the same models on the studied breast cancer data. As a support for the proposed approach, a Python-based workflow has been developed and the plans for its further improvement are finally discussed in the paper.

The paper discusses some aspects of a work in progress aimed at the development of technologies for digitization of Bulgarian folk music and building a digital library with Bulgarian folk songs presented with their music, texts and notes. This library provides digital preservation of the sound recordings, lyrics and notations of more than 1000 Bulgarian folk songs and tools for various types of search and analysis of the available resources. The presentation is focused on the subject ontology especially developed for the occasion and its application in the implementation of a tool for semantics oriented search in the lyrics of songs.

Finding a template location in a query image is a fundamental problem in many computer vision applications, such as localization of known objects, image registration, image matching, and object tracking. Currently available methods fail when insufficient training data are available or big variations in the textures, different modalities, and weak visual features exist in the images, leading to limited applications on real-world tasks. We introduce Self-Supervised Foundation Model for Template Matching (Self-TM), a novel end-to-end approach to self-supervised learning template matching. The idea behind Self-TM is to learn hierarchical features incorporating localization properties from images without any annotations. As going deeper in the convolutional neural network (CNN) layers, their filters begin to react to more complex structures and their receptive fields increase. This leads to loss of localization information in contrast to the early layers. The hierarchical propagation of the last layers back to the first layer results in precise template localization. Due to its zero-shot generalization capabilities on tasks such as image retrieval, dense template matching, and sparse image matching, our pre-trained model can be classified as a foundation one.