Arhivi Dogodki

V ponedeljek, 3. avgusta 2026, bodo ob 16:00 uri izvedeni dve predavanji v okviru PONEDELJKOVEGA SEMINARJA RAČUNALNIŠTVA IN INFORMATIKE Oddelkov za Informacijske znanosti in tehnologije UP FAMNIT in UP IAM. ČAS/PROSTOR: 3. avgust 2026 ob 16.00 prek Zoom-a. 1. predavanje: ============ ————————————————— PREDAVATELJICA: Pika POVH MAVRIČ ————————————————— Pika Povh Mavrič is currently completing her master’s studies in Computer Science at UP FAMNIT. She works as a data analyst in the Customer Relationship Management department at a bank, where she applies and deepens her expertise in data analytics. Her work involves automating processes, improving data quality, creating various reports and analyses, and applying data-driven decision-making to everyday processes within the department. The topic of her paper aligns well with her work in the banking sector. —————————————————————————————————— NASLOV: Machine learning models for predicting personal loan acquisition —————————————————————————————————— POVZETEK: In modern retail banking, the traditional approach of mass marketing consumer loans is becoming highly inefficient and often leads to customer fatigue. To address this issue, this research project focuses on developing a machine learning model designed to accurately predict a customer’s intent to acquire a personal loan. By analyzing historical data, including demographics, product ownership, and past marketing engagement, the project uncovers hidden behavioral patterns. This predictive approach allows financial institutions to shift towards a more customer-centric strategy, optimizing marketing resources and offering loans only to those who truly need them. Seminar bo potekal v angleškem jeziku. ============================================================================================================= 2. predavanje: ============ ——————————————- PREDAVATELJ: Nemanja CVETIĆ ——————————————- Nemanja Cvetić is a Master’s student in Computer Science with a Bachelor’s degree in Information Technology. His research interests include machine learning, explainable artificial intelligence (XAI), data science, computer science and software engineering. His current research focuses on evaluating model interpretability techniques and their practical applications in machine learning. —————————————————————————————————————————————————————————————— NASLOV: Model interpretability in machine learning: A Comparison of LinearSHAP TreeSHAP, and LIME on the California Housing Dataset —————————————————————————————————————————————————————————————— POVZETEK: This seminar presents an empirical comparison of SHAP (LinearSHAP and TreeSHAP) and LIME as post-hoc explainability methods for machine learning models. Using Linear Regression and Random Forest models trained on the California Housing dataset, the study evaluates the quality, consistency, and interpretability of feature attributions. The results demonstrate that SHAP provides more stable and theoretically grounded explanations, while LIME shows greater variability, particularly for complex non-linear models. Seminar bo potekal v angleškem jeziku. ============================================================================================================= Seminarja bosta potekala online prek aplikacije Zoom s pričetkom ob 16:00 uri na sledeči povezavi: https://upr-si.zoom.us/j/297328207?pwd=S3Zpdk1VR3pjckNtWkQwKzlvcDR5UT09 Meeting ID: 297 328 207 Passcode: 123456789 Vabljeni!

V ponedeljek, 24. avgusta 2026, bodo ob 16:00 uri izvedeni dve predavanji v okviru PONEDELJKOVEGA SEMINARJA RAČUNALNIŠTVA IN INFORMATIKE Oddelkov za Informacijske znanosti in tehnologije UP FAMNIT in UP IAM. ČAS/PROSTOR: 24. avgust 2026 ob 16.00 prek Zoom-a. 1. predavanje: ============ ——————————————- PREDAVATELJ: Nemanja CVETIĆ ——————————————- Nemanja Cvetić is a Master’s student in Computer Science with a Bachelor’s degree in Information Technology. His research interests include machine learning, explainable artificial intelligence (XAI), data science, computer science and software engineering. His current research focuses on evaluating model interpretability techniques and their practical applications in machine learning. —————————————————————————————————————————————————————————————— NASLOV: Model interpretability in machine learning: A Comparison of LinearSHAP TreeSHAP, and LIME on the California Housing Dataset —————————————————————————————————————————————————————————————— POVZETEK: This seminar presents an empirical comparison of SHAP (LinearSHAP and TreeSHAP) and LIME as post-hoc explainability methods for machine learning models. Using Linear Regression and Random Forest models trained on the California Housing dataset, the study evaluates the quality, consistency, and interpretability of feature attributions. The results demonstrate that SHAP provides more stable and theoretically grounded explanations, while LIME shows greater variability, particularly for complex non-linear models. Seminar bo potekal v angleškem jeziku. ============================================================================================================= 2. predavanje: ============ —————————————- PREDAVATELJ: Tilen GIMPELJ —————————————- Tilen Gimpelj is a student completing his 2nd year of the master’s program Data Science at UP FAMNIT. He works at the Department of Applied Natural Sciences under Assoc. Prof. Dr. Marko Jukić, and as a student he works at Novartis as a developer in the Bioprocess Development Department. ——————————————————————————————————————————————– NASLOV: Comparison of clustering algorithms for identifying conserved waters in an E. coli DNA gyrase ——————————————————————————————————————————————– POVZETEK: The seminar compares three algorithms – KMeans, DBSCAN and OPTICS for the purpose of identifying conserved water molecules in the DNA gyrase of E.Coli using water oxygen positions from experimentally determined protein structures aligned to the reference structure 4DUH. Seminar bo potekal v angleškem jeziku. ============================================================================================================= Seminarja bosta potekala online prek aplikacije Zoom s pričetkom ob 16:00 uri na sledeči povezavi: https://upr-si.zoom.us/j/297328207?pwd=S3Zpdk1VR3pjckNtWkQwKzlvcDR5UT09 Meeting ID: 297 328 207 Passcode: 123456789 Vabljeni!

Kot je že v navadi, bo v Kopru 26. avgusta ponovno organizirana Oživela ulica, kjer bo mogoče obiskati tudi stojnico Univerze na Primorskem. Oživela ulica bo tako kot lani potekala na Kidričevi ulici, kjer bo obiskovalce pričakal bogat program. Dogodek bo potekal med 17.00 in 23.30. Vstop je brezplačen!

V ponedeljek, 31. avgusta 2026, bodo ob 16:00 uri izvedeni dve predavanji v okviru PONEDELJKOVEGA SEMINARJA RAČUNALNIŠTVA IN INFORMATIKE Oddelkov za Informacijske znanosti in tehnologije UP FAMNIT in UP IAM. ČAS/PROSTOR: 31. avgust 2026 ob 16.00 prek Zoom-a. 1. predavanje: ============ ————————————– PREDAVATELJ: Matic POŽAR ————————————- Matic Požar is a first year PhD student and teaching assistant at UP FAMNIT. His research interests include graph representation learning, graph algorithms, and influence maximization. ————————————————————————————————————————- NASLOV: A New Approximation Method for the Independent Cascade Influence Function ————————————————————————————————————————- POVZETEK: The Independent Cascade model is one of the most commonly used diffusion models for representing influence spread in networks. Evaluating this model is crucial for solving the well-known influence maximization problem and its variants; however, this evaluation is #P-hard. Consequently, most implementations rely on approximation methods. Monte Carlo simulations can approximate the independent cascade model with arbitrary accuracy, but only by the cost of a high number of samples, making them impractical for large networks. We propose a new sampling-free approximation algorithm and conduct extensive experiments comparing it to existing approaches. Our results show that the proposed method is among the most accurate while being substantially more efficient. Seminar bo potekal v angleškem jeziku. ============================================================================================================= 2. predavanje: ============ ————————————– PREDAVATELJ: Uroš SERGAŠ ————————————– Uroš Sergaš is a teaching assistant of Computer Science at the Faculty of Mathematics, Natural Sciences and Information Technologies and is aa member of Centre for Responsible AI UP. As a researcher specializing in recommender systems and computational social science, his work focuses on applying machine learning methods to address societal challenges. Recently, his work also addresses the issue of AI alignment. He is currently pursuing his PhD in recommender systems. ————————————————————————————————————————————————————— NASLOV: Prompt to Press: Evaluating Human Perception of AI Involvement in News Writing Across Prompt Specificity ————————————————————————————————————————————————————— POVZETEK: Large language models (LLMs) are becoming a common feature in content creation tools, prompting important questions about how design choices influence user trust and engagement in AI-assisted journalism. Beyond output quality, factors such as prompt specificity, model choice, and authorship disclosure are themselves interaction design parameters that influence how users interpret and evaluate AI contributions. Yet, little is known about how these design decisions affect reader perceptions in journalistic contexts. To address this gap, we conducted an experiment with 150 participants who evaluated news articles on the sensitive topic of assisted suicide. The articles systematically varied in authorship (human-written, AI-edited, or AI-generated), stance (pro- or anti-legalization), and prompt specificity (vague, moderate, or highly detailed). Participants rated each article on engagement, subjectivity, and perceived AI involvement, and also provided open-ended justifications for their authorship judgments. Our findings show that prompt specificity and model choice significantly influence perceptions of authorship, underscoring how technical design decisions in AI tools can shape public trust in journalism. Seminar bo potekal v angleškem jeziku. ============================================================================================================= Seminarja bosta potekala online prek aplikacije Zoom s pričetkom ob 16:00 uri na sledeči povezavi: https://upr-si.zoom.us/j/297328207?pwd=S3Zpdk1VR3pjckNtWkQwKzlvcDR5UT09Meeting ID: 297 328 207Passcode: 123456789 Vabljeni!

V ponedeljek, 7. septembra 2026, bodo ob 16:00 uri izvedeni dve predavanji v okviru PONEDELJKOVEGA SEMINARJA RAČUNALNIŠTVA IN INFORMATIKE Oddelkov za Informacijske znanosti in tehnologije UP FAMNIT in UP IAM. ČAS/PROSTOR: 7. september 2026 ob 16.00 prek Zoom-a. 1. predavanje: ============ ———————————— PREDAVATELJ: Jani SUBAN ———————————— Jani Suban is a first-year PhD student and teaching assistant at UP FAMNIT and UL FRI. His research field is algorithms and data structures, with a focus on algorithms on strings. —————————————————————————————————- NASLOV: Knight-merge: string concatenation becomes suffix tree merging —————————————————————————————————- POVZETEK: Suffix trees are a widely used data structure for text indexing and pattern searching. A suffix tree is a path-compressed trie constructed from all suffixes of the input text. Its construction can be performed in linear time with respect to the length of the input text. This raises a new question: can we merge two suffix trees in linear time? We present a new algorithm, called Knight-merge, for merging two suffix trees into a single suffix tree. The algorithm runs in linear time, $O(n_1 + n_2)$, where $n_1$ and $n_2$ are the lengths of the two strings from which the suffix trees were constructed. It reuses the existing suffix trees and adds new nodes only when necessary. The algorithm preserves and computes suffix links, and it improves the time complexity of merging two suffix trees from $O((n_1 + n_2)^2)$ to $O(n_1 + n_2)$. Seminar bo potekal v angleškem jeziku. ============================================================================================================= 2. predavanje: ============ —————————————————– PREDAVATELJ: Nilukshan KRISHNARAM —————————————————– Nilukshan Krishnaram is a PhD student and teaching assistant at UP FAMNIT, where he is affiliated with the HICUP Lab. He holds a B.Sc. (Hons) in Information Systems from the University of Colombo, Sri Lanka. His research interests focus on human–computer interaction, extended reality, user experience design, and visual and perceptual attention guidance. ———————————————————————————————————————————— NASLOV: Look Here! But Did It Help? What Eye Tracking Reveals About Visual Attention Guidance ———————————————————————————————————————————— POVZETEK: Visual attention-guidance techniques are designed to help users find what matters. But, does a cue that grabs attention actually help users look in the right place? To investigate this question, we conducted an exploratory user study comparing paper-informed replications of 18 existing attention-guidance techniques within the same controlled visual-search task. Twelve participants completed 76 adaptive pairwise comparisons of these techniques while we collected subjective judgments alongside eye-tracking data, examining noticeability, preference, perceived findability, target-fixation success, guidance-relative time to first fixation, and replay effort. The results reveal that these measures tell very different stories: highly noticeable techniques were not necessarily the fastest or most reliable at guiding gaze, the most preferred techniques were not always the strongest performers, and some less-preferred techniques still achieved high search success. The findings show that no single measure can fully capture how well an attention-guidance technique works, highlighting the importance of considering both subjective judgments and gaze-based performance. Seminar bo potekal v angleškem jeziku. ============================================================================================================= Seminarja bosta potekala online prek aplikacije Zoom s pričetkom ob 16:00 uri na sledeči povezavi: https://upr-si.zoom.us/j/297328207?pwd=S3Zpdk1VR3pjckNtWkQwKzlvcDR5UT09  Meeting ID: 297 328 207 Passcode: 123456789 Vabljeni!

Mednarodna konferenca o aplikativni statistiki (AS 2026) je uradno letno srečanje Statističnega društva Slovenije. Konferenca že več kot dve desetletji predstavlja interdisciplinarno stičišče, kjer se teoretična statistika povezuje s sodobno podatkovno znanostjo. Vse inforamcije: Applied Statistics 2026

Dogodki Aquaculture Europe so namenjeni povezovanju in komunikaciji z akvakulturnim sektorjem. AE2026 bo vključeval tudi sejemsko razstavo, na kateri bodo regionalna in mednarodna podjetja predstavila svoje najnovejše izdelke in storitve. Več: Aquaculture Europe 2026 | European Aquaculture Society Meetings

Dr. Peter Glasnović bo predstavil nastajanje in razvoj Herbarija Univerze na Primorskem, ki je začel nastajati leta 2008 in danes obsega več kot 6.000 primerkov semenk, praprotnic, mahov in makroalg. Herbarijska zbirka je geografsko osredotočena predvsem na zahodni del Balkanskega polotoka in jugovzhodne Alpe, s posebnim poudarkom na zbiranju in hranjenju rastlinskega materiala iz Istre in slovenske flore. Pomemben mejnik v razvoju Herbarija Univerze na Primorskem predstavlja njegova vključitev v mednarodni register Index Herbariorum leta 2024. S pridobitvijo mednarodne kode se je Herbarij Univerze na Primorskem pridružil mreži več kot 3.500 herbarijev po vsem svetu. V okviru predavanja bo dr. Peter Glasnović predstavil tudi pomen herbarijskih zbirk za botanično raziskovanje ter njihov prispevek k poznavanju in ohranjanju rastlinske raznovrstnosti. Spregovoril bo o prihodnjih načrtih, med katerimi sta ureditev in digitalizacija celotne zbirke, kar bo omogočilo lažji dostop do informacij ter njihovo posredovanje širši javnosti. Dogodek bo potekal v okviru Dnevov evropske kulturne dediščine. Kdaj: ponedeljek, 28. september 2026, ob 19. uri Kje: čitalnica Mestne knjižnice Izola