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.

Abstract
Virtual worlds, particularly those able to provide a three-dimensional physical space, have features that make them suitable to support collaborative activities. These features distinguish virtual worlds from other collaboration tools, but current taxonomies of the field of Computer-Supported Cooperative Work do not account for several distinctive features of virtual worlds, namely those related with non-verbal communication. We intended to find out how the use of an avatar, gestures, spatial sounds, etc., influence collaboration in order to be able to include non-verbal communication in taxonomies of the field Computer-Supported Cooperative Work. Several cases of collaboration in virtual worlds are analysed, to find the impact of these non-verbal characteristics of virtual worlds. We proposed adding the concept of Presence to taxonomies of Computer-Supported Cooperative Work and contribute with guidance for future taxonomy development that includes it as a new dimension. This new dimension of Presence is subdivided into "avatar" and "physical space" subdimensions. In turn, these are divided into "physical appearance", "gestures, sounds and animations" and "focus, nimbus and aura"; "environment" and "objects / artefacts". This new taxonomy-development proposal may contribute to inform better design of virtual worlds in support of cooperative work.

Abstract
Understanding the intellectual landscape of scientific communities and their collaborations has become an indispensable part of research per se. In this regard, measuring similarities among scientific documents can help researchers to identify groups with similar interests as a basis for strengthening collaboration and university-industry linkages. To this end, we intend to evaluate the performance of hybrid crowd-computing methods in measuring the similarity between document pairs by comparing the results achieved by crowds and artificial intelligence (AI) algorithms. That said, in this paper we designed two types of experiments to illustrate some issues in calculating how similar an automatic solution is to a given ground truth. In the first type of experiments, we created a crowdsourcing campaign consisting of four human intelligence tasks (HITs) in which the participants had to indicate whether or not a set of papers belonged to the same author. The second type involves a set of natural language processing (NLP) processes in which we used the TF-IDF measure and the Bidirectional Encoder Representation from Transformers (BERT) model. The results of the two types of experiments carried out in this study provide preliminary insight into detecting major contributions from human-AI cooperation at similarity calculation in order to achieve better decision support. We believe that in this case decision makers can be better informed about potential collaborators based on content-based insights enhanced by hybrid human-AI mechanisms.

Abstract
Application execution requests in cloud architecture and fog paradigm are generally heterogeneous and scheduling in these architectures is an optimization problem with multiple constraints. In this paper, we conducted a survey on the related works on scheduling in cloud architecture and fog paradigm, we identify their limitations, we explore some prospects for improvements and we propose a context-aware scheduling model for fog paradigm. The proposed solution uses Min-Max normalization, to solve heterogeneity and normalize the different context parameters. The priority of requests is set by applying the Multiple Linear Regression analysis technique and the scheduling is done using the Multiobjective Nonlinear Programming Optimization technique. The results obtained from simulations on iFogSim toolkit, show that our proposal performs better compared to the non-context-aware proposals.

Abstract
With the increasing development of human-AI teaming structures within and across geographies, the time is ripe for a continuous and objective look at the predictors, barriers, and facilitators of human-AI scientific collaboration from a multidisciplinary point of view. This paper aims at contributing to this end by exploiting a set of factors affecting attitudes towards the adoption of human-AI interaction into scientific work settings. In particular, we are interested in identifying the determinants of trust and acceptability when considering the combination of hybrid human-AI approaches for improving research practices. This includes the way as researchers assume human-centered artificial intelligence (AI) and crowdsourcing as valid mechanisms for aiding their tasks. Through the lens of a unified theory of acceptance and use of technology (UTAUT) combined with an extended technology acceptance model (TAM), we pursue insights on the perceived usefulness, potential blockers, and adoption drivers that may be representative of the intention to use hybrid intelligence systems as a way of unveiling unknown patterns from large amounts of data and thus enabling novel scientific discoveries.

Abstract
This research aims at investigating which web accessibility and usability tools, with the focus on the ones that warrant automation, are available to assess the quality of interfaces for people with disabilities and/or special needs, enabling them to navigate and interact with web and mobile apps. Our search strategy identified 72 scientific articles of the most rated conferences and scientific journals, from which 16 were considered for the systematic literature review (SLR). We found that, despite the existence of various tools either for web or mobile apps, they are not completely effective, covering less than 40% of all the problems encountered. Also, no tool was found capable of adapting the application interfaces according to the type of disabilities that users may present. Therefore, a new tool could be a welcome advancement to provide full accessible and usable experiences.