1,041 research outputs found
On Distributed Linear Estimation With Observation Model Uncertainties
We consider distributed estimation of a Gaussian source in a heterogenous
bandwidth constrained sensor network, where the source is corrupted by
independent multiplicative and additive observation noises, with incomplete
statistical knowledge of the multiplicative noise. For multi-bit quantizers, we
derive the closed-form mean-square-error (MSE) expression for the linear
minimum MSE (LMMSE) estimator at the FC. For both error-free and erroneous
communication channels, we propose several rate allocation methods named as
longest root to leaf path, greedy and integer relaxation to (i) minimize the
MSE given a network bandwidth constraint, and (ii) minimize the required
network bandwidth given a target MSE. We also derive the Bayesian Cramer-Rao
lower bound (CRLB) and compare the MSE performance of our proposed methods
against the CRLB. Our results corroborate that, for low power multiplicative
observation noises and adequate network bandwidth, the gaps between the MSE of
our proposed methods and the CRLB are negligible, while the performance of
other methods like individual rate allocation and uniform is not satisfactory
On the Combined Effect of Directional Antennas and Imperfect Spectrum Sensing upon Ergodic Capacity of Cognitive Radio Systems
We consider a cognitive radio system, consisting of a primary transmitter
(PUtx), a primary receiver (PUrx), a secondary transmitter (SUtx), and a
secondary receiver (SUrx). The secondary users (SUs) are equipped with
steerable directional antennas. We assume the SUs and primary users (PUs)
coexist and the SUtx knows the geometry of network. We find the ergodic
capacity of the channel between SUtx and SUrx , and study how spectrum sensing
errors affect the capacity. In our system, the SUtx first senses the spectrum
and then transmits data at two power levels, according to the result of
sensing. The optimal SUtx transmit power levels and the optimal directions of
SUtx transmit antenna and SUrx receive antenna are obtained by maximizing the
ergodic capacity, subject to average transmit power and average interference
power constraints. To study the effect of fading channel, we considered three
scenarios: 1) when SUtx knows fading channels between SUtx and PUrx, PUtx and
SUrx, SUtx and SUrx, 2) when SUtx knows only the channel between SUtx and SUrx,
and statistics of the other two channels, and, 3) when SUtx only knows the
statistics of these three fading channels. For each scenario, we explore the
optimal SUtx transmit power levels and the optimal directions of SUtx and SUrx
antennas, such that the ergodic capacity is maximized, while the power
constraints are satisfied
A Semi-automatic Method for Efficient Detection of Stories on Social Media
Twitter has become one of the main sources of news for many people. As
real-world events and emergencies unfold, Twitter is abuzz with hundreds of
thousands of stories about the events. Some of these stories are harmless,
while others could potentially be life-saving or sources of malicious rumors.
Thus, it is critically important to be able to efficiently track stories that
spread on Twitter during these events. In this paper, we present a novel
semi-automatic tool that enables users to efficiently identify and track
stories about real-world events on Twitter. We ran a user study with 25
participants, demonstrating that compared to more conventional methods, our
tool can increase the speed and the accuracy with which users can track stories
about real-world events.Comment: ICWSM'16, May 17-20, Cologne, Germany. In Proceedings of the 10th
International AAAI Conference on Weblogs and Social Media (ICWSM 2016).
Cologne, German
Tweet Acts: A Speech Act Classifier for Twitter
Speech acts are a way to conceptualize speech as action. This holds true for
communication on any platform, including social media platforms such as
Twitter. In this paper, we explored speech act recognition on Twitter by
treating it as a multi-class classification problem. We created a taxonomy of
six speech acts for Twitter and proposed a set of semantic and syntactic
features. We trained and tested a logistic regression classifier using a data
set of manually labelled tweets. Our method achieved a state-of-the-art
performance with an average F1 score of more than . We also explored
classifiers with three different granularities (Twitter-wide, type-specific and
topic-specific) in order to find the right balance between generalization and
overfitting for our task.Comment: ICWSM'16, May 17-20, Cologne, Germany. In Proceedings of the 10th
AAAI Conference on Weblogs and Social Media (ICWSM 2016). Cologne, German
Digital Stylometry: Linking Profiles Across Social Networks
There is an ever growing number of users with accounts on multiple social
media and networking sites. Consequently, there is increasing interest in
matching user accounts and profiles across different social networks in order
to create aggregate profiles of users. In this paper, we present models for
Digital Stylometry, which is a method for matching users through stylometry
inspired techniques. We experimented with linguistic, temporal, and combined
temporal-linguistic models for matching user accounts, using standard and novel
techniques. Using publicly available data, our best model, a combined
temporal-linguistic one, was able to correctly match the accounts of 31% of
5,612 distinct users across Twitter and Facebook.Comment: SocInfo'15, Beijing, China. In proceedings of the 7th International
Conference on Social Informatics (SocInfo 2015). Beijing, Chin
Automatic Detection and Categorization of Election-Related Tweets
With the rise in popularity of public social media and micro-blogging
services, most notably Twitter, the people have found a venue to hear and be
heard by their peers without an intermediary. As a consequence, and aided by
the public nature of Twitter, political scientists now potentially have the
means to analyse and understand the narratives that organically form, spread
and decline among the public in a political campaign. However, the volume and
diversity of the conversation on Twitter, combined with its noisy and
idiosyncratic nature, make this a hard task. Thus, advanced data mining and
language processing techniques are required to process and analyse the data. In
this paper, we present and evaluate a technical framework, based on recent
advances in deep neural networks, for identifying and analysing
election-related conversation on Twitter on a continuous, longitudinal basis.
Our models can detect election-related tweets with an F-score of 0.92 and can
categorize these tweets into 22 topics with an F-score of 0.90.Comment: ICWSM'16, May 17-20, 2016, Cologne, Germany. In Proceedings of the
10th AAAI Conference on Weblogs and Social Media (ICWSM 2016). Cologne,
German
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