7,280 research outputs found
Machine Learning at the Edge: A Data-Driven Architecture with Applications to 5G Cellular Networks
The fifth generation of cellular networks (5G) will rely on edge cloud
deployments to satisfy the ultra-low latency demand of future applications. In
this paper, we argue that such deployments can also be used to enable advanced
data-driven and Machine Learning (ML) applications in mobile networks. We
propose an edge-controller-based architecture for cellular networks and
evaluate its performance with real data from hundreds of base stations of a
major U.S. operator. In this regard, we will provide insights on how to
dynamically cluster and associate base stations and controllers, according to
the global mobility patterns of the users. Then, we will describe how the
controllers can be used to run ML algorithms to predict the number of users in
each base station, and a use case in which these predictions are exploited by a
higher-layer application to route vehicular traffic according to network Key
Performance Indicators (KPIs). We show that the prediction accuracy improves
when based on machine learning algorithms that rely on the controllers' view
and, consequently, on the spatial correlation introduced by the user mobility,
with respect to when the prediction is based only on the local data of each
single base station.Comment: 15 pages, 10 figures, 5 tables. IEEE Transactions on Mobile Computin
GTmoPass: Two-factor Authentication on Public Displays Using Gaze-touch Passwords and Personal Mobile Devices
As public displays continue to deliver increasingly private and personalized content, there is a need to ensure that only the legitimate users can access private information in sensitive contexts. While public displays can adopt similar authentication concepts like those used on public terminals (e.g., ATMs), authentication in public is subject to a number of risks. Namely, adversaries can uncover a user's password through (1) shoulder surfing, (2) thermal attacks, or (3) smudge attacks. To address this problem we propose GTmoPass, an authentication architecture that enables Multi-factor user authentication on public displays. The first factor is a knowledge-factor: we employ a shoulder-surfing resilient multimodal scheme that combines gaze and touch input for password entry. The second factor is a possession-factor: users utilize their personal mobile devices, on which they enter the password. Credentials are securely transmitted to a server via Bluetooth beacons. We describe the implementation of GTmoPass and report on an evaluation of its usability and security, which shows that although authentication using GTmoPass is slightly slower than traditional methods, it protects against the three aforementioned threats
A Unified Approach to Configuration-based Dynamic Analysis of Quadcopters for Optimal Stability
A special type of rotary-wing Unmanned Aerial Vehicles (UAV), called
Quadcopter have prevailed to the civilian use for the past decade. They have
gained significant amount of attention within the UAV community for their
redundancy and ease of control, despite the fact that they fall under an
under-actuated system category. They come in a variety of configurations. The
"+" and "x" configurations were introduced first. Literature pertinent to these
two configurations is vast. However, in this paper, we define 6 additional
possible configurations for a Quadcopter that can be built under either "+" or
"x" setup. These configurations can be achieved by changing the angle that the
axis of rotation for rotors make with the main body, i.e., fuselage. This would
also change the location of the COM with respect to the propellers which can
add to the overall stability. A comprehensive dynamic model for all these
configurations is developed for the first time. The overall stability for these
configurations are addressed. In particular, it is shown that one configuration
can lead to the most statically-stable platform by adopting damping motion in
Roll/Pitch/Yaw, which is described for the first time to the best of our
knowledge.Comment: 6 page, 9 figure
Bivariate Beta-LSTM
Long Short-Term Memory (LSTM) infers the long term dependency through a cell
state maintained by the input and the forget gate structures, which models a
gate output as a value in [0,1] through a sigmoid function. However, due to the
graduality of the sigmoid function, the sigmoid gate is not flexible in
representing multi-modality or skewness. Besides, the previous models lack
modeling on the correlation between the gates, which would be a new method to
adopt inductive bias for a relationship between previous and current input.
This paper proposes a new gate structure with the bivariate Beta distribution.
The proposed gate structure enables probabilistic modeling on the gates within
the LSTM cell so that the modelers can customize the cell state flow with
priors and distributions. Moreover, we theoretically show the higher upper
bound of the gradient compared to the sigmoid function, and we empirically
observed that the bivariate Beta distribution gate structure provides higher
gradient values in training. We demonstrate the effectiveness of bivariate Beta
gate structure on the sentence classification, image classification, polyphonic
music modeling, and image caption generation.Comment: AAAI 202
Massive MIMO Performance - TDD Versus FDD: What Do Measurements Say?
Downlink beamforming in Massive MIMO either relies on uplink pilot
measurements - exploiting reciprocity and TDD operation, or on the use of a
predetermined grid of beams with user equipments reporting their preferred
beams, mostly in FDD operation. Massive MIMO in its originally conceived form
uses the first strategy, with uplink pilots, whereas there is currently
significant commercial interest in the second, grid-of-beams. It has been
analytically shown that in isotropic scattering (independent Rayleigh fading)
the first approach outperforms the second. Nevertheless there remains
controversy regarding their relative performance in practice. In this
contribution, the performances of these two strategies are compared using
measured channel data at 2.6 GHz.Comment: Submitted to IEEE Transactions on Wireless Communications,
31/Mar/201
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Integrating biomedical research and electronic health records to create knowledge-based biologically meaningful machine-readable embeddings.
In order to advance precision medicine, detailed clinical features ought to be described in a way that leverages current knowledge. Although data collected from biomedical research is expanding at an almost exponential rate, our ability to transform that information into patient care has not kept at pace. A major barrier preventing this transformation is that multi-dimensional data collection and analysis is usually carried out without much understanding of the underlying knowledge structure. Here, in an effort to bridge this gap, Electronic Health Records (EHRs) of individual patients are connected to a heterogeneous knowledge network called Scalable Precision Medicine Oriented Knowledge Engine (SPOKE). Then an unsupervised machine-learning algorithm creates Propagated SPOKE Entry Vectors (PSEVs) that encode the importance of each SPOKE node for any code in the EHRs. We argue that these results, alongside the natural integration of PSEVs into any EHR machine-learning platform, provide a key step toward precision medicine
The BURCHAK corpus: a Challenge Data Set for Interactive Learning of Visually Grounded Word Meanings
We motivate and describe a new freely available human-human dialogue dataset
for interactive learning of visually grounded word meanings through ostensive
definition by a tutor to a learner. The data has been collected using a novel,
character-by-character variant of the DiET chat tool (Healey et al., 2003;
Mills and Healey, submitted) with a novel task, where a Learner needs to learn
invented visual attribute words (such as " burchak " for square) from a tutor.
As such, the text-based interactions closely resemble face-to-face conversation
and thus contain many of the linguistic phenomena encountered in natural,
spontaneous dialogue. These include self-and other-correction, mid-sentence
continuations, interruptions, overlaps, fillers, and hedges. We also present a
generic n-gram framework for building user (i.e. tutor) simulations from this
type of incremental data, which is freely available to researchers. We show
that the simulations produce outputs that are similar to the original data
(e.g. 78% turn match similarity). Finally, we train and evaluate a
Reinforcement Learning dialogue control agent for learning visually grounded
word meanings, trained from the BURCHAK corpus. The learned policy shows
comparable performance to a rule-based system built previously.Comment: 10 pages, THE 6TH WORKSHOP ON VISION AND LANGUAGE (VL'17
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