application associations atmospheric chemistry autoencoders biodegradation Boolean matrix decomposition breath analysis causality cheminformatics cinema data mining computational sustainability data mining dynamic time warping emotional response analysis enviPath framework inductive databases large-scale machine learning metabolic pathways movie analysis multi-label classification personalized ads query languages REST Scavenger smell of fear sof time series toxicity
2018 |
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| 1. | Stönner, Christof; Edtbauer, Achim; Derstorff, Bettina; Bourtsoukidis, Efstratios; Klüpfel, Thomas; Wicker, Jörg; Williams, Jonathan Proof of concept study: Testing human volatile organic compounds as tools for age classification of films Journal Article In: PLOS One, 13 (10), pp. 1-14, 2018. Abstract | Links | BibTeX | Altmetric | Tags: application, atmospheric chemistry, breath analysis, cinema data mining, data mining, emotional response analysis, machine learning, movie analysis, smell of fear, sof, time series @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 = {application, atmospheric chemistry, breath analysis, cinema data mining, data mining, emotional response analysis, machine learning, movie analysis, smell of fear, sof, time series}, pubstate = {published}, tppubtype = {article} } 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. | |
2015 |
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| 2. | Wicker, Jörg; Krauter, Nicolas; Derstorff, Bettina; Stönner, Christof; Bourtsoukidis, Efstratios; Klüpfel, Thomas; Williams, Jonathan; Kramer, Stefan Cinema Data Mining: The Smell of Fear Inproceedings In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1235-1304, ACM ACM, New York, NY, USA, 2015, ISBN: 978-1-4503-3664-2. Abstract | Links | BibTeX | Altmetric | Tags: application, atmospheric chemistry, breath analysis, causality, cinema data mining, data mining, emotional response analysis, movie analysis, smell of fear, sof, time series @inproceedings{wicker2015cinema, title = {Cinema Data Mining: The Smell of Fear}, author = {Jörg Wicker and Nicolas Krauter and Bettina Derstorff and Christof Stönner and Efstratios Bourtsoukidis and Thomas Klüpfel and Jonathan Williams and Stefan Kramer}, url = {https://wicker.nz/nwp-acm/authorize.php?id=N10031 http://doi.acm.org/10.1145/2783258.2783404}, doi = {10.1145/2783258.2783404}, isbn = {978-1-4503-3664-2}, year = {2015}, date = {2015-01-01}, booktitle = {Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining}, pages = {1235-1304}, publisher = {ACM}, address = {New York, NY, USA}, organization = {ACM}, series = {KDD '15}, abstract = {While the physiological response of humans to emotional events or stimuli is well-investigated for many modalities (like EEG, skin resistance, ...), surprisingly little is known about the exhalation of so-called Volatile Organic Compounds (VOCs) at quite low concentrations in response to such stimuli. VOCs are molecules of relatively small mass that quickly evaporate or sublimate and can be detected in the air that surrounds us. The paper introduces a new field of application for data mining, where trace gas responses of people reacting on-line to films shown in cinemas (or movie theaters) are related to the semantic content of the films themselves. To do so, we measured the VOCs from a movie theatre over a whole month in intervals of thirty seconds, and annotated the screened films by a controlled vocabulary compiled from multiple sources. To gain a better understanding of the data and to reveal unknown relationships, we have built prediction models for so-called forward prediction (the prediction of future VOCs from the past), backward prediction (the prediction of past scene labels from future VOCs) and for some forms of abductive reasoning and Granger causality. Experimental results show that some VOCs and some labels can be predicted with relatively low error, and that hints for causality with low p-values can be detected in the data.}, keywords = {application, atmospheric chemistry, breath analysis, causality, cinema data mining, data mining, emotional response analysis, movie analysis, smell of fear, sof, time series}, pubstate = {published}, tppubtype = {inproceedings} } While the physiological response of humans to emotional events or stimuli is well-investigated for many modalities (like EEG, skin resistance, ...), surprisingly little is known about the exhalation of so-called Volatile Organic Compounds (VOCs) at quite low concentrations in response to such stimuli. VOCs are molecules of relatively small mass that quickly evaporate or sublimate and can be detected in the air that surrounds us. The paper introduces a new field of application for data mining, where trace gas responses of people reacting on-line to films shown in cinemas (or movie theaters) are related to the semantic content of the films themselves. To do so, we measured the VOCs from a movie theatre over a whole month in intervals of thirty seconds, and annotated the screened films by a controlled vocabulary compiled from multiple sources. To gain a better understanding of the data and to reveal unknown relationships, we have built prediction models for so-called forward prediction (the prediction of future VOCs from the past), backward prediction (the prediction of past scene labels from future VOCs) and for some forms of abductive reasoning and Granger causality. Experimental results show that some VOCs and some labels can be predicted with relatively low error, and that hints for causality with low p-values can be detected in the data. | |