We have developed a novel and comprehensive visualization design for group decision making that can support decision makers in modeling and comparing stakeholder preferences. We have implemented a prototype based on this design, and we have obtained preliminary evidence of its utility by means of a user study, wherein groups of individuals participated in a decision scenario that is held as a standard in group dynamics literature

Smartphone reminding apps can compensate for memory impairment after acquired brain injury (ABI). In the absence of a caregiver, users must enter reminders themselves if the apps are going to help them. Poor memory and apathy associated with ABI can result in failure to initiate such configuration behaviour and the benefits of reminder apps are lost. ForgetMeNot takes a novel approach to address this problem by periodically encouraging the user to enter reminders with unsolicited prompts (UPs). An in situ case study investigated the experience of using a reminding app for people with ABI and tested UPs as a potential solution to initiating reminder entry. Three people with severe ABI living in a post-acute rehabilitation hospital used the app in their everyday lives for four weeks to collect real usage data. Field observations illustrated how difficulties with motivation, insight into memory difficulties and anxiety impact reminder app use in a rehabilitation setting. Results showed that when 6 UPs were presented throughout the day, reminder-setting increased, showing UPs are an important addition to reminder applications for people with ABI. This study demonstrates that barriers to technology use can be resolved in practice when software is developed with an understanding of the issues experienced by the user group.

Smartphone reminder applications (apps) have the potential to help people with memory impairment after acquired brain injury (ABI) to perform everyday tasks. Issues impacting the uptake of reminder apps for this group are still poorly understood. To address this, three focus groups were held with people with memory impairments after ABI and ABI caregivers (N=12). These involved a discussion about perceptions of, and attitudes towards, reminder apps combined with usability reflections during a user-centred design session (Keep Lose Change) after a walkthrough of an existing reminder app -- Google Calendar. Framework analysis revealed six key themes that impact uptake of reminder apps; Perceived Need, Social Acceptability, Experience/Expectation, Desired Content and Functions, Cognitive Accessibility and Sensory/Motor Accessibility. Analysis of themes revealed issues that should be considered by designers and researchers when developing and testing reminding software for people with memory impairment following ABI.

As it becomes more difficult to increase single-threaded performance, focus on multi-core processor designs increases. However, individual core performance is still important, especially for long-executing applications such as in scientific computing. Based on scientific application needs as modeled by SPEC-FP and a set of applications from Sandia National Labs, we have created several different reconfigurable functional unit (RFU) designs for superscalar multi-processor supercomputers. This paper discusses the design process and evaluates the RFUs' ability to implement instruction dataflow graphs from scientific workloads. Our best-performing RFU design is able to implement 89% of the dataflow graphs in the benchmarks.

Efficient path-planning algorithms are a crucial issue for modern autonomous underwater vehicles. Classical path-planning algorithms in artificial intelligence are not designed to deal with wide continuous environments prone to currents. We present a novel Fast Marching (FM)-based approach to address the following issues. First, we develop an algorithm we call FM* to efficiently extract a 2-D continuous path from a discrete representation of the environment. Second, we take underwater currents into account thanks to an anisotropic extension of the original FM algorithm. Third, the vehicle turning radius is introduced as a constraint on the optimal path curvature for both isotropic and anisotropic media. Finally, a multiresolution method is introduced to speed up the overall path-planning process.