17,839 research outputs found
Optimal Caching and Routing in Hybrid Networks
Hybrid networks consisting of MANET nodes and cellular infrastructure have
been recently proposed to improve the performance of military networks. Prior
work has demonstrated the benefits of in-network content caching in a wired,
Internet context. We investigate the problem of developing optimal routing and
caching policies in a hybrid network supporting in-network caching with the
goal of minimizing overall content-access delay. Here, needed content may
always be accessed at a back-end server via the cellular infrastructure;
alternatively, content may also be accessed via cache-equipped "cluster" nodes
within the MANET. To access content, MANET nodes must thus decide whether to
route to in-MANET cluster nodes or to back-end servers via the cellular
infrastructure; the in-MANET cluster nodes must additionally decide which
content to cache. We model the cellular path as either i) a
congestion-insensitive fixed-delay path or ii) a congestion-sensitive path
modeled as an M/M/1 queue. We demonstrate that under the assumption of
stationary, independent requests, it is optimal to adopt static caching (i.e.,
to keep a cache's content fixed over time) based on content popularity. We also
show that it is optimal to route to in-MANET caches for content cached there,
but to route requests for remaining content via the cellular infrastructure for
the congestion-insensitive case and to split traffic between the in-MANET
caches and cellular infrastructure for the congestion-sensitive case. We
develop a simple distributed algorithm for the joint routing/caching problem
and demonstrate its efficacy via simulation.Comment: submitted to Milcom 201
Knowledge-based Transfer Learning Explanation
Machine learning explanation can significantly boost machine learning's
application in decision making, but the usability of current methods is limited
in human-centric explanation, especially for transfer learning, an important
machine learning branch that aims at utilizing knowledge from one learning
domain (i.e., a pair of dataset and prediction task) to enhance prediction
model training in another learning domain. In this paper, we propose an
ontology-based approach for human-centric explanation of transfer learning.
Three kinds of knowledge-based explanatory evidence, with different
granularities, including general factors, particular narrators and core
contexts are first proposed and then inferred with both local ontologies and
external knowledge bases. The evaluation with US flight data and DBpedia has
presented their confidence and availability in explaining the transferability
of feature representation in flight departure delay forecasting.Comment: Accepted by International Conference on Principles of Knowledge
Representation and Reasoning, 201
Affect Recognition in Ads with Application to Computational Advertising
Advertisements (ads) often include strongly emotional content to leave a
lasting impression on the viewer. This work (i) compiles an affective ad
dataset capable of evoking coherent emotions across users, as determined from
the affective opinions of five experts and 14 annotators; (ii) explores the
efficacy of convolutional neural network (CNN) features for encoding emotions,
and observes that CNN features outperform low-level audio-visual emotion
descriptors upon extensive experimentation; and (iii) demonstrates how enhanced
affect prediction facilitates computational advertising, and leads to better
viewing experience while watching an online video stream embedded with ads
based on a study involving 17 users. We model ad emotions based on subjective
human opinions as well as objective multimodal features, and show how
effectively modeling ad emotions can positively impact a real-life application.Comment: Accepted at the ACM International Conference on Multimedia (ACM MM)
201
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