In this study, we tested the ability of a machine-learning model (ML) to evaluate different user interface designs within the defined boundaries of some given software. Our approach used ML to automatically evaluate existing and new web application designs and provide developers and designers with a benchmark for choosing the most user-friendly and effective design. The model is also useful for any other software in which the user has different options to choose from or where choice depends on user knowledge, such as quizzes in e-learning. The model can rank accessible designs and evaluate the accessibility of new designs. We used an ensemble model with a custom multi-channel convolutional neural network (CNN) and an ensemble model with a standard architecture with multiple versions of down-sampled input images and compared the results. We also describe our data preparation process. The results of our research show that ML algorithms can estimate the future performance of completely new user interfaces within the given elements of user interface design, especially for color/contrast and font/layout.

The integration of the Internet of Things (IoT) and artificial intelligence (AI) is critical to the advancement of ambient intelligence (AmI), as it enables systems to understand contextual information and react accordingly. While many solutions focus on user-centric services that provide enhanced comfort and support, few expand on scenarios in which multiple users are present simultaneously, leaving a significant gap in service provisioning. To address this problem, this paper presents a multi-agent system in which software agents, aware of context, advocate for their users’ preferences and negotiate service settings to achieve solutions that satisfy everyone, taking into account users’ flexibility. The proposed negotiation algorithm is illustrated through a smart lighting use case, and the results are analyzed in terms of the concrete preferences defined by the user and the selected settings resulting from the negotiation in regard to user flexibility.

Augmented reality is currently a very interesting technology due to the possibility of wide use on various affordable devices and at the same time high engagement and motivation potential for the user. These features are especially important in the educational field. As the number of developed prototypes in this area is rapidly increasing, only a limited part of them considers the implementation of accessibility, although accessibility is strongly required especially in educational solutions. The paper presents accessible mobile application for learning about geometry, specifically about geometry bodies, and this topic is often a challenge for children with learning difficulties. In addition to the accessibility option implemented, the actual design of the best-known universal design principles within the application is described, as accessibility relies heavily on them. The results of the application evaluation are presented, leading to the conclusion that this and future AR software solutions have great potential to be included in the curriculum as a tool for inclusive education, but only if the concept of accessibility is properly understood applied.

Deployment of 5G mobile communication networks has released the full potential of Internet of Things, enabling emerging interactive technologies such as Augmented Reality and Virtual Reality, as well as their merge. In this paper, we consider the impact of introducing 5G network on the perceptual user experience for an Augmented Reality application – specifically, the educational mobile web-based one as the most promising with this regard. However, it is an extremely demanding application in which various multimedia processing tasks are performed. Therefore, we used appropriate simulations to validate the expectations that 5G network enabling higher data rates and low latency, would significantly improve the application-layer quality of experience, with a mobile web access in particular. These expectations were found to be fulfilled, as, specifically, the required time for the end user to load the tested web-based AR application and required Script, has been drastically reduced with respect to the previous generations of mobile networks.