45,310 research outputs found
Emergent Leadership Detection Across Datasets
Automatic detection of emergent leaders in small groups from nonverbal
behaviour is a growing research topic in social signal processing but existing
methods were evaluated on single datasets -- an unrealistic assumption for
real-world applications in which systems are required to also work in settings
unseen at training time. It therefore remains unclear whether current methods
for emergent leadership detection generalise to similar but new settings and to
which extent. To overcome this limitation, we are the first to study a
cross-dataset evaluation setting for the emergent leadership detection task. We
provide evaluations for within- and cross-dataset prediction using two current
datasets (PAVIS and MPIIGroupInteraction), as well as an investigation on the
robustness of commonly used feature channels (visual focus of attention, body
pose, facial action units, speaking activity) and online prediction in the
cross-dataset setting. Our evaluations show that using pose and eye contact
based features, cross-dataset prediction is possible with an accuracy of 0.68,
as such providing another important piece of the puzzle towards emergent
leadership detection in the real world.Comment: 5 pages, 3 figure
To which we belong : understanding the role of tradition in interorganizational relations
This article explores tradition in the context of collaboration. We take a view of tradition as rooted in reference groups, which are conceptually distinct from membership groups. Through research in two particular collaborations supporting technology business development in the UK, we find that tradition, as a potential cause of failure or inertia, is inter-organizationally significant. We argue that insight into the nature of tradition - in particular its dynamic interplay with culture in the formation of identity - allows participants to develop some useful language that supports more effective reflective practice in collaboration
Trust-Based Fusion of Untrustworthy Information in Crowdsourcing Applications
In this paper, we address the problem of fusing untrustworthy reports provided from a crowd of observers, while simultaneously learning the trustworthiness of individuals. To achieve this, we construct a likelihood model of the userss trustworthiness by scaling the uncertainty of its multiple estimates with trustworthiness parameters. We incorporate our trust model into a fusion method that merges estimates based on the trust parameters and we provide an inference algorithm that jointly computes the fused output and the individual trustworthiness of the users based on the maximum likelihood framework. We apply our algorithm to cell tower localisation using real-world data from the OpenSignal project and we show that it outperforms the state-of-the-art methods in both accuracy, by up to 21%, and consistency, by up to 50% of its predictions. Copyright © 2013, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved
Tweet, but Verify: Epistemic Study of Information Verification on Twitter
While Twitter provides an unprecedented opportunity to learn about breaking
news and current events as they happen, it often produces skepticism among
users as not all the information is accurate but also hoaxes are sometimes
spread. While avoiding the diffusion of hoaxes is a major concern during
fast-paced events such as natural disasters, the study of how users trust and
verify information from tweets in these contexts has received little attention
so far. We survey users on credibility perceptions regarding witness pictures
posted on Twitter related to Hurricane Sandy. By examining credibility
perceptions on features suggested for information verification in the field of
Epistemology, we evaluate their accuracy in determining whether pictures were
real or fake compared to professional evaluations performed by experts. Our
study unveils insight about tweet presentation, as well as features that users
should look at when assessing the veracity of tweets in the context of
fast-paced events. Some of our main findings include that while author details
not readily available on Twitter feeds should be emphasized in order to
facilitate verification of tweets, showing multiple tweets corroborating a fact
misleads users to trusting what actually is a hoax. We contrast some of the
behavioral patterns found on tweets with literature in Psychology research.Comment: Pre-print of paper accepted to Social Network Analysis and Mining
(Springer
Conversational Sensing
Recent developments in sensing technologies, mobile devices and context-aware
user interfaces have made it possible to represent information fusion and
situational awareness as a conversational process among actors - human and
machine agents - at or near the tactical edges of a network. Motivated by use
cases in the domain of security, policing and emergency response, this paper
presents an approach to information collection, fusion and sense-making based
on the use of natural language (NL) and controlled natural language (CNL) to
support richer forms of human-machine interaction. The approach uses a
conversational protocol to facilitate a flow of collaborative messages from NL
to CNL and back again in support of interactions such as: turning eyewitness
reports from human observers into actionable information (from both trained and
untrained sources); fusing information from humans and physical sensors (with
associated quality metadata); and assisting human analysts to make the best use
of available sensing assets in an area of interest (governed by management and
security policies). CNL is used as a common formal knowledge representation for
both machine and human agents to support reasoning, semantic information fusion
and generation of rationale for inferences, in ways that remain transparent to
human users. Examples are provided of various alternative styles for user
feedback, including NL, CNL and graphical feedback. A pilot experiment with
human subjects shows that a prototype conversational agent is able to gather
usable CNL information from untrained human subjects
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