Zoekresultaten

Creating the Difference

‘Creating the Difference’ is the theme of the 2014 edition of the Chi Sparks conference. It is also the challenge that the Human-Computer Interaction (HCI) community is facing today. HCI is a creative field where practitioners engage in design, production, and evaluation of interactions between people and digital technology. Creating excellent interfaces for people, they make a difference in media and systems that people are eager to use. Usability and user experience are fundamental for achieving this, as are abilities at the forefront of technology, but key to a successful difference is getting the right concepts, addressing genuine, intrinsic, human needs. Researchers and practitioners contribute to this area from theory as well as practice by sharing, discussing, and demonstrating new ideas and developments. This is how HCI creates a difference for society, for individuals, businesses, education, and organizations. The difference that an interactive product or service makes might lie in the concept of it but also in the making, the creation of details and the realisation. It is through powerful concepts and exceptional quality of realisation that innovation is truly achieved. At the Chi Sparks 2014 conference, researchers and practitioners in the HCI community convene to share and discuss their efforts on researching and developing methods, techniques, products, and services that enable people to have better interactions with systems and other people. The conference is hosted at The Hague University of Applied Sciences, and proudly built upon the previous conferences in Arnhem (2011) and Leiden (2009). Copyright van de individuele papers ligt bij de betreffende auteurs.

CyberParks - the interface between people, places and technology

Game developers need Lua air static analysis of Lua using interface models

Game development businesses often choose Lua for separating scripted game logic from reusable engine code. Lua can easily be embedded, has simple interfaces, and offers a powerful and extensible scripting language. Using Lua, developers can create prototypes and scripts at early development stages. However, when larger quantities of engine code and script are available, developers encounter maintainability and quality problems. First, the available automated solutions for interoperability do not take domain-specific optimizations into account. Maintaining a coupling by hand between the Lua interpreter and the engine code, usually in C++, is labour intensive and error-prone. Second, assessing the quality of Lua scripts is hard due to a lack of tools that support static analysis. Lua scripts for dynamic analysis only report warnings and errors at run-time and are limited to code coverage. A common solution to the first problem is developing an Interface Definition Language (IDL) from which ”glue code”, interoperability code between interfaces, is generated automatically. We address quality problems by proposing a method to complement techniques for Lua analysis. We introduce Lua AiR (Lua Analysis in Rascal), a framework for static analysis of Lua script in its embedded context, using IDL models and Rascal.

<i>Circus Noel</i>: A Case Study into Interaction and Interface Design for Cinematic VR

In this paper, we present the case study Circus Noel, a VR tight rope walking act with which the use of movement in cinematic VR was explored. We use the notion of spatial presence as discussed by Ryan, and its potential to induce an ‘illusion of non-mediation’. Seven users were interviewed about their experience. The results of this case study provide insight into the way a user’s body may be represented in cinematic VR by two moving feet, without disrupting the sense of presence.

Algorithmic Affordances in Recommender Interfaces

Recommenders play a significant role in our daily lives, making decisions for users on a regular basis. Their widespread adoption necessitates a thorough examination of how users interact with recommenders and the algorithms that drive them. An important form of interaction in these systems are algorithmic affordances: means that provide users with perceptible control over the algorithm by, for instance, providing context (‘find a movie for this profile’), weighing criteria (‘most important is the main actor’), or evaluating results (‘loved this movie’). The assumption is that these algorithmic affordances impact interaction qualities such as transparency, trust, autonomy, and serendipity, and as a result, they impact the user experience. Currently, the precise nature of the relation between algorithmic affordances, their specific implementations in the interface, interaction qualities, and user experience remains unclear. Subjects that will be discussed during the workshop, therefore, include but are not limited to the impact of algorithmic affordances and their implementations on interaction qualities, balances between cognitive overload and transparency in recommender interfaces containing algorithmic affordances; and reasons why research into these types of interfaces sometimes fails to cross the research-practice gap and are not landing in the design practice. As a potential solution the workshop committee proposes a library of examples of algorithmic affordances design patterns and their implementations in recommender interfaces enriched with academic research concerning their impact. The final part of the workshop will be dedicated to formulating guiding principles for such a library.

Building Smart Vision Blocks

Gepubliceerd in Mikroniek, nr. 6 2018 In manufacturing environments where collaborative robots are employed, conventional computer vision algorithms have trouble in the robust localisation and detection of products due to changing illumination conditions and shadows caused by a human sharing the workspace with the robotic system. In order to enhance the robustness of vision applications, machine learning with neural networks is explored. The performance of machine-learning algorithms versus conventional computer vision algorithms is studied by observing a generic user scenario for the manufacturing process: the assembly of a product by localisation, identification and manipulation of building blocks.