8,283 research outputs found
Closed-loop Bayesian Semantic Data Fusion for Collaborative Human-Autonomy Target Search
In search applications, autonomous unmanned vehicles must be able to
efficiently reacquire and localize mobile targets that can remain out of view
for long periods of time in large spaces. As such, all available information
sources must be actively leveraged -- including imprecise but readily available
semantic observations provided by humans. To achieve this, this work develops
and validates a novel collaborative human-machine sensing solution for dynamic
target search. Our approach uses continuous partially observable Markov
decision process (CPOMDP) planning to generate vehicle trajectories that
optimally exploit imperfect detection data from onboard sensors, as well as
semantic natural language observations that can be specifically requested from
human sensors. The key innovation is a scalable hierarchical Gaussian mixture
model formulation for efficiently solving CPOMDPs with semantic observations in
continuous dynamic state spaces. The approach is demonstrated and validated
with a real human-robot team engaged in dynamic indoor target search and
capture scenarios on a custom testbed.Comment: Final version accepted and submitted to 2018 FUSION Conference
(Cambridge, UK, July 2018
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
Visually-Enabled Active Deep Learning for (Geo) Text and Image Classification: A Review
This paper investigates recent research on active learning for (geo) text and image classification, with an emphasis on methods that combine visual analytics and/or deep learning. Deep learning has attracted substantial attention across many domains of science and practice, because it can find intricate patterns in big data; but successful application of the methods requires a big set of labeled data. Active learning, which has the potential to address the data labeling challenge, has already had success in geospatial applications such as trajectory classification from movement data and (geo) text and image classification. This review is intended to be particularly relevant for extension of these methods to GISience, to support work in domains such as geographic information retrieval from text and image repositories, interpretation of spatial language, and related geo-semantics challenges. Specifically, to provide a structure for leveraging recent advances, we group the relevant work into five categories: active learning, visual analytics, active learning with visual analytics, active deep learning, plus GIScience and Remote Sensing (RS) using active learning and active deep learning. Each category is exemplified by recent influential work. Based on this framing and our systematic review of key research, we then discuss some of the main challenges of integrating active learning with visual analytics and deep learning, and point out research opportunities from technical and application perspectives-for application-based opportunities, with emphasis on those that address big data with geospatial components
Concepts in a Probabilistic Language of Thought
Note: The book chapter is reprinted courtesy of The MIT Press, from the forthcoming edited collection “The Conceptual Mind: New Directions in the Study of Concepts” edited by Eric Margolis and Stephen Laurence, print date Spring 2015.Knowledge organizes our understanding of the world, determining what we expect given what we have already seen. Our predictive representations have two key properties: they are productive, and they are graded. Productive generalization is possible because our knowledge decomposes into concepts—elements of knowledge that are combined and recombined to describe particular situations. Gradedness is the observable effect of accounting for uncertainty—our knowledge encodes degrees of belief that lead to graded probabilistic predictions. To put this a different way, concepts form a combinatorial system that enables description of many different situations; each such situation specifies a distribution over what we expect to see in the world, given what we have seen. We may think of this system as a probabilistic language of thought (PLoT) in which representations are built from language-like composition of concepts and the content of those representations is a probability distribution on world states. The purpose of this chapter is to formalize these ideas in computational terms, to illustrate key properties of the PLoT approach with a concrete example, and to draw connections with other views of conceptual structure.This work was supported by ONR awards N00014-09-1-0124 and N00014-13-
1-0788, by a John S. McDonnell Foundation Scholar Award, and by the Center
for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF - 1231216
Interpretation of complex situations in a semantic-based surveillance framework
The integration of cognitive capabilities in computer vision systems requires both to enable high semantic expressiveness and to deal with high computational costs as large amounts of data are involved in the analysis. This contribution describes a cognitive vision system conceived to automatically provide high-level interpretations of complex real-time situations in outdoor and indoor scenarios, and to eventually maintain communication with casual end users in multiple languages. The main contributions are: (i) the design of an integrative multilevel architecture for cognitive surveillance purposes; (ii) the proposal of a coherent taxonomy of knowledge to guide the process of interpretation, which leads to the conception of a situation-based ontology; (iii) the use of situational analysis for content detection and a progressive interpretation of semantically rich scenes, by managing incomplete or uncertain knowledge, and (iv) the use of such an ontological background to enable multilingual capabilities and advanced end-user interfaces. Experimental results are provided to show the feasibility of the proposed approach.This work was supported by the project 'CONSOLIDER-INGENIO 2010 Multimodal interaction in pattern recognition and computer vision' (V-00069). This work is supported by EC Grants IST-027110 for the HERMES project and IST-045547 for the VIDI-video project, and by the Spanish MEC under Projects TIN2006-14606 and CONSOLIDER-INGENIO 2010 (CSD2007-00018). Jordi GonzĂ lez also acknowledges the support of a Juan de la Cierva Postdoctoral fellowship from the Spanish MEC.Peer Reviewe
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