21,739 research outputs found
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Transfer Learning for Improving Model Predictions in Highly Configurable Software
Modern software systems are built to be used in dynamic environments using
configuration capabilities to adapt to changes and external uncertainties. In a
self-adaptation context, we are often interested in reasoning about the
performance of the systems under different configurations. Usually, we learn a
black-box model based on real measurements to predict the performance of the
system given a specific configuration. However, as modern systems become more
complex, there are many configuration parameters that may interact and we end
up learning an exponentially large configuration space. Naturally, this does
not scale when relying on real measurements in the actual changing environment.
We propose a different solution: Instead of taking the measurements from the
real system, we learn the model using samples from other sources, such as
simulators that approximate performance of the real system at low cost. We
define a cost model that transform the traditional view of model learning into
a multi-objective problem that not only takes into account model accuracy but
also measurements effort as well. We evaluate our cost-aware transfer learning
solution using real-world configurable software including (i) a robotic system,
(ii) 3 different stream processing applications, and (iii) a NoSQL database
system. The experimental results demonstrate that our approach can achieve (a)
a high prediction accuracy, as well as (b) a high model reliability.Comment: To be published in the proceedings of the 12th International
Symposium on Software Engineering for Adaptive and Self-Managing Systems
(SEAMS'17
Recent Advances of Local Mechanisms in Computer Vision: A Survey and Outlook of Recent Work
Inspired by the fact that human brains can emphasize discriminative parts of
the input and suppress irrelevant ones, substantial local mechanisms have been
designed to boost the development of computer vision. They can not only focus
on target parts to learn discriminative local representations, but also process
information selectively to improve the efficiency. In terms of application
scenarios and paradigms, local mechanisms have different characteristics. In
this survey, we provide a systematic review of local mechanisms for various
computer vision tasks and approaches, including fine-grained visual
recognition, person re-identification, few-/zero-shot learning, multi-modal
learning, self-supervised learning, Vision Transformers, and so on.
Categorization of local mechanisms in each field is summarized. Then,
advantages and disadvantages for every category are analyzed deeply, leaving
room for exploration. Finally, future research directions about local
mechanisms have also been discussed that may benefit future works. To the best
our knowledge, this is the first survey about local mechanisms on computer
vision. We hope that this survey can shed light on future research in the
computer vision field
User Constrained Thumbnail Generation using Adaptive Convolutions
Thumbnails are widely used all over the world as a preview for digital
images. In this work we propose a deep neural framework to generate thumbnails
of any size and aspect ratio, even for unseen values during training, with high
accuracy and precision. We use Global Context Aggregation (GCA) and a modified
Region Proposal Network (RPN) with adaptive convolutions to generate thumbnails
in real time. GCA is used to selectively attend and aggregate the global
context information from the entire image while the RPN is used to predict
candidate bounding boxes for the thumbnail image. Adaptive convolution
eliminates the problem of generating thumbnails of various aspect ratios by
using filter weights dynamically generated from the aspect ratio information.
The experimental results indicate the superior performance of the proposed
model over existing state-of-the-art techniques.Comment: International Conference on Acoustics, Speech, and Signal
Processing(ICASSP), 201
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