<?xml version="1.0" encoding="UTF-8"?>
<xml><records><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>2</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Michał Cholewa</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Czołowe funkcje kuliste jako baza do aproksymacji sygnałów o ograniczonym paśmie</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2013</style></YEAR><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Instytut Informatyki Teoretycznej i Stosowanej PAN</style></PUBLISHER><ABSTRACT><style face="normal" font="default" size="100%">Tematem pracy jest analiza algorytmu aproksymującego sygnały o ograniczonym pasmie opartego na czołowych funkcjach kulistych. Obecny stan wiedzy zawiera analizę błędu pesymistycznego dla zawężonego przypadku funkcji o ograniczonej energii oraz oszacowanie błędu w przypadku średnim, jednakże bez uwzględnienia błędu wynikającego z błędu pomiaru i kwantyzacji oraz jittera. Ponieważ oba te czynniki występują w praktycznym zastosowaniu wszelkich algorytmów aproksymacyjnych, gruntowna analiza ich wpływu jest istotna z punktu badania efektywności algorytmu. Szczególnie jitter jest wielkością o znaczącym wpływie na wynik działania algorytmu, jako, że nawet niewielki jitter sprawia, że problem wymyka się spod założeń Lematu Smolyaka, który mówi o istnieniu optymalnego algorytmu liniowego. W pracy przedstawione jest oszacowanie błędu średniego dla rodziny zadanych miar Gaussa i wykazuje, że błąd średni algorytmu można dobrze ograniczyć. To prowadzi do wniosku, że algorytm aproksymacji sygnałów o ograniczonym pasmie oparty o czołowe funkcje kuliste jest algorytmem odpornym na działanie czynników zakłócających takich jak błąd pomiaru i kwantyzacji oraz jitter. Praca zawiera również implementację algorytmu numerycznego wyliczania wartości zadanych czołowych funkcji kulistych.</style></ABSTRACT><title><style face="normal" font="default" size="100%">Czołowe funkcje kuliste jako baza do aproksymacji sygnałów o ograniczonym paśmie</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_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></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Estimation of the number of states for gesture recognition with Hidden Markov Models based on the number of critical points in time sequence</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2012</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Pattern Recognition Letters</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><title><style face="normal" font="default" size="100%">Estimation of the number of states for gesture recognition with Hidden Markov Models based on the number of critical points in time sequence</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_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></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Gesture data modeling and classification based on critical points approximation</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2011</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Advances in Intelligent and Soft Computing, 2011, Volume 95, Computer Recognition Systems 4,</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">95</style></VOLUME><PAGES><style face="normal" font="default" size="100%">365–373</style></PAGES><title><style face="normal" font="default" size="100%">Gesture data modeling and classification based on critical points approximation</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>7</REFERENCE_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/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2013</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Inżynieria Internetu przyszłości część 2</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Oficyna Wydawnicza PW</style></PUBLISHER><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><title><style face="normal" font="default" size="100%">Aplikacje Sieci Świadomej Treści</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_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/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2012</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Przegląd telekomunikacyjny</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><NUMBER><style face="normal" font="default" size="100%">8-9</style></NUMBER><PAGES><style face="normal" font="default" size="100%">1491–1502</style></PAGES><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><title><style face="normal" font="default" size="100%">Aplikacje sieci świadomej treści</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>7</REFERENCE_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%">Leszek Rutkowski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Marcin Korytkowski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Rafal Scherer</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Ryszard Tadeusiewicz</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Lotfi Zadeh</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Jacek Zurada</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Experimental Evaluation of Selected Approaches to Covariance Matrix Regularization</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2015</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Artificial Intelligence and Soft Computing</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer International Publishing</style></PUBLISHER><VOLUME><style face="normal" font="default" size="100%">9120</style></VOLUME><PAGES><style face="normal" font="default" size="100%">391-401</style></PAGES><TERTIARY_TITLE><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></TERTIARY_TITLE><ISBN><style face="normal" font="default" size="100%">978-3-319-19368-7</style></ISBN><title><style face="normal" font="default" size="100%">Experimental Evaluation of Selected Approaches to Covariance Matrix Regularization</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>9</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Piotr Gawron</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Michał Cholewa</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Katarzyna Kara</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Rewolucja stanu - fantastyczne wprowadzenie do informatyki kwantowej</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2016</style></YEAR><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Gliwice</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IITiS PAN</style></PUBLISHER><PAGES><style face="normal" font="default" size="100%">128</style></PAGES><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Świat, jaki znamy – świat wolnych sieci informacyjnych, świat swobodnej wymiany wiedzy i doświadczeń – skończył się nieodwołalnie. Wolny dostęp do wiedzy okazał się szkodliwą ideą, a najbardziej skorzystali na nim zbrodniarze, twórcy broni biologicznej, która nieomal zmiotła cywilizację z powierzchni ziemi. Zdziesiątkowana przez terror, wojny i epidemię ludzkość powoli odradza się pod czujnym okiem Cerbera. Jego agenci mają jeden cel: nie dopuścić, aby wiedza stała się znów powszechnie dostępna.&lt;/p&gt;&#13;
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&lt;p&gt;Jednak wszechobecna cenzura rodzi bunt.&lt;/p&gt;&#13;
&#13;
&lt;p&gt;Niektórzy są nawet gotowi ginąć – i zabijać – za wolny dostęp do informacji. I najwyraźniej czują się dość pewnie, by rzucić Cerberowi wyzwanie – bo czym innym może być zamach na wysokiego dygnitarza organizacji w jej głównej kwaterze? Teraz Cerber musi użyć wszystkich dostępnych środków, by znaleźć zleceniodawców napastnika. Niestety, jego as atutowy, kwantowa hakerka Eve, właśnie zasiada na ławie oskarżonych pod zarzutem zdrady na rzecz grupy terrorystycznej ALICE...&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://depot.ceon.pl/handle/123456789/16807</style></URL><title><style face="normal" font="default" size="100%">Rewolucja stanu - fantastyczne wprowadzenie do informatyki kwantowej</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_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/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2016</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">ISPRS Journal of Photogrammetry and Remote Sensing</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">121</style></VOLUME><PAGES><style face="normal" font="default" size="100%">60 - 76</style></PAGES><URL><style face="normal" font="default" size="100%">http://www.sciencedirect.com/science/article/pii/S0924271616303446</style></URL><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></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_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></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Two Stage SVM Classification for Hyperspectral Data</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2016</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><ISBN><style face="normal" font="default" size="100%">978-989-758-173-1</style></ISBN><title><style face="normal" font="default" size="100%">Two Stage SVM Classification for Hyperspectral Data</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_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></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Performance of Interest Point Descriptors on Hyperspectral Images</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2016</style></YEAR><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 - Volume 3: VISAPP</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><ISBN><style face="normal" font="default" size="100%">978-989-758-175-5</style></ISBN><title><style face="normal" font="default" size="100%">Performance of Interest Point Descriptors on Hyperspectral Images</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Michał Cholewa</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><AUTHOR><style face="normal" font="default" size="100%">Dariusz Kurzyk</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Quantum hidden Markov models based on transition operation matrices</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2017</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Quantum Information Processing</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">16</style></VOLUME><PAGES><style face="normal" font="default" size="100%">101</style></PAGES><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;In this work, we extend the idea of quantum Markov chains (Gudder in J Math Phys 49(7):072105 [3]) in order to propose quantum hidden Markov models (QHMMs). For that, we use the notions of transition operation matrices and vector states, which are an extension of classical stochastic matrices and probability distributions. Our main result is the Mealy QHMM formulation and proofs of algorithms needed for application of this model: Forward for general case and Vitterbi for a restricted class of QHMMs. We show the relations of the proposed model to other quantum HMM propositions and present an example of application.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/s11128-017-1544-8</style></URL><title><style face="normal" font="default" size="100%">Quantum hidden Markov models based on transition operation matrices</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_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/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2018</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Forensic Science International</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">290</style></VOLUME><SECTION><style face="normal" font="default" size="100%">227</style></SECTION><DATE><style face="normal" font="default" size="100%">07/2018</style></DATE><URL><style face="normal" font="default" size="100%">http://www.sciencedirect.com/science/article/pii/S0379073818303657</style></URL><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></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_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/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2018</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Computer and Information Sciences</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer International Publishing</style></PUBLISHER><ISBN><style face="normal" font="default" size="100%">978-3-030-00840-6</style></ISBN><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><title><style face="normal" font="default" size="100%">Adaptive, Hubness-Aware Nearest Neighbour Classifier with Application to Hyperspectral Data</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_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/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2019</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">IEEE Geoscience and Remote Sensing Letters</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">16</style></VOLUME><PAGES><style face="normal" font="default" size="100%">467-471</style></PAGES><DATE><style face="normal" font="default" size="100%">March</style></DATE><title><style face="normal" font="default" size="100%">A Spatial-Spectral Disagreement-Based Sample Selection With an Application to Hyperspectral Data Classification</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_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/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2019</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">PP-RAI'2019</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Wrocław, Poland</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Wroclaw University of Science and Technology</style></PUBLISHER><PAGES><style face="normal" font="default" size="100%">p. 121</style></PAGES><DATE><style face="normal" font="default" size="100%">2019</style></DATE><ISBN><style face="normal" font="default" size="100%">978-83-943803-2-8</style></ISBN><URL><style face="normal" font="default" size="100%">https://arxiv.org/abs/1808.03513</style></URL><title><style face="normal" font="default" size="100%">Band selection with Higher Order Multivariate Cumulants for small target detection in hyperspectral images</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>10</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Michał Cholewa</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%">Multi-sensor dataset for testing merge of Hyperspectral, HD and 3D cloud information for image recognition</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2016</style></YEAR><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><title><style face="normal" font="default" size="100%">Multi-sensor dataset for testing merge of Hyperspectral, HD and 3D cloud information for image recognition</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_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/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2020</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Sensors</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><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><DATE><style face="normal" font="default" size="100%">11/2020</style></DATE><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><URL><style face="normal" font="default" size="100%">https://www.mdpi.com/1424-8220/20/22/6666</style></URL><title><style face="normal" font="default" size="100%">Blood Stain Classification with Hyperspectral Imaging and Deep Neural Networks</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_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/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2020</style></YEAR><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><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><URL><style face="normal" font="default" size="100%">https://doi.org/10.5281/zenodo.3984905</style></URL><title><style face="normal" font="default" size="100%">A Dataset for Evaluating Blood Detection in Hyperspectral Images [Data set]</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_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/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2021</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Forensic Science International</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><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><URL><style face="normal" font="default" size="100%">https://arxiv.org/abs/2008.10254</style></URL><title><style face="normal" font="default" size="100%">A dataset for evaluating blood detection in hyperspectral images</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_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/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2022</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">21st International Conference on Image Analysis and Processing</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Lecce, Italy</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer, Cham</style></PUBLISHER><VOLUME><style face="normal" font="default" size="100%">13231</style></VOLUME><DATE><style face="normal" font="default" size="100%">05/2022</style></DATE><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><URL><style face="normal" font="default" size="100%">https://arxiv.org/abs/2109.13748</style></URL><title><style face="normal" font="default" size="100%">Improving Autoencoder Training Performance for Hyperspectral Unmixing with Network Reinitialisation</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_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/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2023</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Water Supply</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><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><DATE><style face="normal" font="default" size="100%">05/2023</style></DATE><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><URL><style face="normal" font="default" size="100%">https://doi.org/10.2166/ws.2023.118</style></URL><title><style face="normal" font="default" size="100%">Detection of emergent leaks using machine learning approaches</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_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%">Paweł Foszner</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Jakub Bularz</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Maria Ganzha</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Leszek Maciaszek</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Marcin Paprzycki</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dominik Slezak</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Continual learning of a time series model using a mixture of HMMs with application to the IoT fuel sensor verification</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2023</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Proceedings of the 18th Conference on Computer Science and Intelligence Systems, FedCSIS 2023, Warsaw, Poland, September 17-20, 2023</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><URL><style face="normal" font="default" size="100%">https://doi.org/10.15439/2023F1856</style></URL><title><style face="normal" font="default" size="100%">Continual learning of a time series model using a mixture of HMMs with application to the IoT fuel sensor verification</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_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/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2024</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">arXiv preprint arXiv:2406.04926</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><DATE><style face="normal" font="default" size="100%">06/2024</style></DATE><URL><style face="normal" font="default" size="100%">https://arxiv.org/abs/2406.04926</style></URL><title><style face="normal" font="default" size="100%">Through the Thicket: A Study of Number-Oriented LLMs derived from Random Forest Models</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_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><AUTHOR><style face="normal" font="default" size="100%">Katarzyna Kołodziej</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Michał Gorawski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Jakub Koral</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%">Kryspin Musioł</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">‘Just One More Sensor is Enough’ – Iterative Water Leak Localization with Physical Simulation and a Small Number of Pressure Sensors</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2024</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">IEEE Sensors Journal</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><DATE><style face="normal" font="default" size="100%">06/2024</style></DATE><title><style face="normal" font="default" size="100%">‘Just One More Sensor is Enough’ – Iterative Water Leak Localization with Physical Simulation and a Small Number of Pressure Sensors</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_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/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2024</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">arXiv preprint 	arXiv:2410.02772</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><DATE><style face="normal" font="default" size="100%">09/2024</style></DATE><URL><style face="normal" font="default" size="100%">https://arxiv.org/abs/2410.02772</style></URL><title><style face="normal" font="default" size="100%">Efficient Numerical Calibration of Water Delivery Network Using Short-Burst Hydrant Trials</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_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/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2024</style></YEAR><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><title><style face="normal" font="default" size="100%">Calibration of Water Demand Network Through a Machine Learned Digital Twin Model</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_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/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2025</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Journal of Artificial Intelligence and Soft Computing Research</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><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><DATE><style face="normal" font="default" size="100%">03/2025</style></DATE><title><style face="normal" font="default" size="100%">Through the Thicket: A Study of Number-Oriented LLMS Derived from Random Forest Models</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_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/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2025</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">International Conference on Computational Science 2025</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><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><URL><style face="normal" font="default" size="100%">https://link.springer.com/chapter/10.1007/978-3-031-97570-7_15</style></URL><title><style face="normal" font="default" size="100%">Quantum-aware Transformer model for state classification</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Przemysław Sekuła</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Narjes Shayesteh</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Qinglian He</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Sara Zahedian</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Rodrigo Moscosso</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%">Move Over Law Compliance Analysis Utilizing a Deep Learning Computer Vision Approach</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2025</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Applied Sciences</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">15</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">4</style></ISSUE><URL><style face="normal" font="default" size="100%">https://www.mdpi.com/2076-3417/15/4/2011</style></URL><title><style face="normal" font="default" size="100%">Move Over Law Compliance Analysis Utilizing a Deep Learning Computer Vision Approach</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Przemysław Sekuła</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Qinglian He</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Kaveh Sadabadi</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Rodrigo Moscosso</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Thomas Jacobs</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Zachary Laan</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mark Franz</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%">Vehicle-to-Infrastructure System Prototype for Intersection Safety</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2025</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Applied Sciences</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">15</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">17</style></ISSUE><DATE><style face="normal" font="default" size="100%">09/2025</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;This study investigates the use of Autonomous Sensing Infrastructure and Connected and Autonomous Vehicles (CAV) technologies to support infrastructure-to-vehicle (I2V) and infrastructure-to-everything (I2X) communications, including the alerting of drivers and pedestrians. It describes research findings in the following CAV functionalities: (1) Intersection-based object detection and tracking; (2) Basic Safety Message (BSM) generation and transmission; and (3) In-Vehicle BSM receipt and display, including handheld (smartphone) application BSM receipt and user presentation. The study summarizes the various software and hardware components used to create the I2V and I2X prototype solutions, which include open-source and commercial software as well as industry-standard transportation infrastructure hardware, e.g., Signal Controllers. Results from in-lab testing demonstrate effective object detection (e.g., pedestrians, bicycles) based on sample traffic camera video feeds as well as successful BSM message generation and receipt using the leveraged software and hardware components. The I2V and I2X solutions created as part of this research are scheduled to be deployed in a real-world intersection in coordination with state and local transportation agencies.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://www.mdpi.com/2076-3417/15/17/9754</style></URL><title><style face="normal" font="default" size="100%">Vehicle-to-Infrastructure System Prototype for Intersection Safety</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_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/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2025</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">PLOS Computational Biology</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">21</style></VOLUME><SECTION><style face="normal" font="default" size="100%">1</style></SECTION><DATE><style face="normal" font="default" size="100%">09/2025</style></DATE><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><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></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_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/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2025</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Bulletin of the Polish Academy of Sciences Technical Sciences</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><title><style face="normal" font="default" size="100%">Data structure better than labels? Unsupervised heuristics for SVM hyperparameter estimation</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_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/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2026</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Journal of Water Resources Planning and Management - ASCE</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">152</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">4</style></ISSUE><DATE><style face="normal" font="default" size="100%">02/2026</style></DATE><URL><style face="normal" font="default" size="100%">https://doi.org/10.1061/JWRMD5.WRENG-6839</style></URL><title><style face="normal" font="default" size="100%">Efficient Numerical Calibration of Water Delivery Network Using Short-Burst Hydrant Trials</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_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/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2026</style></YEAR><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><title><style face="normal" font="default" size="100%"> Map-Guided Cross-Training for Building Detection</style></title></RECORD></records></xml>
