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--- aira:start [2022/06/21 11:49] – [Schedule] sbk
+++ aira:start [2022/10/11 07:47] – [2022-06-23] sbk
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 ===== Schedule=====
+  * **[DOCTORAL TRACK] 2022.10.13**: Iwona Grabska-Gradzińska [[#20221013|Plot generation of computer narrative game with graph transformations]]
+    * Meeting link: [[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1665473682749?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]]
+    * Presentation slides: TBA
   * **[DOCTORAL TRACK] 2022.06.23** : Bartosz Soból [[#20220623|AI inference acceleration on FPGA]]
-    * Meeting link: [[|MS Teams]]
+    * Meeting link: [[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1655812380995?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 empoyee, ask [[mailto:szymon.bobek@uj.edu.pl|Szymon Bobek]] for access)
     * Presentation slides: TBA
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   * **2021.10.14** [[http://acai2021.org|Challenges and Opportunities for human-centred AI]]: A dialog between Yoshua Bengio (Turing Award) and Ben Shneiderman, moderated by Virginia Dignum.
     * Meeting link: **[[https://hertie-school-org.zoom.us/j/94694805550?pwd=eTZXbHNMdngvajUwanZpZDBTTDRqUT09|Zoom link]]**
+==== 2022-10-13 ====
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+**Speaker**: Iwona Grabska-Gradzińska, PhD Candidate @ Jagiellonian University
+**Title**: Plot generation of computer narrative game with graph transformations
+**Abstract**: The formalization of plot structures both in literature and game design is a big challenge for researchers and different formal models were proposed since the beginning of 20 century. The introduction of a formal model of adventure game plot description –  based on graph representation and the graph generation procedure –  and its application in computer-aided adventure game design is a goal of this presentation.
+Starting from Vladimir Propp theory, which inspired many researchers of literary structures in the field of information science, some theoretical concepts will be introduced: an n-layer graph, a sheaf graph (a graph structure created for modeling adventure games) and generic matching as a tool that determines the possible scope of modification and generic production as a direct representation of the player's actions. The entire game graph can be considered a forest of sheaf subgraphs embedded into a location layer subgraph through roots-locations. All paths from a given location branch to form a sheaf graph. This leads to the definition of a multisheaf graph. Typical modifications of the world resulting from game mechanics most often operate on one or more sheaves.
+Based on the theoretical graph model, the specification of the StoryGraph implementation standard was proposed, currently available as StoryGraph 1.2, which includes data validation, testing plot lines (game simulation), modeling the world, visualization of the world, production, and production hierarchy tree.
+The process of collaborative game design based on the presented model will be also described.
+**Biogram**: Iwona Grabska-Gradzińska, MSc, MA (iwona.grabska@uj.edu.pl) is a PhD Candidate at the Jagiellonian University. She received her M.Sc. degrees in Computer Science and M.A. in Polish philology. She works as a research and teaching assistant at the Institute of Applied Computer Science at Jagiellonian University. Her research interests embrace both computer science and humanities – linguistics, game narrative structures analysis, visual perception (especially eye tracking), recognition, and reasoning.
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+==== 2022-06-23 ====
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+**Speaker**: Bartosz Soból,  PhD Candidate @ Jagiellonian University
+**Title**: AI inference acceleration on FPGA
+**Abstract**:
+Artificial intelligence and neural networks are constantly applied in all sorts of tasks involving data processing. Emerging models are deployed on different kinds of computing platforms: from edge computing, through the cloud, and ending with HPC.
+Modern FPGA-based accelerators and SoCs aim to fulfill different needs in all of the above levels such as high throughput, low latency, high energy efficiency, and flexibility.
+New software stacks are developed to expose high-level interfaces for preparing, optimizing, and deploying existing or custom, implemented in standard machine learning frameworks such as PyTorch or TensorFlow, models on FPGA devices.
+In this talk, I will present modern solutions for accelerating the inference of neural network models on FPGAs as well as examples of usage done by us at the JU and from others.
+**Biograms**:
+**Bartosz Soból** is a first-year Ph.D. 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.
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