Progress in Polish Artificial Intelligence Research 4

Stały URI zbioruhttp://hdl.handle.net/11652/4774



CONTENTS
1. 3D Reconstruction of Non-Visible Surfaces of Objects from a Single Depth View – Comparative Study Staszak R., Michałek P., Chudziński J., Kopicki M., Belter D. ........................................................................................ 19
2. Challenges of Crop Classification from Satellite Imagery with Eurocrops Dataset Aszkowski P., Kraft M. ........................................................................................ 25
3. Do We Always Need AI for Image Colorization? Śluzek A. ........................................................................................ 31
4. Fault Diagnosis in a Squirrel-Cage Induction Motor Using Thermal Imaging Piechocki M., Kraft M., Pajchrowski T. ........................................................................................ 37
5. Objective Hybrid Quality Assessment of Binary Images with the Use of Shallow Neural Networks Kopytek M., Okarma K. ........................................................................................ 43
6. One-point Hough Transform with Centred Accumulator Chmielewski L. J., Bator M., Gajowniczek K. ........................................................................................ 49
7. Pedestrian Detection with High-resolution Event Camera Wzorek P., Kryjak T. ........................................................................................ 55
8. Recognition of Shoplifting Activities in CCTV Footage Using the Combined CNN-RNN Model Kirichenko L., Pichugina O., Sydorenko B., Yakovlev S. ........................................................................................ 61
9. Spotting Advertisements from Above: Billboard Detection and Segmentation in UAV Imagery Ptak B., Dominiak J., Kraft M. ........................................................................................ 67
10. Transformers Neural Networks Applications in Different Computer Vision Tasks Brodzicki A., Piekarski M., Kostuch A., Noworolnik F., Aleksandrowicz M., Wójcicka A., Jaworek-Korjakowska J. ........................................................................................ 73
11. Weak Supervision in Enemy Detection Based on Computer Game Output Video Stream Rajtar J., Szajerman D. ........................................................................................ 81
12. A Comparison of Shallow Explainable Artificial Intelligence Methods against Grammatical Evolution Approach Sepioło D., Ligęza A. ........................................................................................ 89
13. Clustering Dilemmas – A Study of the Request of Homogenicity within Clusters Versus Diversity Between Clusters Kłopotek M. A. ........................................................................................ 95
14. Contextual ES-adRNN with Attention Mechanisms for Forecasting Smyl S., Dudek G., Pełka P. ........................................................................................ 101
15. Graph-Supported Preparation of GIS Machine Learning Datasets Ernst S. ........................................................................................ 107
16. Hashtag Similarity Based on Laplacian Eigenvalue Spectrum Starosta B., Kłopotek M. A., Wierzchoń S. T. ........................................................................................ 113
17. Improvement of Attention Mechanism Explainability in Prediction of Chemical Molecules’ Properties Durys B., Tomczyk A. ........................................................................................ 119
18. On Usefulness of Dominance Relation for Selecting Counterfactuals from the Ensemble of Explainers Stępka I., Lango M., Stefanowski J. ........................................................................................ 125
19. Towards Detection of Unknown Polymorphic Patterns Using Prior Knowledge Kucharski P., Ślot K. ........................................................................................ 131
20. AI-driven Ecodriving and ETA Solutions for Truck Transport Lipiński P., Morawski M., Napieralski P., Nowok P., Zawiślak B., Hojdys L., Lazar M., Lazarek P., Zając N., Pizoń S., Jakubiec R., Sienkiewicz J., Gołąbek S., Kabocik M., Fedrizzi S., Kuliga M., Frączkiewicz M., Malarz M., Puchalski J., Danysz E., Grajcarek M. ........................................................................................ 139
21. Analysis of Surface EMG Signals to Control of a Bionic Hand Prototype Pieprzycki A., Król D., Wawryka P., Łachut K., Hamera M., Srebro B. ........................................................................................ 145
22. Brief Overview of Selected Research Directions and Applications of Process Mining in KRaKEn Research Group Kluza K., Zaremba M., Sepioło D., Wiśniewski P., Adrian W. T., Gaudio M. T., Jemioło P., Adrian M., Jobczyk K., Ślażyński M., Stachuta-Terlecka B., Ligęza A. ........................................................................................ 151
23. Carbon Footprint Reduction of a Petrochemical Process Supported by ML and Digital Twin modelling Kulikowski S., Romanowski A., Sierszeń A. ........................................................................................ 157
24. Digital Twin for Training Set Generation for Unexploded Ordnance Classification Ściegienka P., Blachnik M. ........................................................................................ 163
25. Energy Dissipation Anomalies in Buildings Morawski M., Tomczyk A., Idaczyk M. ........................................................................................ 165
26. Identification of Damaged AIS Data Based on Clustering and Multi-Label Classification Szarmach M., Czarnowski I. ........................................................................................ 167
27. Integrating Anomaly Detection for Enhanced Data Protection in Cloud-Based Applications Czerkas K., Drozd M., Duraj A., Lichy K., Lipiński P., Morawski M., Napieralski P., Puchała D., Kwapisz M., Warcholiński A., Karbowańczyk M., Wosiak P. ........................................................................................ 173
28. Learning Non-Differentiable Graphs of Utility AI Świechowski M. ........................................................................................ 181
29. Lessons Learned from a Smart City Project with Citizen Engagement Ernst S., Zaworski K., Sokołowski P., Salwa G. ........................................................................................ 187
30. Machine Learning for Water Leak Detection and Localization in the WaterPrime Project Głomb P., Romaszewski M., Cholewa M., Koral W., Madej A., Skrabski M., Kołodziej K. ........................................................................................ 193
31. Performance Analysis of Machine Learning Platforms Using Cloud Native Technology on Edge Devices Cłapa K., Grudzień K., Sierszeń A. ........................................................................................ 195
32. RNN-based Phase Unwrapping for Enabling Vital Parameter Monitoring with FMCW Radars Łuczak P., Hausman S., Ślot K. ........................................................................................ 201
33. Statistical Method for Photovoltaic Power Forecasting Basing on Signal Components Decomposition Parczyk P., Burduk R. ........................................................................................ 207
34. Text-to-music Models and Their Evaluation Methods Modrzejewski M., Rokita P. ........................................................................................ 213
35. Towards Ontology-Driven Verification of Car Claims Settlement Pancerz K., Wolski J. ........................................................................................ 219
36. Using Security Games against Wild Dumping Sites Adrian M., Markiewicz J. ........................................................................................ 225
37. VideoAI – System for Synchronization of Electronic Program Guides Wasilewski J., Sochaj B., Gaca A. ........................................................................................ 231
38. Identification of Melanocytic Skin Lesions Using Deep Learning Methods Paja W., Szkoła J., Pancerz K., Sarzyński J., Żychowska M. ........................................................................................ 239
39. Loss Function Influence on Uncertainty Estimation for White Matter Lesions 3D Segmentation in a Shifted Domain Setting Kaczmarska M., Majek K. ........................................................................................ 245
40. Multi-task Learning for Classification, Segmentation, Reconstruction, and Detection on Chest CT Scans Hryniewska-Guzik W., Kędzierska M., Biecek P. ........................................................................................ 251
41. Supporting Surgical Training with the Help of Computer Vision and Machine Learning Methods Forczmański P., Ryder Y. C., Mott N. M., Gross C. L., Yu J. B., Rooney D. M., Jeffcoach D. R., Bidwell S., Anidi C., Rosenthal L., Kim G. J. ........................................................................................ 259
42. A Convolutional and Recurrent Neural Network-based Approach for Speech Emotion Recognition Duch P., Wiatrowska I., Kapusta P. ........................................................................................ 267
43. Aaron Earned an Iron Urn: Speech-to-IPA Models Improve Diagnostic of Pronunciation Olejnik F., Stachowiak R., Krysińska I., Morzy M. ........................................................................................ 273
44. Anonymizer for Polish Language Walkowiak T., Gniewkowski M., Pogoda M., Ropiak N. ........................................................................................ 281
45. A Hybrid Fuzzy-Rough Approach to Handling Missing Data in a Fall Detection System Mroczek T., Gil D., Pękala B. ........................................................................................ 285
46. Customer Churn Analytics Using Monotonic Rules Szeląg M., Słowiński R. ........................................................................................ 287
47. Application of Pawlak’s Conflict Model to Generate Coalitions of Local Tables with Similar Values on Conditional Attributes Przybyła-Kasperek M., Kusztal K. ........................................................................................ 293
48. A Novel DNN-based Image Watermarking Algorithm Kovačević S., Pavlović K., Djurović I. ........................................................................................ 301
49. Autoregressive Label-Conditioned Autoencoder for Controllable Image-To-Video Generation Kubicki K., Ślot K. ........................................................................................ 307
50. Building Energy Use Intensity Prediction with Artificial Neural Networks Stokfiszewski K., Sztoch P., Sztoch R., Wosiak A. ........................................................................................ 313
51. Grounded HyperSymbolic Representations Learned through Gradient-Based Optimization Łuczak P., Ślot K., Kucharski J. ........................................................................................ 319
52. Increasing Skin Lesions Classification Rates using Convolutional Neural Networks with Invariant Dataset Augmentation and the Three-Point Checklist of Dermoscopy Milczarski P., Borowski N., Beczkowski M. ........................................................................................ 325
53. A Novel Learning Multi-Swarm Particle Swarm Optimization Borowska B. ........................................................................................ 337
54. Are Quantified Boolean Formulas Hard for Reason-Able Embeddings? Potoniec J. ........................................................................................ 343
55. Dynamic Mutation Control in Continuous Genetic Algorithms Wieczorek Ł., Ignaciuk P. ........................................................................................ 349
56. Local Energy Redistribution Units for Space Dimensionality Reduction in Data Classification Puchała D. ........................................................................................ 355
57. MPTCP Congestion Control Algorithms for Streaming Applications – Performance Evaluation in Public Networks Łuczak Ł. P., Ignaciuk P., Morawski M. ........................................................................................ 361
58. Optimized Mutation Operator in Evolutionary Approach to Stackelberg Security Games Żychowski A., Mańdziuk J. ........................................................................................ 367
59. Simulation of the Quantum Heat Engine in the Quantum Register Ostrowski M. ........................................................................................ 373
60. Socio-cognitive Flock-based Optimization Urbańczyk A., Czech K., Byrski A. ........................................................................................ 381
61. A New Approach to Learning of 3D Characteristic Points for Vehicle Pose Estimation Nowak T., Skrzypczyński P. ........................................................................................ 389
62. A Reinforcement Learning Framework for Motion Planning of Autonomous Vehicles Orłowski M., Skruch P. ........................................................................................ 395
63. BDOT10k-seg: A Dataset for Semantic Segmentation Kos A., Majek K. ........................................................................................ 401
64. Beacon-based Swarm Search and Rescue Ratnayake S., Figat M. ........................................................................................ 407
65. Intelligent Anticipatory Mobile Robot Networks for Autonomous Fruit Harvesting Skulimowski A. M. J., Karimi M. ........................................................................................ 411
66. Evolution of Robotic System Specification Methodology Figat M., Zieliński C. ........................................................................................ 421
67. Improving RGB-D Visual Odometry with Depth Learned from a Better Sensor’s Output Kostusiak A. ........................................................................................ 429
68. Mixing Synthetic and Real-world Datasets Strategy for Improved Generalization of the CNN Młodzikowski K., Belter D. ........................................................................................ 435
69. NeRF-based RGB-D Images Generation in Robotics – Experimental Study Kulecki B., Belter D. ........................................................................................ 443
70. Predictive User Interface for Emerging Experiences Kapusta P. Duch P. ........................................................................................ 449
71. Semantic Segmentation for Autonomous Drone Delivery SUADD’23 Challenge Mrukwa A., Majek K. ........................................................................................ 451
72. Semi-formal Methods for Security Informed Safety Assessment of Robotic Systems Kharchenko V., Abakumov A., Yakovlev S. ........................................................................................ 457
73. Using Publicly Available Building Data to Improve 3D Map Krygiel K., Majek K., Będkowski J. ........................................................................................ 459
74. AloneKnight – Enabling Affective Interaction within Mobile Video Games Jemioło P., Świder K., Storman D., Adrian W. T. ........................................................................................ 467
75. AMUseBot: Towards Making the Most out of a Task-oriented Dialogue System Christop I., Dudzic K., Krzymiński M. ........................................................................................ 473
76. Hierarchical Distributed Cluster-based Method for Robotic Swarms Mastej B., Figat M. ........................................................................................ 479
77. Lung Xray Images Analysis for COVID-19 Diagnosis Kloska A., Tarczewska M., Giełczyk A., Marciniak B. ........................................................................................ 485
78. On Parameters of Migration in PEA Computing Biełaszek S., Byrski A. ........................................................................................ 491
79. On the Importance of the RGB-D Sensor Model in the CNN-based Robotic Perception Zieliński M., Belter D. ........................................................................................ 495
80. On the Selection of a Machine Learning model in TinyML Devices – Preliminary Study Puślecki T., Walkowiak K. ........................................................................................ 501
81. Valuing Passes in Actions Leading to the Third Zone on the Pitch with Machine Learning Methods Tylka M., Wałęsa S., Girejko K., Kaczmarek J., Grzelak B., Piłka T. ........................................................................................ 507

Przeglądaj

Filtry

202313

Ustawienia

Tematsearch.filters.applied.operator.equals: search.filters.subject.artificial intelligence

Wyniki wyszukiwania

Teraz wyświetlane 1 - 10 z 13
  • Pozycja
    Lung Xray Images Analysis for COVID-19 Diagnosis
    (Wydawnictwo Politechniki Łódzkiej, 2023) Kloska, Anna; Tarczewska, Martyna; Giełczyk, Agata; Marciniak, Beata
    Background: 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
    AMUseBot: Towards Making the Most out of a Task-oriented Dialogue System
    (Wydawnictwo Politechniki Łódzkiej, 2023) Christop, Iwona; Dudzic, Kacper; Krzymiński, Mikołaj
    This 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
    Predictive User Interface for Emerging Experiences
    (Wydawnictwo Politechniki Łódzkiej, 2023) Kapusta, Paweł; Duch, Piotr
    This research paper focuses on the use of predictive techniques to improve interaction with user interfaces in emerging experiences such as Virtual Reality, Augmented Reality, Metaverse, and touchless kiosks and dashboards. We propose the concept of intelligent snapping, which uses gaze tracking, head-pose tracking, hand tracking, as well as gesture recognition and hand posture recognition to catch the intent of the person rather than the actual input.
  • Pozycja
    Are Quantified Boolean Formulas Hard for Reason-Able Embeddings?
    (Wydawnictwo Politechniki Łódzkiej, 2023) Potoniec, Jędrzej
    We aim to establish theoretical boundaries for the applicability of reason-able embeddings, a recently proposed method employing a transferable neural reasoner to shape a latent space of knowledge graph embeddings. Since reason-able embeddings rely on the ALC description logic, we construct a dataset of the hardest concepts in ALC by translating quantified boolean formulas (QBF) from QBFLIB, a benchmark for QBF solvers. We experimentally show the dataset is hard for a symbolic reasoner FaCT++, and analyze the results of reasoning with reason-able embeddings, concluding that the dataset is too hard for them.
  • Pozycja
    Increasing Skin Lesions Classification Rates using Convolutional Neural Networks with Invariant Dataset Augmentation and the Three-Point Checklist of Dermoscopy
    (Wydawnictwo Politechniki Łódzkiej, 2023) Milczarski, Piotr; Borowski, Norbert; Beczkowski, Michał
    In the paper, we show how to tackle the problem of lack of the rotation invariance in CNN networks using the authors’ Invariant Dataset Augmentation (IDA) method. The IDA method allows to increase the classification rates taking into account as an example the classification of the skin lesions using a small image set. In order to solve the problem of the lack of rotation invariance, IDA method was used and the dataset was increased in an eightfold and invariant way. In the research, we applied the IDA methods and compared the results of VGG19, XN and Inception-ResNetv2 CNN networks in three skin lesions features classification defined by wellknown dermoscopic criterions e.g. the Three-Point Checklist of Dermoscopy or the Seven-Point Checklist. Due to Invariant Dataset Augmentation, the classification rate parameters like true positive rate by almost 20%, false positive rate as well as the F1 score and Matthews correlation coefficient have been significantly increased opposite to type II error that has significantly decreased. In the paper, the confusion matrix parameters result in: 98-100% accuracy, 98-100% true positive rate, 0.0-2.3% false positive rate, tests F1=0.95 and MCC=0.95. That general approach can provide higher results while using CNN networks in other applications.
  • Pozycja
    Grounded HyperSymbolic Representations Learned through Gradient-Based Optimization
    (Wydawnictwo Politechniki Łódzkiej, 2023) Łuczak, Piotr; Ślot, Krzysztof; Kucharski, Jacek
    Hyperdimensional computing is a novel paradigm, capable of processing complex data structures with simple operations. Its main limitations lie in the conversion of real world data onto hyperdimensional space, which due to lack of a universal translation scheme, oftentimes requires application-specific methods. This work presents a novel method for unsupervised hyperdimensional conversion of arbitrary image data. Additionally, this method is augmented by the ability of creating HyperSymbols, or class prototypes, provided that such class labels are available. The proposed method achieves promising performance on MNIST dataset, both in translating individual samples as well as producing HyperSymbols for downstream classification task.
  • Pozycja
    A Convolutional and Recurrent Neural Network-based Approach for Speech Emotion Recognition
    (Wydawnictwo Politechniki Łódzkiej, 2023) Duch, Piotr; Wiatrowska, Izabela; Kapusta, Paweł
    Speech emotion recognition (SER) is a crucial aspect of humancomputer interaction. In this article, we propose a deep learning approach, using CNN and RNN architectures, for SER using both convolutional and recurrent neural networks. We evaluated the approach on four audio datasets, including CREMA-D, RAVDESS, TESS, and EMOVO. Our experiments tested various feature sets and extraction settings to determine optimal features for SER. Our results demonstrate that the proposed approach achieves high accuracy rates and outperforms state-of-the-art algorithms.
  • Pozycja
    Performance Analysis of Machine Learning Platforms Using Cloud Native Technology on Edge Devices
    (Wydawnictwo Politechniki Łódzkiej, 2023) Cłapa, Konrad; Grudzień, Krzysztof; Sierszeń, Artur
    This article presents the results of an experiment performed on a machine learning edge computing platform composed of a virtualized environment with a K3s cluster and Kubeflow software. The study aimed to analyze the effectiveness of executing Kubeflow pipelines for simulated parallel executions. A benchmarking environment was developed for the experiment to allow system performance measurements based on parameters, including the number of pipelines and nodes. The results demonstrate the impact of the number of cluster nodes on computational time, revealing insights that could inform future decisions regarding increasing the effectiveness of running machine learning pipelines on edge devices.
  • Pozycja
    Integrating Anomaly Detection for Enhanced Data Protection in Cloud-Based Applications
    (Wydawnictwo Politechniki Łódzkiej, 2023) Czerkas, Konrad; Drozd, Michał; Duraj, Agnieszka; Lichy, Krzysztof; Lipiński, Piotr; Morawski, Michał; Napieralski, Piotr; Puchała, Dariusz; Kwapisz, Marcin; Warcholiński, Adrian; Karbowańczyk, Michał; Wosiak, Piotr
    In this research, anomaly detection techniques and artificial neural networks were employed to address the issue of attacks on cluster computing systems. The study investigated the detection of Distributed Denial of Service (DDoS) and Partition attacks by monitoring metrics such as network latency, data transfer rate, and number of connections. Additionally, outlier detection algorithms, namely Local Outlier Factor (LOF) and COF, as well as ARIMA and SHESD models were tested for anomaly detection. Two types of neural network architectures, multi-layer perceptron (MLP) and recursive LSTM networks, were used to detect attacks by classifying events as “attack” or “no attack”. The study underscores the importance of implementing proactive security measures to protect cluster computing systems from cyber threats.