1,914 research outputs found
Random hypergraphs and their applications
In the last few years we have witnessed the emergence, primarily in on-line
communities, of new types of social networks that require for their
representation more complex graph structures than have been employed in the
past. One example is the folksonomy, a tripartite structure of users,
resources, and tags -- labels collaboratively applied by the users to the
resources in order to impart meaningful structure on an otherwise
undifferentiated database. Here we propose a mathematical model of such
tripartite structures which represents them as random hypergraphs. We show that
it is possible to calculate many properties of this model exactly in the limit
of large network size and we compare the results against observations of a real
folksonomy, that of the on-line photography web site Flickr. We show that in
some cases the model matches the properties of the observed network well, while
in others there are significant differences, which we find to be attributable
to the practice of multiple tagging, i.e., the application by a single user of
many tags to one resource, or one tag to many resources.Comment: 11 pages, 7 figure
Show Me What I Like: Detecting User-Specific Video Highlights Using Content-Based Multi-Head Attention
We propose a method to detect individualized highlights for users on given
target videos based on their preferred highlight clips marked on previous
videos they have watched. Our method explicitly leverages the contents of both
the preferred clips and the target videos using pre-trained features for the
objects and the human activities. We design a multi-head attention mechanism to
adaptively weigh the preferred clips based on their object- and
human-activity-based contents, and fuse them using these weights into a single
feature representation for each user. We compute similarities between these
per-user feature representations and the per-frame features computed from the
desired target videos to estimate the user-specific highlight clips from the
target videos. We test our method on a large-scale highlight detection dataset
containing the annotated highlights of individual users. Compared to current
baselines, we observe an absolute improvement of 2-4% in the mean average
precision of the detected highlights. We also perform extensive ablation
experiments on the number of preferred highlight clips associated with each
user as well as on the object- and human-activity-based feature representations
to validate that our method is indeed both content-based and user-specific.Comment: 14 pages, 5 figures, 7 table
Children’s attention to online adverts is related to low-level saliency factors and individual level of gaze control
Twenty-six children in 3rd grade were observed while surfing freely on their favourite websites. Eye movement data were recorded, as well as synchronized screen recordings. Each online advert was analyzed in order to quantify low-level saliency features, such as motion, luminance and edge density. The eye movement data were used to register if the children had attended to the online adverts. A mixed-effects multiple regression analysis was performed in order to test the relationship between visual attention on adverts and advert saliency features. The regression model also included individual level of gaze control and level of internet use as predictors. The results show that all measures of visual saliency had effects on children’s visual attention, but these effects were modulated by children’s individual level of gaze control
HypTrails: A Bayesian Approach for Comparing Hypotheses About Human Trails on the Web
When users interact with the Web today, they leave sequential digital trails
on a massive scale. Examples of such human trails include Web navigation,
sequences of online restaurant reviews, or online music play lists.
Understanding the factors that drive the production of these trails can be
useful for e.g., improving underlying network structures, predicting user
clicks or enhancing recommendations. In this work, we present a general
approach called HypTrails for comparing a set of hypotheses about human trails
on the Web, where hypotheses represent beliefs about transitions between
states. Our approach utilizes Markov chain models with Bayesian inference. The
main idea is to incorporate hypotheses as informative Dirichlet priors and to
leverage the sensitivity of Bayes factors on the prior for comparing hypotheses
with each other. For eliciting Dirichlet priors from hypotheses, we present an
adaption of the so-called (trial) roulette method. We demonstrate the general
mechanics and applicability of HypTrails by performing experiments with (i)
synthetic trails for which we control the mechanisms that have produced them
and (ii) empirical trails stemming from different domains including website
navigation, business reviews and online music played. Our work expands the
repertoire of methods available for studying human trails on the Web.Comment: Published in the proceedings of WWW'1
Towards Accountable AI: Hybrid Human-Machine Analyses for Characterizing System Failure
As machine learning systems move from computer-science laboratories into the
open world, their accountability becomes a high priority problem.
Accountability requires deep understanding of system behavior and its failures.
Current evaluation methods such as single-score error metrics and confusion
matrices provide aggregate views of system performance that hide important
shortcomings. Understanding details about failures is important for identifying
pathways for refinement, communicating the reliability of systems in different
settings, and for specifying appropriate human oversight and engagement.
Characterization of failures and shortcomings is particularly complex for
systems composed of multiple machine learned components. For such systems,
existing evaluation methods have limited expressiveness in describing and
explaining the relationship among input content, the internal states of system
components, and final output quality. We present Pandora, a set of hybrid
human-machine methods and tools for describing and explaining system failures.
Pandora leverages both human and system-generated observations to summarize
conditions of system malfunction with respect to the input content and system
architecture. We share results of a case study with a machine learning pipeline
for image captioning that show how detailed performance views can be beneficial
for analysis and debugging
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