311,353 research outputs found
Perturbed-History Exploration in Stochastic Linear Bandits
We propose a new online algorithm for minimizing the cumulative regret in
stochastic linear bandits. The key idea is to build a perturbed history, which
mixes the history of observed rewards with a pseudo-history of randomly
generated i.i.d. pseudo-rewards. Our algorithm, perturbed-history exploration
in a linear bandit (LinPHE), estimates a linear model from its perturbed
history and pulls the arm with the highest value under that model. We prove a
gap-free bound on the expected -round regret of
LinPHE, where is the number of features. Our analysis relies on novel
concentration and anti-concentration bounds on the weighted sum of Bernoulli
random variables. To show the generality of our design, we extend LinPHE to a
logistic reward model. We evaluate both algorithms empirically and show that
they are practical
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
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