Designers still often create a specific user interface for every target platform they wish to support, which is timeconsuming and error-prone. The need for a multi-platform user interface design approach that designers feel comfortable with increases as people expect their applications and data to go where they go. We present Gummy, a multiplatform graphical user interface builder that can generate an initial design for a new platform by adapting and combining features of existing user interfaces created for the same application. Our approach makes it easy to target new platforms and keep all user interfaces consistent without requiring designers to considerably change their work practice.

Model-driven development of user interfaces has become increasingly powerful in recent years. Unfortunately, model-driven approaches have the inherent limitation that they cannot handle the informal nature of some of the artifacts used in truly multidisciplinary user interface development such as storyboards, sketches, scenarios and personas. In this chapter, we present an approach and tool support for multidisciplinary user interface development bridging informal and formal artifacts in the design and development process. Key features of the approach are the usage of annotated storyboards, which can be connected to other models through an underlying meta-model, and cross-toolkit design support based on an abstract user interface model.

As social networks offer a vast amount of additional information to enrich standard learning algorithms, the most challenging part is extracting relevant information from networked data. Fraudulent behavior is imperceptibly concealed both in local and relational data, making it even harder to define useful input for prediction models. Starting from expert knowledge, this paper succeeds to efficiently incorporate social network effects to detect fraud for the Belgian governmental social security institution, and to improve the performance of traditional non-relational fraud prediction tasks. As there are many types of social security fraud, this paper concentrates on payment fraud, predicting which companies intentionally disobey their payment duties to the government. We introduce a new fraudulent structure, the so-called spider constructions, which can easily be translated in terms of social networks and included in the learning algorithms. Focusing on the egonet of each company, the proposed method can handle large scale networks. In order to face the skewed class distribution, the SMOTE approach is applied to rebalance the data. The models were trained on different timestamps and evaluated on varying time windows. Using techniques as Random Forest, logistic regression and Naive Bayes, this paper shows that the combined relational model improves the AUC score and the precision of the predictions in comparison to the base scenario where only local variables are used.

Abstract1 Professional video searchers typically have to search for particular video fragments in a vast video archive that contains many hours of video data. Without having the right video archive exploration tools, this is a difficult and time consuming task that induces hours of video skimming. We propose the video archive explorer, a video exploration tool that provides visual representations of automatically detected concepts to facilitate individual and collaborative video search tasks. This video archive explorer is developed by employing a user-centred methodology, which ensures that the tool is more likely to fit to the end user needs. A qualitative evaluation with professional video searchers shows that the combination of automatic video indexing, interactive visualisations and user-centred design can result in an increased usability, user satisfaction and productivity.

Currently, it is difficult for a designer to create user interfaces that are of high aesthetic quality for a continuously growing range of devices with varied screen sizes. Most existing approaches use abstractions that only support form based user interfaces. These user interfaces may be usable but are of low aesthetic quality. In this paper, we present a technique to design adaptive graphical user interfaces by example (i.e. user interfaces that can adapt to the target platform, the user, etc.), which can produce user interfaces of high aesthetic quality while reducing the development cost inherent to manual approaches. Designing adaptive user interfaces by example could lead to a new generation of design tools that put adaptive user interface development within reach of designers as well as developers.