Differences

This shows you the differences between two versions of the page.

--- aira:start [2024/05/28 13:39] – [Schedule Winter 2023] xpm gjn
+++ aira:start [2024/09/06 08:05] (current) – gjn
@@ Line 8: / Line 8: @@
 The program will be published at [[https://aira.geist.re]] in advance
 (a dedicated MS Teams group for announcements is available for those who are interested).
+Scientific coordination: [[https://gjn.re|Grzegorz J. Nalepa]]
 Scientific secretary [[https://szymon.bobek.re|Szymon Bobek]]
-Scientific coordination: [[https://gjn.re|Grzegorz J. Nalepa]]
+===== Schedule Autumn 2024 =====
+AIRA will restart on 10.10.2024, TBC, stay tuned
 ===== Schedule Summer 2024 =====
-  * **[RESEARCH TRACK] 2024.XX.XX**: Jowita Guja [[#20240523| On translating VR into philosophy. Experiences from the EduVRLab Laboratory]]
+  * **[RESEARCH TRACK] 2024.06.27**: José Palma,Juan Botía, Antonio Guillén-Teruel  [[#20240627| Context-Aware learning models: CALM-Project and Concept Drift in Imbalanced Problems]]
-      * Meeting link: [[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1716197447658?context=%7b%22Tid%22%3a%22eb0e26eb-bfbe-47d2-9e90-ebd2426dbceb%22%2c%22Oid%22%3a%22c96e4eee-96f3-4b0f-88ef-fe65310f5f55%22%7d|MS Teams]]
+      * Meeting link: [[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1719232789438?context=%7b%22Tid%22%3a%22eb0e26eb-bfbe-47d2-9e90-ebd2426dbceb%22%2c%22Oid%22%3a%22c96e4eee-96f3-4b0f-88ef-fe65310f5f55%22%7d|MS Teams]]
       * Recording: [[|View]] (if you are not UJ employee, ask Szymon Bobek for access)
-      * Presentation slides: {{ |Download}}
+      * Presentation slides: {{:aira:slides-jose-palma-20240617.pdf |Download I}} {{ :aira:slides-antonio-guillen-20240627.pdf |Download II}}
+  * **[RESEARCH TRACK] 2024.06.06**: Żaneta Kubic [[#20240606| European Cultural Heritage in Virtual Worlds – why and how : introduction to the IMPULSE project]]
+      * Meeting link: [[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1717407321901?context=%7b%22Tid%22%3a%22eb0e26eb-bfbe-47d2-9e90-ebd2426dbceb%22%2c%22Oid%22%3a%22c96e4eee-96f3-4b0f-88ef-fe65310f5f55%22%7d|MS Teams]]
+      * Recording: [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/EfkSlUmXWbRGvq0xS6bBZkcBrrRI2bumOitE0ou0VBKC2w?nav=eyJyZWZlcnJhbEluZm8iOnsicmVmZXJyYWxBcHAiOiJTdHJlYW1XZWJBcHAiLCJyZWZlcnJhbFZpZXciOiJTaGFyZURpYWxvZy1MaW5rIiwicmVmZXJyYWxBcHBQbGF0Zm9ybSI6IldlYiIsInJlZmVycmFsTW9kZSI6InZpZXcifX0%3D&e=3EfKGG|View]] (if you are not UJ employee, ask Szymon Bobek for access)
+      * Presentation slides: {{ :aira:slides-zaneta-kubic-20240606.pdf |Download}}
   * **[DOCTORAL TRACK] 2024.05.16**: Mateusz Bułat [[#20240516| Image analysis for specialised therapy support]]
       * Meeting link: [[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1715342946914?context=%7b%22Tid%22%3a%22eb0e26eb-bfbe-47d2-9e90-ebd2426dbceb%22%2c%22Oid%22%3a%22c96e4eee-96f3-4b0f-88ef-fe65310f5f55%22%7d|MS Teams]]
-      * Recording: [[|View]] (if you are not UJ employee, ask Szymon Bobek for access)
+      * Recording: [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/EZe0bw0DZY5LtU3CCvns-6UBp432OE_5pD3_vs7zBOPhiA?nav=eyJyZWZlcnJhbEluZm8iOnsicmVmZXJyYWxBcHAiOiJTdHJlYW1XZWJBcHAiLCJyZWZlcnJhbFZpZXciOiJTaGFyZURpYWxvZy1MaW5rIiwicmVmZXJyYWxBcHBQbGF0Zm9ybSI6IldlYiIsInJlZmVycmFsTW9kZSI6InZpZXcifX0%3D&e=eU3MPT|View]] (if you are not UJ employee, ask Szymon Bobek for access)
-      * Presentation slides: {{ |Download}}
+      * Presentation slides: {{ :aira:slides-mateusz-bulat-20240515.pdf |Download}}
   * **[RESEARCH TRACK] 2024.05.09**: Jason J. Jung [[#20240509| Deep Learning for Anomaly Detection in Multivariate Time Series: Approaches, Applications, and Challenges ]]
     * Meeting link: [[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1715082246704?context=%7b%22Tid%22%3a%22eb0e26eb-bfbe-47d2-9e90-ebd2426dbceb%22%2c%22Oid%22%3a%22c96e4eee-96f3-4b0f-88ef-fe65310f5f55%22%7d|MS Teams]]
@@ Line 29: / Line 36: @@
       * Meeting link: [[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1713853200005?context=%7b%22Tid%22%3a%22eb0e26eb-bfbe-47d2-9e90-ebd2426dbceb%22%2c%22Oid%22%3a%22c96e4eee-96f3-4b0f-88ef-fe65310f5f55%22%7d|MS Teams]]
       * Recording: [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/EXQKuYkW3VFMr94bDgX3kR8BEDi5SYRLog-xkyoNA0ONJA?nav=eyJyZWZlcnJhbEluZm8iOnsicmVmZXJyYWxBcHAiOiJTdHJlYW1XZWJBcHAiLCJyZWZlcnJhbFZpZXciOiJTaGFyZURpYWxvZy1MaW5rIiwicmVmZXJyYWxBcHBQbGF0Zm9ybSI6IldlYiIsInJlZmVycmFsTW9kZSI6InZpZXcifX0%3D&e=T6Cnc7|View]] (if you are not UJ employee, ask Szymon Bobek for access)
-      * Presentation slides: {{ |Download}}
+      * Presentation slides: {{ :aira:slides-bartek-malkus-20240425.pdf|Download}}
   * **[DOCTORAL TRACK] 2024.04.18**
     * Farnoud Ghasemi [[#20240418| Performance Optimization of the Platforms in Two-sided Mobility Market]] and
@@ Line 44: / Line 51: @@
     * Recording: [[|View]] (if you are not UJ employee, ask Szymon Bobek for access)
     * Presentation slides: {{|Download}}
-  * **[DOCTORAL TRACK] 2024.03.21**: Maciej Szelążek [[#20240321| Using ML and XAI for decision support in Business Intelligence analysis]]
+  * **[DOCTORAL TRACK] 2024.03.21**: Maciej Szelążek [[#20240321| Using ML and XAI for decision support in Business Intelligence analysis]] (//Project CHIST-ERA XPM//)
     * Meeting link: [[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1710834494210?context=%7b%22Tid%22%3a%22eb0e26eb-bfbe-47d2-9e90-ebd2426dbceb%22%2c%22Oid%22%3a%22c96e4eee-96f3-4b0f-88ef-fe65310f5f55%22%7d|MS Teams]]
     * Recording: [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/EXnHujEGfFNBv8fKxFovZWsB6_EgZ-9OpaaaMWxzuSTW8g?e=rQ9U09&nav=eyJyZWZlcnJhbEluZm8iOnsicmVmZXJyYWxBcHAiOiJTdHJlYW1XZWJBcHAiLCJyZWZlcnJhbFZpZXciOiJTaGFyZURpYWxvZy1MaW5rIiwicmVmZXJyYWxBcHBQbGF0Zm9ybSI6IldlYiIsInJlZmVycmFsTW9kZSI6InZpZXcifX0%3D|View]] (if you are not UJ employee, ask Szymon Bobek for access)
@@ Line 321: / Line 328: @@
 ===== Presentation details =====
-==== 2024-05-23 ====
+==== 2024-06-27 ====
 <WRAP column 15%>
-{{ :aira:jowita-guja-foto.jpeg?width=200| }}
+{{ :aira:jose-palma-foto.jpeg?width=200| }}
+{{ :aira:juan-botia-foto.jpeg?width=200| }}
+{{ :aira:antonio-guillen-foto.jpg?width=200| }}
 </WRAP>
 <WRAP column 75%>
-**Speaker**: Dr hab Jowita Guja, Professor @ AGH University of Science and Technology
-**Title**: On translating VR into philosophy. Experiences from the EduVRLab Laboratory
+**Title I**: Context-Aware learning models: CALM-Project by José Palma, Juan Botía, University of Murcia
+**Abstract**:
+In a pandemic scenario, there are a number of technical challenges that need to be addressed in order to be prepared for future pandemics. These challenges relate to the whole life cycle of development. First, since the beginning of a pandemic, it is crucial not only to have reliable sources of data, but also to make this data accessible. In this sense, we need to integrate data from different sources, of different types, most of which have not been collected for modelling purposes, and to monitor the growth of such data with new sources. Second, although the use of predictive models has proved useful, the variable nature of the distributions, not only in scenarios with different samples but throughout the evolution of a pandemic, makes it difficult for such models to be effective. The COVID19 pandemic demonstrated how the predictive models deteriorated as different waves emerged. In addition, the reason for this was different each time: changes in government policies, the appearance of mutations leading to changes in symptoms, their severity and the effect of previous pathologies, and finally the effect of different vaccination campaigns and the type of vaccine, all lead to changes in the concept being modelled, known as concept drift.
+These are the main challenges that CAML project tries to approach. The initial hypothesis of this project is that the development of advanced techniques to detect and characterise concept drift in pandemic scenarios and on different types of data, combining different representation schemes, will not only provide tools to anticipate and react to model degradation, but will also allow the development of techniques to generate medical and preventive knowledge that will help improve the quality of care and, ultimately, better manage situations of extreme stress in the health system.
+**Title II**:  Concept Drift in Imbalanced Problems by Antonio Guillén-Teruel, University of Murcia
 **Abstract**:
-Until a few years ago, VR technology seemed a narrow niche, inaccessible to the average viewer. The rapid development, increase in accessibility and popularity we are now seeing are revealing new potential for immersive technologies. Perhaps they are capable of transforming our lives to a degree comparable to how smartphones have changed them? Based on the EduVRLab Laboratory's research and implementation projects, we will look at the philosophical problems that this perspective reveals to us and also look at Virtual Reality as an area that we can creatively manage while changing the face of philosophy itself.
+We address the challenge of concept drift in imbalanced datasets, which is common in various real-world applications. We build upon the IPIP (Identical Partitions for Imbalance Problems) method, which effectively generates balanced subsets by subsampling the majority class to ensure representation of all minority class instances. This method enhances the performance of ensemble learning models in imbalanced scenarios.
-**Biogram**:
+Additionally, we introduce the UIC (Unbiased Integration Coe) metric, designed to reduce bias caused by the imbalance ratio in datasets. The UIC metric integrates multiple biased measures, inversely weighted by their correlation with the minority class proportion, resulting in a more unbiased evaluation metric.
-Dr hab Jowita Guja, Prof. AGH -- Philosopher and cultural studies scholar, PhD in Cultural and Religious Studies. Her research interests include virtual reality, philosophical anthropology and the analysis of popular culture He heads the Department of Information Technology and Media at the
-Faculty of Humanities at the AGH University of Science and Technology, where he teaches, among other things, cognitive science, cultural theory, contemporary literature and the design and use of VR and AR technologies.
+Our work extends these methods to address concept drift, particularly in the context of passive learning. Concept drift occurs when the underlying data distribution changes over time, a phenomenon observed in dynamic datasets like those tracking COVID-19 patient outcomes. We propose adapting the IPIP method to handle concept drift by updating the model with new data chunks over time, ensuring that minority class instances are adequately represented in each update.
-She is among the founders of the EduVRLab Virtual Reality Research Laboratory at AGH in Krakow. As of 2019, she serves as its director. She is co-author of the experimental app 'VRSophy: The Cave' which is a VR adaptation of a Platonic text.
+Furthermore, we explore the application of the UIC metric to problems involving concept drift in imbalanced data. By adapting UIC for these scenarios, we aim to provide a more reliable measure of model performance over time, despite the evolving data distributions.
+Through extensive experiments on both simulated and real-world datasets, including a COVID-19 patient cohort, we demonstrate the effectiveness of our approach. Our findings suggest that the combination of IPIP and UIC, adapted for concept drift, offers a robust framework for tackling imbalanced data in non-stationary environments. Future work will focus on developing R packages to implement these methods, facilitating their application in various practical settings.
+**Biograms**:
+**José Palma** received a B.S. degree from the University of Las Palmas de Gran Canaria, Spain, in 1990 and a Ph.D. degree from the University of Murcia, Spain, in 1999, both in Computer Science. He has been an Associate Professor of Computer Science in the Department of Information Engineering and Communications and the School of Computer Science at the University of Murcia since 2000, but has been teaching in this department as an Associate Professor since 1996. Prior to joining the University of Murcia, he worked for 6 years in the Department of Computer Science and Systems at the University of Las Palmas de Gran Canaria. He has authored/co-authored more than 90 journal articles, book chapters and congress papers. His research activity focuses on Artificial intelligence in the areas of intelligent data analysis and machine learning,  and specially in medical domains.
+Currently, José Palma is the head of the AIKE research group (Artificial Intelligence and Knowledge Engineering) . He is also a senior member of the IEEE Engineering in Medicine and Biology Society.
+**Juan A. Botía**, is Professor in Computer Science and Artificial Intelligence at University of Murcia (UMU), Spain since September 2018. He also holds an Honorary Senior Research Fellow position at the Institute of Neurology (IoN), University College London (UCL), UK since July, 2017. He has a PhD in Computational Science and Artificial Intelligence (AI) from UMU (March, 2002). His expertise combines a deep knowledge about Artificial Intelligence, the experience of 10 years in the development of bioinformatic pipelines and the application of machine learning on the molecular biology domain. During the last 10 years, he has actively participated in 43 scientific papers indexed at Journal Citation Reports. Within that period he has supervised and completed 7 PhD theses. He is currently supervising 4 PhD students (4 at University of Murcia, 1 at University College London). He has experience of more than 15 years teaching machine learning related subjects and six years of teaching data analysis for bioinformatics.
+**Antonio Guillén-Teruel** graduated in Mathematics from the University of Murcia in 2020. In 2021 he got a Masters degree in Big Data from the same university and started his Ph.D studies in Informatics. The following year he got a Masters degree in Advanced Mathematics at the University of Murcia. His research focuses on imbalanced problems in Machine Learning (ML), including both in regression and classification problems, as well as the study of concept drift in medical domains for imbalanced datasets.
 </WRAP>
 <WRAP clear></WRAP>
+==== 2024-06-06 ====
+<WRAP column 15%>
+{{ :aira:zaneta-kubic-foto.jpg?width=200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**: Żaneta Kubic, Jagiellonian University
+**Title**: European Cultural Heritage in Virtual Worlds – why and how : introduction to the IMPULSE project
+**Abstract**:
+The IMPULSE project was initiated in response to the European Commission's inquiry into the future of digitized cultural heritage in Europe and digitalization processes. A consortium of 11 partners, representing universities and research institutions, non-profit organizations, and entrepreneurs, is conducting research and development to create frameworks contributing to the development of the European Collaboration Cloud for Cultural Heritage. By creating prototypes for using cultural objects in virtual worlds for university education and artistic research, scientists will have a unique opportunity to establish frameworks for utilizing digitized heritage, particularly addressing audience needs, standards, interoperability, and legal frameworks. The project received funding under the Horizon Europe program (Cluster 2) totalling almost 3.4 million euros, starts on February 2024 and will continue until 2027. During the seminar session the main goals, roadmap and broader context of the project will be presented.
+**Biogram**:
+PhD. Data specialist. Project coordinator of Horizon Europe project IMPULSE: IMmersive digitisation: uPcycling cULtural heritage towards new reviving StratEgies (101132704). Senior documentalist in Jagiellonian University Museum. Member of the National Smart Specialisations 12 for Creative Industries Working Group (Polish Ministry of Economic Development and Technology). Una Europa Ambassador and member of the Self Steering Committee on Data Science and Artificial Intelligence.
+Żaneta specializes in introducing innovations in public institutions, with a particular focus on data management and the implementation of science and art collection management systems. Żaneta integrates cultural heritage with the high technologies and has extensive experience implementing IT solutions that support efficient collections management and facilitate access to cultural information and content. Her efforts aim to promote innovative practices in the cultural sector and utilize modern tools for the protection and promotion of cultural heritage.
+</WRAP>
+<WRAP clear></WRAP>