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<xml><records><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%">M. Blachnik</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Głomb</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%">Rafał Scherer</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Ryszard Tadeusiewicz</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Zadeh Lotfia</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%">Do we need complex models for gestures? A comparison of data representation and preprocessing methods for hand gesture recognition</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%">Artificial Intelligence and Soft Computing Lecture Notes in Computer Science 7267</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer Berlin Heidelberg</style></PUBLISHER><PAGES><style face="normal" font="default" size="100%">477–485</style></PAGES><title><style face="normal" font="default" size="100%">Do we need complex models for gestures? A comparison of data representation and preprocessing methods for hand gesture 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%">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>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Joanna Domańska</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Głomb</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Kowalski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Sławomir Nowak</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Modeling of Internet 3D Traffic Using Hidden Markov Models</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2009</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Advances in Intelligent 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 Verlag</style></PUBLISHER><VOLUME><style face="normal" font="default" size="100%">64</style></VOLUME><SECTION><style face="normal" font="default" size="100%">Modeling of Internet 3D Traffic Using Hidden Markov Models</style></SECTION><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Artykuł podejmuje temat modelowania złożoności przesyłu siatek progresywnych 3D.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Modeling of Internet 3D Traffic Using Hidden Markov Models</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%">Piotr Gawron</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Głomb</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Jaroslaw Miszczak</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Zbigniew Puchała</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Stanisław Kozielski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Urszula Stańczyk</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Eigengestures for natural human computer interface</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%">Man-Machine Interactions 2</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Berlin / Heidelberg</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer</style></PUBLISHER><PAGES><style face="normal" font="default" size="100%">49–56</style></PAGES><ISBN><style face="normal" font="default" size="100%">978-3-642-23168-1</style></ISBN><ABSTRACT><style face="normal" font="default" size="100%">We present the application of Principal Component Analysis for data acquired during the design of a natural gesture interface. We investigate the concept of an eigengesture for motion capture hand gesture data and present the visualisation of principal components obtained in the course of conducted experiments. We also show the influence of dimensionality reduction on reconstructed gesture data quality.</style></ABSTRACT><NOTES><style face="normal" font="default" size="100%">arXiv:1105.1293</style></NOTES><title><style face="normal" font="default" size="100%">Eigengestures for natural human computer interface</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%">Krzysztof Grochla</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Tadeusz Czachórski</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%">Andrzej Grzywak</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Internet w społeczeństwie informacyjnym - Podstawowe problemy Internetu</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2004</style></YEAR><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Warszawa</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Wydawnictwa Naukowo-Techniczne</style></PUBLISHER><SECTION><style face="normal" font="default" size="100%">Badania symulacyjne pakietowych sieci optycznych o dowolnych topologiach</style></SECTION><title><style face="normal" font="default" size="100%">Internet w społeczeństwie informacyjnym - Podstawowe problemy Internetu</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%">Krzysztof Grochla</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%">Kształtowanie ruchu w routerach pracujących w systemie Linux</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2002</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Studia Informatica</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><NUMBER><style face="normal" font="default" size="100%">2A</style></NUMBER><title><style face="normal" font="default" size="100%">Kształtowanie ruchu w routerach pracujących w systemie Linux</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%">Krzysztof Grochla</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%">Andrzej Grzywak</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Współczesne problemy sieci komputerowych - Nowe technologie</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2004</style></YEAR><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Warszawa</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Wydawnictwa Naukowo-Techniczne</style></PUBLISHER><SECTION><style face="normal" font="default" size="100%">Kształtowanie ruchu w transmisjach wideo wykorzystujących kompresję falkową</style></SECTION><ABSTRACT><style face="normal" font="default" size="100%">Rozdział przedstawia budowę i wyniki badań symulacyjnych mechanizmu sterowania transmisją sieciową skalowalnego strumienia wideo. 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estimation</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2005</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%"/></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><title><style face="normal" font="default" size="100%">Analysis of fGn and http requests traces using localized multiscale H parameter estimation</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 Grochla</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Representing single-link traffic quality with kernel PCA</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2006</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</style></PUBLISHER><title><style face="normal" font="default" size="100%">Representing single-link traffic quality with kernel PCA</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 Grochla</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Modeling Connection State with Kernel PCA</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2005</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">2nd EuroNGI Workshop on New Trends in Modeling, Quantitative Methods and Measurements</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><title><style face="normal" font="default" size="100%">Modeling Connection State with Kernel PCA</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%">Krzysztof Grochla</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Modelowanie stacji roboczych w sieci ATM przy użyciu symulatora OMNeT++</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2002</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Studia Informatica</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><NUMBER><style face="normal" font="default" size="100%">2A</style></NUMBER><title><style face="normal" font="default" size="100%">Modelowanie stacji roboczych w sieci ATM przy użyciu symulatora OMNeT++</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%">Krzysztof Grochla</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Arkadiusz Sochan</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Andrzej Grzywak</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Internet w społeczeństwie informacyjnym - Podstawowe problemy Internetu</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2004</style></YEAR><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Warszawa</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Wydawnictwa Naukowo-Techniczne</style></PUBLISHER><SECTION><style face="normal" font="default" size="100%">Skalowalna kompresja wideo na potrzeby transmisji sieciowych</style></SECTION><ABSTRACT><style face="normal" font="default" size="100%">Artykuł prezentuje przegląd i badania zagadnienia skalowalności w stosowanych współcześnie technologiach kodowania sygnału wideo. W pierwszej części prezentowane są modele skalowalności przyjęte w stosowanych i opracowywanych standardach kompresji, w drugiej przedstawione są wyniki badań jakości transmisji skalowalnych strumieni w różnorodnych warunkach obciążenia. Całość zamknięta jest podsumowaniem możliwości sterowania transmisją sieciową takich strumieni.</style></ABSTRACT><title><style face="normal" font="default" size="100%">Internet w społeczeństwie informacyjnym - Podstawowe problemy Internetu</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%">Sławomir Nowak</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Experimental rate distortion analysis of image coder with contourlet transform and SPIHT algorithm</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2008</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Theoretical and Applied Informatics</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><title><style face="normal" font="default" size="100%">Experimental rate distortion analysis of image coder with contourlet transform and SPIHT algorithm</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%">Zbigniew Puchała</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%">Context selection for efficient bit modeling of contourlet transform coefficients</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2007</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Theoretical and Applied Informatics</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">19</style></VOLUME><NUMBER><style face="normal" font="default" size="100%">2</style></NUMBER><PAGES><style face="normal" font="default" size="100%">135–146</style></PAGES><ABSTRACT><style face="normal" font="default" size="100%">Contourlet transform is named as one possible successor to the wavelet transform as a tool for representation of image information. It has better properties in representation of edge information, which make it suitable for designing image compression systems. It is not clear, however, how to exploit the statistical structure of the coefficients generated by this transform in effective compression algorithm. In this work, authors propose a general, nonparametric method to achieve context map, thus allowing for efficient application of EBCOT/JPEG 2000 bit modeling and encoding approach to contourlet transformed images. The article presents the derivation of the method and experiments performed on a publicly available image set. Keywords: contourlet transform, coefficient bit modeling, context estimation</style></ABSTRACT><title><style face="normal" font="default" size="100%">Context selection for efficient bit modeling of contourlet transform coefficients</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 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/><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%">Gesture and Sign Language in Human-Computer Interaction and Embodied Communication</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer Berlin Heidelberg</style></PUBLISHER><EDITION><style face="normal" font="default" size="100%">Lecture Notes in Artificial Intelligence</style></EDITION><title><style face="normal" font="default" size="100%">Choosing and Modeling the Hand Gesture Database for a Natural User Interface</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%">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/><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%">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><title><style face="normal" font="default" size="100%">Choosing and modeling hand gesture database for natural user interface</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%">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/><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%">Lecture Notes in Computer Science, Human-Computer Interaction – INTERACT</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">6949</style></VOLUME><title><style face="normal" font="default" size="100%">Unsupervised Parameter Selection for Gesture Recognition with Vector Quantization and Hidden Markov 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%">Przemysław Głomb</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%">Laboratory for the exploration of virtual 3D environments</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2009</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Annual Report 2009</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PAGES><style face="normal" font="default" size="100%">57–59</style></PAGES><title><style face="normal" font="default" size="100%">Laboratory for the exploration of virtual 3D environments</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%">Arkadiusz Sochan</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%">Rafał 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%">Surface Mixture Models for the Optimization of Object Boundary Representation</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2014</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Artificial Intelligence and Soft Computing, Lecture Notes in Computer Science 8467</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PAGES><style face="normal" font="default" size="100%">703–714</style></PAGES><ISBN><style face="normal" font="default" size="100%">978-3-319-07172-5</style></ISBN><title><style face="normal" font="default" size="100%">Surface Mixture Models for the Optimization of Object Boundary Representation</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>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Sławomir Nowak</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Joanna Domańska</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mateusz Nowak</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%">Progressive 3D meshes transmission: traffic generating and simulation evaluation</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2010</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Theoretical and Applied Informatics</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">22</style></VOLUME><NUMBER><style face="normal" font="default" size="100%">4</style></NUMBER><PAGES><style face="normal" font="default" size="100%">247–259</style></PAGES><DATE><style face="normal" font="default" size="100%">11</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">The paper presents issues related to the progressive transmission of 3D meshes in clientserver architecture. A tool generating network traffic for different 3D scenes and scenarios is presented. The objective was to evaluate the transmission dynamics and possible impact of 3D traffic on the amount of data available in VR browsing client, which relates to browsing QoE (Quality of Experience). Simulation studies were carried out and HMM model of traffic generated by 3D server was worked out, planned for use in modeling works on future internet protocols, servicing 3D traffic.</style></ABSTRACT><NOTES><style face="normal" font="default" size="100%">DOI: 10.2478/v10179-010-0012-2</style></NOTES><title><style face="normal" font="default" size="100%">Progressive 3D meshes transmission: traffic generating and simulation evaluation</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%">Sławomir Nowak</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%">REMOTE VIRTUAL REALITY: EXPERIMENTAL INVESTIGATIONSOF PROGRESSIVE 3D MESH TRANSMISSION IN IP NETWORKS</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2010</style></YEAR><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><ABSTRACT><style face="normal" font="default" size="100%">Performance Modelling and Evaluation of Heterogeneous Networks, proc. HETNET 2010.</style></ABSTRACT><title><style face="normal" font="default" size="100%">REMOTE VIRTUAL REALITY: EXPERIMENTAL INVESTIGATIONSOF PROGRESSIVE 3D MESH TRANSMISSION IN IP 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%">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/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2011</style></YEAR><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><NOTES><style face="normal" font="default" size="100%">arXiv:1109.5034</style></NOTES><URL><style face="normal" font="default" size="100%">https://arxiv.org/abs/1109.5034</style></URL><title><style face="normal" font="default" size="100%">Towards a natural gesture interface: LDA-based gesture separability</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></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/><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%">Computer Recognition Systems</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer</style></PUBLISHER><VOLUME><style face="normal" font="default" size="100%">4</style></VOLUME><PAGES><style face="normal" font="default" size="100%">365–373</style></PAGES><DATE><style face="normal" font="default" size="100%">2011</style></DATE><title><style face="normal" font="default" size="100%">The Effect of Multiple Training Sequences on HMM Classification of Motion Capture Gesture 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 Romaszewski</style></AUTHOR><AUTHOR><style face="normal" font="default" 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size="100%">Antoni Zabłudowski</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">3D Mesh Identification Using Random Walks and Hidden Markov Models</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2009</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%"/></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Academy Publishing House EXIT</style></PUBLISHER><PAGES><style face="normal" font="default" size="100%">302–308</style></PAGES><title><style face="normal" font="default" size="100%">3D Mesh Identification Using Random Walks and Hidden Markov Models</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%">Arkadiusz Sochan</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Krzysztof Grochla</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Głomb</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Andrzej Grzywak</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Internet w społeczeństwie informacyjnym - Podstawowe problemy Internetu</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2004</style></YEAR><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Warszawa</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Wydawnictwa Naukowo-Techniczne</style></PUBLISHER><SECTION><style face="normal" font="default" size="100%">Dostęp do usług katalogowych z wykorzystaniem języka XML w aplikacjach WWW</style></SECTION><ABSTRACT><style face="normal" font="default" size="100%">W rozdziale zarysowano koncepcję usług katalogowych i metody dostępu do nich. Przedstawiono architekturę aplikacji WWW i różne rozwiązania stosowane do tworzenia takich aplikacji. Zaprezentowano wyniki badań porównawczych przeprowadzonych dla tych rozwiązań. Na koniec scharakteryzowano opracowaną aplikację WWW korzystającą z usług katalogowych i XML.</style></ABSTRACT><title><style face="normal" font="default" size="100%">Internet w społeczeństwie informacyjnym - Podstawowe problemy Internetu</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%">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 Grochla</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Andrzej Grzywak</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Współczesne problemy sieci komputerowych - Nowe technologie</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2004</style></YEAR><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Warszawa</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Wydawnictwa Naukowo-Techniczne</style></PUBLISHER><SECTION><style face="normal" font="default" size="100%">Transmisje strumieni MPEG-2 z wykorzystaniem usługi VBR sieci ATM</style></SECTION><ABSTRACT><style face="normal" font="default" size="100%">W rozdziale opisano prace badawcze dotyczące transmisji strumieni MPEG-2 poprzez kanał o zmiennej przepływności bitowej w sieci ATM. Prace miały charakter zarówno doświadczalno-pomiarowy, jak i analityczny. Określono zależność funkcyjną pomiędzy parametrami ruchu sieci ATM, natężeniem strumienia wideo a jakością transmisji multimedialnej. Zależność powiązano z</style></ABSTRACT><title><style face="normal" font="default" size="100%">Współczesne problemy sieci komputerowych - Nowe technologie</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%">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. 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Communications in Computer and Information Science 160.</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PAGES><style face="normal" font="default" size="100%">459–464</style></PAGES><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></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%">E. Switonski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">D. Tejszerska</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">M. Gzik</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">W. Wolanski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">B. Gzik-Zroska</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">M. 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Białecki</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%">Andrzej Kwiecień</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Andrzej Grzywak</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Wysokowydajne sieci komputerowe - Zastosowanie i bezpieczeństwo</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2005</style></YEAR><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Wydawnictwa Komunikacji i Łączności</style></PUBLISHER><SECTION><style face="normal" font="default" size="100%">Mechanizm skalowalnej kompresji wykorzystujący standard JPEG2000 w zastosowaniach sieciowych</style></SECTION><ISBN><style face="normal" font="default" size="100%">83-206-1573-9</style></ISBN><ABSTRACT><style face="normal" font="default" size="100%">W rozdziale przedstawiono możliwości wykorzystania standardu JPEG-2000 do kodowania strumieni przesyłanych przez kanał transmisyjny o wysokim współczynniku błędu i start. Scharakteryzowano metodę kodowania i wynikające z niej cechy skalowalności. W dalszej części opisano zastosowany model i przeprowadzone badania dotyczące stabilizacji jakości prezentowanych sekwencji obrazów z wykorzystaniem informacji nadmiarowej. Pokazano, że kodowanie obrazów ruchomych oparte na warstwach i płaszczyznach bitowych w połączeniu z kodami nadmiarowymi poprawia jakość prezentacji.</style></ABSTRACT><title><style face="normal" font="default" size="100%">Wysokowydajne sieci komputerowe - Zastosowanie i bezpieczeństwo</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%">T. Białecki</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%">Andrzej Kwiecień</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Kazimiera Wódz</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Techniczne i społeczne problemy zastosowania Internetu</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2005</style></YEAR><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Wydawnictwa Komunikacji l Łaczności</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Wydawnictwa Komunikacji i Łączności</style></PUBLISHER><SECTION><style face="normal" font="default" size="100%">JPEG 2000 w transmisjach sieciowych - wpływ współczynnika strat na jakość obrazu wideo</style></SECTION><ISBN><style face="normal" font="default" size="100%">83-206-1590-9</style></ISBN><ABSTRACT><style face="normal" font="default" size="100%">W rozdziale przedstawiono analizę mechanizmu utrzymania jakości prezentacji strumienia obrazów JPEG 2000 w warunkach transmisji z utratą danych z wykorzystaniem dodatkowej informacji nadmiarowej. Kolejne podrozdziały przedstawiają: teoretyczną analizę zagadnienia skalowanej transmisji wideo w środowisku o wysokiej stopie błędu z wykorzystaniem informacji korekcyjnej, opracowane stanowisko badawcze, wyniki badań i podsumowanie.</style></ABSTRACT><title><style face="normal" font="default" size="100%">Techniczne i społeczne problemy zastosowania Internetu</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%">Krzysztof Domino</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Głomb</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Zbigniew Łaskarzewski</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Classification of LPG clients using the Hurst exponent and the correlation coeficient</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%">Theoretical and Applied Informatics</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">27</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">1</style></ISSUE><PAGES><style face="normal" font="default" size="100%">13–24</style></PAGES><SECTION><style face="normal" font="default" size="100%">13</style></SECTION><DATE><style face="normal" font="default" size="100%">2015</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;In this paper we present the analysis of the gas usage for different types of buildings. First, we introduce the classical theory of building heating. This allows the establishment of theoretical relations between gas consumption time series and the outside air temperature for different types of buildings, residential and industrial. These relations imply dierent auto-correlations of gas usage time series as well as different cross-correlations between gas consumption and temperature time series for different types of buildings. Therefore, the autocorrelation and the cross-correlation were used to classify the buildings into three classes: housing, housing with high thermal capacity, and industry. The Hurst exponent was calculated using the global DFA to investigate auto-correlation, while the Kendall's τ rank coeficient was calculated to investigate cross-correlation&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.baztech-1ba91297-f921-4524-927c-381996c36512</style></URL><title><style face="normal" font="default" size="100%">Classification of LPG clients using the Hurst exponent and the correlation coeficient</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>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></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/><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</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%">Parameter Estimation for HOSVD-based Approximation of Temporally Coherent Mesh Sequences</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>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Bartosz Grabowski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Wojciech Masarczyk</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Głomb</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Agata Mendys</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Automatic pigment identification from 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%">Journal of Cultural Heritage</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">31</style></VOLUME><PAGES><style face="normal" font="default" size="100%">1 - 12</style></PAGES><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Art objects conservation or historical analysis necessitates a thorough knowledge of materials used by the artist and their subsequent changes. In the case of paintings this requires the ability to correctly identify the pigments that were used for creation or later restoration of the artwork. This is a challenging problem, as the applied method should be non-contact, robust for the wide variety of chemical substances used and straightforward in the interpretation. Recently, the hyperspectral imaging has emerged as a promising measuring methodology for this kind of the artwork analysis; the combination of acquiring spectral information and planar (photography-like) pixel arrangement provides a lot of potential for material characterization. While initial studies of hyperspectral imaging application to art objects analysis are encouraging, the difficulties of working with its multidimensional data are acknowledged; in many cases complex algorithms are required to fully utilize its potential. In this paper, we study the problem of algorithm design for pigment identification based on a hyperspectral image of a painting. We combine various processing steps to achieve a robust solution requiring minimal user intervention. Using a special set of paintings and a reference pigment database we demonstrate the viability of applying this method in the pigment recognition setting. Our results confirm the potential of using hyperspectral imaging in the art conservation setting, and based on them we discuss the potential construction and elements of such an algorithm.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">http://www.sciencedirect.com/science/article/pii/S1296207417306544</style></URL><title><style face="normal" font="default" size="100%">Automatic pigment identification from 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%">Wojciech Masarczyk</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%">Mateusz Ostaszewski</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Effective Training of Deep Convolutional Neural Networks for Hyperspectral Image Classification through Artificial Labeling</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%">Remote Sensing</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">12</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">16</style></ISSUE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Hyperspectral imaging is a rich source of data, allowing for a multitude of effective applications. However, such imaging remains challenging because of large data dimension and, typically, a small pool of available training examples. While deep learning approaches have been shown to be successful in providing effective classification solutions, especially for high dimensional problems, unfortunately they work best with a lot of labelled examples available. The transfer learning approach can be used to alleviate the second requirement for a particular dataset: first the network is pre-trained on some dataset with large amount of training labels available, then the actual dataset is used to fine-tune the network. This strategy is not straightforward to apply with hyperspectral images, as it is often the case that only one particular image of some type or characteristic is available. In this paper, we propose and investigate a simple and effective strategy of transfer learning that uses unsupervised pre-training step without label information. This approach can be applied to many of the hyperspectral classification problems. The performed experiments show that it is very effective at improving the classification accuracy without being restricted to a particular image type or neural network architecture. The experiments were carried out on several deep neural network architectures and various sizes of labeled training sets. The greatest improvement in overall accuracy on the Indian Pines and Pavia University datasets is over 21 and 13 percentage points, respectively. An additional advantage of the proposed approach is the unsupervised nature of the pre-training step, which can be done immediately after image acquisition, without the need of the potentially costly expert’s time.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://arxiv.org/abs/1909.05507</style></URL><title><style face="normal" font="default" size="100%">Effective Training of Deep Convolutional Neural Networks for Hyperspectral Image Classification through Artificial Labeling</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>31</REFERENCE_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/><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><ISBN><style face="normal" font="default" size="100%">978-83-943803-2-8</style></ISBN><title><style face="normal" font="default" size="100%">Unsupervised deep learning approach to hyperspectral anomaly detection</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 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/><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%"/></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Elsevier</style></PUBLISHER><ISBN><style face="normal" font="default" size="100%">978-0-08-102894-0</style></ISBN><URL><style face="normal" font="default" size="100%">https://www.elsevier.com/books/hyperspectral-remote-sensing/pandey/978-0-08-102894-0</style></URL><title><style face="normal" font="default" size="100%">Anomaly detection in hyperspectral remote sensing 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%">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%">Krzysztof Grochla</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Anna Strzoda</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Rafał Marjasz</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Głomb</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Kamil Książek</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Zbigniew Łaskarzewski</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Energy-Aware Algorithm for Assignment of Relays in LPWAN</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%">Transactions on Sensor Networks</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">18</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">4</style></ISSUE><SECTION><style face="normal" font="default" size="100%">Article No. 60</style></SECTION><DATE><style face="normal" font="default" size="100%">11/2022</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;The Low Power Wide Area Networks allow maintaining connectivity with devices over a distance of a few kilometres. However, due&lt;br /&gt;&#13;
to the presence of obstacles the communication range in an urban environment may be shorter, which creates multiple blind spots or&lt;br /&gt;&#13;
areas with limited coverage. The nodes acting as relays may solve this problem by forwarding the data from other devices with poor&lt;br /&gt;&#13;
connectivity. We propose and investigate a novel algorithm that assigns devices to the relay role while keeping the battery constraints.&lt;br /&gt;&#13;
We define the algorithm, describe the simulation testbed, and present performance evaluation in several scenarios, including real-life&lt;br /&gt;&#13;
topologies of IoT networks. The proposed relay selection scheme allows increasing the lifetime of the network by selecting the relay&lt;br /&gt;&#13;
taking into account the current battery capacity. Additionally, it outperforms a previously proposed relay selection algorithm for LP&lt;br /&gt;&#13;
WAN, showing an average 4% decrease in transmission time and an order of magnitude lower computation complexity.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://dl.acm.org/doi/10.1145/3544561?cid=99659913397</style></URL><title><style face="normal" font="default" size="100%">Energy-Aware Algorithm for Assignment of Relays in LPWAN</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%">Bartosz Grabowski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Głomb</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Kamil Książek</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 Autoencoders Performance for Hyperspectral Unmixing Using Clustering</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%">Asian Conference on Intelligent Information and Database Systems</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Ho Chi Minh City, Vietnam</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer</style></PUBLISHER><VOLUME><style face="normal" font="default" size="100%">1716</style></VOLUME><TERTIARY_TITLE><style face="normal" font="default" size="100%">Communications in Computer and Information Science</style></TERTIARY_TITLE><DATE><style face="normal" font="default" size="100%">11/2022</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Hyperspectral cameras acquire images containing information across the electromagnetic spectrum, which convey useful information about the scene. To enable effective analysis of such data, spectral unmixing is often used. It is an important task in hyperspectral imaging, allowing one to obtain the information about spectral endmembers which make up each hyperspectral pixel. This task, traditionally solved with dedicated statistical methods, has recently been explored with deep learning methods. One of the methods well-suited to this task are autoencoders. These neural networks are initialized using multiple random weights, and their initialization often has a significant impact on their efficiency. Because of that, to improve the initialization of autoencoders for the spectral unmixing task, we propose to use the pre-training scheme consisting of clustering-based artificial labeling. We test the approach on two popular hyperspectral datasets, i.e. Samson and Jasper Ridge. Our experiment delivers promising results, improving autoencoders effectiveness in the case of Samson dataset, i.e. for 25-class labeling endmembers’ and abundances’ errors improve by 0.045 and 0.008, respectively. The worse results in the case of Jasper Ridge dataset (improvement of the endmembers’ error by 0.001, and worsening of the abundances’ error by 0.006 for 25-classes labeling) show that more research is required to understand when the proposed approach improves the results of the spectral unmixing. The auxiliary experiments that we also conduct allow us to partially answer that question.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://link.springer.com/chapter/10.1007/978-981-19-8234-7_9</style></URL><title><style face="normal" font="default" size="100%"> Improving Autoencoders Performance for Hyperspectral Unmixing Using Clustering</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%">Anna Strzoda</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Krzysztof Grochla</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Głomb</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Andrzej Madej</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Link failure prediction in LoRa networks</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%"> International Wireless Communications and Mobile Computing Conference, IWCMC</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Marrakesh, Morocco</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><DATE><style face="normal" font="default" size="100%">07/2023</style></DATE><title><style face="normal" font="default" size="100%">Link failure prediction in LoRa networks</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>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%">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/><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%">medXriv</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><DATE><style face="normal" font="default" size="100%">08/2023</style></DATE><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><URL><style face="normal" font="default" size="100%">https://www.medrxiv.org/content/10.1101/2023.08.04.23293640v1</style></URL><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></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%">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/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2023</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 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;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://zenodo.org/record/8171266</style></URL><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></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%">Agnieszka Ciemięga</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Katarzyna Maresz</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%">Paulina Krupska-Wolas</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Krystian Prusik</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Hyperspectral Imaging as a Facile and Non-Destructive Method for Size Analysis of Gold Nanoparticles Deposited on Porous Materials</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%"/></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><title><style face="normal" font="default" size="100%">Hyperspectral Imaging as a Facile and Non-Destructive Method for Size Analysis of Gold Nanoparticles Deposited on Porous Materials</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%">Krisztian Buza</style></AUTHOR><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%">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%">A Simple and Effective Classifier for the Detection of Psychotic Disorders based on Heart Rate Variability Time Series</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%"> Information Technologies – Applications and Theory 2023</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Tatranské Matliare</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Knižnicné a edicné centrum, Fakulta matematiky, fyziky a informatiky, Univerzita Komenského, Mlynská dolina, Bratislava</style></PUBLISHER><VOLUME><style face="normal" font="default" size="100%">3498</style></VOLUME><DATE><style face="normal" font="default" size="100%">09/2023</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;In this paper, we focus on automated detection of schizophrenia and bipolar disorder. For this task, we describe a simple and effective classifier, i.e. convolutional nearest neighbor. It provides a data-driven and objective approach for the detection of schizophrenia and bipolar disorder based on heart rate variability time series. According to our results, our approach is able to distinguish whether the selected person belongs to the patient group with an accuracy of 85% and area under receiver-operator characteristic curve of 0.92.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://ceur-ws.org/Vol-3498/paper28.pdf</style></URL><title><style face="normal" font="default" size="100%">A Simple and Effective Classifier for the Detection of Psychotic Disorders based on Heart Rate Variability Time Series</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%">Bartłomiej Gardas</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Głomb</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Sadowski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Zbigniew Puchała</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Konrad Jałowiecki</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Łukasz Pawela</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Orphee Faucoz</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Pierre-Marie Brunet</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Piotr Gawron</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Matthijs Van Waveren</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mickaël Savinaud</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Guillaume Pasero</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Veronique Defonte</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Hyper-Spectral Image Classification Using Adiabatic Quantum Computation</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%">IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><title><style face="normal" font="default" size="100%">Hyper-Spectral Image Classification Using Adiabatic Quantum Computation</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 Romaszewski</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%">Maciej Skrabski</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%">Machine Learning for Water Leak Detection and Localization in the WaterPrime Project</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%">Wojciechowski A.(Ed.), Lipiński P.(Ed.)., Progress in Polish Artificial Intelligence Research 4, Seria: Monografie Politechniki Łódzkiej Nr. 2437, Wydawnictwo Politechniki Łódzkiej, Łódź 2023, ISBN 978-83-66741-92-8, doi: 10.34658/9788366741928.</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Wydawnictwo Politechniki Łódzkiej</style></PUBLISHER><title><style face="normal" font="default" size="100%">Machine Learning for Water Leak Detection and Localization in the WaterPrime Project</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 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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%">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%">Assessment of symptom severity in psychotic disorder patients based on heart rate variability and accelerometer mobility data</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%">Computers in Biology and Medicine</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">176</style></VOLUME><DATE><style face="normal" font="default" size="100%">05/2024</style></DATE><URL><style face="normal" font="default" size="100%">https://www.sciencedirect.com/science/article/pii/S0010482524006280</style></URL><title><style face="normal" font="default" size="100%">Assessment of symptom severity in psychotic disorder patients based on heart rate variability and accelerometer mobility 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 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 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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%">Piotr Gawron</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Sadowski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Głomb</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Bartłomiej Gardas</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Matthijs Van Waveren</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Clément Forray</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Guillaume 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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%">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>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%">Przemysław Głomb</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Anna Zawadzka</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Challenges associated with the integration of Large Multimodal Models for waste sorting</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%">6th Polish Conference on Artificial Intelligence (PP-RAI 2025)</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><title><style face="normal" font="default" size="100%">Challenges associated with the integration of Large Multimodal Models for waste sorting</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%">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>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Anna Zawadzka</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Głomb</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%">Development of an effective method for detecting small buildings in satellite images</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%">PP-RAI 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;Detecting and monitoring small-scale buildings is a critical challenge in various sectors and can be crucial for various important tasks. Buildings considered small are those with a footprint smaller than 10 × 10 meters, a definition justified by the resolution of Sentinel-2 images, which is 10 meters at best. Examples of tasks for which small building detection is essential include assessment and monitoring of damage caused by natural disasters or war, identification of illegal settlements, detecting new buildings in key military areas, and identification of structures in protected areas. Regrettably, widely available satellite imagery, such as Sentinel-2, provides very limited spatial resolution, making it impossible to efficiently detect small structures. Despite its importance, this issue remains unexplored, with very limited research directly concerning building segmentation and detecting small structures. Traditional segmentation and classification methods exhibit severe limitations, particularly due to small buildings frequently being positioned at pixel boundaries, essentially occupying an insignificant fraction of the original pixel. Additionally, in the context of buildings in complex environments (e.g., surrounded by vegetation or shadows), popular methods such as U-Net with ResNet often fail to deliver reliable results. In this study, we propose a novel approach to this issue by integrating a state-of-the-art object detection model with a deep-learning-based super-resolution network.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Development of an effective method for detecting small buildings in satellite 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%">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><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%">Anna Zawadzka</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%">Architecture Matters: Gender Disparities in Automated Image Moderation</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%">PP-RAI'2026: 7th Polish Conference on Artificial Intelligence</style></SECONDARY_TITLE><DATE><style face="normal" font="default" size="100%">04/2026</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Automated image moderation systems shape online visibility and dataset curation, yet prior work has identified demographic disparities in similarity-based NSFW classifiers. We compare such systems with instruction-tuned vision-language models (VLMs) that generate structured moderation decisions with textual rationales. Using the PHASE-annotated subset of the GCC dataset, &#160;we compute false positive rates overall and by gender.&#160;&lt;br&gt;Results show substantial variation in moderation strictness and in bias direction: two models show more pronounced strictness in moderation of male images, while other two exhibit higher removal rates for female images.&#160;&lt;br&gt;Reasoning-based models do not consistently mitigate bias but, in the proposed pipeline, they offer a transparency layer.&lt;/p&gt;</style></ABSTRACT><title><style face="normal" font="default" size="100%">Architecture Matters: Gender Disparities in Automated Image Moderation</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%">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/><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%">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><title><style face="normal" font="default" size="100%">Enabling Cloud-Level Accuracy in Edge AI through IoT Data Preprocessing</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%">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/><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%">arXiv preprint arXiv </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><URL><style face="normal" font="default" size="100%">https://arxiv.org/abs/2606.22496 </style></URL><title><style face="normal" font="default" size="100%">Enabling Cloud-Level Accuracy in Edge AI through IoT Data Preprocessing</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%">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/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2026</style></YEAR><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><URL><style face="normal" font="default" size="100%">https://zenodo.org/doi/10.5281/zenodo.20783874 </style></URL><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></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%">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/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2026</style></YEAR><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></RECORD></records></xml>
