31,239 research outputs found
Analyzing impacts of change operations in evolving ontologies
Ontologies evolve over time to adapt to the dynamically changing knowledge in a domain. The evolution includes addition of new entities and modification or deletion of obsolete entities. These changes could have impacts on the remaining entities and dependent systems of the ontology. In this paper, we address the impacts of changes prior to their permanent implementation. To this end, we identify possible structural and semantic impacts and propose a bottom-up change impact analysis method which contains two phases. The first phase focuses on analyzing impacts of atomic change operations and the second phase focuses on analyzing impacts of composite changes which include impact cancellation, balancing and transformation due to implementation of two or more atomic changes. This method provides crucial information on the impacts and could be used for selecting evolution strategies and conducting what-if analysis before evolving the ontologies
Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime
This work addresses the problem of semantic image segmentation of nighttime
scenes. Although considerable progress has been made in semantic image
segmentation, it is mainly related to daytime scenarios. This paper proposes a
novel method to progressive adapt the semantic models trained on daytime
scenes, along with large-scale annotations therein, to nighttime scenes via the
bridge of twilight time -- the time between dawn and sunrise, or between sunset
and dusk. The goal of the method is to alleviate the cost of human annotation
for nighttime images by transferring knowledge from standard daytime
conditions. In addition to the method, a new dataset of road scenes is
compiled; it consists of 35,000 images ranging from daytime to twilight time
and to nighttime. Also, a subset of the nighttime images are densely annotated
for method evaluation. Our experiments show that our method is effective for
model adaptation from daytime scenes to nighttime scenes, without using extra
human annotation.Comment: Accepted to International Conference on Intelligent Transportation
Systems (ITSC 2018
Adaptive Deep Learning through Visual Domain Localization
A commercial robot, trained by its manufacturer to recognize a predefined number and type of objects, might be used in many settings, that will in general differ in their illumination conditions, background, type and degree of clutter, and so on. Recent computer vision works tackle this generalization issue through domain adaptation methods, assuming as source the visual domain where the system is trained and as target the domain of deployment. All approaches assume to have access to images from all classes of the target during training, an unrealistic condition in robotics applications. We address this issue proposing an algorithm that takes into account the specific needs of robot vision. Our intuition is that the nature of the domain shift experienced mostly in robotics is local. We exploit this through the learning of maps that spatially ground the domain and quantify the degree of shift, embedded into an end-to-end deep domain adaptation architecture. By explicitly localizing the roots of the domain shift we significantly reduce the number of parameters of the architecture to tune, we gain the flexibility necessary to deal with subset of categories in the target domain at training time, and we provide a clear feedback on the rationale behind any classification decision, which can be exploited in human-robot interactions. Experiments on two different settings of the iCub World database confirm the suitability of our method for robot vision
Middleware-based Database Replication: The Gaps between Theory and Practice
The need for high availability and performance in data management systems has
been fueling a long running interest in database replication from both academia
and industry. However, academic groups often attack replication problems in
isolation, overlooking the need for completeness in their solutions, while
commercial teams take a holistic approach that often misses opportunities for
fundamental innovation. This has created over time a gap between academic
research and industrial practice.
This paper aims to characterize the gap along three axes: performance,
availability, and administration. We build on our own experience developing and
deploying replication systems in commercial and academic settings, as well as
on a large body of prior related work. We sift through representative examples
from the last decade of open-source, academic, and commercial database
replication systems and combine this material with case studies from real
systems deployed at Fortune 500 customers. We propose two agendas, one for
academic research and one for industrial R&D, which we believe can bridge the
gap within 5-10 years. This way, we hope to both motivate and help researchers
in making the theory and practice of middleware-based database replication more
relevant to each other.Comment: 14 pages. Appears in Proc. ACM SIGMOD International Conference on
Management of Data, Vancouver, Canada, June 200
Using real options to select stable Middleware-induced software architectures
The requirements that force decisions towards building distributed system architectures are usually of a non-functional nature. Scalability, openness, heterogeneity, and fault-tolerance are examples of such non-functional requirements. The current trend is to build distributed systems with middleware, which provide the application developer with primitives for managing the complexity of distribution, system resources, and for realising many of the non-functional requirements. As non-functional requirements evolve, the `coupling' between the middleware and architecture becomes the focal point for understanding the stability of the distributed software system architecture in the face of change. It is hypothesised that the choice of a stable distributed software architecture depends on the choice of the underlying middleware and its flexibility in responding to future changes in non-functional requirements. Drawing on a case study that adequately represents a medium-size component-based distributed architecture, it is reported how a likely future change in scalability could impact the architectural structure of two versions, each induced with a distinct middleware: one with CORBA and the other with J2EE. An option-based model is derived to value the flexibility of the induced-architectures and to guide the selection. The hypothesis is verified to be true for the given change. The paper concludes with some observations that could stimulate future research in the area of relating requirements to software architectures
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