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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%">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%">Piotr Zawadzki</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Grzegorz Dziwoki</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Marcin Kucharczyk</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Jan Machniewski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Wojciech Sułek</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Jacek Izydorczyk</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Weronika Izydorczyk</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Piotr Kłosowski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Adam Dustor</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Wojciech Filipowski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Krzysztof Paszek</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%">Quantum Enabled Data Authentication Without Classical Control Interaction</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%">Electronics</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">14</style></VOLUME><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;We present a quantum-assisted data authentication protocol that integrates classical information-theoretic security with quantum communication techniques. We assume only that the participants have access to open classical and quantum channels, and share a random static key material. Building on the Wegman–Carter paradigm, our scheme employs universal hashing for message authentication and leverages quantum channels to securely transmit random nonces, eliminating the need for key recycling. The protocol utilizes polar codes within Wyner’s wiretap channel model to ensure confidentiality and reliability, even in the presence of an all-powerful adversary. Security analysis demonstrates that the protocol inherits strong guarantees from both classical and quantum frameworks, provided the quantum channel maintains low loss and noise.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://www.mdpi.com/2079-9292/14/20/4037</style></URL><title><style face="normal" font="default" size="100%">Quantum Enabled Data Authentication Without Classical Control Interaction</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%">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%">Piotr Zawadzki</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Anna Zawadzka</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Alicja Zawadzka</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Polar wiretap coding for practical quantum confidentiality</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%">Quantum Information Processing</style></SECONDARY_TITLE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;&lt;span style="-webkit-text-stroke-width:0px;background-color:rgb(255, 255, 255);color:rgb(34, 34, 34);display:inline !important;float:none;font-family:Merriweather, serif;font-size:18px;font-style:normal;font-variant-caps:normal;font-variant-ligatures:normal;font-weight:400;letter-spacing:normal;orphans:2;text-align:start;text-decoration-color:initial;text-decoration-style:initial;text-decoration-thickness:initial;text-indent:0px;text-transform:none;white-space:normal;widows:2;word-spacing:0px;"&gt;We present a quantum-assisted protocol that provides a confidential data channel under the assumption of pre-shared keys. The pre-shared key can be reused across multiple sessions, reducing the cost of fresh key generation. The protocol does not require an authenticated classical feedback channel during execution. Confidentiality is ensured by the physical properties of quantum states and information-theoretic preprocessing using polar codes within the Wyner wiretap framework. The construction avoids entanglement and quantum memories, enabling deployment with current hardware. Due to the physics of quantum communication, the protocol is point-to-point with limited throughput, but is suitable for high-assurance, moderate-rate applications.&lt;/span&gt;&lt;/p&gt;</style></ABSTRACT><title><style face="normal" font="default" size="100%">Polar wiretap coding for practical quantum confidentiality</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>
