Adult children often help their older parents maintain independence at home, and this informal caregiving can be overwhelming and challenging to balance with other aspects of life. Yet, adult children's requirements tend to be overlooked when designing technologies to foster aging in place. In this work, we use reflexive thematic analysis to study Reddit content to understand the experiences of adult caregivers and the issues that they face in supporting their parents. The pseudonymous data allowed us to explore three research questions: 1) What are the experiences of adult children as informal helpers for elderly parents? 2) How do adult children currently manage their role as informal helpers? and 3) How might digital technology facilitate communication between aging parents and adult children as informal helpers? Our findings lead to a number of design considerations for technologies that support adult children caring for parents who are aging in place, including: building common ground, establishing boundaries, addressing guilt, encouraging in-person communication, community support for adult children, and adapting to changes in role reciprocity.

When working together, people frequently share information with each other to enable division of labour, assistance, and delegation of responsibility. The literature has explored both synchronous and asynchronous transfer techniques, known as Handoff and Deposit, respectively. However, current cross-device environments tend to only provide a single mechanism. Moreover, we have little understanding of the impact of different techniques on collaborative process. To understand how Handoff and Deposit may be designed to support complex sensemaking tasks, we followed a Research through Design process to iteratively design Handoff and Deposit techniques using paper and digital sketches and high-fidelity prototypes. We consulted the HCI literature to corroborate our findings with studies and descriptions of existing cross-device transfer designs and to understand the potential impact of those designs on mixed-focus collaboration. We learned that as we move away from a restricted physical workspace and leverage the flexibility of digital personal devices, there is a large design space for realizing cross-device transfer. To inform these designs, we provide five design considerations for cross-device transfer techniques: Transfer Acceptance, Action Dependencies, Immediate Usability, Interruption Potential, and Connection Actions.

Building and maintaining common ground is vital for effective collaboration in CSCW. Moreover, subtle changes in a CSCW user interface can significantly impact grounding and collaborative processes. Yet, researchers and technology designers lack tools to understand how specific user interface designs may hinder or facilitate communication grounding. In this work, we leverage the well-established theory of communication grounding to develop a visual framework, called Joint Action Storyboards (JASs), to analyze and articulate how interaction minutiae impact the costs of communication grounding. JASs can depict an integrated view of mental actions of collaborators, their physical interactions with each other and the CSCW environment, and the corresponding grounding costs incurred. We present the development of JASs and discuss its various benefits for HCI and CSCW research. Through a series of case studies, we demonstrate how JASs provide an analysis tool for researchers and technology designers and serve as a tool to articulate the impact of interaction minutiae on communication grounding.

Cross-device environments (XDEs) have been developed to support a multitude of collaborative activities. Yet, little is known about how different cross-device interaction techniques impact group collaboration, including how their impact on independent and joint work that often occurs during group work. In this work, we explore the impact of two XDE data browsing techniques: TOUCH and TILT. Through a mixed-methods study of a collaborative sensemaking task, we show that TOUCH and TILT have distinct impacts on how groups accomplish, and shift between, independent and joint work. Finally, we reflect on these findings and how they can more generally inform the design of XDEs.

This work investigates how two different cross-surface interaction techniques support communication "grounding" during a collaborative sensemaking task. Grounding is the process of establishing mutual knowledge, beliefs, or assumptions during conversation to effectively communicate, and is essential for collaborative analysis. Our study found several specific design features of the studied cross-surface interfaces that either supported or hindered the grounding process. For instance, one technique provided "flexible ownership" of widgets on a shared tabletop that controlled the data connection between the tabletop and users' personal tablets. This feature enabled cooperative interaction strategies known to facilitate the grounding process. We discuss these results, and other cross-surface design features that impact grounding.

This paper addresses in silico prediction of protein structural classes as defined in the SCOP database. The SCOP defines total of 11 classes, while majority of proteins are classified to the 4 classes: all-alpha all-beta alpha/beta, and alpha+beta. The main goals of this paper are to experimentally evaluate the impact of predicted protein secondary structure content on the structural class prediction and to develop a novel protein sequence representation. The experiments include application of three protein sequence representations and four classifiers to prediction of both 4 and 11 structural classes. The predictions are performed using a large dataset of low homology (twilight zone) sequences. The proposed sequence representation includes the predicted structural content, which provides the strongest contribution towards classification, composition and composition moment vectors, hydrophobic autocorrelations, chemical group composition and molecular weight of the protein. The predicted content values are shown on average to improve the prediction accuracy by 3.3% and 4.2% for the 4 and 11 classes, respectively, when compared to sequence representation that does not utilize this information. Finally, we propose a very compact, 20 dimensional sequence representation that is shown to improve the prediction accuracy by 5.1-8.5% when compared with recently published results