1. Enhancing trust in automated 3D point cloud data interpretation through explainable counterfactualsAndreas Holzinger, Niko Lukač, Dzemail Rozajac, Emil Johnston, Veljka Kocic, Bernhard Hoerl, Christoph Gollob, Arne Nothdurft, Karl Stampfer, Stefan Schweng, Javier Del Ser, 2025, original scientific article Abstract: This paper introduces a novel framework for augmenting explainability in the interpretation of point cloud data by fusing expert knowledge with counterfactual reasoning. Given the complexity and voluminous nature of point cloud datasets, derived predominantly from LiDAR and 3D scanning technologies, achieving interpretability remains a significant challenge, particularly in smart cities, smart agriculture, and smart forestry. This research posits that integrating expert knowledge with counterfactual explanations – speculative scenarios illustrating how altering input data points could lead to different outcomes – can significantly reduce the opacity of deep learning models processing point cloud data. The proposed optimization-driven framework utilizes expert-informed ad-hoc perturbation techniques to generate meaningful counterfactual scenarios when employing state-of-the-art deep learning architectures. The optimization process minimizes a multi-criteria objective comprising counterfactual metrics such as similarity, validity, and sparsity, which are specifically tailored for point cloud datasets. These metrics provide a quantitative lens for evaluating the interpretability of the counterfactuals. Furthermore, the proposed framework allows for the definition of explicit interpretable counterfactual perturbations at its core, thereby involving the audience of the model in the counterfactual generation pipeline and ultimately, improving their overall trust in the process. Results demonstrate a notable improvement in both the interpretability of the model’s decisions and the actionable insights delivered to end-users. Additionally, the study explores the role of counterfactual reasoning, coupled with expert input, in enhancing trustworthiness and enabling human-in-the-loop decision-making processes. By bridging the gap between complex data interpretations and user comprehension, this research advances the field of explainable AI, contributing to the development of transparent, accountable, and human-centered artificial intelligence systems.
Keywords: explainable AI, point cloud data, counterfactual reasoning, information fusion, interpretability, human-centered AI
Published in DKUM: 06.03.2025; Views: 0; Downloads: 2
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2. Improved relation extraction through key phrase identification using community detection on dependency treesShuang Liu, Xunqin Chen, Jiana Meng, Niko Lukač, 2025, original scientific article Abstract: A method for extracting relations from sentences by utilizing their dependency trees to identify key phrases is presented in this paper. Dependency trees are commonly used in natural language processing to represent the grammatical structure of a sentence, and this approach builds upon this representation to extract meaningful relations between phrases. Identifying key phrases is crucial in relation extraction as they often indicate the entities and actions involved in a relation. The method uses community detection algorithms on the dependency tree to identify groups of related words that form key phrases, such as subject-verb-object structures. The experiments on the Semeval-2010 task8 dataset and the TACRED dataset demonstrate that the proposed method outperforms existing baseline methods.
Keywords: community detection algorithms, dependency trees, relation extraction
Published in DKUM: 17.01.2025; Views: 0; Downloads: 5
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3. Energy flexibility in aluminium smelting : a long-term feasibility study based on the prospects of electricity load and photovoltaic productionMarko Bizjak, Niko Uremović, Domen Mongus, Primož Sukič, Gorazd Štumberger, Haris Salihagić Hrenko, Dragan Mikša, Stanislav Kores, Niko Lukač, 2024, original scientific article Keywords: energy flexibility, aluminium smelting, renewable energy, virtual battery, solar production
Published in DKUM: 17.12.2024; Views: 0; Downloads: 9
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4. Novel GPU-accelerated high-resolution solar potential estimation in urban areas by using a modified diffuse irradiance modelNiko Lukač, Domen Mongus, Borut Žalik, Gorazd Štumberger, Marko Bizjak, 2024, original scientific article Abstract: In the past years various methods have been developed to estimate high-resolution solar potential in urban areas, by simulating solar irradiance over surface models that originate from remote sensing data. In general, this requires discretisation of solar irradiance models that estimate direct, reflective, and diffuse irradiances. The latter is most accurately estimated by an anisotropic model, where the hemispherical sky dome from arbitrary surface’s viewpoint consists of the horizon, the circumsolar and sky regions. Such model can be modified to incorporate the effects of shadowing from obstruction with a view factor for each sky region. However, state-of-the-art using such models for estimating solar potential in urban areas, only considers the sky view factor, and not circumsolar view factor, due to high computational load. In this paper, a novel parallelisation of solar potential estimation is proposed by using General Purpose computing on Graphics Processing Units (GPGPU). Modified anisotropic Perez model is used by considering diffuse shadowing with all three sky view factors. Moreover, we provide validation based on sensitivity analysis of the method’s accuracy with independent meteorological measurements, by changing circumsolar sky region’s half-angle and resolution of the hemispherical sky dome. Finally, the presented method using GPPGU was compared to multithreaded Central Processing Unit (CPU) approach, where on average a 70x computational speedup was achieved. Finally, the proposed method was applied over a urban area, obtained from Light Detection And Ranging (LiDAR) data, where the computation of solar potential was performed in a reasonable time.
Keywords: solar energy, solar potential, anisotropic diffuse irradiance, LiDAR, GPGPU
Published in DKUM: 17.12.2024; Views: 0; Downloads: 4
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5. Proceedings of the 10th Student Computing Research Symposium : (SCORES'24)2024, proceedings Abstract: The 2024 Student Computing Research Symposium (SCORES 2024), organized by the Faculty of Electrical Engineering and Computer Science at the University of Maribor (UM FERI) in collabora-tion with the University of Ljubljana and the University of Primorska, showcases innovative student research in computer science. This year’s symposium highlights advancements in fields such as ar-tificial intelligence, data science, machine learning algorithms, computational problem-solving, and healthcare data analysis. The primary goal of SCORES 2024 is to provide a platform for students to present their research, fostering early engagement in academic inquiry. Beyond research presen-tations, the symposium seeks to create an environment where students from different institutions can meet, exchange ideas, and build lasting connections. It aims to cultivate friendships and future research collaborations among emerging scholars. Additionally, the conference offers an opportu-nity for students to interact with senior researchers from institutions beyond their own, promoting mentorship and broader academic networking.
Keywords: evaluacija, optimizacija, strojno učenje, podatki, zborniki
Published in DKUM: 26.11.2024; Views: 0; Downloads: 76
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6. Spletni katalog za ocenjevanje in priporočanje knjigVanesa Tot, 2024, undergraduate thesis Abstract: V diplomskem delu predstavimo razvoj spletnega kataloga za ocenjevanje in priporočanje knjig, ki uporablja kolaborativno filtriranje za personalizirano uporabniško izkušnjo. Spoznamo sorodne rešitve na trgu, ki uporabljajo priporočilne sisteme. Obravnavamo glavne pristope priporočilnih sistemov, kot sta kolaborativno in vsebinsko filtriranje in se pri razvoju aplikacije osredotočimo na kolaborativno filtriranje. Na koncu izvedemo analizo in testiramo delovanje izbranega algoritma na množici uporabnikov, kjer preverimo, kako učinkovito algoritem priporoča knjige na podlagi sosednjih uporabnikov.
Keywords: priporočilni sistem, spletna aplikacija, kolaborativno filtriranje, vsebinsko filtriranje
Published in DKUM: 22.10.2024; Views: 0; Downloads: 59
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7. Razvoj programske rešitve za napredni prikaz in obdelavo tabelaričnih podatkov : diplomsko deloTjaša Repič, 2024, undergraduate thesis Abstract: Diplomsko delo se osredotoča na opis in analizo funkcionalnosti vtičnika Spreadsheet implementiranega v programskem jeziku C++ v okviru programske opreme DewesoftX, ki je eno izmed bolj razširjenih orodij v meritveni industriji. V delu podrobno predstavimo implementacije ključnih funkcij vtičnika, vključno z upravljanjem in prilagajanjem tabel, kot so razveljavljanje in ponovno uveljavljanje dejanj ter napredne možnosti za oblikovanje. Poudarek je na izboljšanju uporabniške izkušnje in integraciji s platformo. Diplomsko delo je zaključeno z analizo časa izvajanja in pomnilniške zahtevnosti različnih funkcionalnosti, preizkušenih na različnih velikostih selekcij, ki omogoča natančno oceno obremenitev, ki jih te funkcije predstavljajo za sistem.
Keywords: preglednica, tabelarični podatki, uporabniška izkušnja, vizualizacija podatkov
Published in DKUM: 07.10.2024; Views: 0; Downloads: 36
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8. Abstracts of the 10th Student Computing Research Symposium (SCORES’24)2024, proceedings Abstract: The 2024 Student Computing Research Symposium (SCORES 2024), organized by the Faculty of Electrical Engineering and Computer Science at the University of Maribor (UM FERI) in collaboration with the University of Ljubljana and the University of Primorska, showcases innovative student research in computer science. This year’s symposium highlights advancements in fields such as artificial intelligence, data science, machine learning algorithms, computational problem-solving, and healthcare data analysis. The primary goal of SCORES 2024 is to provide a platform for students to present their research, fostering early engagement in academic inquiry. Beyond research presentations, the symposium seeks to create an environment where students from different institutions can meet, exchange ideas, and build lasting connections. It aims to cultivate friendships and future research collaborations among emerging scholars. Additionally, the conference offers an opportunity for students to interact with senior researchers from institutions beyond their own, promoting mentorship and broader academic networking.
Keywords: student conference, computer and information science, artificial intelligence, data science, data mining
Published in DKUM: 18.09.2024; Views: 0; Downloads: 30
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10. Geometric Shape Characterisation Based on a Multi-Sweeping ParadigmBorut Žalik, Damjan Strnad, David Podgorelec, Ivana Kolingerová, Andrej Nerat, Niko Lukač, Štefan Kohek, Luka Lukač, 2023, original scientific article Keywords: computer science, image analysis, computational geometry, local reflection symmetry
Published in DKUM: 24.05.2024; Views: 300; Downloads: 15
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