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Yang, Yufeng (author)Event-based cameras promise new opportunities for smart vision systems deployed at the edge. Contrary to their conventional frame-based counterparts, event-based cameras generate temporal light intensity changes as events on a per-pixel basis, enabling ultra-low latency with microsecond-scale temporal resolution, low power consumption at...master thesis 2023
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VASILEIOU, ANTONIOS (author)Graph data is widely used in various applications, driving the rapid development of graph-based machine learning methods. However, traditional algorithms tailored for graphs have constraints in capturing intricate node relationships and higher-order patterns. Recent insights from prior research have shed light on comparing different graph neural...master thesis 2023
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van Linn, Joseph (author)Snow is a natural hazard to human life and infrastructure. This motivates current research efforts to understand the granular material. The material point method models snow as a continuum. Application length scales range from the microstructural level to full scale avalanches. This conventional numerical method relies on solely spatially local...master thesis 2023
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Timp, Lennard (author)Electrical load forecasting, namely short-term load forecasting, is essential to power grids’ safe and efficient operations. The need for accurate short-term load forecasting becomes increasingly pressing with increased renewable energy sources, which are stochastic in their power supply. Most forecasting models are focused on the temporal...bachelor thesis 2023
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Martens, Bruno (author)Critical to the safe application of autonomous vehicles is the ability to accurately predict the future motion of agents surrounding the vehicle. This is especially important - and challenging - in urban traffic, where vehicles share the road with Vulnerable Road Users (VRUs) such as pedestrians and cyclists. However, the majority of the...master thesis 2023
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Cong, Shijie (author)Autonomous robots have been widely applied to search and rescue missions for information gathering about target locations. This process needs to be continuously replanned based on new observations in the environment. For dynamic targets, the robot needs to not only discover them but also keep tracking their positions. Previous works focus on...master thesis 2023
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Kaniewski, Tadeusz (author)The computational cost of high-fidelity engineering simulations, for example CFD, is prohibitive if the application requires frequent design iterations or even fully fledged optimization. A popular way to reduce the computational cost and enable fast iteration cycles is to use surrogate models that are trained to predict simulation results from...master thesis 2023
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Croft, Maxime (author)This paper presents a novel approach to regional forecasting of SARS-Cov-2 infections one week ahead, which involves developing a municipality level COVID-19 dataset of the Netherlands and using a spatio-temporal graph neural network (GNN) to predict the number of infections. The developed model captures the spread of infectious diseases within...master thesis 2023
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Gao, Zhan (author), Isufi, E. (author)Stochastic graph neural networks (SGNNs) are information processing architectures that learn representations from data over random graphs. SGNNs are trained with respect to the expected performance, which comes with no guarantee about deviations of particular output realizations around the optimal expectation. To overcome this issue, we propose...journal article 2023
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Zhang, Rongkai (author), Zhang, Cong (author), Cao, Zhiguang (author), Song, Wen (author), Tan, Puay Siew (author), Zhang, Jie (author), Wen, Bihan (author), Dauwels, J.H.G. (author)We propose a manager-worker framework (the implementation of our model is publically available at: https://github.com/zcaicaros/manager-worker-mtsptwr) based on deep reinforcement learning to tackle a hard yet nontrivial variant of Travelling Salesman Problem (TSP), i.e. multiple-vehicle TSP with time window and rejections (mTSPTWR), where...journal article 2023
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Yi, Yangtian (author), Lu, Chao (author), Wang, Boyang (author), Cheng, Long (author), Li, Z. (author), Gong, Jianwei (author)Accurate recognition of driver behaviours is the basis for a reliable driver assistance system. This paper proposes a novel fusion framework for driver behaviour recognition that utilises the traffic scene and driver gaze information. The proposed framework is based on the graph neural network (GNN) and contains three modules, namely, the...journal article 2023
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Rahmani, S. (author), Baghbani, Asiye (author), Bouguila, Nizar (author), Patterson, Zachary (author)Graph neural networks (GNNs) have been extensively used in a wide variety of domains in recent years. Owing to their power in analyzing graph-structured data, they have become broadly popular in intelligent transportation systems (ITS) applications as well. Despite their widespread applications in different transportation domains, there is no...journal article 2023
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Agiollo, A. (author), Bardhi, Enkeleda (author), Conti, M. (author), Lazzeretti, Riccardo (author), Losiouk, Eleonora (author), Omicini, Andrea (author)In the context of Information-Centric Networking, Interest Flooding Attacks (IFAs) represent a new and dangerous sort of distributed denial of service. Since existing proposals targeting IFAs mainly focus on local information, in this paper we propose GNN4IFA as the first mechanism exploiting complex non-local knowledge for IFA detection by...conference paper 2023
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Chen, Yi Hsien (author), Lin, Si Chen (author), Huang, S. (author), Lei, Chin Laung (author), Huang, Chun Ying (author)Malicious binaries have caused data and monetary loss to people, and these binaries keep evolving rapidly nowadays. With tons of new unknown attack binaries, one essential daily task for security analysts and researchers is to analyze and effectively identify malicious parts and report the critical behaviors within the binaries. While manual...journal article 2023
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Xu, J. (author), Abad, Gorka (author), Picek, S. (author)Backdoor attacks have been demonstrated as a security threat for machine learning models. Traditional backdoor attacks intend to inject backdoor functionality into the model such that the backdoored model will perform abnormally on inputs with predefined backdoor triggers and still retain state-of-the-art performance on the clean inputs. While...conference paper 2023
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Presekal, A. (author), Stefanov, Alexandru (author), Subramaniam Rajkumar, Vetrivel (author), Palensky, P. (author)Electrical power grids are vulnerable to cyber attacks, as seen in Ukraine in 2015 and 2016. However, existing attack detection methods are limited. Most of them are based on power system measurement anomalies that occur when an attack is successfully executed at the later stages of the cyber kill chain. In contrast, the attacks on the Ukrainian...journal article 2023
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Liu, Y. (author), Xie, H. (author), Presekal, A. (author), Stefanov, Alexandru (author), Palensky, P. (author)Synthetic networks aim at generating realistic projections of real-world networks while concealing the actual system information. This paper proposes a scalable and effective approach based on graph neural networks (GNN) to generate synthetic topologies of Cyber-Physical power Systems (CPS) with realistic network feature distribution. In order...journal article 2023
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Bai, N. (author), Nourian, Pirouz (author), Luo, Renqian (author), Cheng, Tao (author), Pereira Roders, A. (author)Mapping cultural significance of heritage properties in urban environment from the perspective of the public has become an increasingly relevant process, as highlighted by the 2011 UNESCO Recommendation on the Historic Urban Landscape (HUL). With the ubiquitous use of social media and the prosperous developments in machine and deep learning,...journal article 2023
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Olatunji, Iyiola E (author), Rathee, Mandeep (author), Funke, Thorben (author), Khosla, M. (author)Privacy and interpretability are two important ingredients for achieving trustworthy machine learning. We study the interplay of these two aspects in graph machine learning through graph reconstruction attacks. The goal of the adversary here is to reconstruct the graph structure of the training data given access to model explanations. Based on...conference paper 2023
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Niessen, Lex (author)In the past decade, the application of Neural Networks (NNs) has received increasing interest due to the growth in computing power. In the field of computational mechanics, this has led to numerous publications presenting surrogate models to assist or replace conventional simulation methods. A subset of these networks, referred to as Graph...master thesis 2022