Abstract
The structure and evolution of a scientific research community can be quantitatively assessed taking into account the interactions between scientific agents dispersed geographically. In the recent years, CSCW has stabilized as a cross-disciplinary field suffering significant changes in its core structure, and there is limited understanding about the factors influencing the nature and progress of collaborative computing research. In this paper, we measure the correlation between a set of features related to the influence of collaboration types on the number of citations as well as the geographical distribution of the accumulated contribution to the CSCW literature. Overall, our work can represent a starting point to demonstrate how the study of scientific collaboration can partly explain the variations in the number of citations, frequency of papers, and topics addressed.

Abstract

Abstract
Crowdsourcing has shown to be a valuable problem-solving approach to handle the increasing complexity and scale of tasks for which the current AI algorithms are still struggling. Crowd intelligence can be particularly useful to train and supervise AI systems in a symbiotic, co-evolutionary relationship that raises long-term research challenges to the hybrid, crowd-computing design space. With the increase in the scale of mixed-initiative approaches, we need to gain a better understanding of the implications of crowd-powered systems as a scaffold for AI through the study of massive crowd-machine interactions. In this paper, we identify some open challenges and design implications for future crowd-AI hybrid systems. A framework is also proposed based on the practical challenges of addressing human-centered AI methods and processes.

Abstract
A substantial amount of work is often overlooked due to the exponential rate of growth in global scientific output across all disciplines. Current approaches for addressing this issue are usually limited in scope and often restrict the possibility of obtaining multidisciplinary views in practice. To tackle this problem, researchers can now leverage an ecosystem of citizens, volunteers and crowd workers to perform complex tasks that are either difficult for humans and machines to solve alone. Motivated by the idea that human crowds and computer algorithms have complementary strengths, we present an approach where the machine will learn from crowd behavior in an iterative way. This approach is embodied in the architecture of SciCrowd, a crowd-powered human-machine hybrid system designed to improve the analysis and processing of large amounts of publication records. To validate the proposal's feasibility, a prototype was developed and an initial evaluation was conducted to measure its robustness and reliability. We conclude this paper with a set of implications for design.

Abstract
In this article we present a game interface, using audio input and output, aiming to provide the concept of interactive narrative to users with visual or motor disability. The solution lets users choose the direction of the story, triggering several alternate endings and thus creating a dynamic and creative narrative. The application development process is described here from the design, implementation and evaluation. In the evaluation phase, we performed user tests with five participants with visual and motor disability. Thus, we record three metrics: effectiveness, success of the task (reaching one of the possible endings); efficiency, time needed to complete the story; and satisfaction, comfort and wellbeing of the user during the interaction. The result was positive, all participants successfully completed the application, and there were no withdrawals. Four in five wanted to repeat the experience and try to reach another end of the story.

Abstract