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Learning from AI : new trends in database technology
Recently some researchers in the areas of database data modelling and knowledge representations in artificial intelligence have recognized that they share many common goals. In this survey paper we show the relationship between database and artificial intelligence research. We show that there has been a tendency for data models to incorporate more modelling techniques developed for knowledge representations in artificial intelligence as the desire to incorporate more application oriented semantics, user friendliness, and flexibility has increased. Increasing the semantics of the representation is the key to capturing the "reality" of the database environment, increasing user friendliness, and facilitating the support of multiple, possibly conflicting, user views of the information contained in a database
Collaboration Enabling Internet Resource Collection-Building Software and Technologies
Over the last decade the Library of the University of California, Riverside
and its collaborators have developed a number of systems, service designs,
and projects that utilize innovative technologies to foster better Internet
finding tools in libraries and more cooperative and efficient effort in Internet
link and metadata collection building. The open-source software
and projects discussed represent appropriate technologies and sustainable
strategies that we believe will help Internet portals, digital libraries, virtual libraries,
library catalogs-with-portal-like-capabilities (IPDVLCs), and related
collection-building efforts in academia to better scale and more accurately
anticipate and meet the needs of scholarly and educational users.published or submitted for publicatio
Collaboration in the Semantic Grid: a Basis for e-Learning
The CoAKTinG project aims to advance the state of the art in collaborative mediated spaces for the Semantic Grid. This paper presents an overview of the hypertext and knowledge based tools which have been deployed to augment existing collaborative environments, and the ontology which is used to exchange structure, promote enhanced process tracking, and aid navigation of resources before, after, and while a collaboration occurs. While the primary focus of the project has been supporting e-Science, this paper also explores the similarities and application of CoAKTinG technologies as part of a human-centred design approach to e-Learning
Real-to-Virtual Domain Unification for End-to-End Autonomous Driving
In the spectrum of vision-based autonomous driving, vanilla end-to-end models
are not interpretable and suboptimal in performance, while mediated perception
models require additional intermediate representations such as segmentation
masks or detection bounding boxes, whose annotation can be prohibitively
expensive as we move to a larger scale. More critically, all prior works fail
to deal with the notorious domain shift if we were to merge data collected from
different sources, which greatly hinders the model generalization ability. In
this work, we address the above limitations by taking advantage of virtual data
collected from driving simulators, and present DU-drive, an unsupervised
real-to-virtual domain unification framework for end-to-end autonomous driving.
It first transforms real driving data to its less complex counterpart in the
virtual domain and then predicts vehicle control commands from the generated
virtual image. Our framework has three unique advantages: 1) it maps driving
data collected from a variety of source distributions into a unified domain,
effectively eliminating domain shift; 2) the learned virtual representation is
simpler than the input real image and closer in form to the "minimum sufficient
statistic" for the prediction task, which relieves the burden of the
compression phase while optimizing the information bottleneck tradeoff and
leads to superior prediction performance; 3) it takes advantage of annotated
virtual data which is unlimited and free to obtain. Extensive experiments on
two public driving datasets and two driving simulators demonstrate the
performance superiority and interpretive capability of DU-drive
ARTEMIS: Real-Time Detection and Automatic Mitigation for BGP Prefix Hijacking
Prefix hijacking is a common phenomenon in the Internet that often causes
routing problems and economic losses. In this demo, we propose ARTEMIS, a tool
that enables network administrators to detect and mitigate prefix hijacking
incidents, against their own prefixes. ARTEMIS is based on the real-time
monitoring of BGP data in the Internet, and software-defined networking (SDN)
principles, and can completely mitigate a prefix hijacking within a few minutes
(e.g., 5-6 mins in our experiments) after it has been launched
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