Univerza na Primorskem
Fakulteta za matematiko, naravoslovje in informacijske tehnologije
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:
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PREDAVATELJ: Matic POŽAR
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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.
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NASLOV: A New Approximation Method for the Independent Cascade Influence Function
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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.
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2. predavanje:
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PREDAVATELJ: Uroš SERGAŠ
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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.
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NASLOV: Prompt to Press: Evaluating Human Perception of AI Involvement in News Writing Across Prompt Specificity
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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.
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Seminarja bosta potekala online prek aplikacije Zoom s pričetkom ob 16:00 uri na sledeči povezavi:
Vabljeni!