Abstract
In this paper, we aimed at exploring the use of iconographic navigation to support inclusive and accessible search for Web content through an extension for Google Chrome browser, entitled Extension Icon. Despite Extension Icon was developed to be a solution that allows people with intellectual disabilities to search autonomously using an iconographic navigation, supported by platforms as Vimeo and YouTube, it intends to be an accessible solution for ALL users. Through participatory design, the solution was iteratively developed and with the outcomes it was obtained two versions of this solution. Therefore, in this paper we described the design, implementation and assessment of two Extension Icon versions. Specifically, twenty-eight participants were invited - 18 people with intellectual disabilities and 10 people without of disability - in order to evaluate and participated in the iterative development of the solution. The user preliminary feedbacks showed a major concern regarding the graphical interface therefore it was redesigned to improve and present a more appealing interface. Overall, user tests carried out with the two versions showed and effective, efficient and satisfactory user interaction. © 2020, Springer Nature Switzerland AG.

Abstract
Application execution required in cloud and fog architectures are generally heterogeneous in terms of device and application contexts. Scaling these requirements on these architectures is an optimization problem with multiple restrictions. Despite countless efforts, task scheduling in these architectures continue to present some enticing challenges that lead us to question how tasks are routed between different physical devices, fog nodes and cloud. In fog, due to its density and heterogeneity of devices, the scheduling is very complex and, in the literature, there are still few studies that have been conducted. However, scheduling in the cloud has been widely studied. Nonetheless, many surveys address this issue from the perspective of service providers or optimize application quality of service (QoS) levels. Also, they ignore contextual information at the level of the device and end users and their user experiences. In this paper, we conducted a review of the literature on the main task scheduling algorithms in cloud and fog architecture; we studied and discussed their limitations, and we also explored and suggested some perspectives for improvement.

Abstract
Peripheral artery disease (PAD) main symptom is intermittent claudication, causing pain and limiting the walking abilities of patients, forcing individuals to temporarily stop walking. One treatment advised to counteract the effects of this disease is the practice of physical exercise with monitoring. Currently the monitored exercise programs are applied at the hospital, so some patients have to travel long distances three times a week, with high costs and low adherence of the patients. This paper presents the cocreation process of a mobile application for quantified supervised home-based exercise therapy on PAD patients. The study aimed to design a solution adapted to users' needs, which collects the necessary information for the therapy supervision by health professionals. The users' behaviour with the application allowed the assessment to a set of limitations and potential sources of noise in the supervision data that suggest the evolution to a pervasive solution, by minimizing, or even eliminating, the interaction with the users. The developed tool is a first step towards the creation of a technological ecosystem for the prescription of supervised therapeutic physical exercise, which leverages self-care and allows access to this type of therapy to the entire population. Cardiovascular disease represents a considerable economic burden to society, therefore effective preventive measures are necessary.

Abstract
With cutting edge scientific breakthroughs, human-centred algorithmic approaches have proliferated in recent years and information technology (IT) has begun to redesign socio-technical systems in the context of human-AI collaboration. As a result, distinct forms of interaction have emerged in tandem with the proliferation of infrastructures aiding interdisciplinary work practices and research teams. Concomitantly, large volumes of heterogeneous datasets are produced and consumed at a rapid pace across many scientific domains. This results in difficulties in the reliable analysis of scientific production since current tools and algorithms are not necessarily able to provide acceptable levels of accuracy when analyzing the content and impact of publication records from large continuous scientific data streams. On the other hand, humans cannot consider all the information available and may be adversely influenced by extraneous factors. Using this rationale, we propose an initial design of a human-AI enabled pipeline for performing scientometric analyses that exploits the intersection between human behavior and machine intelligence. The contribution is a model for incorporating central principles of human-machine symbiosis (HMS) into scientometric workflows, demonstrating how hybrid intelligence systems can drive and encapsulate the future of research evaluation.

Abstract
The harsh impacts of extreme weather events like cyclones or precipitation extremes are increasingly being felt with hazardous consequences. These extreme events are exceptions to well-known weather patterns and therefore are not forecasted with current automatic computational methods. In this context, the use of human computation to annotate extreme atmospheric phenomena could provide novel insights for computational forecasting algorithms and a step forward in climate change research by enabling the early detection of abnormal weather conditions. However, existing crowd computing solutions have technological limitations and show several gaps when involving expert crowds. This paper presents a research approach to fulfill some of the technological and knowledge gaps for expert crowds’ participation. A case study on expert annotation of extreme atmospheric phenomena is used as a baseline for an innovative architecture able to support expert crowdsourcing. The full stack service-oriented architecture ensures interoperability and provides an end-to-end approach able to fetch weather data from international databases, generating experts’ visualizations (weather maps), annotating data by expert crowds, and delivering annotated data for processing weather forecasts. An implementation of the architecture suggests that it can deliver an effective mechanism for expert crowd work while solving some of the identified issues with extant platforms. Therefore, we conclude that the proposed architecture has the potential to contribute as an effective annotation solution for extreme weather events. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
This paper presents an action research study aiming to motivate undergraduate students to develop their computer programming learning skills, particularly within the transition from beginner to proficient level. The SimProgramming motivational approach is presented as a didactic proposal for this context. From the results of this iterative research process, we concluded that SimProgramming is a promising tool for teaching computer programming skills in intermediate classes, with potential to be used and/or applied in other educational contexts. © 2021, Springer Nature Switzerland AG.