63,380 research outputs found
Sparsity in Dynamics of Spontaneous Subtle Emotions: Analysis \& Application
Spontaneous subtle emotions are expressed through micro-expressions, which
are tiny, sudden and short-lived dynamics of facial muscles; thus poses a great
challenge for visual recognition. The abrupt but significant dynamics for the
recognition task are temporally sparse while the rest, irrelevant dynamics, are
temporally redundant. In this work, we analyze and enforce sparsity constrains
to learn significant temporal and spectral structures while eliminate
irrelevant facial dynamics of micro-expressions, which would ease the challenge
in the visual recognition of spontaneous subtle emotions. The hypothesis is
confirmed through experimental results of automatic spontaneous subtle emotion
recognition with several sparsity levels on CASME II and SMIC, the only two
publicly available spontaneous subtle emotion databases. The overall
performances of the automatic subtle emotion recognition are boosted when only
significant dynamics are preserved from the original sequences.Comment: IEEE Transaction of Affective Computing (2016
Towards a query language for annotation graphs
The multidimensional, heterogeneous, and temporal nature of speech databases
raises interesting challenges for representation and query. Recently,
annotation graphs have been proposed as a general-purpose representational
framework for speech databases. Typical queries on annotation graphs require
path expressions similar to those used in semistructured query languages.
However, the underlying model is rather different from the customary graph
models for semistructured data: the graph is acyclic and unrooted, and both
temporal and inclusion relationships are important. We develop a query language
and describe optimization techniques for an underlying relational
representation.Comment: 8 pages, 10 figure
Action planning for graph transition systems
Graphs are suitable modeling formalisms for software and hardware systems involving aspects such as communication,
object orientation, concurrency, mobility and distribution. State spaces of such systems can be represented by graph transition systems, which are basically transition systems whose states and transitions represent graphs and graph morphisms. In this paper, we propose the modeling of graph transition systems in PDDL and the application of heuristic search planning for their analysis. We consider different heuristics and present experimental results
Portinari: A Data Exploration Tool to Personalize Cervical Cancer Screening
Socio-technical systems play an important role in public health screening
programs to prevent cancer. Cervical cancer incidence has significantly
decreased in countries that developed systems for organized screening engaging
medical practitioners, laboratories and patients. The system automatically
identifies individuals at risk of developing the disease and invites them for a
screening exam or a follow-up exam conducted by medical professionals. A triage
algorithm in the system aims to reduce unnecessary screening exams for
individuals at low-risk while detecting and treating individuals at high-risk.
Despite the general success of screening, the triage algorithm is a
one-size-fits all approach that is not personalized to a patient. This can
easily be observed in historical data from screening exams. Often patients rely
on personal factors to determine that they are either at high risk or not at
risk at all and take action at their own discretion. Can exploring patient
trajectories help hypothesize personal factors leading to their decisions? We
present Portinari, a data exploration tool to query and visualize future
trajectories of patients who have undergone a specific sequence of screening
exams. The web-based tool contains (a) a visual query interface (b) a backend
graph database of events in patients' lives (c) trajectory visualization using
sankey diagrams. We use Portinari to explore diverse trajectories of patients
following the Norwegian triage algorithm. The trajectories demonstrated
variable degrees of adherence to the triage algorithm and allowed
epidemiologists to hypothesize about the possible causes.Comment: Conference paper published at ICSE 2017 Buenos Aires, at the Software
Engineering in Society Track. 10 pages, 5 figure
GDBAlive: a Temporal Graph Database Built on Top of a Columnar Data Store
International audienceAlthough graph databases have extensively found applications in the relationship-centered era, a time-version support is seldom provided. While current storage systems capture the most recently updated snapshot of the underlying graph, most real world graphs embed a dynamic behavior translating the fact that vertices or edges can join or leave the graph at any time instant. Regarding that, a graph database should faithfully maintain the state of every graph's element permitting the analysis and prediction of the underlying system's performance. Since physical deletions are forbidden in such a scenario, the outgrowing size of data is a crippling restriction steering the interest in this area towards the optimization of the persistent storage. However, capturing and storing the state of the graph as full snapshots adds a storage overhead traded by faster query responses. Accordingly, the choice of an appropriate storage engine should be adapted with the threshold of accepted query latencies and the available storage resources. This paper will recognize the anterior academic work in the era of temporal graph databases while highlighting the existing tradeoff between storage and computation time costs. The implementation of GDBAlive, a temporal graph database using two state-of-the-art techniques Copy+Log and Log, is provided relying on a robust column oriented data store. In order to optimize the responsiveness of temporal queries in terms of computation times, we will introduce two fetching strategies "AsyncFS" and "Forced Fetch" and prove their efficiency on a real dataset
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