Generative Adversarial Networks (GANs) brought rapid developments in generating synthetic images by mimicking structures in the training data. With the list of application of GANs growing drastically, it has lately become an exciting technology to explore for designers to communicate their ideas and arts through technology and create engaging experiences for humans. Nevertheless, translating human experiences to artificial intelligence and creating visually pleasant imagery is a challenging task due to complex semantics of human perception. To address this issue, we introduce an iterative training approach in which the generated images are curated by humans and the most pleasing ones are fed back into the network to retrain. Additionally, we do a factorial analysis to investigate how the aesthetic quality and the diversity are affected by the size of training data and training length. In experiments, we validate that this method can significantly improve the aesthetic quality of generated images regardless of the dataset size and training length, however the use of smaller datasets comes with a cost of reduction in the image diversity and novelty in the output images. The aesthetic bias towards certain contexts can also deteriorate the diversity and affect the model evaluations. On the other hand, no significant relationship has been found regarding the training length, however this could possibly be due to instabilities that happen during the model convergence progress.

As music video streaming occupies a significant market share in how people consume music, gaining an understanding of user behavioural patterns becomes increasingly crucial. This understanding can enable better music video streaming experiences by tailoring them towards more personalized and user-centric designs. Though prior works have emphasized user behaviour during solely listening to music, understanding user actions/clicks while consuming music videos remains largely unexplored. Given the unique experience offered by the combination of audio and visual elements, there is a need for focused research in this area. Therefore this study attempts to bridge this research gap by collecting and analysing a large dataset of streaming sessions from a music video streaming company - XITE. In total, we analyzed 1.8 million sessions from approximately 270,000 unique users. The behaviour exhibited during those sessions is interpreted as a language and modelled using the Language Model - Doc2Vec. This facilitated the conversion of session action sequences into embeddings. Our findings suggest that music video streaming sessions exhibit cohesive user interaction patterns, which can be grouped into distinct clusters, thereby enabling the detection of distinct behavioural patterns across user sessions. Furthermore, previous studies have indicated that user interactions with multimedia streaming platforms can be influenced by the context in which content is consumed. Extending these findings, our analysis of behavioural clusters revealed that certain user behaviours while consuming music videos are associated with specific music video genres and temporal factors. For instance, we discovered that passive sessions predominantly commence around 10 am, while sessions requiring more active engagement typically start in the evening. The insights derived from this study are valuable for improving user-centric design in music video streaming platforms and providing businesses with data-driven recommendations for strategic planning.

Web surveys have increasingly been used to collect data from respondents over the years. They offer several advantages compared to other methods of obtaining data. Researchers benefit from a broad demographic representation to make generalized conclusions, and satisfaction surveys allow employees to explain shortcomings or improvements anonymously. Both examples demand comprehensive information, thereby requiring a lengthy survey. However, dropout increases with the length of a survey, which is a big problem on web surveys as it decreases the statistical significance of the results. Proposed solutions, such as reducing the number of questions or rewarding respondents with an incentive, may not always be feasible due to the preciseness of information required or limited financial capabilities. Achievement primes have been shown to reduce dropout on short surveys targeting extrinsically motivated respondents without additional costs or the need to reduce survey length. As repeated exposure to primes reinforces the stimuli, long surveys may also benefit from achievement primes. In this study, respondents are exposed to a questionnaire of more than 15 minutes on health whilst working behind a computer containing either no prime, passive achievement primes, or active achievement primes. Besides extrinsically motivated respondents, recruited via the crowdworking platform Prolific, intrinsically motivated respondents are also targeted in this study, recruited via snowball sampling. Through a 2 times 3 factorial design, we discovered no statistical difference in dropout, perceived workload, and user engagement across the three questionnaire variants when evaluating intrinsically (N=88) and extrinsically motivated respondents (N=140) individually. By comparing intrinsically with extrinsically motivated respondents, we discovered extrinsically motivated respondents were more engaged and dropped out less.

Due to COVID-19 the overall wellbeing worldwide decreased. Assessing and improving wellbeing became a more important subject. This article describes the design research that uses the My Wellness Check survey created by the Delft University for Technology and aims to create a dashboard for wellbeing. That includes a way of authenticating users to very sensitive data. Also, finding ways how to improve personal wellbeing by using a recommendation system based on different types of filtering and the additional elements that are needed for a recommendation system.

This paper aims to evaluate which automated measures of aesthetic beauty are the best predictors for human ratings of aesthetics and proposes that typicality and novelty may increase the correlation between the two. To study the correlation between these metrics, a literature study was performed to find a select amount of potentially good predictors, a pipeline was created to extract these values from each image within our dataset, a survey was conducted to vote for which images were considered most aesthetic, and finally regression analysis was performed to see which metrics offered highest correlation with the human rating data. From this we could see there were indeed a number of automated metrics that consistently scored high as predictors for the human aesthetic ratings and there was a slight improvement in the fit of the prediction model upon including novelty as a feature. However, at this moment, the improvement is not significant to conclude these features are better at predicting human ratings.

Microtask crowdsource workers are negatively influenced, mentally as well as physically, by the repetitive nature of the tasks they perform. Research is ongoing on whether using a gesture-based input technique could mitigate these negative effects. This paper identifies possible ways that using gestures as an alternative input modality could lead to exclusion by analysing survey responses, where n=10. While further research is necessary, there are indications that this could lead to cultural and physical exclusion of certain groups. This paper is not meant to discourage using gestures as an alternative method of input but is solely meant to bring attention to possible risks to take into account.

The health of micro-task crowdsourcing workers, also called crowdworkers, is something that is overlooked in the micro-task crowdsourcing literature. Due to repetitive tasks, they can develop Repetitive Strain Injuries. To look into other ways of navigating Crowdsourcing Work Environments (CSWEs) outside the mouse and keyboard paradigm, we consider webcam-based hand-tracking in this paper. The main question we considered was which hand gestures were most suitable for navigating CSWEs. By having micro-task crowdworkers (n=14) test five methods of navigating CSWEs, we found that gestures which were considered easiest and most useful were those that specified a single action in an interface catered to hand-tracking controls. Gestures which attempt to directly replace the mouse in a regular mouse-oriented interface were rated lower on usefulness and ease of use. We also found that most crowdworkers were unlikely to use hand gestures for progressing through related subtasks, since they were considered harder than using the keyboard and mouse.