Ponedeljkov seminar računalništva in informatike - Arhiv

V ponedeljek, 29. junija 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: 29. junij 2026 ob 16.00 prek Zoom-a.

1. predavanje:
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PREDAVATELJICA: Lina MEŠIČ
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Lina Mešič je magistrska študentka Računalništva in informatike na UP FAMNIT in trener športnega plezanja.

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NASLOV: Interaktivni sistemi kot orodje za trening športnega plezanja
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POVZETEK:

Športno plezanje je v zadnjem desetletju doživelo izjemen razvoj, s tem pa so v ospredje stopile določene pomanjkljivosti v treniranju in povečale zahteve po kakovostnem treningu. V seminarju bo predstavljen pregled devetih interaktivnih sistemov s področja HCI, ki se uporabljajo za trening športnega plezanja. Od plezalnih sten s projiciranimi igrami, kjer plezalci lovijo navidezne cilje in pozabijo na strah pred višino, prek virtualne resničnosti za varno soočanje s padci, do nosljivih naprav, ki prek vibracij ali zvoka vodijo plezalca med samim vzponom. Na podlagi izbranih študij poskušamo odgovoriti na vprašanja, kje so meje teh sistemov, komu najbolj koristijo in kako jih v prihodnosti oblikovati, da bodo trenerju v pomoč in ne v napoto. Ugotovitve kažejo, da tehnologija seveda ne more nadomestiti trenerja, lahko pa prevzame ponavljajoče se naloge in administracijo ter mu tako olajša delo.

Seminar bo potekal v slovenskem jeziku.

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2. predavanje:
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PREDAVATELJ: Luka MATIČ
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Luka Matič je študent magistrskega študija Računalništva in informatike na UP FAMNIT, ki se osredotoča na biomedicinsko obdelavo signalov in razvoj programske opreme. Njegova raziskovalna področja vključujejo analizo EEG signalov, strojno učenje in obdelavo podatkov v realnem času. Trenutno se ukvarja s primerjavo spektralnih in kepstralnih značilk pri klasifikaciji EEG signalov.

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NASLOV: Uporaba kepstralne analize za klasifikacijo EEG posnetkov
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POVZETEK:

Seminar obravnava primerjavo spektralnih in kepstralnih metod za analizo in klasifikacijo EEG signalov. Predstavljena je ekstrakcija značilk iz EEG podatkov v frekvenčni in kepstralni domeni ter njihova uporaba v okviru metod strojnega učenja. Osrednji poudarek je na primerjalni analizi učinkovitosti obeh pristopov, ki temelji na doseženi točnosti in pravilnosti klasifikacije EEG signalov. Rezultati prispevajo k boljšemu razumevanju vpliva izbire metode na uspešnost klasifikacije EEG signalov.

Seminar bo potekal v slovenskem jeziku.

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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, 22. junija 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: 22. junij 2026 ob 16.00 prek Zoom-a.

1. predavanje:
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PREDAVATELJ: Jovan VUKOVIĆ
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Jovan Vuković is a Master's student in Data Science at the University of Primorska (UP FAMNIT). He previously completed a Bachelor's degree in Computing and Information Technology at the University of Montenegro. His professional background is primarily focused on software engineering, microservice architectures, distributed systems, and large-scale backend development. His current research interests include knowledge graphs, machine learning, data integration, and scalable graph processing.

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NASLOV: Embedding-Based Entity Alignment between Large-Scale Knowledge Graphs: A Case Study of YAGO and OpenAlex
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POVZETEK:

Knowledge graphs have become an important approach for representing and integrating structured information in domains such as web search, digital libraries, recommendation systems, and scientific knowledge management. By representing entities and their relationships as graphs, they enable the discovery of connections that are difficult to capture using traditional relational data models. As the number and size of available knowledge graphs continue to grow, combining information from multiple sources has become an increasingly important challenge.
One of the key problems in this context is entity alignment, the task of identifying entities in different knowledge graphs that refer to the same real-world object. Solving this problem is essential for building integrated knowledge bases, but it becomes particularly challenging when working with large-scale graphs containing hundreds of millions of entities and relationships.
This presentation introduces the entity alignment problem through a case study involving the YAGO and OpenAlex knowledge graphs. It focuses on the first stages of a scalable alignment pipeline, including large-scale RDF preprocessing, extraction of structural and textual information, graph transformation and integer encoding, and the generation of knowledge graph embeddings using PyTorch-BigGraph.
Several embedding models, including TransE, DistMult, and ComplEx, are evaluated through link prediction experiments on large-scale datasets. The obtained results are used to assess how well the learned embeddings capture the underlying graph structure and to identify the most suitable embedding model for the subsequent entity alignment task.

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 UP FAMNIT and a member of the 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: Can News Ranking Reduce Political Polarization? Evidence from an Online Experiment with LLM-Generated News
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POVZETEK:

Algorithmic news ranking and large language models (LLMs) increasingly mediate how citizens receive political information. While personalization is often criticized for reinforcing echo chambers, ranking has also been proposed as a lever for depolarization by shaping exposure to cross-cutting viewpoints. We tested this claim in an online experiment (N=100, Prolific) using five LLM-generated news articles on gun legislation spanning pro–gun freedom to pro–gun control. All participants read the same articles; the only manipulation was article order, instantiated as a random baseline and six stance-aware depolarization-oriented strategies (counter-narrative sandwich, balanced alternation, and directional gradients). Pre–post questionnaires measured ideological selfplacement (feeling thermometer) and affective evaluations of gun-control and gun-freedom advocates. Across six research questions, we tested to see whether such recommender strategies had any reduction in ideological or affective polarization.

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:

https://upr-si.zoom.us/j/297328207?pwd=S3Zpdk1VR3pjckNtWkQwKzlvcDR5UT09

Meeting ID: 297 328 207
Passcode: 123456789

Vabljeni!

V ponedeljek, 15. junija 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: 15. junij 2026 ob 16.00 prek Zoom-a.

1. predavanje:
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PREDAVATELJICA: Ekaterina BOCHVAROSKA
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Ekaterina Bochvaroska received her B.Sc. degree in Computer Science from UP FAMNIT in 2025, where she is currently pursuing the M.Sc. degree in Computer Science. She is currently working as a developer at the Port of Koper,  where she is involved in software development, data analysis, and digital solutions for logistics and port operations. Her research interests include decentralized systems, distributed computing, blockchain technologies, algorithms and data structures.

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NASLOV: RAVEN: A Retention-Aware Verifiable Extension-Block Network for Off-Chain Message Storage and Retrieval
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POVZETEK:

RAVEN is a prototype protocol and reference implementation that stores encrypted-message data in off-chain extension blocks and commits each block to an ordered parent chain through a small anchor record. Nodes keep different amounts of history - archive nodes retain everything, regular nodes only a recent suffix window  and a client retrieves height ranges from whichever nodes can serve them. Every returned block is verified against its canonical, final anchor before acceptance, so corrupt, missing, or orphaned data is detected rather than trusted. The project covers the protocol core, the node runtime, the retention-aware retrieval pipeline, and an experiment that runs the same workload across several strategies on a local Docker Compose demo.

Seminar bo potekal v angleškem jeziku.

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2. predavanje:
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PREDAVATELJ: Andrej MILISAVLJEVIĆ
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Andrej Milisavljević is a student of Data Science at UP FAMNIT, currently researching cheminformatics at UPR with professors Marko Jukić and Jure Pražnikar as mentors. Their research includes methods for protein modeling, binding site detection, ligand docking, and the parallelization and distribution of cheminformatics computational analyses.

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NASLOV: SAVI ionization and conformer generation
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POVZETEK:

In molecular docking simulations used for the discovery of new medicines, the initial conformation and ionization states of the molecules being studied are crucial for ensuring biochemically relevant results and allowing drug-like molecules to be recognized. In this work, we describe a distributed and parallelized pipeline for efficiently computing ionization states and molecular conformations of 1 billion molecules from the Synthetically Accessible Virtual Inventory (SAVI), and the resulting docking-ready dataset of drug-like molecules for use in drug discovery and other related studies.

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, 1. junij 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: 1. junij 2026 ob 16.00 v FAMNIT-VP3.

1. predavanje:
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PREDAVATELJICA: Kosar SEYYEDHOSSEINZADEH
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Kosar Seyedhosseinzadeh is a second-year PhD student and teaching assistant at UP FAMNIT, where she works with the HICUP Lab. She is working on music recommender systems and user modeling. She received her bachelor’s and master’s degrees in computer engineering from Iran, where her master’s research also focused on recommender systems.

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NASLOV: What do Listeners Attend to When Listening to Music? Toward Explainable Music Recommendations
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POVZETEK:

Music recommender systems are good at suggesting songs, but explaining why a song is relevant to a listener is more difficult. This Seminar explores how listeners differ in what they attend to when listening to music, especially lyrics and musical elements such as rhythm, melody, and harmony. The main idea is that understanding these listener orientations can support user modeling for future explainable music recommender systems.

Seminar bo potekal v angleškem jeziku.

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2. predavanje:
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PREDAVATELJ: Asmir KADUŠIĆ
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Asmir Kadušić is a master’s student at UP FAMNIT with a bachelor’s degree in applied mathematics. Currently, he works in the industry at Quant3S as a data scientist/software developer on different kinds of problems. His work includes big data related to commodity trading, forecasting models, database maintenance, and designing solutions for various problems using Microsoft technologies.

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NASLOV: Scallable Parallel Influence Maximization with a Multi-State Diffusion Model
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POVZETEK:

I will present my work related to my master’s thesis. More specifically, I will present the extension I developed which introduces multistate nodes in the network and addresses the IM problem within the existing theoretical limits. The solution is designed for a distributed system of machines using the MPI model, with the goal of solving and running it on large real-world graphs. I also apply the solution to political influence analysis in networks using the soc-pokec relationship dataset.

Seminar bo potekal v angleškem jeziku.

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Seminarja bosta potekala v predavalnici FAMNIT-VP3 s pričetkom ob 16:00 uri.

Vabljeni!