36,257 research outputs found
Recurrent Neural Networks based Obesity Status Prediction Using Activity Data
Obesity is a serious public health concern world-wide, which increases the
risk of many diseases, including hypertension, stroke, and type 2 diabetes. To
tackle this problem, researchers across the health ecosystem are collecting
diverse types of data, which includes biomedical, behavioral and activity, and
utilizing machine learning techniques to mine hidden patterns for obesity
status improvement prediction. While existing machine learning methods such as
Recurrent Neural Networks (RNNs) can provide exceptional results, it is
challenging to discover hidden patterns of the sequential data due to the
irregular observation time instances. Meanwhile, the lack of understanding of
why those learning models are effective also limits further improvements on
their architectures. Thus, in this work, we develop a RNN based time-aware
architecture to tackle the challenging problem of handling irregular
observation times and relevant feature extractions from longitudinal patient
records for obesity status improvement prediction. To improve the prediction
performance, we train our model using two data sources: (i) electronic medical
records containing information regarding lab tests, diagnoses, and
demographics; (ii) continuous activity data collected from popular wearables.
Evaluations of real-world data demonstrate that our proposed method can capture
the underlying structures in users' time sequences with irregularities, and
achieve an accuracy of 77-86% in predicting the obesity status improvement.Comment: 8 pages, 6 figures, ICMLA 2018 conferenc
Network Vector: Distributed Representations of Networks with Global Context
We propose a neural embedding algorithm called Network Vector, which learns
distributed representations of nodes and the entire networks simultaneously. By
embedding networks in a low-dimensional space, the algorithm allows us to
compare networks in terms of structural similarity and to solve outstanding
predictive problems. Unlike alternative approaches that focus on node level
features, we learn a continuous global vector that captures each node's global
context by maximizing the predictive likelihood of random walk paths in the
network. Our algorithm is scalable to real world graphs with many nodes. We
evaluate our algorithm on datasets from diverse domains, and compare it with
state-of-the-art techniques in node classification, role discovery and concept
analogy tasks. The empirical results show the effectiveness and the efficiency
of our algorithm
Modeling Game Avatar Synergy and Opposition through Embedding in Multiplayer Online Battle Arena Games
Multiplayer Online Battle Arena (MOBA) games have received increasing
worldwide popularity recently. In such games, players compete in teams against
each other by controlling selected game avatars, each of which is designed with
different strengths and weaknesses. Intuitively, putting together game avatars
that complement each other (synergy) and suppress those of opponents
(opposition) would result in a stronger team. In-depth understanding of synergy
and opposition relationships among game avatars benefits player in making
decisions in game avatar drafting and gaining better prediction of match
events. However, due to intricate design and complex interactions between game
avatars, thorough understanding of their relationships is not a trivial task.
In this paper, we propose a latent variable model, namely Game Avatar
Embedding (GAE), to learn avatars' numerical representations which encode
synergy and opposition relationships between pairs of avatars. The merits of
our model are twofold: (1) the captured synergy and opposition relationships
are sensible to experienced human players' perception; (2) the learned
numerical representations of game avatars allow many important downstream
tasks, such as similar avatar search, match outcome prediction, and avatar pick
recommender. To our best knowledge, no previous model is able to simultaneously
support both features. Our quantitative and qualitative evaluations on real
match data from three commercial MOBA games illustrate the benefits of our
model.Comment: Note: this is a draft rejected by AIIDE 201
Deep Neural Networks for Optimal Team Composition
Cooperation is a fundamental social mechanism, whose effects on human
performance have been investigated in several environments. Online games are
modern-days natural settings in which cooperation strongly affects human
behavior. Every day, millions of players connect and play together in
team-based games: the patterns of cooperation can either foster or hinder
individual skill learning and performance. This work has three goals: (i)
identifying teammates' influence on players' performance in the short and long
term, (ii) designing a computational framework to recommend teammates to
improve players' performance, and (iii) setting to demonstrate that such
improvements can be predicted via deep learning. We leverage a large dataset
from Dota 2, a popular Multiplayer Online Battle Arena game. We generate a
directed co-play network, whose links' weights depict the effect of teammates
on players' performance. Specifically, we propose a measure of network
influence that captures skill transfer from player to player over time. We then
use such framing to design a recommendation system to suggest new teammates
based on a modified deep neural autoencoder and we demonstrate its
state-of-the-art recommendation performance. We finally provide insights into
skill transfer effects: our experimental results demonstrate that such dynamics
can be predicted using deep neural networks
Combining complex networks and data mining: why and how
The increasing power of computer technology does not dispense with the need
to extract meaningful in- formation out of data sets of ever growing size, and
indeed typically exacerbates the complexity of this task. To tackle this
general problem, two methods have emerged, at chronologically different times,
that are now commonly used in the scientific community: data mining and complex
network theory. Not only do complex network analysis and data mining share the
same general goal, that of extracting information from complex systems to
ultimately create a new compact quantifiable representation, but they also
often address similar problems too. In the face of that, a surprisingly low
number of researchers turn out to resort to both methodologies. One may then be
tempted to conclude that these two fields are either largely redundant or
totally antithetic. The starting point of this review is that this state of
affairs should be put down to contingent rather than conceptual differences,
and that these two fields can in fact advantageously be used in a synergistic
manner. An overview of both fields is first provided, some fundamental concepts
of which are illustrated. A variety of contexts in which complex network theory
and data mining have been used in a synergistic manner are then presented.
Contexts in which the appropriate integration of complex network metrics can
lead to improved classification rates with respect to classical data mining
algorithms and, conversely, contexts in which data mining can be used to tackle
important issues in complex network theory applications are illustrated.
Finally, ways to achieve a tighter integration between complex networks and
data mining, and open lines of research are discussed.Comment: 58 pages, 19 figure
Stable Multiple Time Step Simulation/Prediction from Lagged Dynamic Network Regression Models
Recent developments in computers and automated data collection strategies
have greatly increased the interest in statistical modeling of dynamic
networks. Many of the statistical models employed for inference on large-scale
dynamic networks suffer from limited forward simulation/prediction ability. A
major problem with many of the forward simulation procedures is the tendency
for the model to become degenerate in only a few time steps, i.e., the
simulation/prediction procedure results in either null graphs or complete
graphs. Here, we describe an algorithm for simulating a sequence of networks
generated from lagged dynamic network regression models DNR(V), a sub-family of
TERGMs. We introduce a smoothed estimator for forward prediction based on
smoothing of the change statistics obtained for a dynamic network regression
model. We focus on the implementation of the algorithm, providing a series of
motivating examples with comparisons to dynamic network models from the
literature. We find that our algorithm significantly improves multi-step
prediction/simulation over standard DNR(V) forecasting. Furthermore, we show
that our method performs comparably to existing more complex dynamic network
analysis frameworks (SAOM and STERGMs) for small networks over short time
periods, and significantly outperforms these approaches over long time time
intervals and/or large networks
Multiresolution Tensor Decomposition for Multiple Spatial Passing Networks
This article is motivated by soccer positional passing networks collected
across multiple games. We refer to these data as replicated spatial passing
networks---to accurately model such data it is necessary to take into account
the spatial positions of the passer and receiver for each passing event. This
spatial registration and replicates that occur across games represent key
differences with usual social network data. As a key step before investigating
how the passing dynamics influence team performance, we focus on developing
methods for summarizing different team's passing strategies. Our proposed
approach relies on a novel multiresolution data representation framework and
Poisson nonnegative block term decomposition model, which automatically
produces coarse-to-fine low-rank network motifs. The proposed methods are
applied to detailed passing record data collected from the 2014 FIFA World Cup.Comment: 34 pages, 15 figure
Learning Sports Camera Selection from Internet Videos
This work addresses camera selection, the task of predicting which camera
should be "on air" from multiple candidate cameras for soccer broadcast. The
task is challenging because of the scarcity of learning data with all candidate
views. Meanwhile, broadcast videos are freely available on the Internet (e.g.
Youtube). However, these videos only record the selected camera views, omitting
the other candidate views. To overcome this problem, we first introduce a
random survival forest (RSF) method to impute the incomplete data effectively.
Then, we propose a spatial-appearance heatmap to describe foreground objects
(e.g. players and balls) in an image. To evaluate the performance of our
system, we collect the largest-ever dataset for soccer broadcasting camera
selection. It has one main game which has all candidate views and twelve
auxiliary games which only have the broadcast view. Our method significantly
outperforms state-of-the-art methods on this challenging dataset. Further
analysis suggests that the improvement in performance is indeed from the extra
information from auxiliary games.Comment: 8 + 2 pages, WACV2019 accepte
Dynamic Graph Convolutional Networks
Many different classification tasks need to manage structured data, which are
usually modeled as graphs. Moreover, these graphs can be dynamic, meaning that
the vertices/edges of each graph may change during time. Our goal is to jointly
exploit structured data and temporal information through the use of a neural
network model. To the best of our knowledge, this task has not been addressed
using these kind of architectures. For this reason, we propose two novel
approaches, which combine Long Short-Term Memory networks and Graph
Convolutional Networks to learn long short-term dependencies together with
graph structure. The quality of our methods is confirmed by the promising
results achieved
Link Prediction in Social Networks: the State-of-the-Art
In social networks, link prediction predicts missing links in current
networks and new or dissolution links in future networks, is important for
mining and analyzing the evolution of social networks. In the past decade, many
works have been done about the link prediction in social networks. The goal of
this paper is to comprehensively review, analyze and discuss the
state-of-the-art of the link prediction in social networks. A systematical
category for link prediction techniques and problems is presented. Then link
prediction techniques and problems are analyzed and discussed. Typical
applications of link prediction are also addressed. Achievements and roadmaps
of some active research groups are introduced. Finally, some future challenges
of the link prediction in social networks are discussed.Comment: 38 pages, 13 figures, Science China: Information Science, 201
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