Invitation: Graduation Presentations Friday, January 25th
Posted: 21 January 2019 12:58 PM
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You’re all invited to the Public Graduation Presentations of Max Graven, Stijn van Vilsteren and Antria KKoushi.

When? This Friday (January 25th), 10:40 - 14:05
Where? Gravensteen Building (Pieterskerkhof 6, Leiden), room 0.11

Schedule
10:40 - 11:15 Max Graven
11:20 - 12:00 Max Graven (VR experiment)

12:50 - 13:25 Stijn van Vilsteren
13:30 - 14:05 Antria KKoushi

Additional information
10:40 - 11:15 Max Graven
Title: Heritage Presents: Critical Approaches to New Media and Cultural Heritage
Abstract: New media like Virtual Reality, Augmented Reality and games, are omnipresent in the field of cultural heritage. The technologies for these media have progressed significantly over the last decade and became more accessible for different kinds of scientific, educational and recreational purposes. Nowadays many heritage museums, sites and researchers make use of new media to show reconstructions, provide context and to create interactions. Although there is an increasing amount of developments and studies in this field of interest, most of them do have a pragmatic approach and are focused on the technical aspects of new media. This explorative study tries to provide a more critical insight into the effects of using new media and provides a basic framework that can be used to find components of cultural heritage that are suited for showing through new media. The research uses approaches of scholars from the critical heritage studies discipline, started in the 1990s. Based on the ideas of an Authorised Heritage Discourse (AHD) interactions between audiences and visitors of heritage have been examined. This research consists out of two case studies, a VR exhibition about Mosul, Aleppo, Palmyra, and Leptis Magna, and the archeological game-project ‘RomeinCraft’, together with a VR experiment done in the National Museum of Antiquities in the Netherlands. During the VR experiment visitors of the museum were asked to take a look at a virtual reconstruction of one of the museums’ highlights. The virtual reconstruction was a one-on-one copy of how the real object was displayed in the collection, the only difference is the medium. Results show a decreased interest of the respondents in the aesthetics and materiality of the object, and an increased curiosity to the context and proportions in the virtual environment. Supported by evidence found in the RomeinCraft case, this study shows that visitors are not that interested in individual objects and their value given by heritage experts, but they rather prefer to gain more insights into the creation and purposes of the object, so they can gain a better insight for their own ideas about heritage. A deeper understanding of how new media is interwoven with cultural heritage allows us to go beyond using new media as a ‘gadget’, to engage in debates about the democracy of heritage, and to create meaningful interactions between visitors and their cultural heritage.
Thesis advisors: Max van Duijn and Angus Mol

12:50 - 13:25 Stijn van Vilsteren
Title: “The positive effect of mediated plants on a divergent thinking test in virtual reality”
Abstract: Plants are incorporated in our daily lives in the different ways, the are placed in our homes, outside in parks but also in our offices. In multiple studies it has been proven that they are stress reducing and creative enhancing. But what would happen if the plants where enhanced in such a way that it could lead to even a greater creativity boost? This question is investigated by having two groups of (n=30) participants take part in a drawing game in virtual reality. There were two rooms that looked almost exactly the same but only there was a difference in the colours of the plants. Group 1 had green plants and group 2 had plants that were mediated by using different colours that do not occur in nature. The participant would take part in a drawing game that challenged their creative abilities. The game was inspired by the Torrance Tests of Creative Thinking test where participants where asked to start with a specific shape that was given and use this shape to create a drawing that they could think of. After finishing the drawing people gave their drawing a name and then continued with another shape until they stopped them selves or if they had reached the maximum limit of 30 minutes. All the created drawings were collected as images and the names were written down. These images were scored based on originality, elaboration, the abstractness of the title, originality and the amount of colours they have used. Both groups where compared with each other and the result was that the participants in the group with the mediated plants scored significantly higher.
Thesis advisors: Peter van der Putten and Avinash Changa

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Posted: 21 January 2019 12:59 PM   [ # 1 ]
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13:30 - 14:05 Antria KKoushi
Title: Classification of Borderline Personality Disorder patients from fMRI scans
Abstract: Borderline Personality Disorder (BPD) is a mental illness that affects many people. This disorder causes mood swings, negative self-imaging can lead to self-harm and in some extreme cases to suicide. Some studies suggested that BPD is related to specific brain areas. This is based on fMRI scans of patients with this disorder. The goal of our study is to examine whether a variety of machine learning models could classify different fMRI scans of healthy subjects and BPD patients. We have used two datasets with fMRI scans derived from two Leiden studies focused on BPD. We explored different machine learning techniques like Support Vector Machines (SVM), Decision Tree algorithms (DTs) and Convolutional Neural Networks (CNN). As a part of our research, we also ran some experiments with the dataset itself so that it became possible to extract features from fMRI scan that can be recognized as the Grey Matter, the White Matter, and the Cerebrospinal fluid. Deep learning models, such as CNN, usually require big datasets to be able to distinguish patterns and features and therefore categorize data. To overcome this limitation with our relatively small dataset we added noise, by generating data using standard deviation. With this technique, it was possible to run different experiments with a variety of models and come to the conclusion that the best model for identifying a BPD patient through fMRI scans is a Convolutional Neural Network. The accuracy of such a model using the Cerebrospinal fluid dataset is 91%.
Thesis advisors: Fons Verbeek and Charlotte van Schie

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