Searched for: subject:"graph"
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de Bruijn, Vasco (author)
In cyber security, side-channel attacks (SCA) are of interest because they target the vulnerabilities in implementation rather than inherent vulnerabilities in the algorithm. Profiled SCA is especially interesting as it assumes that the adversary has unlimited access to a clone device that can generate sufficient traces to create a profile of...
master thesis 2021
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Coutino, Mario (author)
To the surprise of most of us, complexity in nature spawns from simplicity. No matter how simple a basic unit is, when many of them work together, the interactions among these units lead to complexity. This complexity is present in the spreading of diseases, where slightly different policies, or conditions,might lead to very different results;...
doctoral thesis 2021
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Bosland, Liam (author)
A common problem in robotics is the simultaneous localization and mapping (SLAM) problem. Here, a robot needs to create a map of its surroundings while simultaneously localizing itself in this map. An unknown environment is assumed. Traditionally, it has been approached through filtering solutions. This paradigm has shifted to pose graph...
master thesis 2021
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Zwaan, Aron (author)
Static Analysis is of indispensable value for the robustness of software systems and the efficiency of developers. Moreover, many modern-day software systems are composed of interacting subsystems written in different programming languages. However, in most cases no static validation of these interactions is applied. In this thesis, we identify...
master thesis 2021
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Isufi, E. (author), Pocchiari, Matteo (author), Hanjalic, A. (author)
Graph convolutions, in both their linear and neural network forms, have reached state-of-the-art accuracy on recommender system (RecSys) benchmarks. However, recommendation accuracy is tied with diversity in a delicate trade-off and the potential of graph convolutions to improve the latter is unexplored. Here, we develop a model that learns...
journal article 2021
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Meijer, D.H. (author), Post, Johan (author), van der Hoek, J.P. (author), Korving, Hans (author), Langeveld, J.G. (author), Clemens, F.H.L.R. (author)
Drinking water distribution networks (WDNs) are a crucial infrastructure for life in cities. Deterioration of this ageing, and partly hidden from view, infrastructure can result in losses due to leakage and an increased contamination risk. To counteract this, maintenance strategies are required to maintain the service level. Information on...
journal article 2021
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Xu, J. (author), Yang, Gongliu (author), Sun, Yiding (author), Picek, S. (author)
The current navigation systems used in many autonomous mobile robotic applications, like unmanned vehicles, are always equipped with various sensors to get accurate navigation results. The key point is to fuse the information from different sensors efficiently. However, different sensors provide asynchronous measurements, some of which even...
journal article 2021
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Budd, J.M. (author), van Gennip, Y. (author), Latz, Jonas (author)
This paper introduces a semi-discrete implicit Euler (SDIE) scheme for the Allen-Cahn equation (ACE) with fidelity forcing on graphs. The continuous-in-time version of this differential equation was pioneered by Bertozzi and Flenner in 2012 as a method for graph classification problems, such as semi-supervised learning and image segmentation....
journal article 2021
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Janssen, R. (author)
journal article 2021
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Morency, M.W. (author), Leus, G.J.T. (author)
Graph signal processing is an emerging field which aims to model processes that exist on the nodes of a network and are explained through diffusion over this structure. Graph signal processing works have heretofore assumed knowledge of the graph shift operator. Our approach is to investigate the question of graph filtering on a graph about...
journal article 2021
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Budd, J.M. (author), van Gennip, Y. (author)
An emerging technique in image segmentation, semi-supervised learning and general classification problems concerns the use of phase-separating flows defined on finite graphs. This technique was pioneered in Bertozzi and Flenner (2012, Multiscale Modeling and Simulation 10(3), 1090-1118), which used the Allen-Cahn flow on a graph, and was then...
journal article 2021
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Cucuringu, Mihai (author), Pizzoferrato, Andrea (author), van Gennip, Y. (author)
We introduce a principled method for the signed clustering problem, where the goal is to partition a weighted undirected graph whose edge weights take both positive and negative values, such that edges within the same cluster are mostly positive, while edges spanning across clusters are mostly negative. Our method relies on a graph-based...
journal article 2021
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van Ardenne, Xavier (author)
Business valuation is a set of procedures used by financial market participants to determine the price they are willing to pay or receive for businesses. Valuations play a crucial role in financial reporting, capital budgeting, and investment analysis. Current approaches to business valuation rely on professional expertise, causing the valuation...
master thesis 2020
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Hauth, Matthias (author)
The determination of geotechnical parameters from in-situ tests heavily relies on the use of empirical correlations relating field measurements to soil properties. A large number of these correlations can be found across the literature, each of them yielding a different outcome for the parameter value. This results in a high variability of the...
master thesis 2020
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van Berkel, Thijs (author)
A watertaxi can reduce the travel time through the city of Rotterdam in comparison with public transport. To make estimations on the average reduced travel time within Rotterdam, a generic method is developed. This method is capable of finding locations for watertaxi jetties. To achieve this, the watertaxi, public transport, and the walking to...
master thesis 2020
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Pietrak, Jakub (author)
Graph Neural Networks are a unique type of Deep Learning models that have a capability to exploit an explicitly stated structure of data representation. By design they carry a strong relational inductive bias, which is a set of assumptions that makes the algorithm prioritize some solutions over another, independent of observed data. This makes...
master thesis 2020
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Pocchiari, M. (author)
Recommender Systems assist the user by suggesting items to be consumed based on the user's history. The topic of diversity in recommendation gained momentum in recent years as additional criterion besides recommendation accuracy, to improve user satisfaction. Accuracy and diversity in recommender systems coexist in a delicate trade-off due to...
master thesis 2020
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Sipko, Tomas (author)
The world is generating more and more network data in many different areas (e.g., sensor networks, social networks and even text). A unique characteristic of these data is the coupling between data values and underlying irregular structure on which these values are defined. Thus, researchers developed Graph Neural Networks (GNNs) to use deep...
master thesis 2020
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Kumar, Paras (author)
With the rapid growth of unstructured data across different mediums, it exposes new challenges for its analysis. To overcome this, data processing pipelines are designed with the help of different tools and technologies for the analysis of data at different stages. One of the applications which we find useful for our company is the creation of...
master thesis 2020
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Doekemeijer, N.A. (author)
Graphs are a ubiquitous concept used for modeling entities and their relationships. Large graphs, present in a variety of domains, are often fundamentally difficult to process because of sheer size and irregular computation structure. In recent years, both academia and industry have committed to designing scalable solutions to efficiently...
master thesis 2020
Searched for: subject:"graph"
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