Abstract
Tailoring of the user experience to each individuals needs and preferences can lead to more accessible solutions. Adaptation has a key role on matching the systems characteristics with the user needs. This can be achieved with personalization (system-driven adaptation) or customization (user-driven adaptation). Personalization have good results on matching the user needs but raises concerns about privacy and how the information is retrieved. Customizations require that the user manually choose the preferences configuration. This article proposed two versions of a web wizard to elicit the accessibility preferences. One version was based on a quiz and the other one presented small interactive activities. The activities version was proposed to help reduce the burden of configuration by implicitly eliciting the user preferences through interactive small activities. The preliminary results with healthy participants suggest that both versions obtained a positive evaluation. However, there was no major difference between each wizard. The causes of these findings are discussed.

Abstract
Despite decades of research and development in named entity resolution, dealing with name ambiguity is still a challenging issue for many bibliometric-enhanced information retrieval (IR) tasks. As new bibliographic datasets are created as a result of the upward growth of publication records worldwide, more problems arise when considering the effects of errors resulting from missing data fields, duplicate entities, misspellings, extra characters, etc. As these concerns tend to be of large-scale, both the general consistency and the quality of electronic data are largely affected. This paper presents an approach to handle these name ambiguity problems through the use of crowdsourcing as a complementary means to traditional unsupervised approaches. To this end, we present "AuthCrowd", a crowdsourcing system with the ability to decompose named entity disambiguation and entity matching tasks. Experimental results on a real-world dataset of publicly available papers published in peer-reviewed venues demonstrate the potential of our proposed approach for improving author name disambiguation. The findings further highlight the importance of adopting hybrid crowd-algorithm collaboration strategies, especially for handling complexity and quantifying bias when working with large amounts of data.

Abstract
Over the past decades, the use of digital technologies to support participatory urban planning and design has been repeatedly described as a crucial instrument and critical building block for tackling historical problems of participation in such processes. Social media, e-participation platforms, and crowdsourcing applications are examples of technologies that can involve citizens in decision-making processes and thus leverage the benefits of collective intelligence. However, despite the extensive use of social media platforms, old problems related to engagement and participation still occur in digital initiatives. Successful collaboration examples between citizens, policymakers, and strategic stakeholders are still scarce based on online social practices. This study aims to introduce a collective intelligence model, which combines crowdsourcing and social storytelling to support participatory urban planning and design from a bottom-up perspective. The paper concludes by discussing a scenario where citizens can engage in mapping, taking photos, sending ideas, or even creating collective stories about their university issues in a post-pandemic future.

Abstract
The use of VR in industrial training contributes to reduce costs and risks, supporting more frequent and diversified use of experiential learning activities, an approach with proven results. In this work, we present an innovative immersive authoring tool for experiential learning in VR-based training. It enables a trainer to structure an entire VR training course in an immersive environment, defining its sub-components, models, tools, and settings, as well as specifying by demonstration the actions to be performed by trainees. The trainees performing the immersive training course have their actions recorded and matched to the ones specified by the trainer.

Abstract
According to the author's knowledge task scheduling in fog paradigm is highly complex and in the literature there are still few studies on it. In the cloud architecture, it is widely studied and in many researches, it is approached from the perspective of service providers. Trying to bring innovative contributions in these areas, in this paper, we propose a solution to the context-aware task-scheduling problem for fog paradigm. In our proposal, different context parameters are normalized through Min Max normalization, requisition priorities are defined through the application of the Multiple Linear Regression (MLR) technique and scheduling is performed using Multi-Objective Non-Linear Programming Optimization (MONLIP) technique.

Abstract
Current research shows discrepancies in the gait pattern of patients with peripheral artery disease (PAD). Some studies suggest a change in gait pattern after the manifestation of claudication pain while others found patients with PAD already show a pathological gait, even before the intermittent claudication arises, and no change once the pain manifests. This exploratory research examines what change in gait pattern can be detected once claudication pain arises with the help of an accelerometer embedded in a smartphone. This study aims to contribute to the development of a process to remotely collect accelerometer data in a mobile health application, which then can be used to analyze gait pattern in patients with PAD on a larger scale. The findings of this exploratory study show that processing and analyzing accelerometer data from smartphone for gait analysis is viable and establishes a methodology for collecting and analyzing PAD patients' data. The major limitation of this study is the small sample size that do not provide the necessary reliability of the findings, about gait pattern changes. Further gait data should be collected to help understanding the gait pattern of PAD patients and build an extended dataset to be analyzed at a larger scale.