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Pozycja Valuing Passes in Actions Leading to the Third Zone on the Pitch with Machine Learning Methods(Wydawnictwo Politechniki Łódzkiej, 2023) Tylka, Mateusz; Wałęsa, Sebastian; Girejko, Kornelia; Kaczmarek, Jakub; Grzelak, Bartłomiej; Piłka, TomaszIn football, the ability to make accurate and effective passes to the third zone of the pitch is a key aspect of a team’s success. Evaluating these passes can provide valuable information about a team’s performance and help coaches and analysts make informed decisions about their tactics and strategies. In this article, we will explore the possibility of using artificial intelligence methods to score passes to the third zone on the field, in comparison to traditional metrics.Pozycja On the Selection of a Machine Learning model in TinyML Devices – Preliminary Study(Wydawnictwo Politechniki Łódzkiej, 2023) Puślecki, Tobiasz; Walkowiak, KrzysztofThe expected development of TinyML-related technologies will necessitate the development of methods for efficient use of energy resources. In this article, we present preliminary study of machine learning (ML) model selection in TinyML devices in order to reach a tradeoff between accuracy and energy consumption. We study various use cases with different ML models. Our research shows that the presented method can improve the TinyML system in terms of operation time at the cost of slightly lower accuracy.Pozycja On the Importance of the RGB-D Sensor Model in the CNN-based Robotic Perception(Wydawnictwo Politechniki Łódzkiej, 2023) Zieliński, Mikołaj; Belter, DominikMobile and manipulation robots operating indoors use RGB-D cameras as the environment perception sensors. To process data from RGB and depth cameras neural networks are applied. These neural-based systems are trained using synthetic datasets due to the difficulties of obtaining ground truth data on real robots. As a result, the neural model used on the real robot does not produce satisfactory performance due to the differences between the images used during training and the inference. In this paper, we show the importance of depth sensor modeling while training the neural network on a synthetic dataset. We show that the obtained neural model can be used on the real robot and process the data from the real RGB-D camera.Pozycja On Parameters of Migration in PEA Computing(Wydawnictwo Politechniki Łódzkiej, 2023) Biełaszek, Sylwia; Byrski, AleksanderMetaheuristics, such as evolutionary algorithms have been proven to be (also theoretically, see works of Vose [1]) universal optimization methods. Skolicki and DeJong [2] researched impact of migration intervals on island models. In this article, we explore different migration intervals and amounts of migrating indyviduals, complementing Skolicki and DeJong’s research. In our experiments we use different ways of selecting migrants and pave the way for further research, e.g. involving different topologies and neighborhoods. Besides sketching out the background and presenting the idea of the algorithm we show the experimental results and discuss them in detail.Pozycja Lung Xray Images Analysis for COVID-19 Diagnosis(Wydawnictwo Politechniki Łódzkiej, 2023) Kloska, Anna; Tarczewska, Martyna; Giełczyk, Agata; Marciniak, BeataBackground: The SARS-CoV-2 pandemic began in early 2020. It paralyzed human life all over the world and threatened our security. Thus, proposing some novel and effective approaches to diagnosing COVID-19 infections became paramount. Methods: This article proposes a method for the classification of chest X-ray images based on the transfer learning. We examined also different scenarios of dataset augmentation. Results: The paper reports accuracy=98%, precision=97%, recall=100% and F1-score=98% in the most promising approach. Conclusion: Our research proofs that machine learning can be used in order to support medics in chest X-ray classification and implementing augmentation can lead to improvements in accuracy, precision, recall, and F1-scores.Pozycja Hierarchical Distributed Cluster-based Method for Robotic Swarms(Wydawnictwo Politechniki Łódzkiej, 2023) Mastej, Bartłomiej; Figat, MaksymThe growing interest in autonomous systems inspired by nature has led to a major shift towards swarm robotics. The main characteristics of swarms are independence from global knowledge, scalability and relatively low cost. However, the design of a swarm system is still a challenging task. Most of the existing research focuses on the task-specific solutions, which are hardly applicable to other solutions. Therefore, in this paper we present the method that provides a general guideline for the design of the swarm systems. The approach is verified in the simulation of the letter formation task.Pozycja AMUseBot: Towards Making the Most out of a Task-oriented Dialogue System(Wydawnictwo Politechniki Łódzkiej, 2023) Christop, Iwona; Dudzic, Kacper; Krzymiński, MikołajThis paper presents AMUseBot, a task-oriented dialogue system designed to assist the user in completing multi-step tasks. Taking into consideration that the fundamental issues with such systems are poor user ratings and high rates of uncompleted tasks, the main goal of the project is to keep the user focused and provide engaging conversations. We approach these problems by the introduction of dynamic multimodal communication and graph-based task management.Pozycja AloneKnight – Enabling Affective Interaction within Mobile Video Games(Wydawnictwo Politechniki Łódzkiej, 2023) Jemioło, Paweł; Świder, Krzysztof; Storman, Dawid; Adrian, Weronika T.Artificial intelligence is used in various contexts, including video games, where it can enhance the game design and adapt content to players’ emotional states through affective computing. In this paper, we present an example of an affective mobile game and compare participants’ opinions after playing two versions of the game, with and without an affective loop. The game was developed using Unity. In the affective version, physiological data is recorded and analysed to detect emotions based on facial expressions and electrodermal activity, which then affects the game. The study with 11 participants showed positive feedback for the game with affective loop.Pozycja Using Publicly Available Building Data to Improve 3D Map(Wydawnictwo Politechniki Łódzkiej, 2023) Krygiel, Krzysztof; Majek, Karol; Będkowski, JanuszIn this paper, we address the problem of 3D Map accuracy. No access to RTK GPS or LIDAR leads to poor accuracy of the map. High-rise buildings cause even greater trajectory errors. We used artificial intelligence methods to integrate publicly available building data and show that it can improve map accuracy from monocular camera and inaccurate GPS receiver. The main novelty is a method of building elevation detection in sparse point cloud data. We match detected elevations with building data and use modified bundle-adjustment algorithm to improve the map. We show that proposed approach decreases the trajectory error.Pozycja Semi-formal Methods for Security Informed Safety Assessment of Robotic Systems(Wydawnictwo Politechniki Łódzkiej, 2023) Kharchenko, Vyacheslav; Abakumov, Artem; Yakovlev, Sergiy