IRF: Institutional Repository FHNW
Welcome to the publication and research database of the FHNW University of Applied Sciences and Arts Northwestern Switzerland.
The institutional repository contains publications, projects and student theses.
Further information can be found in the IRF manual (available in German).
Communities in IRF
Select a community to browse its collections.
- APS FHNW
- HABG FHNW
- HGK Basel FHNW
- HSI FHNW
- HLS FHNW
- HSM Basel FHNW
- HSA FHNW
- HTU FHNW
- HSW FHNW
- PH FHNW
Recently added
Can GenAI be used as co-author to explain SCM concepts. A framework and numerical evidence
(29.04.2024) Wörner, Dominik; Holzapfel, Andreas
GenAI isn't accepted as a scientific co-author. Nevertheless, it’s one way students use to collect information. First, a framework will be presented, how
GenAI could be used as co-author. Second, the framework will be assessed and validated by numerical evidence. Lessons learned and recommendations will be shared and discussed.
06 - Präsentation
Images influencing images: How pictorial context affects the emotional interpretation of art photographs.
(2023) Reymond, Claire; Vornhagen, Jan B.; Pewlowski, Matthew; Opwis, Klaus; Mekler, Eilsa D.
Images are never seen in isolation. Instead, they are perceived within a spatial and temporal tapestry of neighboring images. What impact do other images have on our emotional response toward a particular image? Answers to this basic question have vital implications for a range of fields—especially for visual communication and for curating art, where resources are invested in arranging images within a visual context. Previous studies have provided mixed results, suggesting that juxtaposed images may lead to contrast or assimilation processes increasing and decreasing our liking of an image. But how specific image features in neighboring images (image’s ambiguity or formal similarities between images) modulate our affective interpretation of an image has almost never been explored. In Study 1, we compared the emotion perceived in art photographs (“target” images) when displayed on their own versus when displayed in juxtaposition with negatively or positively valenced nonart (“context”) images. Additionally, we analyzed the influence of the artwork’s perceived ambiguity. In Study 2, we examined the effect of the perceiver’s expertise and the formal similarity between the images on the rated valence of the target image. Our results show that the emotion perceived in the artwork contrasted away from or assimilated toward the valence perceived in the context image depending on which evaluative dimension was activated. Moreover, the influence of negative contextual material on the target image’s valence was more pronounced. We conclude by saying that the evaluative dimension is part of the pictorial context that influences the affective interpretation of an image. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
01A - Beitrag in wissenschaftlicher Zeitschrift
Evaluation of robust outlier detection methods for zero-inflated complex data
(Taylor & Francis, 2019) Templ, Matthias; Gussenbauer, Johannes; Filzmoser, Peter
Outlier detection can be seen as a pre-processing step for locating data points in a data sample, which do not conform to the majority of observations. Various techniques and methods for outlier detection can be found in the literature dealing with different types of data. However, many data sets are inflated by true zeros and, in addition, some components/variables might be of compositional nature. Important examples of such data sets are the Structural Earnings Survey, the Structural Business Statistics, the European Statistics on Income and Living Conditions, tax data or – as in this contribution – household expenditure data which are used, for example, to estimate the Purchase Power Parity of a country.
In this work, robust univariate and multivariate outlier detection methods are compared by a complex simulation study that considers various challenges included in data sets, namely structural (true) zeros, missing values, and compositional variables. These circumstances make it difficult or impossible to flag true outliers and influential observations by well-known outlier detection methods.
Our aim is to assess the performance of outlier detection methods in terms of their effectiveness to identify outliers when applied to challenging data sets such as the household expenditures data surveyed all over the world. Moreover, different methods are evaluated through a close-to-reality simulation study. Differences in performance of univariate and multivariate robust techniques for outlier detection and their shortcomings are reported. We found that robust multivariate methods outperform robust univariate methods. The best performing methods in finding the outliers and in providing a low false discovery rate were found to be the generalized S estimators (GSE), the BACON-EEM algorithm and a compositional method (CoDa-Cov). In addition, these methods performed also best when the outliers are imputed based on the corresponding outlier detection method and indicators are estimated from the data sets.
01A - Beitrag in wissenschaftlicher Zeitschrift
Habitat-dependency of transect walk and pan trap methods for bee sampling in farmlands
(Apicultural Research Association, 2019) Templ, Barbara; Mozes, Edina; Templ, Matthias; Földesi, Rita; Szirák, Ádám; Báldi, András; Kovács-Hostyánszki, Anikó
Bees are the most important group of flower visitors providing an essential ecosystem service, namely pollination. Due to the worldwide decline of bees, there should be standardized sampling methods in place to ensure consistent and comparable results between studies. We compared the two commonly used sampling methods of yellow pan traps and transect walk to determine (i) which habitat variables affect the species composition, abundance and species richness of sampled bee communities, (ii) which method potentially contains sampling bias towards some individuals or groups of bees and (iii) the efficiency of sampling in various habitats. We conducted fieldwork in different agricultural habitats distributed along landscape heterogeneity and topography gradients. Our results showed that the height of vegetation, the average number of flowers and the amount of woody vegetation had the greatest influence on the sampling efficiency. Our survey also demonstrated that sampling by transect walk captured less bees in general, especially in stubble, maize, and cereal fields. We found that Apis mellifera and Bombus spp. were well represented in samples collected by the transect walk method, while the abundance of other genera, especially Dasypoda, Hylaeus and Panurgus was higher in pan traps. Based on the results, we suggest (i) the transect walk method to compare samples of flower-visiting wild bee communities from various habitats of different vegetation and flower characteristics, (ii) application of the transect walk or pan traps to compare similar habitats and (iii) adoption of a comprehensive method which would incorporate both sampling techniques to gain a more complex insight into wild bee species composition.
01A - Beitrag in wissenschaftlicher Zeitschrift
Feedback-based integration of the whole process of data anonymization in a graphical interface
(MDPI, 2019) Meindl, Bernhard; Templ, Matthias
The interactive, web-based point-and-click application presented in this article, allows anonymizing data without any knowledge in a programming language. Anonymization in data mining, but creating safe, anonymized data is by no means a trivial task. Both the methodological issues as well as know-how from subject matter specialists should be taken into account when anonymizing data. Even though specialized software such as sdcMicro exists, it is often difficult for nonexperts in a particular software and without programming skills to actually anonymize datasets without an appropriate app. The presented app is not restricted to apply disclosure limitation techniques but rather facilitates the entire anonymization process. This interface allows uploading data to the system, modifying them and to create an object defining the disclosure scenario. Once such a statistical disclosure control (SDC) problem has been defined, users can apply anonymization techniques to this object and get instant feedback on the impact on risk and data utility after SDC methods have been applied. Additional features, such as an Undo Button, the possibility to export the anonymized dataset or the required code for reproducibility reasons, as well its interactive features, make it convenient both for experts and nonexperts in R—the free software environment for statistical computing and graphics—to protect a dataset using this app.
01A - Beitrag in wissenschaftlicher Zeitschrift