Simulating creativity in GANs with IoT

[This blog post is based on the Artificial Intelligence MSc thesis project from Fay Beening, supervised by myself and Joost de Boo, more information can be found on Fay’s website]

Recently, generative art has been one of the fields where AI, especially deep learning has caught the public eye. Algorithms and online tools such as Dall-E are able to produce astounding results based on large artistic datasets. One class of algorithms that has been at the root of this success is the Generative Adversarial Network (GAN), frequently used in online art-generating tools because of their ability to produce realistic artefacts.

but, is this “””real””” art? is this “””real””” creativity?

To address this, Fay investigated current theories on art and art education and found that these imply that true human creativity can be split into three types: 1) combinational, 2) explorative and 3) transformative creativity but that it also requires real-world experiences and interactions with people and the environment. Therefore, Fay in her thesis proposes to combine the GAN with an Internet of Things (IoT) setup to make it behave more creative.

Arduin-based prototype (image from Fay’s thesis)

She then designed a system that extends the original GAN with an interactive IoT system (implemented in an Arduino-based prototype) to simulate a more creative process. The prototype of the design showed a successful implementation of creative behaviour that can react to the environment and gradually change the direction of the generated images.

Images shown to the participant during the level of creativity task. Images 2 and 6 are creative GAN generated images. Images 1 and 5 are human-made art. Images 3 and 4 are online GAN generated art.

The generated art was evaluated based on their creativity by doing task-based interviews with domain experts. The results show that the the level to which the generated images are considered to be creative depends heavily on the participant’s view of creativity.

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Representing temporal vagueness on the
semantic web for historical datasets

[This post is based on the Master Information Sciences project of Fabian Witeczek and reuses text from his thesis. The research is part of VU’s effort in the Intavia project and was co-supervised by Go Sugimoto]

To represent properly temporal data on the Semantic Web, there is a need for an ontology to represent vague or imprecise dates. In the context of his research, Fabian Witeczek developed an ontology that can be used to represent various forms of such vague dates. The engineering process of the ontology started with a requirements analysis that contained the collection of data records from existing Digital Humanities Linked Data sets containing temporally vague dates: Biographynet and Europeana. The occurrences of vagueness were evaluated, and categories of vagueness were defined.

The categories were evaluated through a survey conducted with domain experts in the digital humanities domain. The experts were also questioned about their problems when working with temporally vague dates. The survey results confirmed the meaningfulness of the ontology requirements and the categories of vagueness which were: 1) Unknown deviation, 2) within a time span, 3) before or after a
specific date, 4) date options, and 5) complete vagueness.

Visualization of the vague date ontology

Based on the findings, the ontology was designed and implemented, scoping to year-granularity only. Lastly, the ontology was tested and evaluated by linking its instances to instances of a historical dataset. This research concludes that the presented vague date ontology offers a clear way to specify how vague dates are and in which regard they are vague. However, the ontology requires much effort to make it work in practice for researchers in digital humanities. This is due to precision and deviation values that need to be set for every record within the datasets.

Example SPARQL query using concepts from the vague dates ontology

More information can be found in the Master Thesis, linked below.

The ontology itself is found in Fabian’s github account

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Comparing Synthetic Data Generation Tools for IoT Data

[This post is based on the Bachelor Information Sciences project of Darin Pavlov and reuses text from his thesis. The research is part of VU’s effort in the InterConnect project and was supervised by Roderick van der Weerdt]

The concepts and technologies behind the Internet of Things (IoT) make it possible to establish networks of interconnected smart devices. Such networks can produce large volumes of data transmitted through sensors and actuators. Machine Learning can play a key role in processing this data towards several use cases in specific domains automotive, healthcare, manufacturing, etc. However, access to data for developing and testing Machine Learning is often hindered due to sensitivity of data, privacy issues etc.

One solution for this problem is to use synthetic data, resembling as much as possible real data. In his study, Darin Pavlov conducted a set of experiments, investigating the effectiveness of synthetic IoT data generation by three different tools:

This table shows the results of one of the two Machine Learning detection tests showing how difficult it is to differentiate the synthetic data from the real one with a Machine Learning model. For two datasets, the result is calculated as 1 minus the average ROC AUC score

Darin compared the tools on various distinguishability metrics. He observed that Mostly AI outperforms the other two generators, although shows similar satisfactory results on the statistical metrics. The output of SDV on the other hand is poor on all metrics. Through this study we aim to encourage future research within the quickly developing area of synthetic data generation in the context of IoT technology.

More details can be found in Darin’s thesis.

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Hearing (Knowledge) Graphs

[This post is based on Enya Nieland‘s Msc Thesis “Generating Earcons from Knowledge Graphs” ]

Three earcons with varying pitch, rythm and both pitch and rythm

Knowledge Graphs are becoming enormously popular, which means that users interacting with such complex networks are diversifying. This requires new and innovative ways of interacting. Several methods for visualizing, summarizing or exploring knowledge have been proposed and developed. In this student project we investigated the potential for interacting with knowledge graphs through a different modality: sound.

The research focused on the question how to generate meaningful sound or music from (knowledge) graphs. The generated sounds should provide users some insights into the properties of the network. Enya framed this challenge with the idea of “earcons” the auditory version of an icon.

Enya eventually developed a method that automatically produces these types of earcon for random knowledge graphs. Each earcon consist of three notes that differ in pitch and duration. As example, listen to the three earcons which are shown in the figure on the left.

Earcon where pitch varies
Earcon where note duration varies
Earcon where both pitch and rythm vary

The earcon parameters are derived from network metrics such as minimum, maximum and average indegree or outdegree. A tool with user interface allowed users to design the earcons based on these metrics.

The pipeline for creating earcons

The different variants were evaluated in an extensive user test of 30 respondents to find out which variants were the most informative. The results show that indeed, the individual elements of earcons can provide insights into these metrics, but that combining them is confusing to the listener. In this case, simpler is better.

Using this tool could be an addition to a tool such as LOD Laundromat to provide an instant insight into the complexity of KGs. It could additionally benefit people who are visually impaired and want to get an insight into the complexity of Knowledge Graphs

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Exploring Automatic Recognition of Labanotation Dance Scores

[This post describes the research of Michelle de Böck and is based on her MSc Information Sciences thesis.]

Digitization of cultural heritage content allows for the digital archiving, analysis and other processing of that content. The practice of scanning and transcribing books, newspapers and images, 3d-scanning artworks or digitizing music has opened up this heritage for example for digital humanities research or even for creative computing. However, with respect to the performing arts, including theater and more specifically dance, digitization is a serious research challenge. Several dance notation schemes exist, with the most established one being Labanotation, developed in 1920 by Rudolf von Laban. Labanotation uses a vertical staff notation to record human movement in time with various symbols for limbs, head movement, types and directions of movements.

Generated variations of movements used for training the recognizers

Where for musical scores, good translations to digital formats exist (e.g. MIDI), for Lanabotation, these are lacking. While there are structured formats (LabanXML, MovementXML), the majority of content still only exists either in non-digitized form (on paper) or in scanned images. The research challenge of Michelle de Böck’s thesis therefore was to identify design features for a system capable of recognizing Labanotation from scanned images.

Examples of Labanotation files used in the evaluation of the system.

Michelle designed such a system and implemented this in MATLAB, focusing on a few movement symbols. Several approaches were developed and compared, including approaches using pre-trained neural networks for image recognition (AlexNet). This approach outperformed others, resulting in a classification accuracy of 78.4%. While we are still far from developing a full-fledged OCR system for Labanotation, this exploration has provided valuable insights into the feasibility and requirements of such a tool.

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Who uses DBPedia anyway?

[this post is based on Frank Walraven‘s Master thesis]

Who uses DBPedia anyway? This was the question that started a research project for Frank Walraven. This question came up during one of the meetings of the Dutch DBPedia chapter, of which VUA is a member. If usage and users are better understood, this can lead to better servicing of those users, by for example prioritizing the enrichment or improvement of specific sections of DBPedia Characterizing use(r)s of a Linked Open Data set is an inherently challenging task as in an open Web world, it is difficult to know who are accessing your digital resources. For his Msc project research, which he conducted at the Dutch National Library supervised by Enno Meijers , Frank used a hybrid approach using both a data-driven method based on user log analysis and a short survey of know users of the dataset. As a scope Frank selected just the Dutch DBPedia dataset.

For the data-driven part of the method, Frank used a complete user log of HTTP requests on the Dutch DBPedia. This log file (see link below) consisted of over 4.5 Million entries and logged both URI lookups and SPARQL endpoint requests. For this research only a subset of the URI lookups were concerned.

As a first analysis step, the requests’ origins IPs were categorized. Five classes can be identified (A-E), with the vast majority of IP addresses being in class “A”: Very large networks and bots. Most of the IP addresses in these lists could be traced back to search engine

indexing bots such as those from Yahoo or Google. In classes B-F, Frank manually traced the top 30 most encounterd IP-addresses, concluding that even there 60% of the requests came from bots, 10% definitely not from bots, with 30% remaining unclear.

The second analysis step in the data-driven method consisted of identifying what types of pages were most requested. To cluster the thousands of DBPedia URI request, Frank retriev

ed the ‘categories’ of the pages. These categories are extracted from Wikipedia category links. An example is the “Android_TV” resource, which has two categories: “Google” and “Android_(operating_system)”. Following skos:broader links, a ‘level 2 category’ could also be found to aggregate to an even higher level of abstraction. As not all resources have such categories, this does not give a complete image, but it does provide some ideas on the most popular categories of items requested. After normalizing for categories with large amounts of incoming links, for example the category “non-endangered animal”, the most popular categories where 1. Domestic & International movies, 2. Music, 3. Sports, 4. Dutch & International municipality information and 5. Books.

Frank also set up a user survey to corroborate this evidence. The survey contained questions about the how and why of the respondents Dutch DBPedia use, including the categories they were most interested in. The survey was distributed using the Dutch DBPedia websitea and via twitter however only attracted 5 respondents. This illustrates

the difficulty of the problem that users of the DBPedia resource are not necessarily easily reachable through communication channels. The five respondents were all quite closely related to the chapter but the results were interesting nonetheless. Most of the users used the DBPedia SPARQL endpoint. The full results of the survey can be found through Frank’s thesis, but in terms of corroboration the survey revealed that four out of the five categories found in the data-driven method were also identified in the top five resulting from the survey. The fifth one identified in the survey was ‘geography’, which could be matched to the fifth from the data-driven method.Frank’s research shows that although it remains a challenging problem, using a combination of data-driven and user-driven methods, it is indeed possible to get an indication into the most-used categories on DBPedia. Within the Dutch DBPedia Chapter, we are currently considering follow-up research questions based on Frank’s research.

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An Augmented Reality App to Annotate Art

[This post is based on the Bachelor project by Jurjen Braam and reuses content from his thesis]

The value of Augmented Reality applications has been shown for a number of different tasks. Most of these show that AR applications add to the immersiveness of an experience. For his Bachelor Project, VU student Jurjen Braam researched to what extent AR technology makes sense for the task of annotating artworks.

To this end, Jurjen built a mobile application which allows experts or laypeople to add textual annotations to artworks in three different modes. One mode doesnt show the artwork, but allows for textual input, the 2nd mode shows the work in an image and allows for localised annotations. The last mode is the AR mode, which projects the artwork in the physical space, using the device camera and screen.

Three modes of the Application (Text, 2D, AR)

Jurjen evaluated the three modes through a small user study, which showed that immersion and enjoyment was highest in the AR mode but that this mode was least efficient. Also, participants indicated that for annotation tasks, larger screens would be preferable.

User evaluation in action

This research was a unique endeavour combining a proven technology (AR) and well-known task (Annotation) which identified interesting possibilities for follow-up research.

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A Voice Service Development Kit for the Kasadaka platform

[This post is written by André Baart and describes his MSc thesis]

While the internet usage in the developing world is still low, the adoption of simple mobile phones is widespread. A way to offer the advantages of the internet to these populations is voice-based information systems. The KasaDaka voice-services platform is aimed at providing voice-services in the context of ICT for Development (ICT4D). The platform is based on a Raspberry Pi and a GSM modem, which enables affordable voice-service hosting, using the locally available GSM network. The platform takes into account the special requirements of the ICT4D context, such as limited internet connectivity and low literacy rates.

This research focuses on lowering the barrier to entry of voice-service development, by reducing the skill set needed to do so. A Voice Service Development Kit (VSDK) is developed that allows the development of voice-services by deploying and customizing provided building-blocks. These building blocks each represent a type of interaction that is often found in voice-services. (for example a menu, user voice input or the playback of a message) The researcher argues that the simplification of voice-service development is an essential step towards sustainable voice-services in the ICT4D context; As this increases the potential number of local voice-service developers, hremoving the dependency on foreign (and thus expensive) developers and engineers. This simplification should ideally be achieved by providing a graphical interface to voice-service development.

The VSDK was evaluated during the ICT4D course at the Vrije Universiteit Amsterdam, where students built applications for various ICT4D use-cases using the VSDK. Afterwards a survey was conducted, which provided insight on the students’ experiences with voice-service development and the VSDK. From the results of the evaluation is concluded that the building-block approach to voice-service development used in the VSDK, is successful for the development of simple voice-services. It allows newcomers to (voice-service) development, to quickly develop (simple) voice-services from a graphical interface, without requiring programming experience.

The VSDK combined with the existing KasaDaka platform provides a good solution to the hosting and development of voice-services in the ICT4D context.

More details can be found in the complete thesis.A slidedeck is included below. You can find the VSDK code on Andre’s Github:


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Dancing and Semantics

This post describes the MSc theses of Ana-Liza Tjon-a-Pauw and Josien Jansen. 

As a semantic web researcher, it is hard to sometimes not see ontologies and triples in aspects of my private life. In this case, through my contacts with dancers and choreographers, I have since a long time been interested in exploring knowledge representation for dance. After a few failed attempts to get a research project funded, I decided to let enthusiastic MSc. students have a go to continue with this exploration. This year, two Information Sciences students, Josien Jansen and Ana-Liza Tjon-a-Pauw, were willing to take up this challenge, with great success. With their background as dancers they did not only have the necessary background knowledge at but also access to dancers who could act as study and test subjects.

The questions of the two projects was therefore: 1) How can we model and represent dance in a sensible manner so that computers can make sense of choreographs and 2) How can we communicate those choreographies to the dancers?

Screenshot of the mobile choreography assistant prototype

Josien’s thesis addressed this first question. Investigating to what extent choreographers can be supported by semi-automatic analysis of choreographies through the generation of new creative choreography elements. She conducted an online questionnaire among 54 choreographers. The results show that a significant subgroup is willing to use an automatic choreography assistant in their creative process. She further identified requirements for such an assistant, including the semantic levels at which should operate and communicate with the end-users. The requirements are used for a design of a choreography assistant “Dancepiration”, which we implemented as a mobile application. The tool allows choreographers to enter (parts of) a choreography and uses multiple strategies for generating creative variations in three dance styles. Josien  evaluated the tool in a user study where we test a) random variations and b) variations based on semantic distance in a dance ontology. The results show that this latter variant is better received by participants. We furthermore identify many differences between the varying dance styles to what extent the assistant supports creativity.

Four participants during the 2nd user experiment. From left to right this shows variations presented through textual, 2D animation, 3D animation, and auditory instructions.

In her thesis, Ana-Liza dove deeper into the human-computer interaction side of the story. Where Josien had classical ballet and modern dance as background and focus, Ana-Liza looked at Dancehall and Hip-Hop dance styles. For her project, Ana-Liza developed four prototypes that could communicate pieces of computer-generated choreography to dancers through Textual Descriptions, 2-D Animations, 3-D Animations, and Audio Descriptions. Each of these presentation methods has its own advantages and disadvantages, so Ana-Liza made an extensive user survey with seven domain experts (dancers). Despite the relatively small group of users, there was a clear preference for the 3-D animations. Based on the results, Ana-Liza also designed an interactive choreography assistant (IDCAT).

The combined theses formed the basis of a scientific article on dance representation and communication that was accepted for publication in the renowned ACE entertainment conference, co-authored by us and co-supervisor Frank Nack.

You can find more information here:

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Speech technology and colorization for audiovisual archives

[This post describes and is based on Rudy Marsman‘s MSc thesis and is partly based on a Dutch blog post by him]

The Netherlands Institute for Sound and Vision (NISV) archives Dutch broadcast TV and makes it available to researchers, professionals and the general public. One subset are the Polygoonjournaals (Public News broadcasts) that are published under open licenses as part of the OpenImages platform. NISV is also interested in exploring new ways and technologies to make interaction with the material easier and to increase exposure to their archives. In this context, Rudy explored two options.

Two stills from the film ‘Steegjes‘, with the right frame colorized. Source: Polygoon-Profilti (producent) / Nederlands Instituut voor Beeld en Geluid  / colorized by Rudy Marsman, CC BY-SA

One part of the research was the autonomous colorization of old black-and-white video footage using Neural Networks. Rudy used a pre-trained NN (Zhang et al 2016) that is able to colorize black and white images. Rudy developed a program to split videos into frames, colorize the individual frames using the NN and then ‘stitch’ them back together into colorized videos. The stunning results were very well received by NISV employees. Examples are shown below.

Tour de France 1954 (colorized by Rudy Marsman in 2016), Polygoon-Profilti (producent) / Nederlands Instituut voor Beeld en Geluid (beheerder), CC-BY SA

Results from the comparison of the different variants of the method on different corpora
Results from the comparison of the different variants of the method on different corpora

In the other part of his research, Rudy investigated to what extent the existing news broadcast corpus, with a voice-overs from the famous Philip Bloemendal  can be used to develop a modern text-to-speech engine with his voice. To do so he have mainly focused on natural language processing and the determination to what extent the language used by Bloemendal in the 1970s is still comparable enough to contemporary Dutch.

Rudy used precompiled automatic speech recognition (ASR) results to match words to sounds and developed a slot-and-filler text-to-speech system based on this. To increase the limited vocabulary, he implemented a number of strategies, including term-expansion through the use of Open Dutch Wordnet and smart decompounding (this mostly works for Dutch, mapping ‘sinterklaasoptocht’ to ‘sinterklaas’ and ‘optocht’. The different strategies were compared to a baseline. Rudy found that a combination of the two resulted in the best performance (see figure). For more information:

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