11,054 research outputs found
Space for Two to Think: Large, High-Resolution Displays for Co-located Collaborative Sensemaking
Large, high-resolution displays carry the potential to enhance single display groupware collaborative sensemaking for intelligence analysis tasks by providing space for common ground to develop, but it is up to the visual analytics tools to utilize this space effectively. In an exploratory study, we compared two tools (Jigsaw and a document viewer), which were adapted to support multiple input devices, to observe how the large display space was used in establishing and maintaining common ground during an intelligence analysis scenario using 50 textual documents. We discuss the spatial strategies employed by the pairs of participants, which were largely dependent on tool type (data-centric or function-centric), as well as how different visual analytics tools used collaboratively on large, high-resolution displays impact common ground in both process and solution. Using these findings, we suggest design considerations to enable future co-located collaborative sensemaking tools to take advantage of the benefits of collaborating on large, high-resolution displays
Large High Resolution Displays for Co-Located Collaborative Intelligence Analysis
Large, high-resolution vertical displays carry the potential to increase the accuracy of collaborative sensemaking, given correctly designed visual analytics tools. From an exploratory user study using a fictional intelligence analysis task, we investigated how users interact with the display to construct spatial schemas and externalize information, as well as how they establish shared and private territories. We investigated the spatial strategies of users partitioned by tool type used (document- or entity-centric). We classified the types of territorial behavior exhibited in terms of how the users interacted with the display (integrated or independent workspaces). Next, we examined how territorial behavior impacted the common ground between the pairs of users. Finally, we recommend design guidelines for building co-located collaborative visual analytics tools specifically for use on large, high-resolution vertical displays
Augmenting Pathologists with NaviPath: Design and Evaluation of a Human-AI Collaborative Navigation System
Artificial Intelligence (AI) brings advancements to support pathologists in
navigating high-resolution tumor images to search for pathology patterns of
interest. However, existing AI-assisted tools have not realized this promised
potential due to a lack of insight into pathology and HCI considerations for
pathologists' navigation workflows in practice. We first conducted a formative
study with six medical professionals in pathology to capture their navigation
strategies. By incorporating our observations along with the pathologists'
domain knowledge, we designed NaviPath -- a human-AI collaborative navigation
system. An evaluation study with 15 medical professionals in pathology
indicated that: (i) compared to the manual navigation, participants saw more
than twice the number of pathological patterns in unit time with NaviPath, and
(ii) participants achieved higher precision and recall against the AI and the
manual navigation on average. Further qualitative analysis revealed that
navigation was more consistent with NaviPath, which can improve the overall
examination quality.Comment: Accepted ACM CHI Conference on Human Factors in Computing Systems
(CHI '23
Designing Individualized Policy and Technology Interventions to Improve Gig Work Conditions
The gig economy is characterized by short-term contract work completed by
independent workers who are paid to perform "gigs", and who have control over
when, whether and how they conduct work. Gig economy platforms (e.g., Uber,
Lyft, Instacart) offer workers increased job opportunities, lower barriers to
entry, and improved flexibility. However, growing evidence suggests that worker
well-being and gig work conditions have become significant societal issues. In
designing public-facing policies and technologies for improving gig work
conditions, inherent tradeoffs exist between offering individual flexibility
and when attempting to meet all community needs. In platform-based gig work,
contractors pursue the flexibility of short-term tasks, but policymakers resist
segmenting the population when designing policies to support their work. As
platforms offer an ever-increasing variety of services, we argue that
policymakers and platform designers must provide more targeted and personalized
policies, benefits, and protections for platform-based workers, so that they
can lead more successful and sustainable gig work careers. We present in this
paper relevant legal and scholarly evidence from the United States to support
this position, and make recommendations for future innovations in policy and
technology
Painterly rendering techniques: A state-of-the-art review of current approaches
In this publication we will look at the different methods presented over the past few decades which attempt to recreate digital paintings. While previous surveys concentrate on the broader subject of non-photorealistic rendering, the focus of this paper is firmly placed on painterly rendering techniques. We compare different methods used to produce different output painting styles such as abstract, colour pencil, watercolour, oriental, oil and pastel. Whereas some methods demand a high level of interaction using a skilled artist, others require simple parameters provided by a user with little or no artistic experience. Many methods attempt to provide more automation with the use of varying forms of reference data. This reference data can range from still photographs, video, 3D polygonal meshes or even 3D point clouds. The techniques presented here endeavour to provide tools and styles that are not traditionally available to an artist. Copyright © 2012 John Wiley & Sons, Ltd
The Challenges in Modeling Human Performance in 3D Space with Fitts’ Law
With the rapid growth in virtual reality technologies, object interaction is
becoming increasingly more immersive, elucidating human perception and leading
to promising directions towards evaluating human performance under different
settings. This spike in technological growth exponentially increased the need
for a human performance metric in 3D space. Fitts' law is perhaps the most
widely used human prediction model in HCI history attempting to capture human
movement in lower dimensions. Despite the collective effort towards deriving an
advanced extension of a 3D human performance model based on Fitts' law, a
standardized metric is still missing. Moreover, most of the extensions to date
assume or limit their findings to certain settings, effectively disregarding
important variables that are fundamental to 3D object interaction. In this
review, we investigate and analyze the most prominent extensions of Fitts' law
and compare their characteristics pinpointing to potentially important aspects
for deriving a higher-dimensional performance model. Lastly, we mention the
complexities, frontiers as well as potential challenges that may lay ahead.Comment: Accepted at ACM CHI 2021 Conference on Human Factors in Computing
Systems (CHI '21 Extended Abstracts
Scim: Intelligent Skimming Support for Scientific Papers
Researchers need to keep up with immense literatures, though it is
time-consuming and difficult to do so. In this paper, we investigate the role
that intelligent interfaces can play in helping researchers skim papers, that
is, rapidly reviewing a paper to attain a cursory understanding of its
contents. After conducting formative interviews and a design probe, we suggest
that skimming aids should aim to thread the needle of highlighting content that
is simultaneously diverse, evenly-distributed, and important. We introduce
Scim, a novel intelligent skimming interface that reifies this aim, designed to
support the skimming process by highlighting salient paper contents to direct a
skimmer's focus. Key to the design is that the highlights are faceted by
content type, evenly-distributed across a paper, with a density configurable by
readers at both the global and local level. We evaluate Scim with an in-lab
usability study and deployment study, revealing how skimming aids can support
readers throughout the skimming experience and yielding design considerations
and tensions for the design of future intelligent skimming tools
Reference face graph for face recognition
Face recognition has been studied extensively; however, real-world face recognition still remains a challenging task. The demand for unconstrained practical face recognition is rising with the explosion of online multimedia such as social networks, and video surveillance footage where face analysis is of significant importance. In this paper, we approach face recognition in the context of graph theory. We recognize an unknown face using an external reference face graph (RFG). An RFG is generated and recognition of a given face is achieved by comparing it to the faces in the constructed RFG. Centrality measures are utilized to identify distinctive faces in the reference face graph. The proposed RFG-based face recognition algorithm is robust to the changes in pose and it is also alignment free. The RFG recognition is used in conjunction with DCT locality sensitive hashing for efficient retrieval to ensure scalability. Experiments are conducted on several publicly available databases and the results show that the proposed approach outperforms the state-of-the-art methods without any preprocessing necessities such as face alignment. Due to the richness in the reference set construction, the proposed method can also handle illumination and expression variation
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