<?xml version="1.0" encoding="UTF-8"?>
<xml><records><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Przemysław 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><year><style face="normal" font="default" size="100%">2023</style></year></dates><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><publisher><style face="normal" font="default" size="100%">Wydawnictwo Politechniki Łódzkiej</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Przemysław Sekuła</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Michał Cholewa</style></author><author><style face="normal" font="default" size="100%">Katarzyna Kołodziej</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Through the Thicket: A Study of Number-Oriented LLMs derived from Random Forest Models</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style face="normal" font="default" size="100%">06/2024</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">arXiv preprint arXiv:2406.04926</style></secondary-title><urls><style face="normal" font="default" size="100%">https://arxiv.org/abs/2406.04926</style></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michał Cholewa</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Katarzyna Kołodziej</style></author><author><style face="normal" font="default" size="100%">Michał Gorawski</style></author><author><style face="normal" font="default" size="100%">Jakub Koral</style></author><author><style face="normal" font="default" size="100%">Wojciech Koral</style></author><author><style face="normal" font="default" size="100%">Andrzej Madej</style></author><author><style face="normal" font="default" size="100%">Kryspin Musioł</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">‘Just One More Sensor is Enough’ – Iterative Water Leak Localization with Physical Simulation and a Small Number of Pressure Sensors</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style face="normal" font="default" size="100%">06/2024</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">IEEE Sensors Journal</style></secondary-title><electronic-resource-num><style face="normal" font="default" size="100%">10.1109/JSEN.2024.3413889</style></electronic-resource-num><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Katarzyna Kołodziej</style></author><author><style face="normal" font="default" size="100%">Michał Cholewa</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Wojciech Koral</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Efficient Numerical Calibration of Water Delivery Network Using Short-Burst Hydrant Trials</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style face="normal" font="default" size="100%">09/2024</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">arXiv preprint 	arXiv:2410.02772</style></secondary-title><urls><style face="normal" font="default" size="100%">https://arxiv.org/abs/2410.02772</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">https://doi.org/10.48550/arXiv.2410.02772</style></electronic-resource-num><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Katarzyna Kołodziej</style></author><author><style face="normal" font="default" size="100%">Michał Cholewa</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Wojciech Koral</style></author><author><style face="normal" font="default" size="100%">Przemysław Sekuła</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Calibration of Water Demand Network Through a Machine Learned Digital Twin Model</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2024</style></year></dates><secondary-title><style face="normal" font="default" size="100%">5th Polish Conference on Artificial Intelligence (PP-RAI 2024)</style></secondary-title><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Katarzyna Kołodziej</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">AI for the Rescue: Tackling Challenges in Polish Waste Management System</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2024</style></year></dates><secondary-title><style face="normal" font="default" size="100%">XII International Conference Environmental Protection and Energy (EPAE 2024)</style></secondary-title><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Przemysław Sekuła</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Michał Cholewa</style></author><author><style face="normal" font="default" size="100%">Katarzyna Kołodziej</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Through the Thicket: A Study of Number-Oriented LLMS Derived from Random Forest Models</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2025</style></year><pub-dates><date><style face="normal" font="default" size="100%">03/2025</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">Journal of Artificial Intelligence and Soft Computing Research</style></secondary-title><electronic-resource-num><style face="normal" font="default" size="100%">https://doi.org/10.2478/jaiscr-2025-0014</style></electronic-resource-num><volume><style face="normal" font="default" size="100%">15</style></volume><issue><style face="normal" font="default" size="100%">3</style></issue><section><style face="normal" font="default" size="100%">279</style></section><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kamil Książek</style></author><author><style face="normal" font="default" size="100%">Wilhelm Masarczyk</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Krisztian Buza</style></author><author><style face="normal" font="default" size="100%">Przemysław Sekuła</style></author><author><style face="normal" font="default" size="100%">Michał Cholewa</style></author><author><style face="normal" font="default" size="100%">Katarzyna Kołodziej</style></author><author><style face="normal" font="default" size="100%">Piotr Gorczyca</style></author><author><style face="normal" font="default" size="100%">Magdalena Piegza</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Deep learning approach for automatic assessment of schizophrenia and bipolar disorder in patients using R-R intervals</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2025</style></year><pub-dates><date><style face="normal" font="default" size="100%">09/2025</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">PLOS Computational Biology</style></secondary-title><electronic-resource-num><style face="normal" font="default" size="100%">10.1371/journal.pcbi.1012983</style></electronic-resource-num><volume><style face="normal" font="default" size="100%">21</style></volume><section><style face="normal" font="default" size="100%">1</style></section><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Schizophrenia and bipolar disorder are severe mental illnesses that significantly impact quality of life. These disorders are associated with autonomic nervous system dysfunction, which can be assessed through heart activity analysis. Heart rate variability (HRV) has shown promise as a potential biomarker for diagnostic support and early screening of those conditions. This study aims to develop and evaluate an automated classification method for schizophrenia and bipolar disorder using short-duration electrocardiogram (ECG) signals recorded with a low-cost wearable device. We conducted classification experiments using machine learning techniques to analyze R-R interval windows extracted from short ECG recordings. The study included 60 participants—30 individuals diagnosed with schizophrenia or bipolar disorder and 30 control subjects. We evaluated multiple machine learning models, including Support Vector Machines, XGBoost, multilayer perceptrons, Gated Recurrent Units, and ensemble methods. Two time window lengths (about 1 and 5 minutes) were evaluated. Performance was assessed using 5-fold cross-validation and leave-one-out cross-validation, with hyperparameter optimization and patient-level classification based on individual window decisions. Our method achieved classification accuracy of 83% for the 5-fold cross-validation and 80% for the leave-one-out scenario. Despite the complexity of our scenario, which mirrors real-world clinical settings, the proposed approach yielded performance comparable to advanced diagnostic methods reported in the literature. The results highlight the potential of short-duration HRV analysis as a cost-effective and accessible tool for aiding in the diagnosis of schizophrenia and bipolar disorder. Our findings support the feasibility of using wearable ECG devices and machine learning-based classification for psychiatric screening, paving the way for further research and clinical applications.&lt;/p&gt;&#13;
</style></abstract><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Katarzyna Kołodziej</style></author><author><style face="normal" font="default" size="100%">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><year><style face="normal" font="default" size="100%">2025</style></year></dates><secondary-title><style face="normal" font="default" size="100%">6th Polish Conference on Artificial Intelligence (PP-RAI 2025)</style></secondary-title><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Katarzyna Kołodziej</style></author><author><style face="normal" font="default" size="100%">Michał Cholewa</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Wojciech Koral</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Efficient Numerical Calibration of Water Delivery Network Using Short-Burst Hydrant Trials</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2026</style></year><pub-dates><date><style face="normal" font="default" size="100%">02/2026</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">Journal of Water Resources Planning and Management - ASCE</style></secondary-title><urls><style face="normal" font="default" size="100%">https://doi.org/10.1061/JWRMD5.WRENG-6839</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">10.1061/JWRMD5.WRENG-6839 </style></electronic-resource-num><volume><style face="normal" font="default" size="100%">152</style></volume><issue><style face="normal" font="default" size="100%">4</style></issue><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aygün Varol</style></author><author><style face="normal" font="default" size="100%">Katarzyna Kołodziej</style></author><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Naser Motlagh</style></author><author><style face="normal" font="default" size="100%">Mirka Leino</style></author><author><style face="normal" font="default" size="100%">Johanna Virkki</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Enabling Cloud-Level Accuracy in Edge AI through IoT Data Preprocessing</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2026</style></year></dates><secondary-title><style face="normal" font="default" size="100%">IEEE Internet of Things Journal</style></secondary-title><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Large language models (LLMs) offer a natural-language interface for interpreting Internet of Things (IoT) sensor data in smart environments; however, cloud deployment introduces latency, privacy, and connectivity concerns. Local LLMs can reduce these limitations, but compact edge-deployable models often show weaker numerical reasoning when raw sensor readings are provided directly. This paper investigates whether prompt-side preprocessing can improve the accuracy--latency trade-off of local LLMs for environmental monitoring. We propose a structured prompt construction framework that transforms raw air-quality and thermal-comfort measurements into progressively enriched textual representations: raw sensor values, threshold-aware descriptions, and compact environmental summary flags. The approach is evaluated using indoor Raspberry Pi/BME680 datasets from Tampere University and outdoor air-quality datasets from Helsinki, Katowice, and Warsaw. We construct a binary LLM query dataset covering air quality, thermal comfort, and joint environmental conditions, and evaluate five local and five cloud LLMs across three prompt variants and two inference modes, with and without chain-of-thought prompting. The results show that prompt enrichment substantially improves local-model accuracy. In No-CoT mode, local accuracy increases from 50.9\% to 81.7\% indoors and from 63.7\% to 89.3\% outdoors from the raw to the most enriched prompt. Local No-CoT inference is the fastest configuration, with mean latency close to 0.22 s, while CoT substantially increases inference time. These findings suggest that lightweight prompt-side preprocessing can narrow the local--cloud performance gap and support low-latency IoT analytics in smart environments.&lt;/p&gt;</style></abstract></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aygün Varol</style></author><author><style face="normal" font="default" size="100%">Katarzyna Kołodziej</style></author><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Naser Motlagh</style></author><author><style face="normal" font="default" size="100%">Mirka Leino</style></author><author><style face="normal" font="default" size="100%">Johanna Virkki</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Enabling Cloud-Level Accuracy in Edge AI through IoT Data Preprocessing</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2026</style></year></dates><secondary-title><style face="normal" font="default" size="100%">arXiv preprint arXiv </style></secondary-title><urls><style face="normal" font="default" size="100%">https://arxiv.org/abs/2606.22496 </style></urls><electronic-resource-num><style face="normal" font="default" size="100%">10.48550/ARXIV.2606.22496 </style></electronic-resource-num><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Large language models (LLMs) offer a natural-language interface for interpreting Internet of Things (IoT) sensor data in smart environments; however, cloud deployment introduces latency, privacy, and connectivity concerns. Local LLMs can reduce these limitations, but compact edge-deployable models often show weaker numerical reasoning when raw sensor readings are provided directly. This paper investigates whether prompt-side preprocessing can improve the accuracy--latency trade-off of local LLMs for environmental monitoring. We propose a structured prompt construction framework that transforms raw air-quality and thermal-comfort measurements into progressively enriched textual representations: raw sensor values, threshold-aware descriptions, and compact environmental summary flags. The approach is evaluated using indoor Raspberry Pi/BME680 datasets from Tampere University and outdoor air-quality datasets from Helsinki, Katowice, and Warsaw. We construct a binary LLM query dataset covering air quality, thermal comfort, and joint environmental conditions, and evaluate five local and five cloud LLMs across three prompt variants and two inference modes, with and without chain-of-thought prompting. The results show that prompt enrichment substantially improves local-model accuracy. In No-CoT mode, local accuracy increases from 50.9\% to 81.7\% indoors and from 63.7\% to 89.3\% outdoors from the raw to the most enriched prompt. Local No-CoT inference is the fastest configuration, with mean latency close to 0.22 s, while CoT substantially increases inference time. These findings suggest that lightweight prompt-side preprocessing can narrow the local--cloud performance gap and support low-latency IoT analytics in smart environments.&lt;/p&gt;</style></abstract></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aygün Varol</style></author><author><style face="normal" font="default" size="100%">Katarzyna Kołodziej</style></author><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Naser Motlagh</style></author><author><style face="normal" font="default" size="100%">Mirka Leino</style></author><author><style face="normal" font="default" size="100%">Johanna Virkki</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">AIRWISE: Environmental Sensor and LLM Query Dataset for Edge AI-Based Air-Quality and Thermal-Comfort Analytics</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2026</style></year></dates><urls><style face="normal" font="default" size="100%">https://zenodo.org/doi/10.5281/zenodo.20783874 </style></urls><electronic-resource-num><style face="normal" font="default" size="100%">10.5281/ZENODO.20783874 </style></electronic-resource-num><abstract><style face="normal" font="default" size="100%">&lt;p&gt;AIRWISE is an open benchmark dataset for edge AI, IoT environmental monitoring, and large language model (LLM) evaluation on real sensor data. It combines indoor air-quality and thermal-comfort measurements, outdoor air-quality and meteorological time series, and a labeled binary question-answering benchmark for threshold-aware reasoning and anomaly detection. The dataset supports the study “Enabling Cloud-Level Accuracy in Edge AI through IoT Data Preprocessing”:&#160;https://doi.org/10.48550/arXiv.2606.22496&lt;br&gt;AIRWISE provides three core resources:&lt;br&gt;Indoor environmental sensor dataset (BME680, 1-minute resolution). 146,000+ minute-averaged readings from Raspberry Pi + Bosch BME680 nodes in three real micro-environments (office, kitchen, hallway) at Tampere, Finland (2025-11-14 to 2025-12-18): temperature, relative humidity, barometric pressure, gas resistance, indoor air quality (IAQ) proxy, z-score anomaly detectors, and limit-annotated long-format tables with ground-truth anomaly flags.&lt;br&gt;Outdoor air-quality and meteorological time series (hourly, full-year 2023). Helsinki (Finland), Katowice and Warsaw (Poland): NO₂, CO, O₃, PM₂.₅, temperature, and relative humidity assembled from national monitoring networks (FMI, GIOŚ, IMGW-PIB), as cleaned single-series city tables (≈8,760 hourly records per city) and limit-annotated long-format tables with anomaly flags.&lt;br&gt;Binary LLM query benchmark (1,440 labeled yes/no questions). 240 natural-language questions per site across six sites, balanced by task type (80 air quality, 80 thermal comfort, 80 joint environmental condition), each with ground-truth answer, violation label, offending factors, and rationale — ready for benchmarking local/edge and cloud LLMs on sensor-grounded reasoning.&lt;br&gt;Also included: reference air-quality and thermal-comfort limits (EU air-quality standards and Finnish/Polish national guidelines); the unmodified institutional source files for the outdoor component (FMI exports, GIOŚ archive workbooks and station metadata, IMGW-PIB synoptic tables); the complete dataset-generation scripts covering every pipeline step (raw sources → cleaned tables → limit-annotated data → LLM queries) with a requirements.txt for a verified minimal Python environment; SHA-256 checksums; and a dependency-free validation script. Schemas, exact row counts, coverage, and usage examples are documented in README.md and MANIFEST.md.&lt;/p&gt;</style></abstract></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michał Romaszewski</style></author><author><style face="normal" font="default" size="100%">Kamil Drejer</style></author><author><style face="normal" font="default" size="100%">Katarzyna Kołodziej</style></author><author><style face="normal" font="default" size="100%">Anna Zawadzka</style></author><author><style face="normal" font="default" size="100%">Stanisław Lewiński</style></author><author><style face="normal" font="default" size="100%">Przemysław Głomb</style></author><author><style face="normal" font="default" size="100%">Marek Ruciński</style></author><author><style face="normal" font="default" size="100%">Michał Krupiński</style></author><author><style face="normal" font="default" size="100%">Krzysztof Gryguc</style></author><author><style face="normal" font="default" size="100%">Przemysław Sekuła</style></author><author><style face="normal" font="default" size="100%">Szymon Sala</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Toward Seasonal Guidelines for Robust Deep-Learning Sentinel-2 Building Detection in Different Area Types</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2026</style></year></dates><electronic-resource-num><style face="normal" font="default" size="100%">10.1109/JSTARS.2026.3742586</style></electronic-resource-num></record></records></xml>
