191,818 research outputs found
Context-Aware Modeling Using Semantic Web and Z Notation
Surveys in user context modeling have shown that the semantic web is one of
the promising approach to represent and structure the contextual information captured
from user’s surrounding environment in a context-aware application. A benefit of
using semantic web language is that it enables application to reason user contextual
information in order to get the knowledge of user’s behavior. However, regarding its
notation format, semantic web is suitable for implementation level or to be consumed
by application run-time.
Context-aware application is a part of distributed computing system. In distributed
computing system, the language used for specification should be distinguished from
the implementation / run-time purpose. This is known as separation of modeling language.
Regarding the context-aware application, for those who are concerned with
specification of context modeling, the language that is used for specification should
also be distinguished from the implementation one.
This thesis aims at proposing the use of formal specification technique to develop
a generic context ontology model of user’s behavior at the Computer and Information
Sciences Department, Universiti Teknologi PETRONAS. Initially, the context ontology
was written in OWL semantic web language. The further process is mapping onto
a formal specification language, i.e. onto Z notation. As a result, specification of context
ontology and its consistency checking have been developed and verified beyond
the semantic web language environment. An inconsistency of context model has been
detected during the verification of Z model, which cannot be revealed by current OWL
DL reasoner.
The context-aware designers might benefit from the formal specification of context
ontology, where the designers could fully use formal verification technique to check
the correctness of context ontology. Thus, the modeling approach in this thesis has
shown that it could complement the context ontology development process, where the
checking and refinement are performed beyond the semantic web reasone
Recurrent Scene Parsing with Perspective Understanding in the Loop
Objects may appear at arbitrary scales in perspective images of a scene,
posing a challenge for recognition systems that process images at a fixed
resolution. We propose a depth-aware gating module that adaptively selects the
pooling field size in a convolutional network architecture according to the
object scale (inversely proportional to the depth) so that small details are
preserved for distant objects while larger receptive fields are used for those
nearby. The depth gating signal is provided by stereo disparity or estimated
directly from monocular input. We integrate this depth-aware gating into a
recurrent convolutional neural network to perform semantic segmentation. Our
recurrent module iteratively refines the segmentation results, leveraging the
depth and semantic predictions from the previous iterations.
Through extensive experiments on four popular large-scale RGB-D datasets, we
demonstrate this approach achieves competitive semantic segmentation
performance with a model which is substantially more compact. We carry out
extensive analysis of this architecture including variants that operate on
monocular RGB but use depth as side-information during training, unsupervised
gating as a generic attentional mechanism, and multi-resolution gating. We find
that gated pooling for joint semantic segmentation and depth yields
state-of-the-art results for quantitative monocular depth estimation
iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects
We address the task of 6D pose estimation of known rigid objects from single
input images in scenarios where the objects are partly occluded. Recent
RGB-D-based methods are robust to moderate degrees of occlusion. For RGB
inputs, no previous method works well for partly occluded objects. Our main
contribution is to present the first deep learning-based system that estimates
accurate poses for partly occluded objects from RGB-D and RGB input. We achieve
this with a new instance-aware pipeline that decomposes 6D object pose
estimation into a sequence of simpler steps, where each step removes specific
aspects of the problem. The first step localizes all known objects in the image
using an instance segmentation network, and hence eliminates surrounding
clutter and occluders. The second step densely maps pixels to 3D object surface
positions, so called object coordinates, using an encoder-decoder network, and
hence eliminates object appearance. The third, and final, step predicts the 6D
pose using geometric optimization. We demonstrate that we significantly
outperform the state-of-the-art for pose estimation of partly occluded objects
for both RGB and RGB-D input
Domain Objects and Microservices for Systems Development: a roadmap
This paper discusses a roadmap to investigate Domain Objects being an
adequate formalism to capture the peculiarity of microservice architecture, and
to support Software development since the early stages. It provides a survey of
both Microservices and Domain Objects, and it discusses plans and reflections
on how to investigate whether a modeling approach suited to adaptable
service-based components can also be applied with success to the microservice
scenario
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