Ponedeljkov seminar računalništva in informatike - Arhiv

V ponedeljek, 26. maja 2025, bo ob 16:00 uri izvedeno
predavanje v okviru PONEDELJKOVEGA SEMINARJA RAČUNALNIŠTVA IN INFORMATIKE
Oddelkov za Informacijske znanosti in tehnologije UP FAMNIT in UP IAM.

ČAS/PROSTOR: 26. maj 2025 ob 16.00 v FAMNIT-VP2.

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PREDAVATELJ: Shlomo BERKOVSKY
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Shlomo Berkovsky is the leader of the Interactive Medical AI research stream at Macquarie University. The stream focuses on the use of Artificial Intelligence and Machine Learning methods to develop usable patient models and personalised predictions of diagnosis and care. The stream also studies how clinicians and patients interact with health technologies and how Large Language Models can improve patient care. His other areas of expertise include user modelling, online personalisation, and behaviour change technologies.

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NASLOV: Personality Sensing: Detection of Personality Traits Using Physiological Responses to Image and Video Stimuli
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POVZETEK:

Personality detection is an important task in psychology, as different personality traits are linked to different behaviours and real-life outcomes. Traditionally it involves filling out lengthy questionnaires, which is time-consuming, and may also be unreliable if respondents do not fully understand the questions or are not willing to honestly answer them. In this article, we propose a framework for objective personality detection that leverages humans' physiological responses to external stimuli. We exemplify and evaluate the framework in a case study, where we expose subjects to affective image and video stimuli, and capture their physiological responses using non-invasive commercial-grade eye-tracking and skin conductivity sensors. These responses are then processed and used to build a machine learning classifier capable of accurately predicting a wide range of personality traits. We investigate and discuss the performance of various machine learning methods, the most and least accurately predicted traits, and also assess the importance of the different stimuli, features, and physiological signals. Our work demonstrates that personality traits can be accurately detected, suggesting the applicability of the proposed framework for robust personality detection and use by psychology practitioners and researchers, as well as designers of personalised interactive systems.

Seminar bo potekal v živo, s pričetkom ob 16:00 uri v učilnici FAMNIT-VP2.

Vabljeni. 

V ponedeljek, 19. maja 2025, bo ob 16:00 uri izvedeno
predavanje v okviru PONEDELJKOVEGA SEMINARJA RAČUNALNIŠTVA IN INFORMATIKE
Oddelkov za Informacijske znanosti in tehnologije UP FAMNIT in UP IAM.

ČAS/PROSTOR: 19. maj 2025 ob 16.00 v FAMNIT-VP2.

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PREDAVATELJ: Vanja MILESKI
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After graduating from the Faculty of Computer and Information Science at the University of Ljubljana in 2015, Vanja Mileski started working at the Jožef Stefan Institute (JSI). He was a Master's student at the International Postgraduate School Jožef Stefan and a student researcher at the JSI. After finishing his Master's studies, he applied his knowledge of data mining in the private sector as a Data Scientist in the retail, telecommunications, banking, stock market and insurance sectors. His current research interests include Time-Series classification, Deep Learning, ResNet and Inception architectures as well as LLMs.

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NASLOV: Deep learning for time series data using Inception and ResNet paradigms
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POVZETEK:

Prediction of customer behaviour represents a pivotal task in the retail sector, focusing on identifying customers with a high risk of attrition. We analyse a multi-year dataset from a large Slovenian retailer, encompassing detailed customer demographics, purchasing behaviours, and spending habits, represented as time series. We incorporate Convolutional Neural Networks (CNNs) and apply them on the time series, where the features can be viewed as one-dimensional vectors. Furthermore, we incorporate ResNet-like skip connections, as well as Inception modules with different kernel sizes in our architecture to improve the predictive performance compared to traditional ML techniques.

Seminar bo potekal v živo, s pričetkom ob 16:00 uri v učilnici FAMNIT-VP2.

Vabljeni.