<?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%">Alper Saylam</style></author><author><style face="normal" font="default" size="100%">Rifat Cikmazel</style></author><author><style face="normal" font="default" size="100%">Nur Kelesoglu</style></author><author><style face="normal" font="default" size="100%">Mert Nakip</style></author><author><style face="normal" font="default" size="100%">Volkan Rodoplu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Energy-Efficient Indoor Positioning for Mobile Internet of Things Based on Artificial Intelligence</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2021</style></year></dates><secondary-title><style face="normal" font="default" size="100%">ASYU2021 </style></secondary-title><electronic-resource-num><style face="normal" font="default" size="100%">10.1109/ASYU52992.2021.9599049</style></electronic-resource-num><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We develop an energy-efficient indoor positioning system based on Artificial Intelligence (AI). In our system, first, at the positioning layer, a Multi-Layer Perceptron (MLP) estimates the current indoor position of an IoT device based on positioning indicators obtained from the anchors. Second, at the forecasting layer, a pair of MLPs estimate the future positions of the device based on the past position estimates obtained when the device woke up as well as the forecast positions of the device during the sleep periods. Third, the device is awakened to send a positioning beacon at intervals over which a significant displacement is predicted to occur by the forecasting layer. Our results demonstrate that our indoor positioning system saves significant energy via adaptive sleep cycles whose duration is determined by the &#160;prediction of a significant displacement. This work establishes a foundation for indoor positioning that utilizes AI-based positioning and trajectory forecasting.&lt;/p&gt;&#13;
</style></abstract><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alper Saylam</style></author><author><style face="normal" font="default" size="100%">Nur Kelesoglu</style></author><author><style face="normal" font="default" size="100%">Rifat Cikmazel</style></author><author><style face="normal" font="default" size="100%">Mert Nakip</style></author><author><style face="normal" font="default" size="100%">Volkan Rodoplu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Dynamic Positioning Interval Based On Reciprocal Forecasting Error (DPI-RFE) Algorithm for Energy-Efficient Mobile IoT Indoor Positioning</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2021</style></year></dates><secondary-title><style face="normal" font="default" size="100%">International Conference on Computer, Information and Telecommunication Systems (CITS)</style></secondary-title><electronic-resource-num><style face="normal" font="default" size="100%">10.1109/CITS52676.2021.9618231</style></electronic-resource-num><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We develop an algorithm called “Dynamic Positioning Interval based on Reciprocal Forecasting Error (DPIRFE)” for energy-efficient mobile Internet of Things (IoT) Indoor Positioning (IP). In contrast with existing IP algorithms, DPIRFE forecasts the future trajectory of a mobile IoT device by using machine learning and dynamically adjusts the positioning interval based on the reciprocal instantaneous forecasting error, thereby dynamically trading off transmit energy consumption against forecasting error. We compare the performance of DPIRFE with respect to the total transmit energy consumption and the average forecasting error against Constant Positioning Interval (CPI) and Positioning Interval based on Displacement (PID) algorithms. Our results show that DPI-RFE significantly outperforms both of these benchmark algorithms with respect to transmit energy consumption while achieving a competitive average forecasting error performance. These results open the way to the design of machine learning based trajectory forecasting algorithms that can be utilized for energy-efficient positioning in next-generation wireless networks.&lt;/p&gt;&#13;
</style></abstract><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Istanbul, Turkey</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nur Kelesoglu</style></author><author><style face="normal" font="default" size="100%">Katarzyna Filus</style></author><author><style face="normal" font="default" size="100%">Joanna Domańska</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">HierAct: a Hierarchical Model for Human Activity Recognition in Game-Like Educational Applications</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style face="normal" font="default" size="100%">01/2024</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">2023 IEEE International Conference on Big Data</style></secondary-title><urls><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/document/10386919</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">10.1109/BigData59044.2023.10386919</style></electronic-resource-num><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Constantly evolving landscape of modern education makes the integration of technology crucial to enable more interactive and immersive learning experiences. One of the key problems in this domain is Human Activity Recognition (HAR), which uses standard smartphone sensors to understand and recognize user movements. While HAR exhibits high potential in this area, its persistent challenge is to differentiating between similar activities, whose occurrences can often lead to misclassification and reduced quality of applications. To tackle this problem, we employ specialized classifiers and use them in a hierarchical manner instead of using monolith multi-class models. We propose HierAct - a Hierarchical model that solves the multi-class human activity recognition problem. We also release a new HAR dataset - EduAct - (https://github.com/iitis/HierAct-Dataset) focused on activities that could be used to create game-like educational applications.Our results show that the performance of the proposed model is substantially better than other state-of-the-art machine learning models. Our work shows the potential of hierarchical classifiers in HAR applications for education, by offering educators and students a more accurate, reliable, and engaging interactive experience.&lt;/p&gt;&#13;
</style></abstract><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Sorrento, Italy </style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mert Nakip</style></author><author><style face="normal" font="default" size="100%">Nur Kelesoglu</style></author><author><style face="normal" font="default" size="100%">Cüneyt Güzeliş</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fire Detection and Risk Assessment via Support Vector Regression with Flattening-Samples Based Augmented Regularization</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%">10/2024</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">Applied Soft Computing</style></secondary-title><electronic-resource-num><style face="normal" font="default" size="100%">10.1016/j.asoc.2024.112023</style></electronic-resource-num><volume><style face="normal" font="default" size="100%">164</style></volume><section><style face="normal" font="default" size="100%">112023</style></section><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We propose a Hybrid Support Vector Regression (SVR) with Flattening-Samples Based Augmented Regularization (Hybrid FSR-SVR) architecture for multi-sensor fire detection and forest fire risk assessment. The Hybrid FSR-SVR is a lightweight architecture built upon the novel Flattening-Samples Based Augmented Regularization (FSR) approach and temporal trends of environmental variables. The FSR approach augments&#160;l2&#160;norm based smoothing term into an&#160;l1-l2&#160;combination, facilitating the integration of&#160;l1&#160;regularization into the SVR method, thereby enhancing generalization with minimal computational load. We evaluate the performance of Hybrid FSR-SVR using two distinct datasets covering indoor and forest fires, benchmarking against 15 machine learning models, including state-of-the-art techniques, such as Recurrent Trend Predictive Neural Network (rTPNN), Long-Short Term Memory (LSTM), Multi-Layer Perceptron (MLP), Gated Recurrent Unit (GRU), and Gradient Boosting. Our findings demonstrate that Hybrid FSR-SVR effectively assesses the risk of forest fire, enabling early preventive measures. Notably, it achieves a remarkable accuracy of 0.95 for forest fire detection and ranks third with 0.88 accuracy for indoor fire detection. Importantly, it exhibits computation times significantly lower – by 1 to 2 orders of magnitude – than the majority of compared techniques. The superior generalization ability of Hybrid FSR-SVR, facilitated by flattening-samples based augmented regularization, allows for high detection performance even with smaller training sets.&lt;/p&gt;&#13;
</style></abstract><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nur Kelesoglu</style></author><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">ZigBeeNet: Decrypted ZigBee IoT Network Traffic Dataset in Smart Home Environment</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%">11/2024</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">Applied Sciences</style></secondary-title><urls><style face="normal" font="default" size="100%">https://www.mdpi.com/2076-3417/14/23/10844</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">10.3390/app142310844</style></electronic-resource-num><volume><style face="normal" font="default" size="100%">14</style></volume><issue><style face="normal" font="default" size="100%">23</style></issue><section><style face="normal" font="default" size="100%">10844</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>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nur Kelesoglu</style></author><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author><author><style face="normal" font="default" size="100%">Piotr Biernacki</style></author><author><style face="normal" font="default" size="100%">Joanna Domańska</style></author><author><style face="normal" font="default" size="100%">Francisco Ferrão</style></author><author><style face="normal" font="default" size="100%">Shahid Mumtaz</style></author><author><style face="normal" font="default" size="100%">Fideline Tchuenbou-Magaia</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Artificial Intelligence for Electronic-based Systems in Safety Applications</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%">02/2025</style></date></pub-dates></dates><urls><style face="normal" font="default" size="100%">https://www.researchgate.net/publication/389204536_Artificial_Intelligence_for_Electronic-based_Systems_in_Safety_Applications</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">10.13140/RG.2.2.16865.42086</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%">Nur Kelesoglu</style></author><author><style face="normal" font="default" size="100%">Marzena Halama</style></author><author><style face="normal" font="default" size="100%">Anna Strzoda</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Enhancing LoRa-based Outdoor Localization Accuracy Using Machine Learning</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%">07/2025</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">IEEE Access</style></secondary-title><urls><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/document/11080010</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">10.1109/ACCESS.2025.3589032</style></electronic-resource-num><volume><style face="normal" font="default" size="100%">13</style></volume><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The Internet of Things is gaining significant relevance, driving increasing interest in location-&lt;br /&gt;&#13;
based services using wireless signals, particularly Low Power Wide Area Network (LPWAN) technology.&lt;br /&gt;&#13;
LoRa (Long Range), together with LoRaWAN, is a prominent LPWAN standard that provides long-range&lt;br /&gt;&#13;
connectivity and low energy consumption, making it viable for IoT-based positioning systems in smart&lt;br /&gt;&#13;
cities. For localization systems leveraging LoRa signals, Machine Learning (ML) approaches are being&lt;br /&gt;&#13;
increasingly explored, as ML-based solutions offer a powerful way to enhance the accuracy of positioning.&lt;br /&gt;&#13;
In this study, we propose various ML approaches for LoRa-based positioning in outdoor environments. We&lt;br /&gt;&#13;
evaluate six different ML models: k-NN, CNN, SVR, ANN, XG-Boost, and LightGBM-using an open-&lt;br /&gt;&#13;
source urban LoRaWAN dataset. We further propose a Hybrid Model that combines convolutional feature&lt;br /&gt;&#13;
extraction with gradient-boosted regression. This architecture integrates the strengths of deep learning and&lt;br /&gt;&#13;
tree-based models, aiming to capture both temporal signal patterns and structured input correlations for&lt;br /&gt;&#13;
improved localization accuracy. The models are trained offline and tested for performance in terms of&lt;br /&gt;&#13;
localization accuracy, mean square error, and computational efficiency. Additionally, we investigate the&lt;br /&gt;&#13;
impact of different Feature Vector (FV) subsets on localization performance by analyzing the significance of&lt;br /&gt;&#13;
LoRaWAN signal attributes. Our results highlight the effectiveness of ML models in enhancing localization&lt;br /&gt;&#13;
accuracy for LoRa-based outdoor positioning systems, demonstrating performance improvements ranging&lt;br /&gt;&#13;
from 10% to 73% compared to previous ML studies in outdoor localization.&lt;/p&gt;&#13;
</style></abstract><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nur Kelesoglu</style></author><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author><author><style face="normal" font="default" size="100%">Joanna Domańska</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">When LLMs Speak ZigBee: Exploring Low-Latency and Reasoning Models for Network Traffic Generation</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%">Scientific Reports</style></secondary-title><urls><style face="normal" font="default" size="100%">https://www.nature.com/articles/s41598-026-37246-y</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">10.1038/s41598-026-37246-y</style></electronic-resource-num><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The generation of realistic network traffic is a critical requirement for testing, simulation, and security evaluation in ZigBee-based IoT systems. In this study, we propose a novel framework that extracts sample ZigBee traffic packets and generates semantically meaningful and protocol-compliant synthetic traffic by using Large Language Models (LLMs) such as GPT-4.1 and GPT-5. Unlike traditional rule-based or statistical generators, our approach is data-driven and incorporates sample-based few-shot learning, prompt engineering, and a human-in-the-loop feedback mechanism. To evaluate the effectiveness of the proposed framework, we conduct two sets of experiments. The first focuses on generating unidirectional traffic to emulate typical device-to-hub communication, while the second extends this setup to bidirectional exchanges, capturing realistic request--response dynamics and interaction patterns. The realism of the generated traffic is assessed using a multi-dimensional evaluation framework that includes statistical similarity measures, such as Jensen--Shannon Divergence, as well as protocol compliance, semantic correctness, temporal consistency, and diversity metrics. In addition, we compare the performance of LLM-based generators with classical deep learning baselines, including recurrent neural networks (RNNs) and generative adversarial networks (GANs). We further analyze the computational cost and the impact of internal reasoning effort on traffic generation by systematically evaluating different GPT-5 reasoning configurations. Experimental results show that both GPT-4.1 and GPT-5 successfully learn the temporal and structural dependencies of ZigBee traffic and significantly outperform RNN and GAN baselines in terms of semantic correctness and long-duration generation. Across all experiments, GPT-4.1 consistently generates traffic that more closely resembles real ZigBee traffic while requiring substantially lower computational resources. These findings highlight that low-latency, non-reasoning LLMs can be particularly well suited for highly structured, protocol-constrained network traffic generation tasks, and demonstrate the potential of LLM-based approaches for realistic IoT traffic generation in research and security evaluation.&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>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nur Kelesoglu</style></author><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author><author><style face="normal" font="default" size="100%">Joanna Domańska</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">CPU Utilization Forecasting in Large-Scale Cloud Clusters Using Gradient Boosting</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%">7th Polish Conference on Artificial Intelligence (PP-RAI'2026)</style></secondary-title><publisher><style face="normal" font="default" size="100%">(This work was partly supported by the National Centre for Research and Development (NCBR) and co-funded by the European Union under the European Funds for ModernEconomy(FENG)Programme(SMARTKonsorcja),projectno.FENG.01.01-IP.01-A0GV/24-00,)</style></publisher><pub-location><style face="normal" font="default" size="100%">Kraków, Poland </style></pub-location></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author><author><style face="normal" font="default" size="100%">Nur Kelesoglu</style></author><author><style face="normal" font="default" size="100%">Joanna Domańska</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Reliability-Aware LLM Reasoning: Handling Uncertainty in Robot Perception</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%">10.08.2026</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">IEEE International Conference on Robot and Human Interactive Communication (IEEE RO-MAN 2026)</style></secondary-title><urls><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/document/11714851</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">10.1109/RO-MAN72286.2026.11714851</style></electronic-resource-num><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Robots operating in human environments must often make decisions based on perceptual information that&lt;br&gt;is uncertain, incomplete, or ambiguous. This paper proposes a reliability-aware reasoning framework that enables large&lt;br&gt;language models (LLMs) to account for perceptual uncertainty when selecting actions in human-robot interaction scenarios.&lt;br&gt;The environment is represented as a structured scene composed of detected objects enriched with confidence estimates, attribute reliability, and spatial uncertainty information. Using this representation, the LLM evaluates candidate objects through a reliability scoring mechanism that integrates multiple sources of perceptual evidence and supports uncertainty-aware decision making.The proposed approach is evaluated using perception episodes with controlled levels of uncertainty and compared with a baseline LLM-based matching strategy that ignores perceptual&lt;br&gt;reliability. Experimental results show that incorporating uncertainty-aware reasoning substantially improves decision&lt;br&gt;robustness under medium and high uncertainty conditions while reducing safety-critical behaviors caused by overconfident&lt;br&gt;autonomous decisions.&lt;/p&gt;</style></abstract><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Kitakyushu, Japan</style></pub-location></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nur Kelesoglu</style></author><author><style face="normal" font="default" size="100%">Joanna Domańska</style></author><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Risk-Aware Response Refinement for Safer LLM-Powered Socially Assistive Robots in Elderly Care</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%">10.08.2026</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">IEEE International Conference on Robot and Human Interactive Communication (IEEE RO-MAN 2026)</style></secondary-title><urls><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/document/11714788</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">10.1109/RO-MAN72286.2026.11714788</style></electronic-resource-num><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Safe and reliable human–robot interaction remains a challenge, especially for Large Language Model (LLM)-powered socially assistive robots in elderly care. This paper proposes a risk-aware response framework that explicitly models the safety implications of user queries. &#160;We introduce a Query Risk Assessment Module (QRAM), which computes a Query Risk Score (QRS) using structured semantic indicators to classify user inputs into different risk levels. Based on this, we develop two strategies: risk-aware response refinement and risk-aware response generation. To assess safety, we introduce the Risk-Aware Response Safety Score (RRSS), a metric that captures both the presence of risk-bearing language and the absence of necessary safety-critical guidance. Experimental results across multiple LLMs demonstrate that incorporating query-level risk awareness reduces response-level risk, with both strategies outperforming raw responses.&lt;/p&gt;</style></abstract><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Kitakyushu, Japan</style></pub-location></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%">Mert Nakip</style></author><author><style face="normal" font="default" size="100%">Nur Kelesoglu</style></author><author><style face="normal" font="default" size="100%">Taylan Mert</style></author><author><style face="normal" font="default" size="100%">Cüneyt Güzeliş</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Self-Supervised Online Learning Algorithm for rTPNN to Reduce False Alarms in Fire Detection</style></title></titles><keywords><keyword><style face="normal" font="default" size="100%">Fire detection</style></keyword><keyword><style face="normal" font="default" size="100%">Self-Supervised</style></keyword><keyword><style face="normal" font="default" size="100%">online learning</style></keyword><keyword><style face="normal" font="default" size="100%">human-in-the-loop</style></keyword><keyword><style face="normal" font="default" size="100%">multi-sensor</style></keyword></keywords><dates><year><style face="normal" font="default" size="100%">2026</style></year><pub-dates><date><style face="normal" font="default" size="100%">07/2026</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">Knowledge-Based Systems</style></secondary-title><keyword><style face="normal" font="default" size="100%"/></keyword><electronic-resource-num><style face="normal" font="default" size="100%">10.1016/j.knosys.2026.116533</style></electronic-resource-num><pages><style face="normal" font="default" size="100%">14</style></pages><volume><style face="normal" font="default" size="100%">350</style></volume><work-type><style face="normal" font="default" size="100%">Research article</style></work-type></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author><author><style face="normal" font="default" size="100%">Nur Kelesoglu</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%">Risk-Aware Semantic Grounding for Trustworthy LLM-Based Robot Planning</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%">European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD)</style></secondary-title><pub-location><style face="normal" font="default" size="100%">Naples, Italy</style></pub-location></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nur Kelesoglu</style></author><author><style face="normal" font="default" size="100%">Łukasz Sobczak</style></author><author><style face="normal" font="default" size="100%">Joanna Domańska</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">From Plots to Words: Model-Aware Multimodal Explanations as a Foundation for Accessible, Non-Visual Interaction</style></title></titles><keywords/><dates><year><style 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