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<xml><records><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michał Cholewa</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Sebastian Opozda</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Arkadiusz Sochan</style></author><author><style face="normal" font="default" size="100%">M. Kosiedowski</style></author><author><style face="normal" font="default" size="100%">E. Kuśmierek</style></author><author><style face="normal" font="default" size="100%">A. Bęben</style></author><author><style face="normal" font="default" size="100%">P. Krawiec</style></author><author><style face="normal" font="default" size="100%">A. Stroiński</style></author><author><style face="normal" font="default" size="100%">Wojciech Burakowski</style></author><author><style face="normal" font="default" size="100%">Piotr Krawiec</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Aplikacje Sieci Świadomej Treści</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2013</style></year></dates><secondary-title><style face="normal" font="default" size="100%">Inżynieria Internetu przyszłości część 2</style></secondary-title><section><style face="normal" font="default" size="100%">Aplikacje Sieci Świadomej Treści</style></section><isbn><style face="normal" font="default" size="100%">978-83-7814-099-3</style></isbn><publisher><style face="normal" font="default" size="100%">Oficyna Wydawnicza PW</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Sebastian Opozda</style></author><author><style face="normal" font="default" size="100%">Arkadiusz Sochan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Choosing and Modeling the Hand Gesture Database for a Natural User Interface</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2012</style></year></dates><secondary-title><style face="normal" font="default" size="100%">Gesture and Sign Language in Human-Computer Interaction and Embodied Communication</style></secondary-title><edition><style face="normal" font="default" size="100%">Lecture Notes in Artificial Intelligence</style></edition><publisher><style face="normal" font="default" size="100%">Springer Berlin Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Sebastian Opozda</style></author><author><style face="normal" font="default" size="100%">Arkadiusz Sochan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Choosing and modeling hand gesture database for natural user interface</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2011</style></year></dates><secondary-title><style face="normal" font="default" size="100%">GW 2011: the 9th International Gesture in Embodied Communication and Human-Computer InteractionWorkshop</style></secondary-title><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Arkadiusz Sochan</style></author><author><style face="normal" font="default" size="100%">Sebastian Opozda</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Unsupervised Parameter Selection for Gesture Recognition with Vector Quantization and Hidden Markov Models</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2011</style></year></dates><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science, Human-Computer Interaction – INTERACT</style></secondary-title><volume><style face="normal" font="default" size="100%">6949</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">M. Kosiedowski</style></author><author><style face="normal" font="default" size="100%">E. Kuśmierek</style></author><author><style face="normal" font="default" size="100%">A. Stroiński</style></author><author><style face="normal" font="default" size="100%">Michał Cholewa</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Sebastian Opozda</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Arkadiusz Sochan</style></author><author><style face="normal" font="default" size="100%">A. Bęben</style></author><author><style face="normal" font="default" size="100%">P. Krawiec</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Aplikacje sieci świadomej treści</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2012</style></year></dates><secondary-title><style face="normal" font="default" size="100%">Przegląd telekomunikacyjny</style></secondary-title><pages><style face="normal" font="default" size="100%">1491–1502</style></pages><number><style face="normal" font="default" size="100%">8-9</style></number><abstract><style face="normal" font="default" size="100%">Artykuł przedstawia aplikacje korzystające z sieci świadomej treści PI CAN opracowanej w ramach projektu Inżynieria Internetu Przyszłości (IIP)1. Są to trzy aplikacje oferujące dostęp do treści multimedialnych: HomeNetMedia dla celów rozrywki, Rozproszone Wirtualne Muzeum dla edukacji oraz Biblioteka Cyfrowa Pacjenta dla przekazywania informacji medycznej. Scharakteryzowana została funkcjonalność sieci PI CAN z punktu widzenia aplikacji wraz z zasadami współpracy z użytkownikami, tj. dostawcami i konsumentami treści. Poszczególne aplikacje omówione zostały ze wskazaniem sposobu wykorzystania sieci świadomej treści oraz implementacji usług zewnętrznych powiązanych z siecią PI CAN specyficznych dla aplikacji. Przedstawione zostały również wyniki integracji poszczególnych aplikacji z siecią PI CAN oraz wyniki przeprowadzonych testów.</style></abstract><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Piotr Gawron</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards a natural gesture interface: LDA-based gesture separability</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2011</style></year></dates><urls><style face="normal" font="default" size="100%">https://arxiv.org/abs/1109.5034</style></urls><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">arXiv:1109.5034</style></notes></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Piotr Gawron</style></author><author><style face="normal" font="default" size="100%">Sebastian Opozda</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Dimensionality reduction of dynamic animations using HO-SVD</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2014</style></year></dates><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Artificial Intelligence</style></secondary-title><pages><style face="normal" font="default" size="100%">757–768</style></pages><volume><style face="normal" font="default" size="100%">8467</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Piotr Gawron</style></author><author><style face="normal" font="default" size="100%">Sebastian Opozda</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Compression of animated 3D models using HO-SVD</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2013</style></year></dates><secondary-title><style face="normal" font="default" size="100%">arXiv:1310.1240</style></secondary-title><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">arXiv:1310.1240</style></notes></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Piotr Gawron</style></author><author><style face="normal" font="default" size="100%">Sebastian Opozda</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Dimensionality Reduction of Dynamic Mesh Animations Using HO-SVD</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2013</style></year></dates><secondary-title><style face="normal" font="default" size="100%">Journal of Artificial Intelligence and Soft Computing Research</style></secondary-title><pages><style face="normal" font="default" size="100%">277–289</style></pages><volume><style face="normal" font="default" size="100%">3</style></volume><number><style face="normal" font="default" size="100%">4</style></number><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">DOI 10.2478/jaiscr-2014-0020</style></notes></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Effect of Multiple Training Sequences on HMM Classification of Motion Capture Gesture Data</style></title></titles><keywords/><dates><year><style 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size="100%">3D Mesh Approximation Using Vector Quantization</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style face="normal" font="default" size="100%">V</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">Advances in Soft Computing</style></secondary-title><pages><style face="normal" font="default" size="100%">71–78</style></pages><volume><style face="normal" font="default" size="100%">57/2009</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Piotr Gawron</style></author><author><style face="normal" font="default" size="100%">Khalifa Djemal</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Natural hand gestures for human identification in a Human-Computer Interface</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style face="normal" font="default" size="100%">10</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">Image Processing Theory Tools and Applications (IPTA), 2014 4th International Conference on</style></secondary-title><pages><style face="normal" font="default" size="100%">404–409</style></pages><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" 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face="normal" font="default" size="100%"/></secondary-title><pages><style face="normal" font="default" size="100%">302–308</style></pages><publisher><style face="normal" font="default" size="100%">Academy Publishing House EXIT</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Arkadiusz Sochan</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Krzysztof Skabek</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Sebastian Opozda</style></author><author><style face="normal" font="default" size="100%">Andrzej Kwiecień</style></author><author><style face="normal" font="default" size="100%">Piotr Gaj</style></author><author><style face="normal" font="default" size="100%">P. Stera</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Virtual Museum as an Example of 3D Content Distribution in the Architecture of a Future Internet</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2011</style></year></dates><secondary-title><style face="normal" font="default" size="100%">Computer Networks 2011. Communications in Computer and Information Science 160.</style></secondary-title><pages><style face="normal" font="default" size="100%">459–464</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Parameter Estimation for HOSVD-based Approximation of Temporally Coherent Mesh Sequences</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2016</style></year></dates><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications</style></secondary-title><electronic-resource-num><style face="normal" font="default" size="100%">10.5220/0005723501380145</style></electronic-resource-num><isbn><style face="normal" font="default" size="100%">978-989-758-175-5</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Michał Cholewa</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Semi-supervised hyperspectral classification from a small number of training samples using a co-training approach</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2016</style></year></dates><secondary-title><style face="normal" font="default" size="100%">ISPRS Journal of Photogrammetry and Remote Sensing</style></secondary-title><urls><style face="normal" font="default" size="100%">http://www.sciencedirect.com/science/article/pii/S0924271616303446</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">http://dx.doi.org/10.1016/j.isprsjprs.2016.08.011</style></electronic-resource-num><pages><style face="normal" font="default" size="100%">60 - 76</style></pages><volume><style face="normal" font="default" size="100%">121</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Michał Cholewa</style></author><author><style face="normal" font="default" size="100%">Krzysztof Domino</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Application of hyperspectral imaging and Machine Learning methods for the detection of gunshot residue patterns</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style face="normal" font="default" size="100%">07/2018</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">Forensic Science International</style></secondary-title><urls><style face="normal" font="default" size="100%">http://www.sciencedirect.com/science/article/pii/S0379073818303657</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">https://doi.org/10.1016/j.forsciint.2018.06.040</style></electronic-resource-num><volume><style face="normal" font="default" size="100%">290</style></volume><section><style face="normal" font="default" size="100%">227</style></section><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Michał Cholewa</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Adaptive, Hubness-Aware Nearest Neighbour Classifier with Application to Hyperspectral Data</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2018</style></year></dates><secondary-title><style face="normal" font="default" size="100%">Computer and Information Sciences</style></secondary-title><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We present an extension of the Nearest Neighbour classifier that can adapt to sample imbalances in local regions of the dataset. Our approach uses the hubness statistic as a measure of a relation between new samples and the existing training set. This allows to estimate the upper limit of neighbours that vote for the label of the new instance. This estimation improves the classifier performance in situations where some classes are locally under-represented. The main focus of our method is to solve the problem of local undersampling that exists in hyperspectral data classification. Using several well-known Machine Learning and hyperspectral datasets, we show that our approach outperforms standard and distance-weighted kNN, especially for high values of k.&lt;/p&gt;&#13;
</style></abstract><isbn><style face="normal" font="default" size="100%">978-3-030-00840-6</style></isbn><publisher><style face="normal" font="default" size="100%">Springer International Publishing</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Arkadiusz Sochan</style></author><author><style face="normal" font="default" size="100%">Krzysztof Skabek</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Matrix and Tensor-Based Approximation of 3D Face Animations from Low-Cost Range Sensors</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2018</style></year></dates><secondary-title><style face="normal" font="default" size="100%">Computer and Information Sciences</style></secondary-title><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Three-dimensional animation is often represented in the form of a sequence of 3D meshes, also called dynamic animation or Temporally Coherent Mesh Sequence (TCMS). Widespread availability of affordable range sensors makes capturing such data easy, however, its huge volume complicates both storage and further processing. One of the possible solutions is to approximate the data using matrix or tensor decomposition. However the quality the animation may have different impact on both approaches. In this work we use the Microsoft Kinect™&#160;to crate sequences of human face models and compare the approximation error obtained from modelling animations using Principal component analysis (PCA) and Higher Order Singular Value Decomposition (HOSVD). We focus on distortion introduced by reconstruction of data from its truncated factorization. We show that while HOSVD may outperform PCA in terms of approximation error, it may be significantly affected by distortion in animation data.&lt;/p&gt;&#13;
</style></abstract><isbn><style face="normal" font="default" size="100%">978-3-030-00840-6</style></isbn><publisher><style face="normal" font="default" size="100%">Springer International Publishing</style></publisher><pub-location><style face="normal" font="default" size="100%">Cham</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michał Cholewa</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Spatial-Spectral Disagreement-Based Sample Selection With an Application to Hyperspectral Data Classification</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style face="normal" font="default" size="100%">March</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">IEEE Geoscience and Remote Sensing Letters</style></secondary-title><electronic-resource-num><style face="normal" font="default" size="100%">10.1109/LGRS.2018.2868862</style></electronic-resource-num><pages><style face="normal" font="default" size="100%">467-471</style></pages><volume><style face="normal" font="default" size="100%">16</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Krzysztof Domino</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Michał Cholewa</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Band selection with Higher Order Multivariate Cumulants for small target detection in hyperspectral images</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style face="normal" font="default" size="100%">2019</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">PP-RAI'2019</style></secondary-title><urls><style face="normal" font="default" size="100%">https://arxiv.org/abs/1808.03513</style></urls><pages><style face="normal" font="default" size="100%">p. 121</style></pages><isbn><style face="normal" font="default" size="100%">978-83-943803-2-8</style></isbn><publisher><style face="normal" font="default" size="100%">Wroclaw University of Science and Technology</style></publisher><pub-location><style face="normal" font="default" size="100%">Wrocław, Poland</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bartosz Grabowski</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Mateusz Ostaszewski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Unsupervised deep learning approach to hyperspectral anomaly detection</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2019</style></year></dates><secondary-title><style face="normal" font="default" size="100%">PP-RAI'2019</style></secondary-title><isbn><style face="normal" font="default" size="100%">978-83-943803-2-8</style></isbn><publisher><style face="normal" font="default" size="100%">Wroclaw University of Science and Technology</style></publisher><pub-location><style face="normal" font="default" size="100%">Wrocław, Poland</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Anomaly detection in hyperspectral remote sensing images</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2020</style></year></dates><secondary-title><style face="normal" font="default" size="100%"/></secondary-title><urls><style face="normal" font="default" size="100%">https://www.elsevier.com/books/hyperspectral-remote-sensing/pandey/978-0-08-102894-0</style></urls><isbn><style face="normal" font="default" size="100%">978-0-08-102894-0</style></isbn><publisher><style face="normal" font="default" size="100%">Elsevier</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kamil Książek</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Bartosz Grabowski</style></author><author><style face="normal" font="default" size="100%">Michał Cholewa</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Blood Stain Classification with Hyperspectral Imaging and Deep Neural Networks</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style face="normal" font="default" size="100%">11/2020</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">Sensors</style></secondary-title><urls><style face="normal" font="default" size="100%">https://www.mdpi.com/1424-8220/20/22/6666</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">https://doi.org/10.3390/s20226666</style></electronic-resource-num><volume><style face="normal" font="default" size="100%">20</style></volume><issue><style face="normal" font="default" size="100%">Recent Advances in Multi- and Hyperspectral Image Analysis</style></issue><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In recent years, growing interest in deep learning neural networks has raised a question on how they can be used for effective processing of high-dimensional datasets produced by hyperspectral imaging (HSI). HSI, traditionally viewed as being within the scope of remote sensing, is used in non-invasive substance classification. One of the areas of potential application is forensic science, where substance classification on the scenes is important. An example problem from that area—blood stain classification—is a case study for the evaluation of methods that process hyperspectral data. To investigate the deep learning classification performance for this problem we have performed experiments on a dataset which has not been previously tested using this kind of model. This dataset consists of several images with blood and blood-like substances like ketchup, tomato concentrate, artificial blood, etc. To test both the classic approach to hyperspectral classification and a more realistic application-oriented scenario, we have prepared two different sets of experiments. In the first one, Hyperspectral Transductive Classification (HTC), both a training and a test set come from the same image. In the second one, Hyperspectral Inductive Classification (HIC), a test set is derived from a different image, which is more challenging for classifiers but more useful from the point of view of forensic investigators. We conducted the study using several architectures like 1D, 2D and 3D convolutional neural networks (CNN), a recurrent neural network (RNN) and a multilayer perceptron (MLP). The performance of the models was compared with baseline results of Support Vector Machine (SVM). We have also presented a model evaluation method based on t-SNE and confusion matrix analysis that allows us to detect and eliminate some cases of model undertraining. Our results show that in the transductive case, all models, including the MLP and the SVM, have comparative performance, with no clear advantage of deep learning models. The Overall Accuracy range across all models is 98–100% for the easier image set, and 74–94% for the more difficult one. However, in a more challenging inductive case, selected deep learning architectures offer a significant advantage; their best Overall Accuracy is in the range of 57–71%, improving the baseline set by the non-deep models by up to 9 percentage points. We have presented a detailed analysis of results and a discussion, including a summary of conclusions for each tested architecture. An analysis of per-class errors shows that the score for each class is highly model-dependent. Considering this and the fact that the best performing models come from two different architecture families (3D CNN and RNN), our results suggest that tailoring the deep neural network architecture to hyperspectral data is still an open problem.&lt;/p&gt;&#13;
</style></abstract><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Małgorzata Domino</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Tomasz Jasiński</style></author><author><style face="normal" font="default" size="100%">Małgorzata Maśko</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparison of the Surface Thermal Patterns of Horses and Donkeys in Infrared Thermography Images</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style face="normal" font="default" size="100%">11/2020</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">Animals</style></secondary-title><pages><style face="normal" font="default" size="100%"> 2201</style></pages><volume><style face="normal" font="default" size="100%">10</style></volume><issue><style face="normal" font="default" size="100%">12</style></issue><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Arkadiusz Sochan</style></author><author><style face="normal" font="default" size="100%">Michał Cholewa</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Dataset for Evaluating Blood Detection in Hyperspectral Images [Data set]</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2020</style></year></dates><urls><style face="normal" font="default" size="100%">https://doi.org/10.5281/zenodo.3984905</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">10.5281/zenodo.3984905</style></electronic-resource-num><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The sensitivity of hyperspectral imaging (imaging spectroscopy) to haemoglobin derivatives makes it a promising tool for detection and classification of blood. However, due to complexity and high dimensionality of hyperspectral images, the development of hyperspectral blood detection algorithms is challenging. To facilitate their development, we present a new hyperspectral blood detection dataset. This dataset consists of 14 hyperspectral images (ENVI format) of a mock-up scene containing blood and visually similar substances (e.g. artificial blood or tomato concentrate). Images were taken over a period of three weeks and differ in terms of background composition and lighting intensity. To facilitate the use of data, the dataset includes an annotation of classes: pixels where blood and similar substances are visible have been marked by the authors. The main intention behind the dataset is to serve as testing data for Machine Learning methods for hyperspectral target detection and classification.&lt;/p&gt;&#13;
</style></abstract><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Arkadiusz Sochan</style></author><author><style face="normal" font="default" size="100%">Michał Cholewa</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A dataset for evaluating blood detection in hyperspectral images</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2021</style></year></dates><secondary-title><style face="normal" font="default" size="100%">Forensic Science International</style></secondary-title><urls><style face="normal" font="default" size="100%">https://arxiv.org/abs/2008.10254</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">https://doi.org/10.1016/j.forsciint.2021.110701</style></electronic-resource-num><volume><style face="normal" font="default" size="100%">320</style></volume><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The sensitivity of imaging spectroscopy to haemoglobin derivatives makes it a promising tool for detecting blood. However, due to complexity and high dimensionality of hyperspectral images, the development of hyperspectral blood detection algorithms is challenging. To facilitate their development, we present a new hyperspectral blood detection dataset. This dataset, published under an open access license, consists of multiple detection scenarios with varying levels of complexity. It allows to test the performance of Machine Learning methods in relation to different acquisition environments, types of background, age of blood and presence of other blood-like substances. We have explored the dataset with blood detection experiments, for which we have used a hyperspectral target detection algorithm based on the well-known Matched Filter detector. Our results and their discussion highlight the challenges of blood detection in hyperspectral data and form a reference for further works.&lt;/p&gt;&#13;
</style></abstract><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Małgorzata Domino</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Tomasz Jasiński</style></author><author><style face="normal" font="default" size="100%">Małgorzata Maśko</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparison of surface thermal patterns of horses and donkeys in IRT images [Data set]</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2020</style></year></dates><urls><style face="normal" font="default" size="100%">https://zenodo.org/record/4085075</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">http://doi.org/10.5281/zenodo.4085075</style></electronic-resource-num><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The dataset contains infrared thermography (IRT) images of horses and donkeys along with annotations of 15 ROIs corresponding to areas with potential impact of large equine muscles. All animals were healthy and rested. Imaged animals: sixteen horses and eighteen donkeys. Measured animal features such as hair length, and skin thickness are included.&lt;/p&gt;&#13;
</style></abstract><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Filip Pałka</style></author><author><style face="normal" font="default" size="100%">Wojciech Książek</style></author><author><style face="normal" font="default" size="100%">Pawel Plawiak</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Kamil Książek</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Hyperspectral Classification of Blood-Like Substances Using Machine Learning Methods Combined with Genetic Algorithms in Transductive and Inductive Scenarios</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style face="normal" font="default" size="100%">03/2021</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">Sensors</style></secondary-title><urls><style face="normal" font="default" size="100%">https://www.mdpi.com/1424-8220/21/7/2293</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">10.3390/s21072293</style></electronic-resource-num><volume><style face="normal" font="default" size="100%">21</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kamil Książek</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Michał Cholewa</style></author><author><style face="normal" font="default" size="100%">Bartosz Grabowski</style></author><author><style face="normal" font="default" size="100%">Krisztian Buza</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Improving Autoencoder Training Performance for Hyperspectral Unmixing with Network Reinitialisation</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style face="normal" font="default" size="100%">05/2022</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">21st International Conference on Image Analysis and Processing</style></secondary-title><urls><style face="normal" font="default" size="100%">https://arxiv.org/abs/2109.13748</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">10.1007/978-3-031-06427-2_33</style></electronic-resource-num><volume><style face="normal" font="default" size="100%">13231</style></volume><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Neural networks, in particular autoencoders, are one of the most promising solutions for unmixing hyperspectral data, i.e. reconstructing the spectra of observed substances (endmembers) and their relative mixing fractions (abundances), which is needed for effective hyperspectral analysis and classification. However, as we show in this paper, the training of autoencoders for unmixing is highly dependent on weights initialisation; some sets of weights lead to degenerate or low-performance solutions, introducing negative bias in the expected performance. In this work, we experimentally investigate autoencoders stability as well as network reinitialisation methods based on coefficients of neurons’ dead activations. We demonstrate that the proposed techniques have a positive effect on autoencoder training in terms of reconstruction, abundances and endmembers errors.&lt;/p&gt;&#13;
</style></abstract><publisher><style face="normal" font="default" size="100%">Springer, Cham</style></publisher><pub-location><style face="normal" font="default" size="100%">Lecce, Italy</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Michał Cholewa</style></author><author><style face="normal" font="default" size="100%">Wojciech Koral</style></author><author><style face="normal" font="default" size="100%">Andrzej Madej</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Detection of emergent leaks using machine learning approaches</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style face="normal" font="default" size="100%">05/2023</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">Water Supply</style></secondary-title><urls><style face="normal" font="default" size="100%">https://doi.org/10.2166/ws.2023.118</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">10.2166/ws.2023.118</style></electronic-resource-num><volume><style face="normal" font="default" size="100%">23</style></volume><issue><style face="normal" font="default" size="100%">6</style></issue><section><style face="normal" font="default" size="100%">2370</style></section><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In this work, we focus on the detection of leaks occurring in district metered areas (DMAs). Those leaks are observable as a number of time-related deviations from zone patterns over days or weeks. While they are detectable given enough time, due to the huge cost of water loss resulting from an undetected leak, the main challenge is to find them as soon as possible, when the deviation from the zone pattern is small. Using our collected observational data, we investigate the appearance of leaks and discuss the performance of several machine learning (ML) anomaly detectors in detecting them. We test a diverse set of six anomaly detectors, each based on a different ML algorithm, on nine scenarios containing leaks and anomalies of various kinds. The proposed approach is very effective at quickly (within hours) identifying the presence of a leak, with a limited number of false positives.&lt;/p&gt;&#13;
</style></abstract><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kamil Książek</style></author><author><style face="normal" font="default" size="100%">Wilhelm Masarczyk</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Iga Stokłosa</style></author><author><style face="normal" font="default" size="100%">Piotr Ścisło</style></author><author><style face="normal" font="default" size="100%">Paweł Dębski</style></author><author><style face="normal" font="default" size="100%">Robert Pudlo</style></author><author><style face="normal" font="default" size="100%">Krisztian Buza</style></author><author><style face="normal" font="default" size="100%">Piotr Gorczyca</style></author><author><style face="normal" font="default" size="100%">Magdalena Piegza</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The analysis of heart rate variability and accelerometer mobility data in the assessment of symptom severity in psychotic disorder patients using a wearable Polar H10 sensor</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style face="normal" font="default" size="100%">08/2023</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">medXriv</style></secondary-title><urls><style face="normal" font="default" size="100%">https://www.medrxiv.org/content/10.1101/2023.08.04.23293640v1</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">10.1101/2023.08.04.23293640</style></electronic-resource-num><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;em&gt;Background and Objective: &lt;/em&gt;Advancement in mental health care requires easily accessible diagnostic and treatment assessment tools. There is an ongoing search for biomarkers that would enable objectification and automatization of the diagnostic and treatment process dependent on a psychiatric interview. Current wearable technology and computational methods make it possible to incorporate heart rate variability (HRV), an indicator of autonomic nervous system functioning and a potential biomarker of disease severity in mental disorders, into accessible diagnostic and treatment assessment frameworks.&lt;br /&gt;&#13;
&lt;em&gt;Methods:&lt;/em&gt; We used a commercially available electrocardiography (ECG) chest strap with a built-in accelerometer, i.e. Polar H10, to record R-R intervals and activity of 30 hospitalized schizophrenia or bipolar disorder patients and 30 control participants for 1.5-2 hours time periods.&lt;br /&gt;&#13;
We performed an analysis to assess the relationship between HRV and the Positive and Negative Syndrome Scale (PANSS) test scores. The source code for the reproduction of all experiments is available on GitHub while the dataset is available in Zenodo.&lt;br /&gt;&#13;
&lt;em&gt;Results and Conclusions:&lt;/em&gt; Mean HRV values were lower in the patient group and negatively correlated with the results of the PANSS general subcategory. For the control group, we also discovered the inversely proportional dependency between the mobility coefficient based on accelerometer data and HRV. This relationship was less pronounced for the patient group. This indicates that HRV and mobility may be promising markers in disease diagnosis.&lt;/p&gt;&#13;
</style></abstract><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kamil Książek</style></author><author><style face="normal" font="default" size="100%">Wilhelm Masarczyk</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Iga Stokłosa</style></author><author><style face="normal" font="default" size="100%">Piotr Ścisło</style></author><author><style face="normal" font="default" size="100%">Paweł Dębski</style></author><author><style face="normal" font="default" size="100%">Robert Pudlo</style></author><author><style face="normal" font="default" size="100%">Piotr Gorczyca</style></author><author><style face="normal" font="default" size="100%">Magdalena Piegza</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">HRV-ACC: a dataset with R-R intervals and accelerometer data for the diagnosis of psychotic disorders using a Polar H10 wearable sensor</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2023</style></year></dates><urls><style face="normal" font="default" size="100%">https://zenodo.org/record/8171266</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">10.5281/zenodo.8171266</style></electronic-resource-num><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The issue of diagnosing psychotic diseases, including schizophrenia and bipolar disorder, in particular, the objectification of symptom severity assessment, is still a problem requiring the attention of researchers. Two measures that can be helpful in patient diagnosis are heart rate variability calculated based on electrocardiographic signal and accelerometer mobility data. The following dataset contains data from 30 psychiatric ward patients having schizophrenia or bipolar disorder and 30 healthy persons. The duration of the measurements for individuals was usually between 1.5 and 2 hours. R-R intervals necessary for heart rate variability calculation were collected simultaneously with accelerometer data using a wearable Polar H10 device. The Positive and Negative Syndrome Scale (PANSS) test was performed for each patient participating in the experiment, and its results were attached to the dataset. Furthermore, the code for loading and preprocessing data, as well as for statistical analysis, was included on the corresponding GitHub repository.&lt;/p&gt;&#13;
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size="100%">10.1109/JSEN.2024.3413889</style></electronic-resource-num><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Katarzyna Kołodziej</style></author><author><style face="normal" font="default" size="100%">Michał Cholewa</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Wojciech Koral</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Efficient Numerical Calibration of Water Delivery Network Using Short-Burst Hydrant Trials</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style face="normal" font="default" size="100%">09/2024</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">arXiv preprint 	arXiv:2410.02772</style></secondary-title><urls><style face="normal" font="default" size="100%">https://arxiv.org/abs/2410.02772</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">https://doi.org/10.48550/arXiv.2410.02772</style></electronic-resource-num><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Katarzyna Kołodziej</style></author><author><style face="normal" font="default" size="100%">Michał Cholewa</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Wojciech Koral</style></author><author><style face="normal" font="default" size="100%">Przemysław Sekuła</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Calibration of Water Demand Network Through a Machine Learned Digital Twin Model</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2024</style></year></dates><secondary-title><style face="normal" font="default" size="100%">5th Polish Conference on Artificial Intelligence (PP-RAI 2024)</style></secondary-title><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Przemysław Sekuła</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Poster: Explainable classification of multimodal time series using LLMs</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style face="normal" font="default" size="100%">04/2024</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">5th Polish Conference on Artificial Intelligence (PP-RAI 2024)</style></secondary-title><pub-location><style face="normal" font="default" size="100%">Warsaw, Poland</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Przemysław Sekuła</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Michał Cholewa</style></author><author><style face="normal" font="default" size="100%">Katarzyna Kołodziej</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Through the Thicket: A Study of Number-Oriented LLMS Derived from Random Forest Models</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2025</style></year><pub-dates><date><style face="normal" font="default" size="100%">03/2025</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">Journal of Artificial Intelligence and Soft Computing Research</style></secondary-title><electronic-resource-num><style face="normal" font="default" size="100%">https://doi.org/10.2478/jaiscr-2025-0014</style></electronic-resource-num><volume><style face="normal" font="default" size="100%">15</style></volume><issue><style face="normal" font="default" size="100%">3</style></issue><section><style face="normal" font="default" size="100%">279</style></section><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Przemysław Sekuła</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Michał Cholewa</style></author><author><style face="normal" font="default" size="100%">Łukasz Pawela</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Quantum-aware Transformer model for state classification</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2025</style></year></dates><secondary-title><style face="normal" font="default" size="100%">International Conference on Computational Science 2025</style></secondary-title><urls><style face="normal" font="default" size="100%">https://link.springer.com/chapter/10.1007/978-3-031-97570-7_15</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">10.48550/arXiv.2502.21055</style></electronic-resource-num><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Entanglement is a fundamental feature of quantum mechanics, playing a crucial role in quantum information processing. However, classifying entangled states, particularly in the mixed-state regime, remains a challenging problem, especially as system dimensions increase. In this work, we focus on bipartite quantum states and present a data-driven approach to entanglement classification using transformer-based neural networks. Our dataset consists of a diverse set of bipartite states, including pure separable states, Werner entangled states, general entangled states, and maximally entangled states. We pretrain the transformer in an unsupervised fashion by masking elements of vectorized Hermitian matrix representations of quantum states, allowing the model to learn structural properties of quantum density matrices. This approach enables the model to generalize entanglement characteristics across different classes of states. Once trained, our method achieves near-perfect classification accuracy, effectively distinguishing between separable and entangled states. Compared to previous Machine Learning, our method successfully adapts transformers for quantum state analysis, demonstrating their ability to systematically identify entanglement in bipartite systems. These results highlight the potential of modern machine learning techniques in automating entanglement detection and classification, bridging the gap between quantum information theory and artificial intelligence.&lt;/p&gt;&#13;
</style></abstract><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Katarzyna Filus</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Mateusz Żarski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Semantic Depth Matters: Explaining Errors of Deep Vision Networks through Perceived Class Similarities</style></title></titles><keywords/><dates/><secondary-title><style face="normal" font="default" size="100%">arXiv preprint</style></secondary-title><urls><style face="normal" font="default" size="100%">arXiv:2504.09956</style></urls><electronic-resource-num><style face="normal" font="default" size="100%"> https://doi.org/10.48550/arXiv.2504.09956</style></electronic-resource-num><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Manish Gupta</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Piotr Gawron</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Potential of quantum machine learning for processing multispectral Earth observation data</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2025</style></year><pub-dates><date><style face="normal" font="default" size="100%">08/2025</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">Bulletin of the Polish Academy of Sciences Technical Sciences</style></secondary-title><urls><style face="normal" font="default" size="100%">https://journals.pan.pl/dlibra/publication/154279/edition/135180/content</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">10.24425/bpasts.2025.154279</style></electronic-resource-num><volume><style face="normal" font="default" size="100%">73</style></volume><issue><style face="normal" font="default" size="100%">5</style></issue><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kamil Książek</style></author><author><style face="normal" font="default" size="100%">Wilhelm Masarczyk</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Krisztian Buza</style></author><author><style face="normal" font="default" size="100%">Przemysław Sekuła</style></author><author><style face="normal" font="default" size="100%">Michał Cholewa</style></author><author><style face="normal" font="default" size="100%">Katarzyna Kołodziej</style></author><author><style face="normal" font="default" size="100%">Piotr Gorczyca</style></author><author><style face="normal" font="default" size="100%">Magdalena Piegza</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Deep learning approach for automatic assessment of schizophrenia and bipolar disorder in patients using R-R intervals</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2025</style></year><pub-dates><date><style face="normal" font="default" size="100%">09/2025</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">PLOS Computational Biology</style></secondary-title><electronic-resource-num><style face="normal" font="default" size="100%">10.1371/journal.pcbi.1012983</style></electronic-resource-num><volume><style face="normal" font="default" size="100%">21</style></volume><section><style face="normal" font="default" size="100%">1</style></section><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Schizophrenia and bipolar disorder are severe mental illnesses that significantly impact quality of life. These disorders are associated with autonomic nervous system dysfunction, which can be assessed through heart activity analysis. Heart rate variability (HRV) has shown promise as a potential biomarker for diagnostic support and early screening of those conditions. This study aims to develop and evaluate an automated classification method for schizophrenia and bipolar disorder using short-duration electrocardiogram (ECG) signals recorded with a low-cost wearable device. We conducted classification experiments using machine learning techniques to analyze R-R interval windows extracted from short ECG recordings. The study included 60 participants—30 individuals diagnosed with schizophrenia or bipolar disorder and 30 control subjects. We evaluated multiple machine learning models, including Support Vector Machines, XGBoost, multilayer perceptrons, Gated Recurrent Units, and ensemble methods. Two time window lengths (about 1 and 5 minutes) were evaluated. Performance was assessed using 5-fold cross-validation and leave-one-out cross-validation, with hyperparameter optimization and patient-level classification based on individual window decisions. Our method achieved classification accuracy of 83% for the 5-fold cross-validation and 80% for the leave-one-out scenario. Despite the complexity of our scenario, which mirrors real-world clinical settings, the proposed approach yielded performance comparable to advanced diagnostic methods reported in the literature. The results highlight the potential of short-duration HRV analysis as a cost-effective and accessible tool for aiding in the diagnosis of schizophrenia and bipolar disorder. Our findings support the feasibility of using wearable ECG devices and machine learning-based classification for psychiatric screening, paving the way for further research and clinical applications.&lt;/p&gt;&#13;
</style></abstract><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michał Cholewa</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Data structure better than labels? Unsupervised heuristics for SVM hyperparameter estimation</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2025</style></year></dates><secondary-title><style face="normal" font="default" size="100%">Bulletin of the Polish Academy of Sciences Technical Sciences</style></secondary-title><electronic-resource-num><style face="normal" font="default" size="100%">DOI: 10.24425/bpasts.2025.155891</style></electronic-resource-num><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michał Cholewa</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Data structure better than labels? Unsupervised heuristics for SVM hyperparameter estimation</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2025</style></year></dates><secondary-title><style face="normal" font="default" size="100%">Bulletin of the Polish Academy of Sciences Technical Sciences</style></secondary-title><electronic-resource-num><style face="normal" font="default" size="100%">10.24425/bpasts.2025.155891</style></electronic-resource-num><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Katarzyna Kołodziej</style></author><author><style face="normal" font="default" size="100%">Michał Cholewa</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Wojciech Koral</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Efficient Numerical Calibration of Water Delivery Network Using Short-Burst Hydrant Trials</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2026</style></year><pub-dates><date><style face="normal" font="default" size="100%">02/2026</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">Journal of Water Resources Planning and Management - ASCE</style></secondary-title><urls><style face="normal" font="default" size="100%">https://doi.org/10.1061/JWRMD5.WRENG-6839</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">10.1061/JWRMD5.WRENG-6839 </style></electronic-resource-num><volume><style face="normal" font="default" size="100%">152</style></volume><issue><style face="normal" font="default" size="100%">4</style></issue><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Krzysztof Grochla</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Jarosław Sotor</style></author><author><style face="normal" font="default" size="100%">Piotr Masłowski</style></author><author><style face="normal" font="default" size="100%">Bartosz Belter</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards AI-assisted Science with PLAI4SCIENCE - new Polish Research Infrastructure</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2025</style></year><pub-dates><date><style face="normal" font="default" size="100%">04/2025</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">6th Polish Conference on Artificial Intelligence (PP-RAI 2025)</style></secondary-title><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Anna Zawadzka</style></author><author><style face="normal" font="default" size="100%">Mateusz Żarski</style></author><author><style face="normal" font="default" size="100%">Kamil Drejer</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Michał Cholewa</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%"> Map-Guided Cross-Training for Building Detection</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2026</style></year></dates><secondary-title><style face="normal" font="default" size="100%">Geoscience and Remote Sensing Letters</style></secondary-title><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aygün Varol</style></author><author><style face="normal" font="default" size="100%">Katarzyna Kołodziej</style></author><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Naser Motlagh</style></author><author><style face="normal" font="default" size="100%">Mirka Leino</style></author><author><style face="normal" font="default" size="100%">Johanna Virkki</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Enabling Cloud-Level Accuracy in Edge AI through IoT Data Preprocessing</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2026</style></year></dates><secondary-title><style face="normal" font="default" size="100%">IEEE Internet of Things Journal</style></secondary-title><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Large language models (LLMs) offer a natural-language interface for interpreting Internet of Things (IoT) sensor data in smart environments; however, cloud deployment introduces latency, privacy, and connectivity concerns. Local LLMs can reduce these limitations, but compact edge-deployable models often show weaker numerical reasoning when raw sensor readings are provided directly. This paper investigates whether prompt-side preprocessing can improve the accuracy--latency trade-off of local LLMs for environmental monitoring. We propose a structured prompt construction framework that transforms raw air-quality and thermal-comfort measurements into progressively enriched textual representations: raw sensor values, threshold-aware descriptions, and compact environmental summary flags. The approach is evaluated using indoor Raspberry Pi/BME680 datasets from Tampere University and outdoor air-quality datasets from Helsinki, Katowice, and Warsaw. We construct a binary LLM query dataset covering air quality, thermal comfort, and joint environmental conditions, and evaluate five local and five cloud LLMs across three prompt variants and two inference modes, with and without chain-of-thought prompting. The results show that prompt enrichment substantially improves local-model accuracy. In No-CoT mode, local accuracy increases from 50.9\% to 81.7\% indoors and from 63.7\% to 89.3\% outdoors from the raw to the most enriched prompt. Local No-CoT inference is the fastest configuration, with mean latency close to 0.22 s, while CoT substantially increases inference time. These findings suggest that lightweight prompt-side preprocessing can narrow the local--cloud performance gap and support low-latency IoT analytics in smart environments.&lt;/p&gt;</style></abstract></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aygün Varol</style></author><author><style face="normal" font="default" size="100%">Katarzyna Kołodziej</style></author><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Naser Motlagh</style></author><author><style face="normal" font="default" size="100%">Mirka Leino</style></author><author><style face="normal" font="default" size="100%">Johanna Virkki</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Enabling Cloud-Level Accuracy in Edge AI through IoT Data Preprocessing</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2026</style></year></dates><secondary-title><style face="normal" font="default" size="100%">arXiv preprint arXiv </style></secondary-title><urls><style face="normal" font="default" size="100%">https://arxiv.org/abs/2606.22496 </style></urls><electronic-resource-num><style face="normal" font="default" size="100%">10.48550/ARXIV.2606.22496 </style></electronic-resource-num><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Large language models (LLMs) offer a natural-language interface for interpreting Internet of Things (IoT) sensor data in smart environments; however, cloud deployment introduces latency, privacy, and connectivity concerns. Local LLMs can reduce these limitations, but compact edge-deployable models often show weaker numerical reasoning when raw sensor readings are provided directly. This paper investigates whether prompt-side preprocessing can improve the accuracy--latency trade-off of local LLMs for environmental monitoring. We propose a structured prompt construction framework that transforms raw air-quality and thermal-comfort measurements into progressively enriched textual representations: raw sensor values, threshold-aware descriptions, and compact environmental summary flags. The approach is evaluated using indoor Raspberry Pi/BME680 datasets from Tampere University and outdoor air-quality datasets from Helsinki, Katowice, and Warsaw. We construct a binary LLM query dataset covering air quality, thermal comfort, and joint environmental conditions, and evaluate five local and five cloud LLMs across three prompt variants and two inference modes, with and without chain-of-thought prompting. The results show that prompt enrichment substantially improves local-model accuracy. In No-CoT mode, local accuracy increases from 50.9\% to 81.7\% indoors and from 63.7\% to 89.3\% outdoors from the raw to the most enriched prompt. Local No-CoT inference is the fastest configuration, with mean latency close to 0.22 s, while CoT substantially increases inference time. These findings suggest that lightweight prompt-side preprocessing can narrow the local--cloud performance gap and support low-latency IoT analytics in smart environments.&lt;/p&gt;</style></abstract></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aygün Varol</style></author><author><style face="normal" font="default" size="100%">Katarzyna Kołodziej</style></author><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Naser Motlagh</style></author><author><style face="normal" font="default" size="100%">Mirka Leino</style></author><author><style face="normal" font="default" size="100%">Johanna Virkki</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">AIRWISE: Environmental Sensor and LLM Query Dataset for Edge AI-Based Air-Quality and Thermal-Comfort Analytics</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2026</style></year></dates><urls><style face="normal" font="default" size="100%">https://zenodo.org/doi/10.5281/zenodo.20783874 </style></urls><electronic-resource-num><style face="normal" font="default" size="100%">10.5281/ZENODO.20783874 </style></electronic-resource-num><abstract><style face="normal" font="default" size="100%">&lt;p&gt;AIRWISE is an open benchmark dataset for edge AI, IoT environmental monitoring, and large language model (LLM) evaluation on real sensor data. It combines indoor air-quality and thermal-comfort measurements, outdoor air-quality and meteorological time series, and a labeled binary question-answering benchmark for threshold-aware reasoning and anomaly detection. The dataset supports the study “Enabling Cloud-Level Accuracy in Edge AI through IoT Data Preprocessing”:&#160;https://doi.org/10.48550/arXiv.2606.22496&lt;br&gt;AIRWISE provides three core resources:&lt;br&gt;Indoor environmental sensor dataset (BME680, 1-minute resolution). 146,000+ minute-averaged readings from Raspberry Pi + Bosch BME680 nodes in three real micro-environments (office, kitchen, hallway) at Tampere, Finland (2025-11-14 to 2025-12-18): temperature, relative humidity, barometric pressure, gas resistance, indoor air quality (IAQ) proxy, z-score anomaly detectors, and limit-annotated long-format tables with ground-truth anomaly flags.&lt;br&gt;Outdoor air-quality and meteorological time series (hourly, full-year 2023). Helsinki (Finland), Katowice and Warsaw (Poland): NO₂, CO, O₃, PM₂.₅, temperature, and relative humidity assembled from national monitoring networks (FMI, GIOŚ, IMGW-PIB), as cleaned single-series city tables (≈8,760 hourly records per city) and limit-annotated long-format tables with anomaly flags.&lt;br&gt;Binary LLM query benchmark (1,440 labeled yes/no questions). 240 natural-language questions per site across six sites, balanced by task type (80 air quality, 80 thermal comfort, 80 joint environmental condition), each with ground-truth answer, violation label, offending factors, and rationale — ready for benchmarking local/edge and cloud LLMs on sensor-grounded reasoning.&lt;br&gt;Also included: reference air-quality and thermal-comfort limits (EU air-quality standards and Finnish/Polish national guidelines); the unmodified institutional source files for the outdoor component (FMI exports, GIOŚ archive workbooks and station metadata, IMGW-PIB synoptic tables); the complete dataset-generation scripts covering every pipeline step (raw sources → cleaned tables → limit-annotated data → LLM queries) with a requirements.txt for a verified minimal Python environment; SHA-256 checksums; and a dependency-free validation script. Schemas, exact row counts, coverage, and usage examples are documented in README.md and MANIFEST.md.&lt;/p&gt;</style></abstract></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Kamil Drejer</style></author><author><style face="normal" font="default" size="100%">Katarzyna Kołodziej</style></author><author><style face="normal" font="default" size="100%">Anna Zawadzka</style></author><author><style face="normal" font="default" size="100%">Stanisław Lewiński</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Marek Ruciński</style></author><author><style face="normal" font="default" size="100%">Michał Krupiński</style></author><author><style face="normal" font="default" size="100%">Krzysztof Gryguc</style></author><author><style face="normal" font="default" size="100%">Przemysław Sekuła</style></author><author><style face="normal" font="default" size="100%">Szymon Sala</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Toward Seasonal Guidelines for Robust Deep-Learning Sentinel-2 Building Detection in Different Area Types</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2026</style></year></dates><electronic-resource-num><style face="normal" font="default" size="100%">10.1109/JSTARS.2026.3742586</style></electronic-resource-num></record></records></xml>
