TU Wien Research Data
Not a member yet
512 research outputs found
Sort by
Analysis of the Neighborhood Parameter on Outlier Detection Algorithms - Evaluation Tests
No full text<h2>Analysis of the Neighborhood Parameter on Outlier Detection Algorithms - Evaluation Tests</h2>
<p>conducted for the paper: <strong>Impact of the Neighborhood Parameter on Outlier Detection Algorithms</strong> by F. Iglesias, C. Martínez, T. Zseby</p>
<h3>Context and methodology</h3>
<p>A significant number of anomaly detection algorithms base their distance and density estimates on neighborhood parameters (usually referred to as <em>k</em>). The experiments in this repository analyze how five different SoTA algorithms (kNN, LOF, LooP, ABOD and SDO) are affected by variations in <em>k</em> in combination with different alterations that the data may undergo in relation to: cardinality, dimensionality, global outlier ratio, local outlier ratio, layers of density, inliers-outliers density ratio, and zonification. Evaluations are conducted with accuracy measurements (ROC-AUC, adjusted Average Precision, and Precision at n) and runtimes.</p>
<p>This repository is framed within the research on the following domains: algorithm evaluation, outlier detection, anomaly detection, unsupervised learning, machine learning, data mining, data analysis. Datasets and algorithms can be used for experiment replication and for further evaluation and comparison.</p>
<h3>Technical details</h3>
<p>Experiments are in Python 3 (tested with v3.9.6). Provided scripts generate all data and results. We keep them in the repo for the sake of comparability and replicability. The file and folder structure is as follows:</p>
<ul>
<li><em>results_datasets_scores.zip</em> contains all results and plots as shown in the paper, also the generated datasets and files with anomaly</li>
<li><em>dependencies.sh</em> for installing required Python packages in a clean environment.</li>
<li><em>generate_data.py</em> creates experimental datasets.</li>
<li><em>outdet.py</em> runs outlier detection with ABOD, kNN, LOF, LoOP and SDO over the collection of datasets.</li>
<li><em>indices.py</em> contains functions implementing accuracy indices.</li>
<li><em>explore_results.py</em> parses results obtained with outlier detection algorithms to create comparison plots and a table with optimal ks.</li>
<li><em>test_kfc.py</em> rusn KFC tests for finding the optimal k in a collection of datasets. It requires <em>kfc.py</em>, which is not included in this repo and must be downloaded from <a href="https://github.com/TimeIsAFriend/KFC/tree/main">https://github.com/TimeIsAFriend/KFC</a>. <em>kfc.py</em> implements the KFCS and KFCR methods for finding the optimal k as presented in: [1]</li>
<li><em>explore_kfc.py</em> parses results obtained with KFCS and KFCR methods to create latex tables.</li>
<li><em>README.md</em> provides explanations and step by step instructions for replication.</li>
</ul>
<h3>References</h3>
<p>[1] Jiawei Yang, Xu Tan, Sylwan Rahardja, Outlier detection: How to Select k for k-nearest-neighbors-based outlier detectors, Pattern Recognition Letters, Volume 174, 2023, Pages 112-117, ISSN 0167-8655, https://doi.org/10.1016/j.patrec.2023.08.020.</p>
<h3>License</h3>
<p>The CC-BY license applies to all data generated with the "generate_data.py" script. All distributed code is under the GNU GPL license. </p>
Chemo-mechanische Toolbox
No full text<p>Dieses Dokument gibt einen Überblick über normierte und neu implementierte Analysetechniken von bituminösen Materialien, die im Verlauf des Projekts genutzt, adaptiert oder aufgebaut wurden. Für jede Methode werden die grundlegenden Prinzipien erklärt und auf spezifische Anwendungen eingegangen. Verweise auf Veröffentlichungen zu den Methoden sind ebenfalls enthalten. Um das Dokument für Anwender_innen nutzbar zu machen, beinhaltet jeder Abschnitt konkrete Fragestellungen, die mit einer Methode beantwortet werden können, sowie deren Vor- und Nachteile, Kosten und Grenzen.</p>
<p>Das vorliegende Dokument stellt dabei kein Lehrbuch über den Hintergrund der analytischen Prinzipien dar und auch keine allumfassende Übersicht von Ergebnissen dieser Methoden in der Literatur. Ziel ist es, ein kompaktes Nachschlagewerk zur Verfügung zu stellen, das Anwender_innen dabei unterstützt, die richtige Methode für Fragestellungen zu Chemie, Mechanik, Mikrostruktur und Alterung von Bitumen zu finden.</p>
Data Management Plan for the FWF-project RIC9773224
No full text<h3>Technical details</h3>
<p>The included file (DMP_OSVP-ER_20241220_V1.0) is the approved version of the Data Managemen Plan for the <span>Project RIC9773224 funded by the Austrian Science Fund (FWF). It will be maintained and updated if required throughout the project duration. Version management will be conducted via this platform (TU Wien Research Data), under the same DOI.</span></p>
The Impact of Traffic Lights on Modal Split and Route Choice: A use-case in Vienna
No full text<p>The data and code scripts used for the analysis in the paper entitled "<strong>The Impact of Traffic Lights on Modal Split and Route Choice: A use-case in Vienna</strong>", submitted to AGILE (Association of Geographic Information Laboratories in Europe) 2024 Conference.</p><p>It comprises three folders within the zip file:</p><ol><li><strong>Data</strong>: Contains the datasets for the analysis.</li><li><strong>Code</strong>: Includes script files essential for conducting the analysis. The scripts are written in Python.</li><li><strong>Results</strong>: Includes the outcomes showcased in the associated paper.</li><li><strong>Visualizations</strong> : Includes a jupyter notebook for the generated plots in the the associated paper.</li></ol><p>Programming Language: Python </p><p>For reproducibility read the README.txt file included in the zip folder.</p><p>All data files are licensed under CC BY 4.0, all software is licensed under MIT License.</p><p>The transportation dynamics within a European city, Vienna, are examined using a multi-graph representation of the city's network. The focus is on time-optimized routing algorithms and the effects of altering the average waiting penalty at traffic lights. The impact of these modifications, whether an increase to 60, 90, or even 150 seconds or a decrease to 10 seconds, is observed in the selection of transportation modes and routes for identical origin and destination pairs. The investigation also extends to whether routes shift towards secondary street networks to avoid traffic lights as the waiting penalty increases. Experimental variations in average waiting time for cars aim to uncover detailed effects on transportation mode choices, route length and time changes, and variations in human energy expenditure. These findings could provide valuable insights into the transportation network and its possibilities and help in urban planning and policy development. The results indicate a shift in transportation mode as the waiting penalty for cars at traffic lights increases, and in some instances, routes are redirected to roads of lower importance such as residential or service roads.</p>
Python IFC Escape Route Model Generator
No full text<h3>Context</h3>
<p>This research data contains the Python project to generate escape route domain models in the IFC format created by researchers from the TU Wien Research Unit Digital Building Process.<br>It is linked to the research paper:<br> "Fischer, S., Urban, H., Schranz, C., Haselberger, M., & Schnabel, F. (2024).<br> Generation of new BIM domain models from escape route analysis results.<br> Developments in the Built Environment, 19, 100499. <a href="https://doi.org/10.1016/j.dibe.2024.100499">https://doi.org/10.1016/j.dibe.2024.100499</a>"</p>
<p>The research paper describes different ways of storing the results of escape route analysis in IFC models. Five different variants have been evaluated. This Python project contains the code to generate the most promising variant "Routes group Segments -- Group". The generated IFC models for all variants for a custom test model and a real-world model are also published, as well as the two initial models:<br>Custom test model for escape route analysis in IFC format: <a href="https://doi.org/10.48436/hx8gz-zw339">https://doi.org/10.48436/hx8gz-zw339</a><br>Real-world test model for escape route analysis in IFC format: <a href="https://doi.org/10.48436/fnmrh-crh59">https://doi.org/10.48436/fnmrh-crh59</a><br>Custom escape route models in IFC format: <a href="https://doi.org/10.48436/dpwd5-33k50">https://doi.org/10.48436/dpwd5-33k50</a><br>Real-World Escape Route Models in IFC format: <a href="https://doi.org/10.48436/rrd14-t1108">https://doi.org/10.48436/rrd14-t1108</a></p>
<h3>Technical details</h3>
<ul>
<li>The project uses the Programming Language Python.</li>
<li>The project was successfully executed with Python 3.10, 3.11, and 3.12.</li>
<li>The most important library is IfcOpenShell (tested for versions 0.7.0 to 0.7.11).</li>
<li>Instructions for downloading and installing IfcOpenShell can be found here: (https://docs.ifcopenshell.org/ifcopenshell-python/installation.html). Herein it is important to install the correct version compatible with the installed Python version.</li>
<li>The input data is provided by JSON files containing the escape route data of the two initial IFC models.</li>
</ul>
<p>Instructions on how to use the code are included in the README.md file in the zip folder.</p>
<p>All data files are licensed under CC BY 4.0, all software files are licensed under MIT License.</p>
<p>The IFC Escape Route Model Generator is also available for the JavaScript programming language: <a href="https://doi.org/10.48436/c35ty-ky950">https://doi.org/10.48436/c35ty-ky950</a></p>
Dataset of Publication "Malware Communication in Smart Factories: A Network Traffic Data Set"
No full text<p>Machine learning-based intrusion detection requires suitable and realistic<br>data sets for training and testing. However, data sets that originate from<br>real networks are rare. Network data is considered privacy sensitive and the <br>purposeful introduction of malicious traffic is usually not possible. In this<br>paper we introduce a labeled data set captured at a smart factory located<br>in Vienna, Austria during normal operation and during penetration tests with different<br>attack types. The data set contains 173 GB of PCAP files, which represent 16 days (395 hours) of factory operation. It includes MQTT, OPC UA, and Modbus/TCP traffic. The captured malicious traffic was originated<br>by a professional penetration tester who performed two types of attacks: (a)<br>aggressive attacks that are easier to detect and (b) stealthy attacks that are<br>harder to detect. Our data set includes the raw PCAP files and extracted<br>flow data. Labels for packets and flows indicate whether packets (or flows)<br>originated from a specific attack or from benign communication. We describe<br>the methodology for creating the data set, conduct an analysis of the data<br>and provide detailed information about the recorded traffic itself. The data<br>set is freely available to support reproducible research and the comparability<br>of results in the area of intrusion detection in industrial networks.</p>
<p>File description:</p>
<p>a_day1, a_day2, s_day1, s_day2, tf_a and tf_s: Main data set, where files starting with "tf" are training files containing only benign, operational data and all other files are attack files containing both, operational data and attack data.</p>
<p>images.zip: Contains descriptive images about the data.</p>
<p>extractions.zip: Contains extracted packets, flows in both labeled and unlabeled form.</p>
<p>a_day_tuesday_dos.zip: additional day of attack traffic containing benign and attack data, including a DoS attack. This day is not labeled.</p>
<div> </div>
<div> </div>
<div> </div>
<div> </div>
<div> </div>
<div> </div>
Custom Test Model for Escape Route Analysis in IFC format
No full text<h2>Description</h2>
<p>The published data is a digital building model (BIM model) in the IFC format (Industry Foundation Classes).<br>The IFC model represents a custom-developed fictional test model. It was created by researchers from the TU Wien Research Unit Digital Building Process.</p>
<h3>Context and methodology</h3>
<ul>
<li>The IFC model was created as test model to validate the newly developed automated Escape Route Analysis for Vienna's BIM-based building permission process.</li>
<li>The custom escape route test model is a fictional five-storey building that contains test scenarios for the requirements for escape routes arising from the relevant building regulations. These include start doors and start rooms with several different escape routes, interconnected spaces, an underground parking garage, and different dimensions of the elements along the routes.</li>
<li>The model was created with the modeling software Archicad 26 and was exported to IFC4 (Reference View).</li>
</ul>
<h3>Technical details</h3>
<ul>
<li>The dataset includes one BIM model in the IFC format (.ifc).</li>
<li>It can be used in any software that supports the IFC format.</li>
</ul>
<h3>Version update</h3>
<p>The updated version includes new properties required to check the fire resistance of elements in general and along escape routes. Property sets have been renamed and German terms have been translated into English. Finally, modelling errors have been corrected.<br><br></p>
Acetylation of alginate enables the production of inks that mimic the chemical properties of P. aeruginosa biofilm
No full text<p>This Excel file contains raw and partially processed data accompanying the publication with the title "Acetylation of alginate enables the production of inks that mimic the chemical properties of <em>P. aeruginosa</em> biofilm". The data presents raw data for GPC (sheet 1), viscosity (sheet 2), processed uniaxial compression tests to show the Young's moduli of the untreated alginate microbeads at different pH (Figure 3) and tobramycin-treated alginate microbeads (figure 6D). Rheological raw data are presented in the sheets Figure 4A - 4D. Figure 5B contains processed data for the diffusion coefficient of FITC-labelled dextran and also the calculated mesh size of the alginate gels. The sheet Figure 6C contains the data for time-resolved shrinkage upon tobramycin treatment.</p>
<p>The data can be used to plot the graphs in any plotting software and to do statistical analysis.</p>
<p> </p>
Dataset for "Non-Perturbative Feats in the Physics of Correlated Antiferromagnets"
No full text<p>The data repository contains the original figures, numerical (raw) data, and plot scripts to reproduce the figures from the publication <a href="https://doi.org/10.1103/ympr-9m73">"Non-Perturbative Feats in the Physics of Correlated Antiferromagnets"</a> at Physical Review Research. The preprint and LaTeX source files are available on <a href="https://doi.org/10.48550/arXiv.2411.13417">arXiv</a>.</p>
<p>The dataset contains paramagnetic and antiferromagnetic (AF) results from dynamical mean-field theory calculations of the two-dimensional Hubbard model at the one- and the two-particle level. The study investigates divergences of the two-particle irreducible vertex function in the coupled charge and spin sectors within the AF phase. The data map the location of these divergences across the AF phase diagram, showing how AF order reduces but does not eliminate the breakdown of the self-consistent perturbation expansion. Additionally, the results capture the changes in the dynamical structure of the corresponding generalized susceptibilities linking them to the crossover from a weak-coupling (Slater) to a strong-coupling (Heisenberg) antiferromagnet and explore potential links to phase-separation instabilities.</p>
<p>Additional information can be found in the README file. A detailed list of the used Python packages is provided in the file 'python_versions.txt'.</p>
<h3><strong>License</strong></h3>
<p>The CC-BY license applies to all the data, PNG and PDF files. All distributed code is under the MIT license.</p>
Lagetypologie der Industriebetriebe im Viertel unter dem Wienerwald bis 1850
No full text<h2>Fotografisches Addendum zu der 1984 an der TU Wien approbierten Dissertation in 8 Teilen</h2><p>Manfred Hösch/Wien-Roßau, im November 2023</p><p>Die über 700 Fotos (Diapositive) gehen zurück auf die Jahre 1982 und 1983. Die Fotografien waren in erster Linie Hilfsmittel für die Orientierung zur planlichen Wiedergabe der ehemaligen Betriebsörtlichkeiten. Sie hatten in ihrer unbearbeiteten Gesamtheit als 'Work in Progress' einen Status, demgegenüber die Veröffentlichung des ästhetisch-edierten Einzelbildes stets als unterlegen empfunden wurde.</p><p>Die Ortsbezeichnungen folgen weitgehend den Konventionen des Österreichischen Amtskalenders sowie des Adressregisters. Die geografischen Koordinaten wurden online erhoben über Google Maps sowie dem NÖ Atlas, der vor allem bei der Suche nach einer möglichst genauen bzw. nächstgelegenen Adresse erste Wahl war. Die einer Ortsbezeichnung nachgereihte achtstellige Nummer folgt der vom Digitalisierungsunternehmen maschinell angewandten Systematik. Die Abkürzungen H-T sowie H-P verweisen auf Text- wie Bildband (Pläne) der akademischen Arbeit.</p><p><i>Supplement zu Dissertation 2 Teile:</i></p><p><i>Lagetypologie der Industriebetriebe im Viertel unter dem Wienerwald bis 1850 : Textband : mit Tabellen</i></p><p><i>catalogplus: </i>https://permalink.catalogplus.tuwien.at/AC00223968</p><p><i>Lagetypologie der Industriebetriebe im Viertel unter dem Wienerwald bis 1850 : Bildband</i></p><p><i>catalogplus: https://permalink.catalogplus.tuwien.at/AC00223968</i></p><h3> </h3>