Artificial Intelligence in Research and Applications Seminar (AIRA)

Speaker: Mateusz Hohol, Associate Professor @ Jagiellonian University

Title: Investigating and facilitating human geometric cognition through VR/AR technologies

Abstract: Learning the principles of geometry, similarly to numerical knowledge, plays a pivotal role in the acquisition of mathematical competencies that are useful in everyday life. What is more, many surveys (e.g., by OECD) suggest the prominent role of the level of abstract geometric competencies in developing individuals' well-being and societies at large. According to the mainstream cognitive science approach, these competencies are deeply hardwired in spatial navigation and object recognition, based upon, respectively, the core system of layout geometry (hippocampus and entorhinal cortex) and the core system of object geometry (lateral occipital complex). These core systems – being responsible for the processing of different geometric properties – are shared with non-human animals, and appear in the early stages of human ontogeny. However, they cannot fully explain a uniquely human form of Euclidean cognition and, even more so, individual differences in its level. Many empirical findings suggest that spatial language and artifacts use (maps, scale objects, etc.) mediate productive combinations of cognitive representations delivered by the core geometric systems. Again, the mainstream approach - grounded in various methodologies - emphasizes that facilitating these productive combinations in children leads to a deeper understanding of Euclidean geometry principles and the general development of abstract thinking. In this talk, I will focus on the contribution of virtual reality-based research to the understanding foundations of geometric cognition and the efficiency of VR-based cognitive trainings serving as STEM assistances. Considering the issue of ecological validity of experiments/interventions, I will also review (indeed scarce) contributions employing augmented reality-based methodologies. In both cases, I will outline perspectives of further studies and interventions.

Biogram: Mateusz Hohol is associate professor at the Jagiellonian University, who is affiliated within the Copernicus Center for Interdisciplinary Studies; he holds Habil. in psychology, Ph.D. in philosophy. His research focuses mainly on experimental psychology of mathematics (numerical and geometric cognition), and on conceptual issues in cognitive (neuro)science. Recently, he has been interested in facilitating cognition through cognitive artifact use. He published “Foundations of geometric cognition” (Routledge 2020). More information at www.hohol.pl/en

Speaker: Michał Zwierzyński, PhD Candidate @ AGH UST

Title: AI methods in computer modeling and recognition of emotions and humanization of computer systems

Abstract: Emotions are temporary shakes of mind with somatic reactions in meantime. They are crucial in human communication, and they let people understand each other with their state of mind. Non-verbal information is the main information shared with another person. Emotion recognition is a crucial element of Affective Computing related to the interdisciplinary field of science including devices and sensors, machine learning, signal processing and psychology. With this knowledge it is possible to create task sequences to automatically recognize emotion. Nowadays with fast growth of intelligent technologies and industry growth, we observe increased demand for technologies that are capable of satisfying clients and finding the right solution. Automatic emotion recognition has its appliance in robotics: in case of projecting intelligent robots that can help humans and communicate with him, in marketing: letting creation personalized advertise in according to emotional state of client, in education: to help improve efficiency of learning process, and knowledge transform, in games to adjust gameplay to player feeling. Current results of paperwork showing that accuracy isn’t satisfying if we assume that models should be universal. Major number of works do not include facts used in emotion recognition problems – personalization. First published works showing increased accuracy of models by using information about psychological features of users like personality to create personalized models. Result is to develop a collection of methods used to create mechanisms and personalization tools of emotional intelligent systems. Every work will be attached to the chosen use-case.

Biogram: Michał Zwierzyński, MSc (mzwierzy@agh.edu.pl) is a PhD candidate at AGH University of Science and Technology. He received his MSc in Computer Science from Kielce University of Technology in 2020. He works as a Front-end developer. His interest is software development, machine learning, especially artificial neural networks and applications in areas where AI can replace humans.

Speaker: Magdalena Wiercioch, PhD Candidate @ UJ

Title: Machine Learning in Drug Discovery: Applications and Techniques

Abstract: Machine Learning has been widely applied in drug discovery. The accurate prediction of molecular properties is a critical ingredient toward the societal and technological progress since it could speed up much research progress, such as drug designing and substance discovery. Also, it would cause more initiatives towards a personalized medicine. However, complete exploring “chemical universes” that potentially include infinite sets of compounds seems to be computationally intractable. In recent years, advances in the development of deep learning models have spawned a mass of promising methods to address the molecular property prediction task. During our presentation we will introduce the related background and share the experiences connected with the developed models that learn and predict molecular properties on unseen data.

Biograms: Magdalena Wiercioch, MSc (magdalena.wiercioch@uj.edu.pl) is a PhD Candidate at the Jagiellonian University. She received her B.Sc. and M.Sc. degrees in Computer Science. She works as a research and teaching assistant at the Institute of Applied Computer Science at the Jagiellonian University. Also she works as a software developer. Her research interests include data representation and supervised learning. In her work she develops and applies machine learning techniques to enhance drug discovery. Another field of her research concerns explainable machine learning, and understanding the process of natural language learning, including embedding spaces and how they relate to language concepts. She participates as a speaker in scientific events and conferences, which are well-known in the IT industry.

Speaker: Maciej Szelążek, PhD Candidate @ AGH UST

Title: Semantic Data Mining Based Decision Support for Quality Assessment in Steel Industry

Abstract: Smart Manufacturing approaches are most often based on the use of machine learning models in tasks, where human cognitive abilities do not allow for efficient processing of available data. The talk is focused on quality management practices and our proposal of the decision support application based on sensor data collected during the steel products manufacturing. We have integrated domain knowledge, Six Sigma principles, ISO 9001:2015 recommendations, machine learning model, and XAI algorithms to create a semantic connection between data stream and human specialists. The original contribution of our research is the enhancement of current state of the art decision support methods grounded on statistical control. Instead of considering the relations between features based on distribution variability, an appropriate design of the experiment allowed us to identify defective products and compute the potential causes of the defect in the automated procedure

Biograms: Maciej Szelążek, MSc (maciej.szelazek@agh.edu.pl) is a PhD student at the AGH UST in Krakow, Poland, Department of Applied Computer Science. He received his MSc degree in Automation and Metrology from AGH UST in 2010. He worked as an data analyst in the Office of Statistical Process Control (SPC) Arcelor Mittal Poland. Participate in creation and development of an analytical system based on a central database integrating distributed data sources, reporting system and Statistica data mining software. He conducted big data multidimensional analyses related to searching for bottlenecks, logistics, cost optimization and limiting the variability of industry processes. He was involved in Process-aware Analytics Support based on Conceptual Models for Event Logs - PACMEL project.

Speakers:

• Przemysław Kazienko, Full Professor @ Wroclaw University of Science and Technology
• Jan Kocoń, Assistant Professor @ Wroclaw University of Science and Technology

Title: Personalized NLP.

Abstract: Many natural language processing tasks, such as classifying offensive, toxic, or emotional texts, are inherently subjective in nature. This is a major challenge, especially with regard to the annotation process. Humans tend to perceive textual content in their own individual way. Most current annotation procedures aim to achieve a high level of agreement in order to generate a high quality reference source. Existing machine learning methods commonly rely on agreed output values that are the same for all annotators. However, annotation guidelines for subjective content can limit annotators' decision-making freedom. Motivated by moderate annotation agreement on offensive and emotional content datasets, we hypothesize that a personalized approach should be introduced for such subjective tasks. We propose new deep learning architectures that take into account not only the content but also the characteristics of the individual. We consider different approaches for learning the representation and processing of data about text readers. Experiments were conducted on several datasets: Wikipedia discussion texts labeled with attack, aggression, and toxicity, opinions annotated with ten numerical emotional categories and humour data. All of our models based on human biases and their representations significantly improve prediction quality in subjective tasks evaluated from an individual's perspective. Additionally, we have developed requirements for annotation, personalization, content processing and validation procedures to make our solutions human-centric.

Biograms:

Przemysław Kazienko, Ph.D. is a full professor and leader of ENGINE - the European Centre for Data Science at Wroclaw University of Science and Technology, Poland. He received his M.Sc. and Ph.D. degrees in computer science with honours, from Wroclaw University of Technology, Poland, in 1991 and 2000, respectively, his habilitation degree from Silesian University of Technology, Poland, in 2009, and professorship from the President of Poland in 2016. He has authored over 300 scholarly and research articles, including 50 in journals with impact factor within a variety of topics related to personalization and subjective tasks in NLP, affective computing and emotion recognition, social network analysis, complex networks, spread of influence, machine learning, incl. relational machine learning - collective classification and multilabel classification as well as sentiment analysis, DSS in medicine, finances and telecommunication, knowledge management, collaborative systems, data mining, recommender systems, information retrieval, and data security. He also initialized and led over 50 projects, including large European ones, chiefly in cooperation with companies with total local budget over €10M. He gave 20 keynote/invited speeches for international audience and served as a co-chair of over 20 and a member of over 60 programme committees of international scientific conferences and workshops as well as a guest editor of eight special issues in prestige journals. He is an IEEE Senior Member, a member of the Editorial Board of several journals including Social Network Analysis and Mining, International Journal of Knowledge Society Research, International Journal of Human Capital and Social Informatics. He is also on the board of Network Science Society.

Jan Kocoń, Ph.D. is involved in the development of language technologies in projects carried out at the Wrocław University of Technology since 2011. His interests focus on NLP in the following areas: information extraction, sentiment analysis, classification of documents and applying deep language models. He is a co-author of methods for recognising proper names (PolDeepNER), and author of solutions for recognising temporal expressions and events in Polish texts. These tools are currently used by scientists in the field of humanities and social sciences in Poland and worldwide. he also managed the ML team on the Sentimenti project, aimed at analyzing emotions and sentiment in the text. In this project more than 20 thousand people were examined and over 18 million annotations about emotions were collected. He was responsible for creating a machine learning mechanism based on deep neural networks such as BiLSTM, BERT and LASER for automatic recognition of emotions in text based on collected data. He documented his experience in NLP with more than 35 scientific publications. Currently he deals with sentiment analysis tasks, cross-lingual transfer of knowledge and deep language models in personalized NLP methods. He is also a main co-ordinator of the task related to the recognition of emotions within the CLARIN-PL-Biz project worth over 130 million PLN.

Speaker: Bartłomiej Nawara, PhD Candidate @ UJ

Title: On open problems of theory of knowledge, based on Luciano Floridi’s theory of semantic information.

Abstract: Floridi's theory of semantic information is one of the most influential attempts to complement the Shannon / Weaver communication theory with a semantic aspect. In his later works, Floridi tries to expand it even more, in order to define General Theory of Information (GOT), an unsolved enlargement of Shannon / Weaver communication theory. However, it requires a clear distinction between data and information. Floridi notes that the level of abstraction at which data analysis ranks is different from the level of abstraction of the informational processes. This distinction is based on two categories of systems: Artificial Intelligent Behavior (AIB), i.e. AI systems, and Natural Intelligent Behavior (NIB). NIB systems are producing knowledge by processing information. While the AIB systems are only capable of experiencing data, knowledge produced by such systems must be somehow different from human produced knowledge. It is due to lack of so-called “anchoring in the world”, captured in the symbol grounding problem. There is currently no meaningful solution to this problem for AI systems. It's clear that the relationship between data, information and knowledge is difficult to describe using a simple hierarchy. Historically, this idea was implemented by the so-called DIKW pyramid, adding an extra degree of wisdom at the very top. Currently, despite the criticism of this approach, there is no in-depth analysis of the above-mentioned ingredients. Krajewski argue that the relation between data, information and knowledge should be expressed in something more than the usual equation signs between them. Open questions that arise in this matter relate, firstly, to defining knowledge produced by AI systems, and secondly, placing such knowledge in the context of theory of semantic information and GOT. Proposed solution must be consistent with SOTA. The presentation will cover Floridi’s semantic information theory, data-information distinction, and try to describe current status and open problems of theory of knowledge based on Floridi’s theory of semantic information.

Biogram: Bartłomiej Nawara. Data Scientist, philosopher and Phd candidate at Jagiellonian University. Area of interest: Information and knowledge theories, philosophy of data science and NLP.

Speaker: dr Michał Araszkiewicz, Assistant Professor @ UJ

Title: Artificial Intelligence and (Human Rights) Law

Abstract: The talk is focused on the main subject of the research area referred to as “Artificial Intelligence and Law”, that is, formal and computational modeling of legal reasoning, argument and prediction. We describe how symbolic AI on the one hand and computational intelligent systems on the other hand are and may be employed to support or to simply perform legal tasks. We also investigate the legal implications of thsis phenomenon with particular emaphasis on the sphere of human rights. The talk is concluded with the paradox following from the use of AI in order to support the performance of legal tasks in the field of the law of the AI.

Biogram: Dr Michał Araszkiewicz is an assistant professor (adiunkt) in the Department of Legal Theory at the Jagiellonian University in Kraków and holds a PhD in legal theory. He is also a legal advisor, partner in Araszkiewicz Cichoń Araszkiewicz Law Firm (acrlegal.pl). Michał Araszkiewicz has published extensively in the field of legal theory and in the area of AI and Law. He specializes in theories of legal reasoning and argumentation, legal interpretation, case-based reasoning as well as in normative aspects of Artificial Intelligence, including the right to explanation. He is currently a member of the Executive Committee of the International Association for Artificial Intelligence and Law (IAAIL) and of the Steering Committee of JURIX. He served as the President of the ARGDIAP association (argdiap.pl) – a NGO focused on the problems of argumentation, dialogue and persuasion. He has co-organized numerous scientific events including the JURIX 2014 (Conference Chair) and three consecutive editions of XAILA – The EXplainable and Responsible AI in Law workshops at JURIXes 2018-2020 and ICAIL2021. In legal practice he specializes in the field of legal regulation of AI as well as in Intellectual Property, Data Protection and broadly understood Protection of Information.

Speaker: Radosław Pałosz, PhD Candidate @ UJ

Title: Explaining the Artificial Intelligence Act

Abstract: Abstract: Artificial Intelligence Act - currently proceeded by lead Committees of European Parliament - is presented as the regulation that should secure EU's position as the leader in promoting safe and innovative research of AI. It is a part of European Approach to excellence and trust in AI development, presented mainly in White Paper on Artificial Intelligence. The basic assumption of the strategy, of which the AIA is to be the most important basis is to provide space for innovative research and implementation of AI systems, whilst maintaining safety measures built around trust. However, adopted style of regulation focused on product safety seems not to be sufficient for reaching this goal. One of the downsides of AIA is a blurry regulation of explainability related subjects and their role in building trust to AI. Starting with lack of clear definition of notions such as 'transparency' and 'explicability', ending on very formal obligations imposed on producers, distributors and users of AI systems, it seems that AIA would serve rather as niuisance than help. In order to improve the regulation it is worth asking how to best balance values that it is meant to promote. Another venue of research is to identify what could be the best way to build trust to AI systems through explainability.

Biogram: Radosław Pałosz (radoslaw.palosz@uj.edu.pl) graduated in law from the Jagiellonian University in 2017. He is a PhD Student at the Department of Legal Theory, Faculty of Law and Administration. His doctoral dissertation concerns relations between virtual worlds of MMOGs and legal institutions. He is a head of an individual project funded by the National Center of Science in the PRELUDIUM 14 grant to describe the process of emergence of complex normative orders in virtual worlds. Radosław is a scholarship holder in the research project “Explainable methods of predictive maintenance” CHIST-era 19 headed by Professor Grzegorz Nalepa. He is also an attorney-at-law in Kraków Bar Association working in one of the largest law firms in Poland, with an expertise in IP, banking and company law.

Speaker: Maciej Mozolewski, PhD Candidate @ UJ

Title: Demonstration of InXAI framework on ensemble classifier ML model

Abstract: EXplainable Artificial Intelligence (XAI) often boils down to presenting the role of variables in the ML model. In our work, we want to show that thanks to XAI you can achieve much more. We propose InXAI framework, which purpose is to help machine learning experts incorporate explainability into their models. With InXAI, we conducted an experiment that consisted in optimizing the ensemble of binary classifiers in such a way that it would be characterized by better XAI evaluation measures, while maintaining the basic metrics of the model, such as Accuracy. In the second part of my presentation, I will show the current directions of my work on XAI in the context of Time Series data.

Biogram: Maciej Mozolewski is a PhD candidate at the Jagiellonian University in Technical Computer Science since 2021. His main area of research is eXplainable AI. His Alma Mater is Jagiellonian University, where he graduated from Psychology and studied Physics for short period. He graduated from Statistical methods in business at the Faculty of Economics of the University of Warsaw. For nearly 10 years, he has worked as a Data Scientist and Software Engineer. Last but not least, he is gaining practice in teaching students and enjoys it more and more.

Speaker: Michał Kuk, PhD Candidate @ AGH UST

Title: Enhancing cluster analysis with explainable AI and multidimensional cluster prototypes

Abstract: Explainable Artificial Intelligence (XAI) aims at introducing transparency and intelligibility into the decision-making process of AI systems. Most often, its applications concentrate on supervised machine learning problems such as classification and regression. Nevertheless, in the case of unsupervised algorithms like clustering, XAI can bring satisfying results as well. In most cases, such an application is based on the transformation of unsupervised clustering task into supervised one. This is achieved by building a classifier on top of labels discovered in the clustering phase and applying XAI methods for the classifier. However, such an approach may cause the information about cluster shape and distribution to be lost. This, on the other hand, can influence the quality of the explanations. To cope with that we developed a novel approach for Cluster Analysis with Multidimensional Prototypes called ClAMP that aids experts in cluster analysis with human-readable rule-based explanations. The developed state-of-the-art explanation mechanism is based on cluster prototypes represented by multidimensional bounding boxes. This allows to represent an arbitrary shaped clusters and combine strengths of local explanations with the generality of the global one.

Biogram: Michał Kuk is a graduate of the AGH University of Science and Technology from the Drilling, Oil and Gas Faculty. In 2017, realized his master's thesis in which he developed an algorithm optimizing the location of new wells. In 2018, he started Ph.D. studies at the same faculty with a specialization in mining and geology. His scientific researches are connected with the optimization of oil and gas production. He uses machine learning algorithms to improve production from the reservoirs. One of his methods has been presented on the SPE student paper contest where he took 2'nd place in the Ph.D. division. Since November 2020 he is a member of the GEIST team. He was involved in Process-aware Analytics Support based on Conceptual Models for Event Logs - PACMEL project and now he is participating in the XPM project (explainable predictive maintenance).

Speaker: Victor Rodriguez-Fernandez, PhD, Assistant Professor @ Universidad Politécnica de Madrid

Title: Modern deep learning approaches for time series

Abstract: Due to their natural temporal ordering, time series data are present in almost every task that requires some sort of human cognitive process. Industries varying from health care, remote sensing and aerospace engineering all now produce time series datasets of previously unseen scale. However, in comparison to the fields of computer vision and natural language processing, the increasing use of time series has not been met with a complete adoption of deep learning-based approaches, and only during the last years have we seen some of the modern ideas from deep learning applied to time series. In this talk, we will go through some recent research in which trending topics such as self-supervised learning, neural-based visual analytics, and new neural architectures are studied for different use cases involving time series.

Biogram: Victor Rodriguez-Fernandez is an assistant professor in the school of computer systems engineering of Universidad Politécnica de Madrid (UPM). He holds a PhD in computer science at the Autonomous University of Madrid. Currently he is part of the Applied Intelligence and Data Analysis (AIDA) research group at UPM, where he is involved with several projects funded by the European Commision. His research interests revolve around practical applications of deep learning.

Speaker: Jakub Jakubowski, PhD Candidate @ AGH UST, Modelling specialist/data scientist @ ArcelorMittal Poland

Title: Explainable anomaly detection in hot-rolling process

Abstract: Hot rolling is a complex manufacturing process, which require very accurate control systems and good maintenance strategies in order to produce high quality products. Faults at any stay of the process may lead to production of defected products, which is a significant economic loss for the manufacturer. One of the possibilities to improve production process is to discover anomalous observations as soon as they appear, because they may be a sign of greater production issues. In our studies we have used deep learning for detection of anomalies in a hot rolling process. More specifically we will focus on the autoencoder neural network architecture with certain extensions which improve the accuracy and reliability of the deep learning models.

Biogram: Jakub Jakubowski has received Bachelor (2016) and Master (2017) degrees in Energy Engineering from AGH University of Science and Technology, Faculty of Fuels and Energy. Since 2018 he is working in ArcelorMittal, world’s largest steel producer, as modelling specialist/data scientists. Responsible for development and implementation of mathematical models in areas like product optimization and production planning. In addition he helps engineers in analysis of big data from industrial processes and development of business intelligence tools. In 2020 he has completed postgraduate studies in Data Science at AGH UST, Faculty of Computer Science, Electronic and Telecommunications. From 2020 he has been a PhD candidate at AGH UST taking part in Implementation Doctorate Programme, combining research and work in industry. His main field of interest is application of AI techniques in industry i.e. in predictive maintenance solutions.

Speaker: Szymon Bobek, Assistant Professor @ the Jagiellonian University

Title: Challenges in Explainable Artificial Intelligence for Industry 4.0

Abstract: Advances in artificial intelligence trigger transformations that make more and more companies entering Industry 4.0 era. These transformations require building infrastructures for gathering and analysing large volumes of data. In many cases, the analysis involves conformance checking between the discovered patterns and the expert knowledge that is in possession of a company. In this talk, I will show how explainable artificial intelligence methods (XAI) can be used to help experts analyse the results of clustering methods. I will also discuss challenges in using existing XAI algorithms for generating explanations for users without data-science experience.

Biogram: Szymon Bobek holds a position of an assistant professor at the Jagiellonian University in Krakow, Poland, Faculty of Physics, Astronomy and Applied Computer Science He received his degree PHD at AGH UST in 2016 in the field of Computer Science (Artificial Intelligence) and science then he works as a member of GEIST research team. His work includes most recently hybrid models for explainable AI. He is author of over 70 research papers in international journals, books and conferences and reviewed research papers for a number of international journals. He participated in national and international research projects as an investigator and cooperated with several companies in applied projects, especially with massive big data processing using machine learning methods. He is in active cooperation with Cambridge University in the area of XAI application in healthcare applications and with AFFCAI (affcai.geist.re) group in the area of application of XAI to processing biomedical signals for Affective computing. Recently, he has been leading a team that developed a series of original tools in the area of eXplainable AI for the applications in industrial AI. He is a co-organizer of Practical Applications of Explainable Artificial Methods special session @ DSAA2021 conference (https://praxai.geist.re).

Speaker: Timos Kipouros, Senior Research Associate @ the University of Cambridge

Title: Visual Analytics for Aerospace and Healthcare Systems Design

Abstract: In this seminar, a systems engineering approach for the trade-off analysis of multiple criteria will be demonstrated with examples in aerospace and healthcare systems. Using a novel multidimensional data visualisation technique, the simultaneous analysis of the trade-offs between multiple stakeholder priorities and decision criteria is possible. Artificial intelligence, computational models, optimisation simulations and data analysis are utilised extensively, and multidimensional visualisation, as will be shown, can play a key role exploiting our fantastic pattern recognition ability in discovering relational information in such datasets and sequentially guiding complex design decisions in socio-technical systems.

Biogram: Timos Kipouros is a Senior Research Associate in the Engineering Design Centre at the University of Cambridge, and a Senior Lecturer at the University of Cranfield on Computational Engineering Design Optimisation. He received a 5-year Diploma in Mechanical and Aeronautical Engineering from the University of Patras, Greece, in 2002, and his PhD in Multi-Objective Aerodynamic Design Optimisation from the University of Cambridge, Jesus College, in 2006. He was also awarded a graduate certificate on Architecture and Systems Engineering from MIT in 2017. Since 2006, he worked as a Research Associate and then as Senior Research Associate in Cambridge where he pioneered the development of a method for post-analysis of optimisation data and engineering design processes using a highly advanced interactive Parallel Coordinates approach. The method has been extended to support interactive computational design, and robust decision-making in the areas of aerospace and healthcare. He has more than 100 publications in international peer reviewed journals, conferences and industrial workshops and has supervised 21 PhD and 60 MSc projects.

Speaker: Bartosz Soból, PhD Candidate @ Jagiellonian University

Title: Recent developments of machine learning in experimental particle physics

Abstract: With modern physics experiments comes the need to process the growing amount of data. Collaborations behind the largest particle physics experiments at LHC estimate their detectors’ data throughput to notably exceed 1 TB/s during upcoming data-taking sessions. This implies that terabytes of multidimensional data will have to be continuously collected, filtered, and processed into the fully reconstructed tracks of each particle passing through the detector. Additionally, in order to be meaningful, every new measurement has to be compared with its Monte Carlo simulated equivalent. In this talk, I will show how machine learning, hardware-accelerated graph, and generative neural networks can help in these two critical areas of computation in experimental particle physics research: track reconstruction and event simulation.

Biogram: Bartosz Soból is a first-year PhD student in Technical Computer Science at Jagiellonian University. He holds a BSc in Computer Mathematics and MSc in Computer Science from Jagiellonian University. Currently, he is a member of PANDA (FAIR, GSI) collaboration where he conducts research on particle tracking algorithms and heterogeneous online processing of experimental data. His professional interests include high performance computing, software optimization for heterogeneous systems and CPU-GPU-FPGA interoperability.

Speaker: Prof. dr hab. inż. Grzegorz J. Nalepa, Professor @ Jagiellonian University

Title: Artificial Intelligence in Industry 4.0: Data, Models, and Knowledge

Abstract: Industry 4.0 is a paradigm shift, or the next industrial revolution, according to some analysts. It is also closely related with the Internet of Things (IoT), Cyber Physical System (CPS), and with information and communications technology (ICT) in general. One of the aspects of I4.0 is the use of extensive monitoring equipment including sensor networks for monitoring of industrial assets. The premise of I4.0 is that the gathered sensor data may serve as means to support decision making in the industrial setting. However, a proper analysis and use of this data poses a number of challenges. First of all, the characteristics of this data make it a typical Big Data with big volume, velocity, variety. Furthermore, typical analytical methods are not applicable. This is why specific data mining methods must be used, with specific machine learning algorithms properly selected and configured. However, in most of the real-life cases the use of machine learning is not enough. Extensive domain knowledge has to be taken into account to prepare as well as interpret the mining process of the industrial data. This requires a development of complex hybrid AI-based approaches combining ML methods with model and knowledge-based ones. Finally, I4.0 imposes number of specific requirements and tasks to be solved by these approaches. For last three years, the GEIST research group has been involved in two international research project funded by the CHIST-ERA scheme. In the most recent one, the XPM project, we are developing novel methods for predictive maintenance tasks in I4.0 using methods of eXplainable AI (XAI). In the first one, the PACMEL project, we have been developing novel methods for high level knowledge-driven analysis of sensor data, as well as conformance checking of business and industrial processes. This short presentation will introduce the challenges as well as results of both projects.

Biogram: Grzegorz J. Nalepa (GJN.re) is a full professor at the Jagiellonian University, formerly at the AGH University of Science and Technology, in Krakow, Poland. He is an engineer with degrees in computer science - artificial intelligence, and philosophy. He also works as an independent expert and consultant in the area of AI (KnowAI.eu). He co-authored over two hundred research papers in international conferences and journals. He has been involved in tens of projects, including R+D projects with number of companies. He authored a book “Modeling with Rules using Semantic Knowledge Engineering” (Springer 2018). In 2012 he received the scientific award of POLITYKA weekly for the most promising scientific achievements in technical sciences in Poland. In 2018 he received a prize for the outstanding monograph in computer science from the Committee of Computer Science of the Polish Academy of Sciences. In 2020 he founded to Jagiellonian Human-Centered AI Laboratory (JAHCAI). His recent interests include applications of AI in Industry 4.0 and business, explainable AI, affective computing, context awareness, as well as intersection of AI with law.

Speaker: dr inż. Krzysztof Kutt, Assistant Professor @ Jagiellonian University

Title: AI with psychology – a few words on affective adaptation and personalisation of intelligent systems

Abstract: Does your smartphone understand how you feel? When you are irritated does it just go silent and stop annoying you? Such technology is not there yet, but the expansion of intelligent systems into new fields of activity means that they are spending more and more time with us. And that raises the need to implement not only algorithms tailored to solve tasks, but also to understand what the user is feeling. This talk will highlight the key findings of research conducted in this area by the AfCAI team (https://afcai.re/): How to understand emotions? How to collect data? How to train models? Why is this technology not yet working and what can be done to improve its effectiveness?

Biogram: Krzysztof Kutt, PhD, is an assistant professor at the Jagiellonian University. He received BSc and MSc degrees in Computer Science at AGH-UST. In 2018 he defended his PhD thesis on methods and tools for collaborative knowledge engineering at AGH-UST. He also received MA degree in Psychology from Jagiellonian University. Currently, as a computer scientist and a psychologist, he is trying to combine these two disciplines together to create something new and better. His research activities focus on the knowledge engineering (knowledge graphs, data semantization), affective computing (collecting and processing sensory and contextual data related to emotions) and ways of user interaction with information systems (including BCI and Neurofeedback systems).

Speaker: Dr hab. inż. Andrzej Grabowski, profesor CIOP-PIB

Title: Telepresence and simulators – modern use of Virtual Reality in robotics and training with possible application of AI

Abstract: Research on virtual reality has been conducted for over half a century. At that time, the development of technology meant that the possibilities of immersion in a virtual environment were much greater, and the potential spectrum of practical application was becoming broader and broader. Additionally, the decreasing cost makes using these technologies more and more justified also from an economic point of view. Three different VR applications will be presented: remote control of mobile robots using the telepresence concept, training a group of firefighters, and building simulators of self-propelled mining machines. The possibilities of improving training simulations by using AI to modify the course of the training scenario in real-time based on the actions taken by the trainee will also be indicated.

Biogram: Prof. Andrzej Grabowski, Ph. D, D. Sc., professor at CIOP-PIB, Head of Virtual Reality Laboratory in Department of Safety Engineering of Central Institute for Labour Protection - National Research Institute. A graduate of the Faculty of Physics at the Warsaw University of Technology. In his work, he conducts research on the use of virtual reality in various fields, including training, cognitive functions and abilities, telepresence, and support for upper limb rehabilitation. He works on the development of VR techniques. For example, in the laboratory he manages, simulators of various types of vehicles and machines, remotely controlled mobile robots, wireless VR gloves with force feedback, and AI-enhanced vision-based safety systems are developed

Speaker: dr hab. inż. Marcin Woźniak, Professor @ Silesian University of Technology

Title: Smart environment – AI meets IoT

Abstract: Smart environments are constantly introduced in various fields of our life to support us in daily duties, work and entertainment. Sensors are used to help detect dangerous situations in human behavior like sudden changes of body pose. We also implement such solutions in control of electric devices. New models and methods are developed to support data processing and decision making processes in these environments. The development of modern computing enabled using Artificial Intelligence with connection to Internet of Things (IoT). The talk is to show and discuss latest advances and ideas in model of data processing and automatic control of processes and devices used in smart environments.

Biogram: Marcin Woźniak received the M.Sc. degree in applied mathematics, the Ph.D. degree and the D.Sc. degree in computational intelligence. M. Woźniak is currently an Associate Professor with the Faculty of Applied Mathematics, Silesian University of Technology. He is a Scientific Supervisor in editions of "The Diamond Grant" and "The Best of the Best" programs for highly talented students from the Polish Ministry of Science and Higher Education. He participated in various scientific projects at Polish, Italian and Lithuanian universities and projects with applied results at IT industry. He was a Visiting Researcher with universities in Italy, Sweden, and Germany. He has authored/coauthored over 200 research papers in international conferences and journals. His current research interests include neural networks with their applications together with various aspects of applied computational intelligence accelerated by evolutionary computation and federated learning models. In 2017 he was awarded by the Polish Ministry of Science and Higher Education with a scholarship for an outstanding young scientist and in 2021 he received award from the Polish Ministry of Science and Higher Education for research achievements. In 2020 M. Woźniak was presented among “TOP 2% Scientists in the World” by Stanford University for his career achievements. Dr. Woźniak was the Editorial Board member or an Editor for Sensors, IEEE ACCESS, Measurement, Frontiers in Human Neuroscience, PeerJ CS, International Journal of Distributed Sensor Networks, Computational Intelligence and Neuroscience, Journal of Universal Computer Science, etc., and a Session Chair at various international conferences and symposiums, including IEEE Symposium Series on Computational Intelligence, IEEE Congress on Evolutionary Computation, etc.

Speaker: Bartłomiej Małkus – PhD candidate @ Jagiellonian University

Title: Financial modeling with applications of machine learning and explainable AI.

Abstract: Financial modeling is a general term that can cover many different processes carried out throughout the financial system. One of the broad definitions of financial model is that it is anything that is meant to calculate, forecast, or estimate financial numbers. Its applications can be found in all kinds of financial institutions, like banks, investment funds, insurance companies, but also non-financial ones, like regular companies, which forecast incomes, outcomes, cash flows etc. The presentation will focus on and explain some of its more specific fields, like financial instruments pricing (derivatives in particular) or risk modeling, tell about the purpose of these and present challenges arising in them. Next, it will show applications of machine learning and explainable AI in the mentioned processes and fields, obstacles which come with applying them and current solutions. The presentation will also cover available financial data sources that may be used for research purposes.

Biogram: Bartłomiej Małkus is a PhD candidate at the Jagiellonian University in Technical Computer Science since 2021. He received BSc and MSc degrees in Computer Science on AGH University of Science and Technology and is currently pursuing MSc in Financial Markets on Cracow University of Economics. His field of interest is application of AI techniques to financial modelling. Commercially, he works in IBM on on-premises data warehouse analytics solutions.