2,464 research outputs found
HFR Code: A Flexible Replication Scheme for Cloud Storage Systems
Fractional repetition (FR) codes are a family of repair-efficient storage
codes that provide exact and uncoded node repair at the minimum bandwidth
regenerating point. The advantageous repair properties are achieved by a
tailor-made two-layer encoding scheme which concatenates an outer
maximum-distance-separable (MDS) code and an inner repetition code. In this
paper, we generalize the application of FR codes and propose heterogeneous
fractional repetition (HFR) code, which is adaptable to the scenario where the
repetition degrees of coded packets are different. We provide explicit code
constructions by utilizing group divisible designs, which allow the design of
HFR codes over a large range of parameters. The constructed codes achieve the
system storage capacity under random access repair and have multiple repair
alternatives for node failures. Further, we take advantage of the systematic
feature of MDS codes and present a novel design framework of HFR codes, in
which storage nodes can be wisely partitioned into clusters such that data
reconstruction time can be reduced when contacting nodes in the same cluster.Comment: Accepted for publication in IET Communications, Jul. 201
Project RISE: Recognizing Industrial Smoke Emissions
Industrial smoke emissions pose a significant concern to human health. Prior
works have shown that using Computer Vision (CV) techniques to identify smoke
as visual evidence can influence the attitude of regulators and empower
citizens to pursue environmental justice. However, existing datasets are not of
sufficient quality nor quantity to train the robust CV models needed to support
air quality advocacy. We introduce RISE, the first large-scale video dataset
for Recognizing Industrial Smoke Emissions. We adopted a citizen science
approach to collaborate with local community members to annotate whether a
video clip has smoke emissions. Our dataset contains 12,567 clips from 19
distinct views from cameras that monitored three industrial facilities. These
daytime clips span 30 days over two years, including all four seasons. We ran
experiments using deep neural networks to establish a strong performance
baseline and reveal smoke recognition challenges. Our survey study discussed
community feedback, and our data analysis displayed opportunities for
integrating citizen scientists and crowd workers into the application of
Artificial Intelligence for social good.Comment: Technical repor
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