document

Digging through the dirt: a general method for abstract discrete state estimation with limited prior knowledge

de Boer, Wouter (author)

Autonomous robots are often successfully deployed in controlled environments. Operation in uncontrolled situations remains challenging; it is hypothesized that the detection of abstract discrete states (ADS) can improve operation in these circumstances. ADS are high-level system states that are not directly detectable and influence system...

master thesis 2023

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Public Transport Delay Pattern Analysis By Unsupervised Learning Approach

Cheng, Yuxing (author)

To analyze latent multiple specific patterns in the line-based public transport daily delay occurrence, a data-driven explorative analysis of public transport daily delay spatial-temporal distribution pattern is performed based on the k-means clustering algorithm. Firstly, we used aggregated daily delay profile to visualize how the delay is...

student report 2023

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G-Rank: Unsupervised Continuous Learn-to-Rank for Edge Devices in a P2P Network

Gold, Andrew (author)

Ranking algorithms in traditional search engines are powered by enormous training data sets that are meticulously engineered and curated by a centralized entity. Decentralized peer-to-peer (p2p) networks such as torrenting applications and Web3 protocols deliberately eschew centralized databases and computational architectures when designing...

master thesis 2023

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Automated discovery of generalized standard material models with EUCLID

Flaschel, Moritz (author), Kumar, Siddhant (author), De Lorenzis, Laura (author)

We extend the scope of our recently developed approach for unsupervised automated discovery of material laws (denoted as EUCLID) to the general case of a material belonging to an unknown class of constitutive behavior. To this end, we leverage the theory of generalized standard materials, which encompasses a plethora of important constitutive...

journal article 2023

document

Automated identification of linear viscoelastic constitutive laws with EUCLID

Marino, Enzo (author), Flaschel, Moritz (author), Kumar, Siddhant (author), De Lorenzis, Laura (author)

We extend EUCLID, a computational strategy for automated material model discovery and identification, to linear viscoelasticity. For this case, we perform a priori model selection by adopting a generalized Maxwell model expressed by a Prony series, and deploy EUCLID for identification. The methodology is based on four ingredients: i. full...

journal article 2023

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Detecting Dish Types in Picnic Deliveries

Noorthoek, Sterre (author)

In addition to delivering groceries at customers’ doorsteps, online supermarket Picnic goes the extra mile by aiming to improve customer satisfaction. For instance, by providing cooking inspiration to customers through a recently launched recipe page in the app. This feature presents new recipes weekly and allows customers to easily add the...

master thesis 2022

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Bayesian-EUCLID: Discovering hyperelastic material laws with uncertainties

Joshi, A. (author), Thakolkaran, P. (author), Zheng, Y. (author), Escande, Maxime (author), Flaschel, Moritz (author), De Lorenzis, Laura (author), Kumar, Siddhant (author)

Within the scope of our recent approach for Efficient Unsupervised Constitutive Law Identification and Discovery (EUCLID), we propose an unsupervised Bayesian learning framework for discovery of parsimonious and interpretable constitutive laws with quantifiable uncertainties. As in deterministic EUCLID, we do not resort to stress data, but...

journal article 2022

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NN-EUCLID: Deep-learning hyperelasticity without stress data

Thakolkaran, P. (author), Joshi, A. (author), Zheng, Y. (author), Flaschel, Moritz (author), De Lorenzis, Laura (author), Kumar, Siddhant (author)

We propose a new approach for unsupervised learning of hyperelastic constitutive laws with physics-consistent deep neural networks. In contrast to supervised learning, which assumes the availability of stress–strain pairs, the approach only uses realistically measurable full-field displacement and global reaction force data, thus it lies...

journal article 2022

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A critical review of discontinuity plane extraction from 3D point cloud data of rock mass surfaces

Daghigh, Hamid (author), Tannant, Dwayne D. (author), Daghigh, Vahid (author), Lichti, Derek D. (author), Lindenbergh, R.C. (author)

Field investigations of geometric discontinuity properties in rock masses are increasingly using three-dimensional point cloud data. These point clouds sample the rock mass surface and are typically acquired by photogrammetry or LiDAR. The automatic segmentation and extraction of planar surfaces from point cloud data have attracted...

review 2022

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Feasibility study of "Invariant Information Clustering for Unsupervised Image Segmentation"

Dimitrov, Yordan (author)

In this paper we analyze the performance of a novel clustering objective that optimizes a neural network to predict segmentation. We challenge the reported results by replicating the original experiments and conducting additional tests to gain an insight into the algorithm. We analyzed the efficiency of the clustering objective on a different...

master thesis 2021

document

Privacy-preserving Clustering of Single-cell RNA Sequencing Data on Intel SGX

Cao, Qingyuan (author)

This project is dedicated to implementing an unsupervised learning clustering method system for processing big data applied in Intel SGX. Intel SGX is a technology developed to meet the needs of the trusted computing industry similarly to ARM TrustZone, but this time for desktop and server platforms. It is a set of safety-related instruction...

master thesis 2021

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Clustering small and medium sized Dutch enterprises using hybrid intelligence

Sharma, Shipra (author)

We are living in a world full of data. Data capture the characteristics of any entity around us, like living species, properties of scientific experimentation, etc. Moreover, data provides a basis for further analysis, reasoning, or decision-making. One of the most common applications of data analysis is to group data into a set of clusters to...

master thesis 2021

document

Evaluation of the SUM-GAN-AAE method for Video Summarization

Trevnenski, Georgi (author)

Video summarization is a task which many researchers have tried to automate with deep learning methods. One of these methods is the SUM-GAN-AAE algorithm developed by Apostolidis et al. which is an unsupervised machine learning method evaluated in this study. The research aims at testing the algorithm's performance on the Breakfast dataset,...

bachelor thesis 2021

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Zero-shot learning in pick-and-place tasks using neuro-symbolic concept learning

van der Sar, martijn (author)

Pick and place systems that operate in a warehouse setting have been studied a lot recently due to the high economic value for e-commerce companies. In this thesis, the focus is on the perception pipeline that performs object recognition given a certain input data stream (typically RGB-D images). Impressive results regarding object recognition...

master thesis 2021

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Statistical Analysis in Cyberspace: Data veracity, completeness, and clustering

Roeling, M.P. (author)

This thesis presents several methodological and statistical solutions to problems encountered in cyber security. We investigated the effects of compromised data veracity in state estimators and fraud detection systems, a model to impute missing data in attributes of linked observations, and an unsupervised approach to detect infected machines in...

doctoral thesis 2021

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Deep Recursive Embedding for High-Dimensional Data

Zhou, Zixia (author), Zu, Xinrui (author), Wang, Yuanyuan (author), Lelieveldt, Boudewijn P.F. (author), Tao, Q. (author)

Embedding high-dimensional data onto a low-dimensional manifold is of both theoretical and practical value. In this article, we propose to combine deep neural networks (DNN) with mathematics-guided embedding rules for high-dimensional data embedding. We introduce a generic deep embedding network (DEN) framework, which is able to learn a...

journal article 2021

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Unsupervised discovery of interpretable hyperelastic constitutive laws

Flaschel, Moritz (author), Kumar, Siddhant (author), De Lorenzis, Laura (author)

We propose a new approach for data-driven automated discovery of isotropic hyperelastic constitutive laws. The approach is unsupervised, i.e., it requires no stress data but only displacement and global force data, which are realistically available through mechanical testing and digital image correlation techniques; it delivers interpretable...

journal article 2021

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Beyond Labeling: Using Clustering to Build Network Behavioral Profiles of Malware Families

Nadeem, A. (author), Hammerschmidt, C.A. (author), Hernandez Ganan, C. (author), Verwer, S.E. (author)

Malware family labels are known to be inconsistent. They are also black-box since they do not represent the capabilities of malware. The current state of the art in malware capability assessment includes mostly manual approaches, which are infeasible due to the ever-increasing volume of discovered malware samples. We propose a novel...

book chapter 2021

document

Contrastive Learning of Visual Representations from Unlabeled Videos

Simion-Constantinescu, Andrei (author)

This thesis presents a novel self-supervised approach of learning visual representations from videos containing human actions. Our approach tackles the complex problem of learning without the need of labeled data by exploring to what extent the ideas successfully used for images can be transferred, adapted and extended to videos for action...

master thesis 2020

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Predicting software vulnerabilities with unsupervised learning techniques

Man, K.W. (author)

As software is produced more and more every year, software also gets exploited more. This exploitation can lead to huge monetary losses and other damages to companies and users. The exploitation can be reduced by automatically detecting the software vulnerabilities that leads to exploitation. Unfortunately, the state-of-the-art methods for this...

master thesis 2020