Augmented reality (AR) can bring a new interactive experience to the collaboration between users. When users are in the same place, there are two modes of joint operation for the same object: parallel-work (PW) and cross-work (CW). PW means two users perform their tasks, while CW means assisting each other. To investigate the difference that collaboration using PW and CW in an AR environment brings to users, we developed a two-person local collaboration system, LoCol. We designed and conducted user experiments by selecting the tasks of adjusting the virtual model of the assembly and adding missing boundaries in the model. The results showed that CW led to a higher sense of social coexistence while reducing workload. In terms of task completion time and accuracy, CW and PW each had advantages. We found that users generally want to reduce unnecessary repetitive operations and frequent movement by working with others. This is likely an important criterion for determining who is better suited for a particular job in either approach.

During component assembly, some operations must be completed by two or more workers due to the size or assembly mode. For instance, in manual riveting, two workers are positioned on either side of a steel plate, which blocks the view. Traditional collaborative approaches limit assembly efficiency and is difficult to ensure accurate and rapid interaction between workers. In this study, we developed an AR-Assisted Cooperative Assembly System (ARCoA) to address the issue. ARCoA allows users to view their partner’s key information that is occluded, including tools, gestures, orientations, and shared markers. Besides, we presented a user experiment that compared this method with the traditional approach. The results indicated that the new system could significantly improve assembly efficiency, system availability, and sense of social presence. Moreover, most users we surveyed preferred ARCoA. In the future, we will incorporate more functions and improve the accuracy of the system to solve complex multi-person collaborative problems.

Interactive tools make data analysis more efficient and more accessible to end-users by hiding the underlying query complexity and exposing interactive widgets for the parts of the query that matter to the analysis. However, creating custom tailored (i.e., precise) interfaces is very costly, and automated approaches are desirable. We propose a syntactic approach that uses queries from an analysis to generate a tailored interface. We model interface widgets as functions I(q) - > q' that modify the current analysis query q, and interfaces as the set of queries that its widgets can express. Our system, Precision Interfaces, analyzes structural changes between input queries from an analysis, and generates an output interface with widgets to express those changes. Our experiments on the Sloan Digital Sky Survey query log suggest that Precision Interfaces can generate useful interfaces for simple unanticipated tasks, and our optimizations can generate interfaces from logs of up to 10,000 queries in >10s.