83,541 research outputs found
An Efficient Data Aggregation Algorithm for Cluster-based Sensor Network
Data aggregation in wireless sensor networks eliminates redundancy to improve bandwidth utilization and energy-efficiency of sensor nodes. One node, called the cluster leader, collects data from surrounding nodes and then sends the summarized information to upstream nodes. In this paper, we propose an algorithm to select a cluster leader that will perform data aggregation in a partially connected sensor network. The algorithm reduces the traffic flow inside the network by adaptively selecting the shortest route for packet routing to the cluster leader. We also describe a simulation framework for functional analysis of WSN applications taking our proposed algorithm as an exampl
Power Aware Routing for Sensor Databases
Wireless sensor networks offer the potential to span and monitor large
geographical areas inexpensively. Sensor network databases like TinyDB are the
dominant architectures to extract and manage data in such networks. Since
sensors have significant power constraints (battery life), and high
communication costs, design of energy efficient communication algorithms is of
great importance. The data flow in a sensor database is very different from
data flow in an ordinary network and poses novel challenges in designing
efficient routing algorithms. In this work we explore the problem of energy
efficient routing for various different types of database queries and show that
in general, this problem is NP-complete. We give a constant factor
approximation algorithm for one class of query, and for other queries give
heuristic algorithms. We evaluate the efficiency of the proposed algorithms by
simulation and demonstrate their near optimal performance for various network
sizes
Semi-supervised Embedding in Attributed Networks with Outliers
In this paper, we propose a novel framework, called Semi-supervised Embedding
in Attributed Networks with Outliers (SEANO), to learn a low-dimensional vector
representation that systematically captures the topological proximity,
attribute affinity and label similarity of vertices in a partially labeled
attributed network (PLAN). Our method is designed to work in both transductive
and inductive settings while explicitly alleviating noise effects from
outliers. Experimental results on various datasets drawn from the web, text and
image domains demonstrate the advantages of SEANO over state-of-the-art methods
in semi-supervised classification under transductive as well as inductive
settings. We also show that a subset of parameters in SEANO is interpretable as
outlier score and can significantly outperform baseline methods when applied
for detecting network outliers. Finally, we present the use of SEANO in a
challenging real-world setting -- flood mapping of satellite images and show
that it is able to outperform modern remote sensing algorithms for this task.Comment: in Proceedings of SIAM International Conference on Data Mining
(SDM'18
What's in a crowd? Analysis of face-to-face behavioral networks
The availability of new data sources on human mobility is opening new avenues
for investigating the interplay of social networks, human mobility and
dynamical processes such as epidemic spreading. Here we analyze data on the
time-resolved face-to-face proximity of individuals in large-scale real-world
scenarios. We compare two settings with very different properties, a scientific
conference and a long-running museum exhibition. We track the behavioral
networks of face-to-face proximity, and characterize them from both a static
and a dynamic point of view, exposing important differences as well as striking
similarities. We use our data to investigate the dynamics of a
susceptible-infected model for epidemic spreading that unfolds on the dynamical
networks of human proximity. The spreading patterns are markedly different for
the conference and the museum case, and they are strongly impacted by the
causal structure of the network data. A deeper study of the spreading paths
shows that the mere knowledge of static aggregated networks would lead to
erroneous conclusions about the transmission paths on the dynamical networks
TopExNet: Entity-Centric Network Topic Exploration in News Streams
The recent introduction of entity-centric implicit network representations of
unstructured text offers novel ways for exploring entity relations in document
collections and streams efficiently and interactively. Here, we present
TopExNet as a tool for exploring entity-centric network topics in streams of
news articles. The application is available as a web service at
https://topexnet.ifi.uni-heidelberg.de/ .Comment: Published in Proceedings of the Twelfth ACM International Conference
on Web Search and Data Mining, WSDM 2019, Melbourne, VIC, Australia, February
11-15, 201
Hybrid multi-layer Deep CNN/Aggregator feature for image classification
Deep Convolutional Neural Networks (DCNN) have established a remarkable
performance benchmark in the field of image classification, displacing
classical approaches based on hand-tailored aggregations of local descriptors.
Yet DCNNs impose high computational burdens both at training and at testing
time, and training them requires collecting and annotating large amounts of
training data. Supervised adaptation methods have been proposed in the
literature that partially re-learn a transferred DCNN structure from a new
target dataset. Yet these require expensive bounding-box annotations and are
still computationally expensive to learn. In this paper, we address these
shortcomings of DCNN adaptation schemes by proposing a hybrid approach that
combines conventional, unsupervised aggregators such as Bag-of-Words (BoW),
with the DCNN pipeline by treating the output of intermediate layers as densely
extracted local descriptors.
We test a variant of our approach that uses only intermediate DCNN layers on
the standard PASCAL VOC 2007 dataset and show performance significantly higher
than the standard BoW model and comparable to Fisher vector aggregation but
with a feature that is 150 times smaller. A second variant of our approach that
includes the fully connected DCNN layers significantly outperforms Fisher
vector schemes and performs comparably to DCNN approaches adapted to Pascal VOC
2007, yet at only a small fraction of the training and testing cost.Comment: Accepted in ICASSP 2015 conference, 5 pages including reference, 4
figures and 2 table
Towards High Performance Video Object Detection
There has been significant progresses for image object detection in recent
years. Nevertheless, video object detection has received little attention,
although it is more challenging and more important in practical scenarios.
Built upon the recent works, this work proposes a unified approach based on
the principle of multi-frame end-to-end learning of features and cross-frame
motion. Our approach extends prior works with three new techniques and steadily
pushes forward the performance envelope (speed-accuracy tradeoff), towards high
performance video object detection
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