38,816 research outputs found
Vehicular Edge Computing via Deep Reinforcement Learning
The smart vehicles construct Vehicle of Internet which can execute various
intelligent services. Although the computation capability of the vehicle is
limited, multi-type of edge computing nodes provide heterogeneous resources for
vehicular services.When offloading the complicated service to the vehicular
edge computing node, the decision should consider numerous factors.The
offloading decision work mostly formulate the decision to a resource scheduling
problem with single or multiple objective function and some constraints, and
explore customized heuristics algorithms. However, offloading multiple data
dependency tasks in a service is a difficult decision, as an optimal solution
must understand the resource requirement, the access network, the user
mobility, and importantly the data dependency. Inspired by recent advances in
machine learning, we propose a knowledge driven (KD) service offloading
decision framework for Vehicle of Internet, which provides the optimal policy
directly from the environment. We formulate the offloading decision of
multi-task in a service as a long-term planning problem, and explores the
recent deep reinforcement learning to obtain the optimal solution. It considers
the future data dependency of the following tasks when making decision for a
current task from the learned offloading knowledge. Moreover, the framework
supports the pre-training at the powerful edge computing node and continually
online learning when the vehicular service is executed, so that it can adapt
the environment changes and learns policy that are sensible in hindsight. The
simulation results show that KD service offloading decision converges quickly,
adapts to different conditions, and outperforms the greedy offloading decision
algorithm.Comment: Preliminary report of ongoing wor
Unsupervised Online Bayesian Autonomic Happy Internet-of-Things Management
In Happy IoT, the revenue of service providers synchronizes to the
unobservable and dynamic usage-contexts (e.g. emotion, environmental
information, etc.) of Smart-device users. Hence, the usage-context-estimation
from the unreliable Smart-device sensed data is justified as an unsupervised
and non-linear optimization problem. Accordingly, Autonomic Happy IoT
Management is aimed at attracting initial user-groups based on the common
interests (i.e. recruitment ), then uncovering their latent usage-contexts from
unreliable sensed data (i.e. revenue-renewal ) and synchronizing to
usage-context dynamics (i.e. stochastic monetization). In this context, we have
proposed an unsupervised online Bayesian mechanism, namely Whiz (Greek word,
meaning Smart), in which, (a) once latent user-groups are initialized (i.e
measurement model ), (b) usage-context is iteratively estimated from the
unreliable sensed data (i.e. learning model ), (c) followed by online filtering
of Bayesian knowledge about usage-context (i.e. filtering model ). Finally, we
have proposed an Expectation Maximization (EM)-based iterative algorithm Whiz,
which facilitates Happy IoT by solving (a) recruitment, (b) revenue-renewal and
(c) stochastic- monetization problems with (a) measurement, (b) learning, and
(c) filtering models, respectively
Deep Bayesian Multi-Target Learning for Recommender Systems
With the increasing variety of services that e-commerce platforms provide,
criteria for evaluating their success become also increasingly multi-targeting.
This work introduces a multi-target optimization framework with Bayesian
modeling of the target events, called Deep Bayesian Multi-Target Learning
(DBMTL). In this framework, target events are modeled as forming a Bayesian
network, in which directed links are parameterized by hidden layers, and
learned from training samples. The structure of Bayesian network is determined
by model selection. We applied the framework to Taobao live-streaming
recommendation, to simultaneously optimize (and strike a balance) on targets
including click-through rate, user stay time in live room, purchasing behaviors
and interactions. Significant improvement has been observed for the proposed
method over other MTL frameworks and the non-MTL model. Our practice shows that
with an integrated causality structure, we can effectively make the learning of
a target benefit from other targets, creating significant synergy effects that
improve all targets. The neural network construction guided by DBMTL fits in
with the general probabilistic model connecting features and multiple targets,
taking weaker assumption than the other methods discussed in this paper. This
theoretical generality brings about practical generalization power over various
targets distributions, including sparse targets and continuous-value ones.Comment: 7 pages, Deep Learning, Probabilistic Machine Learning, Recommender
System, Multi-task Learnin
Bayesian Optimization for Policy Search via Online-Offline Experimentation
Online field experiments are the gold-standard way of evaluating changes to
real-world interactive machine learning systems. Yet our ability to explore
complex, multi-dimensional policy spaces - such as those found in
recommendation and ranking problems - is often constrained by the limited
number of experiments that can be run simultaneously. To alleviate these
constraints, we augment online experiments with an offline simulator and apply
multi-task Bayesian optimization to tune live machine learning systems. We
describe practical issues that arise in these types of applications, including
biases that arise from using a simulator and assumptions for the multi-task
kernel. We measure empirical learning curves which show substantial gains from
including data from biased offline experiments, and show how these learning
curves are consistent with theoretical results for multi-task Gaussian process
generalization. We find that improved kernel inference is a significant driver
of multi-task generalization. Finally, we show several examples of Bayesian
optimization efficiently tuning a live machine learning system by combining
offline and online experiments
Interactive Sensing in Social Networks
This paper presents models and algorithms for interactive sensing in social
networks where individuals act as sensors and the information exchange between
individuals is exploited to optimize sensing. Social learning is used to model
the interaction between individuals that aim to estimate an underlying state of
nature. In this context the following questions are addressed: How can
self-interested agents that interact via social learning achieve a tradeoff
between individual privacy and reputation of the social group? How can
protocols be designed to prevent data incest in online reputation blogs where
individuals make recommendations? How can sensing by individuals that interact
with each other be used by a global decision maker to detect changes in the
underlying state of nature? When individual agents possess limited sensing,
computation and communication capabilities, can a network of agents achieve
sophisticated global behavior? Social and game theoretic learning are natural
settings for addressing these questions. This article presents an overview,
insights and discussion of social learning models in the context of data incest
propagation, change detection and coordination of decision making
Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey
Future buildings will offer new convenience, comfort, and efficiency
possibilities to their residents. Changes will occur to the way people live as
technology involves into people's lives and information processing is fully
integrated into their daily living activities and objects. The future
expectation of smart buildings includes making the residents' experience as
easy and comfortable as possible. The massive streaming data generated and
captured by smart building appliances and devices contains valuable information
that needs to be mined to facilitate timely actions and better decision making.
Machine learning and big data analytics will undoubtedly play a critical role
to enable the delivery of such smart services. In this paper, we survey the
area of smart building with a special focus on the role of techniques from
machine learning and big data analytics. This survey also reviews the current
trends and challenges faced in the development of smart building services
Making life better one large system at a time: Challenges for UAI research
The rapid growth and diversity in service offerings and the ensuing
complexity of information technology ecosystems present numerous management
challenges (both operational and strategic). Instrumentation and measurement
technology is, by and large, keeping pace with this development and growth.
However, the algorithms, tools, and technology required to transform the data
into relevant information for decision making are not. The claim in this paper
(and the invited talk) is that the line of research conducted in Uncertainty in
Artificial Intelligence is very well suited to address the challenges and close
this gap. I will support this claim and discuss open problems using recent
examples in diagnosis, model discovery, and policy optimization on three real
life distributed systems.Comment: Appears in Proceedings of the Twenty-Third Conference on Uncertainty
in Artificial Intelligence (UAI2007
Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning
The ability to intelligently utilize resources to meet the need of growing
diversity in services and user behavior marks the future of wireless
communication systems. Intelligent wireless communications aims at enabling the
system to perceive and assess the available resources, to autonomously learn to
adapt to the perceived wireless environment, and to reconfigure its operating
mode to maximize the utility of the available resources. The perception
capability and reconfigurability are the essential features of cognitive radio
while modern machine learning techniques project great potential in system
adaptation. In this paper, we discuss the development of the cognitive radio
technology and machine learning techniques and emphasize their roles in
improving spectrum and energy utility of wireless communication systems. We
describe the state-of-the-art of relevant techniques, covering spectrum sensing
and access approaches and powerful machine learning algorithms that enable
spectrum- and energy-efficient communications in dynamic wireless environments.
We also present practical applications of these techniques and identify further
research challenges in cognitive radio and machine learning as applied to the
existing and future wireless communication systems
From 4G to 5G: Self-organized Network Management meets Machine Learning
In this paper, we provide an analysis of self-organized network management,
with an end-to-end perspective of the network. Self-organization as applied to
cellular networks is usually referred to Self-organizing Networks (SONs), and
it is a key driver for improving Operations, Administration, and Maintenance
(OAM) activities. SON aims at reducing the cost of installation and management
of 4G and future 5G networks, by simplifying operational tasks through the
capability to configure, optimize and heal itself. To satisfy 5G network
management requirements, this autonomous management vision has to be extended
to the end to end network. In literature and also in some instances of products
available in the market, Machine Learning (ML) has been identified as the key
tool to implement autonomous adaptability and take advantage of experience when
making decisions. In this paper, we survey how network management can
significantly benefit from ML solutions. We review and provide the basic
concepts and taxonomy for SON, network management and ML. We analyse the
available state of the art in the literature, standardization, and in the
market. We pay special attention to 3rd Generation Partnership Project (3GPP)
evolution in the area of network management and to the data that can be
extracted from 3GPP networks, in order to gain knowledge and experience in how
the network is working, and improve network performance in a proactive way.
Finally, we go through the main challenges associated with this line of
research, in both 4G and in what 5G is getting designed, while identifying new
directions for research.Comment: 23 pages, 3 figures, Surve
A QoS Guarantee Strategy for Multimedia Conferencing based on Bayesian Networks
Service Oriented Architecture (SOA) is commonly employed in the design and
implementation of web service systems. The key technology to enable media
communications in the context of SOA is the Service Oriented Communication. To
exploit the advantage of SOA, we design and implement a web-based multimedia
conferencing system that provides users with a hybrid orchestration of web and
communication services. As the current SOA lacks effective QoS guarantee
solutions for multimedia services, the user satisfaction is greatly challenged
with QoS violations, e.g., low video PSNR (Peak Signal-to-Noise Ratio) and long
playback delay. Motivated by addressing the critical problem, we firstly employ
the Business Process Execution Language (BPEL) service engine for the hybrid
services orchestration and execution. Secondly, we propose a novel
context-aware approach to quantify and leverage the causal relationships
between QoS metrics and available contexts based on Bayesian networks (CABIN).
This approach includes three phases: (1) information discretization, (2) causal
relationship profiling, and (3) optimal context tuning. We implement CABIN in a
real-life multimedia conferencing system and compare its performance with
existing delay and throughput oriented schemes. Experimental results show that
CABIN outperforms the competing approaches in improving the video quality in
terms of PSNR. It also provides a one-stop shop controls both the web and
communication services.Comment: 9 page
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