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18th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Widzisz, Karolina Kania, Mateusz Zyla, Joanna Polański, Andrzej Department of Computer Graphics Vision and Digital Systems Silesian University of Technology Akademicka 16 Gliwice Poland Department of Applied Informatics Silesian University of Technology Akademicka 16 Gliwice Poland Department of Data Science and Engineering Silesian University of Technology Akademicka 16 Gliwice Poland

The primary objective of this study was to test the hypothesis that the binary information on the presence or absence of gene expression can sufficiently capture the inherent heterogeneity within single-cell RNA se quencing (scRNA-seq) data. This hypothesis posits that even without detailed expression levels, valuable insights about cellular diversity can be obtained. Utilizing this method can be particularly advantageous when analyzing large datasets, a common scenario in the field of scRNA-seq. In this paper, we evaluate clustering performance and cluster separability of a variety of model-based algorithms and distance-based methods to an alyze both expression level data and threshold-encoded binarized data. We examined the performance of the Bernoulli-mixture model and Gaussian-mixture model. These were compared against traditional clustering techniques such as hierarchical clustering, K-means, and the Louvain algorithm on a range of scRNA-seq datasets. Our findings reveal that mixture models exhibit a lower dependence on the specific dataset compared to distance-based methods. Mixture models, particularly, demonstrate greater efficacy in accurately estimat ing the number of clusters present within the data. Among analyzed algorithms, the Bernoulli-mixture model stands out, outperforming distance-based approaches in several key aspects. Binary data, presence/absence of gene expression, seem to be indeed adequate to capture the heterogeneity of scRNA-seq data when clus tering with methods specifically designed for binary datasets. The implications of this finding are significant, as it opens up new possibilities for simplifying data analysis in scRNA-seq studies without compromising the accuracy of the results. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Data accuracy

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A Wideband 8×8 Slot Array Antenna Using Gap Waveguide MLW Coaxial Line Technology for mmWave Applications

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IEEE Antennas and Wireless Propagation Letters 2025年

作者： Farahbakhsh, Ali Zarifi, Davood Vosoogh, Abbas Bencivenni, Carlo Mrozowski, Michal Gdansk University of Technology Department of Microwave and Antenna Engineering Faculty of Electronics Telecommunications and Informatics Gdansk80-233 Poland Graduate University of Advanced Technology Department of Electrical and computer engineering Kerman Iran Gapwaves Ab Gothenburg Sweden

The paper presents a wideband 8×8-element slot antenna array operating in the E-band. The proposed design employs a low-loss coaxial waveguide transmission line filled with air implemented using multilayer waveguide (MLW) technology by vertically stacking thin, unconnected metal plates to meet the typical radiation pattern and bandwidth requirements of a slot antenna array. To validate the concept, the simulation and measurement results of an 8×8 slot antenna array are presented. The proposed antenna array exhibits |S11| © 2025 IEEE.

关键词： Millimeter waves

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Modelling parking occupancy using algorithm of 3D visibility network

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IEEE Access 2025年

作者： Babic, Matej Ster, Branko Hluchy, Ladislav Baiamonte, Giuliana Cali, Michele Faculty of Information Studies Ljubljanska cesta 31a Novo MestoSI-8000 Slovenia University of Ljubljana Faculty of Computer and Informatics LjubljanaSI-1000 Slovenia Institute of Informatics Slovak Academy of Sciences Dúbravská cesta 9 45 Bratislava845 07 Slovakia University of Catania Electric Electronics and Computer Engineering Department Catania95125 Italy

The modernization of the information communication infrastructure of the regional data transmission network has advanced in order to increase the maximum transmission speed of existing transport routes, ensuring the quality of the service and its reliability. In the present research work, a new approach for the 3D visibility network algorithm has been developed, showing how graph theory applied to wireless sensor networks (WSNs) enables technological development for new solutions in areas such as public infrastructure. The possibility of determining in such networks whether an area of interest is sufficiently covered by a given set of sensors by means of the Voronoi diagram is discussed. The parking dynamics and parking system were modeled with cellular neural networks (CNNs) based on weather conditions, and magnetic parking sensors were replaced with pillar sensors. The proposed method has proven its effectiveness in determining the position of the minimum sensors covering the area of interest, in order to find a solution in the occupation of parking spaces in the presence of different weather conditions. The proposed approach and experimental results offer potential applications in various fields such as lighting and rendering, motion planning, pattern recognition, computer graphics and computational geometry, in order to conduct studies on problems and perspectives of pillar sensor technology while reducing costs compared to magnetic ones. © 2013 IEEE.

关键词： Rendering (computer graphics)

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Design of an Ultra-thin Metamaterial Absorber with Machine Learning-Assisted Absorption Prediction

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Hossain, Md. Jakir Mubashsara, Nafisa Khan, Riasat Matin, Mohammad Abdul Sarigiannidis, Panagiotis Goudos, Sotirios K. North South University Department of Electrical and Computer Engineering Dhaka Bangladesh University of Western Macedonia Department of Informatics and Telecommunications Engineering Kozani Greece Aristotle University of Thessaloniki ELEDIA@AUTH School of Physics Thessaloniki541 24 Greece

An absorber is a block of material employed to absorb a portion of the energy of an incident particle. Research on absorbers is rapidly expanding as a result of their prospective applications in several fields, including wireless communications and military purposes. However, conventional absorbers have dense size and design complexity that make them unsuitable for a multitude of practical applications. Metamaterial-based absorbers (MMAs) have garnered significant attention with respect to their low density, narrow layers, and high absorption capacity. In this paper, we introduce a new ultra-thin trilayered Ni–MgF2–Ni metamaterial absorber structure, which is designed using machine learning. It has a balanced and symmetrical layout with a metal-dielectric-metal arrangement and a high-temperature endurance. Its resonator design is defined by mathematical deduction from the Conference matrix. The performance of MMA has been evaluated at wavelengths ranging from 250 nm to 1150 nm, as well as in various modes-TM, TE, and TEM. The results demonstrate that the proposed MMA has exceptional absorption capacities, with an overall absorption of 96.18% within the test range. It offers excellent mean absorption percentages of 99% in the optical region (350 to 750 nm), 95.23% in the ultraviolet (UV) range, and 94.38% in the near-infrared (NIR) range. Furthermore, MMA exhibits an absorption efficiency of 99.99% at a particular wavelength of 424.93 nm. In this work, various machine learning techniques have been applied to predict the absorption of the proposed MMA design. The XGBoost meta-learner-based Stacking ensemble machine learning technique achieves the highest prediction efficiency. Finally, the explainable AI technique with the LIME framework has been used to analyze the predictions of applied machine learning models. © 2020 IEEE.

关键词： Magnesium compounds

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Judging Phishing Under Uncertainty: How Do Users Handle Inaccurate Automated Advice? 25

Judging Phishing Under Uncertainty: How Do Users Handle Inac...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

作者： Tarini Saka Kalliopi Vakali Adam D G Jenkins Nadin Kokciyan Kami Vaniea School of Informatics University of Edinburgh Edinburgh United Kingdom University of Edinburgh Edinburgh United Kingdom School of Informatics King's College London London United Kingdom Electrical and Computer Engineering University of Waterloo Waterloo Ontario Canada

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Rehearse With User: Personalized Opinion Summarization via Role-Playing based on Large Language Models

arXiv

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arXiv 2025年

作者： Zhang, Yanyue He, Yulan Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Department of Informatics King’s College London United Kingdom The Alan Turing Institute

Personalized opinion summarization is crucial as it considers individual user interests while generating product summaries. Recent studies show that although large language models demonstrate powerful text summarization and evaluation capabilities without the need for training data, they face difficulties in personalized tasks involving long texts. To address this, Rehearsal, a personalized opinion summarization framework via LLMs-based role-playing is proposed. Having the model act as the user, the model can better understand the user’s personalized needs. Additionally, a role-playing supervisor and practice process are introduced to improve the role-playing ability of the LLMs, leading to a better expression of user needs. Furthermore, through suggestions from virtual users, the summary generation is intervened, ensuring that the generated summary includes information of interest to the user, thus achieving personalized summary generation. Experiment results demonstrate that our method can effectively improve the level of personalization in large model-generated summaries. Copyright © 2025, The Authors. All rights reserved.

关键词： Economic and social effects

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A Survey on the Communication of UAVs with Charging and Control Stations

Recent Advances in Computer Science and Communications

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Recent Advances in computer Science and Communications 2025年

作者： Chalkiadaki, Aikaterini Milidonis, Athanasios Mourgelas, Christos Voyiatzis, Ioannis Department of Informatics and Computer Engineering University of West Attica Ag. Spyridonos Str.Athens Egaleo 12243 Greece

Unmanned Aerial Vehicles (UAVs) have a history of over a century of deployment, but in recent decades, they have progressed at a staggering rate. Nowadays, UAVs are used by a large number of civil and military applications. The communication functionality of a UAV with external systems for control and charging is strongly connected with evolving technologies and services. This leads to an increased number of alternatives when designing UAV communications. This review presents the information needed for choosing an efficient communication system between UAVs and two important elements, the Ground Control Station (GCS) and the Charging Station (CS). GCS is responsible for monitoring and controlling the UAV’s units, while CS is used for the formal charging of the UAV. This study aimed at collecting, classifying, and evaluating all of the necessary information in order to obtain the final decision about the kind of communication that is most efficient for a target UAV application. The features of the telemetry open-source protocols are presented for the UAV-GCS communication and evaluated according to the needs of the most significant application domains. Communication between UAVs and CSs is classified depending on the existence of an intermediate server and analyzed considering telemetry protocols and application domains. Communication algorithms are evaluated in terms of time and energy efficiency. Lastly, for the most significant application domains, the most suitable algorithms are matched. © 2024 Bentham Science Publishers.

关键词： application domains charging stations communication control stations telemetry protocols UAVs

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Privacy-Preserving Utilization of Distribution System Flexibility for Enhanced TSO-DSO Interoperability: A Novel Machine Learning-Based Optimal Power Flow Approach

arXiv

引用

arXiv 2025年

作者： Dindar, Burak Saner, Can Berk Çakmak, Hüseyin K. Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe76131 Germany Department of Electrical and Computer Engineering National University of Singapore Singapore117581 Singapore

Due to the transformation of the power system, the effective use of flexibility from the distribution system (DS) is becoming crucial for efficient network management. Leveraging this flexibility requires interoperability among stakeholders, including Transmission System Operators (TSOs) and Distribution System Operators (DSOs). However, data privacy concerns among stakeholders present significant challenges for utilizing this flexibility effectively. To address these challenges, we propose a machine learning (ML)-based method in which the technical constraints of the DSs are represented by ML models trained exclusively on non-sensitive data. Using these models, the TSO can solve the optimal power flow (OPF) problem and directly determine the dispatch of flexibility-providing units (FPUs)—in our case, distributed generators (DGs)-in a single round of communication. To achieve this, we introduce a novel neural network (NN) architecture specifically designed to efficiently represent the feasible region of the DSs, ensuring computational effectiveness. Furthermore, we incorporate various PQ charts rather than idealized ones, demonstrating that the proposed method is adaptable to a wide range of FPU characteristics. To assess the effectiveness of the proposed method, we benchmark it against the standard AC-OPF on multiple DSs with meshed connections and multiple points of common coupling (PCCs) with varying voltage magnitudes. The numerical results indicate that the proposed method achieves performant results while prioritizing data privacy. Additionally, since this method directly determines the dispatch of FPUs, it eliminates the need for an additional disaggregation step. By representing the DSs technical constraints through ML models trained exclusively on nonsensitive data, the transfer of sensitive information between stakeholders is prevented. Consequently, even if reverse engineering is applied to these ML models, no sensitive data can be extracted. This allows

关键词： Reverse engineering

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Enhancing Diabetes Prediction Performance Using Feature Selection Based on Grey Wolf Optimizer with Autophagy Mechanism

SSRN

引用

SSRN 2025年

作者： Sirmayanti Prastyo, Pulung Hendro Mahyati Department of Electrical Engineering Politeknik Negeri Ujung Pandang Makassar Indonesia Department of Informatics and Computer Engineering Politeknik Negeri Ujung Pandang Makassar Indonesia Department of Chemical Engineering Politeknik Negeri Ujung Pandang Makassar Indonesia

Diabetes mellitus, often called a silent killer, is a chronic condition characterized by insufficient insulin production and elevated blood sugar levels, leading to complications in vital organs such as the nerves, eyes, and kidneys. Machine learning is a powerful tool for predicting diabetes;however, noisy features can negatively impact its accuracy, making an effective feature selection essential. This study proposes an improved feature selection approach for diabetes prediction, leveraging the Grey Wolf Optimizer with an integrated Autophagy Mechanism (GWO-AM) on the Pima Indian Diabetes Dataset. The autophagy mechanism, inspired by cellular self-degradation and recycling, is incorporated into GWO to enhance exploration and exploitation. Comprehensive experiments show that GWO-AM significantly improves prediction accuracy and reduces the number of selected features. The GWO-AM obtains a notable accuracy of 90.91% and outperforms existing methods, highlighting its potential to improve computational efficiency and model interpretability in medical data analysis. © 2025, The Authors. All rights reserved.

关键词： Prediction models

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Real-Time Manipulation Action Recognition with a Factorized Graph Sequence Encoder

arXiv

引用

arXiv 2025年

作者： Erdogan, Enes Aksoy, Eren Erdal Sariel, Sanem Artificial Intelligence and Robotics Lab Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey School of Information Technology Halmstad University Halmstad Sweden

Recognition of human manipulation actions in real-time is essential for safe and effective human-robot interaction and collaboration. The challenge lies in developing a model that is both lightweight enough for real-time execution and capable of generalization. While some existing methods in the literature can run in real-time, they struggle with temporal scalability, i.e., they fail to adapt to long-duration manipulations effectively. To address this, leveraging the generalizable scene graph representations, we propose a new Factorized Graph Sequence Encoder network that not only runs in real-time but also scales effectively in the temporal dimension, thanks to its factorized encoder architecture. Additionally, we introduce Hand Pooling operation, a simple pooling operation for more focused extraction of the graph-level embeddings. Our model outperforms the previous state-of-the-art real-time approach, achieving a 14.3% and 5.6% improvement in F1-macro score on the KIT Bimanual Action (Bimacs) Dataset and Collaborative Action (CoAx) Dataset, respectively. Moreover, we conduct an extensive ablation study to validate our network design choices. Finally, we compare our model with its architecturally similar RGB-based model on the Bimacs dataset and show the limitations of this model in contrast to ours on such an object-centric manipulation dataset. Copyright © 2025, The Authors. All rights reserved.

关键词： Human robot interaction

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