Abstract
Recently, citizen involvement has been increasingly used in urban disaster prevention and management, taking advantage of new ubiquitous and collaborative technologies. This scenario has created a unique opportunity to leverage the work of crowds of volunteers. As a result, crowdsourcing approaches for disaster prevention and management have been proposed and evaluated. However, the articulation of citizens, tasks, and outcomes as a continuous flow of knowledge generation reveals a complex ecosystem that requires coordination efforts to manage interdependencies in crowd work. To tackle this challenging problem, this paper extends to the context of urban emergency management the results of a previous study that investigates how crowd work is managed in crowdsourcing platforms applied to urban planning. The goal is to understand how crowdsourcing techniques and quality control dimensions used in urban planning could be used to support urban emergency management, especially in the context of mining-related dam outages. Through a systematic literature review, our study makes a comparison between crowdsourcing tools designed for urban planning and urban emergency management and proposes a five-dimension typology of quality in crowdsourcing, which can be leveraged for optimizing urban planning and emergency management processes.

Abstract
In recent years, social curation technology has been successfully promoted as a means of addressing the problem of involving citizens with news content. Sites like Facebook, Reddit and Storify, just to name a few, allow their users to discuss, comment, share, identify and review news content from various sources. A key goal of this paper is to analyze the process of social validation in the context of Acropolis, a social computing platform that allows citizens to build and share their own narratives about complex or long-term news stories. The sense of social validation determines an increase in self-esteem, self-confidence, and instant happiness, when conveniently engendered in virtual environments.

Abstract
The aim of the present study is to analyse the effect of the alcohol on driving considering distinct blood alcohol concentration up to 0.49 g/l, which is the legal limit in some countries such as Portugal. Additionally, a comparison between the ascending with the descending phases is considered. A driving simulator is used to ensure the safety of the participants. Two questionnaires were applied to analyse the perception of the 20 participants about the alcohol sensation and confidence to drive as well as the driving performance facing two critical events. Also, the number of correct answers to simple questions was analysed to evaluate the cognitive ability. The study results indicate that the cognitive ability and perception of the participants are affected while the driving performance is less affected.

Abstract

Abstract
Trusted open data can be used for auditing, accountability, business development, or as an anti-corruption mechanism. Metadata information can address provenance concerns, and trust issues can somehow be mitigated by digital signatures. Those approaches can trace the data origin, but usually lack information about the transformation process. Creating trust in an open data service through technology can reduce the need for third-party certifications, and creating a distributed consensus mechanism capable of validating all the transformations can guarantee that the datasets are reliable and easy to use. This work aims to leverage blockchain technologies to track open data transformations, allowing consumers to verify the data using a distributed ledger, and providing a mechanism capable of publishing trusted transformed data without relying on third-party certifications. To validate the proposed approach, use cases for data transformation will be used. The consensus protocol must be capable of validating the transformations according to a predefined algorithm, the provider must be capable of publishing verifiable transformed data, and the consumer should be able to check if a dataset originated by a transformation is legit.

Abstract
Software analytics supports data-driven decision making, which allows software practitioners to leverage valuable insights from software data to improve their processes and many quality aspects of the software. In this paper, we present an artifact designed from a set of patterns to support agile teams to plan and manage software analysis activities, named Software Analytics Canvas. Further, we report the study undertaken to evaluate the ease of use and the utility of our canvas from the practitioners' viewpoint, and a participatory design session carried out to collect information about possible artifact improvements. In general, subjects found the artifact useful, but some of them reported difficulties in learning and understanding how to use it. In the participatory design, they pointed out improvement points and a new layout for the canvas components. The results of both studies helped us refine the proposed artifact, improving both the terms used in each element and the layout of the blocks to make more sense for its users.