Abstract
Over the past decade, online crowdsourcing has established itself as an emerging paradigm that industry and government have been using to harness the cognitive abilities of a multitude of users distributed around the world. In this context, microtask crowdsourcing has become the method of choice for addressing a wide range of diverse problems. Microtasks typically require a minimum of time and cognitive effort, but combined individual efforts have made it possible to accomplish great achievements. The goal of this paper is to contribute to the ongoing effort of understanding whether the same success that microtask crowdsourcing has achieved in other domains can be obtained in the field of social news curation. In particular, we ask whether it is possible to turn online news curation, typically a social and collaborative activity on the Web, into a model in which curatorial activities are mapped into microtasks to be performed by a crowd of online users.

Abstract
Infrastructure-as-Code tools, such as Docker and Docker Compose, play a crucial role in the development and orchestration of cloud-native and at-scale software. However, as IaC relies mostly on the development of text-only specifications, these are prone to misconfigurations and hard to debug. Several works suggest the use of models as a way to abstract their complexity, and some point to the use of visual metaphors. Yet, few empirical studies exist in this domain. We propose a visual programming notation and environment for specifying Docker Compose configurations and proceed to empirically validate its merits when compared with the standard text-only specification. The goal of this work is to produce evidence of the impact that visual approaches may have on the development of IaC. We observe that the use of our solution reduced the development time and error proneness, primarily for configurations definition activities. We also observed a preference for the approach in terms of ease of use, a positive sentiment of its usefulness and intention to use. © 2020 ACM.

Abstract
Software documentation is an important part of the captured knowledge of a software project and documentation patterns have often been used as a systematic way to describe good practices on software documentation. Still, many software teams are challenged by what to document, how to keep the documentation consistent and how to make their consumers aware of the relevant documents. A literature review was done over 14 publications and identified 16 quality attributes and 114 patterns about software documentation. This knowledge was analysed and classified and led to the proposal of new categories and relationships between the existing patterns. These are depicted as a new pattern map that provides a new perspective of documentation patterns and can be used to guide teams in adopting software documentation practices. © 2020 Owner/Author.

Abstract
Interactive tabletops offer unique collaborative features, particularly their size, geometry, orientation and, more importantly, the ability to support multi-user interaction. Although previous efforts were made to make interactive tabletops accessible to blind people, the potential to use them in collaborative activities remains unexplored. In this paper, we present the design and implementation of a multi-user auditory display for interactive tabletops, supporting three feedback modes that vary on how much information about the partners' actions is conveyed. We conducted a user study with ten blind people to assess the effect of feedback modes on workspace awareness and task performance. Furthermore, we analyze the type of awareness information exchanged and the emergent collaboration strategies. Finally, we provide implications for the design of future tabletop collaborative tools for blind users. © 2020 ACM.

Abstract
An approach to analyzing Streaming Big Data as it comes in while maintaining the proper context of past events is to employ contiguous visualizations with an increasingly aggressive aggregation degree. This allows for the most recent data to be displayed in detail, while older data is shown in an aggregated form according to how long ago it was received. However, the transitions applied between visualizations with different aggregations must not compromise the understandability of the data flow. Particularly, new data should be perceived considering the context established by older data, and the visualizations should not be perceived as independent or unconnected. In this paper, we present the first study on transitions between two contiguous visualizations, focusing on time series data. We developed several animated transitions between a scatter plot, where all data points are individually represented as they arrive, and other visualizations where data is displayed in an aggregated form. We then conducted a user evaluation to assess the most appealing and effective transitions that allow for the best comprehension of the displayed data for each visualization pair.

Abstract
In InfoVis design, visualizations make use of pre-attentive features to highlight visual artifacts and guide users' perception into relevant information during primitive visual tasks. These are supported by visual marks such as dots, lines, and areas. However, research assumes our pre-attentive processing only allows us to detect specific features in charts. We argue that a visualization can be completely perceived pre-attentively and still convey relevant information. In this work, by combining cognitive perception and psychophysics, we executed a user study with six primitive visual tasks to verify if they could be performed pre-attentively. The tasks were to find: horizontal and vertical positions, length and slope of lines, size of areas, and color luminance intensity. Users were presented with very simple visualizations, with one encoded value at a time, allowing us to assess the accuracy and response time. Our results showed that horizontal position identification is the most accurate and fastest task to do, and the color luminance intensity identification task is the worst. We believe our study is the first step into a fresh field called Incidental Visualizations, where visualizations are meant to be seen at-a-glance, and with little effort.