13,018 research outputs found
Detection-by-Localization: Maintenance-Free Change Object Detector
Recent researches demonstrate that self-localization performance is a very
useful measure of likelihood-of-change (LoC) for change detection. In this
paper, this "detection-by-localization" scheme is studied in a novel
generalized task of object-level change detection. In our framework, a given
query image is segmented into object-level subimages (termed "scene parts"),
which are then converted to subimage-level pixel-wise LoC maps via the
detection-by-localization scheme. Our approach models a self-localization
system as a ranking function, outputting a ranked list of reference images,
without requiring relevance score. Thanks to this new setting, we can
generalize our approach to a broad class of self-localization systems. Our
ranking based self-localization model allows to fuse self-localization results
from different modalities via an unsupervised rank fusion derived from a field
of multi-modal information retrieval (MMR).Comment: 7 pages, 3 figures, Technical repor
Ontology of core data mining entities
In this article, we present OntoDM-core, an ontology of core data mining
entities. OntoDM-core defines themost essential datamining entities in a three-layered
ontological structure comprising of a specification, an implementation and an application
layer. It provides a representational framework for the description of mining
structured data, and in addition provides taxonomies of datasets, data mining tasks,
generalizations, data mining algorithms and constraints, based on the type of data.
OntoDM-core is designed to support a wide range of applications/use cases, such as
semantic annotation of data mining algorithms, datasets and results; annotation of
QSAR studies in the context of drug discovery investigations; and disambiguation of
terms in text mining. The ontology has been thoroughly assessed following the practices
in ontology engineering, is fully interoperable with many domain resources and
is easy to extend
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Update of time-invalid information in Knowledge Bases through Mobile Agents
In this paper, we investigate the use of a mobile, autonomous agent to update knowledge bases containing statements that lose validity with time. This constitutes a key issue in terms of knowledge acquisition and representation, because dynamic data need to be constantly re-evaluated to allow reasoning. We focus on the way to represent the time- validity of statements in a knowledge base, and on the use of a mobile agent to update time-invalid statements while planning for “information freshness” as the main objective. We propose to use Semantic Web standards, namely the RDF model and the SPARQL query language, to represent time-validity of information and decide how long this will be considered valid. Using such a representation, a plan is created for the agent to update the knowledge, focusing mostly on guaranteeing the time-validity of the information collected. To show the feasibility of our approach and discuss its limitations, we test its implementation on scenarios in the working environment of our research lab, where an autonomous robot is used to sense temperature, humidity, wifi signal and number of people on demand, updating the knowledge base with time- valid information
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