Recent Publications

@article{Jonauskaite2019,
title = {A machine learning approach to quantifying the specificity of color-emotion associations and their cultural differences},
author = {Domicele Jonauskaite and Jörg Wicker and Chrisine Mohr and Nele Dael and Jelena Havelka and Marietta Papadatou-Pastou and Meng Zhang and Daniel Oberfeld },
editor = {Andrew Dunn},
year = {2019},
date = {2019-08-26},
journal = {Royal Society Open Science},
abstract = {The link between color and emotion and its possible similarity across cultures are questions that have not been fully resolved. Online, 711 participants from China, Germany, Greece, and the UK associated 12 color terms with 20 discrete emotion terms in their native languages. We propose a machine learning approach to quantify (a) the consistency and specificity of color-emotion associations and (b) the degree to which they are country-specific, on the basis of the accuracy of a statistical classifier in (a) decoding the color term evaluated on a given trial from the 20 ratings of color-emotion association and (b) predicting the country of origin from the 240 individual color-emotion associations, respectively. The classifier accuracies were significantly above chance level, demonstrating that emotion associations are to some extent color-specific and that color-emotion associations are to some extent country-specific. A second measure of country-specificity, the in-group advantage of the color-decoding accuracy, was detectable but relatively small (6.1%), indicating that color-emotion associations are both universal and culture-specific. Our results show that machine learning is a promising tool when analyzing complex datasets from emotion research.},
keywords = {},
pubstate = {forthcoming},
tppubtype = {article}
}

@article{Stonner2018,
title = {Proof of concept study: Testing human volatile organic compounds as tools for age classification of films},
author = {Christof Stönner and Achim Edtbauer and Bettina Derstorff and Efstratios Bourtsoukidis and Thomas Klüpfel and Jörg Wicker and Jonathan Williams},
doi = {10.1371/journal.pone.0203044},
year = {2018},
date = {2018-10-11},
journal = {PLOS One},
volume = {13},
number = {10},
pages = {1-14},
publisher = {Public Library of Science},
abstract = {Humans emit numerous volatile organic compounds (VOCs) through breath and skin. The nature and rate of these emissions are affected by various factors including emotional state. Previous measurements of VOCs and CO2 in a cinema have shown that certain chemicals are reproducibly emitted by audiences reacting to events in a particular film. Using data from films with various age classifications, we have studied the relationship between the emission of multiple VOCs and CO2 and the age classifier (0, 6, 12, and 16) with a view to developing a new chemically based and objective film classification method. We apply a random forest model built with time independent features extracted from the time series of every measured compound, and test predictive capability on subsets of all data. It was found that most compounds were not able to predict all age classifiers reliably, likely reflecting the fact that current classification is based on perceived sensibilities to many factors (e.g. incidences of violence, sex, antisocial behaviour, drug use, and bad language) rather than the visceral biological responses expressed in the data. However, promising results were found for isoprene which reliably predicted 0, 6 and 12 age classifiers for a variety of film genres and audience age groups. Therefore, isoprene emission per person might in future be a valuable aid to national classification boards, or even offer an alternative, objective, metric for rating films based on the reactions of large groups of people.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@inproceedings{Wicker2019,
title = {XOR-based Boolean Matrix Decomposition},
author = {Jörg Wicker and Yan Cathy Hua and Rayner Rebello and Bernhard Pfahringer},
year = {2019},
date = {2019-11-08},
booktitle = {International Conference on Data Mining},
publisher = {IEEE},
abstract = {Boolean matrix factorization (BMF) is a data summarizing and dimension-reduction technique. Existing BMF methods build on matrix properties defined by Boolean algebra, where the addition operator is the logical inclusive OR and the multiplication operator the logical AND. As a consequence, this leads to the lack of an additive inverse in all Boolean matrix operations, which produces
an indelible type of approximation error. Previous research adopted various methods to address such an issue and produced reasonably accurate approximation. However, an exact factorization is rarely found in the literature. In this paper, we introduce a new algorithm named XBMaD (Xor-based Boolean Matrix Decomposition) where the addition operator is defined as the exclusive OR (Xor). This change completely removes the error-mitigation issue of OR-based BMF methods, and allows for an exact error-free factorization. An evaluation comparing XBMaD and classic OR-based methods suggested that XBMAD performed equal or in most cases more accurately and faster.
},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}

@inproceedings{Roeslin2019,
title = {Data integration for the development of a seismic loss prediction model for residential buildings in New Zealand},
author = {Samuel Roeslin and Quincy Ma and Jörg Wicker and Liam Wotherspoon},
editor = {Luiza Antonie and Peter Christen and Erhard Rahm and Osmar Zaïane},
year = {2019},
date = {2019-09-20},
booktitle = {DINA 2019 - Data Integration and Applications Workshop},
abstract = {In 2010-2011, New Zealand experienced the most damaging earthquakes in its history. It led to extensive damage to Christchurch buildings, infrastructure and its surroundings; affecting commercial and residential buildings. The direct economic losses represented 20% of New Zealands GDP in 2011. Owing to New Zealands particular insurance structure, the insurance sector contributed to over 80% of losses for a total of more than NZ$31 billion. Amongst this, over NZ$11 billion of the losses arose from residential building claims and were covered either partially or entirely from the NZ government backed EQC cover insurance scheme. In the process of resolving the claims, the Earthquake Commission (EQC) collected detailed financial loss data, post-event observations and building characteristics for each of the approximately 434,000 claims lodged following the Canterbury Earthquake sequence (CES). Added to this, the active NZ earthquake engineering community treated the
event as a large scale outdoor experiment and collected extensive data on the ground shaking levels, soil conditions, and liquefaction occurrence throughout wider Christchurch. This paper discusses the necessary data preparation process preceding the development of a machine learning seismic loss model. The process draws heavily upon using Geographic Information System (GIS) techniques to aggregate relevant information from multiple databases interpolating data between categories and converting data between continuous and categorical forms. Subsequently, the database is processed, and a residential seismic loss prediction model is developed using machine learning. The aim is to develop a grey-box model enabling human interpretability of the decision steps.},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}

@inproceedings{williams2019what,
title = {What can we learn from the air chemistry of crowds?},
author = {Jonathan Williams and Christof Stönner and Achim Edtbauer and Bettina Derstorff and Efstratios Bourtsoukidis and Thomas Klüpfel and Nicolas Krauter and Jörg Wicker and Stefan Kramer},
editor = {Armin Hansel and Jürgen Dunkl},
url = {https://www.ionicon.com/sites/default/files/uploads/doc/Contributions_8th-PTR-MS-Conference-2019_web.pdf#page=122},
year = {2019},
date = {2019-05-10},
booktitle = {8th International Conference on Proton Transfer Reaction Mass Spectrometry and its Applications},
pages = {121-123},
publisher = {Innsbruck University Press},
address = {Innsbruck},
abstract = {Current PTR-MS technology allows hundreds of volatile trace gases in air to be measured every second at extremely low levels (parts per trillion). These instruments are often used in atmospheric research on planes and ships and even in the Amazon rainforest. Recently, we have used this technology to examine air composition changes caused by large groups of people (10,000-30,000) under real world conditions at a football match and in a movie theater. In both cases the trace gas signatures measured in ambient air are shown to reflect crowd behavior. By applying advanced data mining techniques we have shown that groups of people reproducibly respond to certain emotional stimuli (e.g. suspense and comedy) by exhaling specific trace gases. Furthermore, we explore whether this information can be used to determine the age classification of films.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}