2,581 research outputs found
A multi-INT semantic reasoning framework for intelligence analysis support
Lockheed Martin Corp. has funded research to generate a framework
and methodology for developing semantic reasoning applications to support the
discipline oflntelligence Analysis. This chapter outlines that framework, discusses
how it may be used to advance the information sharing and integrated analytic
needs of the Intelligence Community, and suggests a system I software
architecture for such applications
Context Aware Computing for The Internet of Things: A Survey
As we are moving towards the Internet of Things (IoT), the number of sensors
deployed around the world is growing at a rapid pace. Market research has shown
a significant growth of sensor deployments over the past decade and has
predicted a significant increment of the growth rate in the future. These
sensors continuously generate enormous amounts of data. However, in order to
add value to raw sensor data we need to understand it. Collection, modelling,
reasoning, and distribution of context in relation to sensor data plays
critical role in this challenge. Context-aware computing has proven to be
successful in understanding sensor data. In this paper, we survey context
awareness from an IoT perspective. We present the necessary background by
introducing the IoT paradigm and context-aware fundamentals at the beginning.
Then we provide an in-depth analysis of context life cycle. We evaluate a
subset of projects (50) which represent the majority of research and commercial
solutions proposed in the field of context-aware computing conducted over the
last decade (2001-2011) based on our own taxonomy. Finally, based on our
evaluation, we highlight the lessons to be learnt from the past and some
possible directions for future research. The survey addresses a broad range of
techniques, methods, models, functionalities, systems, applications, and
middleware solutions related to context awareness and IoT. Our goal is not only
to analyse, compare and consolidate past research work but also to appreciate
their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201
When Things Matter: A Data-Centric View of the Internet of Things
With the recent advances in radio-frequency identification (RFID), low-cost
wireless sensor devices, and Web technologies, the Internet of Things (IoT)
approach has gained momentum in connecting everyday objects to the Internet and
facilitating machine-to-human and machine-to-machine communication with the
physical world. While IoT offers the capability to connect and integrate both
digital and physical entities, enabling a whole new class of applications and
services, several significant challenges need to be addressed before these
applications and services can be fully realized. A fundamental challenge
centers around managing IoT data, typically produced in dynamic and volatile
environments, which is not only extremely large in scale and volume, but also
noisy, and continuous. This article surveys the main techniques and
state-of-the-art research efforts in IoT from data-centric perspectives,
including data stream processing, data storage models, complex event
processing, and searching in IoT. Open research issues for IoT data management
are also discussed
Tag-Aware Recommender Systems: A State-of-the-art Survey
In the past decade, Social Tagging Systems have attracted increasing
attention from both physical and computer science communities. Besides the
underlying structure and dynamics of tagging systems, many efforts have been
addressed to unify tagging information to reveal user behaviors and
preferences, extract the latent semantic relations among items, make
recommendations, and so on. Specifically, this article summarizes recent
progress about tag-aware recommender systems, emphasizing on the contributions
from three mainstream perspectives and approaches: network-based methods,
tensor-based methods, and the topic-based methods. Finally, we outline some
other tag-related works and future challenges of tag-aware recommendation
algorithms.Comment: 19 pages, 3 figure
Ways of Applying Artificial Intelligence in Software Engineering
As Artificial Intelligence (AI) techniques have become more powerful and
easier to use they are increasingly deployed as key components of modern
software systems. While this enables new functionality and often allows better
adaptation to user needs it also creates additional problems for software
engineers and exposes companies to new risks. Some work has been done to better
understand the interaction between Software Engineering and AI but we lack
methods to classify ways of applying AI in software systems and to analyse and
understand the risks this poses. Only by doing so can we devise tools and
solutions to help mitigate them. This paper presents the AI in SE Application
Levels (AI-SEAL) taxonomy that categorises applications according to their
point of AI application, the type of AI technology used and the automation
level allowed. We show the usefulness of this taxonomy by classifying 15 papers
from previous editions of the RAISE workshop. Results show that the taxonomy
allows classification of distinct AI applications and provides insights
concerning the risks associated with them. We argue that this will be important
for companies in deciding how to apply AI in their software applications and to
create strategies for its use
Fusing uncertain knowledge and evidence for maritime situational awareness via Markov Logic Networks
The concepts of event and anomaly are important building blocks for developing a situational picture of the observed environment. We here relate these concepts to the JDL fusion model and demonstrate the power of Markov Logic Networks (MLNs) for encoding uncertain knowledge and compute inferences according to observed evidence. MLNs combine the expressive power of first-order logic and the probabilistic uncertainty management of Markov networks.
Within this framework, different types of knowledge (e.g. a priori, contextual) with associated uncertainty can be fused together for situation assessment by expressing unobservable complex events as a logical combination of simpler evidences.
We also develop a mechanism to evaluate the level of completion of complex events and show how, along with event probability, it could provide additional useful information to the operator.
Examples are demonstrated on two maritime scenarios of rules for event and anomaly detection
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